Presentation Transcript

1. Modeling water balance and water productivity in CropSyst model M. Glazirina, D. Turner
2. 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/
4. 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)
5. Evapotranspiration Volatilization Rainfall Percolation Leaching Water rise Input-output fluxes in CropSyst Management: irrigation tillage Fertilization harvest CROP Runoff Soil loss SOIL
6. 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
7. 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. CropSystdata requirments Constant: Changing in time: Used for calibration: Soil: Texture Hydraulic properties (bulk density, PWP, FC) Chemistry (CEC, pH) Soil: Soil moisture NO3-N and NH4-N SOM Salinity Crop: Phenology N-uptake AGB Yield Weather: Precipitation Tmax, Tmin RHmax, RHmin Solar radiation Wind speed Soil: Soil moisture NO3-N and NH4-N SOM Soil salinity Crop model Management: Tillage Irrigation Fertilization Harvest Ground water and salinity
9. Water balance components in CropSyst model
10. 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 water R - Surface runoff (natural) or surface drainage (artificial) DS is the change of water storage
11. Evapotranspiration model comprehensive, precise 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 simple, less precise
12. Δ(RN-G) RN = net radiation [W m-2] G = soil heat flux [W m-2] f(e) = VPD (vapor pressure deficit) [hPa] Penman-Monteith Original: radiation term aerodynamic term Small modification in CropSyst: f(e) = DayFrac x VPDday_mean Fraction of day in daylight
13. Priestley-Taylor Δ (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
14. Surface runoff Two options: SCS curve number(CN) approach (USDA-SCS, 1988) numerical solution Erosion RUSLE parameters: - Steepness (a percentage 0-100) - Slope length (m)
15. Soil water infiltration & redistribution CropSyst provides basically two different models for choice: cascade finite difference
16. 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).
17. 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
18. Soil hydraulics according to Campbell Soil water potential of layer l, ψsl: ψsl = -a xWCl–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 xΘs -b) b =
19. 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 …
20. 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
21. Crop growth in CropSyst
22. 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)
23. GDDs Temperature Photoperiod GDDs Water stress Vernalization
24. Key phenological stages in CropSyst GDDs (°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
25. - ´ = ´ ´ ´ - k LAI B T RUE PAR ( 1 e ) Rad lim Crop-growth – governing equations Eq. 1 [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] * in view of optimum mean daily temperature for growth
26. ´ BTR T = act B PT VPD Crop-growth – governing equations (cont.) Eq. 2 BPT = biomass production (transpiration dependent) BTR = aboveground biomass transpiration coefficient [kg m-2 kPa m-1], often simply called Transpiration Use Efficiency Tact = 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).
27. Transpiration dependent growth CropSyst versions later than 4.12 offer three different modes for calculating BPT: classical Tanner-Sinclair model BTR is a constant, eq. 2 valid 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) Transpiration use efficiency curve
28. Crop growth (optimal) crop growth is governed by the most limiting condition, either radiation (eq. 1) or transpiration (eq. 2).
29. Water limited growth, how? via reducing transpiration…
30. Crop water uptake, WU (= Tact) soil layer n WU =Σ WUl [mm d-1] l=1 WUl = K · Cl/1.5 · (ψsl - ψl) soil water potential leaf water potential root conductance of soil layer l number of seconds per day = 86400
31. 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
32. Stress indexes Stress 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.
33. Climate change impact assessment using CropSyst(by example of wheat growing in Central Asia)
34. Objectives Model calibration and evaluation for wheat grown under currently prevailing climatic conditions in selected agro-eco-zones of the study region Crop model selection Site selection (by AEZ), and data collection (surveys) Crop model calibration Definition of business-as-usual management Generation of daily time-step weather data (historic and future) Modeling the impact of climate change on crop productivity utilizing developed climate change scenarios
35. Potential biophysical impact of climate change on crop production in Central Asia 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 production Changes in precipitation (amount and intensity) Increasing CO2 “carbon fertilization effect”  moderate increase in crop growth Interactions of 1. – 3.
36. 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
37. 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., 2000
38. 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 38
39. Climate change – CO2 concentrations Increase of the atmospheric CO2 concentration as predicted by SRES A1B and A2 (redrawn from IPCC, 2000)
40. Projections of climate change Underlying data base: seven IPCC GCMs  average deviation (delta) from historic climate (temperature and precipitation) of the seven models 40
41. Business-as-usual (BAU) Definition of agronomic management scenarios based on the usual farmer’s practice  Model simulations should reflect reality 41
42. Business-as-usual (BAU) + Information about BAU: From socio-economists team: Fertilizer type Fertilizer amount Week of planting First week of irrigation Last week of irrigation Number of irrigation events Week of harvest National recommendations: Dates of fertilizer application Dates of irrigation Irrigation rates
43. Weather generators LARS-WG stochastic weather generator Developed by M. Semenov (Rothamsted Research of BBSRC) ClimGen modified version of WGEN developed by Gaylon S. Campbell, Washington State University Available at: Available at: http://www.bsyse.wsu.edu/CS_Suite/ClimGen/index.html http://www.rothamsted.bbsrc.ac.uk/mas-models/larswg.php 43
44. Climate change simulations Historic daily meteorological data: precipitation, solar radiation, Tmax, Tmin, RHmax, RHmin, wind speed Weather generator Location Soil: Soil physical properties Nmin and SOM Soil salinity Groundwater Crop: Crop physiology Crop phenology Management: Planting date Irrigation, fertilization Tillage Regional down-scaling Scenario 4 Scenario 3 Generated daily meteo data Scenario 2 Scenario 1 CropSyst Simulations GCM - СС Scenario outputs Current conditions 44
45. Climate change crop model simulation results – major governing factors higher temperatures: faster growth, shorter growing season less time for biomass accumulation higher evaporative demandincrease 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
46. Selected sites 46
47. Climate change projections for the selected sites
48. CC simulation results: Grain yield Akkavak – Mars, (UZ, irrigated)
49. Days from emergence until maturity Example: Kushmanata (UZ) -10 days -12 days
50. (Minimum) temperatures during vegetative growth Change in average temperature across all sites and scenarios
51. Maximum temperatures during flowering
52. Irrigation water requirements Overall: no noteworthy change Some sites: reduction in irrigation water requirement
53. Water use efficiency Grain yield vs. actual transpiration, all Uzbek sites Historic 10 y = 18.3x – 405.6 R² = 0.758 7.5 Yield (Mg/ha) 5 2.5 0 0 100 200 300 400 500 600 Actual transpiration (mm) 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
54. Thank you for your attention! Thank you for your attention! 54 12th CGIAR Steering Committee Meeting for Central Asia and the Caucasus, September 12-14, 2009, Tbilisi, Georgia