1

Modeling water balance and water productivity in CropSyst model

M. Glazirina, D. Turner

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CropSyst model description

3

CropSyst= “Cropping Systems Simulation Model”• programmed in C++ (object-oriented)

by Prof. C. Stöckle and R. Nelson (Visual Basic for Application version available)

• multi-year, multi-crop, daily time step simulation model

• based on the understanding of plants, soil, weather and management interactions– phenological development– photosynthesis and growth– stress effects (water, N, salt, (K))– root water uptake

• Distributed free of charge via http://www.bsyse.wsu.edu/cropsyst/

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CropSyst - some detail• provides a

– generic crop-growth component, allows adaptation/calibration to any crop; species and cultivars are characterized by a set of parameters which determine crop response to the environment

– link to the GIS-software Arc/Info (spatial application)– report format editor for setting up output style, e.g. MS-Excel – fast graphics viewer

• is very well documented, maintained and regularly updated!

More specifically• considers the influence of soil salinity and shallow groundwater table,• allows using a finite difference solution of Richards equation to simulate

water transport.• handles conservation agriculture features (to some extent)

Input-output fluxes in CropSyst

Runoff

Water risePercolationLeaching

Rainfall Evapotranspiration

Volatilization

CROP

SOILSoil loss

Management:irrigationtillage Fertilizationharvest

Crop processes in CropSyst

development growth light interception net photosynthesis biomass

partitioning leaf expansion roots deepening

leaf senescence water uptake nitrogen uptake water stress nitrogen stress light stress

Soil processes in CropSyst

water infiltration water

redistribution runoff evaporation percolation solutes transport salinization nitrogen fixation

residues fate O.M.

mineralization nitrogen

transformations water erosion ammonia

volatilization ammonium

sorption

8

CropSyst data requirmentsSoil:

• Texture• Hydraulic properties

(bulk density, PWP, FC)

• Chemistry (CEC, pH)

Soil:• Soil moisture• NO3-N and NH4-N• SOM• Salinity

Weather:• Precipitation• Tmax, Tmin• RHmax, RHmin• Solar radiation• Wind speed

Management:• Tillage• Irrigation• Fertilization• Harvest

Ground water and salinity

Crop:• Phenology• N-uptake• AGB• Yield

Soil:• Soil moisture• NO3-N and NH4-N• SOM• Soil salinity

Crop model

Constant: Changing in time: Used for calibration:

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Water balance components in CropSyst model

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Water balance equation

P + I = ET + Inf + R + DS Where:

The incoming water balance components:P - precipitation (including snow)I - irrigation

The outgoing water balance components are:ET - Evapotranspiration Inf - Infiltration of waterR - Surface runoff (natural) or surface drainage (artificial)

DS is the change of water storage

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Evapotranspiration model

• Penman-Monteith– data requirements:

precipitation, max. temp., min temp., solar radiation, wind speed, max relative humidity, min relative humidity

• Priestley-Taylor– data requirements:

precipitation, max. temp., min temp., solar radiation

comprehensive, precise

simple, less precise

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Penman-MonteithOriginal:

Small modification in CropSyst:f(e) = DayFrac x VPDday_mean

Fraction of day in daylight

RN = net radiation [W m-2]G = soil heat flux [W m-2]f(e) = VPD (vapor pressure deficit) [hPa]

Δ(RN-G)

radiation term aerodynamic term

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Priestley-TaylorΔ (RN-G) Δ+γ

Priestley-Taylor "constant"• compensates for the elimination of the aerodynamic term

(of the Penman or PM-model)• default 1.26, higher in arid regions

AVOID USING Priestley-Taylor ET in arid regions!

λET = PTc x

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Surface runoffTwo options:1. SCS curve number

(CN) approach (USDA-SCS, 1988)

2. numerical solution

Erosion• RUSLE parameters:

- Steepness (a percentage 0-100)- Slope length (m)

0

0.25

0.5

0.75

1

40 50 60 70 80 90 100

Curve number (CN)

Surf

ace

runo

ff [p

ropo

rtio

n of

ra

infa

ll]

Daily rainfall = 70 mm40 mm

10 mm

PAW=0.5

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Soil water infiltration & redistribution

• CropSyst provides basically two different models for choice:1. cascade2. finite difference

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Cascade model• each given soil layer is defined by:

– water content at saturation (SAT) – water content at drained upper limit (DUL, FC)– the permanent wilting point (PWP)

• The difference between SAT and the current soil water content (Theta, Θ) determines the capacity of the layer to hold additional water

• After infiltration events, a fraction of water in excess of DUL is drained based on a drainage rate constant

• If Theta for the lowest soil layer exceeds DUL, the excess water is assumed to drain out of the profile

• If the potential drainage for a layer is very large, the net drainage may be limited by the saturated hydraulic conductivity (Ks).

