1
MasterarbeitIm Studiengang
“Environmental Management – Management natürlicher Resourcen”
Test and application of hydrological models with a spatialmodelling language (PCRaster) for the discharge simulation of a
wetland dominated catchment in Northern Germany
Vorgelegt von
Xiaoyong Zhang
Kiel, im Oktober 2006
1. Prüferin: Professor Dr. Nicola Fohrer2. Prüfer: Dr. Georg Hörmann
ÖkologiezentrumChristianAlbrechtsUniversität zu Kiel
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Contents P.
List of Figures 4
List of Tables 6
1. Introduction 7
2. The study area 9
2.1 The catchment and its river network 9
2.2 Development of the Treene and Kielstau landscape 10
2.3 Climate 13
2.4 Topography 15
2.5 Soil 15
2.6 Land use 17
2.7 Groundwater and wetland resources 18
3. Literature review 21
3.1 Principles in hydrological modelling and its new development 21
3.2 The model system PCRaster 23
3.2.1 Development and advantages of PCRaster 23
3.2.2 Applications of PCRaster in catchment hydrology modelling 27
4. Development of the KIDS model 30
4.1 Implementation of the KIDS model in PCRaster 30
4.1.1 Available data and preparation of the database 31
4.1.2 Definition of hydrologic processes in KIDS model 40
4.2 Structure of KIDS model scripts 46
4.3 Select of calibration and validation period 47
4.3.1 Start up period 47
4.3.2 Calibration and validation period 48
4.4 Criteria and methods for results comparison 49
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5. Application of the model 52
5.1 Calibration strategy of the KIDS model 52
5.2 Data evaluation 53
5.3 Calibration 54
5.3.1 Parameter estimation 54
5.3.2 Simple conceptual models 56
5.3.3 Preparation for wetland models 61
5.3.3.1 Water budget analysis in Kielstau 62
5.3.3.2 Soil description and reclassification for wetland map 65
5.3.4 Wetland models 70
5.3.5 Parameter adjustment of wetland models 74
5.4 Validation 80
5.5 Discussion 82
5.5.1 Wetland fraction in Kielstau catchment 83
5.5.2 Combination of two different wetland maps 84
5.5.3 Limitations in the simulation of KIDS model 84
5.5.4 The hydrological features of wetlands in the study area and 85
the application in different models – KIDS, SIMPEL, SWAT
6. Conclusion 87
7. References 89
8. Appendixes 94
Appendix I: Kinematic wave function with Manning’s equation 94
Appendix II: Different versions of wetland map with its soil components 96
Appendix III: The script of KIDS model 98
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List of Figures
Figure 2.1 Upper Treene catchment – the area of input database, 9
and Kielstau catchment – the modelled subcatchment
Figure 2.2 River network in the modelled area 10
Figure 2.3 Estimated locations of glacial valleys 11
Figure 2.4 Geomorphologic simulation of the landscape development 11
Figure 2.5 Geomorphologic situation of the study area 12
Figure 2.6 Precipitation in the Upper Treene catchment above Treia 13
Figure 2.7 Seasonal precipitation distribution in the catchment 14
Figure 2.8 Temperature distribution in the catchment 14
Figure 2.9 Elevation map of the Upper Treene catchment 15
Figure 2.10 Soil types in Kielstau catchment 16
Figure 2.11 Land use pattern in Kielstau basin 17
Figure 2.12 Spatial distribution of peatland in SchleswigHolstein 19
Figure 2.13 Typical landscape in the Kielstau catchment 20
Figure 4.1 Digital elevation map (dem.map) 31
Figure 4.2 Left: possible flow directions in a cell; 32
Middle: grid based catchment discretization and concept of flow path;
Right: part of local drain direction map (ldd.map)
Figure 4.3 Slope map (slope.map) 32
Figure 4.4 Channel width map (channelw.map) 33
Figure 4.5 Three weather stations 34
Figure 4.6 Left: precipitation zone map (zonep.map); 34
Right: rain timeseries file (precipitation.tss)
Figure 4.7 Left: evaporation zone map (zonee.map); 36
Right: evaporation timeseries file (evaporation.tss)
Figure 4.8 Left: original soil map with 56 soil types; 37
Right: simplified soil map (soil.map in PCR) with 11 soil types
Figure 4.9 Land use map (landuse.map) 38
Figure 4.10 Left: Manning map for field (Manning.map); 40
Right: Manning map for channel (channelM.map)
Figure 4.11 Simplified flow chart of the KIDS model 41
Figure 4.12 Reduction of ETp to ETa as a function of soil water content 43
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Figure 4.13 Evaluation of the start up period 48
Figure 4.14 Precipitation and discharge variation from 1986 to 1999 48
Figure 5.1 KIDS model calibration process 53
Figure 5.2 Monthly relations between runoff and precipitation (1986 ~ 1999) 54
Figure 5.3 Effect of change in values of model parameters on model efficiency 55
Figure 5.4 Hydrograph of the basic model 57
Q_basic, NS=0.228, RMSE=0.545, R²=0.573
Figure 5.5 Hydrograph of the basic model with spatial distribution of soil field capacity 58
Q_Sfc, NS=0.163, RMSE=0.517, r²=0.606
Figure 5.6 Hydrograph of the basic model with additional lateral flow 59
Q_Lateral, NS=0.99, RMSE=0.67, r²=0.48
Figure 5.7 Hydrograph of the basic model with spatial distribution of Etp 61
Q_EtLu, NS=0.319, RMSE=0.551, r²=0.580
Figure 5.8 Illustration of water budget in Kielstau 62
Figure 5.9 Water budget investigation in Kielstau (1986~1999) 65
Figure 5.10 Coefficients of different wetland models 73
Figure 5.11 Hydrograph of wetland model, with wetland fraction of 28% 74
according to wetland map version 4
Q_EtsV4_Wet, NS=0.625, RMSE=0.293, r²=0.714
Figure 5.12 Test result of wetland models with different wetland ETp factors 76
Figure 5.13 Statistical coefficients of calibrated wetland models 77
Figure 5.14 Hydrograph of combined wetland model 79
Q_EtsV7_Wet3, NS=0.671, RMSE=0.275, r²=0.722
Figure 5.15 Hydrograph of combined wetland model with additional lateral flow 80
Q_EtsV7_Wet3_L3, NS=0.677, RMSE=0.272, r²=0.728
Figure 5.16 Validated hydrograph of combined wetland model 81
Q_EtsV7_Wet3: NS=0.735, RMSE=0.336, r²=0.748
Validated hydrograph of combined wetland model with additional lateral flow
Q_EtsV7_Wet3_L3: NS=0.726, RMSE=0.341, r²=0.741
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List of Tables
Table 2.1 Legend of soil types and the area distributions in Kielstau 16
Table 4.1 Seasonal crop coefficients according to Haude 35
Table 4.2 Legend and area fraction of simplified soil map 37
Table 4.3 Legend of land use map 38
Table 4.4 Manning roughness coefficients for various open channel surfaces 39
Table 5.1 Estimated optimal set of parameters 55
Table 5.2 Parameter setting for the spatial distribution of soil field capacity 58
Table 5.3 ETp adjustments according to land use 60
Table 5.4 Hydrological data of Kielstau catchment (1986~1999), annual averages 64
Table 5.5 Reclassification of soil map – legend comparison 66
Table 5.6 Definition of 8 wetland versions 72
Table 5.7 Different set of wetland ETp factor 75
Table 5.8 Results of parameter calibration of wetland model 76
Table 5.9 Result overview of calibration and validation 82
Table 5.10 Comparison of wetland soils in Treene and Kielstau 83
Table 5.11 Result comparison of SIMPEL, SWAT, KIDS (1994~1999) 86
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Chapter 1 Introduction
Water management in river basins is attracting increasing attention both in Europe and
worldwide. It has become a key issue for European environmental legislation. The Water
Framework Directive (WFD) is one of the EU legislations, which imposed the integrated
protection of water bodies on all EU member countries. River basins represent a key research
area as acting as important resources for ecosystems and human being. In the light of rapidly
changing land use, increasing demand for clean water and the potential of a more extreme
climate in the future, understanding river basin environments has become an area of
considerable importance.
Modelling is now a common tool in the field of hydrologic research. With the rapid
development of computational power, the ability to model the natural water cycle has
progressed enormously over the recent decades. Many new techniques and methodologies
have been raised to facilitate the river basin research. For example, the use of GIS, remote
sensing techniques, river runoff modelling, various modelling assessments, water quality
assessments, river basin hydrology and so on. Among them, quantitative analysis about river
discharge is the base for all other fluxes researches like nutrients or water erosion modelling.
And the methodology of quantitative analysis or discharge modelling is very different from
region to region. The rainfallrunoff relationship is strongly dependent on soil, vegetation and
topographic characteristics of a catchment.
Most hydrologic research projects are located in mountainous regions, where the real
behaviour of the water system is generally easy to observe or measure. The model
development for flat areas is quite different due to the distinctive hydrological characteristics.
Northern Germany is one of such typical regions with very flat relief. Surface runoff is very
low, but the portion coming from near surface groundwater is very high. Additionally, there is
a large fraction of wetlands with considerable influence on the river discharge.
The present study is taken up with the intent of developing a GIS based process oriented
distributed model for the Kielstau catchment in Northern Germany. We employed PCRaster, a
dynamic modelling language, to construct the KIelstau Discharge Simulation models – KIDS
models – for the outlet point of the Kielstau Catchment over a long time span.
This paper will present the calibration process of the hydrological models for discharge
simulation and analyse the special hydrological characteristics in this wetland dominated
basin. One aim of the paper is to show the effects of different conceptual models to simulate
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the runoff with limited input and verification data sets. Another aim is to present and discuss
the importance of wetland functions on the hydrological cycles in this lowland catchment.
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Chapter 2 The study area
2.1 The catchment and its river network
In this study, two rivers and their catchments are concerned: Treene and Kielstau.
The KIDS models were built up with the dataset of Upper Treene catchment, and the
simulated discharge was reported for the outlet point of Kielstau catchment.
The Kielstau is one of the origins of the river Treene, which has a length of 96 km. The Upper
Treene catchment covers 517 km², and the core research area of this study Kielstau
catchment is around 50 km², which is located at the northeast of the Treene catchment (see
Figure 2.1, the Kielstau subcatchment is highlighted at the right corner).
Figure 2.1 Upper Treene catchment – the area of input database, and Kielstau catchment – the
modelled subcatchment (Source: http://www.wasser.sh, modified)
Figure 2.2 indicates the river network within the upper Treene catchment. The river Kielstau
is a small stream in this catchment. It is circa 11 km long, draining the Winderatter See and
discharging originally into the Treßsee, then flowing together with the river Bondenau into
the river Treene. The river channel of Kielstau was changed several decades ago to drain the
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water directly into the river Bondenau, then discharge into the Treßsee. The river Treene
starts from there, till the last outlet point of the Upper Treene catchment – the gauge station
Treia. The remaining downstream part of River Treene after Treia is strongly influenced by
the backflow of water from tide.
Figure 2.2 River network in the modelled area (Dey, 2004)
2.2 Development of the Treene and Kielstau landscape
As mentioned before, the Upper Treene catchment is situated at SchleswigHolstein, Northern
Germany, extending from northeast to southwest.
The landscape of Northern Germany can be generally divided into young moraine area and
old moraine area (Eggemann et al., 2001). Young moraine landscape was formed in the
youngest Ice Age (Weichsel Ice Age, which began at about 70,000 BC, and reached its
maximum extent about 18,000 BC) and shows the most widespread land forms and is
exclusive for SchleswigHolstein. The furthest expansion reached by the last Ice Age is
estimated at around the line between FlensburgSchleswig (see Figure 2.3). The ribbon area
between Hamburg and Flensburg leads to the edge sandur (along Flensburg, Översee,
Schleswig, Fröruper, Rendsburg and Neumünster).
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The old moraine landscape was formed in older Ice Age (Saale Ice Age, occurring between
200,000 and 125,000 years ago) but the forms produced at that time are nowadays rather
washed out.
Figure 2.3 Estimated locations of glacial valleys (after Schmidtke 1992, modified)
The evolution process of the landform during the last Ice Age could be indicated as Figure
2.4.
Figure 2.4 Geomorphologic simulation of the landscape development (after Schmidtke 1992,
modified)
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In SchleswigHolstein, we observe the old moraine landscape in the western part. These Saale
glacial moraines are so called Old Moraines. In the eastern part of the land, which was
reached by the later glaciations, the old moraines have been transformed and covered by
Young Moraines. The whole landscape of SchleswigHolstein is distinguished by three
geomorphologic features: eastern uplands (Östlich Hügelland) is the young moraine
landscape; northern and middle parts belong to the ‘Schleswiger’ geest; then landscape
through swamp lowland turns into ‘Nordfriesische’ marsh (Schmidtke 1992).
East from the mentioned HamburgFlensburg line, landscape is formed by the ground
moraine with sandur, gravel, tillgravel and tillclay (boulder claymaterial comprising of
many stone fractions, e.g. silt and grain size sands). This is where the Kielstau catchment is
located. The Upper Treene catchment extends itself from the hilly morains to the transitional
zone of outwash plain (see Figure 2.6). The glaciers moved back and forth in turns over long
periods of time creating on this small area a mixture of different types of moraines in close
succession, which formed the originates of the landscape nowadays.
Figure 2.5 Geomorphologic situation of the study area
(Source: http://umwelt.landsh.server.de, modified)
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2.3 Climate
The climate in the Treene catchment is determined by its location in a zone of temperate
climatic conditions with frequent weather changes. There is precipitation all the year round.
With the maritime climate environment, neither the daily variations of temperatures nor the
seasonal ones go into severe extremes. Wedged between the North Sea and the Baltic Sea,
watershed is characterized by the moderate temperature and oceanic climate with soft moist
winters and cold and rainy summers.
The climatic data was provided by Deutscher Wetterdienst (DWD) – German Weather
Service. The precipitation data for the modelled area is from 3 measuring stations: Oeversee,
Eggebek and Treia. The temporal distribution of precipitation for the Upper Treene catchment
is given in Figure 2.5. From the figure it is clear that the yearly rainfall of different locations
have no significantly distinctions over the entire watershed. The average yearly precipitation
of Oeversee is 871.6 mm (this is taken as precipitation data valid for the Kielstau Catchment,
since it is the nearest station), while for the entire catchment this average yearly precipitation
equals 863.8 mm (average over the years 1986~1999).
Figure 2.6 Precipitation in the Upper Treene catchment above Treia (database 1986~1999)
When the four seasons are defined as: spring – March to May, summer – June to August,
autumn – September to November, winter December to February, the seasonal rainfall
distribution is plotted in Figure 2.7. For the entire catchment averaged autumn precipitation
(2.97 mm/day, average over the years 1986~1999) is markedly larger than other period, while
0
0.5
1
1.5
2
2.5
3
3.5
Jan
Feb Mar AprMay Ju
n Jul
AugSep Oct
NovDec
mm
/day
a
OeverseeEggebekTreia
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the spring precipitation is the lowest (1.68 mm/day, average over the years 1986~1999)
during the whole year.
Figure 2.7 Seasonal precipitation distribution in the catchment (database 1986~1999)
The temperature data from Flensburg a city near Kielstau Catchment is the only available
data for the entire region. The distribution over the year is given in Figure 2.8. A clear
seasonal effect is visible in the temperature graphs over the year. The annual mean
temperature is 11.05 °C (average over the years 1986~1999). In winter time the belowzero
temperature period is so short that it is not reflected in the average graph, this results in a not
distinguished snowmelt season.
Figure 2.8 Temperature distribution in the catchment (database 1986~1999)
0
0.5
1
1.5
2
2.5
3
3.5
spring(3.4.5) summer(6.7.8) autumn(9.10.11) winter(12.1.2)
mm
/day
.
OeverseeEggebekTreia
0
5
10
15
20
25
Jan
Feb Mar AprMay Ju
n Jul
AugSep Oct Nov
Dec
time
°CFlensburg Temp. mean
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2.4 Topography
In general the upper Treene basin can be characterised by two different areas with a
distinguished topography. The northeast part of the catchment is a hilly moraine landscape
and turns into a flat outwash plain in the western part, characterized by a low hydraulic
gradient.
The whole watershed has poorly drainage due to the low gradient with maximum altitude
difference of 76.4 m (see Figure 2.9).
Since the landscape around Kielstau area was developed during the last Ice Age, the
catchment has a rather flat relief as well. Although parts of the region is covered with small
hills, it has only maximum 30 m elevation difference.
Figure 2.9 Elevation map of the Upper Treene catchment (Dey, 2004)
2.5 Soil
The geological underground of the whole watershed is dominated by pleistocene deposits.
