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Physics-Based Predictive Modeling for Integrated Agricultural and Urban
ApplicationsFei Chen1, Alex Mahalov2, Michael Barlage1, Francisco Salamanca2,
Stephen Shaffer2, Xing Liu3, Dev Niyogi3
1 National Center for Atmospheric Research2 Arizona State University
3 Purdue Univeristy
Agroclimatology Project Directors Meeting, 17 December 2016, San Francisco, CA
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EASM-3: Collaborative Research: Physics-Based Predictive Modeling for Integrated
Agricultural and Urban Applications• PI: Alex Mahalov, Arizona State University• Co-PIs: Fei Chen, Michael Barlage (NCAR), Matei
Georgescu, Carola Grebitus (ASU)
• This talk: Development of the integrated WRF-Urban-Crop model based on the community Noah-MP land model
• Alex Mahalov and Carola Grebitus (10am): Application of WRF-Urban-Crop model to agriculture and socio-economic
• Stephen Shaffer (10:15 am): improvement to the WRF-Urban-Crop model
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Key Messages from latest National Climate Assessment • Climate disruptions to agricultural production have increased in the past 40
years and are projected to increase over the next 25 years. • Many agricultural regions will experience declines in crop and livestock
production from increased stress due to weeds, diseases, insect pests, and other climate change induced stresses.
• The rising incidence of weather extremes will have increasingly negative impacts on crop and livestock productivity because critical thresholds are already being exceeded.
• Agriculture has been able to adapt to recent changes in climate; however, increased innovation will be needed to ensure the rate of adaptation of agriculture and the associated socioeconomic system can keep pace with climate change over the next 25 years.
• Climate change effects on agriculture will have consequences for food security, both in the U.S. and globally, through changes in crop yields and food prices and effects on food processing, storage, transportation, and retailing. Adaptation measures can help delay and reduce some of these impacts.
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Hatfield,etal.,2014:Ch.6:Agriculture.ClimateChangeImpactsintheUnitedStates:TheThirdNationalClimateAssessment,J.M.Melillo,Terese (T.C.)Richmond,andG.W.Yohe,Eds.,U.S.GlobalChangeResearchProgram,150-174.doi:10.7930/J02Z13FR.
Agricultural Adaptation
United Nations Framework Convention on Climate Change estimated that about US$14 billion will be needed annually by 2030 to cope with the adverse impacts of climate change, though this figure could be two or three times greater. - Frankhauser et al. WIREs Climate Change, 2010.
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Current Approach
ClimateChangeprojectedfrom
models
CropModels
AssessmentAdaptationandMitigationStrategy
Nofeedback
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Urbanfarm/garden
Need for Cross-scale Integrated Modeling and Assessment Systems
Quantify complex climate–soil-crop-urban interactions, which is essential for supporting agricultural management strategies and policy decisions at multiple scales - from the globe, to the continent, and to the farm and cities
Global Scales Continental Scales
Farm Scales 7
New generation Noah-MP community land model
Noah-MPimplementedinWRF,WRF-Hydro,andNOAA/NCEPClimateForecastSystem
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Plantingdate
IPA=1(turnongrowth)
GDDDAY
PSN(totalphotosynthesis)
CH20Flux
LAI
allocate
LeafmassStemmassRootmassGrain(Yield)
Solarradiation
Maintenanceresp
Growthresp
Stage1:SeedingStage2:emergenceStage3:initialvegetativeStage4:normalvegetativeStage5:initialreproductiveStage6:physiologicalmaturityStage7:aftermaturityStage8:afterharvesting
turnover
death
Soilmoisture
Noah-MP-Crop:modelingcropgrowth
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Development of the WRF-Crop modelBuilt upon the WRF-Hydro and Noah-MP land-model ensemble modeling framework,
StemMass
CropYieldNoah-MP-Cropwellsimulated2001rainfed cornyieldattheBondville site,IL(left).Red:modelresultsBlue:obs
Liuetal.2016,JGR
Data Requirement for IntegratingNoah-MP-Crop with WRF
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Implementing30-meterUSDA/GMUCropscape croptypeproduct
