Physics-Based Predictive Modeling for Integrated Agricultural and Urban Applications

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Information about Physics-Based Predictive Modeling for Integrated Agricultural and Urban...

Published on December 23, 2016

Author: NIFA_IBCE

Source: slideshare.net

1. Physics-Based Predictive Modeling for Integrated Agricultural and Urban Applications Fei Chen1, Alex Mahalov2, Michael Barlage1, Francisco Salamanca2, Stephen Shaffer2, Xing Liu3, Dev Niyogi3 1 National Center for Atmospheric Research 2 Arizona State University 3 Purdue Univeristy Agroclimatology Project Directors Meeting, 17 December 2016, San Francisco, CA 1

2. 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 2

3. Project over goals 3

4. 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. 4 Hatfield, et al., 2014: Ch. 6: Agriculture. Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 150-174. doi:10.7930/J02Z13FR.

5. 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. 5

6. Current Approach Climate Change projected from models Crop Models Assessment Adaptation and Mitigation Strategy No feedback 6 Urban farm/garden

7. 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

8. New generation Noah-MP community land model Noah-MP implemented in WRF, WRF-Hydro, and NOAA/NCEP Climate Forecast System 8

9. Planting date IPA =1 (turn on growth) GDDDAY PSN ( total photosynthesis) CH20 Flux LAI allocate Leaf mass Stem mass Root mass Grain (Yield) Solar radiation Maintenance resp Growth resp Stage 1: Seeding Stage 2: emergence Stage 3: initial vegetative Stage 4: normal vegetative Stage 5: initial reproductive Stage 6: physiological maturity Stage 7: after maturity Stage 8: after harvesting turnover death Soil moisture Noah-MP-Crop: modeling crop growth 9

10. Development of the WRF-Crop model Built upon the WRF-Hydro and Noah-MP land-model ensemble modeling framework, Stem Mass Crop Yield Noah-MP-Crop well simulated 2001 rainfed corn yield at the Bondville site, IL (left). Red: model results Blue: obs Liu et al. 2016, JGR

11. Data Requirement for Integrating Noah-MP-Crop with WRF 11 Implementing 30-meter USDA/GMU Cropscape crop type product Yellow/green = corn and soybean

12. • 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)Planting Date Harvest Date Seasonal GDD Good performance Sub-optimal performance Development of the WRF-Crop model

13. Need for more USDA data integration 13

14. WRF Expanding WRF Urban Model Capabilities with Noah-MP 14 Noah LSM Noah-MP LSM UCM - urban BEP/BEM - urban Current capability Development capability

15. WRF-Urban application for the Great Phoenix WRF-experiments Land surface model Urban representation Noah-BULK Noah Bulk NoahMP-BULK Noah-MP Bulk Noah-SUCM Noah Single-layer UCM NoahMP-SUCM Noah-MP Single-layer UCM Noah-BEPBEM Noah Multilayer UCM +BEM NoahMP-BEPBEM Noah-MP Multilayer UCM+BEM Salamanca et al. 2016, paper in preparation

16. Comparing WRF Results using Noah vs Noah-MP (Rural areas) Noah-MP Noah 2-m air temperature 10-m wind speed

17. WRF-results for Phoenix urban areas Table 2. Root Mean Square Error and Mean Absolute Error for WRF-modeled 2-m air temperature (oC), 10-m wind speed (ms-1), and 10-m wind direction (o) against four AZMET urban weather stations.

18. WRF-Urban application to Beijing Metro area 18 • Beijing RUC operational 1km model bias is larger than 3km model • Increasing model resolution does not necessarily increase simulation realism

19. Verification Data 19 • 1670 surface stations (T, wind, RH, pressure, precip) • Three flux tower sites (turbulent/radiation fluxes) Miyun 325m tower

20. 20 • 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

21. 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 21

22. Thank you! 22

23. Noah-MP physics options 1. 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 = 0 Two-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 groundwater TOPMODEL with an equilibrium water table (Chen&Kumar,2001) Original Noah scheme BATS surface runoff and free drainage More to be added 23

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