Soils are mainly consisting of Podzol, Gleysol and Luvisol formed in the Saale and Weichsel
Ice Ages. Although the Kielstau is a small river catchment, there are wide variety of soil types
and soil forms in this small area (see Figure 2.10).
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Figure 2.10 Soil types in Kielstau catchment (BGR, 1999)
The legend and area distribution of the major soil types in Kielstau is shown in Table 2.1:
Table 2.1 Legend of soil types and the area distributions in Kielstau
Legend Description Area percentage
1 Binnen Lake 0.42%
2 GleyPseudogley GleyPseudogley 10.60%
3 GleyKolluvium GleyKolluvium 2.25%
4 vergley. Podsol vergleyter Podsol 0.44%
5 Niedermoor Peat 9.11%
6 Parabraunerde Parabraunerde 17.40%
7 FePodsol FePodsol 2.27%
8 Pseudogley Pseudogley 49.98%
9 Pseudo.Braunerde PseudogleyBraunerde 7.53%
As we can see from the soil map and the area distribution of various soils, 62.83% of the
Kielstau basin is dominated by Gley and Pseudogley, which belong to the major wetland soil
types.
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2.6 Land use
Land use affects interception of precipitation, it influences the ratio between infiltration and
surface runoff, and it determines evapotranspiration to a large extent. Changes in land use will
result in complex interactions of various landscape functions. Land use is therefore an
important parameter in hydrological processes (Fohrer et al., 2005). Under presentday
conditions, about almost half of the total area (41.3 %) of the Upper Treene basin is used for
agriculture, 38.9 % is covered with grassland, 11.5 % deciduous forest, and 8.3 % others
(coniferous forest, water, urban area, fallow land).
The Kielstau catchment is mainly a rural area consisting of a variety of different smallsized
habitats. The typical land use in the area is agricultural: 54.88% arable land, 26.89% pasture,
5.8% rangeland, and the remainder consists of forest, builtup area, surface water (see Figure
2.11). The catchment has undergone many changes, especially since the Second World War.
The natural wetlands were drained after the war in a huge effort to get more agricultural land.
Figure 2.11 Land use pattern in Kielstau basin (DLR, 1995)
Agricultural land use is an important element in the chain of rainfall – runoff relations in the
Kielstau basin. Investments in land drainage, external water supply and largescale use of
biocides and fertilizers have increased agricultural production. Land drainage has created a
faster response of runoff to rainfall, increasing flood risks. Increased use of biocides and
fertilizers has led to the pollution of open water systems.
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Since society now demands sustainability, high water quality standards have been set and
these are driving environmental improvement in agricultural production systems. Farmers
want to meet the demands of society by combining their role in food production with other
functions in areas like nature restoration and conservation. The availability of land is expected
to be a limiting factor. Where water is concerned, agriculture will help to reduce land erosion,
conserve water by reducing artificial drainage (restoring the sponge function of the soil) in
peat or wetland places and enhance flood protection through temporary controlled inundation.
2.7 Groundwater and wetland resources
The wide spreading near surface groundwater is a characteristic feature in the Treene
catchment, which plays an important role in the hydrological cycle.
The large parts of the aquifer system of the Treene basin are developed from the Quaternary
classic sediments. These sediments form a complex but continuous system, containing one of
the most important groundwater resources in Germany. The residence time of the
groundwater in the Treene basin varies from a year (shallow groundwater) to hundreds of
years (deeper groundwater). This is also strongly influenced by all kind of wells used for both
drinking water and industrial purposes. In SchleswigHolstein drinking water is obtained
100% from (deeper) groundwater, while in Germany is around 70%, e.g. in the Federal Lands
Sachsen or NordrheinWestfalen only about 40% (http://www.wvtreene.de/).
As the deep groundwater serves as a important drinking water source, the widelydistributing
shallow groundwater within the river basin has a higher influence on the landscape features
and the water cycle.
The abundant source of near surface groundwater and the poor drainage of landscape result in
numerous scattered peatlands (see Figure 2.12) and spacious wetland area in the watershed.
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Figure 2.12 Spatial distribution of peatland in SchleswigHolstein (Trepel, 2004)
Peatlands or wetlands are valuable landscape units both in meaning of ecology and hydrology.
They contribute to the production of food and raw materials, the regulation of global climate,
the improvement of surface water quality, the influence on hydrological cycle.
The connected and spacious wetland area is a typical landscape in the Kielstau region (shown
in Figure 2.13), with a mosaic of distinct mire types in varying de and regeneration states.
Key habitats are different stages of minerotrophic peatlands and ombrotrophic raised bogs
(e.g. large area of peat layer with over tenmeter depth was formed near Winderatter See),
semi natural wet meadows and pastures, groundwater influenced forests, shallow water lakes
and ponds in the lowland, dry and mesophile grasslands and sand heather.
In the past, severe environmental problems were related to the land use on wetlands in this
area. Most wetlands are drained and used for agriculture. Peatlands were used first for peat
cutting and later for agriculture. The wetlands are strongly degraded by drainage and
cultivation, leading to unfavourable degradation of mire soils. Nowadays it is realized that the
different services offered by wetlands necessitate careful planning of their future use.
Although the Kielstau is a small river catchment, its ecological and hydrological importance
can not be ignored, since it is the origin of river Treene and provides mosaic biotopes. At
present it is already included in the framework of nature protected area under the regional
20
authorities and other environmental protection organizations, owing to its importance in the
meaning of river basin management and the unique ecosystem protection.
Figure 2.13 Typical landscape in the Kielstau catchment (picture taken by Georg Hörmann,
2005)
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Chapter 3 Literature review
3.1 Principles in Hydrological modelling and its new development
Hydrological modelling has existed for about 150 years and really developed with computers.
It is defined by Maidment (2000) as “a mathematical representation of the flow of water and
its components on some part of the land surface or subsurface environment”.
The hydrologic cycle is the central focus of hydrology, which is usually the simulation target
of hydrological models. Although the concept of the hydrologic cycle is simple, the
phenomena are enormously complex and intricate. However in the absence of perfect
knowledge, they may be represented in a simplified way by means of the systems concept.
The early development of hydrologic systems theory, which is the base for hydrological
modelling, was closely linked to the formalisation of water resources systems. These were
originally visualised as relatively complex networks consisting of nodes and connections that
could be associated with physical entities and which had relatively simple individual
behaviour. Thus, a basin could be viewed as a set of stores (aquifers, reservoirs, soil, etc.) and
fluxes (through rivers, channels, pipelines, etc.). Each element of the system obeyed well
defined rules (mass balance, Darcy’s law, Dalton’s law, and the like). All this amounted to the
development and application of a robust water resources system. The books of Dooge (1973)
and Eagleson (1970) can be viewed as pillars of such developments.
Water resources system analysis used to concentrate a lot of efforts on accurate, yet robust
representation of basins, which allowed optimisation. Maturity of this line of development has
led to a high level of refinement and to the establishment of hydrology as a science (Gupta
1989, 2001; Basson et al., 1994; etc.). However, emphasis is shifting from quantification to
understanding, probably reflecting the multidisciplinary nature of hydrology. Moreover,
hydrological models have grown in complexity, in the sense that they contain a continuously
increasing number of parts and that processes of each part are described in increasing detail,
while their predictive capabilities remain doubtful at best. It is fair, therefore, to question the
wisdom of such growth and whether simpler models would work just as well (Bronstert et al.,
2005).
The objective of hydrologic system analysis is to study the system operation and predict its
output. A hydrologic system model is an approximation of the actual system. Its inputs and
outputs are measurable hydrologic variables and its structure is a set of equations linking the
inputs and outputs.
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Hydrologic models may be divided into two categories: lumped models and distributed
models (Beven 2001). Lumped models represent a catchment as a single entity and simulate
state variables and fluxes into and out of the catchment as a whole. Distributed models divide
the catchment up into many entities, each representing small parts of the catchment, and the
state variables and fluxes between the entities are determined across the catchment.
Although lumped models are at the lower level of physical complexity, they are frequently
used in practice due to its simplicity, such as those presented by Boughton (1995) and Farmer
et al. (2003). In these models, the whole catchment is represented by a single entity, and the
underlying principle is that a catchment has tow major properties that control most of its
response to drivers – the ability to store water and the rate of release of that water.
Compared with the simple lumped models, distributed models have some advantages due to
the physical basis of the approach and the increasing availability of digital and remotely
sensed spatial data. Physical models allow the determination of the water balance and its
variation across river basins. Several such models are in common use, like SHE model –
European Hydrological Model (Abbott et al., 1986), TOPMODEL – a physically based
variable contributing area model of basin hydrology (Beven and Kirkby, 1979), WATBAL –
a semidistributed, physically based hydrological modelling system (Knudsen et al., 1986).
Spatiallydistributed, physicallybased hydrological modelling at the scale of an entire river
basin requires large input databases. Therefore, a Geographical Information System is a very
useful environment for modelling because it is easy to store, display and maintain data (De
Roo et al., 1989; Burrough and McDonnell, 1998). Thus, linking or integrating models with a
GIS provides an ideal environment for modelling processes in a landscape. Three approaches
exist for modelling within a GIS environment: loose coupling, tight coupling and embedded
coupling (Wesseling et al., 1996). In loose coupling the GIS is used to preprocess the spatial
data into the desired model inputfile format, such as used by De Roo et al. (1989) and Kite et
al. (1996). In tight coupling models and GIS, model input and output can be addressed
directly by the GIS. In embedded coupling the model is written in an integrated programming
language. The advantage is that the user can construct his or her own models as required. An
earlier example of embedded modelling is the LISEM catchment erosion model, which is
described in De Roo et al. (1996). Another widely used hydrological modelling program is
SWAT (Soil and Water Assessment Tool), which was developed in the early 1990s (Arnold et
al., 1993, 1998).
23
However, not many GIS are capable of physicallybased modelling. Some GIS have some
modelling capabilities, such as the FlowAccumulation command in the Arc/Info GIS (De Roo
et al., 2006) and the drain command in the Grass GIS (GRASS 2006). Using these tools, some
transport modelling can be done.
At present, most GIS provides catchment analysis tools for delineation of catchments and
definition of drainage networks. These tools, however, are not sufficient for dynamic
modelling; they are not capable of solving transport operations through the defined network.
Also, simulations in time are often not possible, such as simulating the catchment response to
a rainfall timeseries. Ideally, for transport modelling one should be able to control the amount
and velocity of water (or any other material) through the network: one would want to have
'access' to the algorithms describing this transport.
Therefore, there is a need for a GIS modelling environment that is capable of physically
based transport modelling. PCRaster, a dynamic modelling system for distributed simulation
models (Van Deursen 1995; Wesseling et al. 1996), provides such a GIS modelling
environment, it is thus employed to construct the KIDS model in this study.
3.2 The model system PCRaster
PCRaster is a dynamic modelling system for distributed simulation models. It includes raster
GIS function, a toolkit for visualisation and an easy to understand scripting language for
models.
Hydrological modelling is one of the main applications of PCRaster in environmental
modelling. We used PCRaster to build the spatiotemporal discharge simulation models for
the Kielstau catchment (KIDS models). The information of PCRaster system is available at
http://pcraster.geo.uu.nl/.
3.2.1 Development and advantages of PCRaster
The PCRaster spatial modelling language is an extension of the ideas behind Map Algebra
and the Cartographic Modelling Language proposed by Berry and Tomlin (Berry 1987,
Tomlin 1990). It also includes ideas of iterations used in Dynamic Modelling and concepts for
defining transport equations along the drainage network (in PCRaster referred to as Local
Drain Direction network). Thus, PCRaster allows for developing physicallybased transport
models.
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PCRaster is originally designed and developed at Utrecht University, department of Physical
Geography and is now supported by the company PCRaster Environmental Software that
combines its R&D efforts with Utrecht University. PCRaster is a dynamic modelling system
for distributed simulation models. The main applications of PCRaster are found in
environmental modelling: geography, hydrology, ecology to name a few.
PCRaster is an environmental modelling language embedded in a GIS (Karssenberg, 2002). It
includes sophisticated raster GIS functionality emphasising on analytical capabilities.
PCRaster uses a scripting language for constructing models describing processes through
time. These dynamic simulation models can easily be constructed using the rich set of model
building blocks and functions in PCRaster.
Environmental modelling
Simulating environmental processes with computer models is expanding rapidly in scientific
and applied research in fields such as hydrology, ecology, process geomorphology, land
degradation and crop yield studies. Computer models help us to understand better the
characteristics of environmental processes and the impact of any changes made to them.
However, there are two main difficulties in building environmental computer models. The
first difficulty is that environmental models are relatively complex because they must be both
temporal and spatial due to the spatiotemporality of natural phenomena. In this regard, the
problem is that they are fed by huge amounts of environmental data, often coming from
different kinds of sources such as manual field measurements, automated data loggers or
remotely sensed images. The second difficulty is that the questions which must be solved by a
model are unique for each environmental problem. As a result standard computer models
which cannot be changed by the environmental researcher are often rather unsatisfying.
Computer models are needed that can easily be adapted to the problem under study.
PCRaster offers a unique solution to the problems mentioned. It embeds an environmental
Dynamic Modelling language in a Geographical Information System. The Dynamic
Modelling language is a powerfull instrument for building environmental models which can
be tuned to the problem under study. The GIS part of the package manages storage,
manipulation and visualisation of spatiotemporal data in raster format, without the burden of
data exchange.
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The embedded GIS environment in PCRaster
The GIS part of PCRaster uses a database with a strict data type checking mechanism. This
assists error detection and polymorphic behaviour of the GIS and Dynamic Modelling
operators. In addition data typing improves and speeds up the visualisation routines. PCRaster
automatically chooses the correct way of displaying a map on the screen, taking the kind of
attribute stored in the map into account. For example, classified nominal data (e.g. soil maps)
are automatically displayed on the screen (or printed on paper) with a classified legend and
different colours for the classes, while directional data (e.g. aspect in a terrain) are
automatically displayed with a directional grey scale visualising the topography of the terrain.
Data exchange with other GIS's is supported by easy conversion functions. The geostatistic
module of PCRaster offers state of the art routines for the interpolation of point data to raster
map data. In addition it can be used for conditional or unconditional random field simulation
and the calculation of error propagation in GIS operations and environmental models.
The GIS supports the Dynamic Modelling language. The database has been developed to
contain temporal data, i.e. stacks of raster maps and time series. These can be visualised
through time series plots and animated maps immediately after running the model without
data exchange. This allows easy model identification, calibration and validation without data
exchange.
Dynamic Modelling language of PCRaster
The Dynamic Modelling language is a special purpose computer language for environmental
modelling. It offers a wide range of map operators that can be combined in the same way as in
mathematical computations. With these operators a fully dynamic model can be programmed
which incorporates an inbuilt structure for iteration over time.
The PCRaster Dynamic Modelling language is specially developed for environmental
modelling. It uses spatiotemporal operators with intrinsic functionality especially meant for
construction of spatiotemporal models. For example, a slope map is simply calculated in one
statement with a slope operator acting upon an elevation map.
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The Dynamic Modelling language of PCRaster provides a set of more than 120 spatial and
temporal operators that can be used for building (static) Cartographic Models and Dynamic
Models. These include:
• Point operators
analytical and arithmical functions, Boolean operators, conditional statements,
operators for relations, comparison, rounding, (random) field generation
• Window operators
for calculations in moving windows of variable size (highpass filtering, edge
filtering, moving averages, etc.)
• Area operators
for calculations in specified areas or classes
• Spread operators
for calculation of distances or cost paths over a map
• Geomorphological operators
functions for hillslope and catchment analysis, definition of hydrological topology
• Hydrological operators
for modelling transport (drainage) of material over a local drain direction map with
routing functions
• Time operators
for retrieving and storing temporal data in iterative Dynamic Models
The main advantages of the Dynamic Modelling language are:
1) Models are programmed and structured according to the way of thinking applied in spatial
temporal sciences such as Geography or Geology. It allows researchers in these fields to
construct models by themselves in a relatively short period of time.
2) No specialist programming knowledge is required, all necessary is a familiarity with
mathematical notations. As a result the researcher can easily adapt the model to the specific
problem under study.
3) Models constructed in the PCRaster Environmental Modelling language can easily be
changed or extended and the results can immediately be evaluated using visualisation routines
linked to the language.
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4) The language is embedded in the PCRaster GIS so that data exchange between model and
GIS is unnecessary. Furthermore, spatialtemporal data can be used or stored at any time
during a model run.
In conclusion, PCRaster is a system that brings together environmental modelling and GIS. It
provides data interfaces to other packages (e.g. GIS's, software for statistical analysis) for
spatial data analysis. PCRaster offers a powerful Dynamic Modelling language for building
iterative spatiotemporal environmental models, which is fully integrated in the GIS. It
includes functionality for data storage, manipulation and visualisation, without the burden of
data exchange between model and GIS.
