POSIVA 2012-30
May 2013
POSIVA OY
Olki luoto
FI-27160 EURAJOKI, F INLAND
Phone (02) 8372 31 (nat. ) , (+358-2-) 8372 31 ( int. )
Fax (02) 8372 3809 (nat. ) , (+358-2-) 8372 3809 ( int. )
Posiva Oy
Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto
-Surface and Near-Surface Hydrological Modelling
in the Biosphere Assessment BSA-2012
ISBN 978-951-652-211-4ISSN 1239-3096
Tekijä(t) – Author(s)
Tuomo Karvonen, WaterHope
Toimeksiantaja(t) – Commissioned by
Posiva Oy
Nimeke – Title
SAFETY CASE FOR THE DISPOSAL OF SPENT NUCLEAR FUEL AT OLKILUOTO -SURFACE AND NEAR-SURFACE HYDROLOGICAL MODELLING IN THE BIOSPHERE ASSESSMENT BSA-2012
Tiivistelmä – Abstract The Finnish nuclear waste disposal company, Posiva Oy, is planning an underground repository for spent nuclear fuel to be constructed on the island of Olkiluoto on the south-west coast of Finland. This study is part of the biosphere assessment (BSA-2012) within the safety case for the repository. The surface hydrological modelling described in this report is aimed at providing link between radionuclide transport in the geosphere and in the biosphere systems. The SVAT-model and Olkiluoto site scale surface hydrological model were calibrated and validated in the present day conditions using the input data provided by the Olkiluoto Monitoring Programme (OMO). During the next 10 000 years the terrain and ecosystem development is to a large extent driven by the postglacial crustal uplift. UNTAMO is a GIS toolbox developed for simulating land-uplift driven or other changes in the biosphere. All the spatial and temporal input data (excluding meteorological data) needed in the surface hydrological modelling were provided by the UNTAMO toolbox. The specific outputs given by UNTAMO toolbox are time-dependent evolution of the biosphere objects. They are continuous and sufficiently homogeneous sub-areas of the modelled area that could potentially receive radionuclides released from the repository. Possible ecosystem types for biosphere objects are coast, lake, river, forest, cropland, pasture and wetland. The primary goal of this study was to compute vertical and horizontal water fluxes in the biosphere objects. These data will be used in the biosphere radionuclide transport calculations. The method adopted here is based on calculating average vertical and horizontal fluxes for biosphere objects from the results of the full 3D-model. It was not necessary to develop any simplified hydrological model for the biosphere objects. This report includes modelling results from for the Reference Case (present day climate) and Terr_MaxAgri Case (maximum extent of agricultural areas and climate scenario A2). The fluxes computed in the Reference Case and in the Terr_MaxAgri Case show that the biggest difference in the results is related to precipitation throughfall and horizontal fluxes out of the biosphere object whereas interception and transpiration do not differ very much from each other in the Reference Case and Terr_MaxAgri Case. Influence of shallow wells on water fluxes in biosphere objects were taken into account by assuming that pumping rate is 500 m3/a, which is the amount of water needed to sustain a single household. The sensitivity runs carried out indicate that the uncertainty involved in predicting the yearly precipitation rate for the period of next 10 000 years is the input data that has the biggest influence on vertical and horizontal fluxes in the biosphere objects during the safety assessment period.
Avainsanat - Keywords
Hydrology, transpiration, interception, water flux, surface runoff, snowmelt, frost, biosphere, assessment, well
ISBN
ISBN 978-951-652-211-4 ISSN
ISSN 1239-3096 Sivumäärä – Number of pages
157 Kieli – Language
English
Posiva-raportti – Posiva Report Posiva Oy Olkiluoto FI-27160 EURAJOKI, FINLAND Puh. 02-8372 (31) – Int. Tel. +358 2 8372 (31)
Raportin tunnus – Report code
POSIVA 2012-30
Julkaisuaika – Date
May 2012
Tekijä(t) – Author(s)
Tuomo Karvonen, WaterHope
Toimeksiantaja(t) – Commissioned by
Posiva Oy
Nimeke – Title
TURVALLISUUSPERUSTELU KÄYTETYN YDINPOLTTOAINEEN LOPPUSIJOITUK-SELLE OLKILUODOSSA -PINTA- JA MAAPERÄHYDROLOGINEN MALLINNUS BIOSFÄÄRIARVIOINNISSA BSA-2012 Tiivistelmä – Abstract Posiva Oy suunnittelee loppusijoituslaitoksen rakentamista käytetylle ydinpolttoaineelle Eurajoen Olkiluodon kallioperään. Tämä raportti on osa biosfäärianalyysiä (BSA-2012) ja liittyy Posivan turvallisuusperusteluihin. Raportissa kuvattu pintahydrologian malli toimii linkittää toisiinsa mallit, joilla lasketaan radionuklidien kulkeutumista kallioperässä ja biosfäärissä. Pienten metsäalueiden vesi- ja energiataseiden laskentaan kehitetty SVAT-malli ja Olkiluodon saaren pintahydrologian käyttäytymistä ennustava malli kalibroitiin ja validoitiin käyttäen Olkiluodon monitorointiohjelmassa (OMO) mitattuja suureita. Seuraavien 10 000 vuoden aikana saaren ja sitä ympäröivien alueiden maaston ja ekosysteemin kehittymistä säätelee edellisen jääkauden jälkeinen maanpinnan nousu. Pintahydrologian malli käyttää lähtötietoina maasto- ja ekosysteemiennusteiden laadintaan kehitetyn GIS-työkalun UNTAMO tuottamia korkeus-, maalaji-, kerros-paksuus- ja uomarastereita. Lisäksi UNTAMO:lla laadittiin biosfääriobjektien paikallinen ajallinen ja ajallinen vaihtelu koko ennustejaksolle. Biosfääriobjektit ovat pieniä homogeenisiä alueita, joille voi kulkeutua loppusijoitustiloista mahdollisesti vapautuvia radionuklideja. Tarkastelussa huomioitavat ekosysteemivaihtoehdot ovat merialue, järvi, joki, metsäinen alue, peltoviljelyyn soveltuva alue, laidun ja suo. Tässä raportissa esitettävät päätulokset ovat biosfääriobjektien vaaka- ja pystysuuntaiset virtaukset seuraavien 10 000 vuoden aikana. Näitä tuloksia käytetään myöhemmin biosfäärissä tapahtuvaan radionuklidien kulkeutumisen laskentaan. Biosfääriobjektien virtauksien laskennassa koko alueen vesitaseet laskettiin ensin 3D-mallilla ja näistä tuloksista poimittiin vaaka- ja pystysuuntaiset virtaukset jokaiselle objektille. Tämän menettelytavan ansiosta biosfääriobjekteille ei tarvinnut laatia yksinkertaistettuja malliversioita. Vaaka- ja pystysuuntaiset virtaukset laskettiin sekä referenssitapaukselle (Reference Case), että vaihtoehdolle, jossa maatalousalueiden laajuus oli suurin mahdollinen (Terr_MaxAgri Case). Mallinnustulosten mukaan edellä mainittujen laskentatapausten välillä oli suurimmat erot kasvuston läpi satavassa vesimäärässä ja alueelta vaaka-suunnassa poistuvissa virtauksissa. Latvustopidännän arvot eivät poikenneet merkittävästi toisistaan. Kaivojen vaikutus otettiin huomioon olettamalla, että yhdestä kaivosta pumpataan vuodessa 500 m3, mikä riittää pienen kotitalouden tarpeisiin. Herkkyys- ja epävarmuustarkastelutarkastelujen keskeisin tulos oli se, että seuraavien 10 000 vuoden aikana sadannan ennustamiseen liittyvät epävarmuudet vaikuttavat eniten biosfääriobjektien vaaka- ja pystysuuntaisiin virtauksiin.
Avainsanat - Keywords
Hydrologia, transpiraatio, latvustopidäntä, maaveden virtaus, pintavalunta, sulanta, routa, biosfääriarviointi, kaivo ISBN ISBN 978-951-652-211-4
ISSN ISSN 1239-3096
Sivumäärä – Number of pages 157
Kieli – Language Englanti
Posiva-raportti – Posiva Report Posiva Oy Olkiluoto FI-27160 EURAJOKI, FINLAND Puh. 02-8372 (31) – Int. Tel. +358 2 8372 (31)
Raportin tunnus – Report code
POSIVA 2012-30 Julkaisuaika – Date
Toukokuu 2012
TABLE OF CONTENTS
ABSTRACT TIIVISTELMÄ
PREFACE ....................................................................................................................... 5
TERMS AND ABREVIATIONS ....................................................................................... 7
1 INTRODUCTION .................................................................................................... 9
1.1 Olkiluoto site.................................................................................................. 9
1.2 Safety case and biosphere assessment ..................................................... 10
1.2.1 Biosphere assessment BSA-2012 .................................................. 10
1.2.2 Description of the modelling chain needed in BSA-2012 ................ 11
1.3 Scope of this report ..................................................................................... 15
2 MATERIAL AND METHODS ................................................................................ 17
2.1 Introduction ................................................................................................. 17
2.2 Forest monitoring network in Olkiluoto ........................................................ 18
2.3 Data for the SVAT model ............................................................................ 19
2.3.1 Forest Intensive Monitoring Plots: FIP ............................................ 19
2.3.2 Transpiration, interception and throughfall ...................................... 20
2.3.3 Meteorological data ......................................................................... 21
2.4 Data for the site scale model in the present day conditions ........................ 21
2.4.1 Land use mapping .......................................................................... 21
2.4.2 Soil surface elevation and stream network ..................................... 24
2.4.3 Soil profile thickness and bedrock elevation ................................... 25
2.4.4 Soil type classification ..................................................................... 25
2.4.5 Hydrogeological zones and bedrock data ....................................... 26
2.4.6 Hydrological and hydrogeological monitoring ................................. 26
2.4.7 Pumping and infiltration tests .......................................................... 31
2.5 Hydrological models for the Eurajoki and Lapinjoki basins ......................... 33
2.5.1 Meteorological variables ................................................................. 33
2.5.2 Snow water equivalent and runoff ................................................... 34
2.5.3 Land use data ................................................................................. 34
2.5.4 Digital elevation model .................................................................... 34
2.6 Data for the 2012 safety assessment .......................................................... 39
2.6.1 Regional scale input data provided by the UNTAMO-toolbox ......... 39
2.6.2 Biosphere object delineation ........................................................... 43
2.6.3 Climate scenarios and projected global sea level rise .................... 45
2.7 Models......................................................................................................... 47
2.7.1 Introduction ..................................................................................... 47
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2.7.2 Soil-Vegetation-Atmosphere-Transfer (SVAT) model ..................... 47
2.7.3 Site scale hydrological model (SHYD) ............................................ 50
2.7.4 Hydrological model for the Eurajoki and Lapinjoki basins ............... 51
2.7.5 Regional scale hydrological model for the safety assessment ........ 52
2.7.6 Vertical and horizontal water fluxes in the biosphere objects ......... 53
2.7.7 Modelling guidelines and model calibration and validation ............. 55
3 MODEL CALIBRATION AND VALIDATION ......................................................... 57
3.1 Introduction ................................................................................................. 57
3.2 Calibration and validation of the SVAT model ............................................. 58
3.2.1 Tree stand transpiration .................................................................. 58
3.2.2 Precipitation throughfall and interception ........................................ 60
3.2.3 Overall water balance of FIP plots .................................................. 61
3.3 Calibration and validation of the site scale model SHYD ............................ 66
3.3.1 Snow and frost ................................................................................ 66
3.3.2 Soil temperature .............................................................................. 66
3.3.3 Groundwater level in overburden and hydraulic head in bedrock ........................................................................................... 70
3.3.4 Modelling the influence of shallow wells ......................................... 74
3.3.5 Discharge measurement weirs........................................................ 79
3.3.6 Treatment of geosphere-biosphere interface zone ......................... 82
3.4 Hydrological models for the Eurajoki and Lapinjoki basins ......................... 85
3.4.1 Snow water equivalent .................................................................... 85
3.4.2 Computed and measured runoff at UNTAMO boundary condition points ............................................................................... 86
3.5 Introduction ................................................................................................. 89
3.6 Boundary conditions in Eurajoki and Lapinjoki Rivers ................................ 89
3.7 Regional scale hydrological modelling in the safety assessment ............... 91
3.7.1 Flux in the geosphere – biosphere interface ................................... 91
3.7.2 Regional scale modelling ................................................................ 94
3.7.3 Vertical and horizontal fluxes in the biosphere objects ................... 96
3.7.4 Influence of wells .......................................................................... 104
4 UNCERTAINTY AND SENSITIVITY ANALYSIS ................................................ 107
4.1 Introduction ............................................................................................... 107
4.2 Uncertainty related to conceptual model and modelling philosophy ......... 107
4.3 Sensitivity to climate scenario ................................................................... 108
4.4 Sensitivity to drainage density ................................................................... 109
4.5 Sensitivity to interception and transpiration parameters ........................... 113
4.6 Sensitivity to soil hydraulic parameters ..................................................... 113
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4.7 Evaluation of the most important uncertainty factors ................................ 113
5 SUMMARY AND CONCLUSIONS ...................................................................... 115
REFERENCES ........................................................................................................... 119
APPENDIX A. DESCRIPTION OF SHYD SUBMODELS ........................................... 133
App. A.1 Introduction .......................................................................................... 133
App. A.2 Description of SVAT model .................................................................. 133
App. A.3 Snow accumulation and snowmelt ....................................................... 137
App. A.4 Soil water balance ................................................................................ 139
App. A.5 Soil heat balance ................................................................................. 141
APPENDIX B. PARAMETERS OF THE SVAT MODEL ............................................. 143
APPENDIX C. INPUT DATA FOR THE SURFACE HYDROLOGICAL MODEL ........ 145
APPENDIX D. ADDITIONAL CALIBRATION AND VALIDATION RESULTS IN THE PRESENT DAY CONDITIONS ................................................................... 151
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5
PREFACE
This report has been written by Tuomo Karvonen (WaterHope). The project has been supervised by Ari Ikonen (Posiva Oy). The terrain and ecosystems development simulations needed as input data in the hydrological simulations were carried out by Ari Ikonen. The report was reviewed by prof. Björn Klöve (University of Oulu) and his comments did improve the manuscript significantly.
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TERMS AND ABREVIATIONS
BSA Biosphere assessment as an entirety either regarding the reporting of the assessment process or the process itself. Specifically, BSA-2012 refers to the biosphere assessment of 2009.
FEFTRA Deep bedrock groundwater flow model developed at VTT FET Forest extensive-level monitoring plot, a basic unit of a systematic 100 x 100 m² environmental monitoring grid at Olkiluoto FIP Forest intensive monitoring plot, a part of the environmental monitoring network at Olkiluoto GIS Geographical information system GBIZ Geosphere-biosphere interface zone; the boundary between bedrock and
overburden layers MRK Wet deposition monitoring plot OMO Olkiluoto Monitoring Programme SHYD Olkiluoto surface hydrological model SVAT Soil-Vegetation-Atmosphere-Transfer model used for computing water and
energy balance TESM Terrain and ecosystems development modelling as the sub-process. Specifically
TESM-2012 refers to the effort and reporting within BSA-2012 UNTAMO A GIS toolbox customised for Posiva for TESM
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1 INTRODUCTION Following the guidelines set forth by the Ministry of Trade and Industry (now Ministry of Employment and Economy), Posiva is preparing to submit a construction license application for the final disposal of spent nuclear fuel at the Olkiluoto site, Finland, by the end of the year 2012. Disposal will take place in a geological repository implemented according to the KBS-3 method. The long-term safety section supporting the license application will be based on a safety case that, according to the internationally adopted definition, will be a compilation of the evidence, analyses and arguments that quantify and substantiate the safety and the level of expert confidence in the safety of the planned repository (Hjerpe et al. 2010). Posiva Oy (Posiva) was established in 1995 by the two Finnish nuclear power companies, Teollisuuden Voima Oyj (TVO) and Fortum Power and Heat Oy (Fortum), to implement the final disposal programme for spent nuclear fuel and to carry out the related research, technical design and development (RTD, or TKS, in Finnish). The spent nuclear fuel is planned to be disposed of in a KBS-3 type of repository to be constructed at a depth of about 420 metres in the crystalline bedrock at the Olkiluoto site. Currently, two variants of the KBS-3 method are under consideration, KBS-3V and KBS-3H. In KBS-3V, the canisters are emplaced vertically in individual deposition holes constructed in the floors of deposition tunnels. In KBS-3H, several canisters are emplaced horizontally in a system of 100-300 m long deposition drifts. In both variants, the canisters are surrounded by a swelling clay buffer material that separates them from the bedrock and, in the case of KBS-3H, also separates the canisters one from another along the deposition drifts. The KBS-3V deposition tunnels and other underground openings in both variants are to be backfilled with a low permeability material (Hjerpe et al. 2010). In 2001, the Finnish Parliament ratified the Government’s favourable Decision in Principle on Posiva’s application to locate a repository at Olkiluoto. This decision represents the milestone prior to entering the phase of confirming site characterisation. Following the guidelines set forth by the Ministry of Trade and Industry (now the Ministry of Employment and Economy), Posiva is preparing for the next step of the nuclear licensing of the repository, which involves submitting the construction licence application for a spent fuel repository by the end of 2012. A safety case will be produced to support the licence application. 1.1 Olkiluoto site Olkiluoto is a moderately sized island (currently an approximate area of 12 km2), on the coast of the Baltic Sea, separated from the mainland by a narrow strait. The Olkiluoto nuclear power plant, with two reactors in operation, and a repository for low- and intermediate-level waste are located on the western part of the island. The construction of a new reactor unit (OL3) is underway at the site. The repository for spent fuel will be constructed in the central-eastern parts of the island after the construction licence for the spent fuel repository has been obtained. The construction of an underground rock characterisation facility, called ONKALO, started in June 2004.
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1.2 Safety case and biosphere assessment Posiva is currently producing a safety case to support the construction licence application for a KBS-3 type of repository at the Olkiluoto site. A safety case is a synthesis of evidence, analyses and arguments that quantify and substantiate the long term safety, and the level of expert confidence in the safety, of a geological disposal facility for radioactive waste (IAEA 2006, NEA 2004, 2009). Posiva's plan for the safety case was initially prepared in 2004 (Vieno & Ikonen 2005), and has been revised in 2008 (Posiva 2008). A safety case includes a quantitative safety assessment, which is defined as the process of systematically analysing the ability of the disposal facility to provide the safety functions and to meet technical requirements, and evaluating the potential radiological hazards and compliance with the safety requirements (Posiva 2008). The safety case 2012 comprises altogether fifteen Safety case portfolio main reports and several supporting reports. The present report regarding surface and near-surface hydrological modelling is one of the main reports. The other main reports that are closely linked with this study are Formulation of Radionuclide Release Scenarios, Data Basis for the Biosphere Assessment, Assessment of Radionuclide Release Scenarios, Biosphere Assessment, Terrain and Ecosystems Development Modelling (TESM), Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports. 1.2.1 Biosphere assessment BSA-2012 The overall aims of the biosphere assessment (BSA-2012) in the safety case are:
to describe the future, present, and relevant past conditions at, and prevailing processes in, the surface environment of the Olkiluoto site (Data Basis for the Biosphere Assessment, Biosphere Assessment and Terrain and Ecosystems Development Modelling reports)
to model the transport and fate of radionuclides hypothetically released from the repository through the geosphere to the surface environment (Biosphere Radionuclide Transport and Dose Assessment)
to assess possible radiological consequences to humans and other biota (Dose Assessment for the Plants and Animals)
The surface environment will evolve significantly on a timescale comparable to that of variations in the radionuclide release from the geosphere. For example, areas that are currently sea bottom will develop into terrestrial areas and lakes will be formed over a period of a few millennia. The main approach in the BSA-2012 is to develop a fully dynamic model for the development of the surface environments, radionuclide transport and radiological consequences analysis (Biosphere Assessment). The time window adopted in the present assessment is the period over which the regulatory dose constraints are assumed to apply. It starts at the year of the emplacement of the first canister and lasts for ten millennia; covering the period from the year AD2020 to the year AD12020 (Hjerpe et al. 2010; Biosphere Assessment). In the radionuclide transport modelling process, the fate of radionuclides potentially released to the biosphere is assessed. The main task of this process is to estimate the spatial and
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temporal distribution of each radionuclide in all biosphere objects included in the landscape model. The ecosystem-specific radionuclide transport models used for the biosphere objects are called biosphere object modules (Section 2.6.2) and, in the present assessment, include: forest, wetland, cropland, pasture, lake, river, coast, and sea.
1.2.2 Description of the modelling chain needed in BSA-2012
The repository areas shown in Figures 1-1 are planned to be located in the deep bedrock around 410-420 m below sea level. The only pathway of radionuclides from the repository depth to the bedrock-overburden interface is via upward groundwater flow in the bedrock as illustrated in Figures 1-1c and 1-3. The driving force for the deep pathways is the hydraulic head difference between the recharge areas (land areas inside the present island boundaries) and the discharge areas (sea or lake or terrestrial areas around the present Olkiluoto Island). The hydrogeological zones shown in 1-1b (see Section 2.4.5 for more details) play an important role in the quantification of the deep groundwater fluxes. The dose assessment calculations of the possible release of radionuclides from the repository area can be divided into four main modelling phases:
1) Radionuclide transport from the repository areas to the bedrock-overburden interface (see Figure 1-1c). Radionuclide transport in the bedrock system is not the topic of this report. The descriptions of these modellings are shown in the Formulation of Radionuclide Release Scenarios report and Assessment of Radionuclide Release Scenarios reports included in the safety case. The outcomes of these models are predictions of spatial and time-dependent activities of the most important radionuclides and most probable discharge areas of radionuclides (“future risk areas”, see Figures 1-2 and 4-6).
2) The possible radionuclide discharge areas from step 1) are used to delineate the so called biosphere objects, which may receive radionuclides either via upward flux from bedrock or from the surrounding area via subsurface flow or through irrigation water taken from nearby lake or river. The biosphere objects are continuous and sufficiently homogeneous sub-areas of the modelled area that could potentially receive radionuclides released from the repository. Biosphere object types are coast, lake, river, forest, cropland, pasture and mire. Due to postglacial crustal uplift the ecosystem types change spatially and also over time. UNTAMO toolbox provides detailed information for the hydrological modelling related to the time-dependent delineation and evolution of the biosphere objects. Description of the UNTAMO toolbox and associated modellings are given in the Terrain and Ecosystems Development Modelling report.
3) The hydrological modelling of the vertical and horizontal fluxes in the future risk areas (biosphere objects) is the most important topic of this report. Hydrological modelling starts with the FIP- and MRK-plots (see Sections 2.2 and 2.3), which are small biosphere objects intensively monitored by Posiva in the present day conditions. The sub-model developed is called SVAT-model (Soil-Vegetation-Atmosphere-Transfer). The SVAT-model is then used as a sub-model in the site scale model, which is calibrated and validated using data sets collected by Posiva. Site scale model is further extended to regional scale
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for computation of future hydrological conditions (next 10 000 years) using input data provided by the UNTAMO toolbox in phase 2). The regional scale hydrological modelling is carried out first as a 3D simulation. The final results of the hydrological model are the vertical and horizontal water fluxes in the biosphere objects compiled from the results of the 3D-model.
4) The fourth modelling phase includes the radionuclide transport modelling carried out for the biosphere objects using the fluxes computed in step 3) and subsequent risk analysis (dose assessment modelling for humans and other biota). These modelling are not topics of this report: the analysis and results are given in the Biosphere Radionuclide Transport and Dose Assessment and in Dose Assessment for the Plants and Animals reports.
There does not exist any single model that could handle all the four modelling phases described above. Therefore, the linking of the four models has to be carried out using ASCII-, raster- , shape- and Excel-files. The Assessment of Radionuclide Release Scenarios modelling provides as output data the locations of possible discharge areas of radionuclides as ASCII-files. UNTAMO toolbox provides output data as raster files, shape files and Excel-files. The biosphere radionuclide transport model uses the same input data for biosphere objects than the hydrological model. The vertical and horizontal fluxes for the biosphere objects are delivered as ASCII-files from the hydrological modelling phase to biosphere radionuclide transport calculations. Simple conceptual model of the flux components needed in the biosphere assessment is shown in Figure 1-3 indicating that the hydrological model needs to link vegetation, overburden soils, bedrock and surface water systems (sea, lakes and river) into one model. The modelling philosophy related to the hydrological model is driven by the ultimate goal of the BSA-2012: doses for humans and other biota. In order to reduce the computational burden in the biosphere radionuclide transport calculations the study area is delineated by UNTAMO toolbox into homogenous biosphere objects which may receive radionuclides. The intensive forest monitoring system carried out by Posiva (Section 2.2) produces measured data for calibrating and validating the SVAT-model in the biosphere object scale (FIP- and MRK-areas). The hydrological modelling is then extended from FIP-areas to site and regional scales but the final results of the hydrological modelling (vertical and horizontal fluxes) are given for the biosphere objects. The details of hydrological computations are shown in Chapters 3 and 4.
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Figure 1-1. a) Present shoreline of the Olkiluoto Island, the surrounding sea areas and location of the repository areas (uppermost graph). b) 3D-view of the repository areas (elevation -420..-410 m) together with the most important hydrogeological zones located close to the repository areas. View is from north-east towards south-west (middle graph). c) Conceptual view of the bedrock fluxes needed to be computed in the safety case (lowermost graph, 3D-view from east towards west). The size of the red box shown in b) and c) is 2x2 km2.
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Figure 1-2. a) Map showing the location of the repository areas and possible discharge points of radionuclide pathways from the repository to the terrestrial or aquatic areas around the present island boundaries (AD4020 situation). Sea has receded from the present location due to postglacial crustal uplift and lakes have been formed around the Olkiluoto Island (upper graph), and b) 3D-view (from east towards west) of the modelling area showing the surface water systems (rivers, lakes and sea area), agricultural areas and possible discharge points of the radionuclide pathways. Areas not indicated as agricultural land are primarily forested areas.
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Figure 1-3. Conceptual view of the water fluxes needed to be computed in the safety case. In the bedrock the downward flux inside the present island boundaries is the driving force that causes upward fluxes from the repository depth towards the bedrock-overburden interface. In future conditions vertical water fluxes are possible both in aquatic and terrestrial areas. The fluxes in bedrock fluxes include both bedrock matrix and hydrogeological zones described in Section 2.4.5. The water fluxes indicate in a simplified way also the most important radionuclide pathways from the repository to the biosphere. 1.3 Scope of this report The primary goal of the surface hydrological modelling described in this report is to provide vertical and horizontal water fluxes for the biosphere objects that are potentially receiving radionuclides from the repository. The most important modelling phases related to hydrological modelling in the BSA-2012 are shown below:
1) Calibration and validation of the SVAT-model for computation of the water balance of the FIP- and MRK-areas (small biosphere forest objects).
