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Planning and management of groundwater resources requires modern techniques and
scientific principles. Remote sensing and geographic information system based
multicriteria and analytic hierarchy approaches are used to identify promising areas for
groundwater potential in Souss river basin. In this study, seven parameters that affect
groundwater occ...
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Citations
... In conclusion, the benefits of the integration of flood risk management into wider development management, urban planning and climate change adaption became very clear. Argaz et al., (2019) worked on flood hazard mapping using remote sensing and GIS tools at souss watershed. The study aimed to identify and estimated the flood-hazard areas of the Souss river basin using multi-criteria analysis in a GIS environment. ...
The Federal Polytechnic Ekowe is one of the fast-growing polytechnics within the region, the main campus has been exposed to frequent flood hazards which were responsible for many damages in several areas of the main campus. This study was focused on the development of flood hazard map showing high vulnerability areas, medium/moderate vulnerability areas and low vulnerability areas of the main campus. The primary software used for the analysis and mapping was Esri's ArcGIS 10.8.1, Elevation dataset (Height), Landsat 9 satellite imagery, rainfall data, and digital soil map of the world. The present work introduced a flood hazard assessment methodology, using multi-criteria analysis and classification in the GIS environment. The results for the study showed that built-up area (94.42%) and vegetation (4.33%). The Elevation model showed minimum and maximum heights were 20.474m and 22.702m respectively. The Slope model also showed minimum and maximum slope percentage (s) were 0.00527 and 8.719 respectively. Other results obtained from multi-criteria were 28.61% for elevation and drainage distance, slope (12.71%) and precipitation (9.84%). In conclusion, more drainage infrastructure is required for the mitigation of flood water in the identified critical areas.
... In conclusion, the benefits of the integration of flood risk management into wider development management, urban planning and climate change adaption became very clear. Argaz et al., (2019) worked on flood hazard mapping using remote sensing and GIS tools at souss watershed. The study aimed to identify and estimated the flood-hazard areas of the Souss river basin using multi-criteria analysis in a GIS environment. ...
... It is one of the most critical components of early warning systems used for flood hazard mitigation and prevention of potential future flooding situations (Bhatt et al., 2014). Flood hazard mapping delivers easy-to-read visual results (charts or maps), allowing decision makers to determine risk areas and prioritize mitigation actions for the hotspots (Forkuo, 2010;Wang et al., 2011;Ajin et al., 2013;Argaz et al., 2019). Therefore, it is important to employ natural hazard maps in sustainable urban development and land use planning with actual concerns for the security of the environment and human life (Bathrellos and Skilodimou, 2019). ...
This is an open access journal distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited ARTICLE DETAILS ABSTRACT Natural hazards and disasters threaten human life, damage properties and affect human-populated environments. Flood hazard assessment procedure was carried out using hazard criteria concepts through GIS, hydrological and hydraulic modeling coupling techniques utilizing HEC-HMS for the hydrological modeling and HEC-RAS for the hydraulic modeling. The objective of this study is to use geo-based hydrologic and hydraulic models to develop flood hazard maps of wadi Yutum floodplain areas in southern Jordan, focusing on the Amman-Aqaba Desert Highway along wadi Yutum for different design storm return periods. A GIS-based model to estimate the flood flow at the gauging stations of Wadi Yutum basin, which is the basin that drains into the city of Aqaba, was developed. Most of the developments in Aqaba are located on alluvial wadi fans, which are, unavoidably, subject to the risk of flooding. Initially, Intensity Duration Frequency (IDF) curves were generated to assess the rainfall-runoff situation in the basin. Based on the analysis and modeling, flood hazard maps for 25, 50, and 100-years return periods were generated with peak flows of 1523 m 3 /s, 1813 m 3 /s, and 2111 m 3 /s, respectively. These flood hazard maps aim to identify hazard locations within the flood plains of the target area. A risk map that categorizes three risk classes (high, medium and low) was generated by coupling the flood hazard map with a socioeconomic map. High and medium risk zones identify hazard risk to the current facilities, road and trail users. The most affected element is the Amman-Aqaba Desert highway; the main entrance to Aqaba city, with an average daily traffic volume of 20,182 vehicles. Given that the vehicle occupancy rate on the highway is 1.6 person per vehicle, more than 32,000 persons could be affected on a given day depending on the time and the severity of the storm. Flood hazard, vulnerability, and risk mapping are essential tools for informed decision-making, helping to minimize the impacts of flooding and enhance the resilience of communities in the face of this natural hazard.