3.2.2 Applications of PCRaster in catchment hydrology modelling
Many research projects related to environmental modelling have been based on PCRaster.
This section contains descriptions of some case studies using PCRaster in catchment
hydrology modelling.
The RHINEFLOW family of water balance models
The world climate is expected to react to the enhanced greenhouse effect with changed
patterns of precipitation and temperature distribution in time and space. Consequently,
streamflow is also expected to change in volume and distribution over the year, giving major
concerns to policy makers. Longterm strategies for future river management require
quantitative information on these changes, where hydrological models can often be important
tools. A project was initiated to assess the impact of climate and land use changes on the
discharge pattern of the river Rhine.
A spatial distributed water balance model, called RHINEFLOW (Kwadijk, 1993), was
developed. It aims at obtaining information for the Rhine catchment at a spatial resolution of
the major tributaries and a temporal resolution of a month. The models are often used for
impacts of climate change on water availability in larger river basins (> 30,000 km²).
All models of RHINEFLOW family are gridbased spatial water balance models.
RHINEFLOW1 (Kwadijk, 1993; van Deursen and Kwadijk, 1993), the parent model of this
family, was the first model ever to be developed in PCRaster. The objective of
RHINEFLOW1 was to build a water balance model to analyse the impact of climate change
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on the runoff regime of the Rhine. The model used a geographical database of landuse,
soiltype and elevation data, stored in a raster GIS with a resolution of 3x3 km². It
implemented the water balance for each gridcell in the database, using monthly temperature
and precipitation as input data, and simulating monthly runoff for selected points along the
river Rhine.
Within the framework of the Dutch Research Programme on Climate Change (NOP),
RHINEFLOW2 was developed (Middelkoop et al., 2000). It extends the concepts of the
RHINEFLOW1 model for a 1x1 km² grid size and a 10day timestep.
The MEUSEFLOW model (Middelkoop et al., 2001) has been developed as a direct
derivative of the RHINEFLOW2 model. MEUSEFLOW is the waterbalance model for the
Meuse basin. Special attention in the formulation of the waterbalance for the Meuse has been
paid to the effects of climate change on the lowflow situations in the Meuse basin.
RHINEFLOW1, its successor RHINEFLOW2 and MEUSEFLOW have been used
extensively in scenario studies related to the future discharge regimes of the river Rhine.
Amongst these are the use of these models in the scenario studies and the perspectives within
the framework of the Dutch National Research Programme on Climate Change and the IRMA
SPONGE EU research programme (Middelkoop et al., 2000). By using output of the
RHINEFLOW and MEUSEFLOW models, these models analysed the consequences of
changes in the discharge regime on user functions and lead to the formulation of a number of
additional models.
LISFLOOD Model: A multipurpose modelling tool for flood simulation
The LISFLOOD model (De Roo et al., 2006) is an example of a physicallybased model
written using the PCRaster GIS environment. It simulates river discharge in a drainage basin
as a function of spatial data on topography, soils and land cover.
LISFLOOD is a model that has been developed explicitly for the simulation of floods in large
European drainage basins. It is capable of simulating large areas while still maintaining a high
spatial and temporal resolution, proper flood routing methods and physical process
descriptions. It also assesses the impact of changes in land use, river geometry, and the effects
of climate change on flood risk. LISFLOOD can further simulate special structures such as
water reservoirs and retention areas by indicating their location, size and inflow and outflow
boundary conditions.
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LISFLOOD has been extensively tested for several transnational catchments – amongst them
the Meuse and the Oder. For these catchments highresolution data are available, in particular
in terms of river dimensions.
Flood risk on European Scale is another main applications of the LISFLOOD model. Floods
are usually dealt with at the national level. Since the definition of flood risk varies between
different countries, a transboundary comparison is normally difficult. LISFLOOD overcomes
this difficulty because it is set up for whole Europe and runs for a period of 10 years.
Statistics such as minimum discharge, maximum discharge or flood frequency are calculated
for every grid point, which allows a flood risk map for the whole of Europe to be produced.
EUROSEM in PCRaster
EUROpean Soil Erosion Model (EUROSEM) (Morgan, 1995) is originally a polygon, event
based runoff and erosion model, developed for the assessment of soil erosion risk and the
evaluation of soil protection measures. It was later rewritten in the dynamic modelling code of
PCRaster (Van Dijck, Karssenberg, 2000).
The PCRaster version of EUROSEM differs from the original version mainly in that it is
raster based and written in the environmental modelling language PCRaster. It is built for a
study in the Ouveze river basin, S. France (Van Dijck, 2000).
The version by Van Dijck included only the water components of EUROSEM. It is later
extended with erosion components to account for the pool formation and crust development in
the study of water erosion modelling in the Sahel (Visser, 2005). Due to its physical character
the model requires a large number of input parameters. One of the main advantages of
incorporating EUROSEM in PCRaster is the relatively open data structure, which allows the
model builder to closely follow the erosion simulation and to make model adaptations
relatively easily.
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Chapter 4 Development of the KIDS model
4.1 Implementation of the KIDS model in PCRaster
To investigate the hydrological cycle in the Kielstau catchment, the Kielstau Discharge
Simulation (KIDS) models has been developed in the PCRaster Spatial Modelling language.
This distributed hydrological model is built on the data sources of the Upper Treene
catchment, and allows the water budget simulation across the river basin.
KIDS model simulates the runoff on a daily time step for the selected discharge station – the
Soltfeld gauge station located at the outflow point of Kielstau basin, which is a subcatchment
of the Upper Treene river basin. It is a distributed rainfallrunoff model taking into account
the influences of topography, precipitation variation and evaporation amounts, soil moisture
content, soil type, land use type and wetland fraction.
There are two steps to implement the KIDS model in PCRaster.
The first is to set up strongly typed temporal spatial data which can be either transferred from
GISArcview, or generated by the PCRaster operational system. Major data input includes
digital elevation map, soil map, landuse map, river network map, etc. The PCRaster database
can hold raster maps, point data, tables for cross tabulation between maps and time series for
representation of attributes changing over time. The dynamic behaviour of the database,
needed for dynamic models, is modelled by time series indexed on time and location, and by
stacks of map layers representing the status of the model at different time steps.
The second step is to build conceptual models for defined hydrological processes. In
PCRaster, dynamic models are built using the PCRaster script language. Processes simulated
in the KIDS model are basically precipitation, interception, evapotranspiration, infiltration,
groundwater flow, lateral flow and surface runoff. Models constructed in PCRaster can easily
be changed or extended and the results can immediately be evaluated using visualisation
routines.
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4.1.1 Available data and preparation of the database
Topography data and its derivations
The available topography data for the Upper Treene area is the DHM 50 (Digitales
Höhenmodell with resolution of 50m), provided by the “Office For Land Surveying
SchleswigHolstein” (Landesvermessungsamt SH 1995).
The dem.map (see Figure 4.1) is a digital elevation map based on the DHM 50, in meters
above sea level. It has a grid width of 50 x 50 m with an elevation accuracy of ± 4 m.
Figure 4.1 Digital elevation map (dem.map)
The digital elevation map (DEM) is one of the products of digital mapping technique, which
provides essential tools to closely represent the 3D nature of a natural landscape. The DEM
can be used to extract topographic variables automatically, such as basin geometry, stream
networks, slope, flow direction, etc. from raster data on elevation.
In a rasterbased map like DEM, the flow domain or catchment is divided into an array of grid
or cells, each of which represents an area with average properties. Each cell of such a
discretized catchment has eight possible flow directions as shown in Figure 4.2 (left). The
direction of flow from one cell to other neighbouring cells is ascertained by using eight
direction pour point algorithm (Jain et al., 2004). This algorithm chooses the direction of the
steepest descent among the eight permitted choices. Once the pour point algorithm identifies
the flow direction in each, a celltocell flow path is determined to the catchment outlet
(Figure 4.2, middle). The computational drainage path for the whole catchment (defined in
PCRaster as Local Drain Direction map) is generated in PCRaster with the lddcreate operator
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on the digital elevation map. The ldd.map corresponds closely to the existing natural drainage
consisting of overland flow planes and drainage network. Part of the ldd.map for the
catchment is shown in Figure 4.2 (right) as an illustration. The computation sequencing of the
model starts from the most upstream cells to the downstream cells determined through DEM
analysis. The twodemensional array of DEM cells is rearranged into a onedemensional
array as a cascading system. The numerical solutions have been performed in GIS
environment for each cell of the catchment.
Figure 4.2 Left: possible flow directions in a cell;
Middle: grid based catchment discretization and concept of flow path;
Right: part of local drain direction map (ldd.map)
For model operation, the catchment DEM is utilized to derive the slope map (see Figure 4.3)
for each of the discretized cells and the river network map for the catchment.
Figure 4.3 Slope map (slope.map)
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Then the channel width map (channelw.map) is developed from the river network map. It
defines the increasing width of river channel with the ranging of 1 to 4 meters, from the
upstream like Kielstau to the downstream near Treia (see Figure 4.4).
Figure 4.4 Channel width map (channelw.map)
Meteorological dataset
The meteorological data for the study area is provided by the German Weather Service
(DWD, Deutscher Wetterdienst). The following dataset is available for the KIDS model, each
timeseries spanning the period from 1984 to 1999.
•Daily precipitation data (mm/day) with 3 areal (subbasin) precipitation
•Potential evaporation data (mm/day) using referencing climatic data from
Flensburg Station (10km away from Kielstau)
•Daily runoff data (m³/day) for the selected discharge station (Soltfeld in Kielstau)
Figure 4.5 shows a map (extracted from Arcview) with the location of three rainfall
measurement stations in the catchment, namely Oeversee, Eggebek and Treia. A precipitation
zone map (zonep.map in Figure 4.6) can easily be computed from based on the location of
these stations with Thiessen Polygon Method. The rainfall data for the three rainstations are
stored in the ascii formatted timeseries file precipitation.tss. In Figure 4.6 the data in the rain
timeseries file (precipitation.tss) is plotted out only in 365 timesteps (one year) for simplicity.
It gives rainfall data for the three precipitation zones.
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Figure 4.5 Three weather stations
Figure 4.6 Left: precipitation zone map (zonep.map);
Right: rain timeseries file (precipitation.tss)
Rainfall is the dynamic input. Rain timeseries is a time table with the precipitation measured
at several meteorological stations. Rain zones do not represent the location of the stations but
the area of pixels for which the measurement at that station is the best estimation of the actual
precipitation at each pixel. The rain zones map denotes for each pixel the column number in
the time table. At each time step, timeinputscalar (a time operator in PCRaster) reads each
timestep one line in the rain timeseries file precipitation.tss and assigns the values for each
rain measurement station to the rain zones in the zonep.map.
Another similar procedure of the temporalspatial data input is for the potential
evapotranspiration (ETp).
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In KIDS model, ETp is calculated with the Haudeevaporation method according to German
standard (DVWK, 1996). In contrast to many other calculation methods, the Haude has a very
simple formula as shown below, which is still in wide use in Northern Germany.
ET p = f *(es14 ea14)
With f: Haudecoefficient (dependent on surface cover and month)
es14: saturation vapor pressure at 2pm (hPa)
ea14: actual vapor pressure at 2pm (hPa), calculated with input values of
Relative Humidity (%) at 2pm and Temperature (°C) at 2pm
The correcting coefficient f according to Haude is dependent on the cultured crop and the
month. The different coefficients are listed in Table 4.1, which are specific for northern
Germany.
Table 4.1 Seasonal crop coefficients according to Haude
Month Surface Cover
Fichte
(Coniferous)
Gras
(Grassland)
Laub
(Deciduous)
WW
(Winter wheat)
ZR
(Sugar beet)
1 0.10 0.20 0.10 0.18 0.14
2 0.10 0.21 0.22 0.18 0.14
3 0.10 0.21 0.23 0.19 0.14
4 0.30 0.29 0.30 0.26 0.15
5 0.39 0.29 0.33 0.34 0.23
6 0.33 0.28 0.35 0.38 0.30
7 0.31 0.26 0.33 0.34 0.36
8 0.25 0.25 0.30 0.22 0.32
9 0.25 0.23 0.20 0.21 0.26
10 0.22 0.22 0.10 0.20 0.19
11 0.10 0.20 0.10 0.18 0.14
12 0.10 0.20 0.10 0.18 0.14
Then the daily ETp value after Haude is compiled as time series file evaporation.tss.
Basically the whole catchment area is taken as one evaporation zone (zonee.map in Figure
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4.7). It can be modified as well to have areal discretization across the basin based on other
geographical properties like soil or land use.
Figure 4.7 Left: evaporation zone map (zonee.map);
Right: evaporation timeseries file (evaporation.tss)
Soil map
Information of the soils in the Treene catchment area comes from the digital soil map (BÜK
Bodenübersichtskarte) available in the Arcview format, with a scale of 1:200 000 (BGR
1999). The selected layer of the digital topographic map 1:200 000 (DTK 200) is used as map
basis for the BÜK 200.
The original soil map has 56 soil types, which is more than enough to be handled in KIDS
model. It is then reclassified into a simpler soil map with only 11 soil types according to the
German classification system (Sponagel et al., 2005) for soil types. Details of the
reclassification procedure will be discussed in Chapter 5. The previous original map and
modified soil.map in PCRaster are presented in Figure 4.8. The legend of the new soil.map
and the area fraction of each types are shown in Table 4.2.
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Figure 4.8 Left: original soil map with 56 soil types;
Right: simplified soil map (soil.map in PCR) with 11 soil types
Table 4.2 Legend and area fraction of simplified soil map
Legend Description Area percentage
1 HH / HN Peat 9.94%
2 GGn Normgley 7.10%
3 gPP vergleyter Podsol 25.64%
4 GG Gley 1.34%
5 SS Pseudogley 7.92%
6 BB Braunerde 12.01%
7 PP Podsol 10.89%
8 LLn Normparabraunerde 7.17%
9 SSLL PseudogleyParabraunerde 17.75%
10 MOn Normorganomarsch 0.12%
11 Rohsubstrate Rohsubstrate 0.12%
Land use map
The land use data is provided by the Deutsches Zentrum für Luft und Raumfahrt (DLR
1995). For the land use classification two scenes of the satellite Landsat TM 5 are used. The
spatial dissolution of the classified land use map is 25 x 25 m. The landuse.map and its legend
are shown respectively in Figure 4.9 and Table 4.3. The typical land use in the catchment is
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agriculture and grassland. The fraction of wetland is unknown, but even the lowlying
wetlands are still used as pasture or meadow.
Figure 4.9 Land use map (landuse.map)
Table 4.3 Legend of land use map
Legend Description
1 Acker Agriculture
2 Brache (verbuscht) Fallow land (bushes)
3 Brache (hohes Gras) Fallow land (high grass)
4 Stadt Urban area
5 Wasser Open water
6 Laubwald Deciduous forest
7 Nadelwald Coniferous forest
8 Grünland Grassland
Manning’s Maps
In KIDS model water is routed through the river basin with the kinematic wave function using
the Manning equation (Chow et al., 1988). Manning’s maps are thus needed for modelling
based on other input maps.
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Manning map for field (see Figure 4.10, left) is made by modifying the land use map. The
manning’s n coefficients are defined as follows: forest – 0.15; city – 0.016; others (including
agriculture, water, grassland) – 0.035.
Manning’s n values for open channel need to be defined as well. Reference values of
Manning's n for various open channel surfaces are listed in Table 4.4.
Table 4.4 Manning roughness coefficients for various open channel surfaces
(after Chow et. al, 1988)
Material
Typical
Manning roughness
Coefficient
Concrete 0.012
Gravel bottom with sides – concrete 0.020
– mortared stone 0.023
– riprap 0.033
Natural stream channels
Clean, straight stream 0.030
Clean, winding stream 0.040
Winding with weeds and pools 0.050
With heavy brush and timber 0.100
Flood plains
Pasture 0.035
Field crops 0.040
Light brush and weeds 0.050
Dense brush 0.070
Dense trees 0.100
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According to Chow et al (1988), we take the low value of 0.035 for downstream and high
value of 0.055 for upstream of Treene river. Figure 4.10 (right) shows the channel’s manning
map (channelM.map)
Figure 4.10 Left: Manning map for field (Manning.map);
Right: Manning map for channel (channelM.map)
4.1.2 Definition of hydrologic processes in KIDS model
This chapter explains the implementation of the KIDS model in PCRaster. We construct the
dynamic model with defined hydrological processes corresponding to the conceptual model,
which is assumed to be the actual water cycle simulation for the modelled river basin.
The basic conceptual model for runoff simulation in Kielstau
Conceptual models, which are used to structure scientific expertise and research and which
are the basis of any computer model, are often built as a network of compartments. Such
modelling approaches can be found quite often, particularly in hydrology. Because it is
difficult to simulate the entire system represented in the matrix, for specific case studies and
sites there are considerably less compartments and links. Conceptual models, which include
only parts of the hydrological cycles, are definitely simpler.