1) Calibration and validation of the Olkiluoto site scale 3D surface hydrological model in the present day condition.
2) Calculation of boundary conditions for the UNTAMO toolbox in Eurajoki and Lapinjoki Rivers.
3) Computation of regional scale hydrological model using input data provided by the terrain and ecosystem modelling (Terrain and Ecosystems Development Modelling report).
4) Calculation of vertical and horizontal fluxes and water balance components for the biosphere objects. These data are used in the computation of radionuclide transport in the biosphere objects and dose assessment calculations (Biosphere Radionuclide Transport and Dose Assessment report and Dose Assessment for the Plants and Animals report).
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The modelling results of this study will be calculated for the Reference Case and Terr_MaxAgri Case. The descriptions of the safety case simulation runs are given in the Biosphere Assessment and Terrain and Ecosystems Development Modelling reports. Reference Case uses the meteorological data from the present day climate. In the Terr_MaxAgri Case maximum reasonable extent of agricultural land is assumed, including also small fields (min. 0.5 ha, at present only 0.4% of farms in Satakunta). The climate scenario A2 provided by the Finnish Meteorological Institute (Pimenoff et al. 2012) and briefly described in Chapter 2 is used in the Terr_MaxAgri Case. This report includes the computation of the hydrological behaviour of the biosphere objects and therefore, the residence times of radionuclides in the bedrock system and in the biosphere system are not computed. Moreover, the risk analysis (dose assessment for humans and other biota) falls outside the scope of this study. The residence times in bedrock are the given in the Assessment of Radionuclide Release Scenarios (bedrock) and risk analysis results are shown in Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports.
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2 MATERIAL AND METHODS 2.1 Introduction In July 2004, Posiva Oy began to construct an underground rock characterization facility called ONKALO on Olkiluoto Island. In February 2012, excavation of the ONKALO access tunnel reached -455 m b.s.l., which is the deepest location according to the the existing plans. Construction of ONKALO and subsequent construction of the repository, will affect the surrounding rock mass and the groundwater flow system. It will also affect the chemical environment, not only on the surface but, to a greater extent, at depth. While many changes may be reversible, some may only be partially reversible and some irreversible. In order to determine the magnitude and extent of such effects, a monitoring system has been set up to measure the resulting changes. In December 2003, a programme for monitoring at Olkiluoto during construction and operation of ONKALO was presented (Posiva 2003b). The program is called Olkiluoto Monitoring Programme (OMO). The monitoring results can be divided into two parts: 1) data needed for monitoring the state of the environment during the construction work and 2) the data collected as input for biosphere modelling for long-term safety purposes. Naturally, these data partly overlap and both data sets are used in model calibration and validation (Haapanen 2012; Vaittinen et al. 2012). A summary of observations and measurements is reported annually for each discipline: Hydrology, Geochemistry, Environment, Rock Mechanics and Foreign Materials.
The data sets utilized in this study have been collected mainly in the environmental (Haapanen 20052012; Aro et al. 2010, 2011) and hydrological programme (Ahokas et al. 2005; Tammisto et al. 2006; Klockars et al. 2007; Vaittinen at al. 2008, 2009, 2010, 2011 and 2012). The presentation of the data sets used in various modelling phases both in the present day climate and in future climate is given in this Chapter. As part of the site investigations for disposal of spent nuclear fuel, hydrological and hydrogeological monitoring has been going on in the Olkiluoto area since 1989. The baseline conditions at Olkiluoto have been defined (Posiva 2003a). The baseline report contains information (e.g. groundwater level and hydraulic heads in the bedrock before the construction of ONKALO started) about the site that can be used in calibration and validation of the models. The basic principle of the forest monitoring program that has been set up on the Olkiluoto Island is first described in Section 2.2. The data sets used in the calibration and validation of the plot scale SVAT model are described in Section 2.3, the data utilized in the site scale are discussed in Section 2.4 and catchment scale data used for calibration and validation of the Eurajoki and Lapinjoki models are shown in Section 2.5. A safety case will be produced to support the licence application for the final disposal of spent nuclear fuel at the Olkiluoto site. Data used in computing the hydrological model in future climatic conditions as a part of the safety case are given in Section 2.6. The presentation of the models given in Section 2.7 follows an analogous division that is used for describing the data sets. First, plot scale SVAT model is introduced. Second,
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the Olkiluoto surface hydrological model (SHYD) aimed to calculate site scale hydrological behavior in the present day condition is briefly described. Third, catchment scale models for the Eurajoki and Lapinjoki basins are introduced. Fourth, extension of the site scale hydrological model for the regional scale needed in the 2012 safety case is described. Finally, the principles for obtaining water balance components and vertical and horizontal water fluxes used in the radionuclide dose computations in biosphere objects are outlined. The main procedure for model calibration and validation is also shown in Section 2.7. The main reason for presenting the extensive forest monitoring program in such a detailed manner is that during the next 10 000 years the areas which potentially receive radionuclides from the repository will be either small forested or cropland biosphere objects located under the present sea level. Therefore, these areas cannot be monitored now. However, Posiva has organized detailed forest monitoring to areas (FIP and MRK) that are close to sea level and these plots represent the future forested biosphere objects. Olkiluoto Island was almost completely below sea level 2 500 years ago and forest ecosystems in FIP- and MRK-areas have been developed to the present condition during this period. The same type of development is expected to occur during the next millennia. Therefore, the areas presently located under the sea level will be land areas and most likely either forests or croplands (UNTAMO predicts the terrain and ecosystem development during the next 10 000 years as shown in the Terrain and Ecosystems Development Modelling report). The hydrological behaviour of cropland biosphere objects has not been measured on the Olkiluoto Island due to lack of appropriate field area. The hydrological behaviour of croplands is largely determined by the drainage system and several modeling projects are available that enable the computation of water fluxes in the agricultural fields (Karvonen 1988; Karvonen and Varis 1992; Kleemola and Karvonen 1996; Karvonen et al. 1999 and Warsta et al. 2008) and drainage flux computation has been adopted from these studies.
2.2 Forest monitoring network in Olkiluoto
The forest monitoring system consists of several overlapping levels (Figure 2-1; Aro et al. 2011). The first level is used for following changes in land use by interpreting aerial images. The second level is vegetation-type mapping, the purpose of which was to classify the vegetation and its distribution for use as a basis for the monitoring of primary plant succession caused by the postglacial crustal uplift (about 6 mm/year, e.g. Haapanen et al. 2009) at the plant community level and the possible anthropogenic environmental impact (Haapanen 2009). The third monitoring level (FET, Forest ExTensive monitoring plots) is a grid of systematically located plots which are used to describe biomass distribution of forests and to monitor growth and other changes in tree stands. The last two levels (MRK and FIP) comprise plots where observations are made daily or hourly. The intensity of the sampling efforts increases towards the sixth monitoring level (Figure 2-1; Aro et al. 2011). FIP and MRK areas provide temporal data for model calibration and validation (see Sections 2.3 and 2.4) and the upper monitoring levels shown in Figure 2-1 produce primarily spatial input data needed in
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the site and regional scale models. The specific aim of the hydrological modelling of this study is to provide vertical and horizontal water fluxes and water content of different overburden layers in so called the biosphere objects (see Section 2.6.2). It is important to note that FIP and MRK plots are small biosphere objects which imply that Posiva has organized the monitoring system wisely in such a way that it is possible to obtain measured data from very similar ecosystems that are handled in the safety assessment.
Figure 2-1. Forest monitoring levels. The outermost land-use grid consists of plots at 50 m intervals. These have been visually interpreted for land-use. VCP contains the vegetation polygons, from which the forest resources have also been inventoried. The numbers of currently monitored plots are 485 (FET), 94 (FET sampling plots), 6 (MRK) of which 4 belong to the FIP grid as well. Grids have been modified (plots added/removed) according to increased knowledge of data needs and land-use changes on the island (adapted from Aro et al. 2011). 2.3 Data for the SVAT model 2.3.1 Forest Intensive Monitoring Plots: FIP The functioning of forest ecosystems on the island is studied in Forest Intensive monitoring Plots (FIP). Totally four plots have been established on the Olkiluoto Island as shown in Figure 2-2: FIP4 (Scots pine forest), FIP10 (Norway spruce forest) and FIP11 (young Norway spruce/birch forest). FIP4 and FIP10 established in 2003 represent Oxalis-Myrtillus/grove-like mineral soil forest site types growing on fine-textured till. The third intensive monitoring plot (FIP11) was established in a young Norway spruce and birch stand nearby in late 2006, and the installation of equipment was finished during 2007. The fourth FIP plot (FIP14 was established in an alder stand of herb-rich type in 2009. The instrumentation of the FIP plots is given by Aro et al. (2011, Table 2, p. 9) and detailed list of performed monitoring activities and their frequency on the FIP plots is shown in Haapanen 2011 (Table 12, p. 24).
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Figure 2-2. Forest Intensive Monitoring Plots (FIP-areas) and forest monitoring locations in 2011. Map data: Topographic database by the National Land Survey of Finland (permission 41/MYY/12) and Posiva Oy. Map layout by Jani Helin/Posiva Oy. (adapted from Haapanen 2012). 2.3.2 Transpiration, interception and throughfall Two independent methods are available for estimation of the transpiration rate on Olkiluoto Island: direct measurements and computational methods utilizing hourly values of measured micrometeorological variables (see Section 2.3.3 ) and the Penman-Monteith equation (Appendix A). The tree stand transpiration measurements on Olkiluoto Island were initiated on FIP4 and FIP10 in early May and early June 2007, respectively. Measurement system was enlarged with three new trees on both the plots in April 2010. The aim was to measure tree-level transpiration as a basis for calculating stand transpiration rate and variability in the FIP areas (Aro et al. 2011, Section 3.3, p. 18). Since weather conditions (humidity, wind, radiation) determine the rate of transpiration, the meteorological data collected in the FIP4 weather station can be used in studying the variability of transpiration in relation to variations in local weather. The establishment of the system, calculation of sapwood area and results for 2007 and 2008 were presented earlier in memos by Hökkä (2008a, b). Some problems occurred in sap flow measurements especially during the winter season in 2009 and 2010. Especially some measuring observations were missing which resulted in unreal peaks in calculated transpiration. Therefore calculated values for tree transpiration can be considered reliable only for the period from the end of March to the
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beginning of December and consequently reliable for the period from April to November on month basis (Aro et al. 2011, p. 28). The construction activities and rock crushing (i.e. an underground rock characterisation facility and an access to the spent fuel repository) on the Olkiluoto Island are producing a potentially negative impact on forests, primarily in the form of stone dust. To monitor the effects on the forests, a bulk deposition and stand throughfall MRK-monitoring network with rainwater and snow collectors (Figure 2-3) was established in 2003. The annual precipitation and interception of the tree canopies are also recorded on these plots. Currently four of the MRK monitoring plots are within FIP plots and two in open areas (Figure 2-3). Rainwater is collected every two weeks and snow every four weeks (Aro et al. 2011). 2.3.3 Meteorological data Meteorological observations are mandatory for a nuclear power plant, thus a compre-hensive database of major meteorological parameters is available from a weather mast OL-WOM1 (Figure 2-4). The collection of the database was started in 1994. Within the forest intensive monitoring plots OL-FIP4, OL-FIP10, OL-FIP11 and OL-FIP14 meteorological measurements are recorded once an hour from corresponding weather masts OL-WOM2–5. The parameters monitored include air temperature, minimum and maximum temperature inside the crown layer and above the canopy (latter only on mast OL-WOM2, which reaches above the tree canopies), relative humidity, precipitation (1 m above ground level), soil moisture content, and soil temperature. Photosynthetically active radiation (PAR), solar radiation, air pressure, wind speed and its direction are measured only on OL-WOM2. Meteorological data available from weather masts OL-WOM1 and OL-WOM2 enable the computation of potential evapotranspiration rate using the Penman-Monteith equation. 2.4 Data for the site scale model in the present day conditions 2.4.1 Land use mapping A vegetation classification and mapping were performed on Olkiluoto Island in the summer 2002 as the first step in the planned monitoring of forest and mire ecosystems in the context of the final reposition of nuclear waste. The main island was divided into distinguishable vegetation polygons (patches), and the vegetation type in each polygon was then determined using the classification as described by Miettinen and Haapanen (2002; Rautio el al. 2004). For this and further purposes, a series of aerial photographs were taken in May 2002 at a scale of 1:10 000. The photographs were used as the basis of polygon delineation. Vegetation classification was performed in the field, where the polygons also received their final form. The delineated vegetation polygons are shown in Figure 2-5.
Figure 2-3. Location of OL-MRK plots. Plots OL-MRK2, 4, 10, 11 and 13 are used in interception and throughfall monitoring, but all forest locations (OL-MRK11 and 14 excluded) are subject to needle sampling and analyses. Map data: Topographic database by the National Land Survey of Finland (permission 41/MYY/12) and Posiva Oy. Map layout by Jani Helin/Posiva Oy (adapted from Haapanen 2012).
Figure 2-4. Locations of Olkiluoto weather stations OL-WOM1–6. Map data: Topographic database by the National Land Survey of Finland (permission 41/MYY/12) and Posiva Oy. Map layout by Jani Helin/Posiva Oy (adapted from Haapanen 2011).
Rautio et al. (2004) supplemented the classification carried out by Miettinen and Haapanen (2002) and provided the spatial input data needed in the site scale hydrological model regarding site type, land use class, vegetation class, developmental class, soil type, drainage type etc. The detailed list of attributes available from each polygon are shown in Rautio et al. (2004, Appendices AC). The total area of forest compartments in the area covered in the present study is 571.1 hectares, of which 539.7 hectares (95%) is forest land, 27.2 hectares scrub land and 1.6 hectares waste land the rest being built-up areas, power lines etc. The soils in the area are rather fertile. Fresh mineral soil (Myrtillus site type, MT) is the dominant type with over 60 % coverage. Grove-like mineral soil (Oxalis-Myrtillus site type, OMT) covers around 20%. Only 3.9% of the 571 hectares in the area is peatland (around 22 ha) and most of this (19 ha) is covered by spruce swamp. Pine swamps cover two hectares and open bogs less than one hectare. The average volume of the growing stock in the study area is 100 m3/ha and average annual increment on the forest land is 6.3 m3/ha/year (Rautio et al. 2004). When the tree species are considered Norway spruce is responsible for the most of the volume and increment of growing stock followed by Scots pine and silver birch. The average volume (m3/ha) is highest in OMT-forests (site type:grove-like mineral soil) but since MT-forest (site type: fresh mineral soil) cover largest area most of the total volume as well as increment can be found in these forests.
Figure 2-5. The vegetation patches identified in Olkiluoto Island (Miettinen and Haapanen 2002).
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The Olkiluoto surface hydrological model (SHYD) uses the land use the delineated patch data provided by Miettinen and Haapanen (2002) and Rautio et al. (2004). Patches are homogenous units in terms of land use type, vegetation, soil type etc. and they allow handling of spatial patterns of hydrological variables both above the soil (throughfall from precipitation, potential evapotranspiration, transpiration, interception, snow processes) and in the soil profile (infiltration, soil water fluxes). Moreover, patches provide a framework which enables the model to be used for description of the future evolution of the overburden hydrology at the site during the next 10 000 years.
2.4.2 Soil surface elevation and stream network
The soil surface elevation model or digital elevation model (DEM) is the basis for the the 3D-model hydrological model (Pohjola et al. 2009). Soil surface elevation and bedrock elevation (see Section 2.4.3) must be interpolated to grid corner and centre points. Digital elevation data must be combined with the drainage network (small natural streams and man-made ditches digitized from existing maps) that provides the fastest pathway of surface and groundwater to the sea. Soil surface elevation given in 10x10 m2 grid and the stream network are shown in Figure 2-6. The ditches take water from the grid if groundwater level computed by the model is above the bottom of the ditch (see Appendix A, Equation A-19). Basic assumption for ditch depth can be given as input data but ditch depth can also be given separately for each section if data is available. In this study forest ditch depth was assumed to be 0.9 m based on average depth of the ditches measured in the field.
Figure 2-6. Soil surface elevations and stream network on the Olkiluoto Island in the present day conditions. The present island boundaries are shown in the map.
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2.4.3 Soil profile thickness and bedrock elevation
Soil profile thickness data shown in Figure 2-7 were provided by the UNTAMO-toolbox (Ikonen et al. 2012). The bedrock elevation can be obtained by subtracting the thickness of the soil profile from the soil surface elevation data. The overburden layers are relatively shallow inside the present island boundaries.
Figure 2-7. Soil profile thickness (m) in the present day conditions. Data provided by the UNTAMO-toolbox. The present island boundaries are shown in the map.
2.4.4 Soil type classification
The forest classification defined by Miettinen and Haapanen (2002) and Rautio et al. (2004) included also soil type for all patches and this classification was used as the initial spatial input data to the hydrological model (see Figure 2-8a). One specific aim of the Olkiluoto surface hydrological model is to predict the influence of leakages into ONKALO on changes in groundwater level in overburden layers and hydraulic heads in the bedrock. The changes are being monitored in the OMO-program (Vaittinen at al. 2010, 2011, 2012). The total number of overburden tubes (OL-PVP, see Section 2.4.6) from which regular groundwater level measurements are available is 35. Seventeen OL-PVP-tubes out of 35 are located on fine-textured till and nine tubes are located on sandy till soil so that totally 26 tubes out of 35 are situated on two major soil types classified by METLA on Olkiluoto Island. Therefore, the two most important soil types were divided to sub classes with different soil water retention curve parameters. Delineation of new soil types is shown in Figure 2-8b. Moreover, two new soil types were defined below and around the Korvensuo reservoir (bottom of the reservoir and embankments). The soil type delineation shown in Figure 2-8b is used only in selecting the parameters of the soil water retention curve.
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2.4.5 Hydrogeological zones and bedrock data
The geosphere-biosphere interface zone (GBIZ), or the boundaries between the geosphere and biosphere modelling domains of the safety assessment, is an important issue (e.g. Bioprota 2005; Lahdenperä 2006) and treatment of this zone needs to be site-specific. On the Olkiluoto Island the role of hydrogeological zones is crucial in handling bedrock water fluxes. As part of the programme for the final disposal of spent nuclear fuel, a hydrogeological structure model containing the hydraulically significant zones on Olkiluoto Island has been compiled (Vaittinen et al. 2011a). The structure model describes the deterministic site scale zones that dominate the groundwater flow in the bedrock system. The geometry and the hydrogeological properties related to the groundwater flow for the zones and the sparsely fractured bedrock to be used in the numerical modelling of groundwater flow and geochemical transport and thereby in the safety assessment. The hydrogeological zones and are also included in the SHYD model (see Karvonen 2011, Section 2.5.1) in addition to the sparsely fractured bedrock between the zones. Totally 14 site scale zones are included in the most recent version (Vaittinen at al. 2011): HZ001, HZ008, HZ19A, HZ19B, HZ19C, HZ20A, HZ20B, HZ21, HZ21B, HZ039, HZ099, OL-BFZ100, HZ146 and HZ056. Moreover, five lineaments are included in the structure model. Location of the lineaments and hydrogeological zones is given in Figure 2-9. The treatment of the hydrogeological zones is given by Karvonen (2010, Section 2.4, p. 9-13) and parameterization of the hydraulic properties of the zones and sparsely fractured bedrock are given in Appendix C. . 2.4.6 Hydrological and hydrogeological monitoring Extensive hydrological and hydrogeological monitoring has been carried out since 1989 in Olkiluoto (Ahokas & Herva 1993, Hänninen 1996, Lehtimäki 2001, Voipio et al. 2004 Ahokas et al. 2005, Tammisto et al. 2006, Klockars et al. 2007, and Vaittinen et al. 2008, 2009, 2010, 2011, 2012). Originally surface-based monitoring has been continued and augmented by additional monitoring equipment specifically designed to monitor effects of the ONKALO construction project on the groundwater flow system. Since the construction of ONKALO began, hydrological monitoring has focused on inflows of groundwater into ONKALO, changes induced in the hydraulic head, evolution of the hydraulic properties (i.e. changes in hydraulic conductivity) and changes in groundwater flow rates and directions, both on the surface and in ONKALO. The whole hydrological and hydrogeological system is affected by postglacial crustal uplift (6 mm/y) typical to the Finnish coast in general (Posiva 2003a).
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Figure 2-8. a) Topsoil type in the present day condition (upper map). Soil type taken from the polygon inventory of Rautio et al. (2004). Data outside the polygons provided by the UNTAMO-toolbox. b) Soil type delineation around the ONKALO area.
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Figure 2-9. a) The five lineaments bounding the Olkiluoto Island (upper map). b) The hydrogeological zones included in the basic hydrogeological structure model. Fracture transmissivities in different classes are shown as coloured discs (oriented fractures) or horizontal squares (unoriented fractures). View towards northeast (Vaittinen at al. 2011). The following parameters are included in hydrological OMO-programme monitoring (Posiva 2003b):
groundwater level in overburden tubes (OL-PVP) hydraulic head in shallow (OL-PP) and deep (OL-KR) bedrock drillholes flow conditions in open drillholes groundwater flow across drillholes
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hydraulic conductivity groundwater salinity (in situ EC) leakage of groundwater into tunnels hydrological/water balance in tunnels runoff precipitation (including snow) sea water level infiltration soil frost
The most important data utilized in the Olkiluoto surface hydrological model are groundwater level in overburden tubes (OL-PVP), hydraulic head in shallow (OL-PP, OL-PA, OL-PR, OL-L) and deep (OL-KR) bedrock drillholes, hydraulic conductivity, leakage of groundwater into tunnels, runoff, precipitation and soil frost. Groundwater level in overburden soils is measured in 40 tubes (see Figure 2-10a), hydraulic head in shallow bedrock in 45 drillholes (see Figure 2-10b) and hydraulic head in 31 packed-off deep bedrock drillholes (see Figure 2-10c) including 190 measurement sections. Locations of snow and frost depth measurements are shown in Figure 2-11. Snow depth is measured at 20 points in four different vegetation types: no vegetation (open areas), pine forests, spruce forests and mixed forests. Frost depth is measured in 11 points and most of the frost measurement points are located close to the snowline. Soil temperature measurements are available from four FIP-areas. Measurements sensors are installed at 0.1 m interval to the depth of 0.9 m. (0.1, 0.2,…, 0.9 m). Frost depth and the thickness of the snow cover are measured manually also on OL-FIP4 (2 ground frost measuring points), and on Olkiluodonjärvi and Liiklansuo mires (1 ground frost point in each). On FIP4, an automatic snow depth measuring station has been tested during springs 2011 and 2012. Discharge measurements started in spring 2003 and four overflow weirs were installed (OL-MP1OL-MP4, see Figure 2-11). During the first years these monitored manually once a week and this caused a great uncertainty in the measurements and estimated runoff based on these manual data. Automatic weirs for measuring hourly discharge were installed in late April 2008 (Haapanen 2009). The automatic weirs measure discharge in unit volume over time (l s-1) and these data are converted into runoff (mm d-1 or mm h-1) so that it is in the same unit than precipitation. The computation of the weir measurement results in unit mm d-1 enables the evaluation of the runoff component of the total water balance of the Olkiluoto Island for the four small catchment areas. The runoff values computed from the measured discharge rates are shown in Chapter 3. Unfortunately there have been problems in discharge measurements due to frozen conditions and technical problems related to water level measurements. In the modelling phase only those results are used where reliable measurements are available.
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Figure 2-10. a) Location of groundwater level observation tubes (OL-PVP) (upper map) and b) location of shallow bedrock drillholes (OL-PP, OL-PR, OL-PA and OL-L) (middle map) and c) location of packed-off drillholes (OL-KR) (lowest map). Note! Only 31 drillholes out of 70 were packed-off at the end of year 2011. Soil surface elevation and present shoreline of the Olkiluoto Island are indicated in the map. KR6 is the long-term pumping drillhole.
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2.4.7 Pumping and infiltration tests
A new feature of the SHYD modelling compared to previous assessments is that anthropogenic (shallow) wells are included; both dug in the overburden and drilled in the bedrock. Two specific experiments have been carried out on the Olkiluoto Island that provide measured data to test the capability of the model to predict properly the effect of pumping from shallow well on groundwater level in overburden soils and hydraulic heads in shallow bedrock: 1) infiltration experiment that includes pumping from OL-KR14 and 2) long-term pumping from OL-KR06. Infiltration experiment and pumping from OL-KR14 A field experiment, an infiltration experiment to investigate potential changes in pH and redox conditions and in buffering capacity as well as the hydrogeochemical processes related to groundwater infiltration was started in late 2008 near ONKALO (Pitkänen et al. 2008; Aalto et al. 2011). The idea is to monitor the major infiltration flow path from the ground surface into the upper part of ONKALO at a depth of about 50 to 100 m depending on the observations made during the experiment. Infiltration is activated by pumping a highly transmissive fracture zone in drillhole OL-KR14. The pumping interval is part of site scale hydrogeological feature HZ19A. The pumping rate has varied between 34 l/min (4.35.8 m3/d or 15802100 m3/y), which is around 34 times more than the water supply capacity of 500 m3/y selected for the private well scenario in the 2012 safety assessment (Data Basis for the Biosphere Assessment report). The monitoring program related to the infiltration experiment has been very versatile. The influence of pumping is followed in the nearest drillholes and groundwater observation tubes through hydrogeological measurements, groundwater and microbiological samplings. The infiltration experiment provides excellent data for model testing concerning to the influence pumping from OL-KR14 on groundwater level in overburden soils and hydraulic head in shallow bedrock. Long-term pumping test in drillhole OL-KR06 A long-term pumping test was started in 2001 in drillhole OL-KR6. The primary aim of the study is to obtain information on potential connections via fractures both to the sea and to deep saline groundwater during long-term pumping in the drillhole (Lamminmäki et al. 2008; Pekkanen and Pöllänen 2008; Pekkanen 2010 and 2011). Water level and hydraulic head data collected during long-term pumping from OL-KR06 is also used in testing the Olkiluoto surface hydrological regarding the influence of pumping from shallow well on hydraulic heads in the near-by observation drillholes. Pumping in the drillhole was started on 22 March 2001. Pumping rate has been varied between 18 and 23 l/min (2633 m3/d or 960012100 m3/y), i.e. a much higher amount than the water supply capacity of 500 m3/y selected for the private well scenario in the 2012 safety assessment. Water level in the pumping drillhole has been around 4 m below sea level during pumping and water level was around 1.6 m above sea level before pumping (Pekkanen 2011). The modeling results are shown in Chapter 3.
