Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance Variables
A suitable routing model for predicting future monthly water discharge (WD) is essential for operational hydrology, including water supply, and hydrological extreme management, to mention but a few. This is particularly important for a remote area without a sufficient number of in-situ data, promoti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/7/2025 |
| _version_ | 1797562263186440192 |
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| author | Hok Sum Fok Linghao Zhou Hang Ji |
| author_facet | Hok Sum Fok Linghao Zhou Hang Ji |
| author_sort | Hok Sum Fok |
| collection | DOAJ |
| description | A suitable routing model for predicting future monthly water discharge (WD) is essential for operational hydrology, including water supply, and hydrological extreme management, to mention but a few. This is particularly important for a remote area without a sufficient number of in-situ data, promoting the usage of remotely sensed surface variables. Direct correlation analysis between ground-observed WD and localized passive remotely-sensed surface variables (e.g., indices and geometric variables) has been studied extensively over the past two decades. Most of these related studies focused on the usage of constructed correlative relationships for estimating WD at ungauged locations. Nevertheless, temporal prediction performance of monthly runoff (<i>R</i>) (being an average representation of WD of a catchment) at the river delta reconstructed from the basin’s upstream remotely-sensed water balance variables via a standardization approach has not been explored. This study examined the standardization approach via linear regression using the remotely-sensed water balance variables from upstream of the Mekong Basin to reconstruct and predict monthly <i>R</i> time series at the Mekong Delta. This was subsequently compared to that based on artificial intelligence (AI) models. Accounting for less than 1% improvement via the AI-based models over that of a direct linear regression, our results showed that both the reconstructed and predicted <i>Rs</i> based on the proposed approach yielded a 2–6% further improvement, in particular the reduction of discrepancy in the peak and trough of WD, over those reconstructed and predicted from the remotely-sensed water balance variables without standardization. This further indicated the advantage of the proposed standardization approach to mitigate potential environmental influences. The best <i>R</i>, predicted from standardized water storage over the whole upstream area, attained the highest Pearson correlation coefficient of 0.978 and Nash–Sutcliffe efficiency of 0.947, and the lowest normalized root-mean-square error of 0.072. |
| first_indexed | 2024-03-10T18:25:46Z |
| format | Article |
| id | doaj.art-67e0ba656e3a4283b8270ae3265ada69 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T18:25:46Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-67e0ba656e3a4283b8270ae3265ada692023-11-20T07:01:22ZengMDPI AGWater2073-44412020-07-01127202510.3390/w12072025Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance VariablesHok Sum Fok0Linghao Zhou1Hang Ji2School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaA suitable routing model for predicting future monthly water discharge (WD) is essential for operational hydrology, including water supply, and hydrological extreme management, to mention but a few. This is particularly important for a remote area without a sufficient number of in-situ data, promoting the usage of remotely sensed surface variables. Direct correlation analysis between ground-observed WD and localized passive remotely-sensed surface variables (e.g., indices and geometric variables) has been studied extensively over the past two decades. Most of these related studies focused on the usage of constructed correlative relationships for estimating WD at ungauged locations. Nevertheless, temporal prediction performance of monthly runoff (<i>R</i>) (being an average representation of WD of a catchment) at the river delta reconstructed from the basin’s upstream remotely-sensed water balance variables via a standardization approach has not been explored. This study examined the standardization approach via linear regression using the remotely-sensed water balance variables from upstream of the Mekong Basin to reconstruct and predict monthly <i>R</i> time series at the Mekong Delta. This was subsequently compared to that based on artificial intelligence (AI) models. Accounting for less than 1% improvement via the AI-based models over that of a direct linear regression, our results showed that both the reconstructed and predicted <i>Rs</i> based on the proposed approach yielded a 2–6% further improvement, in particular the reduction of discrepancy in the peak and trough of WD, over those reconstructed and predicted from the remotely-sensed water balance variables without standardization. This further indicated the advantage of the proposed standardization approach to mitigate potential environmental influences. The best <i>R</i>, predicted from standardized water storage over the whole upstream area, attained the highest Pearson correlation coefficient of 0.978 and Nash–Sutcliffe efficiency of 0.947, and the lowest normalized root-mean-square error of 0.072.https://www.mdpi.com/2073-4441/12/7/2025runoffremote sensing hydrologywater balance variable standardizationMekong Delta |
| spellingShingle | Hok Sum Fok Linghao Zhou Hang Ji Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance Variables Water runoff remote sensing hydrology water balance variable standardization Mekong Delta |
| title | Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance Variables |
| title_full | Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance Variables |
| title_fullStr | Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance Variables |
| title_full_unstemmed | Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance Variables |
| title_short | Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance Variables |
| title_sort | mekong delta runoff prediction using standardized remotely sensed water balance variables |
| topic | runoff remote sensing hydrology water balance variable standardization Mekong Delta |
| url | https://www.mdpi.com/2073-4441/12/7/2025 |
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