The KIDS conceptual model is structured as oneway hydrological flux without feedback.
This is a rather simplified approximation of the complex water cycles.
Figure 4.11 is a schematic overview of the model. The basin is viewed as a set of
compartments which stores water – soil, groundwater aquifers, and the additional wetland
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layer. The simulated hydrological fluxes include precipitation, interception,
evapotranspiration, infiltration, overland flow, lateral flow, and groundwater flow. As the
“wetland” storage layer and the “lateral flow” are added to the basic model during the
calibration process, they are indicated as the parts with dashedline in Figure 4.11.
The hydrological cycle of the drainage basin defined in the KIDS model can thus be
considered as a series of storage compartment and flows. The spatially and temporally
distributed field data are available for model running. Aiming at runoff simulation at a
selected location in the catchment, the KIDS model should describe the processes of surface
storage or interception, infiltration and runoff with processbased equations. These processes
are explained below.
Figure 4.11. Simplified flow chart of the KIDS model
Input of precipitation and potential evapotranspiration
KIDS model needs two types of timeseries file: rainfall and ETp on daily base.
Using the PCRaster command timeinputscalar we can attribute these timeseries of point
observations at measuring station to a spatial distribution map at each timestep.
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Interception
The landscape gains water from precipitation, and part of it is intercepted by the vegetation
cover. This temporally stored water will be evaporated eventually and is taken as the first part
of actual evapotranspiration.
In KIDS model the interception capacity is kept constant at 1mm for simplicity
(intercp_max = 1 mm), without considering the annual variation of LAI (Leaf area index) etc.
Nevertheless, different interception capacities can be created using a map of land use.
Infiltration
At the scale of a watershed, spatial variation of soil properties can be considerable. A property
of particular importance is the rate at which rainfall can enter the soil surface. This infiltration
rate is usually expressed as the volume of water entering unit area of the soil surface per unit
time.
For the KIDS model, we assume a simple method. Infiltration is only a function of soil water
content. In this case, maximum infiltration is defined as the volumetric percentage of the soil
water deficit, which is the difference between soil storage capacity and the actual soil water
content. If the soil is saturated, precipitation goes directly to runoff (or saturation overland
flow). The infiltration rate is set at first in the basic model as 30% (inf_factor = 0.3). This can
be either changed with different values for the parameter sensitivity running, or be spatially
discretized depending on pedological information.
Soil water storage
The KIDS model is a 3 compartments storage model. The storage compartments are soil
water storage, groundwater storage and wetland storage that is added in the wetland version
model.
The soil layer is the first compartment which holds water in the basin. At the watershed scale,
the soil characteristics become increasingly variable in space as the scale increases. For
example, there is now substantial evidence that spatial variability in infiltration rate is
typically high (Rose, 2004). However, despite there being substantial spatial variability in
most soil properties, it is found that useful progress in following the general trends in soil
water stored in the root zone of vegetation can be made by adopting suitable typical values for
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soil water characteristics. Especially if soil in the root zone has a good structure with pores
through which water can drain readily, it is found that, a day or two after a soaking rainfall or
thorough wetting, soil drains to an approximately reproducible water content, call the ‘soil
field capacity’ (Rose, 2004). Expressed in terms of volumetric water content, field capacity
will be denoted fc. Because of the rapidity of drainage at water contents higher than fc, at
least for welldraining soils, fc can be regarded as an effective upper limit to the amount of
water that is available to vegetation for uptake by its root system.
For any particular soil and plant species there is also an effective lower limit to soil water
content below which the plant cannot extract water, though it is the pressure head or soil
water suction that is directly involved, rater that water content. This lower limit to availability
is commonly called the ‘permanent wilting point’, wp.
The watercontent difference ( fc wp) is called the ‘available water content’. This is
assumed to distribute equally over the rooting depth for the vegetation.
For calculating the actual evapotranspiration (Eta) from the soil moisture, another soil water
property called ‘soil start reduction’, sr, is added. If there is enough water in the soil then
ETa = ETp. As soon as water content drops below the limit of reduction sr, the amount of
ETa is a linear function of soil water content, decreasing from ETp at the ‘soil start reduction’
to zero in completely dry soil. The calculation mechanism can be expressed in Figure 4.12.
Figure 4.12 Reduction of ETp to ETa as a function of soil water content
The most important soil physical parameters defined for soil water storage in KIDS model are
(all values in mm water):
• soil_field_capacity = 100, upper limit to water content
• soil_start_reduction = 50, water content where ETa < ETp (app. 50% of soil_field_capacity)
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• soil_pwp = 0, lower limit to water content at permanent wilting point
The incoming water flux to the soil layer is the remaining precipitation, and the outgoing
fluxes include part of ETa, lateral flow and groundwater recharge, which are forwarded to the
next hydrological processes.
Groundwater storage
The groundwater layer in KIDS model is recharged from the soil water storage and
discharged into the river runoff as part of the base flow.
The inflow to groundwater consists of two parts: one is the overflow from soil to groundwater
when the sum of the amount of water already stored in the soil and the inputs from
precipitation exceeds the maximum water storage capacity of the soil; the other is a small
water flux (with a constant flux rate ‘soil_gw_flux’) into the groundwater even if the soil
water is not filled up completely. Both parts produce the water infill from wet soil to the
groundwater reservoir. From this reservoir delayed runoff representing the baseflow of the
river is calculated using a linear recession equation with a recession constant ‘gw_factor’.
Therefore the parameters set in the groundwater fluxes are:
• soil_gw_flux = 0.2; water seepage rate from soil to groundwater
• gw_factor = 0.1; groundwater discharge rate to the river baseflow
Both coefficients can be used for calibration in Chapter 5.
Runoff routing
From the conceptual model in Figure 4.11, it is indicated that Runoff in KIDS model is
composed of three parts: the rapid runoff from overland flow and the delayed runoff from soil
lateral flow and the baseflow from groundwater storage (i.e. groundwater outflow).
The next step is to route the runoff into channel. The basin runoff is obtained from the grid
cell water balances by accumulating water production for all cells in a catchment. Although
many GIS are very advanced in data processing and display, current GIS are not capable of
physicallybased modelling. Especially simulating transport of water and pollutants through
landscapes is a problem in a GIS environment. A number of specific routing methods are
needed in a GIS for hydrologic modelling, amongst those numerical solutions of the Saint
45
Venant equations, such as the kinematic wave approximation for transport of surface water in
a landscape.
To calculate runoff in PCRaster we can use a builtin function: accuthresholdflux ( flow_grid,
rain, max_infiltration). The function calculates runoff for every grid cell based on
precipitation and the maximum infiltration into the soil. The flow direction is calculated
according to the flow net we created in section 4.1.1.
However, the 'accu approach' is not sufficient when the timestep of the model approaches
the average traveltime through a cell, or whenever the number of pixels along the transport
path is large. Due to the relatively simple schemes for solving the differential equations
underlying this transport processes, the PCRaster models in this case become numerically
very unstable. A more robust approach is needed. This approach can be found in the
traditional algorithms for flood routing such as the kinematic wave approximation and the
diffusion wave model (Chow et al., 1988).
The PCRaster has recently been extended with a kinematic wave function to allow for
physically based water flow modelling. Thus the hydrologic modelling capabilities have
largely increased with this development of the routing methods. The kinematic wave can be
used for hydrologic catchment models for overland flow routing of runoff and for channel
routing under certain conditions. It cannot be used in case of backwater effects in rivers.
Using the kinematic wave function allows for a more robust method for solving transport
equations.
In KIDS model, the amount of water that actually discharges is computed with the kinematic
wave simulation tool (with manning’s equation, details see Appendix I) using the following
syntax:
Qt = kinematic (Ldd, Qt1, QIn, , , T, DCL),
with:
Ldd map with directions of flow within the flow network (Local Drain Direction map),
Qt1 map with water discharge in direction Ldd in previous time step (e.g., m3/s),
QIn map with addition or subtraction of water to/from the flow (e.g., m3/s),
map with coefficient (Alpha is calculated according to the equation in the
Appendix I, Chow et al., 1988),
map with coefficient (momentum coefficient equals 0.6, Chow et al., 1988),
T time step (e.g., s, day),
DCL map with distance of flow to downstream unit in direction Ldd,
Qt map with water discharge in direction Ldd at current time step (e.g., m3/s).
46
The new discharge Qt at a certain location is calculated from the discharge Qt1 from the
previous time step at the same location and using Qt from neighbouring upstream pixels.
4.2 Structure of KIDS model scripts
In a program like PCRaster for a hydrological model, entities are needed that represent
hydrological objects such as landscapes (maps) and time (time series), while operations at
these entities are needed that represent hydrological processes as cited in Section 4.1.2.
The KIDS model script has four sections, binding, timer, initial and dynamic:
binding
In the binding section, maps, timeseries and other data can be linked to variables in the
scripts. Although this section is optional, it provides a nice way to quickly change
model parameters at one unique point in the script.
timer
This section controls the number of timesteps. It regulates the duration and time slice
of the model through three parameters, starttime, endtime and timeslice. Here we
model 6 years with daily steps (i.e. 2192 steps).
initial
Model initialization is done in the initial section. Some model variables will get their
initial value here. It sets the initial conditions for the model, including maps and non
spatial attributes. These values may be defined with one or more PCRaster operations.
dynamic
All the statements listed in the dynamic section are executed for each timestep.
The dynamic section is the core part of this dynamic model. It defines the operations for each
time step that result in a map of values for that time step. Each time step consists of one or
more PCRaster operations which are performed sequentially.
The practical application depends upon the generic nature of the entities and operators in the
modelling language. A relatively limited number of entities and operators should support a
wide range of models to be constructed, for many different hydrological situations. Dynamic
models in PCRaster are constructed by writing scripts containing series of statements (Van
Deursen, 1995). The language has no explicit structures for iteration, although dynamic
models do iterate in time. The PCRaster script language has a high level definition of
47
modelling. The syntax of the language is based on mathematical equations where each
equation assigns the value of an expression to a single output. Simple operations like loops
and mathematical operations on maps make up the main part of most models and are generic.
A good example is the kinematic wave transport of water needed for the model. Kinematic
wave transport with manning’s equation is a more complicated operation than multiplication.
It is a spatial operation that needs a numerical solution of the kinematic wave equations. In a
system programming language, this operation will take several pages of code to be
implemented; while in PCRaster the kinematic operator is only one line of code operating on
spatial entities (maps) as shown before. The same seems to hold for other complex operations
in PCRaster.
The script of KIDS model is shown in Appendix III.
4.3 Select of calibration and validation period
For calibration and validation of the KIDS model, we used the timeseries for discharge station
Soltfeld at the outlet point of Kielstau catchment. The input data files for the catchment are
available from January 1984 to December 1999. Three factors about the model running time
should be decided for the model evaluation: the start up period, calibration period and
validation period.
4.3.1 Start up period
To set a start up period is for the purpose of a stable model performance during the calibration
process. Usually a computer program needs an adaptation period to read in the initial data
input and to warm up until calculation equilibrium. The length of the start up period can be
quite different depending on the very nature of the modelling language. For example, SWAT
may take around 1000 time steps until it can report a reasonable outcome.
In PCRaster, a test model running is made in order to determine a reasonable start up period.
An identical model was conducted twice with different set of running time period. One is
from January 1984 to December 1987, the other begins from December 1985. Two
hydrographs are plotted on the same time period (02.12.1985~31.12.1987) in Figure 4.13. It
shows very slight difference after 50 time steps, then no difference could be viewed by visual
comparison. Judging from the timeseries data of the runoff output, both values are exactly the
same after 274 steps.
48
This simulation results showed 1yeartime is enough for PCRaster program to warm up.
Figure 4.13 Evaluation of the start up period
4.3.2 Calibration and validation period
Choosing the calibration and validation period is based upon the statistic analysis of
precipitation and discharge data from 1986 to 1999. The daily averages of rainfall and runoff
volumes for each year are drawn in Figure 4.14 together with their mean lines.
Figure 4.14 Precipitation and discharge variation from 1986 to 1999
0
0.5
1
1.5
2
2.5
3
3.5
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Average of Rainfall (mm/day)Average of Discharge (mm/day)mean
49
It is suggested that, an optimal calibration and validation scheme includes a separation
between dry and wet periods for calibration and validation purposes (Klein et. al, 2004). One
of the periods, either the dry period or the wet period, is used for calibrating the model, the
other is used for validation purposes.
In this case, the precipitation and discharge records in the years 1990~1993 were relatively
close to the average level, without large fluctuation. Therefore this period should be
appropriate as calibration period. While the years from 1995 to 1997 were extremely dry, the
catchment received 24% less precipitation than in an average year. It would be better to
separate that period from the calibration process but to include it in the validation period.
So, the calibration and validation periods are set up as follows: with one year model pre
running time for the whole year of 1988, the period from January 1989 to December 1993 is
used for calibration, and the calibrated model will be validated for the period from January
1994 to December 1999.
This calibration and validation scheme includes a separation between dry and wet periods, it
is thus assumed to be optimal for the calibration and validation purposes, which requires a
long record of observations.
4.4 Criteria and methods for results comparison
The process of calibration is always the comparison of two data sets – in most cases time
series like discharge. It is done by selecting a subset of the measured data series, and
comparing simulated results with this selected subset.
When comparing model predictions with observed data, qualitative as well as quantitative
techniques should be employed. While qualitative techniques are typically based on visual
inspection of the results (here are pairs of time series), quantitative techniques try to express
the agreement between model and data numerically in terms of the outcomes of performance
measures. It is often difficult or timeconsuming to judge the significance of these outcomes
objectively. Moreover the various measures typically only highlight specific aspects of the
system and the model. Therefore a judicious combination of several techniques should be
employed for a thorough model assessment. For an overview of methods see Janssen and
Heuberger (1995), Gupta and Sorooshian (1998) and Anderson and Bates (2001).
Two criteria are set to calibrate the KIDS model. The first criterion is that the simulated
annual runoff should correspond with the measured annual runoff, assuming a stable model.
50
This assumption requires a zero change in average annual storage in soil water and ground
water. The second criterion is that the timing and magnitude of peak and low flows (on a daily
time basis) should be well represented.
To meet the second criterion, a mixture of methods or techniques is chosen to determine the
quality of the KIDS model, the degree to which the model describes measured discharges. It
includes:
• NashSutcliffe Index (Nash and Sutcliffe,1970)
• Root mean square error (RMSE)
• Regression coefficient: r²
• Visual comparison
Pi and Oi denote the predicted value and observed value i, and are their means.
The most important criterion is the coefficient of efficiency – NashSutcliffe index. It is in
concern of the modelling efficiency measure, and quantifies the relative improvement of the
employed model over the ‘nominal’ or ‘benchmark’ situation. A NashSutcliffe index of 1
means that the model produces discharge data which are exactly coinciding with the measured
data. An efficiency of 0 denotes that using the mean value of discharge as an estimate for
decade discharge would be an equally good estimator as the model used.
51
The RMSE compares the predictions Pi and observations Oi on an individual level. It
measures ‘Pi –Oi’ in a quadratic sense, therefore it is rather sensitive to outliers. The result is
better when it is closer to zero.
The regression coefficient r² can be used to compare the model prediction and the
observation. The deviation of the r² from 1 is a useful indicator for the modeldata agreement.
The regression result should be carefully used especially if there is much variability in
observations or model prediction.
The last criterion used for calibration besides those statistical techniques is the visual
comparison of the two time series. It can reveal patterns which cannot be detected by the
statistical methods.
52
Chapter 5 Application of the model
5.1 Calibration strategy of the KIDS model
Model calibration is a critical phase in the modelling process, and the need for a well
established calibration strategy is obvious. Therefore a systematic approach for the KIDS
model calibration is developed which is guided by the intended model use. The specific
purpose of the KIDS model is the prediction of longterm dynamical feature of river
discharge value. This objective serves as the main guiding principle in building and
calibrating the model.
Considering this background, some activities should be explicitly acknowledged prior to the
calibration process: determining the necessity of building up the model and evaluating the
basic data before simulation. The fundamental first step in organizing a simulation model
involves a detailed analysis of all existing and proposed components of the system, the
collection and preparation of dataset, and model implementation with defined hydrological
settings, as described in the previous chapters. The second is to examine and evaluate the
measured data, in order to know the natural behaviour of the physical system. Since the
developed simulation model should demonstrate a consistent behaviour with the actual
system, this step is essential for the success of model calibration. Details will be discusses in
Section 5.2.