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Figure 2-11. a) Location of the discharge measurement weirs (OL-MP1OL-MP4), snow depth measurement line and frost depth measurement points (upper map). b) Delineation of the catchment areas lower map, adapted from Haapanen 2011). Present shoreline of the Olkiluoto Island is shown in the map.
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Figure 2-12. Location of groundwater level observation tubes (OL-PVP2129), shallow bedrock drillholes (OL-PP6669) and deep bedrock drillholes (OL-KR1518) around the pumping drillhole OL-KR14 of the infiltration experiment.
2.5 Hydrological models for the Eurajoki and Lapinjoki basins
The aim of the hydrological models of the Eurajoki and Lapinjoki basins (Figure 2-13a) is to provide time dependent boundary conditions for the UNTAMO toolbox. The boundary conditions are needed in those points (Figure 2-13b) where Eurajoki and Lapinjoki Rivers discharge into the 2012 safety assessment modelling area. The models are used to predict discharge coming from Eurajoki and Lapinjoki rivers to the area surrounding the present Olkiluoto Island in future climate conditions (next 10 000 years) but the model calibration needs to be carried out in the present climate conditions.
2.5.1 Meteorological variables
The meteorological variables needed in the hydrological model include daily values for precipitation, air temperature and potential evapotranspiration. Daily data from the above mentioned variables were available from Olkiluoto for the period 1993-2009 and these data were used as the reference data. Areal precipitation in the Eurajoki and Lapinjoki basins is bigger than the value measured in Olkiluoto and a monthly correction was made to Olkiluoto data using data obtained from the Hydrological Yearbooks published by the SYKE (www.ymparisto.fi). The computation period was 01.10.1993-30.09.2010, i.e. total length of the period was 17 years.
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Correction factor for potential evapotranspiration was computed separately for each subcatchment of the River Eurajoki and River Lapinjoki based on long-term water balance studies: PETLONG=PRECLONG - RUNOFFLONG PETCORR=PETLONG/PETOLKILUOTO where PRECLONG is cumulative precipitation, RUNOFFLONG is measured runoff and PETLONG is estimated evapotranspiration rate over the whole computational period, PETOLKILUOTO is estimated evapotranspiration in Olkiluoto and PETCORR is the correction factor for evapotranspiration rate.
2.5.2 Snow water equivalent and runoff
The two most important measured values for the calibration of the hydrological model are snow water equivalent (or snow depth) and runoff. These data were obtained from the database HERTTA maintained by the Finnish Environment Institute (SYKE) in the OIVA-database (www.ymparisto.fi). Snow water equivalent measurements were available from two stations in the Eurajoki Basin (Yläne and Kauttuankoski, Figure 2-13a) and one station in the Lapinjoki basin (Lapinjoki/snow). Four discharge measurements stations were used in calibration of the model in the Eurajoki basin (Pappilankoski, Pyhäjärvi_luusua, Pyhäjoki and Yläneenjoki, Figure 2-13a) and one discharge station was available from the Lapinjoki basin (Ylinenkoski, Figure 2-13a). Discharge values (m3 s-1) given in the database were converted to runoff rates (mm d-1).
2.5.3 Land use data
The Finnish Environment Institute (SYKE) provides the CLC2006-land use/land cover raster data in a 25x25 m2 grid for the whole Finland (OIVA-database). Production of the CLC2000 and updated version CLC2006 database of Finland is based on automated interpretation of satellite images and data integration with existing digital map data. Continuous land cover variables were transformed into 44 discrete land use classes (Figure 2-14). The description of the land use classes and proportion of each land use class in the Eurajoki basin is given in Table 2-1 and the corresponding data for the Lapinjoki basin is shown in Table 2-2.
2.5.4 Digital elevation model
Digital elevation model (DEM) is used to delineate the stream network needed in the hydrological models. In the present study the freely downloadable version of 3’’ (arcsec) DEM was used (Hormann 2010). The resolution of the DEM is around 90x90 m2. Due the low resolution of the DEM the locations of the main rivers of Eurajoki and Lapinjoki rivers were digitized to ensure their exact placement. Flow accumulation raster computed from the DEM was used only to provide local stream network densities. All the calculations related to computation of flow accumulation raster and delineation of the local stream network were performed using ArcGis 9.3 (ESRI™) software (http://www.esri.com).
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Figure 2-13. a) Delineation of the Eurajoki and Lapinjoki basins (upper map) and b) location of discharge measurements points in the River Eurajoki (Pappilankoski) and River Lapinjoki (Ylinenkoski).
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Figure 2-14. Land use classification of the catchment areas of the River Eurajoki and River Lapinjoki based on CORINE-data provided by SYKE (OIVA 2010).
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Table 2-1. CORINE land use classification of the catchment area of the River Eurajoki. Class number, area of each land use class (km2), % from total area and lands use class description shown in the Table. Total area of Eurajoki basin is 1336 km2.
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Table 2-2. CORINE land use classification of the catchment area of the River Lapinjoki. Class number, area of each land use class (km2), % from total area and lands use class description shown in the Table. Total area of Lapinjoki basin is 462 km2.
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2.6 Data for the 2012 safety assessment 2.6.1 Regional scale input data provided by the UNTAMO-toolbox During the next 10 000 years the terrain and ecosystem development is to a large extent driven by the postglacial crustal uplift (see Figures 2-152-18). Full details regarding changes in soil surface elevations, surface water development and associated effects on ecosystem types are predicted by the UNTAMO toolbox and the results are shown in the Data Basis for the Biosphere Assessment and Terrain and Ecosystems Development Modelling reports and only a brief summary is given here. UNTAMO is a GIS toolbox developed for simulating land-uplift driven or other changes in the biosphere. The toolbox consists of modules to represent land uplift and delineation of the sea area, surface-water bodies, terrestrial and aquatic erosion, accumulation of organic matter, terrestrial vegetation, aquatic vegetation, faunal habitats, human settlement and land use (Terrain and Ecosystems Development Modelling). All the spatial and temporal input data (excluding meteorological data) needed in the surface hydrological modelling are provided by the UNTAMO toolbox. The maximum soil surface elevation inside the present island boundaries is around 18 m above sea level but due to postglacial crustal uplift the highest soil surface elevation will be around 54 m according to UNTAMO predictions (Figure 2-15). In the beginning of the 10 000 year period land uplift is around 0.006 m/a and at the end of the period around 0.0025 m/a. Because of the land uplift sea recedes from the present location around 20 km westwards by the year AD12020 (see Figure 2-16). The safety assessment modelling area regarding ecosystem changes and thereby hydrological development needs to cover the terrestrial area shown in Figure 2-16. The land area inside the Olkiluoto Island is around 12 km2 but the modelling area in the 2012 safety assessment around the present island boundaries is much larger. The maximum extent of the UNTAMO output data covers an area which is 48 km wide in the west-east direction and 20 km in the south-north direction (see Figure 2-16).
Figure 2-15. Influence of postglacial crustal uplift on maximum soil surface elevation (m) inside the present island boundaries during the period AD2020AD12020. Postglacial land uplift is estimated by the UNTAMO toolbox (Terrain and Ecosystems Development Modelling).
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Figure 2-16. Maximum extent of the safety assessment area provided by the UNTAMO toolbox (Terrain and Ecosystems Development Modelling). Present shoreline location is shown in the map. Soil surface elevation, lakes, rivers and sea area are given as predicted for the year AD12020. The development of the surface water systems (rivers, lakes) together with the location of the coastal areas is shown in Figures 2-17 for the present day condition (initial state of the terrain and ecosystem modelling) and for years AD4020 and AD6020 and in Figure 2-18 for the years AD8020, AD10020 and AD12020, respectively. The UNTAMO predictions are available for 500 year time steps (AD2020, AD2520, etc.) but all maps are not shown. According to UNTAMO simulations sea will recede from the present day location within the next 15002 000 years (Figure 2-17b) in such a way that the areas around the present island boundaries are terrestrial land with the exception of a few lakes located around the Olkiluoto Island. The influence of postglacial crustal uplift on soil surface elevation is much bigger than the possible effect of global sea level rise in various climate scenarios projected by Finnish Meteorological Institute for the next 10 000 years (Pimenoff et al. 2012, Section 4.3, Figure 18). According to UNTAMO simulations the postglacial crustal uplift will be almost 3 m during the next 500 years and maximum total global sea level rise within the same period is around 1 m as predicted by Pimenoff et al. (2012, Figure 18b, p. 48). During the next 1500 years postglacial crustal is estimated to be around 8 m and the corresponding maximum estimate on global sea level rise is around 2 m caused by melting of northern hemisphere ice sheets and 2 m due to thermal expansion of ocean water (Pimenoff et al. 2012, Figures 18b and 18d). By the year AD9000 maximum global sea level rise could be around 8.0 m (Pimenoff et al. 2012) but according to UNTAMO results the postglacial crustal after 7 000 years from the present day is around 29 m (see Figure 2-15). The surface hydrological model utilizes directly the raster files (cell size 10x10 m2) regarding soils surface elevation, flow accumulation, soil type and thickness of overburden mineral layers and peat layers. Location of lakes and sea areas are available as polygons and these data were converted also into raster data (10x10 m2 cells).
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Figure 2-17. Influence of land uplift on soil surface elevation, extent of sea areas and location of cropland areas (yellow), lakes and rivers. Present island boundaries are shown in all graphs. a) Present day, b) AD4020 and c) AD6020.
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Figure 2-18. Influence of land uplift on soil surface elevation, extent of sea areas and location of cropland areas (yellow), lakes and rivers. Present island boundaries are shown in all graphs. a) AD8020, b) AD1020 and c) AD12020.
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Location of rivers and small streams were obtained from flow accumulation rasters. Flow accumulation raster shows the number of upslope cells that flow into each cell. A stream is assumed to develop in areas where the flow accumulation raster exceeds a predefined threshold value. The threshold for small streams was selected to be 5 ha and influence of this parameter on stream density and horizontal fluxes will be discussed in Chapter 5.
2.6.2 Biosphere object delineation
The conditions in the surface environment in the year 2020, which is the assumed emplacement time of the first canister, define the initial state of the biosphere and this is the starting point for the terrain and ecosystem modelling. The forecasts made by UNTAMO toolbox (Terrain and Ecosystems Development Modelling) are used together with the radionuclide release pattern provided in the Formulation of Radionuclide Release Scenarios report to define the biosphere objects. They are continuous and sufficiently homogeneous sub-areas of the modelled area that could potentially receive radionuclides released from the repository. The specific aim of the surface hydrological modelling of this study is to compute vertical and horizontal water fluxes for the biosphere objects (see Section 2.7.6 and Chapter 4) to be later on used in the Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports. The delineation of the biosphere objects is described in detail in the Terrain and Ecosystems Development Modelling report and only a brief summary of the biosphere object data is given here. The spatial location of the biosphere objects is predefined in the delineation process but the ecosystem type of each object changes over time due to postglacial crustal uplift and the associated biosphere development process. Possible ecosystem types for biosphere objects are coast, lake, river, forest, cropland, pasture and wetland. The delineation of the biosphere objects around the shoreline of the present island are shown in Figure 2-19a and all objects inside the modelling area are given in Figure 2-19b. Some of the objects close to the present island boundaries are those that potentially may receive radionuclides from the repository (direct discharge from bedrock) and others may receive radionuclides from the surrounding terrestrial objects or from irrigation water. The delineated objects further away from the Olkiluoto Island can receive radionuclides only from irrigation water that is flowing in the rivers that pass Olkiluoto Island either via northern or southern route (Figure 2-19). Total number of biosphere objects which are at some time step terrestrial areas (forest, wetland, cropland or pasture) is 195. Average area of these objects is 8.85 ha, maximum object area is 375 ha and minimum area is 0.027 ha. Moreover, there are totally 54 biosphere objects, which will eventually be rivers. UNTAMO gives as output data for each object also a biotype in addition to ecosystem type. Possible biotopes for forest areas are rock forest biotope, heath forest biotope, grove biotope and mire biotope. In agricultural areas UNTAMO suggests one of the following biotopes: cereal, sugar beet, potato, pea, vegetable and berries and fruits (Terrain and Ecosystems Development Modelling).
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UNTAMO provides several attribute data for all ecosystem types and biotypes the and for all time steps starting from AD2020 and ending at AD12020. The attributes used in the surface hydrological model are shown in Table 2-3.
Figure 2-19. a) Delineation of the biosphere objects around the present island shoreline (upper map) and b) object delineation in the ecosystem inside the whole modelling area. Total number of terrestrial objects is 88 in the northern route and 115 in the southern route. Different colors are used only to show the biosphere object delineation.
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Table 2-3. List of UNTAMO attributes provided as output data from UNTAMO toolbox and used as input data in the hydrological modelling.
2.6.3 Climate scenarios and projected global sea level rise
In order to estimate the future climate and sea level evolution in Olkiluoto on a time-scale of 10 000 years, Pimenoff et al. (2012) analysed climate simulations of the Earth System models MPI/UW (climate – ice sheet – carbon cycle model) and UVic (climate – carbon cycle model). The analysed model simulations suggest that anthropogenic greenhouse gas emissions will cause global climate to warm 0.3 to 8 degrees during the current millennium depending on level of emissions. Further, simulations suggest that the cooling of the climate back to its pre-industrial state will take more than 10 000 years. Model simulations with low, intermediate and high emissions suggest that during the next 10 000 years, a temperate climate will continue in Olkiluoto (Pimenoff et al. 2012). Three different climate scenarios A1B, B1 and A2 were available for computing the boundary conditions in the Eurajoki and Lapinjoki Rivers for the UNTAMO-toolbox and calculating vertical and horizontal fluxes in the safety assessment modelling area shown in Figure 2-16 (maximum extent of the area) during the next 10 000 yeas. Mean atmospheric CO2 concentration (uppermost graph), annual mean air temperature (middle graph) and annual precipitation and estimated potential evapotranspiration (PET) rates (lowest graph) are shown for all the scenarios. In the Reference Case of the safety assessment data from present day conditions is used as input data: average yearly precipitation is around 550 mm/a, annual mean air temperature is 5.8 °C and estimated potential evapotranspiration rate is 370 mm/a. The climate scenario B1 is very similar to the present day climate regarding annual precipitation but mean air temperature and potential evapotranspiration rate are lower in this scenario compared to present day values. In scenario A1B precipitation is around 10-15 % higher than nowadays and in scenario A2 around 600 mm/a during the next 2 000 years and slightly below the present day values after that. The uncertainties related to global sea level rise are considerable. The main results of projected global sea level rise are shown in Figure 18 of Pimenoff et al. (2012, p. 48). The influence of postglacial crustal uplift on soil surface elevation is much bigger than the possible effect of global sea level rise in various climate scenarios projected by
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Finnish Meteorological Institute for the next 10 000 years as discussed earlier in Section 2.5.1 of this report.
Figure 2-20. Climate scenarios A1B, B1 and A2 available for computing boundary conditions in the Eurajoki and Lapinjoki Rivers for the UNTAMO-toolbox and calculating vertical and horizontal fluxes in the safety assessment modelling area during the next 10 000 years. Mean atmospheric CO2 concentration (uppermost graph), annual mean air temperature (middle graph) and annual precipitation and potential evapotranspiration (PET) rates (lowest graph) shown for all scenarios.
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2.7 Models
2.7.1 Introduction
The presentation of the models given in this section follows an analogous division that is used for describing the data sets. First, plot scale SVAT model utilizing the data collected from the Wet Deposition Monitoring Network (MRK) and Forest Intensive Monitoring Plots (FIP) is introduced. Second, the Olkiluoto surface hydrological model (SHYD) aimed to calculate site scale hydrological behavior in the present day condition is briefly described. Third, catchment scale models for the Eurajoki and Lapinjoki basins are briefly introduced. Fourth, extension of the site scale hydrological model for the regional scale needed in the 2012 safety assessment is described. Finally, the principles for obtaining water balance components and vertical and horizontal water fluxes used in the radionuclide dose computations in biosphere objects are outlined. The equations used in the models are shown in Appendix A. The SVAT model shown in Section 2.7.2 is a sub-model of the site scale hydrological model SHYD described in Section 2.7.3. SHYD is also used in safety assessment modelling carried out in the regional scale (Section 2.7.5). The computations of the vertical and horizontal fluxes for each delineated biosphere object are based on results of the full 3D-model, i.e. it is not necessary to develop any simplified hydrological model for the biosphere objects (Section 2.7.6). Model calibration and validation principles are outlined in Section 2.7.7.
2.7.2 Soil-Vegetation-Atmosphere-Transfer (SVAT) model
Models that solve the water pathway in the soil, vegetation and atmosphere continuum are here called SVAT model (Soil-Vegetation-Atmosphere-Transfer, Figure 2-21). In the SVAT model the main emphasis is devoted to the computation of water the energy balance of components of the FIP and MRK plots (Sections 2.3.1 and 2.3.2) located on Olkiluoto Island. Ínterception and transpiration of different vegetation types are at a very crucial role in the SVAT model. Hydrological processes quantified in energy and water balance modelling of forest stands include precipitation throughfall, interception, evaporation, transpiration and snow accumulation and snowmelt. The soil water model is the one described in Section 2.7.3 and Appendix A (App. A.4). Soil water sub-model computes vertical and horizontal water fluxes in unsaturated and saturated soils, overland flow and flow to small ditches or natural streams. Moreover, a sub-model for computing soil heat balance is included. List of variables computed in the SVAT model is given in Table 2-4. The SVAT model is a sub-model of the site scale model described in Section 2.7.3 and the regional scale model introduced in Section 2.7.5 and therefore the energy and water balance components computed in the SVAT model can be utilized in larger scale models.
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P=precipitation, T=transpiration from overstorey and understorey, I=evaporation of intercepted water from overstorey and understorey, S=storage of intercepted water in overstorey and understorey, P-T=precipitation throughfall, Sap=sap flow in the trunk, R=root water uptake, E=evaporation from soil surface, qV=vertical downward or upward flux in the soil profile at different depths, qL=lateral flux at in the soil profile at different depths, qB=flux through the overburden-bedrock interface.
Figure 2-21. The general scheme of water transfer pathways in the SVAT system. Both vertical and horizontal water fluxes in the soil are computed. Extraction of soil water by plant roots takes place from several layers. Lower boundary condition of the soil water sub model is infiltration into the bedrock or discharge out of the bedrock system.
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Table 2-4. List of variables computed by the SVAT model developed to analyze the water and energy balance components of the Forest Intensive Measurements Plots (FIP) and Wet Deposition Monitoring Network (MRK) located on Olkiluoto Island.
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2.7.3 Site scale hydrological model (SHYD)
Olkiluoto surface hydrological model SHYD (Karvonen 2008, 2009a, 2009b and 2011) is a tool that can be used to study the water balance components at the Olkiluoto site and to evaluate the effect of pumping and leakages into ONKALO and repository panels on groundwater level in overburden soils and in shallow and deep bedrock drillholes. The surface hydrological model version is a site scale model that computes water fluxes for the whole Olkiluoto Island (9 km2) and model area can be also extended outside the present island boundaries as shown in the regional scale version of the model (Section 2.7.5). The hydrogeological zones described in Section 2.4.5 have an important role in the calculation of water fluxes in the bedrock system as shown by the OMO-hydrological program (Vaittinen at al. 2011b) and modelling carried out by Löfman et al. 2009, 2010 and Löfman and Karvonen (2012) and Karvonen (2011). Fourteen site scale hydrogeological zones and five lineaments are included in the model utilizing the geometry data given by Vaittinen et al. (2011a).
The amount of data available for calibrating the SHYD model are very large and it include both site scale hydrological data (e.g. Vaittinen et al. 2011b, 2012) and detailed data from Forest Intensive Monitoring plots (FIP-areas), snow and frost depth measurements and runoff measurements from four small catchment areas (e.g. Haapanen 2010, 2011) and influence of pumping from shallow well (Aalto et al. 2011 and Pekkanen 2010 and 2011).
The Olkiluoto surface hydrology model includes the following sub-models:
SVAT model described in Section 2.7.2 and App. A.2 snow accumulation and snow melt using degree-day model soil heat balance including computation of frost depth calculation of soil water content in the overburden for calculation of spatial and
temporal distribution of infiltration and surface runoff subsurface runoff flow in the shallow overburden soil system as a solution of
3D-unsatured/saturated flow (Richards equation) open channel flow in ditches solved with a simplified form of the Saint Venant
equations recharge to and discharge from overburden soils to the underlying bedrock flow in the bedrock system solved with a 3D-submodel where bedrock and
fracture zones have been calculated separately The overall model combines all the sub-models into one computer program. The reason for linking all the sub-models is that the processes are coupled so closely with each other that it would not be possible to solve them separately. E.g. soil temperature and frost depth calculations are influenced by soil water status; runoff is dependent on soil water content, frost depth and groundwater level in the overburden; recharge from overburden to the bedrock is dependent on pressure head difference between the overburden and bedrock. In the Olkiluoto surface hydrological model overburden and bedrock are combined into one single numerical solution and overburden-bedrock interface can be seen as the layer where hydraulic properties change from soil values to bedrock data. The model links
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unsaturated and saturated soil water in the overburden and groundwater in bedrock into one continuous pressure system. Flux at the interface between overburden and bedrock can be calculated since the location of the first bedrock node in the vertical direction can be obtained from bedrock elevation data. Therefore, in the surface hydrological model no simplifications are needed in the treatment of the biosphere-geosphere interface. Olkiluoto surface hydrological model includes three different options for estimating the influence of hydrogeological zones on water flow in the bedrock system and only a brief summary of the method 3 used in this study is given here. Hydrogeological zones are thin plates located in 3-D space and a realistic way to treat them in the model is to include them as explicit structures and calculate water flow in these zones using finite element method. Moreover, water exchange between rock matrix and fracture zones needs to be calculated. Detailed description of method 3 has been given by Karvonen (2010, Section 2.4, p. 9-14 and Appendix B.4, p. 80-84). Development of method 3 can be motivated by the fact that hydrogeological zones can transmit the pressure effect over a large area as shown e.g. by Vaittinen et al. 2011b (Tables 37 and 3-8).
2.7.4 Hydrological model for the Eurajoki and Lapinjoki basins
The hydrological models of Eurajoki and Lapinjoki basins are needed only to provide the time dependent boundary condition for the UNTAMO toolbox as described in Section 2.5. The boundary conditions are needed in the points (Figure 2-13b) where Eurajoki and Lapinjoki Rivers discharge into the 2012 safety assessment modelling area. The description of the hydrological used in the Eurajoki and Lapinjoki basins has been described in Karvonen et al. (1999). The model is relatively simple compared to the SHYD model due to the fact that data available from the Eurajoki and Lapinjoki basins are not as detailed as the input data from the safety assessment modelling area. The hydrological model is based on the sub-division of the catchment into smaller units by a generation of so-called "hydrologically similar units" or "patch types". In the application of the model to Eurajoki and Lapinjoki basins patch types were generated based on CORINE land use data (Section 2.5.3 and Tables 2-1 and 2-2). The basic idea is to aggregate areas of hydrologically similar behaviour, e.g., land use, slope, and vegetation. Instead of having completely distributed 3D-structure, the model can be considered semi-distributed. The methodology of catchment subdivision into smaller Hydrologically Similar Units is here called a HSU-concept. Within each HSU a mathematical model is needed to describe the water balance of so called "characteristic profiles". The characteristic profile is the largest unit that can be handled mathematically still maintaining the idea of hydrologically similar regime. An agricultural characteristic profile is a cross-section between two parallel open ditches or subsurface drains. A typical spacing between the drains is 12-20 m, which is the length of the characteristic profile. The vertical water movement in the cross-section is calculated by solving the Richards equation for unsaturated-saturated flow (Karvonen 1988). The lateral movement towards subsurface drains or open ditches is calculated using the Hooghoudt’s equation (e.g. Feddes et al., 1978; Skaggs, 1980; Karvonen, 1988). A high hydraulic conductivity of saturated soil
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increases drainage flow and lowers efficiently the water table depth between the drains, which decreases the extent of the areas producing saturation overland flow. For forest areas the characteristic profile is called hillslope and the length of the hillslope can vary from few meters up to hundreds of meters. The water balance of the quasi-two-dimensional hillslope is represented using the unsaturated and saturated calculation modules. During excessive rainfall or snowmelt the lowest section of the profile becomes completely saturated (exfiltration areas) and a certain fraction of the hillslope area contributes to direct overland flow. Bogs and mires are represented using the characteristic profile of a forest hillslope with a small slope and peat soil water retention curve. The peat mining areas are treated mathematically in a similar way as the agricultural cross-sections with open ditches. Lakes, reservoirs, stream channels and impervious areas comprise a characteristic profile which instantly produces runoff. Total runoff from the characteristic profiles is an input to the channel network. The channel processes are described using the geomorphologic instantaneous unit hydrograph (GIUH) as suggested by Rodríguez-Iturbe and Valdés (1979) and Rodríguez-Iturbe (1993).
2.7.5 Regional scale hydrological model for the safety assessment
The site scale hydrological model introduced in Section 2.7.3 and in App. A.4 was used as the basis of the regional scale model that covers a much larger area than the present Olkiluoto Island (see Figure 2-16). The terrestrial area of the regional scale model increases over time due to the postglacial crustal uplift (see Figures 2-17 and 2-18). The same 3D-model that solves the Richards equation in the site scale model (see App. A.4) is used also in the regional scale with the exception of the treatment of the bedrock-overburden interface. In site scale model the bedrock is computed at the same time with the overburden but in regional scale model the bedrock fluxes are computed in two steps. In the first step of the analysis steady-state recharge/discharge to/from bedrock is computed for all computational pixels and these results are stored as raster files and used as the lower boundary condition of the regional scale model in the second step. This simplification implies that 3D-model needs to be calculated dynamically only for the overburden soils. The accuracy of this simplification will be discussed in the regional scale modelling of the 2012 safety assessment in Section (4.3.1).
