Suitable Rainwater Harvesting Research Articles

Rainwater harvesting suitability of ground tanks located on stock watering places across Western NSW

ABSTRACT A stock watering place (SWP) is a type of travelling stock reserve, and Western Local Land Services (WLLS) is the controlling authority of SWPs in the semi-arid Western region of New South Wales, Australia. Over half SWPs have ground tanks that were installed in the 1800s to provide water for travelling stock. The location of many SWP ground tanks is thought to have been influenced by traditional Aboriginal knowledge, however, their position for capturing contemporary surface flows has not previously been assessed. This study aims to use multicriteria analysis within a Geographic Information System (GIS) to investigate whether SWP ground tanks are at the most suitable rainwater harvesting (RWH) location. ArcGIS was used to analyse data to identify RWH suitability based on slope, soil group, and drainage density. These layers were then weighted and combined to produce a final RWH suitability map. Results show that most (50.7%) ground tanks are in high RWH suitability locations, 47.9% are in medium RWH suitability, 1.4% are in low suitability, and none are in unsuitable locations. These results indicate that 1800s RWH suitability selection methods were highly effective. The findings of this study will contribute to the WLLS SWP knowledge base and will assist in prioritising SWPs for works to improve RWH efficiency, thereby ensuring reliable stock water on SWPs.

Suitability of rainwater harvesting in saline and arsenic affected areas of Bangladesh

A major portion of Bangladesh is currently experiencing a scarcity of safe drinking water because of arsenic contamination, high salinity and human-induced pollution. The objectives of this study were to identify locations with a high scarcity of drinking water and suitability of harvesting rainwater. Kriging interpolation algorithms of Geographical Information System (GIS) was employed to identify the probable water scarce zones as well as suitable zones of harvesting rain water from the available data of secondary sources. Statistical methods were employed to cluster, correlate, and regress variables such as rainfall, salinity, and As. The results showed that groundwater quality in the southwestern parts of Bangladesh is saline with high concentration (>10000 μS/cm). On the other hand, the northeastern and southwestern parts of Bangladesh are also vulnerable to arsenic contamination (60 %–97 % of tubewells), compared to other regions. The rainfall zonation map, covering the years 1951–2022, indicated that the Sylhet division had the highest potential for rainfall (ranging from 2600 to 3900 mm). From this study it was demonstrated that Sylhet, Noakhali, Bhola, Barishall, Patuakhali, Bagerhat, and Khulna were identified as suitable places for sustainable rainwater harvesting (RWH). The findings of this study may play significant role towards achieving sustainable potable water supply in vulnerable zones, if they receive attention from policymakers.

Rainwater Harvesting Site Selection for Drought-Prone Areas in Somali and Borena Zones, Oromia Regional State, Ethiopia: A Geospatial and Multi-Criteria Decision Analysis

Rainwater collection systems play a crucial role in enhancing water availability in regions with low precipitation. This study focused on identifying potential rainwater harvesting (RWH) locations in drought-prone areas, specifically the Borena zone of Oromia and the regional states of Somali. This research leveraged geospatial techniques and a multi-criteria decision analysis (MCDA) to assess feasible RWH sites. The dataset comprises essential factors such as rainfall, drainage density, slope, soil texture type, and land use/land cover. These thematic layers serve as inputs for analysis, with each factor being weighted using the Analytical Hierarchy Process (AHP) method based on its significance. Reclassifying factors into subclasses facilitates suitability analysis. The weighted linear combination (WLC) technique is applied to identify and prioritize potential rainwater harvesting (PRWH) locations based on four suitability classes: highly suitable, moderately suitable, low suitability, and unsuitable. Our findings reveal that 1% of the study area, covering approximately 3288 km2, is highly suitable for RWH. Areas with moderate suitability constitute approximately 12% (37,498 km2), while regions with low suitability, representing the majority, encompass about 75% (242,170 km2). Additionally, 13% (41,000 km2) of the study area is deemed unsuitable for RWH. The proposed technique for identifying suitable RWH sites is adaptable to other low-precipitation regions. However, before implementing RWH structures, further research is imperative. This study proposed the exploration of socioeconomic variables in future research and urged for an in-depth examination of various aspects of environmental sustainability. Our research paves the way for adapting rainwater harvesting systems to align with community needs and life cycles while also exploring the socio-economic and environmental dimensions of sustainability for future study. The insights offer promising solutions to address the urgent issues associated with water scarcity. This should include comprehensive site depictions, an exploration of social and economic activities, and the meticulous preparation of a cost-benefit analysis.