Then the calibration phase follows. Usually the model calibration is referred as the process of
finding a set parameter values with which the model performs optimally considering the
described criteria, while the model structure stays the same during the calibration process. In
this study, both aspects are included in the calibration process, with more focusing on the
determination of the best conceptual model or the optimal structure of the model which can
give the closest simulation of the observed data.
This calibration approach is decided by the nature of effectiveness of the applied techniques.
With the PCRaster modelling language, it is quite easy to modify the algorithms in the model.
Meanwhile, the parameter sensitivity test can only be done manually in PCRaster. In order to
reduce the parameter uncertainty in simulation results, a preliminary parameter sensitivity or
uncertainty analysis is performed. Based on the results, a standard set of parameters is
determined for the later calibration. Details are explained in Section 5.3
53
With the estimated parameters, the central part of the calibration process begins with the test
of different conceptual models, by modifying the algorithms as necessary to improve the
accuracy of the model. Then the model predictions are analysed and compared with
measurement data. This is performed not only qualitatively by visually inspecting the
agreement between observed data and model predictions, but also quantitatively in terms of
statistical methods. Subsequently the structure and parameters of the model are adjusted such
that the ‘datatomodel agreement’ is satisfactory to run validation.
The involved issues in this calibration process are indicated in Figure 5.1, and will be
discussed in more detail in the subsequent sections.
Figure 5.1 KIDS model calibration process
5.2 Data evaluation
The objective of the KIDS model is to simulate the discharge of Kielstau river. Using the
available data from the official gauge station Soltfeld, the characteristic of the runoff is
54
examined by comparing the seasonal relationship between runoff value and the precipitation
amount.
Based on the data records from the year 1986 to 1999, we calculate the average monthly
precipitation and runoff for each month through the whole year and then plot the ratio of
runoff/precipitation comparing with the seasonal variation of rainfall.
From Figure 5.2, it can be concluded that the hydrology of the Kielstau area is characterized
by a seasonal distribution of runoff/precipitation relation. The average runoff/precipitation
ratio is 0.318. During the months from May to October, which is the high precipitation
season, there is almost no positive correlation between runoff and precipitation. There are
probably two reasons for that. One is the high evaporation during that period, the other is the
fraction of wet area with large part of shallow groundwater distribution in Kielstau catchment,
which may have a high influence on this seasonal imbalance.
Figure 5.2 Monthly relations between runoff and precipitation (19861999)
5.3 Calibration
5.3.1 Parameter estimation
As mentioned in Section 5.1, a preliminary parameter estimation is performed in order to
reduce the parameter uncertainty in simulation results. The analysis is done for the following
parameters in the basic model:
‘Inf_factor’ (infiltration rate);
‘Soil_capacity/reduct’ (Soil_field_capacity/soil_strat_reduction);
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Jan
Feb Mar Apr May Jun Jul
Aug Sep Oct
Nov Dec
Run
off /
Prec
ipita
tion
.
0
20
40
60
80
100
120
Prec
ipita
tion
(mm
/mon
th)
.
ratio runoff/precipitation
average of monthly sum precipitation (mm)
55
‘soil_gw_flux’ (water seepage rate from soil to groundwater);
‘gw_factor’ (groundwater discharge rate to the river baseflow).
The parameter ‘Intercp_max’ is not included in the analysis, because it has little effect on the
model result from the previous modelling experience.
The analysis is carried out for the selected parameters by altering the initial values within
reasonable bounds. Results of this analysis are illustrated in Figure 5.3. The effect of change
in parameter values on the model performances is indicated by the per cent change in the
NashSutcliffe index. A positive change can be interpreted as improvement over the initial
situation with the previous set of parameter values.
Figure 5.3 Effect of change in values of model parameters on model efficiency
From the analysis result, the parameter values, which report a positive change in the Nash
Sutcliffe index, are chosen as the standard set for the model simulation. The values of
estimated initial set of parameters (presented below in Table 5.1) will be kept all the same
during the conceptual model calibration process, if nothing special is mentioned.
Table 5.1 Estimated initial set of parameters
Parameter Standard value
Intercp_max 1 (mm)
Inf_factor 0.4
Soil_capacity/reduct 200 /100 (mm)
soil_gw_flux 0.1
gw_factor 0.05
56
5.3.2 Simple conceptual models
As introduced in Section 4.1.2, we build up a basic conceptual model for the runoff
simulation in Kielstau. The structure of the hydrological cycle is corresponding to the flow
chart shown in Figure 4.11, but without the additional wetland layer.
This section discusses the first step of the conceptual model calibration process –
modification based on this simple model. Since PCRaster is capable to integrate temporal
spatial distributed data into the hydrological modelling, modifications on the basic model
include spatial distribution of soil field capacity, added lateral flow, and spatial distribution of
ETp referring to land use type.
The basic model – ‘Q_basic’
With the estimated set of parameters in the basic model, the modelled discharge value,
Q_basic, is plotted for the calibration period (Jan. 1989 ~ Dec. 1993) in Figure 5.4, compared
with the observed discharge data. The statistical coefficients are shown below the hydrograph.
The negative value of NashSutcliffe index demonstrates clearly that the simulation model
can not reproduce the basic features of measured data. However the r² value of 0.573 shows
the model can predict the discharge fluctuation in some extent.
Judging from the visual comparison, Q_basic has an overall higher value except some peak
point. The sum of the daily measured runoff data for the calibrated 5 years is 1183 mm, while
the predicted sum is 2248 mm, which is nearly 2 times more. And it has extremely more
discharge during summer and autumn period. Considering the presented daily precipitation in
Figure 5.4, the higher simulated runoff is a systematic reaction of the rainfall: the runoff starts
to rise immediately after rain in the summer and autumn time. This ‘normal’ phenomenon is
deviated from the natural hydrological mechanism as we observed in Kielstau region and
discussed in Section 5.2.
Q_basic is a start point for later model modification. It will serve as a reference line, being
plotted together with the newly modeled discharge value, in order to show the differences,
especially improvement of various models compared to the basic model.
57
Figure 5.4 Hydrograph of the basic model
Q_basic, NS = 0.228, RMSE = 0.545, r² = 0.573,
Sum predicted discharge = 2248 mm (Sum observed discharge = 1183 mm).
The basic model with spatial distribution of soil field capacity – ‘Q_sfc’
In the basic model, the whole catchment has an identical value of soil water storage capacity,
with the corresponding parameters ‘soil_field_capacity’=100, and ‘soil_start_reduction’=50.
Actually the spatial variation in this soil property is considerable, depending on soil texture,
rooting depth of vegetation, stoniness of soil, depth of lithic contact, etc (Kwadijk, 1993).
As to test this parameter sensitivity, we relabelled the soil map into a spatialdistributed
maximum soil storage capacity map according to the soil properties of the main soil types,
ranging 100~400 mm. The parameter setting follows the values in Table 5.2.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1Jan
89
1Jul8
9
1Jan90
1Jul9
0
1Jan
91
1Jul9
1
1Jan92
1Jul9
2
1Jan
93
1Jul9
3
Dis
char
ge
(m³/
s)
a
0
10
20
30
40
50
Pre
cipi
tatio
n (m
m/d
ay)
.
Observed data
Q_basicPrecipitation
58
Table 5.2 Parameter setting for the spatial distribution of soil field capacity
Soil type soil_field_capacity / soil_start_reduction (mm)
Peat 400 / 200
Gley 300 / 150
Pseudogley 250 / 125
Braunerde 200 / 100
Podsol 100 / 50
Q_Sfc, the simulated runoff of the modified basic model with spatial distribution of soil field
capacity, is shown in Figure 5.5.
Since the modification increases the soil storage capacity as a whole, the total runoff has
decreased compared to Q_basic. All statistical coefficients prove a better performance of the
model. Although Q_Sfc has nearly the same values as Q_basic in peak flows, it presents
delayed runoff in low flows, which matches closer to the observed data.
Q_Sfc brings some valuable changes to simulation, and this can be used as an element for
further modification.
Figure 5.5 Hydrograph of the basic model with spatial distribution of soil field capacity
Q_Sfc, NS = 0.163, RMSE = 0.517, r² = 0.606,
Sum predicted discharge = 2150 mm (Sum observed discharge = 1183 mm).
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1Jan
89
1Jul8
9
1Jan
90
1Jul9
0
1Jan
91
1Jul9
1
1Jan
92
1Jul9
2
1Jan
93
1Jul9
3
Disc
harg
e (m
³/s)
a
Observed dataQ_basicQ_Sfc
59
The basic model added with lateral flow – ‘Q_lateral’
Up to now, the modelled river discharge consists of two parts, the rapid runoff from surface
flow and the delayed runoff from groundwater discharge. In this model, an additional
subsurface flow is included as part of the river discharge – lateral flow from soil layer. This is
done by introducing another parameter in the model, the water outflow rate from the available
soil water storage, namely ‘lateral_factor’.
Several values of ‘lateral_factor’ are tested, from 0.1 to 0.03, among which the model with
lateral_factor=0.03 produces the best result, Q_lateral. This result is in accordaqnce with the
characteristic of the Kielstau catchment – very small share of lateral flow is expected as well
as a small share of surface runoff due to its flat relief. From Figure 5.6, the added lateral flow
strongly increases runoff during the low flow seasons, causing by this additional water
extraction from soil compartment to river discharge. The overall sum of lateral flow is 632
mm. The lateral flow with ‘lateral_factor’ of 0.03 intrigues large amount of water outflow in
river discharge, but has little influence on the peak flows. This makes the hydrograph seem to
be worse that the basic model. For later parameter calibrations, the value of ‘lateral_factor’
can be set within a much lower range, like 0.001~0.005, if the simulated low flow is intended
to be increased till the level of measured runoff.
Figure 5.6 Hydrograph of the basic model with additional lateral flow
Q_Lateral, NS = 0.99, RMSE = 0.67, r² = 0.48,
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1Jan
89
1Jul8
9
1Jan9
0
1Jul9
0
1Jan
91
1Jul9
1
1Jan
92
1Jul9
2
1Jan9
3
1Jul9
3
Disc
harg
e (m
³/s)
a
Observed dataQ_basicQ_lateral
60
Sum predicted discharge = 2880 mm (Sum observed discharge = 1183 mm).
The basic model with spatial distribution of ETp adjusted by land use – ‘Q_EtLu’
Different land use types can be used to adjust the calculated potential evapotranspiration
(ETp). For example, a land use coefficient can be introduced into the model: 0.4 for bare land,
1.0 for open water, 1.1 for forest (due to the high transpiration), and so on (Kwadijk, 1993).
The ETp value in KIDS model is calculated after Haude year, with crop coefficient for
grassland, referred as ‘Haude_Gras’. As introduced in Section 3.3.1, Haude method contains
different correcting coefficient dependent on the land use cover. We take those coefficients
for the corresponding land use type. For those which is not covered by the Haude method, the
ETp is calculated by multiplying ‘Haude_Gras’ with a introduced land use coefficient. Table
5.3 gives an overview of the methods to define ETp value for all land use types. ETa value is
limited by the amount of available water.
Table 5.3 ETp adjustments according to land use
Land use ETp
1 Agriculture Haude_WW
2 Fallow land (bushes) Haude_Gras
3 Fallow land (high grass) Haude_Gras
4 Urban area 0.4 * Haude_Gras
5 Open water 1.0 * Haude_Gras
6 Deciduous forest Haude_Laub
7 Coniferous forest Haude_Fichte
8 Grassland Haude_Gras
The modelled result after introducing this spatial distribution of ETp adjusted by land use,
Q_EtLu, is reported in Figure 5.7. It has very little difference with Q_basic, which means the
adjustment cannot improve the basic model efficiently. This method should not deserve more
emphasis in further calibrations.
61
Figure 5.7 Hydrograph of the basic model with spatial distribution of ETp
Q_EtLu, NS = 0.319, RMSE = 0.551, r² = 0.580,
Sum predicted discharge = 2265 mm (Sum observed discharge = 1183 mm).
To sum up this section about the basic model calibrations, all model modifications mentioned
above are tried to test the model reactions to different adjustment, and to give some hints for
the next step of calibration in order to improve the model performance.
Although not much simulation enhancement is observed during the process, all modelled
results present a typical deviation from the measured runoff, which has a special characteristic
as analysed in Section 5.2. After summer, measured runoff starts much later than the
simulated one, in spring however, the values are quite similar. A new method must be found
out to solve this seasonal mismatch between the simulated and observed data.
5.3.3 Preparation for wetland models
The first analysis of the Kielstau data set in Section 5.2 shows a low runoff / precipitation
ratio, with an average value of 0.318. Whereas for SchleswigHolstein, runoff is typically
slightly below 50% of precipitation (Schmidtke, 1992). Moreover, the Kielstau region is
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1Jan
89
1Jul8
9
1Jan90
1Jul9
0
1Jan9
1
1Jul9
1
1Jan
92
1Jul9
2
1Jan93
1Jul9
3
Dis
char
ge (m
³/s)
a
Observed dataQ_basicQ_EtLu
62
observed to be a very flat landscape with large area covered by wet surface, which must be an
indispensable element in the local hydrological cycle. A possible explanation for the lower
discharge in summer and autumn (see Figure 5.2) could be, either the calculated
evapotranspiration is too low, or the area with additional storage (e.g. wetland) is ignored, or
a part of both.
Further analysis need to be performed to check the justification of these assumptions. Here we
make firstly a water budget analysis for the Kielstau catchment, then create a socalled
wetland map in order to facilitate the integration of wetland function in KIDS model.
5.3.3.1 Water budget analysis in Kielstau
The idea of a water budget describes the partitioning of water incoming to and outgoing from
a system of defined volume over a given time period. The inputs may be either as
precipitation (rainfall, snowfall, or dew) or as irrigation water. A water budget applies the
constraint of mass conservation to the water input. Precipitation can either infiltrate into the
soil or, if an excess of precipitation over infiltration occurs, generate overland flow, though
river flow is commonly dominantly dependent on subsurface flow.
A water budget or water balance is constructed over a particular volume of soil (watershed)
and for a selected time period.
Figure 5.8 Illustration of water budget in Kielstau
63
Now consider the fluxes shown in Figure 5.8 to be volumetric amounts (in m³) for the entire
watershed of Kielstau area A accumulated over the chosen budget time period (which could
be a week, a month, or a year, here we choose one year). It will be assumed that the rainfall P,
and the evapotranspiration ET over a time period are average values for the entire watershed.
Hence the total volume of rainfall input for the defined time period across the Kielstau
watershed with area A will be A*P. Similar comments apply to ET. Then the water budget
equation expressing conservation of mass of water for the entire watershed volume is given
by
A*P = S + SWC + Uv + Uh + A*ET (Soil water balance)
Where
A*P = total precipitation received on the surface of the soil volume;
S = total volume of surface runoff;
SWC = increase in soil water stored in the watershed volume;
Uv, Uh = vertical and horizontal volumetric flow of water from the
soil volume;
A*ET = total evapotranspiration from the surface of the volume.
Realizing that the factors of S, Uv, Uh are intermediate water fluxes that will ultimately flow
into river as discharge or fill in the groundwater storage. It can be expressed in equation as:
S +Uv + Uh = GW + Q. After replacing it into the water budget equation, we get a new
equation for water balance at steady state: A*P = SWC + GW + A*ET + Q.
Most water in a catchment is commonly stored within the soil profile and aquifers where
groundwater resources are abundant. The amount of water accumulated in soil or groundwater
forms the watercontent profiles in a watershed. In hydrological studies of watersheds, the
period of time chose for the analysis is a ‘hydrological year’, at the commencement and end
of which watercontent profiles are judged to be identical (Rose 2004). Thus, for a
hydrological year, with the term SWC + GW = 0, the water budget equation turns into
A*P = A*ET + Q (Areal water balance).
We replace the terms in the equation with the measured or calculated data for Kielstau
watershed to check the water balance status. The amount of precipitation is given by the total
volume of water collected per unit area per day, expressed by the depth of water in mm. The
potential evapotranspiration rate is calculated after Haude with crop coefficient of grassland.
The river discharge value expressed in m³/s comes from measured data of gauge station
64
Soltfeld, which locates at the outlet point of Kielstau watershed. The average daily values for
the whole year of precipitation, evapotranspiration, and river discharge are applied for the
whole catchment. All data from the year 1986 to 1999 is listed in Table 5.4.