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Finite difference model• builds on the Richards equation

– common flow equation for (un)saturated flow in porous media (as a soil can be considered)

– is a parabolic non-linear partial differential equation of secondary order, which is solved numerically by a finite difference approach

• requires a parameterization (continuous form) of the soil hydraulic properties via:

– soil water retention characteristics pF-curve– soil hydraulic conductivity SHC-curve

CropSyst uses the so-called Campbell approach

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Soil hydraulics according to Campbell• Soil water potential

of layer l, ψsl:

ψsl = -a x WCl –b

whereas

a = e (ln(33) + b x ln(WC-33))

[ln(-1500/-33] [ln(WC-33/WC-1500)]

• Soil hydraulic conductivity:

K = Ks x (Θ/ Θs) c

whereas

c = 2b + 3

• air entry potential = (-a x Θs -b)

b =0

0.05

0.10.15

0.20.250.3

0.350.4

0.450.5

1 10 100 1000 10000 100000Soil water potential [hPa]

Soi

l moi

stur

e [c

m³ c

m- ³]

Loam, observed

van Genuchten

Campbell

0.0001

0.001

0.01

0.1

1

10

100

1000

1 10 100 1000 10000h [hPa]

K [c

m/d

]

van Genuchten

Campbell

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Output• Daily report• Seasonal report• Annual report• specific files:

– cum_water_depth.xls

– hydraulic_properties.xls

– water_content.xls– water_depth.xls– water_potential.xls– …

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Output• Water entering soil = Precipitation + Irrigation - Interception (crop&residue)• Precipitation • Irrigation • Crop water Interception • Residue water Interception • Evapotranspiration = Soil evaporation + Transpiration + Residue evaporation • Soil evaporation • Transpiration • Residue evaporation• Infiltration• Soil water depletion

Potential and actual

Water entering soil - Evapotranspiration – Infiltration = Soil water depletion

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Crop growth in CropSyst

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Crop Development• Crop development is the progression of a crop

through phenological stages.• The proper simulation of crop development

(phenological stages) is crucial – as it determines the length of time when the crop

interacts with the environment– as it allows matching specific physiological conditions

of a crop to specific environmental conditions.• Crop development is governed by growing degree days

(GDDs)

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GDDs

GDDs

Vernalization

Photoperiod

Water stress

Temperature

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Key phenological stages in CropSystGDDs (°C days) from seeding to• Emergence • maximum rooting depth• Peak LAI (end of vegetative growth)• begin Flowering• begin Grain filling• Maturity

Also expressed in GDDs:• Leave duration

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Crop-growth – governing equations

[kg m-2 day-1]BRad = biomass production

(radiation-dependent)Tlim = temperature-

dependent limiting factor*

RUE = radiation use efficiency [kg MJ-1]

PAR = photosynthetic active radiation [MJ m-2 day-1]

k = radiation extinction coefficient [-]

LAI = leaf area index [m2 m-2]

)e1(PARRUETB LAIklimRad

´--´´´=

0

0.25

0.5

0.75

1

0 1 2 3 4 5 6LAI [m2 m-2]

Ads

orbe

d ra

diat

ion

[frac

tion]

k = 0.5k = 0.6k = 0.7k = 0.8k = 0.9k = 1

1- e(-k*LAI)

Eq. 1

* in view of optimum mean daily temperature for growth

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Crop-growth – governing equations (cont.)

BPT = biomass production (transpiration dependent)BTR = aboveground biomass transpiration coefficient [kg m-2 kPa

m-1], often simply called Transpiration Use EfficiencyTact = actual transpiration [m d-1]VPD = vapor pressure deficit [kPa] Assumptions/Preconditions• Maintenance and growth respiration losses are

accounted for in the experimental determination of BTR

• The difference between leaf and atmospheric vapor density can be approximated by the atmospheric deficit expressed as the atmospheric vapor pressure deficit (VPD).

VPDTBTRB act

PT´

= Eq. 2

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Transpiration dependent growth• CropSyst versions later than 4.12 offer

three different modes for calculating BPT:1. classical Tanner-Sinclair model

• BTR is a constant, eq. 2 valid2. FAO AquaCrop water

productivity• BTR is a constant; VPD is not considered; equation 2 not used; unit of water productivity is g biomass/kg water)

3. Transpiration use efficiency curve

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Crop growth

• (optimal) crop growth is governed by the most limiting condition, either– radiation (eq. 1) or– transpiration (eq. 2).

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Water limited growth, how?