Application of GIS & RS in Rainwater Harvesting for an Arid Region

In dry areas with variable rainfall, water shortages severely affect the livelihoods of local populations. Rainwater harvesting from land sites and roof-tops is a readily available solution to this problem. This study presents a GIS & RS-based methodology to identify potential land sites and roof-top rainwater harvesting potential using easily obtainable and freely available data. Slope, land cover, soil, and drainage maps were used to identify suitable rainwater harvesting land sites, which were further analyzed for surface runoff using the curve number method. This research also incorporated obtainable socio-economic factors in identifying the suitable locations of dams considering the implementation stage. Based on the proposed criteria, 44 suitable locations for dams were identified and validated using a validation map and Google Earth images. CROPWAT model analysis revealed that paddy water demand in Batticaloa would increase threefold if all available land for paddy cultivation was utilized. Therefore, abandoned lands can be utilized by erecting dams at those identified locations. Analysis of Google Earth images also revealed that roof rainwater harvesting would be sufficient to meet the basic water demand in the area. This study demonstrates that rainwater harvesting can be an effective strategy for addressing water scarcity in dry areas.

A GIS-based approach to determine the priority area for rainwater harvest in Kupang

Kupang, a pivotal economic and administrative hub in Nusa Tenggara Timur, Indonesia, faces recurring droughts resulting in crop failures, food insecurity, and health problems. Addressing this water crisis entails rainwater harvesting as a potential solution. This study establishes a comprehensive framework for prioritizing rainwater harvesting areas in Kupang. Using the weighted overlay method and open-source GIS software, our approach involves three stages: identifying drought and flood priority zones, defining suitability indicators, and quantifying the potential of rainwater harvesting systems. By leveraging climatological, geological, and hydrological information, we determine priority areas for drought and flood management. Our analysis of rainwater harvesting suitability integrates climatological information, land use and land cover mapping, runoff potential assessment, and water use index. Quantification of rainwater harvesting system requirements hinges on rooftop area and population data. The entire wards in Kupang are classified as high priorities for drought mitigation. Therefore, rainwater harvesting is a viable strategy for all wards. The most prioritized and in-demand wards, Oesapa, Sikumana, Lasiana, and Liliba, have the potential to install a minimum of 12,000 rainwater harvesting systems, each with a 1500-liter volume barrel.

A holistic multi-tiered decision framework for evaluating rainwater harvesting potential in arid regions: A case study of the southeastern basin of Djibouti

Rainwater harvesting (RWH) stands as a strategy to mitigate the widespread challenge of water scarcity in arid and semi-arid regions worldwide. The practice of capturing and storing stormwater via different RWH technologies during wet periods can serve as a means of replenishing stressed local aquifers. However, identifying optimal locations conducive to the successful implementation of RWH initiatives accounts for a multi-tiered and intricate challenge. Furthermore, contemporary methodologies utilized to assess the potential of RWH often adhere to conventional paradigms, exhibiting a dearth of sophisticated mathematical foundations. Hence, this study proposes a hybrid approach that integrates the fuzzy analytic hierarchy process (AHP) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) algorithms to delineate suitable RWH sites. The proposed framework was subsequently applied in the southeastern basin of Djibouti, a nation facing acute water stress. The assessment of RWH potential in this research is predicated upon twelve decision criteria, organized into three principal dimensions: physical, environmental, and socio-economic aspects. The results highlighted the vitality of environmental (47.67%) and physical (37.98%) dimensions, whereas the socio-economic (14.35%) cluster was deemed less significant according to the established decision framework. Furthermore, the resultant suitability map portrays that a substantial area of the basin, approximately 872.5 km2 (equivalent to 64.25%), exhibits moderate to very high suitability for the implementation of diverse RWH structures, including check dams, percolation tanks, and infiltration ponds. The reliability of the analysis is verified through sensitivity analysis, underscoring the robustness of the decision model. This contributes to the empowerment of decision-makers and the research community, facilitating the creation of accurate RWH potential maps, and thereby advancing effective water resource management strategies for water stressed regions.

