Environmental Hazards of Surface Water Resources due to heavy metals from the wastewater sites: A case study: Integration of HEC-RAS, HEC-HMS, and GIS in Flood Hazard Mapping in Scarcity Rainfall Region, Sohag, Egypt

Environmental Hazards of Surface Water Resources due to heavy metals from the wastewater sites: A case study: Integration of HEC-RAS, HEC-HMS, and GIS in Flood Hazard Mapping in Scarcity Rainfall Region, Sohag, Egypt | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental Hazards of Surface Water Resources due to heavy metals from the wastewater sites: A case study: Integration of HEC-RAS, HEC-HMS, and GIS in Flood Hazard Mapping in Scarcity Rainfall Region, Sohag, Egypt Bosy A. El-Haddad, Ahmed M. Youssef, Shaymaa Rizk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7035710/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Environmental Earth Sciences → Version 1 posted 7 You are reading this latest preprint version Abstract Only land application is accessible for sewage wastewater disposal in Upper Egypt's Nile Valley. The lowland desert zone, located between the farmed floodplain and the Eocene Limestone plateau, features wastewater disposal installations. These wastewater disposal sites are located in the mouths of wadis, which are vulnerable to flooding. They are located near farmed floodplains, reclaimed lands, residential areas, surface water systems, irrigation canals, and the River Nile. In this work, untreated wastewater forms enormous uncontrolled pools on the ground. Geochemical studies have revealed that wastewater ponds and the surrounding soil are contaminated with heavy metals and bacteria, posing a significant environmental risk. Flood modeling was developed for the Al-Kola Basins, Sohag, Egypt, to generate flood hazard and ecological risk maps by integrating GIS, HEC-HMS, and HEC-RAS techniques. Due to the scarcity of rainfall data, the last recorded rainfall event in 1994 was used as a reference to determine the water runoff rate using HEC-HMS. The study was done using a digital elevation model with 12.5 m resolution. Based on HEC-RAS modeling, it was found that the average water depth increased from 1.48 m to 2.29 m, the average velocity increased from 2.41m/s to 3.76m/s, and the water spread risen from 26–37% of the entire basin area for the first scenario (rainfall = 20mm) and the second scenario (rainfall = 60mm), respectively. Our findings show that heavy metals contaminate the area due to anthropogenic activities, and floodwater can transport these polluted materials towards irrigation canals and the River Nile. Engineering recommendations were made to mitigate these critical environmental risks that could compromise human health. Environmental sustainability Flood risk modeling Heavy metals Wastewater problems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Hazards of natural, technological, or anthropogenic origin (floods, landslides, earthquakes, wildfires, droughts, and wastewater) may cause loss of life, property damage, and health hazards (Shi 2019; Gunjyal et al., 2023). These hazards can disrupt the existing societal order and living conditions due to hazardous events, resulting in physical, economic, and social damages that exceed a society’s adaptive capacity (Jia et al., 2021; Jonkman et al., 2024). Floods are among the worst natural disasters, displacing people and costing money (Vinet et al., 2019; Jonkman et al., 2024). Between 2000 and 2019, flood occurrences increased by 23% above the previous annual average of 163 flood events, resulting in economic losses exceeding $ 600 billion, which accounted for 22% of global disaster losses (Devitt et al., 2023). Climate change and global warming, land-use modifications, urbanization, and higher population density in flood-prone areas, new hydraulic structures, and river section changes have increased flood risk globally in recent years (Tabari 2020; Barati et al., 2023). Rentschler et al. (2023) found an 85% rise in urban flood-prone regions between 1985 and 2015 using satellite images, putting one-fourth of the world's population at danger. Future changes in severe weather, such as significant precipitation events, may impact the frequency and intensity of flooding (Winsemius et al., 2016). Blöschl et al. (2019) found that flood patterns in Europe varied, with some areas experiencing an increase of 11% and others a decrease of 23%. Understanding the relationship between flood risk and climate and societal changes is a major fundamental challenge for improving communities' resilience to water-related disasters by enabling cost-effective flood risk management and mitigation (Bosseler et al., 2021). According to the Emergency Events Database (EM-DAT) (Delforge et al., 2025), 9,865 events causing natural disasters have been recorded globally between 2000 and 2023 (www.emdat.be). These events were found in Africa, the Americas, Asia, Europe, and Oceania. Approximately 40% of these disasters were recorded as flood-related problems that are occurring more frequently due to global warming and