Search Results (15)

Inrecent years, there has been a notable increase in utilising multiple criteria decision-making (MCDM) methods in practical problem solving. The advancement of enhanced decision models with greater capabilities, coupled with technologies like geographic information systems (GIS) and artificial intelligence (AI), has fueled the application of MCDM techniques across various domains. To address the scarcity of irrigation water resources in Bortala, Northwest China, the selection of a dam site has been approached using a hybrid model integrating a multipolar Fuzzy set and a plithogenic Fuzzy hypersoft set along with a GIS. This study considered criteria such as a geological layer, slope, soil type, and land cover. Four potential and reasonably suitable dam locations were identified using a dam construction suitability map developed for Bortala. Ultimately, we showcased the benefits of the innovative method, emphasizing an open, transparent, and science-based approach to selecting optimal dam sites through local studies and group discussions. The results highlight the effectiveness of the hybrid approach involving a fuzzy hypersoft set and plithogenic multipolar fuzzy hypersoft set in addressing the challenges of dam site selection. Full article

Iraq, including the investigated watershed, has endured destructive floods and drought due to precipitation variability in recent years. Protecting susceptible areas from flooding and ensuring water supply is essential for maintaining basic human needs, agricultural production, and industry development. Therefore, locating and constructing storage structures is a significant initiative to alleviate flooding and conserve excessive surface water for future growth. This study aims to identify suitable locations for Runoff Harvesting (RH) and dam construction in the Hami Qeshan Watershed (HQW), Slemani Governorate, Iraq. We integrated in situ data, remotely sensed images, and Multi-Criteria Decision Analysis (MCDA) approaches for site selection within the Geographical Information Systems (GIS) environment. A total of ten criteria were employed to generate the RH suitability maps, including topographic position index, lithology, slope, precipitation, soil group, stream width, land cover, elevation, distance to faults, and distance to town/city. The weights of the utilized factors were determined via Weighted Linear Combination (WLC) and Analytic Hierarchy Process (AHP). The resulting RH maps were validated through 16 dam sites preselected by the Ministry of Agriculture and Water Resources (MAWR). Findings showed that the WLC method slightly outperformed AHP regarding efficiency and exhibited a higher overall accuracy. WLC achieved a higher average overall accuracy of 69%; consequently, it was chosen to locate new multipurpose dams for runoff harvesting in the study area. The overall accuracy of the 10 suggested locations in HQW ranged between 66% and 87%. Two of these sites align with the 16 locations MAWR has recommended: sites 2 and 5 in the northwest of HQW. It is noteworthy that all MAWR dam sites were situated in medium to excellent RH zones; however, they mostly sat on ineffective geological localities. It is concluded that a careful selection of the predictive factors and their respective weights is far more critical than the applied methods. This research offers decision-makers a practical and cost-effective tool for screening site suitability in data-scarce rugged terrains. Full article

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. Full article

In arid areas, the forecast of runoff is problematic for ungauged basins. The peak discharge of flashfloods and rainwater harvesting (RWH) was assessed by the integration of GIS, the RS tool and hydrologic modeling. This approach is still under further improvement to fully understand flashflood and rainwater harvesting potentialities. Different morphometric parameters are extracted and evaluated; they show the most hazardous sub-basins. Vulnerability potential to flooding is high relative to steep slopes, high drainage density, and low stream sinuosity. Using hydrologic modeling, lag time, concentration time, peak discharge rates, runoff volume, rainfall, and total losses are calculated for different return periods. The hydrologic model shows high rainfall rates, and steep slopes are present in the southeastern part of the study area. Low rainfall rates, moderate–high runoff, and gentle slopes are found in the central and downstream parts, which are suitable sites for rainwater harvesting. An analytic hierarchy process is utilized for mapping the best sites to RWH. These criteria use land-cover, average annual max 24 h rainfall, slope, stream order, and lineaments density. About 4% of the basin area has very high potentialities for RWH, while 59% of the basin area has high suitability for RWH. Ten low dam sites are proposed to impact flooding vulnerability and increase rainwater-harvesting potentialities. Full article

