An introduction to the crop model GLAM Sanai LI Email:
[email protected] APEC Climate Center 12 Centum 7-ro, Haeundae-gu, Busan, 612-020, Republic of Korea
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Introduction
Crop modelling methods Empirical and semi-empirical methods + +
Low input data requirement Can be valid over large areas May not be valid as climate, crop or management change
Process-based +
Simulates nonlinearities and interactions Extensive calibration is often needed skill is highest at plot-level
What is the appropriate level of complexity?
Depend on the yield-determining process on the spatial scale of interest (Sinclair and Seligman, 2000)
General Large Area Model for Annual Crops (GLAM) Aims to combine:
Challinor et. al. (2004) the benefits of more empirical approaches (low input data requirements, validity over large spatial scales) with the benefits of the process-based approach (e.g. the potential to capture intra-seasonal variability, and so cope with changing climates)
Yield Gap Parameter to account for the impact of differing nutrient levels, pests, diseases, non-optimal management etc. 4
GLAM (General Large-Area Model for annual crops) • Process based crop model • Specifically designed for use on large spatial scales - simulates climatic influences on crop growth and development - low input data requirements
Typical climate model grid cell – GLAM can be run on this spatial scale
GLAM – Inputs and outputs INPUTS Daily weather data: OUTPUTS
- Rainfall - Solar radiation - Min temperature - Max temperature
Soil water balance
Soil type
\
GLAM Planting date
Leaf canopy
Root growth
Biomass
Crop Yield
General Large Area Model for Annual Crops (GLAM) d(HI)/dt Pod yield
Biomass transpiration efficiency
Root system Development stage
Transpiration
Leaf canopy
CYG
water
radiation temperature rainfall RH
Soil water
stress
the flowchart of the GLAM model structure and the processes of yield and biomass formation
General Large Area Model for Annu al Crops (GLAM): some parameters • Thermal duration: Determines development rate o Predict weather extremes at sensitive stages (e.g. flowering) • Transpiration efficiency to calculate biomass o Yield changes under elevated CO2
• Maximum rate of change of LAI: determines growth of leaves o Check model consistency by looking at Specific Leaf Area • Yield gap parameter: time-independent site-specific parameter to account for the impact of differing nutrient levels, pests, diseases, non-optimal management etc. o Process-based: acts on LAI to determine an effective LAI o In practice, YGP can bias correct input weather data o It is not the sole determinant of mean yield, however
Simulation of developmental stages daily effective temperature (oC)
t TT
ti 1
ti
(T eff T b ) dt
i development stage thermal time
(oCd)
Time (days)
base temperature (oC)
In GLAM, the simulation of developmental stages is controlled by accumulated thermal time. Once the thermal time accumulation tTT reaches the specified thermal time for a given stage, next stage begins
Modelling crop growth: biomass in GLAM H2O CO2
Stomatal conductance During the summer, cumulative photosynthesis increases linearly with cumulative transpiration
above ground biomass transpiration efficiency
photosynthesis is not modeled directly, but it is represented by transpiration efficiency Maximum normalized transpiration efficiency
W t
ET T T min , E TN , max V
daily actual transpiration
Vapor presser deficit
maximum transpiration efficiency 10
Modelling canopy in GLAM Leaf Area Index effective LAI
yield gap parameter
S L L ,1 C YG min t max t S cr maximum LAI expansion rate
S
TT TTopt
the soil water stress factor •Water stress factor reduces leaf expansion • decrease in leaf area index affect radiation interception and transpiration, and hence crop yield
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Modelling crop growth: Yield in GLAM Yield=biomass*harvest index (HI)
Harvest index =yield/biomass Biomass is allocated to yield by harvest index from the beginning of gain-flilling, if there is no any stress, harvest index linearly increase with time 12
Water Balance in GLAM model Evaporation + Transpiration Rain
net soil water input Sw=Trainfall-Ttranspiration-Tevaporation-Trunoff-Tdrainage All of the non-runoff rain goes through the first soil layer first, then the total water infiltration into the soil is distributed into NSL (No. of soil layers) vertical soil layers
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Water Balance in GLAM Runoff-US Soil Conservation Service method (USDASCS, 1964) R is the runoff P is the precipitation S is the amount of water that can soak into the soil S = ksat ksat is saturated hydraulic conductivity of the soil Kks is emperiacal constant
Infiltration rate=precipitation -runoff All of the on-runoff rainfall goes through the first soil layer firstly. When the soil water content is greater than the drainage limit, then the excess soil water is infiltrated into the next soil layers and the soil water content in each layer is simulated.. 14
Water Balance in GLAM -drainage Cd1, Cd2, Cd3 are empirical constants θdul is drained upper limit
F accounting for simultaneous inflow from the layer above Qi is the incoming water flux from the layer above
FD is the drainage rate
θ is soil water content θs is the initial value of θ
If the soil moisture is greater than dul at the start of a timestep, the incoming water from above is percolated to the lower layers
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potential evapotranspiration rate-Priestley Taylor
RN - net all-wave radiation G - soil heat flux, G = CGRNe−kL , CG constant, k-constant λ - the latent heat of vaporisation of water Δ = ∂esat/∂T - slope of the saturation-vapour pressure versus the temperature curve γ- ratio of the specific heat of air at constant pressure to the latent heat of vaporisation of water α -PriestleyTaylor coefficient, is a function of VPD(vapour pressure deficit) The energy-limited evaporation and transpiration rates (Ee and e , respectively) are TT described using the simpler Priestley–Taylor equation (Priestly and Taylor, 1972)
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Transpiration and evaporation potential evapotranspiration rate
maximum possible energy-limited evapotranspiration Is given by G=0
energy-limited evaporation
energy-limited transpiration potentially extractable soil water
te(z) is the time of first root uptake in layer z kDIF is the uptake diffusion coefficient zmax is depth of soil profile
transpiration rate
evaporation rate
The available soil water is partitioned into transpiration and evaporation according to water demand where necessary 17
Model calibration
GLAM Calibration-Yield gap parameter (YGP)
GLAM run with YGP, varying from 0.05-1 in step of 0.05. The optimal value is chosen by minimizing the Root Mean Square Error (RMSR) between observed yields and simulated yields
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GLAM – Calibration GLAM simulates the impact of weather on crop yields. It does not directly simulate the impact of other factors such as nutrient deficiencies, pests, diseases, weeds The yield gap parameter is a time-independent site-specific parameter that accounts for these factors.