climate change (Jerome Glago, 2021). Population growth, urbanization, and industrialization have led to the destruction of vegetation, soil loss, and an increase in artificial and impermeable surfaces in urban areas (Zipperer et al., 2020). The destructive effect of floods is more severe in areas with inadequate permeable surfaces and scarce underground drainage channels (Sung et al., 2018; Peker et al., 2024; Tabasi et al., 2025). Floods can significantly contribute to the transfer of contaminated elements and pollute surface waters (Balaram et al., 2023). However, it is challenging to prevent flood disasters (Wang et al., 2022). Therefore, studies that determine potential flood impact areas and disaster damage using techniques such as geographic information systems, remote sensing, and hydrological modeling are crucial for managers and decision makers (Amatebelle et al., 2025). If the cross-sectional capacity is exceeded at various points along a wadi route, floods may cause severe damage in basins and downstream areas. It is essential to obtain timely and reliable flood data and accurate information about flood risks by interpreting flood maps to avert potential post-event disasters and reduce damage in flood-susceptible areas (Grigg 2023). The GIS environment can accurately extract hydrological factors based on the digital elevation model (DEM), including catchment and networks characteristics (Chowdhury 2023). The first step in obtaining various flood maps and risk maps is to perform flood modeling, including hydrological and hydraulic modeling (Yamani et al., 2016). Flood hazard mapping is essential for risk reduction and risk management. Hydrodynamic and hydraulic models have been used to create flood danger maps for decades, aiding land use planning. These models use physically based equations and detailed terrain definitions to identify flood-prone areas and estimate flood depth and flow velocity for different return periods (D'Angelo et al., 2022; Karim et al., 2023), which are important for flood hazard assessment. The US Army Corps of Engineers developed the HEC-HMS and HEC-RAS software, which are abbreviations for the Hydrologic Engineering Center-Hydrologic Modeling System and the Hydrologic Engineering Center-River Analysis System, respectively (Hashemyan et al., 2015). They are used in various hydrological simulations. Worldwide, several researchers employ HEC-HMS and HEC-RAS to understand both hydrological and hydraulic processes, including simulating urban floods, agricultural floods, flash floods in arid and semi-arid areas, flood frequency assessments, flood warning systems, reservoir spillway capacity evaluations, and stream restoration planning (Zhang et al., 2022). HEC-HMS enables us to predict flood peak discharges and runoff volumes. In addition, various studies integrate HEC-HMS and HEC-RAS in a GIS environment to understand the flood susceptibility areas, for flood prediction, and establish inundation maps. Numerical models are essential for developing a hydraulic model and applying hydraulic analysis using the HEC-RAS program, which is suitable for performing hydraulic calculations (Demir et al., 2021). In recent years, the integration of geographic information systems (GIS) with hydrological and hydraulic modeling has significantly enhanced numerical flood modeling (Abdessamed and Abderrazak 2019). The HEC-HMS and HEC-RAS are numerical models that utilize mathematical equations to calculate water flow rates and evaluate flood risks and other related hazards qualitatively (Kordi-Karimabadi et al., 2025). Flood modeling is a technical method for acquiring high-accuracy information regarding key flood factors, including runoff, storage, and velocity. Heavy metals (HMs) pose a significant threat to the environment and human health globally (Awad et al., 2021). The Ministry of Environmental Protection and the Ministry of Land and Resources, PRC, report that several heavy metals pollution sources and their high soil levels harm soil quality, fertility, food safety, and human health (Angon et al., 2021). Effluent discharges and wastewater activities in the Al-Kola area, Sohag, Egypt, have resulted in increased pollution levels for over 30 years (Youssef et al., 2011). High levels of heavy metals characterize wastewater operations in the Sohag desert zones (Rizk and Elhaddad 2023). Geochemical components of heavy metals in this region are little studied. In general, overall heavy metal content does not indicate environmental toxicity. A low mobile quantity may be more damaging than a high immobile amount, depending on chemical form, species, and solid-phase features, which greatly impact redistribution (Briffa et al., 2020). Precipitation, ion exchange, water compounds, stability, and plant uptake govern these types (Hama Aziz et al., 2023). This study focuses on simulating the occurrence of floods in the Al-Kola area basins, Sohag, Egypt. This area was impacted by a dyke failure in 2002, which held the untreated wastewater ponds, leading to the movement of pollutants to