Rainwater harvesting is an important step towards maximizing the water availability and land productivity in arid and semi-arid areas. The present study shows that the area of Ghazi Tehsil within Khyber Pakhtunkhwa Province, Pakistan, has great potential for rainwater harvesting due to its feasible climatic and topographic conditions. This area of 348 km² normally receives high rainfall annually, but, due to hilly terrain, the bulk of rainwater is lost in the runoff process. In order to enhance agricultural output for such a large area, the practice of rainwater harvesting is a sustainable and decisive approach. However, the selection of appropriate sites for rainwater harvesting on a large scale presents a critical challenge. In such areas, geospatial technology has proved very decisive in the identification of potential sites. In this study, we have used the HEC-GeoHMS tool (ArcGIS 9.3) to compute a curve number to represent the effects of rainfall against the hydrological soil group and landcover. Subsequently, the curve number was used as an input parameter in the soil conservation service runoff-curve number (SCS-CN) method to estimate surface runoff potential for different combinations of landcover and hydrological soil groups. It was observed that runoff was higher in mountainous areas and relatively low in plain areas. Finally, to identify the potential sites for rainwater harvesting, weighted overlay analysis-based related thematic map layers were further reclassified, and weights were assigned according to the technical guidelines of suggested international standards and under consideration of the study area’s topographic, hydrological, and climatic factors. As a result, about 20% of the area was found suitable, 52% less suitable, and 29% as not suitable. Furthermore, relative suitability was assigned to the results of suitable sites as an input for the identification of potential sites for different rainwater harvesting storage structures. These results show that 10% of the area was suitable for farm ponds, 5.74% for check dams, 21.5% for Nigarims, and 8.9% was found to be suitable for gully plugs. The comparison of our GIS-derived and field-based results spatially affirms that the analyzed results were agreeably overlaid in the context of spatial results for check dams, gully plugs, and Nigarims. Full article

As the global climate continues to change, extreme weather events such as hurricanes and heavy rainfall are becoming more frequent. Subsequently, flooding and standing water disrupt and negatively impact many communities. The use of nature-based solutions (NBS) is an innovative and sustainable approach to flood mitigation. Geospatial research and applications have developed rapidly to identify and map broad regions in the world, as well as specific locations for NBS. We conducted a geospatial analysis in ArcGIS Pro to identify areas where NBS, referred to as “FloodWise” practices in this study, could be sited in the North Carolina Coastal Plain to strategically reduce flooding and provide water quality and habitat improvement. The study provides a spatially explicit application of integrated remote sensing, scientific and professional knowledge, and extant databases to screen diverse variables and identify potential specific NBS opportunities and sites. The practices modeled in this study are wetland restoration, afforestation, agroforestry, “water farming” (which uses a combination of dry dams and berms), and stream restoration. Maps of specific areas and tracts in the county for the NBS practices in Robeson County, North Carolina were developed based on the land ownership size, biophysical characteristics, current land uses, and water management opportunities. Land suitability locations revealed in these maps can be used in future resilience planning initiatives to reduce floodwaters on North Carolina’s rural landscapes. The geospatial analysis methodologies employed in this study can be followed to model NBS locations for flood reduction and water storage opportunities in other counties in Eastern North Carolina or other regions with similar topographies and land-type characteristics. Full article

Intensive agricultural production accompanied by the climate change impacts in post-Colonial rural landscapes have continuously increased the demand for water resources and coastal areas, showing an unprecedented water supply crisis. By taking extreme weather conditions and rainfall events for future trends, a resilient water storage facility for the landscape requires the collaborative approach of natural systems and simulation modelling techniques to develop sustainable future scenarios. In this study, an ecological suitability model is used to identify potential sites for the construction of multi-purpose dams. As part of the model structure, multi factors are classified using the patterns of changing landscapes, and then weighted overlay analysis is conducted on a Geographic Information System (GIS) platform. Compared to previous studies, this paper derives its principal impact parameters and projections based on historical land cover information. The suitability maps that are generated visually guide the geographical location of the multi-purpose dams and indicate the areas from highly suitable to least suitable, clarifying the possibility of building blue infrastructure alongside the waterways in west-central Barwon. The workflow proposes a resilient water system based on existing land characteristics and measures that future water storage capacity will be a valid increase of approximately 1.5 times. This strategy alleviates water scarcity during the dry season to benefit traditional agricultural activities. Digital calculations are utilized to demonstrate the feasibility of the experimental results, providing a methodology for regulating the distribution and supply of river flows throughout the year while retaining runoff in a hierarchical pattern at precipitation periods. Full article