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, India

Water scarcity in arid and semi-arid regions is a critical global concern, necessitating innovative solutions to address increasing water demands in these vulnerable areas. This study focuses on tackling this challenge by identifying and classifying rainwater harvesting zones based on their potentiality and comparing the performance of two machine learning models, Artificial Neural Network (ANN) and Random Forest (RF), for optimizing rainwater harvesting strategies. The study area is Purulia, a district in India. Extensive literature review was conducted to identify key factors influencing rainwater harvesting. Open-source remotely sensed data were employed to pinpoint rainwater harvesting potential zones. A multi-criteria decision-making technique was applied to assess the importance of various factors. Results indicated that rainfall, slope, runoff potential, soil, land cover, and drainage density are the six crucial factors for selecting suitable rainwater harvesting locations. Approximately 2% of the area is unsuitable, 8% is poorly suitable, 33% is moderately suitable, 45% is highly suitable, and the remaining 12% is extremely suitable in Purulia. Two predictive models were developed, with the RF algorithm demonstrating nearly 99% accuracy. Finally, remedial techniques for mitigating water scarcity through rainwater harvesting are discussed separately for urban and rural areas. This research article 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.

Integrated geospatial approach for adaptive rainwater harvesting site selection under the impact of climate change

The impact of global climate change on water resources is a pressing concern, particularly in arid and semi-arid regions, where water shortages are becoming increasingly severe. Rainwater harvesting (RWH) offers a promising solution to address these challenges. However, the process of selecting suitable RWH sites is complex. This paper introduces a comprehensive methodology that leverages various technologies and data sources to identify suitable RWH locations in the northern region of Iraq, considering both historical and future scenarios. The study employs remote sensing and geographic information systems to collect and process geospatial data, which are essential for the site selection process. AHP is utilized as a decision-making tool to assess and rank potential RWH locations based on multiple criteria, helping to prioritize the most suitable sites. The WLC approach is used to combine and weigh various factors, enabling a systematic evaluation of site suitability. To account for the uncertainty associated with future climate conditions, a stochastic weather generator is employed to simulate historical and future precipitation data for period (1980–2022) and (2031–2100). This ensures that the assessment considers changing climate patterns. Historical precipitation values ranged from 270 to 490 mm, while future projections indicate a decrease, with values varying from 255 to 390 mm. This suggests a potential reduction in available water resources due to climate change. The runoff for historical rainfall values ranged from 190 mm (poor) to 490 mm (very good). In the future projections, runoff values vary from 180 mm (very poor) to 390 mm (good). This analysis highlights the potential impact of reduced precipitation on water availability. There is a strong correlation between rainfall and runoff, with values of 95% for historical data and 98.83% for future projections. This indicates that changes in precipitation directly affect water runoff. The study incorporates several criteria in the model, including soil texture, historical and future rainfall data, land use/cover, slope, and drainage density. These criteria were selected based on the nature of the study region and dataset availability. The suitability zones are classified into four categories for both historical potential and future projections of RWH zones: very high suitability, covering approximately 8.2%. High suitability, encompassing around 22.6%. Moderate suitability, constituting about 37.4%. Low suitability, accounting for 31.8% of the study region. For the potential zones of RWH in the future projection, the distribution is as follows: very high suitability, approximately 6.1%. High suitability, around 18.3%. Moderate suitability, roughly 31.2%. Low suitability, making up about 44.4% of the study region. The research's findings have significant implications for sustainable water resource management in the northern region of Iraq. As climate change exacerbates water scarcity, identifying suitable RWH locations becomes crucial for ensuring water availability. This methodology, incorporating advanced technology and data sources, provides a valuable tool for addressing these challenges and enhancing the future of water management to face of climate change. However, more investigations and studies need to be conducted in near future in the study region.