urban and agricultural areas and the irrigated canals, causing severe health problems. For about 30 years, the wadi's low-lying areas were occupied by wastewater ponds and woody farms covering an area of 11 km 2 . Geochemical analysis indicated that heavy metals contaminate the soil and water in the ponds. The question that arises is, what if a severe rainstorm occurred “due to climate change”, could these polluted soils and water move and contaminate the surface water elements (irrigated canals and the River Nile)? Based on that, an integration of HEC-HMS, HEC-RAS, and GIS models was carried out in flood modeling to evaluate the contamination elements in the surrounding areas. To overcome the scarcity of rainfall records, various assumptions were made based on the past flood event in 1994 as a reference point (Q r )(about 40mm). To conduct the hydrological model, five scenarios were used: Q 20 (20 mm), Q 30 (30 mm), Q 40 (40 mm), Q 50 (50 mm), and Q 60 (60 mm), to calculate peak discharge and runoff volume at each rainfall value. Moreover, two scenarios, Q20 (20 mm) and Q60 (60 mm), were used for hydraulic 2D flow modeling using HEC-RAS. Manning’s “n” values and curve number maps were estimated based on field work and soil and land use maps of the area. The inundation depth and velocity were calculated using the HEC-RAS model. Subsequently, a flood hazard map was developed based on water depth and velocity using a GIS model. The impact of flood distribution on transporting polluted soils towards critical elements in the downstream areas of the wadis was evaluated and discussed. 2. Al-Kola Basins The Al Kola basins are part of the East Limestone Plateau basins, located in Sohag Governorate, Egypt. These basins drain their waters towards the west, where agricultural areas of the Nile floodplain, urban centers, the irrigated canal, and the River Nile (Fig. 1 ). The River Nile flows from south to North through the Upper Egypt governorates, one of which is the Sohag city. It finally reaches the Mediterranean Sea. The current basin's area is about 100 km 2 . The main wadis that dissect the area are Wadi Deir El-Hadied, Wadi Naziza, and Wadi El-Kiman (Fig. ). All wadis originate from the eastern limestone plateau. Close to the River Nile, these wadies move through a low desert zone, which was occupied by the wastewater facilities (wastewater plant, ponds, and farm lands). The slope of the wadis in the low desert zone ranges between 0.008 and 0.015with an average of 0.012 m/m. To the west, there is the new floodplain of the Nile occupied by urban and agricultural activities. The Nag Hamady canal runs from south to north and the River Nile to the west. In addition, the Aswan-Cairo highway runs through the area parallel to the River Nile. Another highway crosses the area, which is Sohag – Red Sea – Cairo. The altitude of the basin area ranges between 44 m above sea level (at the River Nile in the west) and 500m above sea level to the east, above the limestone plateau. In the study area, where an arid climate prevails, heavy storms sometimes form flash floods. In this study, Al-Kola basins were modeled using HEC-HMS hydrological processes. Then, the critical sites located in these basins were evaluated using HEC-RAS models (inundation models and the impact of wastewater sites on the downstream elements) (Fig. 1 ). In the east of Sohag governorate, there have been many flood events in the past that caused loss of life and property. Two historical events are documented here: one flood event in 1994 and another in 2016, both of which caused severe damage to people's property and infrastructure. The region's critical importance in terms of floods due to the failure of the wastewater pond dike is emphasized by the 2002 flood, which was recorded in a study as a flood causing a serious environmental problem (Fig. 1 e and f). Climate change has had a severe impact on the globe, particularly in the Arab regions. Understanding the impact and the inundation areas can help avoid serious environmental problems to the surface water (irrigation canals and the River Nile). And mitigate severe damage to urban and agricultural areas. In addition, since 2000, the wadi courses and the old flood plain in the area have been occupied by a wastewater disposal site (plant, farm, woods and shrubs, and wastewater depressions). Geochemical investigations of the soil samples collected from the area indicated that these soils are contaminated with heavy metals. To the west of the study area, there are critical elements, including agricultural activities, urban centers, irrigation canals, and the River Nile. 3. Materials and Methods 3.1. HEC-HMS Model Data and Base Model Setup To construct the HEC-HMS model, this research required the use of both geographical and meteorological data. In Table 1 , the characteristics of all of the input data that were utilized are presented. Data from the ALOS PALSAR satellite, which was obtained from the Alaska Satellite Facility (ASF) website ( https://asf.alaska.edu/ ). The DEM data is a Synthetic Aperture