... These layers were divided into five categories using ArcGIS' categorization tool. As previously reported (Argaz et al., 2019;Ogato et al., 2020), locations at higher elevations suffered fewer incidences of flooding, whereas those at lower elevations were more susceptible to flooding. ...
Regular floods are part of people's lives in various regions of the world, recurring with varying magnitudes and frequencies to which people have adapted for centuries. In contrast, flooding resulting from extreme hydro-meteorological events and occurring at unexpected magnitudes and frequencies can cause loss of lives, livelihoods, and infrastructure, such as the one in Ilaje Ondo state, Nigeria. This study is focused on pinpointing Flood Vulnerable Zones (FVZ) within Ilaje, Ondo state, Nigeria, employing a combination of Geographic Information System (GIS) and Multi-criteria Decision-Making Analysis (MCDM) based on the Analytical Hierarchy Process (AHP) model within a geospatial framework. The assessment of flood vulnerability encompasses seven indicators, including precipitation, drainage density, distance from the river, distance to land, slope, land use, land cover, and elevation. Subsequently, a flood vulnerability map is generated for the study area, delineating five vulnerability groups: very high, high, moderate, low, and very low. The resulting evaluation reveals that a significant portion of Ilaje, primarily in its eastern, western, and central regions, falls under very high and highly vulnerable zones, covering approximately 1037.4 km² out of 1444 km. The results generated showed the lowest mean annual rainfall of the area between 1,756mm and 1,834mm and the highest mean annual rainfall was between 2,068mm and 2,440mm. Digital elevation model (DEM), slope, drainage density, and distance to river and land were generated from the SRTM. 154km square was vulnerable, 207.3km square was least vulnerable, 676.9km square was moderately vulnerable, and 306.5km square was highly vulnerable in the study area.
... Since water can easily scour away the soil's top layer, heavy precipitation or frequent cyclones increase the likelihood of soil erosion. Lineaments, which can be straight or curved, are geomorphic features associated with geological features such faults and lithological contacts (Argaz et al., 2019). Soil erosion is affected by infiltration and surface runoff, both of which are impacted by the lineament . ...
This study aims to draw a scientific framework for plotting soil erosion susceptibility in the Chittagong Hill Tracts of Bangladesh by comparing existing approaches. Data-driven machine learning techniques (including Classification and Regression Tree (CART), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF)) and a knowledge-based approach (AHP) are used in this study to pinpoint areas of Chit-tagong that are particularly susceptible to soil erosion while taking into account 18 soil erosion-regulating parameters. Furthermore, the effectiveness of the selected data-driven machine learning models and knowledge-based models was assessed by utilizing soil erosion and non-erosion sites. When evaluating the fidelity of each model using the ROC and AUC, the RF model was shown to be the most accurate and predictive. There is no poor performer among these models; all have AUCs greater than 67 % (RF = 0.86, ANN = 0.73, SVM = 0.67, CART = 0.67, and AHP = 0.82). According to the findings of the Random Forest model, approximately 71.55 percent of the area exhibited a moderate level of susceptibility to soil erosion. In relation to the land area, the high and low zones accounted for 16.91 percent and 11.54 percent, respectively. The specific area shares of 2256.25, 9548.08, and 1539.67 square kilometers were attributed to the high, moderate, and low danger zones, respectively. The best models' results after comparing models of data-driven and knowledge-based approaches can help to estimate soil erosion risk zones and provide insight into establishing appropriate policies to minimize this issue. In addition, the methods used in this research might be applicable to assessing the vulnerability and risk of soil erosion events in other areas. As they begin long-term planning to reduce soil erosion, local authorities and policymakers will find the study's results on practical policies and management options quite helpful.
... It is a common phenomenon that flood situations are probably created in lower elevated flat areas compared to higher elevations and has a considerable influence on the propagation of floods. Argaz et al. [15] mentioned that flooding was less of an issue for higher elevations, and vice versa. ...
Floods are one of the natural disasters causing economic, social, and environmental damage around the world, including in Sri Lanka. Flood susceptibility mapping is essential for flood prevention and mitigation measures. This study aimed to develop and validate a flood susceptibility map for the Kanakarayan Aru River Basin in Sri Lanka. Primary data were collected from water professionals and experts from Provincial Irrigation-Northern Province, Provincial Irrigation-Eastern Province, Disaster Management Centre, and Irrigation Department to decide the significance of flood causative factors. Secondary data of rainfall, digital elevation model, and GIS-based thematic data layers were collected from different agencies. GIS-based spatial multi-criteria decision analysis and analytical hierarchy process method were used for the study. A total of eight flood causative factors, i.e., elevation, slope, precipitation, land use and land cover, river proximity, drainage network density, topographic wetness index, and soil types were identified. Results show that the three mostrelevant factors of flood risk were precipitation (33%), drainage density network (17%), and surface slope (11%). The very high, high, and moderate flood risks occupy 12.5%, 23.4%, and 27.1% of the river basin areas, respectively. The validation process is executed based on the map's comparison of the historical flood locations of the different flood-susceptible zones and it provides a significant accuracy.
