Modified water balance model for groundwater recharge estimations using cloud integration
Abstract Groundwater recharge is an essential element of enhancing global water governance. This is conspicuous over areas like West Bengal, India, which face natural and manmade water resource challenges. This particular study aims at improving the estimation of groundwater recharge using the Modif...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Civil Engineering |
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| Online Access: | https://doi.org/10.1007/s44290-025-00162-7 |
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| author | Arup Kumar Das Saurabh Kumar Gupta Suraj Kumar Singh Pradeep Kumar Rawat Shruti Kanga |
| author_facet | Arup Kumar Das Saurabh Kumar Gupta Suraj Kumar Singh Pradeep Kumar Rawat Shruti Kanga |
| author_sort | Arup Kumar Das |
| collection | DOAJ |
| description | Abstract Groundwater recharge is an essential element of enhancing global water governance. This is conspicuous over areas like West Bengal, India, which face natural and manmade water resource challenges. This particular study aims at improving the estimation of groundwater recharge using the Modified Water Balance Model (MWBM), which has been integrated with Google Earth Engine (GEE) and high-resolution remote sensing data here in application for groundwater. The method used consists of MODIS land surface temperature and CHIRPS precipitation data efficiently maps groundwater recharge estimation for various districts of West Bengal. The MWBM utilizes the geospatial analytic capabilities of GEE and above calculations in MWBM in creating recharge estimations that are geographically referenced. The majority of the results showed significant differences in the spatial recharge characteristics of the aquifers across the study region. High recharge was found in Alipurduar and Jalpaiguri district because of the high rainfall but low and constant recharge potential in Bankura and Purba Bardhaman districts due to less permeable rock layers. Within MWBM, improvements in groundwater management include the use of remote sensing techniques as well as modernization of computational processes to enhance recharge estimates. The present study not only aims at improving the accuracy of recharge estimation methods but also suggests a workable approach in the context of water resource management plans. |
| format | Article |
| id | doaj-art-84125c51124b4c9aab62621135113560 |
| institution | Kabale University |
| issn | 2948-1546 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Civil Engineering |
| spelling | doaj-art-84125c51124b4c9aab626211351135602025-01-19T12:38:20ZengSpringerDiscover Civil Engineering2948-15462025-01-012112910.1007/s44290-025-00162-7Modified water balance model for groundwater recharge estimations using cloud integrationArup Kumar Das0Saurabh Kumar Gupta1Suraj Kumar Singh2Pradeep Kumar Rawat3Shruti Kanga4Centre for Climate Change and Water Research, Suresh Gyan Vihar UniversityCentre for Climate Change and Water Research, Suresh Gyan Vihar UniversityCentre for Sustainable Development, Suresh Gyan Vihar UniversityAsian International UniversityDepartment of Geography, School of Environment and Earth Sciences, Central University of PunjabAbstract Groundwater recharge is an essential element of enhancing global water governance. This is conspicuous over areas like West Bengal, India, which face natural and manmade water resource challenges. This particular study aims at improving the estimation of groundwater recharge using the Modified Water Balance Model (MWBM), which has been integrated with Google Earth Engine (GEE) and high-resolution remote sensing data here in application for groundwater. The method used consists of MODIS land surface temperature and CHIRPS precipitation data efficiently maps groundwater recharge estimation for various districts of West Bengal. The MWBM utilizes the geospatial analytic capabilities of GEE and above calculations in MWBM in creating recharge estimations that are geographically referenced. The majority of the results showed significant differences in the spatial recharge characteristics of the aquifers across the study region. High recharge was found in Alipurduar and Jalpaiguri district because of the high rainfall but low and constant recharge potential in Bankura and Purba Bardhaman districts due to less permeable rock layers. Within MWBM, improvements in groundwater management include the use of remote sensing techniques as well as modernization of computational processes to enhance recharge estimates. The present study not only aims at improving the accuracy of recharge estimation methods but also suggests a workable approach in the context of water resource management plans.https://doi.org/10.1007/s44290-025-00162-7Groundwater rechargeModified water balance modelGoogle earth engine (GEE)Remote sensing dataSustainable water resource managementSpatial analysis |
| spellingShingle | Arup Kumar Das Saurabh Kumar Gupta Suraj Kumar Singh Pradeep Kumar Rawat Shruti Kanga Modified water balance model for groundwater recharge estimations using cloud integration Discover Civil Engineering Groundwater recharge Modified water balance model Google earth engine (GEE) Remote sensing data Sustainable water resource management Spatial analysis |
| title | Modified water balance model for groundwater recharge estimations using cloud integration |
| title_full | Modified water balance model for groundwater recharge estimations using cloud integration |
| title_fullStr | Modified water balance model for groundwater recharge estimations using cloud integration |
| title_full_unstemmed | Modified water balance model for groundwater recharge estimations using cloud integration |
| title_short | Modified water balance model for groundwater recharge estimations using cloud integration |
| title_sort | modified water balance model for groundwater recharge estimations using cloud integration |
| topic | Groundwater recharge Modified water balance model Google earth engine (GEE) Remote sensing data Sustainable water resource management Spatial analysis |
| url | https://doi.org/10.1007/s44290-025-00162-7 |
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