Radar (SAR) with a spatial resolution of 12.5 m. The DEM data was employed to delineate the stream networks and sub-basins; in addition, the morphometric parameters were extracted. To calculate the time of concentration (T c ) for the hydrological model, the curve number (CN) values were computed. To achieve this, an overlay of land-use data (digitized from the Landsat OLI 30m and verified using high-resolution Google Earth images) and soil map (extracted from the geologic map with a scale of 1:250,000 and verified by the field work) was performed. Data on land-use types and the Hydrologic soil group map were resampled to 30-m resolution (Fig. 2 a, 2 b). The CN grid map was created using ArcGIS (spatial analysis tools, overlay, weighted overlay) by combining both land-use and soil-type maps (Fig. 2 c). The overlapping method computed CN values for each land use and soil type. CN maps were generated as raster data over the whole region. Next, calculate the average CN values within each subbasin to match the HEC-HMS input requirement for one average CN value per subbasin. Final step: compute time of concentration (T c ) and lag time (T lag ) values for each subbasin were estimated using Equations (1–3) as shown in Table 2 . 𝑇 𝑐 = 𝑙^0.8*(𝑆+1)^0.7/1140*𝑌^0.5 (1) S = (1000/CN) − 10, (0 < CN < 100) (2) T l𝑎𝑔 = 0.6*𝑇 𝑐 (3) Where T lag = lag time, T c = time of concentration, l = longest flow length, Y = basin slope ( % ), S = maximum potential retention, and CN = curve number. Rainfall data were gathered from historical records and from the observations by the Meteorological Service ( https://www.visualcrossing.com/weather-history ). As the rain is scared in the area five rainfall scenarios were applied, each with a single value; the first scenario used the highest rainfall recorded at the time of the 1994 flood event (reference point “Q r ”), which was approximately 40 mm. Two scenarios before and two after the reference value, as follows Q 20 = 20mm, Q 30 = 30mm, Q 50 = 50mm, and Q 60 = 60mm, were applied. Q 50 and Q 60 were attributed to any unexpected rainstorm that occurs in the future due to climate change, assuming an increase of the maximum rainfall in 1994 by 25% and 50% (1.25*Q r = 50mm; 1.5*Q r = 60mm). To make these values valid, the rainstorm that occurred in Assiut in 1994 was 60mm. Many studies have documented the impact of climate change on Egypt (NBI 2012; Siam and Elthahir 2017 ; Jungudo 2023 ). Table 1 Input data used in the HEC-HMS and HEC-RAS models. Data Data Type Source Source Final Resolution Intended Purpose (s) DEM Geospatial ALOS PALSAR https://asf.alaska.edu/ (accessed on 01 February 2025) 12.5 m Basin and stream delineation, and extraction of morphometric parameters LULC Landsat OLI https://earthexplorer.usgs.gov/ (accessed on 01 February 2025) 30 m CN grid map generation (from a combination of land-use and soil maps) was used to extract CN values for each sub-basin. Soil type Paper map Modified from the Geological map and field work. 1- Conco 1985 (1:250,000) 2- Field reconnaissance 30 m Rainfall Meteorological Rain gauge 1- Historical records 2- https://www.visualcrossing.com/weather-history/ (accessed on 01 February 2025) 1- 40 mm in 1994 2- Records from 1970 to 2024 Five values were used 20, 30, 40, 50, and 60 mm. Table 2 Morphometric parameters for sub-basins (Area, maximum flow length (MFL), basin slope (BS), average curve number (CN average )), maximum potential retention (S), time of concentration (t c ), and lag time (T lag ) characteristics. Basin Sub-basin Area Km 2 MFL (km) BS (m/m) CN average S T c (min) T lag (min) B1 B1 8.1 8.41 0.13896 81.51 2.27 26.3 15.8 B2 B2 28.5 17.10 0.13973 82.70 2.09 44.7 26.8 B3 SB3 7.3 5.04 0.10545 84.81 1.79 13.6 8.2 SB4 21.2 10.66 0.09264 84.74 1.80 23.3 14.0 SB5 21.8 16.31 0.15856 81.73 2.24 47.3 28.4 B4 B4 14.2 16.56 0.12174 81.99 2.20 41.6 25.0 3.2. HEC-HMS Model and Parameters Adjustment HEC-HMS version 4.12 software offers several methods for simulating rainwater flow in a basin, and it utilizes parameter-based models. The model consists of three primary components: the basin module, the meteorological module, and the control specifications module. It allows them to choose from a variety of options. To carry out the simulations, various techniques are employed, including the "SCS Curve Number" method for loss calculation, the “SCS loss” method for the transformation method, and the "Muskingum" method for routing. Table 2 shows the curve number “CN”, time of concentration “T c ”, and lag time “T lag ” values for each basin. In the meteorological part, data on precipitation were entered as a single value. SCS–type II was used for the rainfall value distribution, in which 60% of the rainfall occurred in two hours, and the remaining 40% was distributed over the rest of the 24 hours. The control specifications section sets the simulation period's start and end timings, which in our case is the duration of the flood hydrograph. The flowchart in Fig. 3 summarizes HEC-HMS modeling. Parameter modification was performed by comparing