Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026085 |
Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets | |
Dembele, Moctar1,2; Hrachowitz, Markus2; Savenije, Hubert H. G.2; Mariethoz, Gregoire1; Schaefli, Bettina1,3 | |
2020 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:1 |
文章类型 | Article |
语种 | 英语 |
国家 | Switzerland; Netherlands |
英文摘要 | Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of the traditional streamflow-only calibration. However, combining multiple data sources in model calibration requires a meaningful integration of the data sets, which should harness their most reliable contents to avoid accumulation of their uncertainties and mislead the parameter estimation procedure. This study analyzes the improvement of model parameter selection by using only the spatial patterns of satellite remote sensing data, thereby ignoring their absolute values. Although satellite products are characterized by uncertainties, their most reliable key feature is the representation of spatial patterns, which is a unique and relevant source of information for distributed hydrological models. We propose a novel multivariate calibration framework exploiting spatial patterns and simultaneously incorporating streamflow and three satellite products (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, and Gravity Recovery and Climate Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data set is used for model evaluation. A bias-insensitive and multicomponent spatial pattern matching metric is developed to formulate a multiobjective function. The proposed multivariate calibration framework is tested with the mesoscale Hydrologic Model (mHM) and applied to the poorly gauged Volta River basin located in a predominantly semiarid climate in West Africa. Results of the multivariate calibration show that the decrease in performance for streamflow (-7%) and terrestrial water storage (-6%) is counterbalanced with an increase in performance for soil moisture (+105%) and evaporation (+26%). These results demonstrate that there are benefits in using satellite data sets, when suitably integrated in a robust model parametrization scheme. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000520132500044 |
WOS关键词 | REMOTELY-SENSED EVAPOTRANSPIRATION ; SMOS SOIL-MOISTURE ; PARAMETER-ESTIMATION ; LAND-SURFACE ; WATER-RESOURCES ; PROCESS REPRESENTATION ; VEGETATION DYNAMICS ; DATA ASSIMILATION ; EARTH OBSERVATION ; GRACE DATA |
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/280463 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Lausanne, Inst Earth Surface Dynam, Fac Geosci & Environm, Lausanne, Switzerland; 2.Delft Univ Technol, Fac Civil Engn & Geosci, Water Resources Sect, Delft, Netherlands; 3.Univ Bern, Inst Geog GIUB, Bern, Switzerland |
推荐引用方式 GB/T 7714 | Dembele, Moctar,Hrachowitz, Markus,Savenije, Hubert H. G.,et al. Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets[J]. WATER RESOURCES RESEARCH,2020,56(1). |
APA | Dembele, Moctar,Hrachowitz, Markus,Savenije, Hubert H. G.,Mariethoz, Gregoire,&Schaefli, Bettina.(2020).Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets.WATER RESOURCES RESEARCH,56(1). |
MLA | Dembele, Moctar,et al."Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets".WATER RESOURCES RESEARCH 56.1(2020). |
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