Table 5.4 Hydrological data of Kielstau catchment (1986~1999), annual averages
Year
Average of Precipitation
(mm/day)
Average of ETp
(mm/day)
Average of Discharge
(m³/s)
1986 2.218 1.188 0.354
1987 2.435 0.959 0.393
1988 2.781 1.122 0.653
1989 2.059 1.509 0.270
1990 2.671 1.279 0.424
1991 2.283 1.257 0.446
1992 2.259 1.444 0.348
1993 2.401 1.126 0.458
1994 2.818 1.344 0.645
1995 2.075 1.510 0.523
1996 1.372 1.294 0.109
1997 1.984 1.480 0.176
1998 3.201 1.046 0.675
1999 2.859 1.307 0.611
Next is to calculate the water budget. The left part of the equation is water input to the
catchment: Input = Annual mean Precipitation (mm/day) * Area of Kielstau (51.92 km²)
The right part of the equation is water output from the catchment:
Output = Annual mean ETp (mm/day) * Area of Kielstau (51.92 km²) + River Discharge
(m³/s) * 86400 (s/day)
The results are presented in Figure 5.9 below. We could see that the water budgets were not in
balance assuming the calculated ETp is close to the actual values in the watershed. There
were output deficit (here it is defined as the difference between ‘Input’ and ‘Output’) in 12
years out of these 14 years. If it is desired to reset the water budget in balance, one possibility
could be to enhance the evaporation rate. The average ratio of ‘output deficit’ / ‘ETp’ through
65
the years from1986 to 1999 is 31.6%. this indicates that the evaporation rate can be increased
by about 30%.
Figure 5.9 Water budget investigation in Kielstau (1986~1999)
We decide to modify the existing model gradually instead of increasing the evaporation rate
for the whole catchment. This leads to a new model structure with an additional wetland
fraction, which is shown in Figure 4.11. The ‘wetland’ is defined as an area with higher
potential evaporation rate and no water limit for actual evaporation.
5.3.3.2 Soil description and reclassification for wetland map
As introduced in Section 4.1.1, the landscape of the study area displays a remarkable range of
soil types, resulting from the residual deposits of Ice Ages, variations on geology, topography,
vegetation and other organisms and other factors combined to influence soil formation. To
bring convenience to modelling, we need to find way to classify such variety of soils.
Due to the fact that these are typical soil types found in Germany, it is not suitable to make
reference to some purported international soil classifications, like the World Reference Base
for Soil Resources (FAO, 1998). Because of the evolving nature of soil science, no
universally accepted system of classifying soils exists. Many countries have continued with
developing and refining their own national classifications, as for example in Germany where
0
30000
60000
90000
120000
150000
18000019
86
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Wat
er V
olum
e (m
³/day
)
.
Input (rain)Output (ETp+Q)
66
Bodenkundliche Kartieranleitung by AG Boden (Sponagel et al., 2005) German
classification system for the soil types (in the following text referred as KA5) – has been
adopted.
Following the German classification system after KA5, similar soils are grouped into new
classes that are given distinctive names. Details of reclassification are shown in Table 5.5. In
the column ‘new legend code’ are the principal soil types, which includes the subtypes the
soil listed in the column ‘original value’.
Table 5.5 Reclassification of soil map – legend comparison
New legend code Description Original value1 HH / HN Peat HHn : Hh
HNn : Hn;Hn/S:Hn/Lg;Hn/UbTbHNn : HnHNn : Hn;Hn/SHHn : Hh; Hh/Hn; HPP : H\SHHn : Hh;Hh/S; HPP : H\Sa, Ssdr
2 GGn Normgley GGn : Sp/UTb;Sp/Lg; HNn : Hn/SpGGn, GMn : Sa/Ssdr; HNn : Hn/S
3 gPP vergleyter Podsol gPPn, GGPP, pGGn : Sa/Ssdr; HNn : HnGGPP, pGGn : Sp;Sp//Lg; HNn : Hn/SGGPP : Sa/Ssdr; HPP : H\Ssdr; HNn :Hn/S
4 GG Gley ABGG : SfGGYK : SLz/Lg; GGn : Sp
5 SS Pseudogley SSn : UTg; SSLL : Lg; BBn : SgGGSS : Sfl//Lg; GGn, gPPn : Sp/LgpSSn, pSSBB : Sap//Lg; BB/LL : Sp//LgSSn, GGn : Sp/UTb;SflLfl/LgSSn : Sp/Ugf; BBn : Sp/Sgf
6 BB Braunerde BBn : Sp/Sg(Sgf)pBBn : Sp/SgPPBB : Sap//Sg; pSSBB : Sap//LgSSBB : Sp//Lg; sLLn : SflLfl/LgPPBB, PPn : Sap; SSBB, SSPP : Sap//LgpBBn : Sp/Sg; SSBB : Sp/LgsBBn, SSn : Sp//Lg;Sp/UTbpBBn : Sap/SsdrBBn : Sp/Sg;LLn, SSLL : Lg;Lfl//Sg
67
pBBn : Sp/Sg; pSSn : Sfl/LgpBBn : Sp/SsdrsBBn, SSBB : Sp//Lg; YKn : Sz/Sp
7 PP Podsol PPRQ, PPn : SdgPPn, GGPP : SagPPn, GGPP : Sap; gSSPP : Sap//LggSSPP, GGPP, PPn : Sap//Lg; PPn : SaKippsubstratPPn : SdBBPP : Sa/SsdrBBPP, pBBn : Sap//SggPPn, GGPP : Sap/SsdrBBPP, PPn : Sa/SsdrSSPP, pSSBB, pSSn : Sap//Lg
8 LLn Normparabraunerde LLn : Lg; BBLL : Lp
9 SSLLPseudogleyParabraunerde SSLL, SSn : Lg; Lg//Mg
10 MOn Normorganomarsch MOn : Thpm/Ssdr;Thpm/Tpm11 Rohsubstrate Rohsubstrate Rohsubstrate : Sg, Sgf
From the soil name in the table we could see that, in the German classification system the soil
type with the dominant features is called last, e.g. ParabraunerdePseudogley: the gleyic
features of the Pseudogley are dominant. Names of horizons, texture, soil density, humus
content and rooting intensity are recorded according to the KA5 as well.
Individual soils from the simplified soil classification are described below in terms of their
definitions or major properties, and possibly their model of formation.
HN/HH, Niedermoor/Hochmoor
A ‘Moor’ land is wetland or peatland, which is constantly influenced by groundwaters or
precipitation water. Depending upon shape, structure and the kind of the water supply one
differentiates Hochmoor (or bog) and Niedermoor (or fen).
Niedermoore, also mentioned as Flachmoore, are wetlands, which are interspersed with
nutrientrich groundwater to the surface; while Hochmoore are enriched by rain water. The
‘Moor’ wetlands form in lowering, river valleys, for small hollows and at slopes within the
68
range of spring water withdrawals. In the sense of soil science, those soils are understood as
more than 30% from organic substance existing and their organic layers at least 30 cm.
GG, Gley, soil with Ah/GPfofile; GGn, Normgley, soil with Ah/Go/GrPfofile; &
SS, Pseudogley, soil with Ah/S(e)w/SdPfofile
Most soil classifications divide the gley soils into surfacewater gleys (also known as
stagnogleys or pseudogley) and gleys proper, or groundwater gleys. The former having a
slowly pemeable lower subsoil, leading to a "perched" water table, the latter being in low
ground or basin situations where the natural groundwater table is constantly high enough to
influence the soil profile.
Gleye rank among the soils, which are affected by the groundwater. The groundwater level
stands here more highly than 1.3 m under surface. Gleye develop under the influence of
oxygenpoor groundwater. The groundwater influence is temporarily enough to 40 cm under
the upper soil layer.
The name Pseudogley was selected, because this soil in a part of its characteristics resembles
the “genuine” Gley. Contrary to the Gley the Pseudogley is not affected by the groundwater
but impounded water. The precipitation water can seep not completely or only very slowly.
This fact leads to oxygen deficiency and thus to the solution and relocation of iron and
manganese. Pseudogleye have usually a low pH value.
The common feature of the poorly draining gley soils is that, under periodic or permanent
water logging, the subsoil experiences a lack of oxygen within the pore space. Consequently
under anaerobic conditions the insoluble iron oxides (which cause the characteristic yellow,
brown or reddishbrown colour to soils with adequate aeration) are reduced chemically and
the ferric iron changed to ferrous iron prior to translocation from the soil profile. Minerals
with iron in the ferrous form impart a grey or bluishgrey colour to the subsoil. The gleying
process is not necessarily permanent and where intermittent, intense mottling and grey
colours are characteristic. Where surface wetness is a feature throughout the year, the
horizons are generally rich in organic matter, often intergrading into peat deposits.
BB, Braunerde, soil with Ah/Bv/CPfofile
The general term Braunerde or ‘brown earth’, denotes an extensive grouping of soils,
generally free draining, with altered subsoil horizons, usually brown or reddish brown, in
69
which iron oxides are bonded to silicate clays. Under natural conditions of broadleaf forest a
litter rich in nutrients and organic matter is generated and with intense biological activity, the
litter is rapidly decomposed prior to incorporation into the mineral topsoil. A darkcoloured
surface Ah horizon (h denotes enriched with humus), of variable depth, overlies subsoil
horizons (B horizons) with distinctive brown colours, the main feature being a gradual
lightening in colour as the organic matter and iron content decreases with depth.
Given their adequate depth, free drainage and relatively high natural nutrient levels, these
soils are amongst the most fertile soils and are used extensively for agriculture. By virtue of
their occurrence in favourable climatic areas and on flat or gently undulating terrain they have
also been used extensively for settlement and industry.
LL, Parabraunerde, soil with Ah/Al/Bt/CProfile
Parabraunerde is designated as a soil, with which clay minerals were shifted from the upper
layer into the lower layer. Parabraunerde is a far common type of soil in the moderatehumid
climate from primarily calcareous loose rock. The supplies of plantavailable water and the
content of natural nutrients in Parabraunerde are usually high, it is thus predominantly used
for agricultural production.
SSLL, PseudogleyParabraunerde, soil with Ah/Al/AlSw/BtSd/C or Ah/SwAl/Sd
Bt/Sd/C Profile
The soils are classified as PseudogleyParabraunerden which have been more or less
influenced by gleyic processes due to stagnant water following the forming of a dense, clayey
horizon by lessivation. Some show weak signs of Pseudogleyprocesses due to stagnant water
in the profile and weathering/iron oxide forming processes. In the case of heavy influence of
water logging, these soils have to be classified as secondary Pseudogley.
PP, Podsol, soil with Ahe/Ae/Bh/Bs/CPfofile
gPP, vergleyter Podsol
Podsol belongs to an extensive group of leached, acid soils. It has a distinctive light coloured
horizon immediately beneath superficial, organic horizons. A number of subgroups are
identifiable and most podsols are free draining.
70
Podsols are generally infertile, nonproductive soils. By virtue of their occurrence in cool, wet
conditions, often within steep hill terrain, they have been used principally for forestry and
recreation rather than for largescale settlement or widespread intensive agriculture.
MOn, Normorganomarsch, is typical humus marsh or organic marsh. MOn and Rohsubstrate
(raw substrate) cover very small area in the catchment, both respectively 0.12% of the whole
area (see Table 4.2). Thus they are not in the consideration of major soil types.
After the investigation into the soil properties, the soil types which have similar
characteristics of wetland can be chosen as possible components of wetland area in the
catchment. Among various definitions on wetlands, we take a broad approach in determining
the wetlands. Under the RAMSAR Convention (Davis 2004), wetlands are defined as:
“areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary,
with water that is static or flowing, fresh, brackish or salt, including areas of marine water the
depth of which at low tide does not exceed six metres”.
So wetlands are areas where water is the primary factor controlling the environment and the
associated plant and animal life. They occur where the water table is at or near the surface of
the land, or where the land is covered by shallow water.
Corresponding to the hydrological properties of wetland, soils, which have common features
of poor drainage, being constantly influenced by groundwater or precipitation water, periodic
or permanent water logging in soil profiles, can be classified as wetland. From the previous
introduction, those soils include: Hoch/Niedermoor, Gley, Pseudogley, and possibly
Pseudogleyparabraunerde.
5.3.4 Wetland models
Hydrologically, a wetland is distinguished from adjacent upland areas by the presence of
water, which creates alternatingly or permanently saturated conditions (Bradley and Gilvear,
2000).
Evaporation is frequently the most significant loss of water from a wetland as noted by
Tagaki et al. (1998). This is also supported by a summery of studies on hydrological functions
of wetlands (Bullock and Acreman, 2003). Among all the collated reference studies, there is
strong evidence that wetlands evaporate more water than other land types, such as grassland,
71
forests or arable land. Two third of studies conclude that wetland increase average annual
evaporation or reduce average annual river flow. From the data analysis in Kielstau, the river
runoff reveals similar feature influenced by higher evaporation from wetlands.
But investigation of the wetland evaporation rate in Kielstau suffers from a lack of reliable
measurements, as climatic data are not collected routinely from wetland areas, data from
nearby stations in nonwetland areas may not be representative of the wetland for the
computation of evaporation. In a review of evapotranspiration rates measured in Danish
wetland studies, it is found that wetland evaporation in Denmark is by a factor of 1.3 to 1.5
higher than grassland evaporation (Andersen, 2003).
Thus, within the framework provided the waterbudget investigation, we set a factor of 1.3
higher than the reference evaporation for wetland area, intending to model a realistic
hydrological wetland conditions in KIDS. Another consequential effect of water stagnation is
substantial water storage within wetlands. This results in no limitation of available water to
support the assumed higher potential evaporation transferring into actual evaporation.
All these lead to a transformation of conceptual model from the basic one to the wetland
model. The following three modifications are introduced to the wetland model:
1. Creating wetland map using soil data. Wetland is made of selected soil types
(mainly including HHHN/Peat, GG/Gley, SS/Pseudogley, and SSLL/Pseudogley
Parabraunerde).
2. Spatial distribution of different potential Evapotranspiration value according to the
wetland map, where the wetland has a 1.3 times higher ETp than nonwetland area.
The ETp of nonwetland area is calculated after Haude with land use cover of
grassland.
3. Evaporation from wetland is never limited by water, i.e. ETa =ETp. The needed
amount of water for evaporation is first subtracted from the water storage in soil, then
the subsurface fluxes that will otherwise discharge into the river runoff. This iterative
cycle will going on until the water balance in wetland (‘wet_balance’ in the model
script) is filled up to zero.
Eight wetland versions, from ‘Q_EtsV1_Wet’ to ‘Q_EtsV8_Wet’, with increasing wetland
fraction are tested in the modified wetland model. Table 5.6 presents the essential settings for
all created wetland versions: the composed soil types, the corresponding area fractions in
Upper Treene and Kielstau catchments. All wetland maps are attached in Appendix II.
72
Table 5.6 Definition of 8 wetland versions
Models Composed soil types Wetland Fraction
(Upper Treene)
Wetland Fraction
(Kielstau)
Basic / 0.00% 0.00%
Q_EtsV1_Wet 1_peat 9.94% 9.11%
Q_EtsV2_Wet 1_peat, 2_GGn 17.04% 9.11%
Q_EtsV3_Wet 1_peat, 2_GGn, 4_GG, 5_SS 26.30% 21.96%
Q_EtsV4_Wet 1_peat, 9_SSLL 27.68% 59.09%
Q_EtsV5_Wet 1_peat, 2_GGn, 9_SSLL 34.79% 59.09%
Q_EtsV6_Wet 1_peat, 5_SS, 9_SSLL 35.60% 77.22%
Q_EtsV7_Wet 1_peat, 2_GGn, 4_GG, 9_SSLL 36.13% 61.34%
Q_EtsV8_Wet 1_peat, 4_GG, 5_SS, 9_SSLL 36.95% 79.47%
With the implementation of wetland models, the simulated runoff has substantial changes
proven by the three coefficients shown in Figure 5.10. And different setting of wetland
fractions has great effect on the model performance. The statistical indexes of Q_EtsV1_Wet
and Q_EtsV2_Wet with small wetland fractions only improve in a small range. While the
fourth version, Q_EtsV4_Wet, which has about 28% wetland fraction, produces the best
simulation. As the wetland area increasing till 36.95%, a decline of the efficiency index is
observed. The optimal wetland fraction should be 28%.
Not only the total area of wetland plays a role in affecting model result, the location of
wetland as well. From the values of wetland fraction in Kielstau catchment (Table 5.6), some
wetland models have the identical wetland area in Kielstau, e.g. Q_EtsV1_Wet and
Q_EtsV2_Wet, Q_EtsV4_Wet and Q_EtsV5_Wet. This may be the reason that they show
quite similar coefficients in Figure 5.10. Because the soil type GGn is not in the Kielstau river
basin, to include it in wetland area or not won’t have influence on the discharge at Soltfeld.
73
Figure 5.10 Coefficients of different wetland models
The simulated discharge of model Q_EtsV4_Wet, which has the best NashSutcliffe index
among the eight wetland models, is plotted in Figure 5.11, comparing together with the
observed value and the basic model. It shows the integration of wetland fraction in the model
creates a better fit of the simulated and measured runoff for the Kielstau basin. During the low
flow periods, Q_EtsV4_Wet decreases the large amount of excess runoff which is the typical
systematic error created by Q_basic. Water is extracted by the additional wetland storage and
evaporated rapidly during summer and autumn seasons. Thus this function of wetland makes
the modified model represent the observed low runoff/precipitation characteristics in a better
way.