Rainwater harvesting for agriculture development using multi-influence factor and fuzzy overlay techniques

Rainwater harvesting (RWH) is an essential technique for enhancing agricultural development, particularly in regions facing water scarcity or unreliable rainfall patterns. Water shortage, however, is one of the key causes of low crop production especially in mountainous regions like the Khyber Pakhtunkhwa province where most rainwater is lost by runoff. Therefore, rainwater harvesting could be a suitable to make better use of runoff and increase crop production. The study focuses on selecting suitable rainwater harvesting sites in District Karak to enhance agriculture by utilizing multi-influence factor (MIF) and fuzzy overlay techniques. We considered seven factors, i.e., land use land cover (LULC), slope, geology, soil, rainfall, lineament, drainage density, to create a ranking system to understand its application in site selection analysis. The results were combined into one overlay process to produce a rainwater harvesting suitability map. The weighted overlay analysis of the MIF model results reveals that 167.96 km2 area has a very high potential for rainwater harvesting, 874.17 km2 has a high potential, 1182.92 km2 has a moderate and 354.50 km2 has a poor potential for rainwater harvesting. The fuzzy overlay analysis revealed that 257.53 km2 has a very high potential for rainwater harvesting, 896.56 km2 area is classified as high, 1018.30 km2 moderate, and 407.7 km2 has poor potential for rainwater harvesting. The findings of this research work will help the policymakers and decision-makers construct various rainwater harvesting structures in the study area to overcome the water shortage problems.

The Application of the Analytic Hierarchy Process and GIS to Map Suitable Rainwater Harvesting Sites in (Semi-) Arid Regions in Jordan

The current study is based on the evaluation of the assessment of rainwater harvesting (RWH) in (semi- arid) regions. Where, this study aimed to assess the implementation of RWH by developing a methodology that can be easily applied to identify rainwater harvesting locations in Hasa Basin in southwest Jordan, through integration between the Multiple Criteria Decision Models (MCDM) using an analytic hierarchy process (AHP), and Geographic Information System (GIS). The main factors considered to achieve the aim of the study were rainfall intensity, runoff, slope, flood susceptibility, soil texture, geology, land use/ cover (LULC), elevation, rivers, faults, settlement centers, roads, wells. These were reclassified and weighted to map the levels of rainwater harvesting in the study area. Rainwater harvesting suitable sites map obtained for the study area showed that areas with high and very high suitability formed, respectively, about 11.14% and 1.17%, while areas with low and very low suitability, in contrast, constituted about 46.09% and 9.68 %, respectively, of the total area of the study area.

Identification of Suitable Rainwater Harvesting Sites Using Geospatial Techniques With AHP in Chacha Watershed, Jemma Sub-Basin Upper Blue Nile, Ethiopia

Rainfed agriculture in Ethiopia has failed to produce enough food, to achieve the increasing demand for food. Pinpointing the appropriate site for rainwater harvesting (RWH) have a substantial contribution to increasing the available water and enhancing agricultural productivity. The current study related to the identification of the potential RWH sites was conducted at the Chacha watershed central highlands of Ethiopia which is endowed with rugged topography. The Geographic Information System with Analytical Hierarchy Process was used to generate the different maps for identifying appropriate sites for RWH. In this study, 11 factors that determine the RWH locations including slope, soil texture, runoff depth, land cover type, annual average rainfall, drainage density, lineament intensity, hydrologic soil group, antecedent moisture content, and distance to the roads were considered. The overall analyzed result shows that 10.50%, 71.10%, 17.90%, and 0.50% of the areas were found under highly, moderately, marginally suitable, and unsuitable areas for RWH, respectively. The RWH site selection was found highly dependent on a slope, soil texture, and runoff depth; moderately dependent on drainage density, annual average rainfall, and land use land cover; but less dependent on the other factors. The highly suitable areas for rainwater harvesting expansion are lands having a flat topography with a soil textural class of high-water holding capacity that can produce high runoff depth. The application of this study could be a baseline for planners and decision-makers and support any strategy adoption for appropriate RWH site selection.