Runoff water harvesting (RWH) is considered as an important tool for overcoming water scarcity in arid and semi-arid regions. The present work focuses on identifying potential RWH sites in the Wadi Watir watershed in the south-eastern part of the Sinai Peninsula. This was carried out by means of significant integration of the analytical hierarchy process (AHP), distributed spatial model, geographical information system (GIS), watershed modeling system (WMS), and remote sensing techniques (RS). This integration of modern research tools has its own bearing on the accurate identification of optimum RWH sites, which could be relied upon in developmental planning for arid environments. Eight effective RWH parameters were chosen to apply a multi-parametric decision spatial model (MPDSM), namely the overland flow distance, volume of annual flood, drainage density, maximum flow distance, infiltration number, watershed slope, watershed area and watershed length. These parameters were used within ArcGIS 10.1© as thematic layers to build a distributed hydrological spatial model. The weights and ranks of each model parameter were assigned according to their magnitude of contribution in the RWH potentiality mapping using a pairwise correlation matrix verified by calculating the consistency ratio (CR), which governs the reliability of the model application. The CR value was found to be less than 0.1 (0.069), indicating acceptable consistency and validity for use. The resulting MPDSM map classified the watershed into five categories of RWH potential, ranging from very low to very high. The high and very high classes, which are the most suitable for RWH structures, make up approximately 33.24% of the total watershed area. Accordingly, four retention dams and seven ground cisterns (tanks) were proposed in these areas to collect and store the runoff water, whereby these proposed RWH structures were chosen according to the soil type and current land-use pattern. The resulting MPDSM map was validated using a topographic wetness index (TWI) map, created for the watershed. This integrative and applied approach is an important technique which can be applied in similar arid environments elsewhere. Full article

Optimal site selection of a dam is one of the crucial tasks in water resource management. In this study, a dam suitability stream model (DSSM) is utilized to identify potential sites for constructing multi-purpose dams. In DSSM, each input parameter is weighted using the analytic hierarchy process (AHP), and then weighted overlay analysis is performed in a Geographical Information System (GIS) environment. Compared to the previous studies, this study showed different results based on the crucial parameter that is “stream order”. Two resultant site suitability maps are prepared to differentiate the importance of stream order. Each of the resulting maps visualizes four classes of suitability from highly suitable to least suitable. The proposed sites will store water for a variety of uses at the local and regional level and reduce flood risk, which can be very useful for hydrologists and disaster risk managers. Full article

Rainwater-harvesting (RWH) agriculture has been accepted as an effective approach to easing the overexploitation of groundwater and the associated socioeconomic impacts in arid and semiarid areas. However, the stability and reliability of the traditional methods for selecting optimal sites for RWH agriculture need to be further enhanced. Based on a case study in Tehran Province, Iran, this study proposed a new decision support system (DSS) that incorporates the Best-Worst Method (BWM) and Fuzzy logic into a geographic information system (GIS) environment. The probabilistic analysis of the rainfall pattern using Monte Carlo simulation was conducted and adopted in the DSS. The results have been demonstrated using suitability maps based on three types of RWH systems, i.e., pans and ponds, percolation tanks, and check dams. Compared with traditional methods, the sensitivity analysis has verified that the proposed DSS is more stable and reliable than the traditional methods. Based on the results, a phase-wise strategy that shifts the current unsustainable agriculture to a new paradigm based on RWH agriculture has been discussed. Therefore, this DSS has enhanced the information value and thus can be accepted as a useful tool to ease the dilemma resulting from unsustainable agriculture in arid and semiarid areas. Full article

Background: An epilepsy prevalence of 4.4% was documented in onchocerciasis-endemic villages close to the Maridi River in South Sudan. We investigated the role of the Maridi dam in causing an onchocerciasis-associated epilepsy epidemic in these villages. Methods: Affected communities were visited in November 2019 to conduct focus group discussions with village elders and assess the OV16 seroprevalence in 3- to 9-year-old children. Entomological assessments to map blackfly breeding sites and determine biting rates around the Maridi River were conducted. Historical data regarding various activities at the Maridi dam were obtained from the administrative authorities. Results: The Maridi dam was constructed in 1954–1955. Village elders reported an increasing number of children developing epilepsy, including nodding syndrome, from the early 1990s. Kazana 2 (the village closest to the dam; epilepsy prevalence 11.9%) had the highest OV16 seroprevalence: 40.0% among children 3–6 years old and 66.7% among children 7–9 years old. The Maridi dam spillway was found to be the only Simulium damnosum breeding site along the river, with biting rates reaching 202 flies/man/h. Conclusion: Onchocerciasis transmission rates are high in Maridi. Suitable breeding conditions at the Maridi dam, coupled with suboptimal onchocerciasis control measures, have probably played a major role in causing an epilepsy (including nodding syndrome) epidemic in the Maridi area. Full article