... Given the significant temporal and spatial variability of flood events, a systematic and continuous spatial and temporal evaluation of flood hazard, as well as an awareness of its drivers, is critical for flood management (Tang et al. 2021). Flood hazard assessment enables researchers to pinpoint the probability of a flood occurring in a particular location or time frame (Argaz et al. 2019). This assessment requires an examination of the physical and geographical characteristics of flood inducers (Tanoue et al. 2016). ...
... Flood hazard mapping According to the literature review (Mngutyo and Ogwuche 2013;Kazakis et al. 2015;Argaz et al. 2019;Shariati et al. 2020;Ogato et al. 2020), seven factors, including height , slope , drainage density , NDVI, land cover, geology, and rainfall, were selected as the effective criteria of flooding hazard in the studied area. These characteristics correspond to the Environmental Protection Agency's and Interior Ministry's standards and requirements, as well as global experience (Table 1). ...
... Among these layers, drainage density and NDVI were inversely related to the flood. However, elevation and slope were directly associated with the flood hazard (Argaz et al. 2019;Ogato et al. 2020). After rasterization of the criteria, the rasterized layers were standardized using different fuzzy membership functions based on the library and field studies. ...
Among natural hazards and disastrous outcomes, floods have tremendous impacts on infrastructure as well as on humans. In the present study, we determined flood hazard using several hydro-geomorphic factors in the Darab watershed. For this purpose, the Analytical Hierarchical Process (AHP) and fuzzy logic were used in the initial phase of the mapping process to overlap layers. The kernel density and zonal statistics were employed to calculate flood hazard density and flood hazard zones, respectively. In addition, the spatial statistics were considered to identify floods with high and low clusters in flood hazardous zones using ArcGIS Pro. The results showed that the Gamma operator (0.9) with the AUC value of 0.8924 was the optimal operator for flood zonal statistics. Only 26.45% of the study area had high flood intensity, and only two high-hazard flooding clusters were detected in the Darab watershed. The heighest value of flood was recognized in the north and northeast of the study area, which corresponded to small towns with less than 5000 inhabitants. Our experiments demonstrated that the integration of geospatial analysis with spatial statistics may provide a reliable method to assess flood hazard.
... A combined application of GIS and MCDA was carried out by Gigovic et al. (2017) to present a reliable GIS multi-criteria methodology for hazard mapping of flood-prone regions in urban areas of Siberia. Similarly, Argaz et al. (2019) introduce a flood hazard assessment methodology using multi-criteria analysis in a GIS environment for the Souss watershed, Morocco. Ramkar and Yadav (2021) used an integrated geospatial technique and Multiple Criteria Decision-Making technique (MCDM) approach to create a food risk index map of river basins with limited data. ...
The rainfall in the upper catchments associated with the hydro peaking events disturbs the regular flow regimes and is often responsible for downstream flooding in alluvial plains. The unregulated release from a hydropower dam abruptly alters the flow dynamics and inundates the adjacent floodplains. In this work, an integrated modeling framework is developed by linking a semi-distributed hydrological model, a two–dimensional hydrodynamic model with a multi-criteria decision approach, to investigate the downstream flooding of Beki River in Assam, India due to the storm runoff and release from the Kurichu Dam, Bhutan. A 48-h rainfall event and dam release flow from 16/06/2022–18/06/2022 is selected for the hydrological simulations. The computed hydrograph is used as an upstream boundary condition in the 2D model to generate the different flow scenarios up to the confluence point of the Brahmaputra River. The results indicate that a peak of 4970.86 m3/s is attained at downstream with an attenuation of 5.976%. The velocity in the stream ranges between 0.77 and 1.14 m/s, and near the meanders, it reaches up to 1.31 m/s. The computed hazard map indicates that some embankments and the road networks in the four revenue circles are significantly affected due to flooding. The proposed methodology and outcomes of the study will be useful for the effective management and monitoring of floods, especially in flood-prone regions.
