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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Project over goals

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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

Need for more USDA data integration

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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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Thank you!

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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