Integrated Modelling, Scenario Assessment & Decision...

Integrated Modelling, Scenario Assessment & Decision Support

Final Project Workshop INTECRAL, March 2017

University of Jena Annika KünneSven KralischJuliana Santos

codematix GmbH Jena Carsten BuschChristian Schwartze

• Pressures on River Basins– Overexploitation & pollution

(population growth, deforestation, intenseagricultue)

– Climate Change(increasing dry periods, more extreme events)

• Impacts– Degradation of land and water resources

(e.g. soil, wetlands) and hydrologicalecosystem services

– Floods, droughts, water scarcity, waterpollution, soil erosion, landslides, …

Challenges

Final Project Workshop INTECRAL, March 2017 2

…to provide knowledge-based support for integrated land and water resources management

Overall Objectives

1. Understand processes and their interactions

2. Create computer models of integrated eco-hydrological systems

3. Evaluate scenarios and assess their impacts using computer models



From Environmental Observations and Measurements…

4

…to Abstraction and Generalization…



…to a Conceptual Model…

Driving data: P, T, …

ET

Interception Snow

Soilwater

Groundwater

Upper zone

Lower zone

Additional data: Radiation …

Runoff

Surface RO

Interflow 1

Interflow 2

Baseflow

Infiltration DPS

MPS LPS

MPS LPS

MPS LPS

HRU 1

HRU 4

HRU 2 HRU 3

HRU 5

HRU 6

Reach 1 Reach 3

Reach 2

SVNQ

Study Area

Guapi-Macacu river basin: 1265 km² Dois Rios river basin: 3120 km²Muriaé river basin: 8165 km²



Spatial Data Time Series Data

Eco-hydrological System Analysis

Example Results: Remote Sensing

Land Use

Classification

Muriaé River Basin


Example Results: Hydrological Modelling Rio Dois Rios

Efficiency CriteriaR²: 0.84 , Nash Sutcliffe: 0.65, logNash Sutcliffe: 0.81, PBIAS: 21.58

0

20

40

60

80

100

120

1400

200

400

600

800

1000

1200

1400

12/25/2003 5/8/2005 9/20/2006 2/2/2008 6/16/2009

Pre

cip

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[m

m]

Ru

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m³/

s]

Precipitation Observed J2K / PM


Example Results: Modelling

Simulated base flow, interflow and surfacerunoff [l/m²] withinthe Dois Rios Basin(2006 – 2013)


Example Results: Modelling (2)

Simulated surfacerunoff [l/m²] withinthe Dois Rios Basin(2006 – 2013)

Efficiency criteria:NS: 0.81logNS: 0.85R²: 0.82PBIAS: -4.49


Example Results: Climate Scenarios

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5

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10/10/2006 7/6/2009 4/1/2012 12/27/2014 9/22/2017 6/18/2020 3/15/2023

Simulated Runoff

Past FutureToday

?


Climate Input Data for Modelling from Cordex

EC-Earth RCP 4.5 vs. EC-Earth RCP 8.5

Example Results: Hydrological Modelling of Climate Scenarios for Rio Dois Rios

RCP 4.5: ΔT = 1.8°C

RCP 8.5: ΔT = 3.1°C


15Final Project Workshop INTECRAL, March

2017

EC-Earth Precipitation1976 - 2005

EC-Earth RCP 8.52011 - 2040

EC-Earth RCP 8.52041 - 2070

EC-Earth RCP 8.52071 - 2100

DJF

MAM

JJA

SON

16Final Project Workshop INTECRAL, March

2017

EC-Earth Total Runoff1976 - 2005

EC-Earth RCP 8.52011 - 2040

EC-Earth RCP 8.52041 - 2070

EC-Earth RCP 8.52071 - 2100

DJF

MAM

JJA

SON


Main Differences – Runoff from 1975 until 2100

Dry season –becomes dryer


Main Differences – Runoff from 1975 until 2100

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Historic Runoff 1976 - 2005 Future Trend 2071 - 2100

High Flows and Floods from 1975 until 2100

1975 - 2005 2011 – 2040 2041 – 2070 2071 - 2100

Small FloodsLarge Floods

More floods during wet season


Irrigation demand = soil water deficit

Options: 1. Drip irrigation

2. Flood irrigation

3. Sprinkler irrigation

Where: Barracão dos Mendes

Scenario: Irrigation Simulation

(Pictures © Wikimedia Commons)


Increasing environmental data availability, growing computer hardware performance and improving eco-hydrological process understanding

… generate new challenges:

• Information management

• Model/data integration

• Making data and processingworkflows easily usable

• Communication of results

Technical Challenges


Technical Solution

Integrated Land Management

System

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• Eco-hydrological modelling• need for land-use and land-cover information (model parametrization)

expert knowledge: in hydrological modelling and remote sensing

• On-demand, browser- and wizard-driven, workflow-based• HRUweb: delineation of modelling entities