Upper boundary conditions for the regional scale model are precipitation and potential evapotranspiration rates and air temperature for calculation of snow accumulation and snowmelt. The surface water development (coastal areas, lakes and rivers) needed as input data in the regional scale model are provided by the UNTAMO toolbox. Sea areas, lakes are main rivers (Eurajoki ja Lapinjoki) used the prescribed head boundary conditions and small streams act as conditional sink points in the model: flux computed from towards stream is positive if groundwater level computed with the model is above the stream bottom and otherwise flux is zero. The equations used for computing the flux to river/stream are given in App. A.4).
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2.7.6 Vertical and horizontal water fluxes in the biosphere objects
The specific aim of the hydrological modelling is to provide vertical and horizontal fluxes for the biosphere objects described in Section 2.6.2. The conceptualization of the layers (compartments) in the biosphere object layers is determined by the structure of the model that computes the radionuclide transport in the biosphere and is used to carry out the dose assessment scenarios (Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports). The layers and flux components for aquatic objects are shown in Figure 2-22, for forest and mire objects in Figure 2-23a, for cropland and pasture objects in Figure 2-23b. Since the biosphere objects are continuous and sufficiently homogeneous sub-areas of the modelling area and their spatial locations are known, it is possible to compile fluxes for each biosphere layer from the results of the 3D-model. Vertical fluxes are areally averaged values from all 10x10 m2 pixels inside the delineated ecosystem objects. Vertical fluxes are aggregated to correspond the storages of the conceptualized version of the dose assessment model (Figures 2-22 and 2-23). The number of vertical layers in the 3D surface hydrological model in overburden layers is 10 and results from several layers of the 3D-model are combined into the layers shown in Figures 2-22 and 2-23. Horizontal fluxes are computed by summing the horizontal inflows and outflows through the biosphere object boundaries. Moreover, soil water content and water amount in various layers are computed. The method adopted here is based on calculating average vertical and horizontal fluxes for biosphere objects from the results of the full 3D-model, i.e. it was not necessary to develop any simplified hydrological model for the biosphere objects. The drawback of the method is that it requires a lot of computer time due to the need to solve the 3D-model in unsaturated-saturated soils.
Figure 2-22. Flux and water balance components computed from the regional scale model for aquatic biosphere objects. FijDown and FijUp denote vertical fluxes, Fhor horizontal fluxes, Wcont soil water content in each layer and WAmount is Wcont multiplied by the thickness of each layer.
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Figure 2-23. Flux and water balance components computed from the regional scale model for terrestrial biosphere objects. a) Forest and mire ecosystems (upper graph). FFlux to well computed used only in forest ecosystems. b) cropland ecosystems and pasture biotopes (lower graph). Irrigation fluxes (FIrrig) are not used in pastures. FijDown and FijUp denote vertical fluxes, Fhor horizontal fluxes, Wcont soil water content in each layer and WAmount is Wcont multiplied by the thickness of each layer, Fwell is discharge to well.
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2.7.7 Modelling guidelines and model calibration and validation Examples of general methodologies for modelling guidelines have been given e.g. by Schlesinger et al. (1979), Andersson and Woessner (1992), Rykiel (1996) and Refsgaard et al. (2004). However, they use different terminology and have significant differences with respect to the underlying scientific philosophy. Refsgaard et al. (2004) propose a framework for quality assurance guidelines, including a consistent terminology and a foundation or a methodology bridging the gap between scientific philosophy and pragmatic modelling. The primary perspective of this study is in pragmatic modelling but the modelling guidelines suggested by Refsgaard et al. (2004) are followed to the extent that it is possible even though the temporal and spatial scales are very different in various sub-models, which makes it difficult to strictly follow the suggested guidelines. Refsgaard et al. (2004) suggest the following five key principles for pragmatic modelling:
1) A terminology that is internally consistent. 2) It is not possible to carry out a universal code verification or universal model
validation, but these terms must always be restricted to clearly defined domains of applicability. This is a necessary assumption for the consistency of the terminology and methodology and must be emphasized explicitly in any guidelines.
3) Validation tests against independent data that have not also been used for calibration are necessary in order to be able to document the predictive capability of a model.
4) Model predictions achieved through simulation should be associated with uncertainty assessments where amongst others the uncertainty in model structure and parameter values should be accounted for.
5) A continuous interaction between manager and modeller is crucial for the success of the modelling process.
Regarding key principle 1), Refsgaard et al. (2004) make distinction between the conceptual model, the model code and the site-specific model. Conceptual model is a description of reality in terms of verbal descriptions, equations, governing relationships or ‘natural laws’ that aim to describe reality. It is the developer’s perception of the key hydrological and ecological processes in the study area (perceptual model) and the corresponding simplifications and numerical accuracy limits that are assumed acceptable in order to achieve the purpose of the modelling. A conceptual model thus includes both a mathematical description (equations) and a descriptions of flow processes, river system elements, ecological structures, geological features, etc. that are required for the particular purpose of modelling (Refsgaard et al. 2004). The conceptual models used in this study are described in Appendix A. The site-specific model includes all the necessary input data, boundary conditions and parameter values and these have been documented in Chapter 3 and Appendices B and C. Model calibration is the procedure of adjustment of parameter values of a model to reproduce the response of reality within the range of accuracy specified in the performance criteria. However, the terms validation and verification are used with
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different, and sometimes interchangeable, meaning by different authors. Refsgaard et al. (2004) state that distinction should be made between model verification (adequacy of computer programme) and model validation (adequacy of site-specific model). The ability of a given model code to adequately describe the theory and equations defined in the conceptual model by use of numerical algorithms is evaluated through the verification of the model code. The methodologies used for code verification include comparing a numerical solution with an analytical solution or with a numerical solution from other verified codes. Model validation is the substantiation that a model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model. The term validation is weaker than the term verification (Refsgaard et al. 2004). According to Refsgaard et al. (2004) the model code needs to be verified. Code verification is a substantiation that a model code is in some sense a true representation of a conceptual model within certain specified limits or ranges of application and corresponding ranges of accuracy. According to key principle 2) code verification cannot be done universally but it can is done for some sub-models. In this study the code verification was carried out for the 3D numerical solution of the Richards equation and the 2D model that computes flow in fractures. The code verification of the 3D model was done by comparing computed results of numerical model against 3D analytical solution of Tracy (2007) (see Karvonen 2010, Appendix B.3). Analytical solution of Tang and Jiao (2001) was used in the code verification of the 2D fracture model (see Karvonen 2010, Appendix B.4). Others sub-models (e.g. SVAT model) could not be code verified with analytical solutions but extensive data sets are available for calibration and validation of these models. The key principle 3) states that the model validation must be carried out against independent data, i.e. data that have not been used during calibration. In this study the amount of data is exceptionally large and therefore approximately 50-60 % of the data were used for calibration and rest of the data were utilized as independent validation data. Refsgaard (2001) points out that in addition to validation, the model should be tested to show how good it can perform the kind of task for which it is specifically intended to be applied. In this study a very important aim of the model is to calculate the vertical and horizontal fluxes in the biosphere objects. Computed fluxes and water balance terms are compared in Chapter 4 to existing field data collected in FIP and MRK plots, which actually are small biosphere objects. Model predictions are in this study associated with uncertainty assessments where the uncertainties in model structure and parameter values are accounted for (see Chapter 5) to the extent it is possible to carry out in this study since the models are very complex. Finally, Refsgaard et al . (2004) suggest in key principle 5) that continuous interaction between manager and modeller is crucial for the success of the modelling process. This particular study started in 2007 and several interim reports have been published during these years (Karvonen 2008, 2009a, 2009b, 2009c, 2010, 2011) and Löfman and Karvonen (2012) and the managers representatives have given valuable feedback for the modeller throughout the process.
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3 MODEL CALIBRATION AND VALIDATION 3.1 Introduction The calibration and validation of the models given in this Chapter follows an analogous division that is used for describing the present day data sets in Sections 2.22.5 and models of this study (Section 2.7). First, calibration and validation of plot scale SVAT model utilizing the data collected from the Wet Deposition Monitoring Network (MRK) and Forest Intensive Monitoring Plots (FIP) is introduced in Section 3.2. FIP and MRK plots are in practice small biosphere objects and detailed modelling of these plots provide valuable information for evaluating the validity of the hydrological modelling results carried out for forest biosphere objects in the safety assessment phase (see Chapter 4). Second, the calibration and validation results of different sub-models (snow, soil heat balance, soil water balance, treatment of wells) of the Olkiluoto surface hydrological model (SHYD, Karvonen 2008, 2009a, 2010, 2011) aimed to calculate site scale hydrological behavior in the present day conditions are given in Section 3.3. Third, calibration and validation of catchment scale models for the Eurajoki and Lapinjoki basins is described in Section 3.4. Coefficient of determination Reff is commonly used to evaluate how well measured and computed values fit with each other. Correlation coefficient R between measured and computed values can be computed as the square root of the coefficient of determination. Reff. and R can be computed from Equation (3-1):
N
iiiE
N
iAveriM
eff
M
EMeff
CMS
MMS
RR
SSS
R
1
2
1
2 (5-1)
where N is the number of measurements, Mi is the measured value at time i , MAver is the mean value of measured variable and Ci is the computed value at time i. SM is the variance of measured values values multiplied by N-1 and SE is the cumulative sum of the square of errors. Maximum value for Reff is 1.0, which indicates perfect fit between measured and computed values, i.e. SE=0. It this study Reff is used to evaluate goodness of fit for those sub-models where measured values are available on daily level. If measurements are available on monthly basis (e.g. yearly interception % from precipitation above canopy or groundwater level in overburden) or cumulative value of special interest (precipitation throughfall) Reff is not computed.
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3.2 Calibration and validation of the SVAT model
3.2.1 Tree stand transpiration
Sap flow has been measured in several stems in Scots pine (FIP4) and Norway spruce (FIP10) dominated stands since 2007 (Section 2.3.2) and these values are used to derive measured estimate of the transpiration flux of the stands (Hökkä 2008; Aro et al. 2010, 2011). The calculated transpiration rates are based on computing canopy energy balance using measured global radiation above the canopy, air temperature, relative humidity and wind speed. The most important parameters needed in the Penman-Monteith equation are resistances to water vapor transport from canopy to atmosphere. Aerodynamic resistance was computed using the equations given by Koivusalo and Kokkonen (2002). Canopy conductances gc,oc and gc,uc were computed using the model suggested by Kellomäki and Wang (2000). In the present model the relative influence of various meteorological or soil variables on canopy conductance is defined by functions f(Da), f(h), f(Ta) and f(Ca) taken from Kellomäki and Wang (2000) and function f(Rs) adopted from Dolman and Nonhebel (1988) (see Appendix A, Eqs. (A-9) and (A-10)). Da is vapour pressure deficit, h is soil water potential, Ta is air temperature, Ca is ambient concentration CO2, and Rs is global radiation (W m-2). Canopy resistance is the reciprocal of canopy conductance as shown in Eq. (A-11) in Appendix A. The parameter values needed in the canopy conductance are given in Table B-3 (Appendix B). The only parameters which were calibrated based on measured sap flow data are the maximum values of the canopy conductances gc,oc,MAX and gc,uc,MAX, which represent conductance in optimum conditions. The measurement period was around 4.5 years for FIP4 (9.5.2007-31.12.2011) and one year shorter for FIP10 (6.6.2007-31.12.2010) since the device was not functioning properly in FIP10 during 2011 (Haapanen et al. 2012). Measured and computed monthly and cumulative transpiration rates are shown in Figure 3-1 for Scots pine stand and for Norway spruce stand. According to Haapanen (2012) some problems have occurred in sap flow measurements especially during the winter season in 2009–2011. Some measuring observations were missing which resulted in artificial peaks in estimated transpiration. Thus the estimates can be considered reliable only for the period from the end of March to the beginning of December, and consequently for the period from April to November on a monthly basis. In 2011, more severe problems occurred in the sap flow measurement of OL-FIP10. The measurement systems had several breaks during January–April 2011. In May, the operation was recovered until mid summer, after which the loggers produced data of bad quality, and finally another logger was broken. Therefore no data on transpiration on the Norway spruce stand OL-FIP10 is available for the year 2011 (Haapanen 2012). Three summer periods were used for model calibration in both stands and two years were available for model validation with independent data set in FIP4 but only one year in FIP10 due to malfunctioning of the measurement system. The measured and computed monthly transpiration rates (Figure 3-1, upper graph) in Scots pine stand (FIP4) are in relatively good agreement with each other during the calibration and validation period. However, the model overestimates the transpiration during years 2007 and 2008. Haapanen (2008) and Hökkä (2008) have described difficulties related
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to sap flow measurements on plot FIP4 which might partly explain the relatively low transpiration rates especially during 2007 compared to FIP10. The Olkiluoto SVAT model overestimates the transpiration rate for summer months (May-September) for plot FIP4 by around 11 % during the measurement period (measured sum for summer months is 490 mm and computed sum 540 mm, respectively). There has been a clear decrease in measured monthly transpiration rates on plot FIP10 as shown in Figure 3-1 (lower graph). According to measurements monthly transpiration in the Norway spruce dominated stand was clearly lower in 2010 than in 20072009, and there is a decreasing trend in the stand level transpiration of the spruce stand. Total sum of measured sap flow rates and computed transpiration fluxes are almost the same during the summer months of years 20072010 on plot FIP10 (460 mm for measured and 470 mm for computed) but during 2010 measured values were clearly lower than the computed ones. This may be attributed to the measurement difficulties on plot FIP10 which caused that 2011 measured results are not available.
Figure 3-1. Measured and computed monthly values (mm/d) for tree stand transpiration. a) Scots pine stand (FIP4) and b) Norways spruce stand (FIP10). Calibration period indicated in the graphs.
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3.2.2 Precipitation throughfall and interception
Canopy interception and stand throughfall sub-model was originally developed by Koivusalo and Kokkonen (2002) and it is described in Appendix A. The model includes also calculation of stem flow (water running along the branches and stem of the tree). Stem flow reduces the amount of precipitation falling to measurement gauge located under the canopy. The Olkiluoto SVAT model calculates two throughfall values: one with stem flow excluded and the other with stem flow included. The first one is used in comparing the measured throughfall with the computed one and the latter is used as the infiltration rate in the soil sub-model (upper boundary condition of the water flow model). Hourly values of meteorological variables were used as input data to the model. Measured precipitation above canopy is needed as model input data. This variable is not measured in weather stations WOM2-5 and therefore values from WOM1 were used. This station is not located in forest areas and therefore WOM1 precipitation values were scaled with open area measurements of MRK plots to get the correct cumulative precipitation above the canopy of the FIP plots. The parameter values used in the interception and throughfall sub model are given in Appendix B in Table B-1 for overstorey and in Table B-2 for understorey canopy. Some of the parameter values were taken from Koivusalo and Kokkonen (2002) but the most important parameters were calibrated based on measured data in FIP plots. The source of each parameter value is given in Tables B-1 and B-2. Measured versus computed monthly and cumulative stand throughfall and yearly values for interception (% from precipitation) are shown in Figures 3-23.5 for all four FIP plots. The computation period is 1.9.2004-30.09.2012 for FIP4, 23.5.2005-30.09.2012 for FIP10, 28.6.2007-30.09.2012 for FIP11 and 03.11.2009-30.09.2012 for FIP14. The calibration period for plot Scots pine stand (FIP4) is 01.09.200431.05.2009 and validation period 01.06.2009-30.09.2012. The calibration period for Norway spruce stand (FIP10) is 01.05.200531.05.2009 and validation period 01.06.2009-30.09.2012. The calibration period for young birch stand (FIP11) is 01.07.200731.05.2010 and validation period 01.06.2010-30.09.2012 and the calibration period for alder stand (FIP14) is 01.11.200931.05.2011 and validation period 01.06.2011-30.09.2012. The comparison of measured and computed precipitation throughfall values show that the model performs very well for cumulative throughfall rates but does not reproduce in detail the monthly rates. Estimated stem flow was around 11% of precipitation for the Scots pine forest (FIP4), around 5 % for Norway spruce forest (FIP10) and about 3 % for young Norway spruce/birch forest (FIP11) and around 6 % for alder stand. The reason for higher stem flow in pine forest is that the angle between stem and branches is such that it “collects” more easily water that is flowing out of the interception storage. Yearly interception measurements are available from the four MRK plots (Haapanen et al. 2010, 2011, 2012). It is necessary to point out that MRK4=FIP4, MRK10=FIP10, MRK11=FIP11 and MRK14=FIP14. Interception is expressed as % from above canopy precipitation: (precipitation above the canopy – computed stand throughfall) /
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(precipitation above the canopy) x 100. Measured values of precipitation above the canopy were here taken from open MRK plots (Haapanen 2007, 2008, 2009, 2010, 2011, 2012). Measured annual values of interception (%) are shown in the lowest graphs of Figures 3-23-5 for different MRK plots. The SVAT model reproduces relatively well the yearly interception rates both in the calibration and in the validation period. Average measured interception rate for MRK4 is 35 % and the corresponding computed value is 36 %. For plot MRK10 the measured and computed values are 31 % and 29 %, for plot MRK11 10 % and 9 % and for plot MRK14 24 % and 26 %, respectively. Measured and computed interception rate is smaller for FIP11 compared to the other plots. This result is realistic since interception is likely to be smaller in young birch forest stand compared e.g. to Scots pine and Norway spruce forests.
3.2.3 Overall water balance of FIP plots
Olkiluoto SVAT model can be used to compute overall water balance of the FIP plots both for the vegetation and for the soil profile. Here FIP plots can be considered as representatives of the biosphere objects used in the safety assessment. Water balance for the vegetation means that on long-term basis average precipitation above the canopy equals the sum of total interception and precipitation throughfall. The input fluxes to the overburden layers are precipitation throughfall, flux from bedrock (here upward flux is denoted as positive inflow) and horizontal inflow fluxes to the plot. Outflows from the plot are transpiration (tree and understorey transpiration) and horizontal outflow. The water balance results are shown in Table 3-1 for all the four plots indicating that in young birch stand the computed horizontal outflow is around 15-30 mm/a higher than in other stands mainly due to small interception rate compared to other stands. Runoff is not measured on FIP plots but on small catchments (Section 2.4.6) and the runoff measurements cannot be compared to the horizontal outflow results shown in Table 3-1. Weir measurements given in Section 3.3.5 are 170 mm/a for weir OL-MP1, 165 mm/a for weir OL-MP2, 195 mm/a for weir OL-MP3 and 180 mm/a for weir OL-MP4, i.e. relatively close to the computed horizontal outflows of the FIP plots. Values shown in Table 3-1 will be compared to with the flux results of calculated for the forest biosphere objects in Section 4.3.3.
Table 3-1. Computed water balance terms for the FIP plots. Precipitation above the canopy was scaled to the present day average yearly precipitation 550 mm/a.
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Figure 3-2. a) Measured and computed monthly throughfall in Scots pine stand (MRK4) (uppermost graph), b) measured and computed cumulative throughfall in Scots pine stand (middle graph) and c) measured and computed yearly interception of precipitation by the tree crowns (% of precipitation in the open area plots) in Scots pine stand (lowest graph). Model calibration period is 01.09.200431.05.2009 and validation period 01.06.2009-30.09.2012.
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Figure 3-3. a) Measured and computed monthly throughfall in Norway spruce stand (MRK10) (uppermost graph), b) measured and computed cumulative throughfall in Norway spruce stand (middle graph) and c) measured and computed yearly interception of precipitation by the tree crowns (% of precipitation in the open area plots) in Norway spruce stand (lowest graph). Model calibration period is 01.05.200531.05.2009 and validation period 01.06.2009-30.09.2012.
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Figure 3-4. a) Measured and computed monthly throughfall in young birch stand (MRK10) (uppermost graph), b) measured and computed cumulative throughfall in young birch stand (middle graph) and c) measured and computed yearly interception of precipitation by the tree crowns (% of precipitation in the open area plots) in young birch stand (lowest graph). Model calibration period is 01.07.200731.05.2010 and validation period 01.06.2010-30.09.2012.
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Figure 3-5. a) Measured and computed monthly throughfall in alder stand (MRK10) (uppermost graph), b) measured and computed cumulative throughfall in alder stand (middle graph) and c) measured and computed yearly interception of precipitation by the tree crowns (% of precipitation in the open area plots) in alder stand (lowest graph). Model calibration period is 01.11.200931.05.2011 and validation period 01.06.2011-30.09.2012.
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3.3 Calibration and validation of the site scale model SHYD
3.3.1 Snow and frost
Calibration period for the snow model and soil heat balance model is indicated in the graphs. Calibration period covered four snow seasons (2006-2007, 2007-2008, 2008-2009 and 2009-2010) and selection of the period was based on the fact that snow depth was thick during winter 2009-2010 and it was useful to include this into the calibration set. Four winters from the beginning of the measurement period were almost snow-free and data not representative enough for calibration of the parameters influencing snow accumulation and snowmelt. Results of snow model calibration and validation are shown in Figures 3-6 and 3-7 for the four different vegetation types: open areas (no vegetation), Scots pine forest, Norway spruce forest and deciduous forest. Snow accumulation and snow melt are very well described with the degree-day model originally developed by Vehviläinen (1992). In the calibration period the coefficient of determination Reff computed with Equation (3-1) was 0.83 for open areas, 0.89 for Scots pine forest, 0.91 for spruce forest and 0.92 for deciduous forest. In the validation period the Reff-values were only slightly lower than during the calibration period: 0.79 for open areas, 0.87 for Scots pine forest, 0.88 for spruce forest and 0.89 for deciduous forest. Simulation of frost depth comes from the solution of combined water and heat balance equations and results are here shown in the same graphs than snow depth results (Figures 3-6 and 3-7). Frost depth is interpolated in the model as the level where soil temperature falls below the freezing point T0 (usually 0 °C). The mid points of the layers are located at depths 0.1, 0.3, 0.6, 1.0, 1.5 m, etc. in the vertical direction (see Table C-1 in Appendix C). Computed and measured frost depths in open areas and various types of forest areas are shown in Figure 3-3. Computed values are in quite good agreement with measured with the exception of open areas and Scots pine forests during the validation period (winter 2004-2005) when computed frost depth is about 20-30 cm deeper than the measured one. Snow depth during that winter was very small and in these conditions frost depth was overestimated by the model due to (insulation effect of snowpack is difficult to predict in these type conditions). Coefficient of determination was not computed for frost depth simulations due to large variation in measured values.
3.3.2 Soil temperature
Soil heat balance model solves both soil temperature and frost depth. Soil temperature is very closely linked with soil moisture and therefore water balance and heat balance models are coupled together. The solution of the heat balance is complicated by the latent heat caused by freezing and thawing of soil water. In the freezing phase a lot of energy is released when soil water is frozen and in spring an equivalent amount of energy is needed to melt the ice. Soil temperature measurements were started in autumn 2004 and measured data from four FIP plots were available for calibration and validation of the model. Measurements
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sensors were installed at 0.1 m interval to the depth of 0.9 m. (0.1, 0.2,.. 0.9 m). During the measurement period the highest soil temperatures were lower than usually measured in Finnish soils (e.g. Rankinen et al. 2004). This might be due to thick organic layer in the measurement sites. Organic layer is an efficient insulator and tends to dampen the amplitude of the temperature variations. Measured and computed soil temperatures at four depths (10, 20, 40 and 90 cm) are shown in Figure 3-8 for Scots pine forest (FIP4). Computed peak values are around 1-2 °C higher than the measured values at 10 cm and 20 cm depths. At greater depths computed and measured soil temperature values are in much better agreement with each other. Coefficient of determination (Reff) was 0.91 and 0.92 for the first two depths (10 and 20 cm) and 0.95-0.96 for deeper layers both during the calibration and validation period. Calibration period of the heat balance model is indicated in the graphs (Figure 3-8) and other periods of the data were used for independent testing of the model.
Figure 3-6. Measured and computed snow and frost depth (cm) a) in open areas (upper graph) and b) in Scots pine forest (lower graph). Calibration period indicated in the graphs.
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Calibration and validation results of other FIP plot are shown in Appendix D: Norway spruce in Figure D-1 (FIP10), young birch stand in Figure D-2 (FIP11) and alder stand in Figure D-3 (FIP14). The results of the other forest areas are comparable with the Scots pine results shown in Figure 3-8. Coefficient of determination was 0.83 and 0.81 for the first two depths (10 and 20 cm) in Norway spruce forest (FIP10) and 0.94-0.95 for deeper layers during the calibration and validation period. For young birch forest (FIP11) and alder forest (FIP14) Reff-values were around 0.90 for the first two depths and 0.95-0.96 for deeper layers. Initial values for soil heat balance parameters needed are the specific heat capacity of the soils CS, parameters of the thermal conductivity of soil (KT/P1, KT/P2 and KT/P3), bulk density D and parameter that defines the fraction of unfrozen water content as a function of soil temperature Tice were taken from literature (Karvonen 1988 and Rankinen at al. 2004). The calibrated values are given in Appendix C.
Figure 3-7. Measured and computed snow and frost depth (cm) a) in Norway spruce forest (upper graph) and b) in deciduous forest (lower graph). Calibration period indicated in the graphs.
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Figure 3-8. Measured and computed soil temperature at 10, 20, 40 and 90 cm depths in Scots pine forest (FIP4). Calibration period indicated in the graphs.
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3.3.3 Groundwater level in overburden and hydraulic head in bedrock
The spatial and temporal variation of groundwater level and hydraulic head in shallow bedrock drillholes is caused by seasonal changes in precipitation and evapotranspiration. Vertical fluxes are directed mainly downwards during spring and autumn and upwards during summer periods when evapotranspiration normally exceeds precipitation. Horizontal fluxes in the overburden are important especially during rainy periods and these fluxes can be predicted properly only if the seasonal variation of groundwater level variation can be reproduced in the model. Groundwater level in overburden soils is measured in 40 tubes (see Figure 2-10a) and hydraulic head in shallow bedrock in 45 drillholes (see Figure 2-10b). Moreover, hydraulic head is measured in 31 packed-off deep bedrock drillholes (see Figure 2-10c) including 190 measurements. In packed-off drillholes different measurement sections in the bedrock are isolated from each other by packers that prevent water flowing in the drillhole: open drillhole would not allow hydraulic head measurements deeper in the bedrock. In OL-KR drillholes there are usually 4-8 packed-off measurement sections in one drillhole The most important parameters needed in the hydrological model of the overburden soils are given in Appendix C in Table C-4: saturated water content S, residual water content R, parameters and of the van Genuchten function (van Genuchten 1980) and saturated hydraulic conductivity K. Initial values for parameters of the soil water retention curve were estimated using pedotransfer functions developed by Jauhiainen (2004). Grain size distribution curves needed in the pedotransfer function were obtained from Huhta (2007, 2008, 2009 and 2010). Saturated hydraulic conductivity was taken from the results of the slug tests carried out in OL-PVP tubes studies (Tammisto et al. 2005; Tammisto and Lehtinen 2006; Keskitalo and Lindgren 2007; Keskitalo 2008 and 2009; Hinkkanen 2011). The bedrock hydraulic conductivity data and properties of the hydrogeological zones used in all simulations were taken from Löfman and Karvonen (2012) and data are listed in Appendix C.