For public safety, especially for people who dwell in the valley that is located downstream of a dam site, as well as the protection of economic and environmental resources, risk management programs are urgently required all over the world. Despite the high safety standards of dams because of improved engineering and excellent construction in recent times, a zero-risk guarantee is not possible, and accidents can happen, triggered by natural hazards, human actions, or just because the dam is aging. In addition to that is the impact of potential climate change, which may not have been taken into account in the original design. A flood risk management program, which is essential for protecting downstream dam areas, is required. Part of this program is to prepare an inundation map to simulate the impact of dam failure on the downstream areas. The Baysh dam has crucial importance both to protect the downstream areas against flooding, to provide drinking water to cities in the surrounding areas, and to use the excess water for irrigation of the agricultural areas located downstream of the dam. Recently, the Kingdom of Saudi Arabia (KSA) was affected by extraordinary rainstorm events causing many problems in many different areas. One of these events happened along the basin of the Baysh dam, which raised the alarm to the decision makers and to the public to take suitable action before dam failure occurs. The current study deals with a flood risk analysis of Wadi Baysh using an integration of hydrologic and hydraulic models. A detailed field investigation of the dam site and the downstream areas down to the Red Sea coast has been undertaken. Three scenarios were applied to check the dam and the reservoir functionality; the first scenario at 100- and 200-year return period rainfall events, the second scenario according to the Probable Maximum Precipitation (PMP), and the third scenario if the dam fails. Our findings indicated that the Baysh dam and reservoir at 100- and 200-year rainfall events are adequate, however, at the PMP the water will spill out from the spillway at ~8900 m³/s causing flooding to the downstream areas; thus, a well-designed channel along the downstream wadi portion up to the Red Sea coast is required. However, at dam failure, the inundation model indicated that a vast area of the section downstream of the dam will be utterly devastated, causing a significant loss of lives and destruction of urban areas and agricultural lands. Eventually, an effective warning system and flood hazard management system are imperative. Full article

Prediction of possible landslide areas is the first stage of landslide hazard mitigation efforts and is also crucial for suitable site selection. Several statistical and machine learning methodologies have been applied for the production of landslide susceptibility maps. However, the performance assessment of such methods have conventionally been carried out by utilizing existing landslide inventories. The purpose of this study is to investigate the performances of landslide susceptibility maps produced with three different machine learning algorithms, i.e., random forest, artificial neural network, and logistic regression, in a recently constructed and activated dam reservoir and assess the external quality of each map by using pre- and post-event photogrammetric datasets. The methodology introduced here was applied using digital surface models generated from aerial photogrammetric flight data acquired before and after the dam construction. Aerial photogrammetric images acquired in 2012 and 2018 (after the dam was filled) were used to produce digital terrain models and orthophotos. The 2012 dataset was used for producing the landslide susceptibility maps and the results were evaluated by comparing the Euclidian distances between the two surface models. The results show that the random forest method outperforms the other two for predicting the future landslides. Full article

Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites. Full article

The Middle East is an inherently dry zone. It has experienced severe drought for the last seven years, and climate change has made the situation worse. The Dohuk governorate has been suffering from an appalling water crisis. One possible way of relieving this water crisis is by properly harvesting the rainwater. Rainwater harvesting is a widely used method of storing rainwater in the countries presenting with drought characteristics. Several pieces of research have derived and developed different criteria and techniques to select suitable sites for harvesting rainwater. The main aim of this research was to identify and select suitable sites for the potential erection of dams, as well as to derive a model builder in ArcMap 10.4.1. The model combined several parameters, such as slope, runoff potential, land cover/use, stream order, soil quality, and hydrology to determine the suitability of the site for harvesting rainwater. To compute the land use/cover categories, the study depended on Landsat image data from 2018. Supervised classification was applied using the ENVI 5 software, while the slope mapping and drainage order were extracted using a digital elevation model. Inverse distance weighting (IDW) was used for the spatial interpolation of the rain data. The results demonstrated that suitable areas for water harvesting, are located in the middle and northern part of the research area, and in intensively cultivated zones. The main soil texture in these suitable sites was loam, while the rainfall rate amounted to 750 to 900 mm. This research shows that 15% and 13% of the area studied can be categorized as having excellent and good suitability for water harvesting, respectively. Furthermore, 21% and 27% of the area studied were of moderate and poor suitability, while the remaining 24% were not suitable at all. Full article