Using Remote Sensing Data-Based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments
文献类型：期刊论文
| 作者 | Huang, Qi4,5; Qin, Guanghua2,4; Zhang, Yongqiang5; Tang, Qiuhong5 ; Liu, Changming5; Xia, Jun3; Chiew, Francis H. S.1; Post, David1
|
| 刊名 | WATER RESOURCES RESEARCH
 |
| 出版日期 | 2020-08-01 |
| 卷号 | 56期号:8页码:21 |
| 关键词 | remote sensing evapotranspiration PML runoff prediction bias correction |
| ISSN号 | 0043-1397 |
| DOI | 10.1029/2020WR028205 |
| 通讯作者 | Zhang, Yongqiang(yongqiang.zhang2014@gmail.com) |
| 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 (nonbias-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. Key Points Using bias-corrected remote sensing data to calibrate hydrological model shows great potential especially in ungauged catchments Compared to raw PML-AET, bias-corrected PML-AET improves runoff prediction noticeably and adding GRACE shows limited benefit Gridded application performs better than lumped catchment modeling application for maximizing the benefit from the spatial PML-AET data |
| WOS关键词 | SOIL-MOISTURE ; DATA SETS ; RAINFALL ; PRECIPITATION ; EVAPOTRANSPIRATION ; SATELLITE ; BASIN ; REGIONALIZATION ; IDENTIFICATION ; SIMULATIONS |
| 资助项目 | CAS Talent Program ; National Natural Science Foundation of China[41971032] ; National Natural Science Foundation of China[51879172] ; Second Tibetan Plateau Scientific Expedition and Research[2019QZKK0208] |
| WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000582701700077 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | CAS Talent Program ; National Natural Science Foundation of China ; Second Tibetan Plateau Scientific Expedition and Research |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156757]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Yongqiang |
| 作者单位 | 1.CSIRO Land & Water, Canberra, ACT, Australia 2.Sichuan Univ, State Key Lab Hydraul & Mt River Engn, Chengdu, Peoples R China 3.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Peoples R China 4.Sichuan Univ, Coll Water Resource & Hydropower, Chengdu, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Qi,Qin, Guanghua,Zhang, Yongqiang,et al. Using Remote Sensing Data-Based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments[J]. WATER RESOURCES RESEARCH,2020,56(8):21. |
| APA | Huang, Qi.,Qin, Guanghua.,Zhang, Yongqiang.,Tang, Qiuhong.,Liu, Changming.,...&Post, David.(2020).Using Remote Sensing Data-Based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments.WATER RESOURCES RESEARCH,56(8),21. |
| MLA | Huang, Qi,et al."Using Remote Sensing Data-Based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments".WATER RESOURCES RESEARCH 56.8(2020):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。