National Institute of Food and Agriculture/USDA

1. (a) (a) • Ongoing CEESMO Projects • I. Calderon1,2,*andM. Kafatos1,2 • 1School of Earth and Environmental Sciences, Schmid College of Science & Technology, Chapman University, Orange, CA, USA, 2Center of Excellence in Earth Systems Modeling & Observations, Chapman University, Orange, CA, USA | *smiley@chapman.edu (a) (c) (b) (e) (d) National Institute of Food and Agriculture/USDA Multi-Model Simulations and Satellite Observations for Assessing Impacts of Climate Variability on the Agro-ecosystems in California and Southwestern United States M. Kafatos, H. M. El-Askary, N. Hatzopoulos, J. Kim, D. Medvigy C. Tremback and R. Walko., D. Stack, B. Myoung, S. H. Kim. Introduction ApsimRegions is a one-way nested climate-crop modeling framework that links gridded weather data (rain, temperature, radiation, etc.) with the point-specific Agricultural Production Systems sIMulator (APSIM) crop model. ApsimRegions was a tool developed with the goal of better understanding the impacts of climate, soils, and management decisions on crop yields in the Southwestern US. Crop models simulate the growth and development of crops in response to environmental drivers (Fig. 1). Traditionally, process-based crop models have been run at the local farm level for management scenario optimization. Few previous studies have used these models over large geographic regions. In particular, assessment of regional-scale yield variability due to climate change requires the merging of high-resolution, regional-scale, datasets, many of which have been unavailable until recently. Objectives The goal of this USDA study is to inter-compare climate model driven APSIM yields (Fig.2) and to create a model ensemble to best forecast crop yields while creating an automated framework for extending the process-based APSIM crop model for use at regional scales. Python was chosen to create this framework because of its flexibility, extensive libraries, and quick prototyping capabilities. These qualities led to the successful development of a streamlined, integrated, modeling framework. Conclusions This study has shown that by aggregating the results of a process-based crop model on a regional scale, potential yields can be simulated over large geographic regions. ApsimRegions can be used in other regions of the world, and even globally, if provided ample computing power and data. Additionally, farmers can use it for determining the best large scale management strategies. We have successfully created a framework for extending an existing process-based crop model for use at regional scales. Using the programming language Python, an automated pipeline was created to merge and process the results from Regional Climate Models (RCMs) and the APSIM crop model. Acknowledgements This work is supported by NIFA (Award No. 2011-67004-30224), under the joint NSF-DOE-USDA Earth System Models (EaSM) program. • NASA Science Mission Directorate’s Earth Science Division • Feasibility Assessment of Using Satellite Data for Enhancing Wildland Fire Decision Support and Warning Systems • S. Nghiem, M. Kafatos, F. Fujioka, X. Liu, N. Hatzopoulos, D. Stack, B. Myoung, S. H. Kim., C. Tremback, L. Rodriguez, H.M. El-Askary. • Introduction • The National Weather Service (NWS) weather forecasters produce weather data and forecasts for fire weather support at all levels from local to national. NWS is the prime agency responsible for issuing Fire Weather Watches (FWW) and Red Flag Warnings (RFW), and the use of these products is related directly to the fire preparedness activities for firefighting agencies, as well as the actions of private citizens. NWS fire weather forecast is crucial to the Weather Information Management System (WIMS), an updated computerized version of the National Fire Danger Rating System (NFDRS), used to estimate both live and dead fuel moisture values as implemented in WIMS and FireFamily Plus. All wildfire systems and organizations can benefit from using NASA remote sensing data for an assessment of potential enhancements in both wildfire warning systems and national wildfire decision support systems. • Figure 1. Fuel Dryness Map from MODISEVI Figure 2. Real-time MODIS satellite imagery • Objectives • The objective of this proposal is to investigate the feasibility of satellite remote-sensing measurements and derived products for enhancing wildfire operational systems such as the NFDRS, the Wildland Fire Decision Support System (WFDSS) and wildfire systems for FWW and RFW, with a multi-institutional and multi-agency interdisciplinary team. The approach will utilize NASA satellite remote sensing data, including soil moisture and vegetation observations. • Conclusions • This ongoing study has shown that it is feasible to use satellite data (Terra and Aqua MODIS) available through NASA to produce more reliable, objective measurements of fuel moisture – more accurately and at much greater frequency than what is currently being done. At the present time, we are dependent on aperiodic, point source estimates of fuel moisture made by independent county fire agencies. By utilizing NASA’s satellites, however, we can improve both the accuracy and objectivity of the time-critical warning and fire resource dispatch decisions. Utilizing remote sensing data will provide the missing link between precipitation, soil moisture, live fuel moisture, dead fuel moisture and resultant fire behavior. Continuous coverage by satellite products will help with a better evaluation of the fire environment. • Acknowledgements • This work is supported by NASA (Award No. NNN12AA01C), under the SMD’s Earth Science Division. Figure 2. USDA study plan: comparing models. Figure 1. Theoretical crop yield model.