• webIMX: remote sensing image classification

Data Processing for Eco-Hydrological Modelling

parameter / maps

…parameter / maps

HRUs

landuse classification

…

central workflow management


Putting it all together…

webIMX

e.g.:

• densitynormalizationmode

• minimum cellsize

• training sites

• minimumobject size

• …

HRUweb

e.g.:

• physiographicdata inputs• DEM• soils• landuse

• reclass rules

• minimumsubbasins size

• minimum HRU size

• …

webIMXworkflow = Taverna Models

HydrologicalModel

(e.g. Jams/J2000)

e.g.:

• modelparameters

• HRUs

• …

…

…

HRUwebworkflow = Taverna Models


Technical SolutionG

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System

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• data inputs – via upload or Google Drive / Dropbox

• data processing with ILMSimage(server-side image processing library)

– image preparation

– image segmentation

– automatic classification

– training (class references)

– supervised classification

– data export

webIMX – what it does…


• intermediate data and results• available for download in

result browser

• classification results (GeoTiff/Shape)

• segmentation results / cells

• optional vector smoothing

webIMX Results

Landcover map based on Landsat images 2011Final Project Workshop INTECRAL, March 2017 27

Technical SolutionR

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• data inputs – via upload or individual data

catalog

• data processing with open-source GRASS-GIS– data preparation

– watershed basin analysis

– data reclassification

– GIS overlay analysis and generalization

– subbasin-by-subbasin approaches

– flow routing based topologies (HRUs/reaches)

– collecting statistics (per HRU)

– data provision using established GIS formats

HRUweb – what it does…


HRUweb – wizard steps and maps (selection)


• intermediate data• available at

any time via result browser

• particularlyraster maps, shapefiles

• final GIS dataset containing• HRU geodataset

• HRU parameter table

• stream network

• reach routing information

• subbasins / watersheds

HRUweb Results


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System


Data Management


• INTECRAL River Basin Information System (RBIS)

– Data management, sharing, analysis and visualization

– Web-based, open-source

– Full read/write access to all data

– Standard interfaces to external apps

• Data collected

– 200+ time-series of climate, rain, water quality & quantity

– 80+ geospatial datasets

– …

http://intecral.uni-jena.de/rbis

http://intecral.uni-jena.de/rbis


INTECRAL-RBIS Data Types

Time series• measured and simulated

time series

• data source description

Time series• measured and simulated

time series

• data source description

Geodata• interactive maps• data exploration• web-map services

Geodata• interactive maps• data exploration• web-map services

Observations• Study sites• Field measurements

• Soil• Vegetation

Observations• Study sites• Field measurements

• Soil• Vegetation

Metadata based on ISO standardsMetadata based on ISO standards

Documents• text documents• pictures/movies• raw/binary files

Documents• text documents• pictures/movies• raw/binary files

Technical SolutionR

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Library Model

JAMS Modelling System

Design

Prepare data

Parameterize

Run

Calibrate

Analyze

Design

Prepare data

Parameterize

Run

Calibrate

Analyze

Design

Prepare data

Parameterize

Run

Calibrate


• Automated modelling workflow

1. Build model locally

2. Upload model data to server

3. Start simulation on server

4. Download results from server

Web-based Modelling

WWW


Summary

• Collected data and developed models allow forhydrological modelling and scenario impactassessment in the whole state ofRio de Janeiro

• Web-based tools ensure easy andflexible processing of requiredinput data

• Models can be adaptedto future requirements


Training course– Hydrological

Modelling withJAMS / J2KJune 2015,

Nova Friburgo

Training course –Geo-Hydrological Tools for River Basin Management, November 2016, Niterói

Upcoming course: Next week (March 20th - 24th) in Niterói


Funded by



Ritter, Michael E. The Physical Environment: an Introduction to Physical Geography.20160912. http://www.earthonlinemedia.com/ebooks/tpe_3e/title_page.html

• Initial model setup Rio Dois Rios (2006 –2013)

Modeling Results Dois Rios (preliminary)

42

R² Nash-Sutcliffe Log Nash-Sutcliffe Pbias

0.76 0.56 0.55 9.74


Spatial Modeling Results Dois Rios

43

Simulated spatialbase flow, interflowand surface runoff[l/m²] within theDois Rios watershedat station Dois Rios from 2006 – 2013.


44Final Project Workshop INTECRAL, March 2017

45Final Project Workshop INTECRAL, March 2017

Environmental Modelling

• Data preprocessing

– Data analysis (proof of homogeneity)

– Data correction

– Trend detection

46

Mann-Kenndall Test for Station Campos (mean annualtemperature andrelative humidity1961 – 2013).

Data Acquisition

Data Management

ModellingIDSS Development


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