model simulations of flow rates for five scenarios with the relevant historical data of the 1994 flood. Table 3 lists adjustment parameters for the HEC-HMS model. In the flood management plan, five scenarios were applied, as follows Q 20 (20 mm), Q 30 (30 mm), Q r (40 mm), Q 50 (50 mm), and Q 60 (60 mm). Table 3 Modeling process and adjustment factors applied in the current study. Modeling Process Method Type Model Parameters Unit Applied Fitting Values Loss Method SCS Curve Number Initial Abstraction Millimeter Automatic (0.2S) CN Unitless Variable for each subbasin Impervious surface % 0–20 Transform Method SCS unit Hydrograph time of concentration-Tc Minutes Variable for each subbasin Storage coefficient Hour 0 Routing Method Muskingum Lag time- T lag Minutes Variable for each subbasin 3.3. HEC-RAS Model The HEC-RAS (Hydrological Engineering Centre River Analysis System) version 6.7 software, developed by the US Army Corps, is a widely used tool for flood modeling in hydrodynamic simulations and is freely available ( https://www.hec.usace.army.mil/software/hec-ras/ ). For simulating water flow, the HEC-RAS model can perform simulations in both one-dimensional steady flow and two-dimensional unsteady flow. Throughout the stream network, flow analysis can be done by using geometric and hydraulic calculation processes. Users of the 2D HEC-RAS model have the option of selecting from one of three sets of equations: the 2D diffusion wave equations, the shallow water equations, and the Navier–Stokes equations. In the current model, the 2D diffusion wave equations were taken into consideration. These equations continue to be valid even when assumptions of shallow water breakdown are made, such as when crossing a hydraulic jump. When using HEC-RAS for hydraulic modeling, the initial step includes the collection of high-resolution digital elevation model (DEM) data. 12.5 meters is the resolution of the DEM data that was utilized in this investigation. For the purpose of hydraulic modeling, the DEM was clip to the cover the study region, which consisted of the Al-Kola basins. Following the completion of the second step, the size of the calculation mesh was chosen to be 25 meters, which was consistent with the DEM. As part of the research, surface Manning "n" values were calculated by using CN data in conjunction with field work. The data on the unsteady flow were gathered from the HEC-HMS model. Following that, the various wadi slopes were identified inside the GIS environment, and the model was executed with the boundary conditions set as the inflow hydrograph and the normal depth. The flowchart of the HEC-RAS modeling process is shown in Fig. 3 . 3.4. Flood Hazard Maps During floods, the most prevalent method by which people sustain injuries or lose their lives is due to their unconscious actions. Most of the time, people are ignorant of the strength of rushing water, and as a result, they put themselves and the people around them at risk when they unknowingly cross current pathways. Within its "Risk to People" guideline, the United Kingdom's Department of Environment, Food, and Rural Affairs (DEFRA) defines flood danger levels. This guidance outlines the actions that individuals should take and those that they should avoid when confronted with flood conditions. To determine the amount of flood danger, the human guide highly recommends using the hazard rating approach, as described in Eq. (4). HR = d * (v + n) + DF (4) where HR is the hazard rating value; d is the inundation depth (m); v is the flow velocity (m/s); DF is the debris factor; and n is a constant, which is 0.5. Table 4 provides a list of debris factors that are suitable for a variety of inundation depths and velocities, as well as the predominant land use. The four intervals in Table 5 are used to establish the flood danger rate levels, which are determined according to the flood. One must first determine the debris factor that is chosen from Table 4 , taking into account the change in water velocity and/or inundation depth, and then enter the value of this factor into Eq. (4). To carry out hydraulic modeling, it is necessary to specify the flood velocity (v) and flood depth (d) expressions in Eq. (4) for each cell on the surface. As a result, HR values are calculated for each pixel, and the HR map is created by carrying out the same procedure for each element. Table 4 Selection of the debris factor based on depth, velocity, and different land use types. Depth and Velocity Bare land Pasture/Arable Woodland Urban 0.00–0.25 m 0 0 0 0 0.25–0.75 m 0 0 0.5 1 d > 0.75 m and/or v > 2 0 0.5 1 1 Table 5 Flood hazard rate threshold value, flood hazard level, and flood hazard description based on Defra, Environment Agency Flood Risks to People (2006). HR threshold ( HR = d × ( v + 0.5) + DF ) Flood Hazard Level Description < 0.75 Low Caution: Flood zone with shallow flowing water or deep standing water 0.75–1.25 Moderate Dangerous for some (e.g., children): flood zone with deep or high-speed water 1.25–2.00 Significant Dangerous for most people: flood zone with high-speed water > 2 Extreme Dangerous for all: Extreme danger: flood zone with deep, high-speed water 4. Results The HEC-HMS model was run using rainfall data from five scenarios, with values of 20 mm, 30 mm, 40 mm, 50 mm, and 60 mm. The peak flow rates obtained for the Bains outlets S1 to S4 are given in Table 6 . Additionally, the peak discharge at the different rainfall values (20, 30, 40, 50, and 60 mm) and for various basins' outlets is shown in Fig. 4 . Flood hydrographs of the model simulations at the output points for different basins are shown in Fig. 5 . The computed peak flow rates for the five scenarios, as calculated using the HEC-HMS model for the other basins affected in the area. Table 6 Peak discharge for Q 20 to Q 60 at outlets of different basins. Peak Flow Rate Calculated by Using Basin’s outlet Peak Flow Rate for Various Scenarios Q 20 (m 3 /s) Q 30 (m 3 /s) Q 40 (m 3 /s) Q 50 (m 3 /s) Q 60 (m 3 /s) HEC-HMS Model S1 9.0 18.2 29.4 42.2 56.0 S2 25.4 51.2 82.3 117.2 154.9 S3 44.5 89.0 142.3 201.9 265.9 S4 12.8 25.9 41.8 59.7 79.1 The DEFRA approach was employed to assess the levels of risk posed by various sites in the region (Table 5 ). To transform the raster data of water depth values and water velocities that were acquired by flood modeling with HEC-RAS into a debris factor, the Spatial Analysis Tools (Reclassify function in the ArcGIS 10.8.2) were used. After that, Eq. (5) was applied to this raster data with the assistance of the Spatial Analysis Tools—Raster Calculator function, and hazard maps were created according to hazard classes. The hazard maps that are generated by the DEFRA approach are shown in Fig. 8 . These maps are based on the link between flood depth and flow rates. There is a greater likelihood of hypothetical scenarios occurring on the right side of the research area. Figure 9 shows the hazard level class percentage for each hazard zone level. For the first scenario, assuming a rainfall of 20mm, based on the hazard level map, the hazard level class areas are 25.9, 16.2, 19.1, 18.5, and 24.3 km² for No, low, moderate, significant, and extreme hazard zones, respectively. It was found that the extreme hazard is located along the narrow valleys situated in the central and eastern portions of the study area (the upstream and middle stream areas of the basins). However, the western part of the study area, where the flood plain of the wadis that occupied by the wastewater areas to the east and the urban areas, agricultural, and surface water elements (irrigation canal and the River Nile) are characterized by low to moderate hazard level for wadis 1, 2, and 4 and from moderate to significant hazard for wadi 4. For the second scenario, assuming a rainfall of 60mm, based on the hazard level map, the hazard level class areas are 24.5, 7.1, 11.3, 17.3, and 43.7 km² for No, low, moderate, significant, and extreme hazard zones, respectively. It was found that the extreme hazard zone is located along the narrow valleys situated in the central and eastern portions of the study area (the upstream and middle stream areas of the basins). These extreme areas have a higher proportion than the extreme zone of the first scenario. However, the western part of the study area, where the flood plain of the wadis that occupied by the wastewater areas to the east and the urban areas, agricultural, and surface water elements (irrigation canal and the River Nile) are characterized by low to significant hazard level for wadis 2, and 4 and from significant to extreme hazard for wadis 1 and 4. Unfortunately, verification of the hydraulic model could not be carried out because no recorded data were available. Despite this, when the historical flood spots in the region in 1994 are taken into consideration, it is clear that the model's findings provide water distributions in areas comparable to those based on the region's population. The fact that this scenario has occurred suggests that the past floods in the area may have been observed, and the water distributions indicate that the model's conclusions are consistent. The reasons for this are both the lower elevations of the regions on the west side, where agriculture, urban, and waste elements are present. The floodwater, based on the depth and velocity models for both 20mm and 60mm scenarios, reaches the surface water elements and could impact their quality. The floodwater can carry wastewater, contaminated soils, and heavy metals with it. Water levels of up to extreme hazards were observed in some low-elevation areas with the rainfall of 60mm. 5. Discussion 5.1. Heavy metals mobility and contamination level To determine the mode of occurrence of heavy metals and their effects on the environment based on their mobility (Ozcan and Altundag 2013 ; Awad et al., 2021 ). The Tessier et al. ( 1979 ) approach for trace element speciation in soil has been widely used to estimate the mobility of heavy metals and their environmental impacts. Five fractions are determined in the fractionation methods (F1, F2, F3, F4, and F5) as follows: exchangeable, carbonates, reducible, oxidizable, and residual, respectively. F1 to F4 represent the mobility of the heavy metals, whereas F5 is used for residual. For Cd, Co, Cu, Ni, Pb, and Zn, the mobility values are 89.9–97.7%, 43.8–73.3%, 80.2–95.2%, 35.9–76.0%, 80.7–96.5%, and 58.2–93.1%, respectively. With an average value of 81.3% for Zn, 43.3% for Ni; Due to wastewater leaking from Al-Kola wastewater ponds, the soil samples had high concentrations of hazardous heavy metals, exceeding the global background value (Kabata-Pendias and Pendias, 2011 ). Due to heavy metal concentration, Zn, Ni, Pb, Co, Cu, and Cd concentrations are maximum at 305.0, 43.3, 41.6, 24.5, 22.3, and 8.9 mg/kg. Higher concentrations of heavy metals in the studied region indicate anthropogenic origins. Recent studies have shown soil heavy metal contamination (Kumar et al., 2019 ; Ahmad et al., 2021 ; Singh et al., 2024 ). Based on the Contamination index (Bali and Sidhu 2021 ), which measures the proportion of the concentration of heavy metals to the concentration of the background level of each element (international soil samples). Results indicated that the contamination index ranged from 1.6–2.7 for 1 to 3 for Co and 1.1–2.6 for Ni, indicating moderate contamination; 0.77–10.2 for Zn indicates low to very high contamination, 13.4–37.5 for Cd showing very high contamination, and 0.93–2.7 Pb and 0.18–1.2 for Cu indicate low to moderate contamination. 5.2. Flood simulation and its implications Numerous studies in both national and international literature utilize the combination of HEC-RAS and HEC-HMS. Due to the HEC-HMS model's complete automation, it has been extensively used in the literature over the last several years. The HEC-HMS model fills a significant gap in hydrological–hydraulic modeling research, as its primary function is to simulate flow and analyze it in comparison to observed data. This research revealed that the findings of the HEC-HMS were in agreement with the floods observed in the past (1994 and 2016). Recent research has shown that the resolution of the digital elevation model (DEM) is a crucial component that directly affects operational sensitivity in flood simulation. To accomplish this goal, high-resolution digital elevation model data with a resolution of 12.5 meters were used in this research. Additionally, processing surface details of wadis and other features on a digital elevation model (DEM) enables the study area to be transformed into detailed topography. For this purpose, a 12.5 m digital elevation model (DEM) was acquired for the study. This digital surface model was created using remote sensing methods, which not only deliver speed and economy but also convey the most recent state of the terrain. The land use and soil data used for this purpose are the latest and most up-to-date, and roughness assignments are based on literature. Flood hydrograph peak flow rates obtained using HEC-HMS are consistent with those reported before in the 1994 and 2016 flood events. It can be seen that the HEC-HMS results coincide with the previous event results. Since these findings demonstrate that the HEC-HMS and HEC-RAS models are trustworthy and usable models for flood modeling and flood hazard mapping, it may be concluded that these models deserve praise. Due to its modeling capabilities, the DEFRA technique is crucial for flood management studies, both before and during crises, as well as for prioritizing access to disaster victims (search and rescue) following floods. This is because the DEFRA approach also helps prioritize access to victims of disasters. Instead of being produced by constructions or characteristics that impact water flow hydrology, it seems that the dimensional insufficiency of wadi sections triggers floods to pass incoming flow. The water that exceeds its cross-section quickly spreads over the surrounding region, where wastewater ponds and farms are located. The velocity of the floodwater is great, and it has the potential to transport contaminants in a westerly direction toward essential components, such as metropolitan centers, agricultural regions, and surface water bodies. The area can be made more resistant to the risk of flooding by implementing structural measures, such as constructing a drainage channel on the appropriate slope. Additionally, flow rates and velocities can be more effectively regulated by lowering the base slope. 