Assessing the potential of Rainwater Harvesting (RWH) for sustaining small-scale irrigated coffee farming in the Bigasha watershed, Uganda

A study was carried out during 2022 to assess the potential of rainwater harvesting for sustaining small-scale irrigated coffee farming in the Bigasha watershed of Isingiro District, south-western Uganda. In this study, remote sensing, Geographical Information System (GIS), the Quantum GIS Soil and Water Assessment Tool (QSWAT+) and Crop Water and Irrigation Requirements Program (CROPWAT) models were utilised to assess the potential of rainwater harvesting (RWH) that can be used for irrigation. The criteria used to identify suitable RWH sites were based on four factors namely; topography, soils, land use land cover and rainfall. It was found out that seventy per cent of the watershed is hilly with slopes greater than 10 per cent and half of the area is covered by Leptosols. Three temporal land use land cover (LULC) maps (of 1999, 2010 and 2022), were derived using the K-nearest neighbor (KNN) algorithm. These LULC databases were used to assess the impacts of land use land cover changes on the runoff hence potential for RWH. The annual average rainfall received in the study area over the 21-year period studied (2000 – 2020) was 954 mm which is adequate for irrigation if harvested. The QSWAT+ model simulated runoff depths from 62, 125 and 114 Hydrologic Response Units (HRUs) of the 1999, 2010 and 2022 LULC maps respectively, were used to estimate potential RWH sites. The 114 HRUs from the 2022 LULC map could generate an annual average surface runoff volume of 1.39 million cubic meters (MCM) which could potentially irrigate 101.8 per cent of the current coffee production areas in the Bigasha watershed. The prediction accuracy of the Kagera river basin model was very good with NSE (R2) of 0.81(0.82) for calibration and 0.87(0.88) for validation. The Kagera watershed model fitted parameters were further used to calibrate the Bigasha watershed model. This was done because the Bigasha is a sub-basin of Kagera and it does not have its own gauged outlet.

Identification of Suitable Sites Using GIS for Rainwater Harvesting Structures to Meet Irrigation Demand

This study uses a multi-criteria decision analysis approach based on geographic information system (GIS) to identify suitable sites for rainwater harvesting (RWH) structures (such as farm dam, check dam and contour bund) to meet irrigation demand in Greater Western Sydney region, New South Wales, Australia. Data on satellite image, soil, climate, and digital elevation model (DEM) were stored in GIS layers and merged to create a ranking system, which were then used to identify suitable RWH (rainwater harvesting) areas. The resulting thematic layers (such as rainfall, land use/land cover, soil type, slope, runoff depth, drainage density, stream order and distance from road) were combined into one overlay to produce map of RWH suitability. The results showed that 9% of the study region is ‘very highly suitable’ and 25% is ‘highly suitable’. On the other hand, 36% of the area, distributed in the north-west, west and south-west of the study region, is ‘moderately suitable’. While 21% of the region, distributed in east and south-east part of the region, has ‘low suitability’ and 9% is found as ‘unsuitable area’. The findings of this research will contribute towards wider adoption of RWH in Greater Western Sydney region to meet irrigation demand. The developed methodology can be adapted to any other region/country.

Remote sensing and multi-criterion analysis for identifying suitable rainwater harvesting areas

Water scarcity and soil erosion are the main constraints small holder farmers are facing in Tigray, the northern most part of Ethiopia. Both very high and very low precipitation can cause a damage to agriculture which is the case in semi-arid regions like Tigray. While too little rainfall cannot support the growth of crops resulting in crop failure, the short but intense rainfall also causes a runoff thereby washing away essential soil nutrients. Installation of different micro/macro-catchment rainwater harvesting can address both water scarcity and soil erosion if they are properly designed prior to construction. This research was intended to develop a methodology for identifying suitable rainwater harvesting (rwh) sites by using weighted overlay analysis. It also utilizes Ahp (analytical hierarchy process) as effective multi-criterion decision-making tool in eastern Tigray at Kilte Awlaelo district on an area of 1001 km2. This method was chosen because it is simple to use, cost effective, flexible and widely adopted. Physical, hydrological, climate and socio-economic aspects were taken into account during criteria selection. The result indicated four suitability classes with 8.74% highly suitable areas (85.25 km2), 56% suitable areas (550.75 km2), 30.8% moderately suitable areas (303.2 km2) and 4.46% less suitable areas (43.87 km2). The produced rwh suitability map was also validated by both ground truth on google earth pro and a field trip to the study site. In situ and ex situ rwh including bench terraces, wells, and exclosure areas were identified during the field visit that verified the suitability model. Finally, depending on weight and scale of criteria and sub-criteria that matched to each identified suitable areas, different micro-catchment and macro-catchment techniques of water harvesting are recommended. This methodology can be utilized as decision-making tool for rwh practitioners, local and foreign organizations working on soil water conservation programmes and policy-makers during their early planning stages.

