Presentation by Sander de Vries (Wageningen University) at the Symposium on catchment hydrology and WFlow, during Delft Software Days - Edition 2017. Tuesday, 24 October 2017, Delft.

1. LINTUL-WFLOW
Sander de Vries, Wageningen Plant Research
Willem van Verseveld, Deltares
Integrating a crop model in the wflow framework

2. kindofgreen.nl
About
 Sander de Vries, PhD
 WUR - Wageningen Plant Research (formerly PRI)
● BU Agrosystems Research
 WUR - Wageningen Uni – Plant Production Systems
 Previous:
● International Rice Research Institute (IRRI)
● International Potato Center (CIP, Indonesia)
● Bejo Vegetable Seeds B.V.
● Keygene N.V.
● Utrecht Sustainability Institute @ Utrecht Uni (USI)
 Crop modelling, GIS & more @ kindofgreen.nl

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Crop growth modelling... why?
 Curiosity:
● How to explain variations in crop growth?
● Integrating crop physiology, agro-meteorology, soil
science & agronomy quantitatively?
● => Knowledge gaps => Research prioritization
 After proper validation, practical use in:
● Explorative studies
● Crop “ideotyping”
● Field-level decision support, yield forecasting, risk
analysis, etc.

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Explorative studies
IRRI, 2000; De Vries, 2000
IRRI, Rice Supply and Demand Analysis, 2010 scenario

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Crop breeding - ideotyping
Sharma et al., Afr. J. Agr. Res., 2013

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Yield forecasting
Combined with long-term
weather forecast

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... and crop insurance!
www.g4indo.org

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‘Old school’: C.T. de Wit
Photo: K. Suyk, Anefo (Nationaal Archief)

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Crop growth modelling:
 A model can be defined as a simplified representation
of reality...
 ‘School of De Wit’ crop growth models:
1. Dynamic, time based (no regression models)
2. Hierarchical levels of organization (e.g. cells,
leaves, canopy, crop)
3. State variables (e.g. weights) are updated at each
time-step, by rate variables (e.g. carbon flux).
4. Explanatory: rate calculations based on knowledge
of the underlying biophysical processes
5. Deterministic (as opposed to stochastic)
Bouman et al., 1996

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Crop growth modelling?
Biomass (carbohydrate) partitioning over time

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LINTUL - How does it work?
DVS = 0 DVS = 1 DVS = 2
Image: International Rice Research Institute (IRRI) –
Rice Knowledge Bank

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Partitioning
 Phenological development rate determined by:
● Thermal time
● Photoperiod
● Vernalization
 E.g. thermal time:
● DAVTMP = 0.5 * (TMMN + TMMX)
● DTEFF = DAVTMP – TBASE
● TBASE = 8 (rice)

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Biomass production
Crop growth
g / (m2 * week)
Intercepted PAR, MJ / (m2 * week)
Circles: barley
Triangles: wheat
Simplest option: a fixed Light Use Efficiency (LUE)
E.g. Gallagher & Biscoe, 1978

14. LINTUL = Light INterception & UtiLization
0
L
Lai (L) counted from top of canopy
*
E.g. Coombs et al., 1985
 GTOTAL = LUE * PARINT
 PARINT = 0.5 * DTR * (1.- EXP(-K*LAI))

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Production Levels
Lovenstein et al., 1995; Van Ittersum and Rabbinge,
1997; Van Ittersum et al., 2013

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Effect of water limitation?
Basically:
-> half the water req.?
-> half the production!
Briggs & Shantz, 1914

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LINTUL so far: point or field-based
 Originally
programmed in
FORTRAN-related
FST
 No explicit area
dimension

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Integration LINTUL into wflow
 Basis: LINTUL3 (Shibu et al., 2010)
 From FST to PCRaster-Python
 From point-based to array/grid based (Brantas, Java)
 Water balance-related matter to be handled by WFLOW-
SBM instead of LINTUL’s built-in water balance module.
data exchange WFLOW-LINTUL  WFLOW-SBM?
 Input data?
 ‘Rice mask’?
 DEWS
 Calibration

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Start from wflow_sceleton.py

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Data exchange via BMI
 WFLOW-LINTUL  BMI  WFLOW-SBM
 Basic Model Interface (from CSDMS, Community Surface
Dynamics Modelling System?) => Deltares
● Data exchange each simulation time step (i.e.:
each day)
WFLOW_
SBM
WFLOW_
LINTUL
BMI
RDEPTH
LAI
TRANSpot,
TRANSact,
PAW

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Proof of Concept (movie clip)

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In DEWS...

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Crop Mask?
 Ability to start/stop
crop growth
simulations for
individual grid cells,
based on grid cell
value (0/1)
● ‘Crop profile’
 Data to be supplied
by SAR (satellite)
data

24. kindofgreen.nlSimulated LAI – Brantas Catchment (movie clip)

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Simulated Rice Yield (g/m2) – Brantas Catchment
Movie Clip

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Calibration
 RMSE between simulated and observed rice yields over
all grid cells calculated for each year, summed over 15
years of data.
● Minimized by tuning LUE

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E.g. 2008, sub-district level data

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Yield Data

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Simulated Yield

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Simulated – Observed Yields

31. Terima Kasih!
sander.devries@wur.nl
Image credit: DocChewBacca
cc-by-sa-2.0 license