Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach
Abstract In order to enhance the simulation of BMPs (Best Management Practices) reduction effects in unmonitored watersheds, in this study, we combined the physically-based hydrological model Soil & Water Assessment Tool (SWAT) and the data-driven model Bi-directional Long Short-Term Memory (Bi-...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-44531-7 |
| _version_ | 1827634775650729984 |
|---|---|
| author | Xianqi Zhang Yu Qi Haiyang Li Shifeng Sun Qiuwen Yin |
| author_facet | Xianqi Zhang Yu Qi Haiyang Li Shifeng Sun Qiuwen Yin |
| author_sort | Xianqi Zhang |
| collection | DOAJ |
| description | Abstract In order to enhance the simulation of BMPs (Best Management Practices) reduction effects in unmonitored watersheds, in this study, we combined the physically-based hydrological model Soil & Water Assessment Tool (SWAT) and the data-driven model Bi-directional Long Short-Term Memory (Bi-LSTM), using the very-high-resolution (VHR) Land Use and Land Cover (LULC) dataset SinoLC-1 as data input, to evaluate the feasibility of constructing a water environment model for the Ba-River Basin (BRB) in central China and improving streamflow prediction performance. In the SWAT-BiLSTM model, we calibrated the top five SWAT parameters sorted by P-Value, allowing SWAT to act as a transfer function to convert meteorological data into base flow and storm flow, serving as the data input for the Bi-LSTM model. This optimization improved the Bi-LSTM's learning process for the relationship between the target and explanatory variables. The daily streamflow prediction results showed that the hybrid model had 9 regions rated as "Very good," 2 as "Good," 2 as "Satisfactory," and 1 as "Unsatisfactory" among the 14 regions. The model achieved an NSE of 0.86, R2 of 0.85, and PBIAS of −2.71% for the overall daily streamflow prediction performance during the verification period of the BRB. This indicates that the hybrid model has high predictive accuracy and no significant systematic bias, providing a sound hydrodynamic environment for water quality simulation. The simulation results of different BMPs scenarios showed that in the scenarios with only one BMP measure, stubble mulch had the best reduction effect, with average reductions of 17.83% for TN and 36.17% for TP. In the scenarios with a combination of multiple BMP measures, the combination of stubble mulch, soil testing and formula fertilization, and vegetative filter strip performed the best, achieving average reductions of 42.71% for TN and 50.40% for TP. The hybrid model provides a novel approach to simulate BMPs' reduction effects in regions without measured hydrological data and has the potential for wide application in BMP-related decision-making. |
| first_indexed | 2024-03-09T15:17:59Z |
| format | Article |
| id | doaj.art-e1879d78c99a409dbc4ad8f69576767f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:17:59Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e1879d78c99a409dbc4ad8f69576767f2023-11-26T12:58:27ZengNature PortfolioScientific Reports2045-23222023-10-0113111510.1038/s41598-023-44531-7Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approachXianqi Zhang0Yu Qi1Haiyang Li2Shifeng Sun3Qiuwen Yin4Water Conservancy College, North China University of Water Resources and Electric PowerWater Conservancy College, North China University of Water Resources and Electric PowerWater Conservancy College, North China University of Water Resources and Electric PowerWater Conservancy College, North China University of Water Resources and Electric PowerWater Conservancy College, North China University of Water Resources and Electric PowerAbstract In order to enhance the simulation of BMPs (Best Management Practices) reduction effects in unmonitored watersheds, in this study, we combined the physically-based hydrological model Soil & Water Assessment Tool (SWAT) and the data-driven model Bi-directional Long Short-Term Memory (Bi-LSTM), using the very-high-resolution (VHR) Land Use and Land Cover (LULC) dataset SinoLC-1 as data input, to evaluate the feasibility of constructing a water environment model for the Ba-River Basin (BRB) in central China and improving streamflow prediction performance. In the SWAT-BiLSTM model, we calibrated the top five SWAT parameters sorted by P-Value, allowing SWAT to act as a transfer function to convert meteorological data into base flow and storm flow, serving as the data input for the Bi-LSTM model. This optimization improved the Bi-LSTM's learning process for the relationship between the target and explanatory variables. The daily streamflow prediction results showed that the hybrid model had 9 regions rated as "Very good," 2 as "Good," 2 as "Satisfactory," and 1 as "Unsatisfactory" among the 14 regions. The model achieved an NSE of 0.86, R2 of 0.85, and PBIAS of −2.71% for the overall daily streamflow prediction performance during the verification period of the BRB. This indicates that the hybrid model has high predictive accuracy and no significant systematic bias, providing a sound hydrodynamic environment for water quality simulation. The simulation results of different BMPs scenarios showed that in the scenarios with only one BMP measure, stubble mulch had the best reduction effect, with average reductions of 17.83% for TN and 36.17% for TP. In the scenarios with a combination of multiple BMP measures, the combination of stubble mulch, soil testing and formula fertilization, and vegetative filter strip performed the best, achieving average reductions of 42.71% for TN and 50.40% for TP. The hybrid model provides a novel approach to simulate BMPs' reduction effects in regions without measured hydrological data and has the potential for wide application in BMP-related decision-making.https://doi.org/10.1038/s41598-023-44531-7 |
| spellingShingle | Xianqi Zhang Yu Qi Haiyang Li Shifeng Sun Qiuwen Yin Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach Scientific Reports |
| title | Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach |
| title_full | Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach |
| title_fullStr | Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach |
| title_full_unstemmed | Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach |
| title_short | Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach |
| title_sort | assessing effect of best management practices in unmonitored watersheds using the coupled swat bilstm approach |
| url | https://doi.org/10.1038/s41598-023-44531-7 |
| work_keys_str_mv | AT xianqizhang assessingeffectofbestmanagementpracticesinunmonitoredwatershedsusingthecoupledswatbilstmapproach AT yuqi assessingeffectofbestmanagementpracticesinunmonitoredwatershedsusingthecoupledswatbilstmapproach AT haiyangli assessingeffectofbestmanagementpracticesinunmonitoredwatershedsusingthecoupledswatbilstmapproach AT shifengsun assessingeffectofbestmanagementpracticesinunmonitoredwatershedsusingthecoupledswatbilstmapproach AT qiuwenyin assessingeffectofbestmanagementpracticesinunmonitoredwatershedsusingthecoupledswatbilstmapproach |