The soil water retention curve parameters have been calibrated in several phases (Karvonen 2008, 2009) and the most recent calibration was carried out in spring 2010 (Karvonen 2011). The calibrated parameter values are shown in Appendix C. Model validation period covers the measurements since May 2010.
Measured and computed temporal variation of groundwater level in overburden OL-PVP-tubes is shown for eleven tubes in Figure 3-9 and for ten tubes in Figure D-4 in Appendix D for the measurement period 01.10.200431.10.2012. In some monitoring stations A, B and C-tubes (perforated section at different depths) have been installed and only the A-tubes are drawn since the measured and modeled groundwater levels are very close to each other in A-, B- and C-tubes. In some tubes measurement period is still short and these results are not shown here. Basic data of groundwater observation tubes installed in overburden is given in Vaittinen et al. (2010b, Appendix 2, p. 97) and map of existing OL-PVP-tubes is given in Figure 2-10a.
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Figure 3-9. Measured and computed groundwater level in eleven overburden tubes (OL-PVP). Location of tubes is shown in Figure 2-10a.
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The measured and computed temporal variation in groundwater level during the years 2004-2012 varied between 1.0-3.0 m in overburden tubes. The temporal variation predicted by the surface hydrological model is in good agreement with the measured variation in most of the groundwater tubes. The biggest difference between measured and computed variation is in tubes OL-PVP12 (Figure D-4, uppermost graph), OL-PVP8A (Figure D-4, middle graph) and OL-PVP13 (Figure D4, middle graph). The simulation results were good for the other tubes shown in Figure 3-9 and in Figure D-4 in Appendix D. There are at least three possible reasons for too small temporal variation estimated by the model. Firstly, estimated evapotranspiration may have been too small compared with the true value. Secondly, the shape of the water retention curve close to saturation can be slightly erroneous in the area surrounding the OL-PVP tubes. Thirdly, various types of construction works (roads, paved areas, ditches etc.) carried out during the years 2004-2012 may influence in some of the tubes. Measured and computed temporal variation of hydraulic head in shallow bedrock drillholes OL-PP, OL-L, OL-PR and OL-PA are shown in Figure 3-10 and in Figures D-5D-7 in Appendix D for the period 01.10.2004-31.10.2012. Basic data of shallow holes drilled in bedrock are given in Vaittinen et al. (2010b, Appendix 2, p. 98-99) and map of existing OL-PP, OL-L, OL-PA and OL-PR-drillholes is given in Figure 2-10b. The difference between maximum and minimum hydraulic head during year 2004-2012 was around 1.0-3.5 m in the shallow bedrock drillholes. The biggest measured temporal variation was seen in drillhole OL-L1 (Figure 3-10, uppermost graph), which is located above the ONKALO. Modeled variation is good with the exception of drillholes OL-PP8 (Figure 3-10, lowest graph) located almost at sea level, OL-L3 (Figure 3-10, uppermost graph), OL-L26 (Figure D-7, lower graph), OL-PP7 (Figure D-6, middle graph), OL-L15 (Figure, lowest graph), OL-L1 (Figure 3-10, uppermost graph) and OL-PP55 and OL-PP56 (Figure D-6, lowest graph). In the other drillholes the temporal variation computed with the model corresponds relatively well with the measured variation. The temporal variation is relatively well predicted in drillholes OL-PP1 and OL-PP5 (Figure 3-10, middle graph), in OL-PP2 and OL-PP9 (Figure D-5, uppermost graph) and OL-PA2/3 (Figure D-5, middle graph) but the timing of the groundwater level rise after autumn rains is delayed 2-3 weeks compared to the measured values. In these drillholes the most probable reason for the delayed response is that the overburden layers above the bedrock surface are not properly parameterized. Another explanation might be influence of various construction works (roads, paved areas etc.) during 2004-2012. The temporal variation in shallow bedrock drillholes was only slightly smaller than the corresponding value in overburden tubes. This indicates that the hydraulic connection between overburden and shallow bedrock is very good and overburden and bedrock are in the same continuous pressure system, which is one of the basic assumptions of the surface hydrological model.
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Figure 3-10. Measured and computed hydraulic head in eleven shallow bedrock drillholes (OL-PP, OL-PA, OL-L). Location of drillholes is shown in Figure 2-10b.
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The Olkiluoto surface hydrological model computes also the hydraulic head in deep bedrock. Comparison of measured and computed heads in packed-off deep drillholes falls outside the main scope of this study and therefore these comparisons are not shown. Karvonen (2011, Appendix 5, p. 131-147) has given graphs for all packed-off drillholes.
3.3.4 Modelling the influence of shallow wells
A new feature in the 2012 safety assessment compared to the TSM2009-case (Hjerpe et al. 2010) is the need to include influence of shallow wells in the radionuclide dose analysis (Biosphere Radionuclide Transport and Dose Assessment report). The effect of pumping from wells on vertical and horizontal fluxes in the biosphere objects (Section 2.6.2) is computed with the SHYD model. Therefore, it necessary to test the SHYD model against measurements carried out in field experiments which include pumping from shallow well. As shown in Section 2.4.7 two specific experiments carried out by Posiva provide valuable measured data to test the model: 1) infiltration experiment and pumping from OL-KR14 (Pitkänen et al. 2008; Aalto et al. 2011 and 2) long-term pumping from OL-KR06 (Lamminmäki et al. 2008; Pekkanen and Pöllänen 2008; Pekkanen 2010 and 2011). Infiltration experiment and pumping from OL-KR14 The location of monitoring points (groundwater level in overburden tubes OL-PVP2129 and hydraulic head in shallow bedrock drillholes OL-PP6669 around the pumping drillhole OL-KR14 is shown in Figure 2-12. Pumping from the packed-off section of 13.0-18 m in OL-KR14 started 9.12.2008. The quick drawdown in packed-off section in OL-KR14 was around 4 m. The measured quick drawdowns varied between 0.2 m (OL-PP68) and 1.0 m (OL-PP66) and the corresponding computed quick drawdowns varied between 0.3–1.5 m (Figure 3-11). Measured total drawdown at the end of year 2011 varied between 0.8 and 2.9 m and the corresponding computed values were 2.2 – 2.8 m (Table 3-2). The drawdowns in OL-PP66–69 caused by pumping from OL-KR14 were bigger than the effect of ONKALO during the infiltration experiment. Measured OL-KR14 drawdowns varied between 0.3 m and 2.1 m and computed values were 0.5–2.4 m. Measured drawdowns caused by ONKALO leakages were between 0.5–0.8 and computed values were 0.4–0.7 m (Table 3-2). Measured and computed drawdowns in shallow bedrock drillholes OL-PP66–69 were relatively close to each other. The largest total and OL-KR14 pumping drawdowns can be seen in drillhole OL-PP69, which is the closest drillhole to the pumping drillhole. The biggest difference between measured and computed total drawdown is seen in drillhole OL-PP68 (farthest from the pumping hole). Measured and computed hydraulic heads in OL-PP66–69 are shown in Figure 3-11. The seasonal variation in shallow bedrock drillholes OL-PP66–69 caused by dry and wet meteorological periods varied between 0.7 and 1.3 m in different drillholes (Figure 3-11). The effect of a break in pumping in the period 9.3.2010 – 27.4.2010 can be clearly seen as a rapid recovery of heads when pumping was ceased (1.0-1.6 m) and quick drawdown after pumping was restarted.
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Figure 3-11. Measured and computed hydraulic head in drillholes OL-PP66 and OL-PP67 (upper graph) and in drillholes OL-PP68 and OL-PP69 (lower graph). Pumping from OL-KR14 started 09.12.2008. Note the quick drawdown in hydraulic head after pumping was started. Table 3-2. Measured and computed total drawdown, drawdown caused by ONKALO and OL-KR14 pumping drawdown at the end of year 2011 in shallow bedrock observation holes OL-PP66–69 around pumping drillhole OL-KR14.
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In overburden observation tubes OL-PVP21–29 measured drawdowns were zero at the end year 2011 for all tubes (Table 3-3). The computed drawdowns were very small (<0.03 m) at the end of 2011 in all tubes except OL-PVP27, where the computed total effect was 0.11 m. The modeled drawdowns at the end of year 2011 and maximum effect of ONKALO and OL-KR14 pumping during the experiment period are shown in Table 3-3. The maximum effect during the experiment was biggest in tube OL-PVP27 (1.02 m), which is located in the area where zones HZ19C and local zone HZInf7 intersect and are close to soil surface.
Measured and computed groundwater levels in overburden tubes OL-PVP21–29 are shown in Figure 3-12. The temporal variation in groundwater level between start of the pumping and end of year 2011 was around 2.5-3.0 m in overburden tubes OL-PVP21, OL-PVP22, OL-PVP24, OL-PVP25 and OL-PVP27 and 1.3-2.1 m in tubes OL-PVP23, OL-PVP26, OL-PVP28 and OL-PVP29. The temporal variation predicted by the surface hydrological model was around 0.2-0.4 m too small compared to the measured one. One possible explanation for the fact that the model cannot capture in full detail the very complex interaction between overburden soils and the underlying bedrock is the lack of detailed local fracture zone geometry around the pumping drillhole. Only three local hydrogeological zones are included in the present model. According to the baseline report of the infiltration experiment (Aalto et al. 2011) the local fracture zone network should be denser than the one used in the surface hydrological model.
The detailed monitoring carried out in the infiltration experiment provided valuable measurements from the effect of pumping on hydrological behaviour of overburden soils and shallow bedrock. Comparison of measured and computed results showed that the Olkiluoto surface hydrological model can capture the most important features related to the pumping effects and the model can be used to predict the influence of shallow wells on water fluxes in the biosphere objects for the safety assessment needs (see Chapter 4).
Table 3-3. Computed total drawdown, drawdown caused by ONKALO and OL-KR14 pumping drawdown at the end of year 2011 and maximum effect during the infiltration experiment in overburden tubes OL-PVP21–29 around pumping drillhole OL-KR14. Measured drawdowns at the end of year were zero for all OL-PVP21–29 tubes.
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Figure 3-12. Measured and computed groundwater level in overburden tubes OL-PVP21-23 (upper graph), in tubes OL-PVP24-26 (middle graph) and in tubes OL-PVP27-29 (lower graph). Pumping from OL-KR14 started 09.12.2008. Long-term pumping test in drillhole OL-KR06 A long-term pumping test was started in 22 March 2001 in drillhole OL-KR6. Pumping rate has varied between 18 and 23 l/min (2633 m3/d or 960012100 m3/a), i.e. a much
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higher amount than the water supply capacity of 500 m3/y selected for the private well scenario in the 2012 safety assessment. Water level in the pumping drillhole has been around 4 m below sea level (Figure 3-13, upper graph) during pumping and water level was around 1.6 m above sea level before pumping (Pekkanen 2011). The measured and modelled water levels in the open drillhole are shown in Figure 3-13 together with the pumping rate. The monitoring system related to this experiment is not as detailed as in the infiltration experiment case. The closest hydraulic head measurement drillhole is located at a distance of 75 m from the pumping drillhole (OL-EP04). Measured and computed hydraulic heads at four different depths are shown in Figure 3-13 (lower graph) indicating that computed values are around 1 m above the measured values in deeper measurement sections L2L4. Close to soil surface (OL-EP04, section L1) modelled values are in quite good agreement with the measured data. The model can capture relatively well the hydraulic effects of long-term pumping from OL-KR06.
Figure 3-13. Measured and computed water level in the pumping drillhole OL-KR06 and pumping rate (upper graph) and measured and modelled hydraulic heads in four measurement sections of the drillhole OL-EP04.
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3.3.5 Discharge measurement weirs
Manual discharge measurements started in spring 2003 and four overflow weirs were installed (OL-MP1OL-MP4). Manual data measured once a week were unreliable and these results are not shown here. Automatic weirs for measuring hourly discharge have been used since late April 2008. The automatic weirs measure discharge in unit volume over time (l s-1) and it is necessary to convert measured discharge into runoff (mm d-1 or mm h-1) so that it is in the same unit than precipitation. Catchment areas needed to convert l s-1 to mm h-1 of the are as follows: 0.364 km2 for OL-MP1, 0.131 km2 for OL-MP2, 0.182 km2 for OL-MP3, and 0.323 km2 for OL-MP4, The computation of the weir measurements in unit mm d-1 enables the evaluation of the runoff component of the total water balance of the Olkiluoto Island. Moreover, runoff measurements are useful in the estimation of the reliability of the weir measurements. Model calibration period was 26.04.200830.09.2010 and model validation period was 1.10.201030.06.2012. Measured and computed daily and cumulative runoff rates are shown in Figures 3-143-17.
Figure 3-14. a) Measured and computed daily runoff rate in discharge weir OL-MP1 (upper graph) and b) cumulative runoff during the measurement period (lower graph) in OL-MP1. Two cumulative computed curves are shown: including only the days where measurement is available (Cum runoff/Computed) and for the whole period (Cum runoff/Total computed). Model calibration period is 26.4.200830.09.2010 and model validation period is 01.10.2010-30.06.2012.
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Two cumulative computed curves are shown in the graphs. The first curve denoted as Cum runoff/Computed includes only those days where measurements are available and the other cumulative includes the whole measurement period (Cum runoff/Total computed). Cumulative computed runoff values are in good agreement with the measured cumulative rates with the exception of OL-MP4 for summer 2010 (Figure 3-17, lower graph). Daily values are not predicted equally well and especially the highest measured runoff rates are underestimated (upper graphs in Figures). One possible reason for underestimation of the highest runoff rates is that the preferential flow paths (fast bypass routes like root channels, worm holes) are not properly described in the model.
Figure 3-15. a) Measured and computed daily runoff rate in discharge weir OL-MP2 (upper graph) and b) cumulative runoff during the measurement period (lower graph) in OL-MP2. Two cumulative computed curves are shown: including only the days where measurement is available (Cum runoff/Computed) and for the whole period (Cum runoff/Total computed). Model calibration period is 26.4.200830.09.2010 and model validation period is 01.10.2010-30.06.2012.
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Figure 3-16. a) Measured and computed daily runoff rate in discharge weir OL-MP2 (upper graph) and b) cumulative runoff during the measurement period (lower graph) in OL-MP2. Two cumulative computed curves are shown: including only the days where measurement is available (Cum runoff/Computed) and for the whole period (Cum runoff/Total computed). Model calibration period is 26.4.200830.09.2010 and model validation period is 01.10.2010-30.06.2012.
The coefficient of determination Reff was 0.67 for weir OL-MP1 during the calibration period and 0.64 for the validation period. The corresponding values for weir OL-MP2 were 0.60 for calibration and 0.46 for validation period, for weir OL-MP3 0.60 /calibration) and 0.51 (validation). The Reff were lowest for weir OL-MP4: 0.49 for calibration and 0.44 for validation period. The low Reff values can also be explained by the fact that automatic calibration methods cannot be used due to the long computer time needed to run one simulation (12-24 hours for desktop computer). Therefore, the main emphasis in calibration was to reproduce the cumulative runoff well enough.
Computed average yearly runoff rates for the measurement period were 170 mm/a for weir OL-MP1, 165 mm/a for weir OL-MP2, 195 mm/a for weir OL-MP3 and 180 mm/a for weir OL-MP4. The site scale model predicts relatively well the cumulative runoff values and this is the most important result needed in the safety assessment. The computational period in the radionuclide transport modelling is several hundred or thousand years and daily maximum values of runoff are not essential.
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Figure 3-17. a) Measured and computed daily runoff rate in discharge weir OL-MP4 (upper graph) and b) cumulative runoff during the measurement period (lower graph) in OL-MP4. Two cumulative computed curves are shown: including only the days where measurement is available (Cum runoff/Computed) and for the whole period (Cum runoff/Total computed). Model calibration period is 26.4.200830.09.2010 and model validation period is 01.10.2010-30.06.2012. 3.3.6 Treatment of geosphere-biosphere interface zone The treatment of the hydraulic connection in the geosphere-biosphere interface zone (GBIZ) is important in the safety assessment. Therefore, it is necessary to check that the hydraulic consistency in the interface between the surface hydrological model (Karvonen 2009a, 2010 and 2011) and the deep groundwater flow model FEFTRA (Löfman et al. 2009, 2010) is maintained. There is a requirement for making existing models more robust for the treatment of the GBIZ whilst ensuring that a greater artificial block between geosphere and biosphere models is not created (BIOPROTA 2005). It is crucial to recognize that there is a region of space that should overlap the geosphere and biosphere model domains (Vieno and Ikonen 2005; Lahdenperä 2006). The primary focus in the Olkiluoto surface hydrological model is to compute water balance and water fluxes in the overburden soils and in the shallow bedrock (<50 m) and evaluate the effect of vegetation on the behavior of the system (Karvonen 2009b). The deep bedrock and the site scale hydrogeological zones are included in the surface
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hydrological model to provide the necessary overlap (see above) with the deep groundwater flow model FEFTRA (Löfman et al. 2009, 2010) and Löfman and Karvonen (2012). The need for using two different models in computing water and solute fluxes in the geosphere-biosphere continuum is that there exist no single model that can handle accurately all the relevant processes. The hydraulic consistency in the boundary between the surface hydrological model and deep groundwater flow model FEFTRA is maintained if the flux in the uppermost part of the bedrock is the same in both models. The hydraulic properties of the bedrock matrix and site scale hydrogeological zones were calibrated by Löfman et al. (2009, 2010) and the same values were used in the surface hydrological model. Therefore, the fluxes computed by the two approaches in the uppermost part of the bedrock are the same if the pressure head profiles coincide in the uppermost 50 m of the bedrock. The site scale hydrogeological zones from the year 2008 (Update 2008, Vaittinen et al. 2009b) were used in all simulations (both FEFTRA and surface hydrological model). The transmissivity values of the zones were taken from Löfman et al. (2010, Table 4-2, p. 30) and hydraulic conductivities in the bedrock system given by Löfman at el. (2010, Table 4-1, p.29) were utilized in all the model simulations. Detailed results of the comparison of SHYD and FEFTRA results with each other and with baseline head measurements are given in Karvonen (2011, Chapter 4) and only a brief summary is shown here. According to Karvonen (2011) the pressure head profiles computed with the Olkiluoto surface hydrological model (SHYD) and deep groundwater flow model (FEFTRA) are in good agreement with each other in the uppermost 100 m of the bedrock indicating the fluxes computed with the two approaches are close to each other. The computed profiles are also relatively well in agreement with the measured baseline head profiles in most of the drillholes (Figure 3-18). In three drillholes out of 18 there is some deviation between the SHYD and FEFTRA results. The overall conclusion drawn by Karvonen (2011) is that the hydraulic treatment of the geosphere-biosphere interface can be done by computing hydraulic heads at level z=0 m with the Olkiluoto surface hydrological model and using these values as head boundary condition in the deep groundwater flow model FEFTRA. Comparison of measured and computed baseline heads (=hydraulic heads before the construction of ONKALO) provide also supporting evidence for recharge/discharge rates computed with the SHYD model. Spatial recharge/discharge rates cannot be measured directly in the field and the only way to validate the model in this respect is to ensure that the model computes correctly the hydraulic head profiles.
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Figure 3-18. Measured (Ahokas et al. 2008) and calculated baseline heads (present day) along the deep drillholes OL-KR07-OL-KR12. SHYD2010 results were computed with the Olkiluoto surface hydrological model, FEFTRA2010 simulations were carried out using the pressure head boundary condition provided by the surface hydrological model and FEFTRA2008 results were computed using the surface boundary condition given by Löfman et al. (2009, Figure 3-12, p.48). Soil surface and bedrock elevations are indicated for each drillhole.
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3.4 Hydrological models for the Eurajoki and Lapinjoki basins
3.4.1 Snow water equivalent
Snow water equivalent measurements were available from two stations in the Eurajoki Basin (Yläne and Kauttuankoski, Figure 2-13a) and one station in the Lapinjoki basin (Lapinjoki/snow). The degree-day snow model developed by Vehviläinen (1992) and modified by Karvonen (2008) to include computation of snow density and snow depth in addition to snow water equivalent. Snow model was calibrated at the same time with the catchment runoff model (Section 3.4.2). Model calibration covered the period 01.09.199331.08.2001 and model validation period was 01.09.200131.08.2009. The calibration and validation results are shown in Figure 3-19. During the calibration period the coefficient of determination Reff was 0.83 for the Kauttuankoski station, 0.77 for the Yläne Eura station and 0.71 for the Lapinjoki station. The validation results were slightly lower for all stations: 0.79 for the Kauttuankoski station, 0.73 for the Yläne Eura station and 0.67 for the Lapinjoki station. The most important reason for the relatively low Reff-values is that scaled precipitation values based on Olkiluoto data were used instead of data measured inside the catchment areas.
Figure 3-19. Measured and computed snow water equivalent in Eurajoki and Lapinjoki basins. a) Calibration period (upper graph) and b) validation period (lower graph). Kauttuankoski and Yläne Eura snow monitoring stations are located in the Eurajoki basin.
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3.4.2 Computed and measured runoff at UNTAMO boundary condition points
The specific aim of the hydrological models of the Eurajoki and Lapinjoki basins (Figure 2-13a) is to provide time dependent boundary conditions for the UNTAMO toolbox at those points (Figure 2-13b) where Eurajoki and Lapinjoki Rivers discharge into the 2012 safety assessment modelling area. Four discharge measurements stations were used in calibration of the model in the Eurajoki basin (Pappilankoski, Pyhäjärvi_luusua, Pyhäjoki and Yläneenjoki, Figure 2-13a) and one discharge station was available from the Lapinjoki basin (Ylinenkoski, Figure 2-13a). The discharge stations closest to the boundary condition points are Pappilankoski in the Eurajoki River and Ylinenkoski in the Lapinjoki River. The hydrological model developed by Karvonen et al. (1999) (see also Section 2.7.4) was calibrated and validated for the same periods than the snow model (calibration 01.09.199331.08.2001 and validation 01.09.200131.08.2009). Measured and computed cumulative runoff for 10 day periods for the Pappilankoski observation station in Eurajoki River are shown in Figure 3-20. The measured and computed 10 d runoff sums are in quite good agreement with each other both for peak rates and low flow periods. Coefficient of determination Reff was 0.86 for the calibration period and almost the same for the validation period (Reff =0.85).
Figure 3-20. Measured and computed 10 d runoff sums in the Pappilankoski station located in the Eurajoki River. a) Calibration period (upper graph) and b) validation period (lower graph).
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Measured and computed cumulative runoff for 10 day periods for the Ylinenkoski observation station in Lapinjoki River are shown in Figure 3-21. The measured and computed 10 d runoff sums are in a slightly better agreement with each other in the Lapinjoki River than in the Eurajoki River. Coefficient of determination Reff was 0.89 both for the calibration validation periods.
Figure 3-21. Measured and computed 10 d runoff sums in the Ylinenkoski station located in the Lapinjoki River. a) Calibration period (upper graph) and b) validation period (lower graph). As overall conclusion it can be stated that the hydrological models developed for the Eurajoki and Lapinjoki basins provide reliable results for predicting 10 day runoff sums in the present climate conditions.
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4 HYDROLOGICAL MODELLING FOR 2012 THE SAFETY ASSESSMENT
4.1 Introduction
The most important modelling phases related to hydrological modelling in the 2012 safety case are given in Section 1.2.2 and 1.3. The calibration and validation of the Olkiluoto site scale surface hydrological model in the present day condition is given in Chapter 3. The next step is to compute the boundary conditions for the UNTAMO toolbox in Eurajoki and Lapinjoki Rivers (Section 4.2). In the modelling chain the next step is the computation of the terrain forecasts with UNTAMO toolbox for the next 10000 years using input data provided by the Data Basis for the Biosphere Assessment report and boundary conditions for the Eurajoki and Lapinjoki Rivers (Terrain and Ecosystems Development Modelling). The delineation of the biosphere objects (Terrain and Ecosystems Development Modelling) is the key process that provides input data for the regional scale modelling (Section 4.3.2). Finally, vertical and horizontal fluxes in the biosphere objects are compiled from results of the regional scale model (Section 4.3.3) The main aim of this Chapter is to present the modelling results from for the Reference Case and Terr_MaxAgri Case (maximum extent of agricultural areas). The descriptions of the safety case simulation runs – Reference Case and Terr_MaxAgri - are given in the in Biosphere Assessment and Terrain and Ecosystems Development Modelling reports.
4.2 Boundary conditions in Eurajoki and Lapinjoki Rivers
The hydrological modelling of the Eurajoki and Lapinjoki basins gave reliable results for predicting 10 day runoff sums in the present climate conditions as shown in Section 3.4.2. The time dependent input data needed in the UNTAMO toolbox are mean discharge MQ and average annual maximum discharge MHQ over the period AD2020AD12020. Regarding the uncertainties included in the meteorological input data (climate scenarios A2, A1B and B1 given in Section 2.6.3) it can be concluded that the hydrological models of Eurajoki and Lapinjoki basins can be used to provide reliable boundary conditions needed in the UNTAMO toolbox. Mean discharge (MQ) is used in the UNTAMO toolbox for delineation of the surface water systems (lakes and streams), which are in a very crucial role in the hydrological modelling of the regional scale (section 4.3). The MQ- and MHQ-values computed for the Eurajoki and Lapinjoki basins are given in Figures 4-1 and 4-2. In the present day conditions MQ is 8.3 m3/s and MHQ is 24 m3/s in the Eurajoki River. In the Lapinjoki River the corresponding values are 3.3 m3/s for MQ and 12.2 m3/s for MHQ. The climate will remain temperate during the next 10 000 in all climate scenarios. MQ is highest based on data from scenario A2 during the first 500 years but lowest throughout rest of the period. Climate scenario B1 provides practically the same results then present day climate.
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Figure 4-1. Computed mean discharge MQ (upper graph) and average annual maximum discharge MHQ (lower graph) in the Eurajoki River for the three climate scenarios A2, A1B and B during the period AD2020AD12020. Mean discharge MQ in the present day situation in Eurajoki River is 8.3 m3/s and MHQ is 24 m3/s.
