High-Resolution Soil Moisture Data Reveal Complex Multi-Scale Spatial Variability Across the United States

被引:24
作者
Vergopolan, Noemi [1 ,2 ,3 ]
Sheffield, Justin [4 ]
Chaney, Nathaniel W. [5 ]
Pan, Ming [6 ]
Beck, Hylke E. [7 ]
Ferguson, Craig R. [8 ]
Torres-Rojas, Laura [5 ]
Eigenbrod, Felix [4 ]
Crow, Wade [9 ]
Wood, Eric F. [1 ]
机构
[1] Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA
[2] Princeton Univ, Atmospher & Ocean Sci Program, Princeton, NJ 08544 USA
[3] NOAA, Geophys Fluid Dynam Lab, Princeton, NJ 08540 USA
[4] Univ Southampton, Sch Geog & Environm Sci, Southampton, Hants, England
[5] Duke Univ, Dept Civil & Environm Engn, Durham, NC 27706 USA
[6] Univ Calif San Diego, Scripps Inst Oceanog, Ctr Western Weather & Water Extremes, San Diego, CA 92103 USA
[7] European Commiss, Joint Res Ctr JRC, Ispra, Italy
[8] SUNY Albany, Atmospher Sci Res Ctr, Albany, NY 12222 USA
[9] USDA, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA
基金
英国科研创新办公室;
关键词
soil moisture; landscape; heterogeneity; scaling; spatial variability; hyper-resolution; LAND-SURFACE; SCALE; HYDROBLOCKS; DROUGHT; PRECIPITATION; HETEROGENEITY; AVAILABILITY; BIODIVERSITY; SYSTEM; MODEL;
D O I
10.1029/2022GL098586
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site-specific studies, little is known about how SM varies locally (1-100-m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long-standing challenge in hydrology. We reveal the striking variability of local-scale SM across the United States using SMAP-HydroBlocks - a novel satellite-based surface SM data set at 30-m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local-scale complexity yields a remarkable and unique multi-scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1-km resolution, with complete loss expected at the scale of current state-of-the-art SM monitoring and modeling systems (1-25 km resolution).
引用
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页数:13
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