Identifying rainwater harvesting sites using integrated GIS and a multi-criteria evaluation approach in semi-arid areas of Ethiopia

In recent years, East Africa has been suffering from severe droughts. The availability of water is crucial to socioeconomic development and ecosystem services in the region. In order to address the pressing issue of water scarcity in the Wag Himra zone, a study will identify viable rainwater harvesting (RWH) sites. Geographical Information System with a multi-criteria evaluation system was used to identify suitable RWH sites based on land use and cover, soil texture, runoff depth, slope, drainage density, and considering road and town constraints. The runoff depth was estimated using the soil conservation service curve number model, and the land use/cover image classification was undertaken using ArcGIS. By using weighted overlay analysis, sites that are potentially suitable for RWH were identified. Based on the hydrological and socioeconomic characteristics of the study area and available literature, the weight of the criteria was determined using the Analytical Hierarchical Process. The findings of the study indicate that only 0.02% of the study area is considered highly suitable, 2.59, 12.26, 61.76, and 21.1% are rated as moderately suitable, marginally suitable, less suitable, and not suitable for RWH, respectively, and 2.29% is labeled a constraint for RWH. It is possible to harvest and store rainwater in the study area to meet increasing water demand. These findings aim to assist decision-makers, planners, and managers to find sites, invest in water resources, and use RWH as an alternative water source.

Identification of suitable sites for rainwater harvesting using GIS-based multi-criteria approach in Nusa Penida Island, Bali Province, Indonesia

The critical step in increasing water availability and land productivity in areas with freshwater scarcity, such as arid and semi-arid, is identifying suitable sites for Rain Water Harvesting (RWH). Identifying RWH suitable sites is site-specific due to a wide variety of a region’s characteristics. Biophysical (slope, soil texture, drainage density, land use), hydrological (runoff potential), and socio-economic (distance to road, distance to river, distance to settlement) parameters of the study area were implemented integrating multi-criteria decision analysis (analytical hierarchy processes) and Geographic Information System (GIS) to evaluate RWH suitable sites in Nusa Penida Island, Indonesia. This study’s parameters and hybrid method were effective tools for identifying RWH suitable sites. The result also indicated that approximately 38% of the Nusa Penida Island is highly suited for RWH. These areas are characterized by dryland farming as the dominant land use, gentle slope, high runoff potential, high drainage density, and moderately fine soil texture. The initial identification of RWH potential sites could be valuable information in completing water conservation programs for several purposes. This study’s approach also contributes to developing a suitable RWH identification methodology, especially for dry regions in Indonesia.

Determining the suitability of rainwater harvesting for the achievement of sustainable development goals in Wadi Watir, Egypt using GIS techniques

Scarcity of water has emerged as a major problem globally due to climate change and population growth, especially in semi-arid and arid regions. Egypt is currently experiencing serious water challenges due to its limited water resources. The water challenges require optimal and sustainable water management, which should be linked to sustainable development goals. In this context, rainwater harvesting (RWH) is an efficient technique for sustainable management of water resources. This research aims to determine the optimal implementation of RWH systems considering the biophysical and socioeconomic characteristics of the study area. This research combines geographic information systems, remote sensing, multi-criteria analysis, and hydrological modeling in a case study in Wadi Watir in the Sinai Peninsula, Egypt. It then determines optimal sites for RWH structures using a combination of Boolean analysis, weighted linear combination, and depression depth technique. The study's findings are linked to the sustainable development goals to develop a sustainable RWH plan for the first time. The results indicated that 19% (666 km2) of the study area is highly suitable for RWH. The possibility of RWH is restricted to 16% (573 km2) of the watershed. The study results identified 12 optimal sites for checking dams and 14 locations for percolation tanks along the streams. This research puts forward a novel technique to address the challenges of water scarcity along with socioeconomic and environmental pressures while achieving sustainable development goals.