... Topographical criteria are highly significant for investigating flood-susceptible regions due to their direct and indirect effects on hydrological properties and flood occurrence (Argaz et al., 2019;Tang et al., 2020). Initially, the topographical factors were extracted from Advanced Land Observing Satellite DEM (ALOS PALSAR DEM), at 12.5 m resolution, and prepared using ArcGIS10.8 ...
... According to a review of the literature, the locations most prone to flood incidents, because of overland flow, are those adjacent to the river (Rincón et al., 2018;Das and Gupta, 2021). Flooding is typically less frequent in locations distant from watercourses, and areas near rivers experience regular flood events (Argaz et al., 2019;Rehman et al., 2019). There is no general consensus on the particular distance that might increase vulnerability, although it varies among rivers (Mahmoud and Gan, 2018). ...
... Flow accumulation is another important factor widely used in determining zones vulnerable to flooding (Das, 2020;Argaz et al., 2019). It shows the overall flow from upstream areas to a certain cell in the watershed (Lappas and Kallioras, 2019), and is directly related to the occurrence of floods. ...
In recent decades, floods have been the most common, complex, and destructive natural calamities worldwide. Hence, for inclusive flood risk assessment, creating flood susceptibility mapping to demarcate flood-vulnerable zones is fundamental for decision makers. To assess flood-prone locations in the Akre, Iraqi Kurdistan Region, fundamental for susceptibility mapping was undertaken using geographic information systems, remote sensing, and an analytic hierarchy process model. To assess flood susceptibility, the geographic information systems framework used 15 ideal causative factors for flooding: altitude, slope, distance to streams, flow accumulation, drainage density, rainfall, soil type, lithology, curvature, topographic wetness index (TWI), topographic roughness index stream power index, stream transport index, land use/land cover, and normalized difference vegetation index. The factors contributing to flooding were optimally weighted with respect to the proposed model. The final flood susceptibility map was reclassified into five different classes of susceptibility to flooding: very low (16.64% of the study area); low (19.53%); moderate (38.92%); high (17.83%); and very high (7.08%). The area under curve values for the predicted rate and success rate of the AHP model were 0.956 and 0.971, respectively. Therefore, the results were accurate and reliable. The AHP model is a powerful method for fundamental for susceptibility mapping to mitigate the serious impacts of flooding and assist scholars, local governments and policymakers in future master planning.
... According to the literature, flood reduction measures range from traditional (or manual) to technology intervention monitoring systems and reservoir water monitoring to complement flood control mechanisms. In this study, Earth remote sensing products and GIS tools were used to produce flood forecasting information and flood risk maps to reduce flooding impact and damage [70,71]. ...
... In order to provide timely flood forecasting and early warnings, it is important to play a role in flood management systems through scientific and technical approaches. In various literature, monitoring of the impacts of riverine flooding and the flood risk management tool is considered [71]. In some cases, the effectiveness of flood risk management systems can be reduced through the variability of flooding and incompatibility of different approaches. ...
Hydrologic extreme events such as flooding impact people and the environment and delay sustainable development in flood-prone areas when it is excessive. The present study developed a seasonal floodwater forecast system for the Awash and Omo-Gibe basins of Ethiopia using the 2021 rainy season (June to September) as a temporal case study. In Ethiopia, there is no seasonal forecasting system available to cope with the recurrent flooding impacts instead of exercising ineffective and traditional monitoring approaches. The satellite-driven precipitation and temperature forecasts, observed rainfall, discharge, reservoir water levels, land cover, and soil data were used in the hydrologic (HEC-HMS) and hydraulic (HEC-RAS) models, spreadsheet, and GIS applications. The results obtained were forecasts of the runoff, reservoir water levels, and storage. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), percent of bias (Pbias), and Kling–Gupta efficiency (KGE) were used to evaluate the model’s performance in addition to plots as presented in the manuscript. The R2 values obtained for the Koka and Gibe-3 reservoirs’ inflows (water levels) were 0.97 (0.95) and 0.93 (0.99), respectively, and the NSE values were 0.90 (0.88) and 0.92 (0.95) for each reservoir. Similarly, the water levels (meter) and storage (Mm3) for the Koka and Gibe-3 reservoirs at the end of the 2021 flood season were 111.0 (1467.58) and 890.8 (13,638.5), respectively. Excess floodwater can be maintained in and released from reservoirs depending on the future water uses and flood monitoring activities downstream. In addition, the flood inundation extents from Earth remote sensing satellite observation and model results were examined and showed agreement.
