Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables
Basin water supply, planning, and its allocation requires runoff measurements near an estuary mouth. However, insufficient financial budget results in no further runoff measurements at critical in situ stations. This has recently promoted the runoff reconstruction via regression between the runoff a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/4/999 |
| _version_ | 1827652850232066048 |
|---|---|
| author | Hok Sum Fok Yutong Chen Linghao Zhou |
| author_facet | Hok Sum Fok Yutong Chen Linghao Zhou |
| author_sort | Hok Sum Fok |
| collection | DOAJ |
| description | Basin water supply, planning, and its allocation requires runoff measurements near an estuary mouth. However, insufficient financial budget results in no further runoff measurements at critical in situ stations. This has recently promoted the runoff reconstruction via regression between the runoff and nearby remotely-sensed variables on a monthly scale. Nonetheless, reconstructing daily runoff from individual basin-upstream remotely-sensed climatic variables is yet to be explored. This study investigates standardized data regression approach to reconstruct daily runoff from the individual remotely-sensed climatic variables at the Mekong Basin’s upstream. Compared to simple linear regression, the daily runoff reconstructed and forecasted from the presented approach were improved by at most 5% and 10%, respectively. Reconstructed runoffs using neural network models yielded ~0.5% further improvement. The improvement was largely a function of the reduced discrepancy during dry and wet seasons. The best forecasted runoff obtained from the basin-upstream standardized precipitation index, yielded the lowest normalized root-mean-square error of 0.093. |
| first_indexed | 2024-03-09T21:07:57Z |
| format | Article |
| id | doaj.art-357e58f152fc401b8082b03d9c3dd672 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:07:57Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-357e58f152fc401b8082b03d9c3dd6722023-11-23T21:55:28ZengMDPI AGRemote Sensing2072-42922022-02-0114499910.3390/rs14040999Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic VariablesHok Sum Fok0Yutong Chen1Linghao Zhou2School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaBasin water supply, planning, and its allocation requires runoff measurements near an estuary mouth. However, insufficient financial budget results in no further runoff measurements at critical in situ stations. This has recently promoted the runoff reconstruction via regression between the runoff and nearby remotely-sensed variables on a monthly scale. Nonetheless, reconstructing daily runoff from individual basin-upstream remotely-sensed climatic variables is yet to be explored. This study investigates standardized data regression approach to reconstruct daily runoff from the individual remotely-sensed climatic variables at the Mekong Basin’s upstream. Compared to simple linear regression, the daily runoff reconstructed and forecasted from the presented approach were improved by at most 5% and 10%, respectively. Reconstructed runoffs using neural network models yielded ~0.5% further improvement. The improvement was largely a function of the reduced discrepancy during dry and wet seasons. The best forecasted runoff obtained from the basin-upstream standardized precipitation index, yielded the lowest normalized root-mean-square error of 0.093.https://www.mdpi.com/2072-4292/14/4/999daily runoff forecastMekong BasinGRACE gravimetryTRMM precipitationENSO |
| spellingShingle | Hok Sum Fok Yutong Chen Linghao Zhou Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables Remote Sensing daily runoff forecast Mekong Basin GRACE gravimetry TRMM precipitation ENSO |
| title | Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables |
| title_full | Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables |
| title_fullStr | Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables |
| title_full_unstemmed | Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables |
| title_short | Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables |
| title_sort | prospects for reconstructing daily runoff from individual upstream remotely sensed climatic variables |
| topic | daily runoff forecast Mekong Basin GRACE gravimetry TRMM precipitation ENSO |
| url | https://www.mdpi.com/2072-4292/14/4/999 |
| work_keys_str_mv | AT hoksumfok prospectsforreconstructingdailyrunofffromindividualupstreamremotelysensedclimaticvariables AT yutongchen prospectsforreconstructingdailyrunofffromindividualupstreamremotelysensedclimaticvariables AT linghaozhou prospectsforreconstructingdailyrunofffromindividualupstreamremotelysensedclimaticvariables |