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Figure 4-2. Computed mean discharge MQ (upper graph) and average annual maximum discharge MHQ (lower graph) in the Lapinjoki River for the three climate scenarios A2, A1B and B during the period AD2020AD12020. Mean discharge MQ in the present day situation in Lapinjoki River is 3.3 m3/s and MHQ is 12.2 m3/s. 4.3 Regional scale hydrological modelling in the safety assessment
4.3.1 Flux in the geosphere – biosphere interface
The site scale hydrological model introduced in Section 2.7.3 and in App. A.4 is used as the basis of the regional scale model that covers a much larger area than the present Olkiluoto Island (see Figure 2-16). The same 3D-model that solves the Richards equation in the site scale model is used also in the regional scale with the exception of the treatment of the bedrock-overburden interface. In site scale model the bedrock is computed at the same time with the overburden but in regional scale model the bedrock fluxes are computed in two steps. In the first step of the analysis steady-state recharge/discharge to/from bedrock is computed for all computational pixels and for all
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time steps (AD2020, AD2520, etc.) and these results are stored as raster files and used as the lower boundary condition of the regional scale model in the second step. This simplification implies that the regional scale 3D-model needs to be calculated dynamically using daily time step only for the overburden soils and recharge from bedrock is given as recharge/discharge raster in (pixel size is 10x10 m2), i.e. recharge/discharge varies spatially but is constant for each time step within each pixel. The computation of recharge/discharge rates and validity of the above mentioned simplification is discussed below.
The SHYD model was used to compute vertical fluxes at the geosphere-biosphere interface as a function of time (AD2020–AD12020) using the present day climate data (safety assessment Reference Case). Average downward flux inside the present island boundaries is given in Figure 4-3 as a percentage from the present day precipitation 550 mm/a. Spatial values for recharge to bedrock (positive downward flux) or discharge from bedrock (negative upward flux) are shown Figure 4-5 for three time steps: AD3020, AD5020 and AD12020.
According to Figure 4-3 the computed recharge inside the present island boundaries increases from approximately 1.2 % to 1.6 % during 1500 years because sea recedes from the area as shown in Figure 2-17. After year AD3500 computed average flux increases slowly and is around 1.7 % at the end of the computational period (AD12020). The downward vertical flux inside the present island boundaries is the recharge rate that discharges (flows upwards) through bedrock interface around the present island boundaries. Some fraction of the recharge flows also through the repository areas and may cause radionuclides to be discharged outside the present island boundaries. Details of these computations are shown in the Assessment of Radionuclide Release Scenarios report.
Figure 4-3. Average downward flux inside the present island boundaries as a function of time (AD2020AD12020) for the reference case. Flux is given in % from the present day precipitation 550 mm/a.
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Figure 4-4. Computed recharge/discharge rates (% from the present day precipitation 550 mm/a) at overburden-bedrock interface in the Reference Case. Recharge (downward flux) is positive and discharge (upward flux) is negative a) AD3020, b) AD5020 and c) AD12020. Present shoreline is shown in the maps. The thin lines around the present island boundaries are the vertical lineaments where discharge from bedrock is higher than in the bedrock matrix.
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The bedrock system in Olkiluoto is transport limited (e.g. Karvonen 2008, p. 23) and can transmit only about 1.2 % from the yearly precipitation in the present day situation (Figure 4-3), i.e. it cannot transmit all the surface water available for bedrock recharge. There is more supply of water in the overburden than the bedrock system can transport. The recharge rate inside the island boundaries is driven by the hydraulic head difference between the island and the receiving (discharge) areas: sea areas in the present day and terrestrial areas or lakes in future conditions (Figure 4-5). Increase in head difference is caused by the fact that sea recedes from the area due to postglacial crustal uplift. In the present day condition average head difference is around 4 m but after 1500-2000 years the head difference will be around 12 m (Figure 4-5). The seasonal variation in groundwater level causes small changes in the hydraulic head difference between areas inside the island boundaries and the discharge areas. According to model results the average seasonal variation inside the present island boundaries is around 0.3-0.4 m in the present day condition and is slightly bigger in future conditions (0.5-0.6 m). The effect of seasonal variation in groundwater level on the head difference is around 10 % (0.4 m out of 4) at present and about 4-5 % (0.5-0.6 m out of 12 m) during the period AD3520AD12020. Therefore, it is acceptable to use a steady-state value for recharge/discharge rate within one time step the regional scale modelling. The spatial values for three time steps are shown in Figure 4-4.
Figure 4-5. Average hydraulic head difference (m) between areas inside the present Olkiluoto Island boundaries and the discharge areas around the island (safety assessment Reference Case). 4.3.2 Regional scale modelling The regional scale hydrological model uses raster files created with UNTAMO toolbox as model input data: soil surface elevation, soil thickness, soil profile data (depth of different soils layers), top soil type, location of cropland, forest and peat areas, flow accumulation raster, location of coastal areas and lakes. Moreover, the river network is available as polyline data. The size of computational pixel (grid cell) is the same as UNTAMO output data: 10x10 m2 and computational grid is created automatically from the input rasters.
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The hydraulic head boundary conditions are: at coastal areas H=0 and at lake cells H is the water level in each lake. Lake water levels are delivered by UNTAMO toolbox. Eurajoki and Lapinjoki Rivers are defined as head boundary conditions. Smaller streams act as conditional flux boundary conditions: if groundwater level computed with the model is above the stream bottom, water flows from the cell to the stream according to Equation (A-19) in Appendix A. Depth of small stream depth is assumed to be 0.9 m. Location of small streams is obtained from flow accumulation raster, which shows the number of upslope cells that flow into each cell. A stream is assumed to develop in areas where the flow accumulation raster exceeds a predefined threshold value. The threshold for small streams was selected to be 5 ha and influence of this parameter on stream density and horizontal fluxes will be discussed in Chapter 5. The upper boundary conditions for the regional scale model are precipitation and potential evapotranspiration rates and air temperature for calculation of snow accumulation and snowmelt. In addition to raster data UNTAMO toolbox provides input data for parameterization of the SVAT model of terrestrial biosphere objects: vegetation height, interception fraction, biomass (forest areas), leaf area index, saturated hydraulic conductivity, thickness of soil layer and organic matter layers and ditch interval (croplands). These attributes are provided separately for each object and during each time step. The spatial location of the biosphere objects is predefined before the delineation process but the ecosystem type of each object changes over time due to postglacial crustal uplift and the associated biosphere development process. Possible terrestrial ecosystem types for biosphere objects are forest, cropland, pasture and wetland. The surface hydrological model was not computed as a continuous simulation over the whole period of 10 000 years since the UNTAMO data are available for 500-year intervals. Therefore, regional scale model was used to compute “snapshots” of the hydrological conditions for the same time points that UNTAMO forecasts were made (AD2020, AD2520, .., AD12020). The surface hydrological model was calculated for a period of seven years using daily input data for the meteorological variables needed in the model (precipitation, air temperature, potential evapotranspiration and global radiation). The meteorological data was taken from Olkiluoto present day data (years 2005-2011) and scaled in future time steps in such a way that average values over the seven year period corresponded the selected values from the climate scenarios A2, A1B and B1. With this procedure it was possible to utilize also the SVAT model in the regional scale modelling. During the computation of the regional model daily values for vertical and horizontal fluxes in the biosphere objects (see 4.3.3) were summed to o0btain average flux rates needed in the radionuclide dose assessment model (Biosphere Radionuclide Transport and Dose Assessment report). Fluxes to wells were compiled at the same time with the vertical and horizontal fluxes (see Section 4.3.4 for information regarding the selection of well placement and influence of well).
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4.3.3 Vertical and horizontal fluxes in the biosphere objects
The specific aim of the hydrological modelling in the biosphere assessment BSA-2012 is to provide vertical and horizontal fluxes for the biosphere objects described in Section 2.6.2. The conceptualization of the layers (compartments) in the biosphere objects is determined by the structure of the model that computes the radionuclide transport in the biosphere and is used to carry out the dose assessment scenarios (Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports). The layers and flux components for aquatic objects are shown in Figure 2-22, for forest and wetland objects in Figure 2-23a, and for cropland and pasture objects in Figure 2-23b. It can be argued that the selected modelling strategy to use the 3D regional scale model in calculating vertical and horizontal fluxes for the biosphere objects is “too detailed” regarding the uncertainty of the input data (climate scenarios, land use data and parameterization of the SVAT model). The other option would have been to use simpler models for the biosphere objects (separate model for each object and each time step). However, the total number of terrestrial biosphere object is very large, e.g. 195 in the Reference Case, and since the total number of time steps is 21 (10 000 years and 500 year interval) the number of “simple models” would have been very large even though all biosphere are not active during the whole computation period. The main difficulty with the simple models would have been the determination of horizontal boundary conditions (object do not follow any catchment subdivision) and moreover, water may flow from one biosphere object to another before it reaches coastal area. In the method adopted here it was not necessary to develop any simplified hydrological model for the biosphere objects. Computed vertical fluxes are areally averaged values from all 10x10 m2 pixels inside the delineated ecosystem objects. Vertical fluxes are aggregated to represent the storages of the conceptualized version of the dose assessment model (Figures 2-22 and 2-23). The number of vertical layers in the 3D surface hydrological model in overburden layers is 10 and results from several layers of the 3D-model are combined into the layers shown in Figures 2-22 and 2-23. Horizontal fluxes are computed by summing the horizontal inflows and outflows through the biosphere object boundaries. Moreover, soil water content and water amount in various layers are computed. The method adopted here is based on calculating average vertical and horizontal fluxes for biosphere objects from the results of the full 3D-model. The drawback of the method is that it requires a lot of computer time due to the need to solve the 3D-model in unsaturated-saturated soils.
Vertical and horizontal fluxes were computed for the Reference Case and the Terr_MaxAgri Case (maximum reasonable extent of agricultural land including also small fields). Present day climate was used in the Reference Case and climate scenario A2 (Section 2.6.3) was used in the Terr_MaxAgri Case. UNTAMO data were available in the Terr_MaxAgri Case only from the northern route. Delineation of the biosphere objects in the Reference Case and in the Terr_MaxAgri Case around the present island shoreline are shown in Figure 4-6. Total number of terrestrial objects is 88 (northern route) in Reference Case and 336 in the Terr_MaxAgri Case.
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Figure 4-6. a) Delineation of the biosphere objects in the Reference Case in the northern route around the present island shoreline (upper map) and b) object delineation in the Terr_MaxAgri Case (lower map). Total number of terrestrial objects is 88 in Reference Case and 336 in the Terr_MaxAgri Case. Different colors are used only to show the biosphere object delineation.
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Water balance components in the Reference Case In the Reference Case the total number of biosphere objects which are at some time step terrestrial areas (forest, wetland, cropland or pasture) is 195. Average area of these objects is 8.85 ha, maximum object area is 375 ha and minimum area is 0.027 ha. Moreover, there are totally 54 biosphere objects, which will eventually be rivers. UNTAMO gives as output data for each object also a biotype in addition to ecosystem type. Possible biotopes for forest areas are rock forest biotope, heath forest biotope, grove biotope and mire biotope. In agricultural areas UNTAMO suggests one of the following biotopes: cereal, sugar beet, potato, pea, vegetable and berries and fruits (Terrain and Ecosystems Development Modelling). Input data regarding irrigation comes also from UNTAMO toolbox. Average, maximum and minimum vertical and horizontal water fluxes and water contents in different layers are given in Table 4-1 for the in the Reference Case for forest, cropland, pasture and wetland biosphere objects. Moreover, contribution of wells is given for those forested objects that include well. The water balance components and conceptualization of the vegetation and vertical soil profile are shown in Figure 2-23. Cumulative distribution of the most important flux components are shown in Figure 4-7 for forest objects and in Figure 4-8 for cropland biosphere objects. Cumulative distributions are not shown for wetland and pasture objects since the number of these objects is small compared to forest and cropland objects. The results shown in Table 4-1 and in Figure 4-7 can be compared with the measurements carried out in the FIP and MRK plots for transpiration (Section 3.2.1), interception and precipitation throughfall (Section 3.2.2) and water balance computations of the FIP plots (Section 3.2.3). The measured and computed values shown in Chapter 3 fall within the cumulative distributions given in Figure 4-7 and maximum and minimum values for those fluxes given in Table 4-1. Water balance components in the Terr_MaxAgri-case
In the Terr_MaxAgri Case (maximum extent of agricultural area including small fields) the total number of biosphere objects in the northern route which are at some time step terrestrial areas (forest, wetland, cropland or pasture) is 336. Average area of these objects is 3.25 ha, maximum object area is 56 ha and minimum area is 0.02 ha. Moreover, there are totally 29 biosphere objects, which will eventually be rivers. The average size of the biosphere object is smaller in the Terr_MaxAgri Case compared to the Reference Case (8.85 ha). Precipitation is smaller in the Terr_MaxAgri Case (500-550 mm/a during the years AD4020AD12020) compared to the Reference Case (550 mm/a) (Figure 2-20). Average, maximum and minimum vertical and horizontal water fluxes and water contents in different layers are given in Table 4-2 for the in the Terr_Max_Agri Case for forest, cropland, pasture and wetland biosphere objects. The water balance components and conceptualization of the vegetation and vertical soil profile are shown in Figure 2-23.
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Table 4-1. Vertical and horizontal water fluxes, water content in different layers and flux to wells (only forest areas) in the Reference Case for terrestrial biosphere objects (forest, cropland, pasture and wetland). Average, maximum and minimum values are given for all ecosystem types. Unit of fluxes is mm/a, water content is given in unit m3/m3. Fluxes to wells are given from those biosphere objects that include well. Names of flux and water balance terms are shown in Figure 2-23. Empty cells in the Table indicate that these variables are not included in the conceptualized version of the ecosystem object.
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Figure 4-7. Cumulative distribution of vertical and horizontal fluxes in forest biosphere objects. a) Upward flux from bedrock to overburden soils, b) upward flux from middle mineral layer to root zone, c) downward flux from root zone to middle mineral layer, d) transpiration, e) interception and f) total horizontal outflow from object.
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Figure 4-8. Cumulative distribution of vertical and horizontal fluxes in cropland biosphere objects. a) Upward flux from bedrock to overburden soils, b) upward flux from middle mineral layer to root zone, c) downward flux from root zone to middle mineral layer, d) transpiration, e) interception and f) total horizontal outflow from object.
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Table 4-2. Vertical and horizontal water fluxes, water content in different layers and flux to wells (only forest areas) in the Terr_MaxAgri Case for terrestrial biosphere objects (forest, cropland, pasture and wetland). Average, maximum and minimum values are given for all ecosystem types. Unit of fluxes is mm/a, water content is given in unit m3/m3. Fluxes to wells are given from those biosphere objects that include well. Names of flux and water balance terms are shown in Figure 2-23. Empty cells in the Table indicate that these variables are not included in the conceptualized version of the ecosystem object.
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Comparison of Reference and Terr_MaxAgri Case fluxes Exact comparison of horizontal and vertical fluxes in the Reference Case and in the Terr_MaxAgri Case is not possible since the number of biosphere objects is different and object delineation is not the same. The results given in Tables 4-1 (Reference Case) and in Table 4-2 (Terr_MaxAgri Case) show that the biggest difference in the results is related to precipitation throughfall and horizontal fluxes out of the biosphere object whereas interception and transpiration do not differ very much from each other in the Reference Case and Terr_MaxAgri Case. Potential evapotranspiration is slightly smaller in the Terr_MaxAgri case and therefore average transpiration and interception are 1-2 % bigger in the Reference case. Precipitation throughfall is around 20-25 mm/a smaller in the Terr_MaxAgri Case as compared to the Reference Case for all ecosystem types (forest, cropland, pasture and wetland) due to smaller precipitation in the Terr_MaxAgri Case. In forested areas FHor3 is the biggest outflow component (horizontal water flux from the upper mineral layer) and this result agrees with the site scale hydrological modelling in Olkiluoto (Karvonen 2010, 2011), which show that horizontal fluxes are significant only when groundwater level is close to soil surface (depth 0-60 cm). Similar results were obtained by Laine-Kaulio (2011) in tracer experiments carried out in forested hillslopes. According to Laine-Kaulio tracer experiments implied, together with the model applications, that the real, large-scale hydraulic conductivities near and at saturation, and in particular in the upper 50 cm of the soil profile, are very high compared to the rest of the profile and significant horizontal water fluxes were seen when groundwater level was close to soil surface. The assumption in computing water fluxes in the agricultural objects is that field drainage is efficient (drain spacing data provided by the UNTAMO toolbox), which indicates that drainage flux is the biggest horizontal outflow component in cropland and pasture ecosystems. In the model drainage flux includes FHor2 and 10-30 % of FHor1 depending on the profile thickness. FHor2 is the flux from middle mineral layer, which represents depth interval 30-100 cm. FHor3 includes both horizontal flux in the root zone and surface runoff. The computed drainage fluxes (FHor2+10-30% from FHor1) shown in Tables 4-1 and 4-2 are in within that range measured in Finnish conditions (e.g. Turtola and Paajanen 1995; Kukkonen et al. 2004; Turtola et al. 2007). The modelled outflow component FHor3 which includes also surface runoff is around 10-15 % from total runoff. In the field experiments carried out in clay soil in eastern part of Finland (Kukkonen et al. 2004) surface runoff component was small (0.1-13.9 % from total runoff) whereas in heavy clay in southern part of Finland surface runoff was more than 50 % from total runoff (Turtola and Paajanen 1995). In measurements and modellings carried out by Warsta et al. (2008) and Warsta (2011) in heavy clayey soil in southern Finland surface runoff component was around 35-40 %. According to results shown in Tables 4-1 and 4-2 horizontal outflow in wetland biosphere objects occurs mainly close to surface (FHor3). Measured data from wetland objects were not available but the computed results seem realistic since the surface layers are much more conductive than the deep layers.
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4.3.4 Influence of wells
The SHYD model allows addition of wells as sink points in computational grid. Overburden and drilled wells can be treated in the same way in the model. The water pumped from wells is assumed to be taken from different soil or bedrock layers depending on the hydraulic conductivity of each layer. The model computes drawdown caused by pumping from well and divides the well pumping (assumed to be 500 m3/a for single household well, see Biosphere Assessment report) for different layers. Well fluxes are then converted to unit m/a by dividing them by the area of delineated biosphere object. Radionuclides discharging from bedrock to overburden are assumed to be fully mixed in the layers of the biosphere objects and radionuclides can flow horizontally to well or as runoff out of the object and vertically towards the rooting zone. Influence of wells in the Reference Case The selection of the number and location of wells (biosphere object) are based on the delineation of the soil types provided by the UNTAMO toolbox and modelling carried out with the SHYD model. Wells can only be placed into objects where soil type is coarse, medium or fine mineral soil and drawdown caused by well is such that computed groundwater level does not fall below bedrock-overburden level. Hydraulic conductivity of clay and gyttja soils is not high enough to sustain a well. In the Reference Case the assumption is that one biotope needs to sustain one well. Number of wells in the Reference Case is shown in Table 4-3 indicating maximum number of wells is 15. Biotope areas in forests change over time and therefore number of wells may change over time, i.e. it may happen that location that previously could sustain a well cannot do it later on due to change groundwater flow conditions. Wells are assumed to be located only to forest objects and not inside agricultural, pasture or wetland objects. Fluxes from well are given in the same unit – m/a (mm/a) - than horizontal and vertical fluxes implying that these fluxes can be used in the model in the same way than other flux components. Well pumping is 500 m3/a (single household well) and well fluxes are computed by dividing QWell1, QWell2 and QWell3 by the area of the object. QWell1+QWell2+QWell3=500 m3/a and QWell1 is pumping from deep overburden, QWell2 from middle mineral layer and QWell3 from upper mineral layer. Cumulative distribution of flux to wells is shown in Figure 4-9 (left graph) for forest biosphere objects, which include a well. Moreover, cumulative distribution of the sum of horizontal outflows from deep overburden (lower mineral layer) and middle mineral layers are shown in Figure 4-9 (right graph) for the case with wells included and distribution of corresponding fluxes in the same biosphere objects but no pumping included. The difference between the two curves shows the effect of pumping on horizontal outflows from biosphere objects with wells included.
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Figure 4-9. Cumulative distribution of flux to wells (mm/a) in forest biosphere objects (left graph) and cumulative distribution of sum of horizontal outflows from deep overburden (lower mineral layer) and middle mineral layers with wells included and distribution of corresponding fluxes in the same objects than wells but no pumping included. The difference between the two curves shows the effect of pumping on horizontal outflows from biosphere objects with wells included.
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Table 4-3. Number of wells in the northern and southern route for different time steps of computation. Biotope areas in forests change over time and therefore number of wells change over time and some wells may disappear due to the fact that it cannot anymore sustain a well.
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5 UNCERTAINTY AND SENSITIVITY ANALYSIS
5.1 Introduction
The first part of the uncertainty analysis (Section 5.2) is related to the conceptual model and the modelling philosophy selected by Posiva to link several models for computing radionuclide transport in the geosphere and biosphere systems and to carry out the dose assessment calculations. This chain of models includes the hydrological model of this study. The uncertainty related to treatment of geosphere-biosphere interface is also discussed in Section 5.2. The computation of vertical and horizontal water fluxes and in the biosphere objects was carried out using full 3D surface hydrological model. The benefit of the method adopted here is that it was not necessary to develop any simplified hydrological model for the biosphere objects. Fluxes for biosphere objects were obtained by averaging vertical and horizontal fluxes over the boundaries of each biosphere object during each time step. The drawback of the method used here is that computations are very time consuming and it is not possible to carry out the sensitivity analysis by changing one parameter value at a time and computing the effect of the change in output variables. The most important input data that have effect on water fluxes are climate scenario (5.3), drainage density (5.4), interception and transpiration parameters (5.5) and soil hydraulic properties (5.6). The most important outputs of the model are vertical and horizontal fluxes in various types of biosphere objects (forests, croplands, pastures and wetlands). These fluxes will be used in the radionuclide transport and dose assessment calculations (Biosphere Radionuclide Transport and Dose Assessment report). The parameter uncertainty has a direct influence on the results and the sensitivity analysis provides a means of estimating the uncertainty of the results. The sensitivity of the model output was estimated by examining how changes in the four above mentioned input data groups influence vertical and horizontal flux components in various ecosystem types. Moreover, the aim is to compare the uncertainty included in the estimation of the input data needed in the computation.
5.2 Uncertainty related to conceptual model and modelling philosophy
The conceptual model of water and radionuclide fluxes in the geosphere-biosphere system is shown in Figure 1-3. There does not exist a single model that could handle both water and radionuclides transport calculations and evaluation of the risk associated with the possible releases. Therefore, Posiva has decided that the modelling of the effects of possible releases from repository will be done in a chain of models:
1) radionuclide transport from repository areas to geosphere-biosphere interface 2) delineation of the biosphere objects that may receive radionuclides 3) computation of water fluxes in the biosphere objects 4) computation of biosphere radionuclide transport in the biosphere objects 5) dose assessment for humans and other biota
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The uncertainties caused by the chain of models are the related to the interface zones between various models. Radionuclide transport in bedrock does not use the recharge/discharge computed with the model presented in this study but pathway routes described in the Assessment of Radionuclide Release Scenarios report. Fortunately, the uncertainty caused by flux through the bedrock-overburden interface is small in computing the water balance of the biosphere objects. As shown in Section 4.3.1 the bedrock system in Olkiluoto is transport limited and can transmit only about 1.2 % (67 mm/a) from the yearly precipitation in the present day situation and 1.7 % (89 mm/a) at the end of the biosphere assessment period (AD12020, Figure 4-3). The uncertainty related to precipitation in future climate (section 5.3) is much bigger than the uncertainty included in the flux rate at the geosphere-biosphere interface zone. Moreover, the results given in Section 3.3.6 show that the pressure head profiles computed with the Olkiluoto surface hydrological model (SHYD) and deep groundwater flow model (FEFTRA) are in good agreement with each other and with the measurements in the uppermost 100 m of the bedrock implying that the bedrock fluxes computed with the two approaches are close to each other. This provides additional reliability that the upward flux rates computed for the biosphere objects (Fsource in Tables 4-1 and 4-2) are of correct magnitude. The biosphere radionuclide transport model uses the fluxes computed in this study and there does not exist any specific interface that would add uncertainty to the results. The uncertainty related to hydrological model and its input data are discussed in Sections 5.35.7.
5.3 Sensitivity to climate scenario
Precipitation and potential evapotranspiration rates are the most important input data provided by the selected climate scenario. In the Reference Case the present day climate was used and in Terr_MaxAgri Case climate scenario A2 was chosen. Comparison of fluxes in the Reference case (Table 4-1) and in the Terr_MaxAgri Case (Table 4-2) provides information on the effect of precipitation and evapotranspiration on water fluxes. Increase in precipitation increases throughfall and horizontal fluxes and also vertical fluxes especially in the uppermost layers of the soil profile. Yearly precipitation in scenario A2 is on the average 20-30 mm/a smaller than in the Reference Case and this is also the difference in precipitation throughfall and in the sum of horizontal outflows from various ecosystem types (forests, croplands, pastures and wetlands) between Terr_MaxAgri Case and Reference Case. Potential evapotranspiration rates were close to each other in the Reference Case and in the Terr_MaxAgri Case and therefore effect of evapotranspiration rate cannot be seen by comparing the flux components shown in Tables 4-1 and 4-2. Decrease in potential evapotranspiration rate has only a minor effect on interception since energy used for evaporating water from the tree foliage or crop canopy is used first and if there is still energy available the rest is used for transpiration. Therefore, the effect of change in potential evapotranspiration rates between various climate scenarios influences primarily the transpiration rate. On yearly level decrease in transpiration increases
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relatively linearly the runoff components (e.g. Vakkilainen 2009) and uncertainty in estimation of potential evapotranspiration rate has a direct effect on horizontal flux components.