0.3
0
0.3
0.6
0.9
Basic
Q_EtsV1_Wet
Q_EtsV2_Wet
Q_EtsV3_Wet
Q_EtsV4_Wet
Q_EtsV5_Wet
Q_EtsV6_Wet
Q_EtsV7_Wet
Q_EtsV8_Wet
Coe
ffic
ient
s
a
NashSutcliffeRMSEr²
74
Figure 5.11 Hydrograph of wetland model, with wetland fraction of 28% according to
wetland map version 4
Q_EtsV4_Wet, NS = 0.625, RMSE = 0.293, r² = 0.714,
Sum predicted discharge = 1508 mm (Sum observed discharge = 1183 mm).
5.3.5 Parameter adjustment of Wetland models
The output of wetland models confirmed it is a reasonable way to improve the simulation
efficiency of KIDS model. But calibration cannot get underway until the model is performing
properly. The model results up to now still leave large space for further calibration. More
adjustments to parameters based on the wetland model are made and results are discussed in
the following sequence.
Adjustment of ETp factor for wetlands
We can see from the hydrograph of Q_EtsV4_Wet in Figure 5.11, the simulated runoff does
not follow the fluctuations of high flow and low flow quite well. This cannot be simply solved
by increasing or decreasing the runoff through the whole year, but probably by a time
differentiation setting of some hydrological inputs. In our wetland model, it could be done by
changing the ETp factor for wetlands.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.51J
an89
1Jul8
9
1Jan
90
1Jul9
0
1Jan
91
1Jul9
1
1Jan
92
1Jul9
2
1Jan
93
1Jul9
3
Disc
harg
e (m
³/s)
aObserved dataQ_basicQ_EtsV4_Wet
75
Eight different methods to reset the wetland ETp factor are tested in the model
Q_EtsV4_Wet, although all coefficients settings are not verified with the actual data. The
detailed methodology is listed in Table 5.7. Each ETp factor is defined by two elements, one
is the reference evaporation value, e.g., ETp rate after Haude with land cover of grassland,
namely ‘Haude_Gras’; the other is the monthly coefficients, ranging from 0.8 to 1.5. These
eight models with different ETp settings are run just for a sensitivity test.
Table 5.7 Different set of wetland ETp factor
Model Ref. ETp Monthly coefficients
Jan. Feb. Mar.Apr. May Jun. Jul. Aug.Sep. Oct. Nov.Dec.
Q_EtsV4_Wet Haude_Gras 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3
Q_EtL_sV4_Wet Haude_Laub 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3
Q_EtW_sV4_Wet Haude_WW 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3
Q_EtG1_sV4_Wet Haude_Gras 1.3 1.0 1.3 1.3 1.3 1.3 1.5 1.5 1.3 1.3 1.3 1.3
Q_EtG2_sV4_Wet Haude_Gras 1.3 1.3 1.3 1.5 1.5 1.5 1.3 1.3 1.3 1.3 1.3 1.3
Q_EtG3_sV4_Wet Haude_Gras 1.5 1.5 1.5 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3
Q_EtG4_sV4_Wet Haude_Gras 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.5 1.5 1.5
Q_EtG5_sV4_Wet Haude_Gras 0.8 0.8 0.8 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3
Q_EtG6_sV4_Wet Haude_Gras 1.3 1.3 1.3 1.3 1.3 1.3 0.8 0.8 0.8 1.3 1.3 1.3
The influence of those different settings for wetland ETp factor on NashSutcliffe, RMSE, r²
of the eight tested models is summarized in Figure 5.12. The accuracy of the wetland model is
not greatly improved by this approach. Some even make the results worse. Only
Q_EtG3_sV4_Wet has a slight improvement in the statistical indexes compared to
Q_EtsV4_Wet.
76
Figure 5.12 Test result of wetland models with different wetland ETp factors
Parameter calibration of wetland model
In the succession of parameter calibration of the basic model (discussed in Section 5.3.2),
similar procedure will continue for the wetland model with reference to the achieved progress
in Section 5.3.2.
Four parameters are chosen for the calibration in this part: ‘Inf_factor’, ‘gw_factor’,
‘lateral_factor’ and ‘soil_field_capacity’. Explanations and comments on predicted runoff of
each calibrated wetland model are summarized in Table 5.8 below, and the results are
elaborated by the coefficients of each model (shown in Figure 5.13) as well.
Table 5.8 Results of parameter calibration of wetland model
Model Name Description Comments on result
Q_EtsV4_W_I1 Wetland modelQ_EtsV4_Wet with‘Inf_factor’ = 0.1
It creates a lot of peaks in runoff because of higherproportion of overland flow, and it is not to beexpedted to have a lot of overland flow due to theflat relief. This soil infiltration rate is too low.
Q_EtsV4_W_I3 Wetland modelQ_EtsV4_Wet with‘Inf_factor’ = 0.3
The model has little sensitivity to this change. Theresult comes out with only two more peak pointson 17.10.1991 and 25.11.1992.
0.3
0.0
0.3
0.6
0.9
Q_EtsV
4_Wet
Q_EtL_
sV4_
Wet.
Q_EtW
_sV4_
Wet.
Q_EtG
1_sV
4_W
et.
Q_EtG
2_sV
4_W
et.
Q_EtG
3_sV
4_W
et.
Q_EtG
4_sV
4_Wet.
Q_EtG
5_sV
4_W
et.
Q_EtG
6_sV
4_W
et.
Different set of ETp for Wetland
Coe
ffici
ents
a
NashSutcliffeRMSEr²
77
Q_EtsV4_W_Ia Spatial distribution of‘inf_factor’, forwetland is 0.1, nonwetland 0.4.
It leads to more peaks similar as Q_EtsV4_W_I1.
Q_EtsV4_W_gw3 Wetland modelQ_EtsV4_Wet with‘gw_factor’ = 0.03
The effect of a lower groundwater discharge rate(compared to the standard set ‘gw_factor’ = 0.05)is to stretch the hydrograph in the horizontaldirection. The curve of predicted runoff becomesmore flat and the total amount of runoff doesn’tchange much.
Q_EtsV4_W_L3 Wetland modelQ_EtsV4_Wet with‘lateral_factor’ = 0.003
To add an additional lateral flow from soil waterstorage in this low range between 0.001 and 0.003is proven to be an efficient way to simulate thelow flows. This can be applied to the model forvalidation.
Q_EtsV4_W_sfc Spatial distribution of‘soil_field_capacity’,for wetland is 400mm,nonwetland 200mm.
It results in a small improvement of coefficientsand little change can be observed from itshydrograph.
Figure 5.13 Statistical coefficients of calibrated wetland models
0.3
0.0
0.3
0.6
0.9
Q_EtsV
4_Wet
Q_EtsV4_w_I1.
Q_EtsV4_w_I3.
Q_EtsV4_w_Ia.
Q_EtsV4_w_gw3.
Q_EtsV4_w_L3.
Q_EtsV4_w_sfc.
Calibration of Q_EtsV4_Wet
Coe
ffici
ents
a
NashSutcliffeRMSEr²
78
Combination of two different wetland maps in wetland model
Wetlands are typically small heterogeneous landscape features, characteristically wetter than
the surrounding land (Gavin and Agnew, 2003). In the calibration of wetland models, we
created eight wetland maps with various wetland fractions. The application of high
evaporation for wetland, which consists of two steps in the model, based upon the same
wetland map. In practice, wetlands often comprise a complex mosaic of wet and dry patches
and, the composition could vary with the change of weather or seasons. For such
inhomogeneous sites, it is not necessary to apply the modification to the identical wetland
area all through the model running.
We choose any two from the eight wetland maps and apply the two modification steps to the
selected two different wetland maps separately. For example, model Q_EtsV7_Wet3 means:
spatial distribution of ETp is corresponding to the wetland map No. 7 (where the wetland has
a 1.3 times higher ETp than nonwetland area) and evaporation from wetland area defined by
the wetland map No. 3 is not limited by water, i.e. ETa =ETp.
Twelve Combinations of two different wetland maps are tried in the wetland model. Among
them, Q_EtsV7_Wet3 produces the best simulation, with NashSutcliffe index 0.671. The
predicted runoff of Q_EtsV7_Wet3 is shown in Figure 5.14. Comparing to Q_EtsV4_Wet, it
reduces the extra runoff efficiently during the low flow seasons, and has same good
performance in peak flows.
79
Figure 5.14 Hydrograph of combined wetland model
Q_EtsV7_Wet3, NS = 0.671, RMSE = 0.275, r² = 0.722,
Sum predicted discharge = 1308 mm (Sum observed discharge = 1183 mm).
However the values of low flows modelled by Q_EtsV7_Wet3 are too low to reflect the
variations of the observed runoff. In some periods, it even decreases to zero. From the
previous experiences of parameter calibration for wetland model, an additional lateral flow
has good function in avoiding fast decrease of runoff during the low flows.
We tested the model Q_EtsV7_Wet3 with different setting of parameter ‘lateral_factor’,
ranging 0.001~0.003. The one with ‘lateral_factor’ = 0.003 can produce the closest values of
low flows to the measure runoff, as shown in Figure 5.15.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1Jan
89
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9
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1Jan9
3
1Jul9
3
Dis
char
ge (m
³/s)
a
Observed dataQ_EtsV4_WetQ_EtsV7_Wet3
80
Figure 5.15 Hydrograph of combined wetland model with additional lateral flow
Q_EtsV7_Wet3_L3, NS = 0.677, RMSE = 0.272, r² = 0.728,
Sum predicted discharge = 1390 mm (Sum observed discharge = 1183 mm).
In the Section 5.3, the whole calibration procedure of the KIDS model demonstrates that the
model integrated with wetland function can reproduce the observations reasonably. Judging
by the evaluation criteria, among those wetland models after some parameter adjustment, both
Q_EtsV7_Wet3 and Q_EtsV7_Wet3_L3 models are considered to perform properly and will
be used for model validation.
5.4 Validation
Model validation is desirable following completion of the calibration step. For the purpose of
validation, the calibrated forms of the wetland model, Q_EtsV7_Wet3 and
Q_EtsV7_Wet3_L3, are applied to the selected period from January 1994 to December 1999.
Note that the data related to validation were not used earlier for the calibration. Figure 5.16
presents the results of model validation.
As can be seen from the plots of observed and simulated hydrographs, both models generally
predict the overall shape of hydrograph. The peak runoff, time to peak and total volume of
0
0.5
1
1.5
2
2.5
3
3.5
4
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1Jan89
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9
1Jan
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1Jan9
1
1Jul9
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1Jan92
1Jul9
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1Jan93
1Jul9
3
Dis
char
ge (m
³/s)
aObserved dataQ_EtsV7_Wet3Q_EtsV7_Wet3_L3
81
runoff computed from the models compare reasonably well with the observed values. But
each has its own advantages over the other. The NashSutcliffe index of Q_EtsV7_Wet3
reaches 0.735, other two coefficients RMSE and r² are better than those of
Q_EtsV7_Wet3_L3 as well. The sum predicted discharge 1746 mm is closer to the total
observed discharge 1667 mm. However, the low flows prediction of model Q_EtsV7_Wet3 is
less accurate as compared to that of model Q_EtsV7_Wet3_L3. To decide which model is
better depends on the selected criterion.
Figure 5.16 Validated hydrograph of combined wetland model
Q_EtsV7_Wet3: NS=0.735, RMSE=0.336, r²=0.748,
Sum predicted discharge=1746 mm.
Validated hydrograph of combined wetland model with additional lateral flow
Q_EtsV7_Wet3_L3: NS=0.726, RMSE=0.341, r²=0.741,
Sum predicted discharge=1846 mm.
(Sum observed discharge = 1667 mm).
This model validation is operated for discharge simulation in an additional time period not
used in the calibration. Moreover, the chosen period for validation includes the extremely dry
year from 1995 to 1997. The validation result showed the calibrated model can reasonably
reproduce the actual data, the KIDS model is therefore considered fully suitable for
0
0.5
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4
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5
1Jan94
1Jul9
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95
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1Jul9
6
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1Jul9
7
1Jan98
1Jul9
8
Dis
char
ge (m
³/s)
a
Observed dataQ_EtsV7_Wet3Q_EtsV7_Wet3_L3
82
application to discharge simulation in other periods for Kielstau catchment. For an overview
of the KIDS model scripts see Appendix III.
5.5 Discussion
After several steps of calibration and test of model validation, the proposed KIDS model
written in PCRaster is proven to be capable of predicting the general hydrological patterns in
Kielstau catchment.
Table 5.9 gives a result overview of the main conceptual models from the calibration and
validation. As the first attempt to calibrate the basic model, several modifications are tested to
improve the model performance, like the spatial distribution of surface infiltration rate, water
storage capacity of soils, different evapotranspiration value according to the land use. With
these efforts, the model outcome (indicated by the NashSutcliffe index) is only enhanced in a
small range, and the seasonal mismatch of the runoff/precipitation ratio cannot be solved. The
NS index of all the basic models is below zero.
With a model structural adaptation to the wetland version, the performance of KIDS model is
enhanced significantly, which can be proven from both the fluctuations of high or lowflows
in the hydrograph, and the goodnessoffit coefficients. The result of validation also shows
that the calculated discharge fluctuations from the wetland model can represent the observed
discharge over longer periods reasonably well.
Table 5.9 Result overview of calibration and validation
Calibration (89~93)
NS RMSE r² Sum predicted
runoff (mm)
Sum observed runoff Q_basic 0.228 0.545 0.573 2248
1183 mm Q_EtsV4_Wet 0.625 0.293 0.714 1508
Q_EtsV7_Wet3 0.671 0.275 0.721 1308
Q_EtsV7_Wet3_L3 0.677 0.272 0.728 1390
Validation (94~99)
Sum observed runoff Q_EtsV7_Wet3 0.735 0.336 0.748 1746
1667 mm Q_EtsV7_Wet3_L3 0.726 0.341 0.741 1846
83
In the view of the wetland assumption and the algorithm implemented in the KIDS wetland
model, there are some aspects need to be concerned.
5.5.1 Wetland fraction in Kielstau catchment
In the Kielstau Catchment, although a large area of peatland and wet surface is observed
(Trepel 2004), there is no accurate mapping data of wetlands for this region. This is a crucial
question concerning the wetland model calibration. In this study, it is solved by creating a
series of wetland map versions based upon the available soil information. Among those
wetland models with different definition of wetland area, Q_EtsV4_Wet, which has about
28% wetland fraction in the Upper Treene catchment, produces the best simulation. In order
to find out the corresponding wetland fraction in our core investigation area – Kielstau
catchment, we calculated the area distributions of all soil types for both Treene and Kielstau
region. Table 5.6 has presented the calculation results for all versions of wetland map. For the
model Q_EtsV4_Wet, it is assumed that the wetland fraction in Kielstau catchment is 59.09%,
which is much larger that the estimated fraction of 30%.
This overestimated wetland fraction originates from the soil data. From Table 5.10, we could
see that one of the main ‘wetland’ soil type ‘9_SSLL’ covers almost a half area (49.98%) of
Kielstau catchment. It is no doubt that the wetland model will be implemented with large
fraction of wetland if this soil type is classified as wetland.
To create wetland map using the present soil data is a fuzzy approach to construct the wetland
model. More precise information about extent and location of the wetlands is needed to get a
better estimation of the wetland in the catchment.
Table 5.10 Comparison of wetland soils in Treene and Kielstau
Legend code Legend_Treene map Legend_Kielstau map Area in Kielstau
1 HH / HN Hoch/Niedermoor Niedermoor 9.11%
2 GGn Normgley / /
3 gPP vergleyter Podsol / /
4 GG Gley GleyKolluv 2.25%
5 SS Pseudogley PseudBraun 7.53%
GleyPseudo 10.60%
6 BB Braunerde / /
84
7 PP Podsol / /
8 LLn Normparabraunerde / /
9 SSLL
Pseudogley
Parabraunerde Pseudogley 49.98%
10 MOn Normorganomarsch / /
11 Rohsubstrate Rohsubstrate / /
5.5.2 Combination of two different wetland maps
The better simulation computed by the wetland model, like Q_EtsV7_Wet3, with the
combination of two different wetland maps, indicates another estimation of the location and
characteristics of wetland in Kielstau catchment.
By checking the wetland maps in Appendix II, Version 3 and Version 7 share a common
wetland area about 11.36% in Kielstau, the rest wetlands are located at different sites. The
methodology of applying the assumed algorithm to different wetland maps in the model
implies that wetland is not a uniform area, and its properties vary with space and time, which
is the typical dynamic characteristic of natural system and makes the researches on it
challengeable.
