Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR024949 |
A Unified Data-Driven Method to Derive Hydrologic Dynamics From Global SMAP Surface Soil Moisture and GPM Precipitation Data | |
Mao, Yixin1; Crow, Wade T.2; Nijssen, Bart1 | |
2020-02-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:2 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Data sets provided by the Soil Moisture Active Passive (SMAP) and the Global Precipitation Measurement (GPM) satellite missions contain rich information about land surface hydrologic processes.In this study, a unified regression method is proposed and applied to these global data sets to investigate surface soil moisture (SSM) dynamics. Two forms of regressors are implemented: 1) the linear regressors of SSM and precipitation flux and 2) the linear regressors of SSM and precipitation flux with an additional interaction term. Regression results based on 3 years of global SMAP and GPM data show that the unified regression method can identify the SSM characteristics found by several recent studies, including the SSM exponential decay rate, the fraction of precipitation retained in the surface soil layer, and the effective depth of hydrologic storage. Additionally, including the interaction regressor provides a novel way to derive the sensitivity of infiltration/runoff partitioning to antecedent SSM without the need for streamflow observations. These SMAP/GPM regression results are compared with those derived from a global SSM data set simulated by the variable infiltration capacity model. Relative to the satellite data, variable infiltration capacity retains moisture longer in the top layer, retains too much precipitation input in that layer, and exhibits levels of sensitivity of runoff/infiltration partitioning to top-layer soil moisture that generally match SMAP especially in humid regions. This study demonstrates that the regression-based method can recover useful process-level insight from SMAP SSM retrievals and is a viable tool for evaluating the representation of surface processes in hydrologic models. Plain Language Summary In the past few years, new satellite missions have been launched to make frequent observations of near-surface soil moisture and rainfall over most global land areas. In this study, we analyze these satellite observations to learn how near-surface soil moisture changes over time. Based on this analysis, we can determine for each location: 1) how fast near-surface soil moisture declines after rainfall, 2) how much rainfall enters the soil versus how much runs off, and 3) how rainfall partitioning is affected by near-surface soil moisture. The first two questions have been studied before, but our method is simpler and reaches the same conclusions. Our method is also able to answer the third question, which has not been looked at before. Finally, we analyze a model-based near-surface soil moisture data set in the same way and compare the results with our observation-based finding. In this way, we pinpoint aspects of the mathematical model that do not correctly mimic the real-world evolution of near-surface soil moisture. This insight will guide model upgrades that better capture reality. |
英文关键词 | SMAP satellite soil moisture dynamics regression hydrologic model data-driven |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000535672800048 |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/280498 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA; 2.ARS, Hydrol & Remote Sensing Lab, USDA, Beltsville, MD USA |
推荐引用方式 GB/T 7714 | Mao, Yixin,Crow, Wade T.,Nijssen, Bart. A Unified Data-Driven Method to Derive Hydrologic Dynamics From Global SMAP Surface Soil Moisture and GPM Precipitation Data[J]. WATER RESOURCES RESEARCH,2020,56(2). |
APA | Mao, Yixin,Crow, Wade T.,&Nijssen, Bart.(2020).A Unified Data-Driven Method to Derive Hydrologic Dynamics From Global SMAP Surface Soil Moisture and GPM Precipitation Data.WATER RESOURCES RESEARCH,56(2). |
MLA | Mao, Yixin,et al."A Unified Data-Driven Method to Derive Hydrologic Dynamics From Global SMAP Surface Soil Moisture and GPM Precipitation Data".WATER RESOURCES RESEARCH 56.2(2020). |
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