Yellow/green=cornandsoybean
• For a normal year (2013), WRF-Crop predicted crop yield is good in corn dominated regions (Iowa, Illinois, Indiana) near where the model was calibrated (right)
• Challenge: improve model performance beyond calibration region for its global applications using spatially varying parameters, e.g., planting/harvest dates, growing degree day (below)
Corn yield ratio (modeled / observed) in % for 73 USDA zones (e.g., <100 implied underprediction)PlantingDate HarvestDate SeasonalGDD
GoodperformanceSub-optimalperformance
Development of the WRF-Crop model
WRF
Expanding WRF Urban Model Capabilities with Noah-MP
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Noah LSM Noah-MP LSM
UCM - urban BEP/BEM - urban
Current capability
Development capability
WRF-Urban application for the Great Phoenix
WRF-experiments Landsurface model Urbanrepresentation
Noah-BULK Noah Bulk
NoahMP-BULK Noah-MP Bulk
Noah-SUCM Noah Single-layerUCM
NoahMP-SUCM Noah-MP Single-layerUCM
Noah-BEPBEM Noah Multilayer UCM+BEM
NoahMP-BEPBEM Noah-MP MultilayerUCM+BEM
Salamanca et al. 2016,paperinpreparation
Comparing WRF Results using Noah vs Noah-MP (Rural areas)
Noah-MP
Noah
2-mairtemperature 10-mwindspeed
WRF-results for Phoenix urban areas
Table2.RootMeanSquareErrorandMeanAbsoluteErrorforWRF-modeled2-mairtemperature(oC),10-mwindspeed(ms-1),and10-mwinddirection(o)againstfourAZMET
urbanweatherstations.
WRF-Urban application to Beijing Metro area
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• Beijing RUC operational 1km model bias is larger than 3km model
• Increasing model resolution does not necessarily increase simulation realism
Verification Data
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• 1670 surface stations (T, wind, RH, pressure, precip)• Three flux tower sites (turbulent/radiation fluxes)
Miyun
325mtower
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• Using a revised parameter table that reduces urban heat storage and anthropogenic heat
• Improved (blue) bias in most locations
Correctly parameterize urban processes improve WRF regional simulations
Outreach• WRF-Crop modeling capability released in WRF v3.8 in April
2016• WRF-Urban-Crop with Noah-MP is planned to be released in
spring 2017 • Have responses/requests from many groups• Connection to other NIFA projects: U2U• Publications related to WRF-Urban-Crop development
regional climate studies• Sharma et al. 2016: Green and Cool Roofs to Mitigate Urban Heat Island Effects in Chicago Metropolitan
Area: Evaluation with a Regional Climate Mode, Environ. Res. Lett., Vol 11, 6.• Barlage et al. 2016: Impact of physics parameterizations on high-resolution weather prediction over
complex urban areas. J. Geophys. Res., 121, 4487–4498, doi:10.1002/2015JD024450.• Yang et al. 2016: Assessing the impact of hydrological processes on urban meteorology using an integrated
WRF-Urban modelling system. J. Hydrometeor., 17, 1031-1046.• Sharma et al. 2016: Regional climate modeling of urban meteorology: A sensitivity study. International
Journal of Climatology DOI: 10.1002/joc.4819.• Liu, et al. 2016: Noah-MP-Crop: Introducing Dynamic Crop Growth in the Noah-MP Land-Surface Model.
J. Geophys. Res.,in press.• Huang et al. 2016: Estimate of boundary-layer depths over Beijing, China, using Doppler lidar data during
SURF-2015. Boundary Layer Meteorol., in press.• Li et al. 2016: Introducing and evaluating a new building-height categorization based on the fractal
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Noah-MP physics options1. Leaf area index (prescribed; predicted)2. Turbulent transfer (Noah; NCAR LSM)3. Soil moisture stress factor for transpiration (Noah; SSiB; CLM)4. Canopy stomatal resistance (Jarvis; Ball-Berry)5. Snow surface albedo (BATS; CLASS)6. Frozen soil permeability (Noah; Niu and Yang, 2006)7. Supercooled liquid water (Noah; Niu and Yang, 2006)8. Radiation transfer:
Modified two-stream: Gap = F (3D structure; solar zenith angle; ...) ≤ 1-GVF
Two-stream applied to the entire grid cell: Gap = 0Two-stream applied to fractional vegetated area: Gap = 1-GVF
9. Partitioning of precipitation to snowfall and rainfall (CLM; Noah)10. Runoff and groundwater:
TOPMODEL with groundwaterTOPMODEL with an equilibrium water table (Chen&Kumar,2001)Original Noah schemeBATS surface runoff and free drainage
More to be added23