5.4 Sensitivity to drainage density
In forested and wetland areas drainage density (amount of small streams) is obtained from flow accumulation rasters delivered by the UNTAMO toolbox. Flow accumulation raster shows the number of upslope cells that flow into each cell. A stream is assumed to develop in areas where the flow accumulation raster exceeds a predefined threshold value. In the Reference Case the threshold for small streams was selected to be 5 ha and two sensitivity cases were computed: threshold value was selected to be 2.5 ha or 10 ha. The effect of the threshold value on stream density is shown in Figure 5-1 for the 5, 2.5 and 10 ha cases. Decrease in the threshold value increases the amount of natural streams and in the model this increases the number of stream pixels in the hydrological model (average distance to stream pixel decreases). Drainage density in croplands and pastures is characterized by the drain spacing. Values suggested by the UNTAMO toolbox vary between 12 – 20 m depending on the soil type of the field and these values in in full accordance with the drain spacing used in Finland for various soils types: 12 m for clay soils and 20 m for coarse soils (Peltomaa 2009). Small drain spacing implies high drainage density and vice versa. The effect of drainage density on vertical and horizontal fluxes in various types of biosphere objects is shown in Tables 5-1 (lower density) and 5-2 (higher density). The Reference Case results are shown in Table 4-1. In the lower drainage density case the threshold value for stream delineation was 10 ha and drain spacing in croplands and pastures was assumed to be 20 % higher than suggested by the UNTAMO toolbox. In the higher density case upslope area needed for stream was 2.5 ha and drain spacing in agricultural objects was assumed to be 20 % smaller than in the Reference Case. Comparison of results given in Tables 4-1 (Reference Case) and in Table 5-1 (lower drainage density) show that in forested areas computed total horizontal outflow is almost the same in Tables 4-1 (223 mm/a) and 5-1 (228 mm/a) but flux component FHor3 is higher in Table 5-1 (177 mm/a) compared to the Reference Case (155 mm/a in Table 4-1). The reason for this is that low stream density in forested areas implies that average groundwater level is higher and relative fraction of flow in the upper mineral soil layer increases. Opposite effect can be seen by comparing Tables 5-2 (higher drainage density) and Reference Case. In forested areas FHor3 is 134 mm/a in the higher drainage density case and 155 mm/a in the Reference Case. In agricultural areas the effect 20 % increase/decrease in drainage density is small (less than 10 mm/a) on yearly average fluxes. This can be seen by comparing the computed fluxes given in Tables 4-1, 5-1 and 5-2. Drainage density influences the seasonal behaviour of groundwater level in agricultural fields but over longer periods 20 % increase in drain spacing does not influence significantly the yearly drainage fluxes.
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Figure 5-1. Influence of upslope area (the threshold value for small streams) on stream network density for the time step AD6020. a) Upslope area is 5 ha (Reference Case), b) upslope area is 2.5 ha and c) upslope area is 10 ha.
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Table 5-1. Vertical and horizontal water fluxes, water content in different layers and flux to wells (only forest areas) for terrestrial biosphere objects (forest, cropland, pasture and wetland) assuming that the drainage density is lower than in the Reference Case. Threshold value for steam is 10 ha and drain spacing in agricultural areas is 20 % higher than in the Reference Case. Average, maximum and minimum values are given for all ecosystem types. Unit of fluxes is mm/a, water content is given in unit m3/m3. Fluxes to wells are given from those biosphere objects that include well. Names of flux and water balance terms are shown in Figure 2-23. Empty cells in the Table indicate that these variables are not included in the conceptualized version of the ecosystem object.
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Table 5-2. Vertical and horizontal water fluxes, water content in different layers and flux to wells (only forest areas) for terrestrial biosphere objects (forest, cropland, pasture and wetland) assuming that the drainage density is higher than in the Reference Case. Threshold value for steam is 2.5 ha and drain spacing in agricultural areas is 20 % smaller than in the Reference Case. Average, maximum and minimum values are given for all ecosystem types. Unit of fluxes is mm/a, water content is given in unit m3/m3. Fluxes to wells are given from those biosphere objects that include well. Names of flux and water balance terms are shown in Figure 2-23. Empty cells in the Table indicate that these variables are not included in the conceptualized version of the ecosystem object.
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5.5 Sensitivity to interception and transpiration parameters
Interception has been measured on the Olkiluoto Island in four FIP-areas and transpiration in two FIP-areas as shown in Sections 2.3 and 3.2. The calibration and validation of the SVAT model that computes interception and transpiration is given in Section 3.2. Computed transpiration and interception rates were shown to agree well with the measured values (Figures 3-13-5). Decrease in interception implies higher precipitation throughfall and also higher transpiration rate since more energy is available for transpiration if interception is reduced. Therefore, the effect of change in interception does not have the equally strong effect on horizontal and vertical fluxes in the biosphere objects.
5.6 Sensitivity to soil hydraulic parameters
Horizontal hydraulic conductivities influences in the corresponding way than change in drainage density. Increase in horizontal K-value lowers the groundwater level since smaller gradient is needed around the stream cells or subsurface drains to transport the runoff to streams/drains and decrease in K-value has the opposite effect. More water will flow in subsurface soils below the root zone when K-values are high as compared to the case when horizontal conductivities are small. According to simulations carried out with the 3D-model, 50 % decrease in horizontal hydraulic conductivity increased the horizontal outflow component FHor3 by 20-30 mm/a when compared to the Reference Case, i.e. the effect is of the same magnitude than decrease in drainage density as shown in Section 5.3 and in table 5-1. In the vertical direction the distances are much smaller than in the horizontal direction and therefore, the uncertainty in vertical hydraulic conductivity influences less on water fluxes on yearly water fluxes.
5.7 Evaluation of the most important uncertainty factors
The most important input data that have effect on vertical and horizontal water fluxes in biosphere objects are climate scenario, drainage density, interception and transpiration parameters and soil hydraulic properties. Interception and transpiration have been measured on the Olkiluoto Island in small FIP and MRK plots, which are small biosphere objects. Therefore, the uncertainty in the parameters related to interception and transpiration is much smaller than uncertainty included in the three other input data (climate scenario, drainage density and soil hydraulic properties). The results shown in Section 5.3 imply that in forested areas the effect of lower drainage density (threshold value for stream delineation 10 ha instead of 5 ha) on outflow component FHor3 was approximately 20-25 mm/a. The influence of 50 % decrease in horizontal hydraulic conductivity increased the horizontal outflow component FHor3 by 20-30 mm/a when compared to the Reference Case (Section 5.5).
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The results given in Section 5.2 showed that increase in precipitation increases throughfall and horizontal fluxes and also vertical fluxes especially in the uppermost layers of the soil profile. Yearly precipitation in scenario A2 (520530 mm/a) is on the average 20-30 mm/a smaller than in the Reference Case and this is also the difference in precipitation throughfall and in the sum of horizontal outflows from various ecosystem types (forests, croplands, pastures and wetlands) between Terr_MaxAgri Case and Reference Case. Pimenoff et al. (2011) formulated three plausible climate evolution at Olkiluoto on a regional scale for the next 120 000 years and in these scenarios yearly precipitation varied between 600 and 700 mm/a for the time period AD2000AD12000. The climate scenarios of Pimenoff et al. (2011) would imply 100-150 mm/a higher horizontal outflow as compared to the Reference Case (present day climate). Uncertainty in drainage density or soil hydraulic parameters would not cause this high effect with realistic parameter values. Therefore, it can be concluded that the uncertainty involved in predicting the yearly precipitation rate for the period of next 10 000 years is the input data that has the biggest influence on vertical and horizontal fluxes in the biosphere objects during the safety assessment period.
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6 SUMMARY AND CONCLUSIONS The Finnish nuclear waste disposal company, Posiva Oy, is planning an underground repository for spent nuclear fuel to be constructed on the island of Olkiluoto on the south-west coast of Finland. The safety case 2012 comprises altogether fifteen Safety case portfolio main reports and several supporting reports. The present report regarding surface and near-surface hydrological modelling is one of the main reports. The other main reports that are closely linked with this study are Formulation of Radionuclide Release Scenarios, Data Basis for the Biosphere Assessment, Assessment of Radionuclide Release Scenarios, Biosphere Assessment, Terrain and Ecosystems Development Modelling (TESM), Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports. The overall aims of the biosphere assessment (BSA-2012) in the safety case are to describe the future, present, and relevant past conditions at, and prevailing processes in, the surface environment of the Olkiluoto site (Biosphere Assessment and Terrain and Ecosystems Development Modelling reports), to model the transport and fate of radionuclides hypothetically released from the repository through the geosphere to the surface environment (Biosphere Radionuclide Transport and Dose Assessment) and to assess possible radiological consequences to humans and other biota (Dose Assessment for the Plants and Animals). The time window adopted in the present assessment is the period over which the regulatory dose constraints are assumed to apply. It starts at the year of the emplacement of the first canister and lasts for ten millennia; covering the period from the year AD2020 to the year AD12020. The surface hydrological modelling described in this report is aimed at providing links between Biosphere Assessment and Terrain and Ecosystems Development Modelling reports and Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports. The most important modelling phases described in this report are calibration and validation of the Olkiluoto site scale surface hydrological model in the present day condition, calculation of boundary conditions for the UNTAMO toolbox in Eurajoki and Lapinjoki Rivers, computation of regional scale hydrological model using input data provided by Terrain and Ecosystems Development Modelling report and calculation of water balance components for the biosphere objects delineated. Computation of radionuclide transport and dose assessment in the biosphere objects uses fluxes computed in this study (Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports). The descriptions of the safety case simulation runs – Reference Case and Terr_MaxAgri are given in the in Biosphere Assessment and Terrain and Ecosystems Development Modelling reports. In July 2004, Posiva Oy began to construct an underground rock characterization facility called ONKALO on Olkiluoto Island. Construction of ONKALO and subsequent construction of the repository, will affect the surrounding rock mass and the groundwater flow system. It will also affect the chemical environment, not only on the surface but, to a greater extent, at depth. While many changes may be reversible, some may only be partially reversible and some irreversible. In order to determine the magnitude and extent of such effects, Posiva has set up a monitoring system called Olkiluoto Monitoring Programme (OMO). The monitoring results can be divided into
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two parts: 1) data needed for monitoring the state of the environment during the construction work and 2) the data collected as input for biosphere modelling for long-term safety purposes. Naturally, these data partly overlap and both data sets are used in this study for model calibration and validation.
During the next 10 000 years the terrain and ecosystem development is to a large extent driven by the postglacial crustal uplift. Full details regarding changes in soil surface elevations, surface water development and associated effects on ecosystem types are predicted by the UNTAMO toolbox and the results are shown in the Data Basis for the Biosphere Assessment and Terrain and Ecosystems Development Modelling reports. UNTAMO is a GIS toolbox developed for simulating land-uplift driven or other changes in the biosphere. The toolbox consists of modules to represent land uplift and delineation of the sea area, surface-water bodies, terrestrial and aquatic erosion, accumulation of organic matter, terrestrial vegetation, aquatic vegetation, faunal habitats, human settlement and land use (Terrain and Ecosystems Development Modelling). All the spatial and temporal input data (excluding meteorological data) needed in the surface hydrological modelling are provided by the UNTAMO toolbox. The forecasts made by UNTAMO toolbox (Terrain and Ecosystems Development Modelling) are used together with the radionuclide release pattern provided in the Formulation of Radionuclide Release Scenarios report to define the biosphere objects. They are continuous and sufficiently homogeneous sub-areas of the modelled area that could potentially receive radionuclides released from the repository. The specific aim of the surface hydrological modelling of this study is to compute vertical and horizontal water fluxes for the biosphere objects to be later on used in the Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports. The spatial location of the biosphere objects is predefined in the delineation process but the ecosystem type of each object changes over time due to postglacial crustal uplift and the associated biosphere development process. Possible ecosystem types for biosphere objects are coast, lake, river, forest, cropland, pasture and wetland. Some of the objects close to the present island boundaries are those that potentially may receive radionuclides from the repository (direct discharge from bedrock) and others may receive radionuclides from the surrounding terrestrial objects or from irrigation water. The models used in this study were tested starting from plot scale SVAT model that utilizes the data collected from the Wet Deposition Monitoring Network (MRK) and Forest Intensive Monitoring Plots (FIP). In the second phase, the calibration and validation of the Olkiluoto surface hydrological model (SHYD) aimed to calculate site scale hydrological behavior in the present day condition is given. In the third phase calibration and validation results of the catchment scale models for the Eurajoki and Lapinjoki basins are introduced. The extension of the site scale hydrological model for the regional scale needed in the 2012 safety assessment is described in the fourth step. Finally, the principles for obtaining water balance components and vertical and horizontal water fluxes used in the radionuclide dose computations in biosphere objects are outlined. The method adopted here is based on calculating average vertical and horizontal fluxes for biosphere objects from the results of the full 3D-model, i.e. it was not necessary to develop any simplified hydrological model for the biosphere objects.
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The drawback of the method is that it requires a lot of computer time due to the need to solve the 3D-model in unsaturated-saturated soils.
The key principle in the application of various sub-models was that model validation was carried out against independent data, i.e. data that was not used during calibration. In this study the amount of data is exceptionally large and therefore approximately 50-60 % of the data were used for calibration and rests of the data were utilized as independent validation data. The model calibration and validation were successful both in the plot scale (interception and transpiration) and in the site scale (snow accumulation and snow melt, depth to groundwater table and cumulative runoff from small sub-catchments). The specific aim of the hydrological modelling in the biosphere assessment BSA-2012 is to provide vertical and horizontal fluxes for the biosphere objects. The conceptualization of the layers (compartments) in the biosphere object layers is determined by the structure of the model that computes the radionuclide transport in the biosphere and is used to carry out the dose assessment scenarios (Biosphere Radionuclide Transport and Dose Assessment and Dose Assessment for the Plants and Animals reports). This report includes modelling results from for the Reference Case (present day climate) and Terr_MaxAgri Case (maximum extent of agricultural areas and climate scenario A2). The regional scale hydrological model uses raster files created with UNTAMO toolbox as model input data: soil surface elevation, soil thickness, soil profile data (depth of different soils layers), top soil type, location of cropland, forest and peat areas, flow accumulation raster, location of coastal areas and lakes. Moreover, the river network is available as polyline data. In addition to raster data UNTAMO toolbox provides input data for parameterization of the SVAT model of terrestrial biosphere objects (vegetation height, interception fraction, biomass in forested areas, leaf area index, saturated hydraulic conductivity, thickness of soil layer and organic matter layers and drain spacing in agricultural areas). These attributes are provided separately for each object and during each time step. Possible terrestrial ecosystem types for biosphere objects are forest, cropland, pasture and wetland. The vertical and horizontal fluxes computed in the Reference Case in the biosphere objects were compared with the measurements carried out in the FIP and MRK plots for transpiration, interception and precipitation throughfall and water balance computations of the FIP plots. Measured values were within the cumulative distributions computed from the results of the biosphere objects. The fluxes computed in the Reference Case and in the Terr_MaxAgri Case show that the biggest difference in the results is related to precipitation throughfall and horizontal fluxes out of the biosphere object whereas interception and transpiration do not differ very much from each other in the Reference Case and Terr_MaxAgri Case. Precipitation throughfall was around 20-25 mm/a smaller in the Terr_MaxAgri Case as compared to the Reference Case for all ecosystem types (forest, cropland, pasture and wetland) due to smaller precipitation in the Terr_MaxAgri Case.
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A new feature in the 2012 safety assessment compared to the TSM2009-case is the need to include influence of shallow wells in the radionuclide dose analysis (Biosphere Radionuclide Transport and Dose Assessment report). The SHYD model allows addition of wells as sink points in computational grid. Overburden and drilled wells can be treated in the same way in the model. The SHYD model was tested against two experiments carried out in Olkiluoto which include pumping from shallow well (infiltration experiment and pumping from OL-KR14 and long-term pumping from OL-KR06). Comparison of measured values with computed results showed that the Olkiluoto surface hydrological model can capture the most important features related to the pumping effects and the model can be used to predict the influence of shallow wells on water fluxes in the biosphere objects for the safety assessment needs. In the safety assessment computations water pumped from wells is assumed to be taken from different soil or bedrock layers depending on the hydraulic conductivity of each layer. The model computes drawdown caused by pumping from well and divides the well pumping (assumed to be 500 m3/a for single household well) for different layers. Well fluxes are the converted to unit m/a by dividing them by the area of delineated biosphere object. The selection of the number and location of wells (which biosphere object) are based on the delineation of the soil types provided by the UNTAMO toolbox. Wells can only be placed into objects where soil type is coarse, medium or fine mineral soil. The computation of vertical and horizontal water fluxes and in the biosphere objects was carried out using full 3D surface hydrological model. The drawback of the method used here is that computations are very time consuming and it is not possible to carry out the sensitivity analysis by changing one parameter value at a time and computing the effect of the change in output variables. The most important input data that have effect on water fluxes are climate scenario, drainage density, interception and transpiration parameters and soil hydraulic properties. The sensitivity runs carried out indicate that the uncertainty involved in predicting the yearly precipitation rate for the period of next 10 000 years is the input data that has the biggest influence on vertical and horizontal fluxes in the biosphere objects during the safety assessment period.
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REFERENCES Safety case portfolio main reports Safety case for the disposal of spent nuclear fuel at Olkiluoto - Formulation of Radionuclide Release Scenarios 2012. Eurajoki, Finland: Posiva Oy. POSIVA 2012-08. ISBN 978-951-652-189-6. Safety case for the disposal of spent nuclear fuel at Olkiluoto - Data Basis for the Biosphere Assessment BSA-2012. Eurajoki, Finland: Posiva Oy. POSIVA 2012-28. ISBN 978-951-652-209-1. Safety case for the disposal of spent nuclear fuel at Olkiluoto - Assessment of Radionuclide Release Scenarios for the Repository System 2012. Eurajoki, Finland: Posiva Oy. POSIVA 2012-09. ISBN 978-951-652-190-2. Safety case for the spent nuclear fuel disposal at Olkiluoto - Biosphere Assessment BSA-2012. Eurajoki, Finland: Posiva Oy. POSIVA 2012-10. ISBN 978-951-652-191-9. Safety case for the disposal of spent nuclear fuel at Olkiluoto - Terrain and ecosystems development modelling in the biosphere assessment BSA-2012. Eurajoki, Finland: Posiva Oy. POSIVA 2012-29. ISBN 978-951-652-210-7. Safety case for the disposal of spent nuclear fuel at Olkiluoto - Radionuclide transport and dose assessment for humans in the biosphere assessment BSA-2012. Eurajoki, Finland: Posiva Oy. POSIVA 2012-31. ISBN 978-951-652-212-1. Safety case for the disposal of spent nuclear fuel at Olkiluoto - Dose assessment for the plants and animals in the biosphere assessment BSA-2012. Eurajoki, Finland: Posiva Oy. POSIVA 2012-32. ISBN 978-951-652-213-8. Safety case portfolio supporting reports Olkiluoto Site Description 2011. Eurajoki, Finland: Posiva Oy. POSIVA 2012-02. ISBN 978-951-652-179-7. Olkiluoto Biosphere Description 2012 Eurajoki, Finland: Posiva Oy. POSIVA 2012-06. ISBN 978-951-652-187-2) Other references Aalto, P. (ed.), Helin, J., Lindgren, S., Pitkänen, P-, Ylä-Mella, M., Ahokas, H., Heikkinen, E., Klockars, J., Lahdenperä, A-M., Korkealaakso, J., Lamminmäki, T., Pedersen, K., Karvonen, T. 2011. Baseline report for infiltration experiment. Posiva Working Report 2011-25.
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Vaittinen, T., Ahokas, H., Klockars, J., Nummela, J., Penttinen, T.,Tammisto, E. & Karvonen. T. 2008. Results of monitoring at Olkiluoto in 2007. Hydrology. Working Report 2008-23. Vaittinen,T., Ahokas, H., Klockars, J., Nummela, J., Penttinen, T., Tammisto, E., Karvonen, T., 2009. Results of monitoring at Olkiluoto in 2008, Hydrology. Working Report 2009-43. Vaittinen, T., Ahokas, H., Klockars, J., Nummela, J., Pentti, E., Penttinen, T., Karvonen T. 2010. Results of monitoring at Olkiluoto in 2009. Hydrology. Posiva Working Report 2010-43. Vaittinen, T., Ahokas, H., Nummela, J. and Paulamäki, S. 2011a. Hydrogeological Structure Model of the Olkiluoto Site - Update in 2010. Working Report 2011-65. Posiva Oy. Eurajoki, Finland. Vaittinen,T., Ahokas, H., Klockars, J., Nummela, J., Pentti, E., Penttinen, T., Tammisto, E., Karvonen, T. and Lindgren, S. 2011b. Results of monitoring at Olkiluoto in 2010, Hydrology. Working Report 2011-43. Posiva Oy. Eurajoki, Finland. Vaittinen,T., Ahokas, H., Klockars, J., Nummela, J., Pentti, E., Penttinen, T., Tammisto, E., Karvonen, T. and Lindgren, S. 2012. Results of monitoring at Olkiluoto in 2011, Hydrology. Working Report 2012-43. Posiva Oy. Eurajoki, Finland. Vakkilainen, P. 2009. Hydrologian perusteita. Maan vesi- ja ravinnetalous. Ojitus, kastelu ja ympäristö, luku 3 (toim. Paasonen-Kivekäs, Peltomaa, R., Vakkilainen P. ja Äijä, H.). Salaojayhdistys ry, Helsinki. p. 67-111. Vehviläinen, B. 1992. Snow cover models in operational watershed forecasting. Publications of Water and Environment Research Institute 11, National Board of Waters and the Environment, Finland, 112 pp. Vieno, T. 1997. WELL-97 - A stylized well scenario for indicative dose assessment of deep repositories. VTT Energy, Technical Report SPAVTT-2/97. Vieno, T. & Ikonen, A. 2005. Plan for Safety Case of spent fuel repository at Olkiluoto. POSIVA 2005-01. Voipio, S., Tammisto, E., Lehtimäki, T., & Ahokas, H. 2004. Summary of Long-Term Hydrological Monitoring at the Olkiluoto Site. Eurajoki, Finland: Posiva Oy. 188 p. Working Report 2003-42. Vuorela, A., Penttinen, T. & Lahdenperä, A-M. 2009. Review of Bothnian Sea shorelevel displacement data and use of a GIS tool to estimate isostatic uplift. Eurajoki, Finland: Posiva Oy. Working Report 2009-17. 191 p.
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APPENDIX A. DESCRIPTION OF SHYD SUBMODELS App. A.1 Introduction The most important sub-models used in the hydrological modelling in the surface and near-surface hydrological modelling in the biosphere assessment BSA-2012 are introduced in this Appendix. The SVAT model (Soil-Vegetation-Atmosphere-Transfer) solves water and energy balance small forest plots. Representative plots (FIP and MRK) are monitored in the present day conditions and in future conditions SVAT model is used to compute water and balance for biosphere objects (see Section 2.6.2). Snow accumulation and snowmelt sub-model is needed both at present and in simulations that cover the period of next 10 000 years. Climate projections shown in Section 2.6.3 indicate that temperate conditions will prevail during the biosphere assessment time window (AD2020AD12020). Soil water and heat balance sub-models are needed in the site and regional scale hydrological models. App. A.2 Description of SVAT model Models that solve the water pathway in the soil, vegetation and atmosphere continuum are here called SVAT- model (Soil-Vegetation-Atmosphere-Transfer). In the SVAT model described here the main emphasis is devoted to the computation of water the energy balance of components of the four FIP plots located on Olkiluoto Island. Ínterception and transpiration of different vegetation types are at a very crucial role in the SVAT model. Hydrological processes quantified in energy and water balance modelling of forest stands include precipitation throughfall, interception, evaporation, transpiration, snow accumulation and melt, soil and ground water movement, overland flow, horizontal subsurface flow and flow to forest ditches. Moreover, soil heat balance is included. The importance of each process depends on the site properties, size of the area and climatic characteristics, as well as on the modelling objectives. Interception submodel The form of precipitation is determined on the basis of the air temperature. Below temperature TL (°C) precipitation falls as snow and above temperature TU as rain. Between TL and TU the proportion of snowfall (and rain) is a linear function of the air temperature. The share of precipitation falling between the trees is computed as a product of the total precipitation P (m h-1) and the sky-view fraction fS (-). The remaining precipitation, i.e. (1 − fS)P, can be intercepted in the overstorey canopy and it forms the input to the interception procedure of this storage. The interception model adopted from Koivusalo and Kokkonen (2002) is a modified version from the interception model proposed by Aston (1979). The following equation is used to determine the depth of intercepted water during a time-step, WI (m): IISI CtPfk
III eICICW /)1(00 )()( (A-1)
where I0 is the canopy storage (m) in the beginning of the computation time-step, CI is the interception capacity (m), and kI (-) is a model parameter and PI (m h-1) is the precipitation input for the interception model during and t (h) is length of one time step. The capacity
134
CI is higher for interception of snow (CIS) than for interception of rain (CIW). The interception capacity is much lower in understorey vegetation. PI is above canopy precipitation PA during one time step in overstorey computation and in understorey calculations PI is the fraction that passes through the overstorey. The change in the canopy storage during a computation time-step, ΔI (m), can be written as ΔI =WI − EI (A-2) where EI (m) is the depth of interception evaporation during a time-step. Evaporation rate out of the interception storage, ER (m h-1), is calculated using a combination equation of the Penman-Monteith type (Monteith, 1965):
)(
/)(
SL
reecSRE aadpan
R (A-3)
where S (hPa°C-1) is the gradient of the saturated vapor pressure – temperature curve, Rn is the net radiation in the canopy (W m-2), ρa is the air density (kg m-3), cp is the specific heat of air (Jkg-1°C -1), ed (hPa) is the saturation vapor pressure at air temperature, ea (hPa) is the vapor pressure of air, ra is the aerodynamic resistance of vapor transport (s m-1), L is the latent heat of vaporization (Jkg-1), and γ (hPa°C-1) is the psychrometric constant. The depth of interception evaporation during one time step is then EI=ERt. The aerodynamic resistance ra is calculated according to the eddy diffusion theory assuming equal resistances to transfer of heat, vapour and momentum. Although this assumption is known not to be strictly valid, it is commonly used in a hydrological context (Calder, 1990; Lundberg et al., 1998; Lundberg and Halldin, 1994; Wigmosta et al., 1994; Koivusalo and Kokkonen 2002). The wind speed is logarithmic above the canopy, exponential inside the canopy and logarithmic above the ground (Choudhury and Monteith, 1988; Dolman, 1993; Koivusalo and Kokkonen 2002). Aerodynamic resistance ra is computed by integrating the reciprocal of the eddy diffusion coefficient over the range from d0+z0o to zr (Dolman 1993; Koivusalo and Kokkonen 2002):
1lnln1
000 /)(0
00
0
0
02
hdznn
h
r
o
r
ra
oenK
h
dh
dz
z
dz
ukr (A-4)
o
r
rh
z
dz
dhukK
0
0
002
ln
)( (A-5)
where k is von Karman constant (0.41), ur (m s-1) is wind speed at reference height zr (m), h0 (m) is vegetation height, d0 is the zero-plane displacement height (=0.63h0), z0o is the roughness length of the canopy (=0.13h0), n is extinction coefficient, Kh is the logarithmic diffusion coefficient in the canopy (m2 s-1). For understorey computation zr is 2 m and wind speed is transferred from above canopy value to inside canopy wind speed using the exponential wind speed profile inside the canopy.
