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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 #370831

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 prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States

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

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/5/2020
Publication Date: 5/11/2020
Citation: Sohoulande Djebou, D.C., Martin, J.H., Szogi, A.A., Stone, K.C. 2020. Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.125053.
DOI: https://doi.org/10.1016/j.jhydrol.2020.125053

Interpretive Summary: The conterminous United States (US) 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 water cycle with consequences on freshwater availability. In the case of the US, the contrast between dry and wet regions 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 solutions for the management of limited freshwater resources. Particularly, the development of predictive models is needed to understand the future availability of freshwater resources. Therefore, this study developed a predictive model for quantifying the monthly variation of freshwater resources using climate variables including total precipitation, number of wet days, air temperature, and potential evapotranspiration. The approach builds on the achievements of the Gravity Recovery and Climate Experiment (GRACE) satellite mission by determining the footprints of freshwater resource variation using a multivariate model. The model estimates are highly improved when it includes lag times between the response and the climate variables. Even though, its predictive power is unevenly distributed across the conterminous US, the model can be used to predict and monitor freshwater resources for the locations which show high model performances.

Technical Abstract: The conterminous United States (US) 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. 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.