Current Research

Current research involves improvements to allow the models to run at higher spatial resolution—that is, improve clarity, so the model can be accurately applied to smaller physical blocks. The result will be a model to better meet the irrigation scheduling needs of nut tree farmers.

Simulations have been setup and tested for both WRF and WRF-ACASA models. We have tested the stability of the model at different spatial resolutions—from 16 km down to 250 meters wide. We have tested  model  improvements with different land cover data.

Input data sources: 
• WRF-ACASA uses either input data from a Global Climate Model and/or reanalysis data1/
• Weather data, including inputs from MODIS and GOES satellites2/
• Direct measurements from field studies and flux towers at the test sites 

Initial Results

Our initial results are promising to provide an analysis system that can be automated and be used to predict Eta ahead for several days. More than a decade’s worth of model simulations have been carried out in various test configurations and scenarios. WRF-ACASA has proven to be numerically robust.

Land cover data3/ has been added to the standard WRF input data library, and this addition has greatly improved the resolution of differences among surfaces across the landscape. With inclusion of a sophisticated surface layer scheme such as ACASA, WRF can take full advantage of more detailed information about the characteristics of the land cover.

Estimates of Evapotranspiration (shown at left), show the ability of the WRF-ACASA model to realistically resolve patterns of ETa across the Californian landscape with its highly variable biomes and water availability.

1/Sources of reanalysis data: NCEP - NARR (National Center for Environmental Prediction - North American Regional Reanalysis Project) and NNRP ( NCEP and National Center for Atmospheric Research Reanalysis Project)
2/ MODIS NASA satellite, GOES NOAA satellite
3/ California Augmented Multisource Land Map

Next Steps

Our work will focus on accurately simulating the effects of vegetation variability on estimating ETa. This will provide better characterization of the state of the vegetation in orchards.

We are preparing an interactive map interface for access via gadgets of location-based ETa and relevant weather and climate data.

In parallel with improved model predictions, we are working on a web-based tool to allow farmers to modify inputs that will let them better evaluate the consequences of management decisions.