| Autor: |
Fauzia, L. Surinaidu, Abdur Rahman, Shakeel Ahmed |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 11, Iss 1, Pp 1-16 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-021-90898-w |
| Popis: |
Abstract Extensive change in land use, climate, and over-exploitation of groundwater has increased pressure on aquifers, especially in the case of crystalline rocks throughout the world. To support sustainability in groundwater management require proper understating of groundwater dynamics and recharge potential. GIS based studies have gained immense popularity in groundwater exploration in recent years because they are fast and provide recent information on the resource for future growth. Thus, the present study utilized a GIS-based Weighted Overlay Index (WOI) model to identify the potential recharge zones and to gain deep knowledge of groundwater dynamics. The in situ infiltration tests have been carried out, which is the key process in groundwater recharge and is neglected in many cases for WOI. In the WOI, ten thematic layers from the parameters influencing and involved in the recharge process are considered to identify potential recharge zones. The results suggested a significant underestimation of recharge potential without considering site-specific infiltration rates that one needs to be considered. The present WOI model considered in situ infiltration information and classified the entire area into four recharge zones, good, moderate, poor, and very poor. The final integrated map compared with the real-time field data like water level fluctuation and infiltration to analyse occurrence and quantification of recharge. The estimated average groundwater draft is 21.9 mcm, while annual renewable recharge is only 5.7 mcm that causing a continuous fall of the groundwater table. The study is useful in selecting regions with more focussed recharge studies and suggested the need of reducing groundwater demand by changing cropping patterns through a predictive decision support tool. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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