Arid and semi-arid regions experience limited rainfall and frequent droughts, which adversely affect natural resources and livelihoods, ultimately hindering economic development. Climate change exacerbates water scarcity, highlighting the need for optimal water resource utilisation, particularly through rainwater harvesting. Identifying suitable sites for rainwater harvesting has become a critical research focus, employing remote sensing and geographical information systems (GIS) technologies.
This paper “Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions: A case study on Purulia” aims to develop a model that efficiently identifies rainwater harvesting potential zones using principal variables, combining geographical information systems methods with multi-criteria decision-making and machine learning techniques.
The study focuses on Purulia, a drought-prone district in West Bengal, India, emphasising the necessity for precise categorisation of land for effective rainwater harvesting practices. This research embraces a comprehensive approach to address water-related concerns, offering a replicable framework applicable globally, with a specific focus on arid and semi-arid regions.
The study focuses on identifying suitable rainwater harvesting potential zones in the Purulia district, West Bengal, which is part of the Chhottanagpur plateau, a region prone to water scarcity due to its arid climate, non-perennial rivers, and unsuitable soil conditions. The district receives uneven rainfall, with higher rainfall in the eastern part and lower in the west. Six key factors—rainfall, slope, soil texture, drainage density, runoff coefficient, and land cover—were selected as criteria to identify rainwater harvesting potential zones.
Data for these parameters were collected from various sources, and their significance in rainwater harvesting was evaluated using a Multi-Criteria Decision-Making (MCDM) approach, specifically the Analytical Hierarchical Process (AHP). This technique assigns weightage to each factor based on pairwise comparisons, with rainfall being given the highest importance due to its critical role in this arid region. The overlay analysis was performed using ArcGIS software, where each criterion was classified into five categories of suitability, ranging from "not suitable" to "very highly suitable" for rainwater harvesting.
In addition to the MCDM approach, machine learning algorithms were employed to create predictive models for rainwater harvesting potential zones identification. Two algorithms—Artificial Neural Network (ANN) and Random Forest (RF)—were used. Random Forest was preferred because of its higher accuracy. The study area was divided into five zones based on rainwater harvesting potential: not suitable, low suitability, moderate suitability, high suitability, and very high suitability.
Analysis of factors for identifying potential rainwater harvesting zones
Purulia exhibits characteristics typical of areas near the Tropic of Cancer, featuring undulating geomorphology in some locations. An examination of variables influencing rainwater harvesting potential reveals that rainfall is uneven across the region. The western part receives the least rainfall, averaging less than 1,300 mm annually, affecting about 17% of the area. Rainfall increases eastward, with approximately 15% of the area receiving more than 1,350 mm. A third of the central region experiences rainfall between 1,400 mm and 1,500 mm yearly, while only 12% of the extreme eastern area receives about 1,500 mm annually.
Around 50% of the land features a gentle slope of less than 2.5°, with about 40% of the area having slopes under 6.5°. The topography is relatively flat for nearly 90% of the region, with only 6% experiencing undulation up to 12.5°. The western section primarily consists of plateau regions with minor hills, where slope angles range from 22.5° to 60°.
Runoff potential in the study area varies from negligible to 56%. The maximum runoff coefficient occurs in gentle plains used for agriculture, while hilly and rangeland areas show the least runoff potential. Approximately 17% of the area has minimal runoff, while 27% of the area shows runoff between 28% and 38%. High runoff potential is identified in 13% of the area, with values reaching up to 56%.
The plateau's characteristics are evident in the soil texture, with around 65% of the land covered by clay and loam, while 21% consists of purely loamy soil. Mixed soil types of sand and loam account for only 12% of the area, with loamy properties dominating the region.
The drainage network is reflected in drainage density, with 30% of the area having minimal density (0 to 0.33 km/km²) and only 5% showing maximum density (over 2 km/km²). The remaining 63% of the area has densities ranging from 0.33 to 2.01 km/km².
Purulia's economy relies mainly on primary and secondary activities, with land use dominated by agriculture. Approximately 56% of the land is agricultural, although much of it remains barren for significant portions of the year. About 16% of the area is covered by trees and vegetation, particularly dense forests in the hills. The topography and climate also support native herbaceous vegetation, which occupies nearly one-fifth of the total area.
The analysis of rainwater harvesting potential zones reveals that less than 2% of the study area is unsuitable for rainwater harvesting, primarily located in the western section. Only 8% is deemed poorly suitable, indicating that the potential for rainwater harvesting is nearly non-existent there. Approximately one-third of the western and southern regions are classified as having moderate potential. Notably, around 45% of the area is highly suitable for rainwater harvesting, with the central eastern part showing the greatest potential and considered "very highly suitable", covering less than 12% of the total area.
The study highlighted the importance of spatial and machine learning-based analysis for managing water scarcity in dryland regions like Purulia. These techniques can help policymakers and planners develop sustainable water management strategies by optimising the use of rainwater, especially in regions with limited water resources. The findings suggest that regions with gentle slopes, higher rainfall, and loamy soil textures are most suitable for rainwater harvesting. Additionally, the study recommends further exploration of machine learning models for greater accuracy in spatial prediction.
The analysis highlighted that specific rainwater harvesting techniques should be applied separately to urban and rural areas. Urban methods focus on recharge pits, trenches, and wells, while rural areas benefit from techniques like gully plugs, contour bunds, percolation tanks, and check dams.
The study emphasises that the Random Forest model's framework could be applied to other arid and semi-arid regions globally, using publicly available datasets. By identifying rainwater harvesting potential zones, Purulia can improve agricultural irrigation and water access, leading to overall socio-economic development. This integrated approach can be replicated in similar water-scarce regions worldwide to tackle drought and water shortages effectively.
The study outlines a practical method for mitigating water scarcity in Purulia through rainwater harvesting, highlighting the importance of spatial modelling, geographical information systems, and machine learning for water resource management.