• via reducing transpiration…

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Crop water uptake, WU (= Tact) n

WU = Σ WUl [mm d-1]l=1

WUl = K · Cl/1.5 · (ψsl - ψl)

leaf water potential

soil water potential

number of seconds per day = 86400

root conductance of soil layer l

soil layer

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A range of other "water" factors• Act. to pot. transpiration ratio that limits leaf area growth• Act. to pot. transpiration ratio that limits root growth• Maximum daily water uptake• ET crop coefficient at full canopy• Leaf water potential at the onset of stomatal closure• Wilting leaf water potential• Leaf duration sensitivity to water stress• Phenological sensitivity to water stress• Initial leaf area index• fraction of max. LAI at physiological maturity

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Stress indexesStress index is determined as one minus the ratio of actual to

overall potential biomass growth for each day of the growing season.

Potential growth is defined as the growth calculated from potential transpiration (Trpot) substituted for Tract.

Actual biomass growth is obtained after growth limitations have been applied.

This overall stress index is partitioned into light, temperature, water, and nitrogen stress indices. These quantities are used as indicators of the plant response to environmental conditions. All these indices range from 0 to 1, where 0 is no stress and 1 is maximum stress.

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Climate change impact assessment using CropSyst

(by example of wheat growing in Central Asia)

Objectives1. Model calibration and evaluation for wheat grown under

currently prevailing climatic conditions in selected agro-eco-zones of the study region

a. Crop model selection b. Site selection (by AEZ), and data collection (surveys)c. Crop model calibration

2. Definition of business-as-usual management3. Generation of daily time-step weather data (historic and

future)4. Modeling the impact of climate change on crop

productivity utilizing developed climate change scenarios

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Potential biophysical impact of climate change on crop production in Central Asia

1. Increasing temperature– warmer winter and early spring (winter crops)

better early crop growth, less damage by frost– hotter late-spring, hotter summer

crop heat stress (lower grain production)– shorter cropping cycle

lower biomass production2. Changes in precipitation (amount and intensity)3. Increasing CO2

– “carbon fertilization effect” moderate increase in crop growth• Interactions of 1. – 3.

Model selection criteria• Capacity to handle the impact of climate change on crop growth:

– CO2 response– temperature response (cold & hot)– water stress (rainfall variability)

• Capacity for reasonable prediction of– impact of shallow groundwater (GW-module; upward movement of

water in the soil)– salinity response (saline soils)– evapotranspiration in arid environments– response to soil conservation measures (zero-tillage, surface residue

retention)

• Availability of further, useful modeling tools, such as– automatic irrigation

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CO2 fertilization effect in CropSyst • increase in radiation use efficiency (ε) by a G-ratio factor• decrease in canopy conductance, increase of WUE

Tubiello et al., 200037

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Generation of weather data• Historic: data bases of national met-services, ICARDA and

www• Future: using greenhouse gas emission scenarios of IPCC, 2007

– A2: pessimistic; assumes a continuous population growth, increasing divergence between regions, less transfer of technological innovations

– A1b: neither optimistic nor pessimistic; assumes population stabilization, continued globalized world, balance between fossil-intensive and non-fossil energy sources

• Future periods:– immediate future: 2011-2040– mid-term future: 2041-2070– long-term future: 2071-2100

Increase of the atmospheric CO2 concentration as predicted by SRES A1B and A2 (redrawn from IPCC, 2000)

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Climate change – CO2 concentrations

40 40

No Name Country Year Resolution (degrees)

01 BCCR-BCM2.0 Norway 2005 2.8 x 2.8

02 CSIRO-MK3.0 Australia 2001 1.9 x 1.9

04 MIROC3.2 Japan 2004 2.8 x 2.8

08 CGCM3.1(T63) Canada 2005 2.8 x 2.8

09 CNRM-CM3 France 2005 2.8 x 2.8

10 ECHAM5/MPI-OM Germany 2003 1.9 x 1.9

12 GFDL-CM2.0 USA 2005 2 x 2.5

Projections of climate change• Underlying data base: seven IPCC GCMs

average deviation (delta) from historic climate (temperature and precipitation) of the seven models

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Business-as-usual (BAU)

• Definition of agronomic management scenarios based on the usual farmer’s practice

Model simulations should reflect reality

From socio-economists team:1. Fertilizer type2. Fertilizer amount3. Week of planting4. First week of irrigation5. Last week of irrigation6. Number of irrigation events7. Week of harvest

National recommendations:+1. Dates of fertilizer

application2. Dates of irrigation3. Irrigation rates

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Business-as-usual (BAU)Information about BAU:

BAU Planting date Fertilizer application

Irrigation

optimal Depending on location

highest recommendedaverage average/median average

sub-optimal lowest water stressed

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ClimGen modified version of WGEN developed by Gaylon S. Campbell, Washington State University

Available at:http://www.bsyse.wsu.edu/CS_Suite/ClimGen/index.html

LARS-WG stochastic weather generatorDeveloped by M. Semenov(Rothamsted Research of BBSRC)

