Integration of process oriented
hydrologic and water quality models
balancing between complexity and practicability
Piet Groenendijk
Overview
Background
Why are models coupled?
Some examples
Recent developments with respect to model
coupling
Experiences from EuroHarp
Lessons learned
Why are models coupled?
Accurate representation of interactions between
different domains
Integrated assessments on water allocation,
environmetal impacts, strategies to adapt to
climate change, etc etc
“co-production of knowledge”
Processes of planning and decision making no longer
hierarchical but the product of complex interactions
between governmental and non-govermental
organisations and the general public
Why are models coupled?
Uncoupled
Iteratively
coupled
Coupled simultaneous
solution
Modules in Nationwide groundwater model
(NHI)
Rainfall
ETref
Soil
Basin or catchment
Sections of-water
distribition model
Land use
Irrigation
Main surface
waters
MetaSWAP in a nutshell
Pn
Ebs
T
Discretization
root zone
subsoil
Root zone
Flow dynamics
instantaneous spreading
of precipitation in root
zone
sequence of steadystate flow situations in
subsoil
qp
qc
Shallow
subsoil
Pb
Phreatic level
10
Calculation procedure
Preprocessing: derive meta relations for driving
variables in simplified model by use of detailed
model
Link simplified model to MODFLOW
Perform simulation runs
Postprocessing: dis-aggregate results to the
discretization level of the detailed model
Verification with results of detailed model
0.00E+00
2.00E-01
1
0.00E+00
28 55 82 109 136 163 190 217 244 271 298 325 352
-2.00E-01
1
28 55 82 109 136 163 190 217 244 271 298 325 352
-2.00E-01
-4.00E-01
-4.00E-01
-6.00E-01
Hgw_MetaSWAP
-6.00E-01
Hgw_SWAP
Hgw_MetaSWAP
Hgw_SWAP
-8.00E-01
-8.00E-01
-1.00E+00
-1.00E+00
-1.20E+00
-1.20E+00
-1.40E+00
-1.40E+00
5.00E-01
0.00E+00
1
28 55 82 109 136 163 190 217 244 271 298 325 352
-2.00E-01
0.00E+00
1
-4.00E-01
27 53 79 105 131 157 183 209 235 261 287 313 339 365
-6.00E-01
-5.00E-01
-1.00E+00
-1.50E+00
Hgw_MetaSWAP
Hgw_SWAP
-8.00E-01
Hgw_MetaSWAP
Hgw_SWAP
-1.00E+00
-1.20E+00
-1.40E+00
-2.00E+00
-1.60E+00
-2.50E+00
-1.80E+00
12
Published:
Vadose Zone Journal, 2008, 7:769-781
Journal of Hydrology, accepted, July 2011
Recent development in GENESIS project:
Link with crop production
Photosynthesis
and dry matter
production
Soil and vadose
zone
Groundwater
flow
Results
More dynamics in evapotranspiration
Climate scenario
Crop growth starts earlier, and ends earlier
Grassland: internal compensation occurs;
vulnerability to climate change is limited;
arable crops are more vulnerable
actual transpiration is more sensitive to the drier
conditions if the feedback from the vegetation is
included, it resulted to higher recharge rates in
dry years
MetaSwap linked to MODFLOW in Nationwide
groundwater model:
Computation time:
30 years
Timestep 1 day
550 000 grids
6 days runtime, 50% MODFLOW + 50% MetaSwap
25x – 100x as fast as a full Richards’ equation based
model
Results are used by national and regional
authorities, decision makers
Groundwater quality modelling
Kromme Rijn
GENESIS case study;
Groundwater quality modelling (1)
Atmosphere
Manure and fertilizers
N2
NH 3
Crops
Surface runoff
ANIMO:
Leaching and
transformation of
carbon, nitrogen and
phosphorus in soils
1D vertical columns, with lateral sink-terms
for transport to other areas or surface water
systems
CO2
Fresh Org.
Matter
Exudates
Diss. Org.
Matter
CO2
CO2
Humus / biomass
Tile drains
Field ditches
sorption diff-precip
PO 4
NH4
Crop uptake
CO2
NO3
sorption
Groundwater
Streams and canals
Grw quality modelling (3)
Coupling of ANIMO - RT3D:
Every timestep
Preservation of
concentration and mass
fluxes at model interfaces
Prototype was successful
Technical improvements:
faster by parallellization
Verification ANIMO - RT3D link
Flux of water exchange
Time (days)
Flux weighted Nitrate concentration
Time (days)
Lessons learned Model linking projects
Progress made last 5 years, new insights gained
Water quantity: operational for advise
Water quality: academic level
New challenges:
technical and data issues to be solved
Further testing in pilot projects
How to calibrate and validate linked large scale models
Most detailed is not always the best !!!
EuroHarp: Model approaches to quantify diffuse
losses
NineAssisting end-users in choosing
appropriate models for the job,
assessment of results and
information on model uncertainty
Low
Level of
complexity
High
Annual predictions based on
export coefficient approach
Data of 17
Catchments
Data oriented
(empirical/
statistical
Methods differ profoundly in
their complexity, level of
process representation and
data requirements
Daily simulations of water flow
and nutrient concentrations
Process oriented
(deterministic)
EU-project EUROHARP
(Special issue JEM, 2009)
Lessons learned EuroHarp
Models are needed to assess the fate of fertilizers and the
effectiveness of mitigation options. However:
Modeller:
Huge influence of the modeller’s choices
problem definition, handling of input data, expected outputs and
interpretation of results;
Models:
Lumped parameters / modelling may only be applicable
under the specific circumstances they have been calibrated
for; uncertainty of scenario analysis !!!
Complex models: The results may be correct for the wrong
reasons; can have huge economic consequences !!!!
Lessons learned EuroHarp
Model result: “25% model, 50% modeller, 25% good luck”
Impact assessment of mitigation options are often based on
modelling
There is no such thing as a best model.
Evaluate intermediate results (hydrological aspects, crop
uptake etc.).
Use different models and modellers for the same
catchment/river basin before you decide what to do!!!
Main conclusion
sustainable watershed management:
Demands for
Integrated assessments on water allocation,
environmental impacts, strategies to adapt to
climate change
Requires often (not always) linked models
Interaction with stakeholders
Problem definition
Data – demand
Cooperation
between scientists (research groups)
Consider ensemble modelling
Thank you for attention
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