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ARS Home » Southeast Area » Florence, South Carolina » Coastal Plain Soil, Water and Plant Conservation Research » Research » Publications at this Location » Publication #370932
Research Project: Managing Water Availability and Quality for Sustainable Agricultural Production and Conservation of Natural Resources in Humid Regions

Location: Coastal Plain Soil, Water and Plant Conservation Research

Title: Climate driven forecast of land water storage anomalies across the CONUS

Author
item Sohoulande, Clement
item Martin, Jerry
item Szogi, Ariel
item Stone, Kenneth - Ken

Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 3/6/2020
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: The conterminous United States (CONUS) extends over a region of contrasting climates with an uneven distribution of freshwater resources. Under climate change, most predictions concord on critical disturbances in the terrestrial hydrological cycle with consequences on freshwater resources availability. In the case of the US, an exacerbation of the contrast between dry and wet regions is expected and could drastically affect local ecosystems, agriculture practices, and communities. Hence, efforts to better understand spatial and temporal patterns of freshwater resources are needed to better plan and anticipate responses. Particularly, understanding the future of land water resources anomalies requires the development of predictive models. This study developed a predictive modeling approach to quantify monthly land liquid water equivalence thickness anomaly (LWE) using climate variables including total precipitation (PRE), number of wet day (WET), air temperature (TMP), and potential evapotranspiration (PET). The approach builds on the achievements of the Gravity Recovery and Climate Experiment (GRACE) satellite mission by determining LWE footprints using a multivariate regression on principal components model. Model estimates improve significantly by considering lag times between the response (i.e. LWE) and the climate variables. For instance, the performance evaluation of the model with a lag time consideration shows 0.5=R2=0.8 for 41.2% of the conterminous US, and RMSE=0.05 for 66.4% of the territory. Even though, its predictive power is unevenly distributed across the conterminous US, the model can be used to predict and monitor freshwater resources anomalies for the locations which show high model performances.