Available at:http://www.rothamsted.bbsrc.ac.uk/mas-models/larswg.php

Weather generators

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Crop:• Crop

physiology

• Crop phenology

Management:• Planting

date• Irrigation,

fertilization• Tillage

Soil:• Soil physical

properties• Nmin and SOM• Soil salinity• Groundwater

Historic daily meteorological data: precipitation, solar radiation, Tmax, Tmin, RHmax, RHmin, wind speed

CropSyst Simulations

Current conditions

Scenario outputs

Weather generator

Location

GCM - СС

Scenario 4

Scenario 3

Scenario 2

Scenario 1

Regional down-scaling

Generated daily

meteodata

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Climate change simulations

Climate change crop model simulation results – major governing factors

• higher temperatures:→ faster growth, shorter growing season

less time for biomass accumulation→ higher evaporative demand

increase in crop water requirements→ “warmer” (less cold) winters and springs

less frost damage, faster early growth in spring→ hotter late spring and summer

increased risk of sterility of flowers • higher precipitation:

→ more water for the crop→ increased risk of nitrate leaching

• higher concentration of CO2:→ carbon fertilization effect

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Country Site name AEZTajikistan Faizabad 1032

Shahristan 532Khorasan 510Bakht 510Spitamen 510

Kyrgyzstan Uchkhoz 510Zhany pakhta 510Daniyar 510KyrNIIZ 510

Country Site name AEZKazakhstan Shieli 310

Vozdvizhenka 521Petropavlovsk 821Kostanay 521

Uzbekistan Khorezm 310Syrdarya 510Kushmanata 510Kuva 310Akkavak (2 experiments) 510

Selected sites

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Climate change projections for the selected sites

CC simulation results: Grain yield

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0

2

4

6

H I M L I M L H I M L I M L H I M L I M L

A1b A2 A1b A2 A1b A2

Suboptimal Mgmt. Average Mgmt. Optimal Mgmt.

Yiel

d (M

g/ha

)

LSD (0.42 Mg/ha)

* ***

Akkavak – Mars, (UZ, irrigated)

210

220

230

240

H I M L I M L

A1b A2

Days

from

em

erge

nce

till m

atur

ity

Avg. ( ±SD)

Min

Max

Example: Kushmanata (UZ)

Days from emergence until maturity

49

-10 days -12 days

(Minimum) temperatures during vegetative growth

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Immediate future

Mid-term future

Long-term future

Avg. + 0.8 + 1.7 + 2.9Range 0.6-1.0 1.4-2.4 2.2-4.1

Change in average temperature across all sites and scenarios

Maximum temperatures during flowering

51

20

25

30

35

40

I M L I M L

Hist. A1b A2

T (°

C)

Astana

50-year Max.

95%-Perc.

Avg. (±SD)

Irrigation water requirements

• Overall: no noteworthy change• Some sites: reduction in irrigation water requirement 52

0

50

100

150

200

250

H I M L

Irrig

ation

(mm

)

Shieli

0

100

200

300

400

H I M L

Irrig

ation

(mm

)

Kuva

Subopt. Mgmt. A1b

Subopt. Mgmt. A2

Avg. Mgmt. A1b

Avg. Mgmt. A2

Optimal Mgmt. A1b

Optimal Mgmt. A2

0

100

200

300

400

H I M L

Irrig

ation

(mm

)

Kuva

Subopt. Mgmt. A1b

Subopt. Mgmt. A2

Avg. Mgmt. A1b

Avg. Mgmt. A2

Optimal Mgmt. A1b

Optimal Mgmt. A2

0

100

200

300

400

H I M L

Irrig

ation

(mm

)Kuva

Subopt. Mgmt. A1b

Subopt. Mgmt. A2

Avg. Mgmt. A1b

Avg. Mgmt. A2

Optimal Mgmt. A1b

Optimal Mgmt. A2

Grain yield vs. actual transpiration, all Uzbek sites

Transpiration Use efficiency increased from 18.3 kg/ha/mm under historic (CO2) conditions to 25.8 kg/ha/mm in the long-term future

Water use efficiency

53

0

2.5

5

7.5

10

0 100 200 300 400 500Yi

eld

(Mg/

ha)

Actual transpiration (mm)

Long-termMid-termImmediateHistoric

Slope (kg/ha/mm):25.822.520.318.3

y = 18.3x – 405.6R² = 0.758

0

2.5

5

7.5

10

0 100 200 300 400 500 600

Yiel

d (M

g/ha

)

Actual transpiration (mm)

Historic

5412th CGIAR Steering Committee Meeting for Central Asia and the Caucasus, September 12-14, 2009, Tbilisi, Georgia

Thank you for your attention!Thank you for your attention!