Identifying suitable sites for rainwater harvesting using GIS & Multi – Criteria Decision Making techniques in Badghis Province of Afghanistan

In Afghanistan, about 1/3 (12.5 mln) of the total population have no access to stable water supplies such as groundwater extraction, stream flow diversion, and reservoir storage. In areas with limited water sources, rainwater harvesting (RWH) can improve the availability of potable water. Due to RWH flexibility and applying different criteria, it can be used in various settings, making RWH a viable option for local communities. Within this study, suitable RWH sites were identified based on modern technological approaches (GIS and Multi-Criteria Decision Making (MCDM)) considering different biophysical criteria selected according to the requirements of the target area environment. The Analytical Hierarchy Process (AHP), Weighted Linear Combination (WLC) Model and hydrology tools were used as MCDM and GIS-based decisions, respectively. The proposed methodology was implemented in the target area of 11,772 km2. The obtained land suitability map was divided into five (5) RWH zones: highly suitable (7.84% of the total area), suitable (21.85%), moderately suitable (31.15%), marginally suitable (27.85%), and not suitable (11.31%). The research results show that highly suitable and suitable sites (3,495.47 km2) are located in mountainous zones with good elevation potential, proper valley shapes, and large catchment areas. RWH practices in these areas can be considered as renewable and sustainable alternatives to water demand saturation. In addition to being a solution in terms of providing water to water-scarce areas, RWH installations represent a good climate change response and water resource management means.

Integrating GIS-based FAHP and WaTEM/SEDEM for identifying potential RWH areas in semi-arid areas

Rainwater harvesting (RWH) is an alternative technique to potentially overcome the increasing need for water. However, identifying suitable sites for RWH remains a complex task. The present study offers an approach that can enable water managers to use the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SEDEM) as a spatially soil erosion and sediment delivery model, Geographic Information System (GIS) and Fuzzy Analytical Hierarchy Process (FAHP) method to identify potential RWH sites. For this purpose, several data sources were employed to generate needful thematic layers. Five criteria: land use/land cover (LULC), slope, soil texture, drainage density and runoff were used. The Soil Conservation Service-Curve Number (SCS-CN) method was utilized for preparing a yearly runoff potential map. Then, the thematic layers were weighted for generating RWH suitability map. The results show that the average annual runoff is 248 mm. Moreover, approximatly 76.1% of the study area falls within slight soil erosion class. The classified RWH suitability map showed that 6.9% of the watershed falls within an extremely suitable category. Prediction accuracy of the developed map of RWH suitability showed an area under the curve value (AUC) of 59.6% for FAHP method in this study. It is concluded that involving a spatial soil erosion and sediment delivery model with GIS-based FAHP represents a valuable approach for locating suitable RWH sites. So, this method can be used in other parts of the world.

Rainwater Harvesting Using GIS Technique: A Case Study of Diyala Governorate, Iraq

To improve the management of water resources in Iraq, there are several methods, including the use of rainwater harvesting techniques. In this study, the Digital Elevation Model (DEM) and Landsat satellite imagery were used under the GIS environment to identify the suitable zones for rainwater harvesting. The accomplishment of rainwater harvesting systems strongly depends on their technical designing and identifying the suitable sites. Six criteria have been used to identify the rainwater harvesting sites in the Diyala governorate. The procedure of identifying the suitable sites for rainwater harvesting was applied twice for the Diyala governorate. Firstly, it was applied by using the criteria of rainfall, slope, stream order, distance to roads, and land use, and secondly, rainfall, slope, stream order, distance to roads, and Normalized Difference Vegetation Index (NDVI) criteria were used for this purpose. As a result, the study area was divided into three suitability zones: low, moderate, and high according to the specific criteria that were used to identify the rainwater harvesting suitable sites. It was found that in the application of land use criterion the low suitability zone represents 26%, 58% represents the moderate, and 16% for the high suitability zone, while in the method of NDVI it was found that 29% represents the zone that has low suitability, 57% represents the moderate, and 14% represents the high suitability zone. The compared results led to conclude that the land use is the most influential criterion for identifying the rainwater harvesting suitability sites and found that most of the Eastern parts of Diyala governorate are promising areas for rainwater harvesting and ArcGIS is a very useful, time-saving, and cost-effective tool for identifying the rainwater harvesting suitable sites.