5.5.3 Limitations in the simulation of KIDS model
Although the KIDS model can reproduce the observed runoff patterns reasonably well and the
goodnessoffit coefficients (e.g. NashSutcliffe index = 0.735) of the model validation is
acceptable, there is still lot of space left for further improvement.
The present prediction exhibits some weakness in reaching the peak flows. The simulation of
the runoff variations is too rough and the discharge decrease from peak flows to low flows is
always faster than the observed system. It is possible to try more modifications, but further
calibration of KIDS model is primarily limited by the amount and quality of the available
data, in relation to the required data accuracy for a more efficient model. Additional
limitations are the effectiveness of the applied technique and the availability of time. Those
are the most usual problems in model calibrations. As a consequence, to get the closest
simulation is not the only objective of the calibration, it should provide information on the
modelling uniqueness and uncertainties left in the model, which will be adequately accounted
for in subsequent model applications. This is exactly what can be achieved from this study.
85
5.5.4 The hydrological features of wetlands in the study area and the application in
different models – KIDS, SIMPEL, SWAT
It is widely accepted that wetlands have a significant influence on the hydrological cycle.
However, what hydrological functions and to which extent wetlands are performing in the
water cycle is a rather complicate question. Emphasizing on the water quantity functions of
wetlands, there are many examples where wetlands reduce floods, recharge groundwater or
augment low flows. Less recognised are some cases where wetlands increase floods, act as a
barrier to recharge, or reduce low flows. And there are various types of wetlands influenced
by topography, climate, groundwater, connectivity with stream flow, etc. Some studies reveal
strong concurrence of some hydrological features for certain wetland types, some other show
diversity of functions for apparently similar wetlands (Bullock and Acreman, 2003).
Therefore, to ensure a reliable conclusion on wetland functions in an individual area, it is
essential to make investigation in the field periodically.
As research in the Kielstau Catchment has just started, there is not much data support for
verification. It is thus difficult to make definitive statements regarding the role of wetlands in
water storage, groundwater recharge or discharge, runoff production, etc.
In this study, the likely function of wetlands is identified through an assessment of measured
data. A seasonal distribution of the relation between runoff and precipitation is observed and
the runoff / precipitation ratio is relatively lower than that of the neighbouring area. This
special hydrological feature cannot be reproduced properly by the basic model approach.
Then an additional factor ‘wetland’ was introduced to the KIDS model accordingly, with one
conspicuous hydrological function – larger amount of water loss though evaporation from
wetland than from nonwetland portions of the catchment. This modification gives the most
significant enhancement of the KIDS model.
The discharge simulation process described in this study confirms that wetlands play an
important role in the water cycle of Kielstau catchment. This view is also strengthened by
some other studies, as PCRaster is not the only modelling language that is used to construct
hydrological models for Kielstau catchment. The other two methods are SIMPLE and SWAT.
SIMPEL (Hörmann, 1997) is a onedimensional soil water model system programmed with
Excelworksheets. It can be used to calculate the water budgets of ecosystems (evaporation,
seepage, soil water content of a soil column, etc.) and surface runoff for small catchments.
The SIMPEL family contains a series of model versions for the simulation of the water
86
budget in Kielstau. Among them the wetland version can produce a satisfying simulation with
seasonal characteristics similar to the observed data.
SWAT (Soil and Water Assessment Tool) is a physically based, continuous time model that
can be used under a widerange of different environmental conditions (Arnold et al., 1993,
1998; Arnold and Fohrer, 2005). It is thus widely applied in many countries all over the
world. In 2004, a study to calculate the water balance in the Upper Treene catchment using
SWAT was conducted by Dey (2004). The runoff simulations were modelled for 8 official
gauge stations in the basin, including the outlet point of the Kielstau catchment – Soltfeld.
Another research application with SWAT on the Kielstau Catchment is currently under the
investigation of Tavares (2006, in preparation). This study focuses on both stream flow
simulation and instream chemistry assessment. We take the quantitative simulation result to
compare with that of SIMPEL and KIDS models.
Despite the difference in the nature of these three modelling languages, they share something
in common to improve the model performance: a structural adaptation of wetland. The
algorithm implemented in SIMPLE and KIDS is quite similar: higher evaporation rate from
wetland area and unlimited water supply for that. While the approach of SWAT is slightly
different: special types of water bodies (like wetlands, ponds, reservoirs) with greater soil
water storage capacity, and additional artificial subsurface drainage are introduced into the
model, since those parts are embedded prototypes in SWAT. Table 5.11 gives an overview of
the simulation results from different models. Since the results are recalculated for the
validation period from 1994 to 1999, the values are different from those cited in the
references.
Table 5.11 Result comparison of SIMPEL, SWAT, KIDS
(for time period 1994~1999, sum observed runoff = 1667 mm)
Model type NashSutcliffe RMSE r² Sum predicted
runoff (mm)
Simpel Wet 0.661 0.380 0.666 1484
SWAT 0.643 0.390 0.670 1968
KIDS Wet 0.735 0.336 0.748 1746
All these studies support the notion that wetland is an important factor in hydrological
research and water management in the Kielstau catchment.
87
Chapter 6 Conclusion
This study demonstrates the development of a DEMbased process oriented distributed
rainfallrunoff model with a spatial modelling language PCRaster for a wetland dominated
catchment – Kielstau catchment – in Northern Germany.
The proposed KIDS model (Kielstau Discharge Simulation model) is designed and applied for
the discharge simulation of the outlet point located at the Soltfeld gauge station in Kielstau
catchment. The physically based KIDS model is capable of handling catchment heterogeneity
in term of distributed information of landuse, slope, soil and rainfall. It was built using the
data set of the Upper Treene catchment and reported results for the selected outlet point in
Kielstau catchment.
The paper describes the study area, introduces model and methodology, presents the
calibration process and model validation, and discusses the model results relative to the
special hydrological characteristics in this wetland dominated catchment.
The application of the KIDS model to simulate the runoff of Kielstau river yielded the
following conclusions or recommendations:
• Among the series of conceptual models tested during the calibration process, only
the wetland version can reproduce similar runoff with the observed data, specified by the
seasonal differentiated relationship between precipitation and runoff values. From a
hydrological point of view, the study clearly showed that the application on individual
catchments with individual physical characteristics always needed adjustments in the most
critical parts of the water cycle. Kielstau catchment is observed to be a very flat lowlying
region, with dominating peatland and wet surfaces, widely distributed near surface
groundwater layer. These characteristics exert a strong influence on the hydrological cycle in
the river basin. It is essential to take this into account for the modelling success.
• The KIDS model has well simulated the hydrographs of runoff at the outlet of
Kielstau catchment. From the validation result, NashSutcliffe index reaches 0.735, RMSE
0.336 and r² 0.748, which is reasonable well for general modelling predictions. The total
predicted runoff volume of 1746 mm for the years 1994~1999 is very close to the observed
data 1667 mm. Some features like the hydrograph shape, values of peak and low flows, time
to peak are well demonstrated by the model results. In this context, this GISbased
hydrological model written in PCRaster could be viewed as an efficient alternative to simulate
runoff in Kielstau.
88
• The calibration process and the simulation results indicate that wetland has a high
impact on the water balance in the catchment. The wellperforming wetland model is
modified on the basic model with additional evapotranspiration of about 30% from wetlands,
which leads to a decrease in river discharge during the summer and autumn seasons. From the
modelling experience, it is suggested that evapotranspiration is a fundamental and here a
major component of the hydrological cycle influenced by wetlands. However, this is deduced
from the calibration and validation of the model, which is only based on the comparison of
observed and computed runoffs at the outlet of the catchment. A complete validation for the
wetland functions is still not achievable due to the absence of relative verification data. And
the river basin management in such a wetland dominated catchment requires knowledge of
what hydrological functions and to which extent wetlands are performing in the water cycle,
which needs sufficiently extensive measured data. Modelling is an important accompaniment
to measurement, but is no substitute for it. Conclusions of wetland functions in the study area
cannot only be based on the assumptions and modelling test alone, it must be supported by
hydrological data. Nevertheless, the success of the KIDS model modified with additional
wetland function strengthens the view that wetlands must be an important part of integrated
water resources management in the Kielstau river basin.
• The present runoff prediction of the KIDS model still exhibits weaknesses in specific
components. To make further calibration will severely increase the demand of the required
data for the model and the effectiveness of employed techniques. However, no matter how
sufficient data support exists and how complex or sophisticated the models are, all hydrologic
models are approximations of reality, so the output of the actual system can hardly be forecast
with certainty. With this regard, models are more valuable to give us a framework to assemble
our process understanding and to explore the system behaviours implied by our understanding
of natural systems. The application of KIDS model demonstrates noticeable and valuable
information on the likely function of wetland through the water cycle simulation, and will
provide analysis aids for further research in the Kielstau catchment.
89
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8. Appendixes
Appendix I
Kinematic wave function with Manning's equation(Source: http://pcraster.geog.uu.nl/runoff/, access date 02/04/06)
The Manning's equation is
R hydraulic radius (m)S friction slope (m/m)n manning's roughness coefficient ()V flow velocity (m/s)
Manning's equation is valid for SI units, with R in meters and V in meters per second, S isdimensionless.
Water routing over local drain direction network according to the kinematic wave with theManning's equation.
The main part of the description below is taken from Chow et al, 1988. The kinematic wavemodel assumes negligible accelaration and pressure terms in the momentum equation, and thewave motion is described by the equation of continuity. In practice, this means that thekinematic wave can only be used with relatively steep slopes of drainage, where the slope ofthe water surface can be assumed to be equal to the slope of the water bottom surface.
The kinematic wave model is defined by the following two equations. Units given are unitsused by the 'kinematic' function of PCRaster.
Equation of continuity:
(1)
Q Flow (m3/s)x distance in flow direction (m)A cross sectional area of flow (m2)t time (s)q lateral inflow ((m3/s)/m)
Equation of momentum:
(2)S0 gravity force termSf friction force term
The momentum equation can also be expressed in the form:
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(3)
By combining equation (3) and Manning's equation written with S0=Sf and R=A/P we get:
(4)
P wetted perimeter (m)
and in this case:
(5)
(6)The variables q, 'alpha' and 'beta' are input to the 'kinematic' function of Pcrcalc.
Example scriptThe script simulates all phases of a rainstorm. The bottom width of the channel is given byBw may be different for each cell. Notes to numbered statements in the script:(1) The length of a cell is used in several lines of the script. It is calculated here once forincreasing execution speed.
(3) Generates distance to downstream cell for use in kinematic function and for calculatinglateral inflow per metre along the stream. The function 'downstreamdist' assigns a 'zero' to apit cell which is unrealistic for routing purposes. These 'zero' values are replaced with thecelllength value by the 'max' statement.
(4) Statement (7) divides by the root of Slope, so Slope may not be zero. The function 'max'sets it to a minimum of a very low value (0.001).
(5) and (6) Assumption of no flow at the start of the model run. The binding sets these to avery very low value, representing no flow. Setting to a value of zero instead, may result inproblems with the kinematic wave function.
(7) and (8) Calculate terms for Alpha only once (increases execution speed).
(9) Calculates Alpha according to equation (5) in the 'equations' section above.
(10) Lateral inflow is the netto result of rain, interception, surface storage and infiltration.May be negative (netto outflow, eg, in case infiltration is greater than netto rain), zero, orpositive.
(11) The function 'kinematic' needs lateral inflow per distance along the stream.
(12) The input map Q is the result of this statement from the previous timestep (i.e., Q_old).See 'kinematic' in PCRaster manual (PCRaster 2006) for further details.
(13) Calculates water depth (m) and dividing by bottom width of channels.
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Appendix II
Different versions of wetland map with its soil components
Version 1: 1_peat Version 2: 1_peat (2_GGn)
Version 3: 1_peat Version 4: 1_peat (2_GGn) 9_SSLL 4_GG 5_SS
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Version 5: 1_peat Version 6: 1_peat (2_GGn) 5_SS 4_GG 9_SSLL 9_SSLL
Version 7: 1_peat Version 8: 1_peat (2_GGn) 4_GG 4_GG 5_SS 9_SSLL 9_SSLL
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Appendix III
The script of KIDS model
## Kielstau Discharge Simulation Model, integrated with wetland function## written by X. Zhang, Kiel University, Germany## Date: June 2006## modified based on model_basic.mod and model_wetland_all.mod
bindingPTSS = precipit.tss;pzones = zonep.map;ETSS = evapo_soil_G1o3.tss; # evaporation from wetland, 1.3*Haude_Grasezones = zonee_soil_v7.map;outlet = dispoints.map; # map with 8 runoff sampling locationsslope = slope.map; # developed from dgm.map using slope operatorwetland = wetland_v3.map;
#kinematic function Beta=scalar(0.6); LDD=ldd.map; N=Manning.map; # Manning's N; ChannelN=channelM.map; # channel's Manning value ChannelWidth=channelw.map; T=scalar(86400);
areamapclone.map;
timer1 5479 1;reportdefault= 365 + 365 ..endtime;
initialswc = scalar(0.001);intercp_max = scalar(1.0); # interception capacity constant at 1mmsoil_field_capacity = scalar(200.0); # water content (mm) at field capacitysoil_start_reduct = scalar(100.0); # water content where Eta < ETpinf_factor = 0.4; # infiltration percentage of soil water deficitgw_content = scalar(0.001);soil_gw_flux = 0.1; # water seepage from soil to groundwatergw_factor = 0.05; # groundwater outflow as base flowlateral_factor = 0.003; # ratio of lateral flow from soil_water_contentQ_sum = scalar(0.001);day = 0;
wet_balance = scalar(0.001);wet = scalar(0.001);
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# kinematic function CL=celllength(); CA=cellarea(); DCL=max(downstreamdist(LDD),CL); Q=scalar(0.00000001); H=scalar(0.00000001); # initial water height(m) slope = if(slope eq 0, 0.000001, slope); # cover the 0 value in slope.map with 0.000001
# term for Alpha AlphaFact=(ChannelN)/(sqrt(slope))**Beta; AlphaPower=(2/3)*Beta; #Power for Alpha; WH=0.0000001; FlowWidth= ChannelWidth; Q=0.0001;
dynamic
Pr2 = timeinputscalar(PTSS, pzones);Ev2 = timeinputscalar(ETSS, ezones);
day = if (day >365 then 0 else day + 1);
intercp_content = min(Pr2, intercp_max); # possible interceptionintercp = min(Ev2, intercp_content); # water loss (evaporation) from interception
rest_et = Ev2 intercp;rest_precipit = Pr2 intercp; # water input to soil before infiltration
max_infiltration = (soil_field_capacity swc)*inf_factor; # infiltrated waterrunoff_surface = max(rest_precipit max_infiltration, 0); # rapid surface runoff
rest_precipit = rest_precipit runoff_surface;
swc = swc + rest_precipit; # soil water contentETa = if (swc > soil_start_reduct then rest_et else rest_et*(swc/soil_start_reduct)); #actual evapotranpirationswc = swc min(ETa, swc);
# part wetlandETa = if(wetland == 1 then rest_et else if(swc > soil_start_reduct then rest_et elserest_et*(swc/soil_start_reduct)));temp1 = min(ETa,swc); # negativ if wetland evaporation higher than soil waterwet_balance = if(wetland == 1 then (wet_balance (ETatemp1)) else wet_balance) ;swc = swctemp1;
lateral_flow = swc*lateral_factor;swc = swc lateral_flow;
overflow = max(swcsoil_field_capacity,0); # ground water fluxgw_flux = if(swc>soil_start_reduct then swc*soil_gw_flux else 0);
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gw = overflow + gw_flux;swc = swc gw;
gw_content = gw_content + gw;gw_outflow = gw_content * gw_factor;gw_content = gw_content gw_outflow;
Runoff = runoff_surface + lateral_flow + gw_outflow; # runoff composed by three parts
# fill up wetland deficit with runoff temp2 = if (wet_balance<0 then min(wet_balance,Runoff) else 0) ; Runoff = Runofftemp2; wet_balance = wet_balance + temp2 ; #report wet = wet_balance ;
SumR=Runoff/1000; #to m^3WH=WH+SumR; # water height
################################# runoff calculation using one of kimematicALPHA = AlphaFact*((FlowWidth+2*WH)**AlphaPower);QIn=SumR*CA/T/DCL;
Q = kinematic(LDD, Q, QIn, ALPHA, Beta, T,DCL);
V=Q/CA; # flow velocity(m3/s), CA is cell areaWH=if(FlowWidth >0.001, 1000*(ALPHA*(Q**Beta))/FlowWidth,0); #wh in mm unit################################
Q_sum = Q_sum + Q;report Q_EtsV7_wP3_L3.tss=timeoutput(outlet,Q);
