Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020WR028205 |
Using Remote Sensing Data‐based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments | |
Qi Huang; Guanghua Qin; Yongqiang Zhang; Qiuhong Tang; Changming Liu; Jun Xia; Francis H. S. Chiew; David Post | |
2020-08-12 | |
发表期刊 | Water Resources Research
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出版年 | 2020 |
英文摘要 | Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored, and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (non‐bias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data, and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions. |
领域 | 资源环境 |
URL | 查看原文 |
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/287813 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Qi Huang,Guanghua Qin,Yongqiang Zhang,等. Using Remote Sensing Data‐based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments[J]. Water Resources Research,2020. |
APA | Qi Huang.,Guanghua Qin.,Yongqiang Zhang.,Qiuhong Tang.,Changming Liu.,...&David Post.(2020).Using Remote Sensing Data‐based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments.Water Resources Research. |
MLA | Qi Huang,et al."Using Remote Sensing Data‐based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments".Water Resources Research (2020). |
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