Ground–Surface Water Assessment for Agricultural Land Prioritization in the Upper Kansai Basin, India: An Integrated SWAT-VIKOR Framework Approach

Prioritizing agricultural land use is a significant challenge for sustainable development in the rapidly urbanizing, semi-arid riverine basins of South Asia, especially under climate variability and water scarcity. This study introduces a systematic framework combining remote sensing and geospatial data with the Soil and Water Assessment Tool (SWAT) model, morphometric analysis, and VIKOR-based Multi-Criteria Decision Analysis (MCDA) to effectively identify Agricultural Land Prioritization (AgLP) areas in the Upper Kansai Basin, India, while reducing the environmental impact, in line with Sustainable Development Goals (SDGs). The SWAT model simulation reveals varied hydrological patterns, with basin water yields from 965.9 to 1012.9 mm and a substantial baseflow (~64% of total flow), emphasizing essential groundwater–surface water interactions for sustainable agriculture. However, the discrepancy between percolation (47% of precipitation) and deep recharge (2% of precipitation) signals potential long-term groundwater challenges. VIKOR analysis offers a robust prioritization framework, ranking SW4 as the most suitable (Qi = 0.003) for balanced hydrological and morphometric features, in agreement with the SWAT outcomes. SW4 and SW5 display optimal agricultural conditions due to stable terrain, effective water retention, and favorable morphometric traits (drainage density 3.0–3.15 km/km2; ruggedness 0.3–0.4). Conversely, SW2, with high drainage density (5.33 km/km2) and ruggedness (2.0), shows low suitability, indicating risks of erosion and poor water retention. This integrated AgLP framework advances sustainable agricultural development and supports SDGs, including SDG 2 (Zero Hunger), SDG 6 (Clean Water), SDG 13 (Climate Action), and SDG 15 (Life on Land). Incorporating hydrological dynamics, land use, soil properties, and climate variables, this approach offers a precise assessment of agricultural suitability to address global sustainability challenges in vulnerable riverine basins of developing nations.

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Publication Details

Journal
Water
Published
2025-03-19
DOI
https://doi.org/10.3390/w17060880
Citations
13
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
10.62

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article

Ground–Surface Water Assessment for Agricultural Land Prioritization in the Upper Kansai Basin, India: An Integrated SWAT-VIKOR Framework Approach

Abhilash Kumar Chandel, Nassir Al‐Arifi, Santanu Banerjee, Mahmoud E. Abd-Elmaboud et al.
13 citations
Water
Hydrology and Watershed Management Studies
10.62
article

Ground–Surface Water Assessment for Agricultural Land Prioritization in the Upper Kansai Basin, India: An Integrated SWAT-VIKOR Framework Approach

Abhilash Kumar Chandel, Nassir Al‐Arifi, Santanu Banerjee, Mahmoud E. Abd-Elmaboud, Gupinath Bhandari, Youssef M. Youssef, Sudipto Halder
article en
13 citations

Abstract

Prioritizing agricultural land use is a significant challenge for sustainable development in the rapidly urbanizing, semi-arid riverine basins of South Asia, especially under climate variability and water scarcity. This study introduces a systematic framework combining remote sensing and geospatial data with the Soil and Water Assessment Tool (SWAT) model, morphometric analysis, and VIKOR-based Multi-Criteria Decision Analysis (MCDA) to effectively identify Agricultural Land Prioritization (AgLP) areas in the Upper Kansai Basin, India, while reducing the environmental impact, in line with Sustainable Development Goals (SDGs). The SWAT model simulation reveals varied hydrological patterns, with basin water yields from 965.9 to 1012.9 mm and a substantial baseflow (~64% of total flow), emphasizing essential groundwater–surface water interactions for sustainable agriculture. However, the discrepancy between percolation (47% of precipitation) and deep recharge (2% of precipitation) signals potential long-term groundwater challenges. VIKOR analysis offers a robust prioritization framework, ranking SW4 as the most suitable (Qi = 0.003) for balanced hydrological and morphometric features, in agreement with the SWAT outcomes. SW4 and SW5 display optimal agricultural conditions due to stable terrain, effective water retention, and favorable morphometric traits (drainage density 3.0–3.15 km/km2; ruggedness 0.3–0.4). Conversely, SW2, with high drainage density (5.33 km/km2) and ruggedness (2.0), shows low suitability, indicating risks of erosion and poor water retention. This integrated AgLP framework advances sustainable agricultural development and supports SDGs, including SDG 2 (Zero Hunger), SDG 6 (Clean Water), SDG 13 (Climate Action), and SDG 15 (Life on Land). Incorporating hydrological dynamics, land use, soil properties, and climate variables, this approach offers a precise assessment of agricultural suitability to address global sustainability challenges in vulnerable riverine basins of developing nations.

WaterVol. 17(6)
Bidhan Chandra Krishi Viswavidyalaya (IN), Suez University (EG), Mansoura University (EG), Jadavpur University (IN), Chhatrapati Shahu Ji Maharaj University (IN), King Saud University (SA), Virginia Tech (US)
King Saud University
Openalex Percentile: Top 2%
Hydrology and Watershed Management Studies
10.62
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