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Net radiation The net radiation (W m-2) adsorbed by the overstorey and understorey canopy, respectively (Rn,oc and Rn,uc) can be calculated according to equations (A-7):
4)1()1( cslsn TRaRRaR (A-7a)
nocn RfSR )1(, (A-7b)
nSucn RfR , (A-7c)
where a is albedo, Rs (W m-2) is measured global radiation, Rl (W m-2) is net long-wave heat radiation, Tc is canopy surface temperature, is emissivity of the surface and is Stefan-Boltzman constant. Fraction (1-fS) of net radiation is taken up by the overstorey canopy and sky-view fraction fS by the understorey canopy. Transpiration flux Computation of transpiration rates of the overstorey, LToc (W m-2) and understorey, LTuc (W m-2) can be done using the Penman-Monteith equation:
)1(
/)(
,
,
,,
oca
occ
ocaadpaocnoc
r
rS
reecSRLT
(A-8a)
)1(
/)(
,
,
,,
uca
ucc
ucaadpaucnuc
r
rS
reecSRLT
(A-8b)
Conversion of transpiration rates LToc from unit W m-2 to the same unit than precipitation is give (m h-1) can be done Canopy resistance Resistance of the overstorey canopy, rc,oc (s m-1) and understorey canopy, rc,uc, needed in Eqs. (A-8a) and (A-8b) can be obtained by using the concepts defined by Kellomäki and Wang (2000). Resistance is calculated from canopy conductances gc,oc and gc,uc (m s-1). Canopy conductances were calculated from Eqs. (A-9) and (A-10): )()()()()(,,, asaaMAXoccocc CfRfTfhfDfgg (A-9a)
)()()()()(,,, asaaMAXuccucc CfRfTfhfDfgg (A-9b)
136
350/1/1)(
)/()(
)(1)(
)/(1)(
)/(1)(
88
6
2max5
1
3
1
1
7
4
2
agga
kgss
aga
kg
kgaa
CkkCf
kRRf
TTkTf
khhf
kDDf
g
g
g
(A-10)
where gc,oc,MAX and gc,uc,MAX are maximum conductances for overstorey and understorey canopies. gc,oc,MAX was calibrated from existing transpiration (sap flow) measurements and and gc,uc,MAX was estimated from literature data. Relative influence of various meteorological or soil variables on canopy conductance is defined by functions f(Da), f(h), f(Ta) and f(Ca) taken from Kellomäki and Wang (2000) and function f(Rs) adopted from Dolman and Nonhebel (1988). Da is vapour pressure deficit (kPa), h is soil water potential (MPa), Ta is air temperature, Ca is ambient concentration CO2 (ppm), Rs is global radiation (W m-2) and kg1..kg8 are parameters of the relative conductivity functions (see Appendix B). Canopy resistances were calculated as the reciprocal of the corresponding conductivities.
uccucc
occocc
gr
gr
,,
,,
/1
/1
(A-11)
Stem flow In forest areas stem flow, PSTEM, is the fraction of rainfall and canopy snowmelt that flowing along the stems of the trees. Precipitation gauges located under the trees do not catch this part of total rainfall and it has to be removed from total throughfall so that measured and computed throughfall can be compared with each other.
ICWC
CSPf
MAXSTEMSTEM
WMPP
PfefP CEXTSTEM
)1(,
, (A-12)
where fSTEM,MAX and fSTEM,EXT are parameters to be calibrated. The parameter values are different for pine, spruce and deciduous forests. PC is sum of rainfall above canopy PW and canopy snowmelt MC subtracted by the depth of intercepted water WI. Throughfall PT during a computation time-step through the overstorey can be written as PT = [(1− fS)P −WI] - PSTEM + fSP +US, (A-13) where US is the snow unloading during a computation time-step. When the air temperature increases above the freezing point and the canopy storage is greater than CIW, unloading of the intercepted snow (in excess of CIW) occurs. Occurrence of snow unloading is checked
137
in the beginning of the computation time step before the depth of intercepted water WI is determined according to Eq. (A-1) (Koivusalo and Kokkonen 2002). App. A.3 Snow accumulation and snowmelt Snow accumulation and snowmelt must be modelled both in the canopy storages and on the ground in order to get the overall water balance of FIP plots. Detailed snow water equivalent and snow depth measurements were not available from the forest intensive plot. Therefore, a simple degree-day model was selected as a basis for the snowmelt computations:
MAXMCUMCUMMINM
MBAM
KKMKKK
TTKM
);1(
)( , (A-14)
where M is snowmelt (m h-1), KM is degree-day factor (m h-1 °C-1), TA is average temperature of air and TB,M is base temperature for melting, KMIN and KMAX are the minimum and maximum values for degree-day factor, MCUM (m) is cumulative snowmelt during that winter/spring and KCUM is a parameter. The reason to use the cumulative snowmelt as an explaining variable of the degree-day factor KM has been explained by Vehviläinen (1992). During the snowmelt period, the physical properties of snow change considerably. Snow becomes more granulated, its density increases and, the albedo of the snow surface drops from 0.8 to about 0.5. This results in increased degree-day factor towards the end of the melting period. Mathematically this phenomenon is taken into account by increasing KM as a function of the cumulative snowmelt MCUM. According to Kuusisto (1984) snowmelt parameter KM is approx. 1.0410-4 m h-1 °C-1 (2.5 mm °C-1 d-1) in clearings and approx. 1.1710-4 m h-1 °C-1 (2.8 mm °C-1 d-1) in forests, the coverage of crowns being 20 %. From this point snowmelt parameter declines linearly and reaches the value of 510-5 (1.2 mm °C-1 d-1) when the coverage of the crowns is 80 %. According to Hiitiö (1982) degree-day factor for different terrain types were: 9.310-5 m h-1 °C-1 (2.24 mm °C-1 d-1) for spruce forests, 1.1710-4 m h-1 °C-1 (2.8 mm °C-1 d-1) for deciduous forests and 1.2510-4 m h-1 °C-1 (3.0 mm °C-1 d-1) for open areas. Calculation of water retention capacity of the snowpack Snowmelt or rainfall increases the density of snow causing the water to store in the snow until it becomes sufficiently "wet". The quantity of water exceeding this limiting value is given as "yield" to the rainfall-runoff model. The quantity of water stored in snow cover SLIQ (mm) is a function of the water equivalent of snow SWE (mm) and thus a continuously changing factor. Moreover, the maximum water holding capacity of snow fCAP (fraction) changes as a function of the structure of the snowpack: the water retention capacity has its maximum relative value in the beginning of the snow accumulation period and relative value decreases with the age of the snowpack. Thus the development of the liquid water retention capacity fCAP is opposite that for the degree-day factor. Mathematically the calculation of this phenomenon is taken into
138
account by decreasing fCAP as a function of the cumulative snowmelt MCUM (mm) according to Eqs. (A-15) (Vehviläinen 1992).
WECAPLIQ
MINCCAPCUMCUMMAXCCAP
SfS
ffMCff
,, );1( (A-15)
where fC,MAX and fC,MIN are the maximum and minimum liquid retention capacity of the snow cover (fractional values) , CCUM (m-1) is an empirical parameter, SLIQ (m) is the maximum amount of liquid water in the snowpack and SWE (m) is the snow water equivalent. According to Vehviläinen (1992), average values and standard deviations of the parameter values based on model calibration studies are as follows: fC,MAX 0.17 and 0.084, fC,MIN 0.05 and 0.028 and CCUM 0.018 and 0.019. According to field measurements the water retention capacity fCAP varies usually between 0.05...0.15 (Kuusisto 1984). Calculation of refreezing process Refreezing of the liquid water is included in the model. It is defined in this case by the diurnal average temperature so that, below the threshold temperature TB,F, refreezing MF (positive value) is calculated according to the following formula (Vehviläinen 1992):
FBAF
FBAe
AFBFF
TTM
TTTTKM F
,
,,
;0
;)(
(A-16)
where TB,F (°C), refreezing parameter KF (m °C-1 h-1) and empirical exponent eF have to be calibrated. According to Vehviläinen (1992), average values and standard deviations of the parameters are as follows: TB,F –1.7 and 1.2 (°C), KF 6.310-5 and 5.810-5 m h-1 °C-1 1and 1.4 and eF 0.36 and 0.44, respectively. Calculation of snow density and depth Snow density S,I (kg m-3) and snow depth DS,i (m) for day i are calculated using equations (A-17):
iS
iWEiS
MAXiS
iSiS
iSNEWiSiSPACKiS
SD
PD
PD
,
,,
,
,1,
,1,1,,
)1(
(A-17)
where S,i-1 and DS,i-1 are snow density (kg dm-3) and snow depth (m) during the previous day, PS,i (m) is the amount of new snow falling during day i, NEW is density of new snow (around 100 kg m-3) and PACK is an empirical "snowpacking" parameter that takes into account the fact that snow density increases when the snow cover is
139
ageing. Typical values for PACK are around 0.01..0.02 indicating that during each day snow density is increased around 1-2 % if there is no new snow during the day. However, it is easy to figure out that equation (A-17) can lead to too dense snowpack if the snow free period during the winter is long enough. Therefore the model limits the snow density to a maximum value MAX (around 300350 kg m-3) that cannot be exceeded. App. A.4 Soil water balance In the Olkiluoto surface hydrological model (Karvonen 2009a, 2010, 2011) overburden and bedrock were combined into one single numerical solution and overburden-bedrock interface can be seen as the layer where hydraulic properties change from soil values to bedrock data. The model links unsaturated and saturated soil water in the overburden and groundwater in bedrock into one continuous pressure system. Flux at the interface between overburden and bedrock can be calculated since the location of the first bedrock node in the vertical direction can be obtained from bedrock elevation data. Therefore, in the surface hydrological model no simplifications are needed in the treatment of the biosphere-geosphere interface. For the flow of water in saturated or unsaturated soil the solution of the 3D Richards equations was used (Karvonen 2008, Appendix T.2.3, p. 76-78):
TTzyx QSz
HhK
zy
HhK
yx
HhK
xt
HhC
)()()()( (A-18)
where H is the hydraulic head (h+z) (m), h is soil water pressure head (m), z is vertical coordinate (m), t is time (d), Kx(h), Ky(h) and Kz(h) (m d-1) are the hydraulic conductivities of the soil. C(h) is differential water capacity (m-1), which is defined as the derivative of the soil water retention curve, C(h)=d/dh, where is volumetric soil water content (-), ST is sink term caused by evapotranspiration (m3m-3d-1) , QT is the term that takes into account flow to forest ditches and subsurface drains (m3m-3d-1). The numerical solution of this equation was done using the finite volume method and code verification of the model given by Karvonen (2010, Appendix B.3, p. 70-79). Olkiluoto surface hydrological model includes three different options for estimating the influence of hydrogeological zones on water flow in the bedrock system. The detailed description of the three methods has been given by Karvonen (2010, Section 2.4, p. 9-13). In this study the method 3 was chosen. In this method the hydrogeological zones are thin plates located in 3-D space and water flow in these zones was calculated using the finite element method. Moreover, water exchange between rock matrix and fracture zones needs to be calculated. Detailed description of this method has been given by Karvonen (2010, Section 2.4, p. 9-13 and Appendix B.4, p. 80-84). Evapotranspiration term ST is calculated using the Penman-Monteith equation (e.g. Tamm 2002). Root depth was assumed to be 0.4 m and sink term assumes triangular distribution of roots indicating that a greater proportion of evapotranspiration is taken from the top soil.
140
Flow to agricultural drains is calculated from the Hooghoudt equation (Feddes et al. 1978) and flow to small streams (qR(t) from forest areas is calculated from Equation (A-19):
BijBij
RijBR ZGWLifx
ZGWLfLzTtq
;5.0
)()( (A-19)
where TB(z) is the transmissivity calculated by integrating the hydraulic conductivity from the bedrock level ZB to groundwater level (GWL), Lij is length of stream section in pixel ij, GWLij is computed groundwater level in pixel ij, ZB is elevation of stream bottom in pixel ij (soil surface elevation minus stream depth 0.9 m), parameter fR is influence of additional resistance around the stream (1.0 if no additional resistance) and x is pixel width (10 m here). Flux qR(t) is zero if GWLij < ZB.
GWL
ZB
B dzzKzT )()( (A-20)
van Genuchten model of soil water retention curve Van Genuchten (1980) proposed an approximation for the water retention characteristic
hhSe 1 (A-21)
where h is soil pressure head (soil matric potential), α [m-1], β [-] are empirical constants and γ = 1 - 1/β. Effective saturation Se of the soil is defined as
rs
re hS
(A-22)
where θ is the volumetric water content of the soil, θs is saturated water content and θr is residual water content.. Water content θ can be solved from equations (B-3) and (B-4) when the pressure head h is known. Unsaturated hydraulic conductivity K(h) of a soil can be described with the product of saturated hydraulic conductivity KS and relative hydraulic conductivity KR(h).
2/12/1 11
)(
eeR
RS
SShK
KKhK
(A-23)
Approximation of the differential moisture capacity In solving the governing equation, the differential moisture capacity C of Eq. (A-20) has to be evaluated. Tests with numerical solution methods have shown that non-linear changes in C tend to produce mass conservation errors, whereas the non-linear changes in hydraulic conductivity mostly affect the internal water distribution (Celia et al. 1990). Karvonen
141
(1988) and Celia et al. (1990) have presented algorithms that maintain the water balance of the Richards equation. Here the method suggested by Karvonen (1988) is used. The most accurate approximation for C(t+1/2) would be:
)()1(
)()1()2/1(
thth
tttC
(A-24)
where is soil moisture content, h is soil matric potential and t+1 refers to new, unknown value and t to known value from the present time step. C(t+1/2) calculated using equation (A-24) is the exact approximation which should be used when the change in soil matric potential is h(t+1)-h(t) and the corresponding change (t+1)-(t) should be calculated. The solution method for using approximation given in Eq. (A-24) can be summarized as follows: 1° At the beginning of each time step an explicit approximation of the change in water
content is calculated for the first nodal point and for other nodes initial estimate can be the value taken from the previous time step. An estimate of the soil moisture content of each nodal point can be obtained, denoted by °(t+1).
2° Approximation of soil water potential for each nodal point can be calculated using new water content values estimated in stage 1° ( h°(t+1) as a function of °(t+1))
3° Differential moisture capacities can be estimated now from (A-24) where (t+1) is replaced by °(t+1) and h(t+1) by h°(t+1).
4° New pressure head values can be calculated by solving the equation (A-20). If convergence is not attained, a new iteration is necessary and within this new cycle the stages 1° through 3° are repeated. At the end of the iteration procedure, h°(t+1) is equal to h(t+1).
The main merit of the procedure described above is the accurate calculation of mass balances (Karvonen 1988). Hence, it is possible to use longer time steps and still maintain reliable water balance calculations. App. A.5 Soil heat balance The equations describing the combined heat and water flow are given by Eqs. (A-25) and (A-26) (Karvonen 1988):
z
TqC
z
TK
zt
IL
t
TC WWTFIS
)( (A-25)
TW
I Sz
hhK
zt
I
t
hhC
1)()(
(A-26)
where z is a space coordinate (m), t is time (d), T is soil temperature (°C), KT is soil thermal conductivity (J m-1 °C-1), LF is latent heat of fusion of water (J kg-1), CS is volumetric specific heat of soil (J m-3 °C-1), I and W are density of ice and water (kg m-3), respectively, CW is specific heat of water (J kg-1 °C-1), qW is flow of water (m d-1), I is volumetric ice content, h is soil water potential, C(h) is differential moisture capacity (m-1) and K(h) is unsaturated hydraulic conductivity of the soil matrix (m/d)
142
and ST the sink term representing the volume of water taken up by the roots. Soil heat balance is calculated using 1D-solution for each grid, since the horizontal heat fluxes in the soil are negligible compared to vertical fluxes. A difficulty in the numerical solution of (A-25) and (A-26) is the inclusion of the ice term, since it generally dominates the solution (e.g. Karvonen 1988). To avoid numerical difficulties the assumption is often made that there exists a unique relationship between unfrozen water content uf and soil temperature T in frozen soil.
0;)(0
TeT ICET
TT
Suf (A-27)
where S is the saturated water content, T0 is the freezing point for h=0 m (usually 0 °C) and TICE is a parameter, which defines the shape of the curve. TICE is a small value for coarse soils (0.52) and a bigger value for silt/clay soils (2.04.0).
143
APPENDIX B. PARAMETERS OF THE SVAT MODEL
Table B-1. Parameter values of overstorey (trees) interception models.
Table B-2. Parameter values of understorey interception models.
Table B-3. Parameter values of canopy conductance models.
144
Table B-4. Parameter values of snow accumulation and snowmelt models.
145
APPENDIX C. INPUT DATA FOR THE SURFACE HYDROLOGICAL MODEL
Figure C-1. Delineation of the soil types of the Olkiluoto Island used in site scale modeling of the Olkiluoto Island.
146
Table C-1. Thickness of vertical layers z (m), depth of layer midpoint (m) and cumulative depth (m) in the 3D model (numbering starts from top). Total number of layers in the vertical direction is 18 and main emphasis is in the description of the overburden layers (thickness usually less than 4 m).
147
Table C-2. The calibrated parameter values of the soil water retention curves of the soil types classified on Olkiluoto Island. Saturated water content S, residual water content R, parameters (m-1)and of the van Genuchten function and saturated hydraulic conductivity (m s-1). Parameter values defined separately for top soil (0-0.4 m) and bottom soil (>0.4 m).
148
Table C-3. The hydraulic properties (the hydraulic conductivity K, the fracture aperture 2b, the fracture spacing 2a, the flow porosity f) of the sparsely fractured rock (SFR) between the hydrogeological zones (HZ) of the structural model 2010 (Vaittinen et al. 2011a; Löfman and Karvonen 2012, Table 4-5). The hydraulic conductivity represent the bedrock inside the Well Characterized Area (the rock volume where the drill hole investigations have been focused). The lack of information on the HZs outside the WCA2010 has been compensated by using five-fold conductivity outside the WCA2010. Based on the calibration conducted in Löfman et al. (2009) the horizontal/vertical anisotropy ratio of 10 in the uppermost 50 m layer of rock was used (i.e. the vertical component of K was decreased to 3.0·10-8 m/s).
149
Table C-4. The hydraulic properties (the transmissivity T, the fracture aperture 2b, the fracture spacing 2a, the flow porosity f, the thickness dave) of the hydrogeological zones (HZ) in the structural model 2010 (Vaittinen et al. 2011a; Löfman and Karvonen 2012, Table 4-3). The graphs of the depth dependent transmissivities are presented in Figure 5.1-2.
150
Figure C-4. The depth dependent transmissivities of zones HZ001, HZ099, BFZ100 and HZ146 (Posiva 2011; Vaittinen et al. 2011a; Löfman and Karvonen 2012, Figure 4-3).
151
APPENDIX D. ADDITIONAL CALIBRATION AND VALIDATION RESULTS IN THE PRESENT DAY CONDITIONS
Figure D-1. Measured and computed soil temperature at 10, 20, 40 and 90 cm depths in Norway spruce stand (FIP10). Calibration period indicated in the graphs.
152
Figure D-2. Measured and computed soil temperature at 10, 20, 40 and 90 cm depths in young birch stand (FIP11). Calibration period indicated in the graphs.
153
.
Figure D-3. Measured and computed soil temperature at 10, 20, 40 and 90 cm depths in alder stand (FIP14). Calibration period indicated in the graphs.
154
Figure D-4. Measured and computed groundwater level in ten overburden tubes (OL-PVP). Location of tubes is shown in Figure 2-10a.
155
Figure D-5. Measured and computed hydraulic head in shallow bedrock drillholes (OL-PP, OL-PA, OL-L). Location of drillholes is shown in Figure 2-10b.
156
Figure D-6. Measured and computed hydraulic head in shallow bedrock drillholes (OL-PP, OL-PA, OL-L). Location of drillholes is shown in Figure 2-10b.
157
Figure D-7. Measured and computed hydraulic head in shallow bedrock drillholes (OL-PP, OL-PA, OL-L). Location of drillholes is shown in Figure 2-10b.
LIST OF REPORTS
23.5.2013
POSIVA-REPORTS 2012
_______________________________________________________________________________________
POSIVA 2012-01 Monitoring at Olkiluoto – a Programme for the Period Before Repository Operation Posiva Oy ISBN 978-951-652-182-7 POSIVA 2012-02 Microstructure, Porosity and Mineralogy Around Fractures in Olkiluoto
Bedrock Jukka Kuva (ed.), Markko Myllys, Jussi Timonen, University of Jyväskylä Maarit Kelokaski, Marja Siitari-Kauppi, Jussi Ikonen, University of Helsinki Antero Lindberg, Geological Survey of Finland Ismo Aaltonen, Posiva Oy ISBN 978-951-652-183-4
POSIVA 2012-03 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Design Basis 2012 ISBN 978-951-652-184-1 POSIVA 2012-04 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Performance Assessment 2012 ISBN 978-951-652-185-8 POSIVA 2012-05 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Description of the Disposal System 2012 ISBN 978-951-652-186-5 POSIVA 2012-06 Olkiluoto Biosphere Description 2012 ISBN 978-951-652-187-2 POSIVA 2012-07 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Features, Events and Processes 2012 ISBN 978-951-652-188-9 POSIVA 2012-08 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Formulation of Radionuclide Release Scenarios 2012 ISBN 978-951-652-189-6 POSIVA 2012-09 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Assessment of Radionuclide Release Scenarios for the Repository System 2012 ISBN 978-951-652-190-2
POSIVA 2012-10 Safety case for the Spent Nuclear Fuel Disposal at Olkiluoto - Biosphere Assessment BSA-2012 ISBN 978-951-652-191-9 POSIVA 2012-11 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Complementary Considerations 2012 Posiva Oy ISBN 978-951-652-192-6 POSIVA 2012-12 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Synthesis 2012 ISBN 978-951-652-193-3 POSIVA 2012-13 Canister Design 2012 Heikki Raiko, VTT ISBN 978-951-652-194-0 POSIVA 2012-14 Buffer Design 2012 Markku Juvankoski ISBN 978-951-652-195-7 POSIVA 2012-15 Backfill Design 2012 ISBN 978-951-652-196-4 POSIVA 2012-16 Canister Production Line 2012 – Design, Production and Initial State of the Canister Heikki Raiko (ed.), VTT Barbara Pastina, Saanio & Riekkola Oy Tiina Jalonen, Leena Nolvi, Jorma Pitkänen & Timo Salonen, Posiva Oy ISBN 978-951-652-197-1 POSIVA 2012-17 Buffer Production Line 2012 – Design, Production, and Initial State of the Buffer Markku Juvankoski, Kari Ikonen, VTT Tiina Jalonen, Posiva Oy ISBN 978-951-652-198-8 POSIVA 2012-18 Backfill Production Line 2012 - Design, Production and Initial State of the Deposition Tunnel Backfill and Plug ISBN 978-951-652-199-5 POSIVA 2012-19 Closure Production Line 2012 - Design, Production and Initial State of Underground Disposal Facility Closure Ursula Sievänen, Taina H. Karvonen, Saanio & Riekkola Oy David Dixon, AECL Johanna Hansen, Tiina Jalonen, Posiva Oy ISBN 978-951-652-200-8
POSIVA 2012-20 Representing Solute Transport Through the Multi-Barrier Disposal System by Simplified Concepts Antti Poteri. Henrik Nordman, Veli-Matti Pulkkanen, VTT Aimo Hautojärvi, Posiva Oy Pekka Kekäläinen, University of Jyväskylä, Deparment of Physics ISBN 978-951-652-201-5 POSIVA 2012-21 Layout Determining Features, their Influence Zones and Respect Distances at the Olkiluoto Site Tuomas Pere (ed.), Susanna Aro, Jussi Mattila, Posiva Oy Henry Ahokas & Tiina Vaittinen, Pöyry Finland Oy Liisa Wikström, Svensk Kärnbränslehantering AB ISBN 978-951-652-202-2 POSIVA 2012-22 Underground Openings Production Line 2012- Design, Production and Initial State of the Underground Openings ISBN 978-951-652-203-9 POSIVA 2012-23 Site Engineering Report ISBN 978-951-652-204-6 POSIVA 2012-24 Rock Suitability Classification, RSC-2012 Tim McEwen (ed.), McEwen Consulting Susanna Aro, Paula Kosunen, Jussi Mattila, Tuomas Pere, Posiva Oy Asko Käpyaho, Geological Survey of Finland Pirjo Hellä, Saanio & Riekkola Oy ISBN 978-951-652-205-3 POSIVA 2012-25 2D and 3D Finite Element Analysis of Buffer-Backfill Interaction Martino Leoni, Wesi Geotecnica Srl ISBN 978-951-652-206-0 POSIVA 2012-26 Climate and Sea Level Scenarios for Olkiluoto for the Next 10,000 Years Natalia Pimenoff, Ari Venäläinen & Heikki Järvinen, Ilmatieteen laitos ISBN 978-951-652-207-7 POSIVA 2012-27 Geological Discrete Fracture Network Model for the Olkiluoto Site, Eurajoki, Finland: version 2.0 Aaron Fox, Kim Forchhammer, Anders Pettersson, Golder Associates AB Paul La Pointe, Doo-Hyun Lim, Golder Associates Inc. ISBN 978-951-652-208-4 POSIVA 2012-28 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Data Basis for the Biosphere Assessment BSA-2012 ISBN 978-951-652-209-1
POSIVA 2012-29 Safety Case For The Disposal of Spent Nuclear Fuel at Olkiluoto - Terrain and Ecosystems Development Modelling in the Biosphere Assessment BSA-2012 ISBN 978-951-652-210-7 POSIVA 2012-30 Safety Case for the Disposal of Spent Nuclear Fuel at Olkiluoto - Surface and Near-surface Hydrological Modelling in the Biosphere Assessment BSA-2012 ISBN 978-951-652-211-4