Crop yield
It also acts to bias correct weather
1.00 Yield Gap Parameter = 0.80
0.05
GLAM – The Yield Gap Parameter (YGP) methods You can choose how the yield gap parameter reduces simulated yields. Options include acting on: • EOS: end-of-season yield •LAI: Leaf area index • ASW: the available soil water
Roots
YGP = 0.2 YGP=1.0 Soil Properties Uptake of water
YGP = 0.2 YGP = 1.0 YGP = 1.0
YGP = 0.2
Leaf Area Index
Potential rate of transpiratio n
Rate of transpiratio n
Soil Water Stress Factor
Biomass
Crop Yield
Harvest Index
Simulating the floods effect on wheat
Flooding effect There is increased risk of crop losses due to flooding and excess precipitation crop damage from flooding and excess soil moisture is not included or not well simulated by some dynamic crop models GLAM- New schedule of surface water storage, infiltration and waterlogging
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Simulating the impact of flood on wheat in China
Correlation coefficient between observed wheat yield and rainfall in China from 1985 to 2000
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Water Balance in GLAM -Surface water storage and runoff PPTi
ETi
Surface storage
Runoff
Drainage
More frequent heavy rainfall may increase surface water storage and cause crop loss due to excess soil moisture
Infiltration method : the infiltration capacity of the soil is assumed to be affected the soil water content INF is infiltration rate (cm/day) P is precipitation (cm/day) SURFSTORAGE is surface water storage SWsat is saturated volume soil water SW is volume soil water content in the soil layer SWdul is drained upper limit DZ is the depth of soil layer
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The response of transpiration to waterlogging days for winter wheat in China (Hu et al,2004)
•Waterlogging can result in the death of root cells, due to a reduction in oxygen availability. •Excessive soil moisture limits root growth and absorption of soil water, consequently decreasing crop transpiration
Parameterizing the flood effect Method (Hu et al,2004): simulate flood effect by introducing a damage function that limited the plant's transpiration and roots growth roots when soil is greater than the field capacity
WSF is water stress factor WSFC0 is the sensitivity of different crops to waterlogging f(TW; PDT) is the response of transpiration to waterlogging days from empirical function of experimental data Kwl is the ratio of the lower limit of soil water content under waterlogging stress to field capacity SW is soil water content SWFC is field compacity SWSAT is saturated soil water content
Comparison of soil water content in the first soil layer with and without surface water storage in water balance model of GLAM
with surface water storage the simulated soil water content is slightly higher than that without surface storage
Probability distribution function of correlation coefficient between observed and simulated wheat yield in east China
Blue line : infiltration is calculated from original GLAM model without flood Green line: infiltration is calculated from original GLAM model with flood Red line: infiltration is simulated to by the modified infiltration with flood. the original model with waterlogging stress is better then without waterlogging stress. The modified GLAM model was better than the original model 30
Simulating the flood effect Comparison between observed yield and simulated yield with and without flooding effect from 1985 to 2000 at 0.5◦ grid cell (31.75◦N; 120.25◦E)
the modified model improved yield predictions in years with serious flooding damage year 1991 and 1998
Comparison of correlation coefficient between observed and simulated yield at the 0.5◦ scale in east China from 1985 to 2000 With flooding effect, yield predictions showed a better agreement with observed yield compared with no flooding effect
original
modified models
Model performance
Evaluation of model consistency-RUE
Radiation Use Efficiency (RUE=biomass/radiation intercepted) of winter wheat :1.58 g MJ-1, spring wheat: 1.34 g MJ-1 Measured RUE of 1.81 g MJ-1 in semi-arid environment by O’Connell et al.(2004) 34
Correlation between observed and simulated yield at county(70-129km)/city(80-128km) level and field level in China 0.8
Correlation(R)
0.7 0.6
Rainfedcounty(city) Rainfed-field
0.5 0.4 0.3 0.2
Significant level
0.1 0 Guyuan
Guyang
Spring wheat
Huma
Zhengzhou Beijing
Winter wheat
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Comparison of simulated and observed wheat yield (kg/ha) at 0.5o scale across China
(a) Observations
(b) Simulations
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Validation of GLAM-Wheat in China -Difference between observed and simulated mean wheat yield (%) in China (correlation r= 0.83,p
