Data from: Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States

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.

本研究数据关联于题为《气候驱动的陆地水储量异常预测：美国本土水资源监测展望》的学术论文（https://doi.org/10.1016/j.jhydrol.2020.125053）。
该研究聚焦于美国本土（CONUS）——这一区域气候类型多样且差异显著，淡水资源分布亦极不均衡。
在气候变化背景下，美国本土内干湿区域的气候反差预计将进一步加剧，进而对当地生态系统、农业生产及社区造成严重影响。
因此，亟需深入解析淡水资源的长期时空分布特征，以制定应对策略并提前做好预判。
自2002年起，重力恢复与气候实验（GRACE）及GRACE后续任务（GRACE-FO）的卫星观测数据，以前所未有的精度提供了大尺度陆地水储量变化的估算结果。
然而，GRACE任务有限的运行寿命与观测间隙，催生了对类GRACE数据重建方法的研究需求。
本研究提出了一种预测建模方法，利用总降水量（PRE）、降水日数（WET）、气温（TMP）及潜在蒸散发（PET）等气候变量，实现逐月液态水当量厚度异常（LWE）的量化。
该方法依托GRACE任务的研究成果，通过引入滞后信号的主成分多元回归模型，确定液态水当量的空间分布特征。
针对引入滞后信号的模型进行性能评估，结果显示美国本土41.2%的区域其决定系数R²满足0.5 ≤ R² ≤ 0.8。
但该模型的预测能力存在显著的区域分布不均性。
对于模型表现优异的区域，本方法可有效用于淡水资源异常的预测与监测。
本研究中作为输入使用的预处理数据已随本数据集一并提供，其中包含文中提及的各类地图的GIS文件。