Data from: Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States
Sohoulande, Clement et al. (2021), Data from: Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States, Dryad, Dataset, https://doi.org/10.5061/dryad.qnk98sfdz
These research data are associated with the manuscript entitled “Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States” (https://doi.org/10.1016/j.jhydrol.2020.125053). The study focused on the conterminous United States (CONUS) which extends over a region of contrasting climates with an uneven distribution of freshwater resources. Under climate change, an exacerbation of the contrast between dry and wet regions is expected across the CONUS and could drastically affect local ecosystems, agriculture practices, and communities. Hence, efforts to better understand long-term spatial and temporal patterns of freshwater resources are needed to plan and anticipate responses. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite observations provide estimates of large-scale land water storage changes with an unprecedented accuracy. However, the limited lifetime and observation gaps of the GRACE mission have sparked research interest for GRACE-like data reconstruction. 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 GRACE mission by determining LWE footprints using a multivariate regression on principal components model with lag signals. The performance evaluation of the model with a lag signals consideration shows 0.5 ≤ R2 ≤ 0.8 for 41.2% of the CONUS. However, the model’s predictive power is unevenly distributed. The model could be useful for predicting and monitoring freshwater resources anomalies for the locations with high model performances. The processed data used as inputs in the study are here provided including the GIS files of the different maps reported.
Methods are described in the manuscript https://doi.org/10.1016/j.jhydrol.2020.125053
Descriptions corresponding to each figure and table in the manuscript are placed in the Read Me.docx file that is included as part of the Dryad dataset.