6. Conclusions In this study, flood propagation and flood hazard mapping of the Al-Kola area in Sohag, Egypt, were examined using the DEFRA method, incorporating GIS, HEC-HMS, and HEC-RAS. Peak flows using HEC-HMS were obtained for five scenarios: Q 20 , Q 30 , Q 40 , Q 50 , and Q 60 floods. Additionally, in two scenarios, Q20 and Q60, the flood maps indicated that most of the area was highly affected by flood events during these scenarios. The results of the modeling scenarios showed that the average water levels in the study area increased from 1.48 m for the first scenario (rainfall = 20 mm) to 2.29 m for the second scenario (rainfall = 60 mm). In addition, considering that the average velocity in the region for the first scenario (rainfall = 20 mm) is 2.41 m/s, and for the second scenario (rainfall = 60mm), the average velocity is 3.74 m/s. The area studied was a wastewater area (wood farms and cultivated areas). It was determined that the majority of the flood-affected areas had an “Extreme” level of hazard for the first scenario and the second scenario, which ranged from 23.4 to 42%, respectively. This study makes the first contribution to the literature on coupled hydrological and hydraulic modeling to assess the impact of wastewater sites on surface water elements, agricultural areas, and urban areas in the Al-Kola area. The findings can be applied to inform management strategies for basins. To generalize and assess the trustworthiness of the results, the hydrologic and hydraulic models used in this research can be combined and applied to various flood-prone areas. It is feasible to utilize models to manage flood management and simulate real-time inundation. This will help to limit the amount of items that are lost and avoid any potential harm that may be caused by flooding. It is envisaged that flood effects in this region can be addressed by establishing a flood channel that can convey the flood water to the irrigation canal and prevent the flood water from carrying any pollutants with it. Declarations Author Contribution Bosy A. El Haddad, Ahmed M. Youssef, and Shaymaa Rizk designed the experiments, ran models, analyzed the results, and wrote and reviewed the manuscript. References Abdessamed D, Abderrazak B (2019) Coupling HEC-RAS and HEC-HMS in Rainfall–Runoff Modeling and Evaluating Floodplain Inundation Maps in Arid Environments: Case Study of Ain Sefra City, Ksour Mountain. SW of Algeria. Environ. Earth Sci., 78, 586. Ahmad W, Alharthy RD, Zubair M, Ahmed M, Hameed A, Rafique S (2021) Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk. Sci Rep 11, 17006. https://doi.org/10.1038/s41598-021-94616-4 Amatebelle CE, Owolabi ST, Ogundeji AA, Okolie CC (2025) A systematic analysis of remote sensing and geographic information system applications for flood disaster risk management. Journal of Spatial Science, 1–27. https://doi.org/10.1080/14498596.2025.2476973 Angon PB, Islam MdS, KC S, Das A, Anjum N, Poudel A, Suchi SA (2024) Sources, effects and present perspectives of heavy metals contamination: Soil, plants and human food chain, Heliyon, 10(7), e28357, https://doi.org/10.1016/j.heliyon.2024.e28357. Awad M, Liu Z, Skalicky M, Dessoky ES, Brestic M, Mbarki S, Rastogi A, El Sabagh A (2021) Fractionation of Heavy Metals in Multi-Contaminated Soil Treated with Biochar Using the Sequential Extraction Procedure. Biomolecules, 17;11(3):448. doi: 10.3390/biom11030448. PMID: 33802758; PMCID: PMC8002428. Balaram V, Copia L, Kumar US, Miller J, Chidambaram S (2023) Pollution of water resources and application of ICP-MS techniques for monitoring and management—A comprehensive review, Geosystems and Geoenvironment, 2(4): 100210, https://doi.org/10.1016/j.geogeo.2023.100210. Bali AS, Sidhu GPS (2021) Heavy metal contamination indices and ecological risk assessment index to assess metal pollution status in different soils, Editor(s): Vinod Kumar, Anket Sharma, Artemi Cerdà, Heavy Metals in the Environment, Elsevier, p 87-98, https://doi.org/10.1016/B978-0-12-821656-9.00005-5. Barati AA, Zhoolideh M, Azadi H, Lee J-H, Scheffran J (2023) Interactions of land-use cover and climate change at global level: How to mitigate the environmental risks and warming effects, Ecological Indicators, 146, 109829, https://doi.org/10.1016/j.ecolind.2022.109829. Blöschl G, Hall J, Viglione A, Perdigão RAP, Parajka J, Merz B, Lun D, Arheimer B, Aronica GT, Bilibashi A, （2019） Changing climate both increases and decreases European river floods. Nature 573, 108–111. Bosseler B, Salomon M, Schlüter M, Rubinato M (2021) Living with Urban Flooding: A Continuous Learning Process for Local Municipalities and Lessons Learnt from the 2021 Events in Germany. Water, 13(19), Article 19. https://doi.org/10.3390/w13192769 Briffa J, Sinagra E, Blundell R (2020) Heavy metal pollution in the environment and their toxicological effects on humans, Heliyon, 6(9): e04691, https://doi.org/10.1016/j.heliyon.2020.e04691. Chowdhury MdS (2023) Modelling hydrological factors from DEM using GIS, MethodsX, 10, 102062, https://doi.org/10.1016/j.mex.2023.102062. D’Angelo C, Passalacqua P, Fiori A, Volpi E (2022) Identification of flood‐prone areas with GeoFlood: Lessons learned from the Tiber River case study. Journal of Flood Risk Management, 15(2), e12795. https://doi.org/10.1111/jfr3.12795 Delforge D, Wathelet V, Below R, Sofia CL, Tonnelier M, van Loenhout JAF, Speybroeck N (2025) EM-DAT: the Emergency Events Database, International Journal of Disaster Risk Reduction, 124, 105509, https://doi.org/10.1016/j.ijdrr.2025.105509. Demir V, Beden N, Ülke A (2021) Review and Comparison of Flood Modeling Methods. Eur. J. Sci. Technol. 1013–1021 Devitt L, Neal J, Coxon G, Savage J, Wagener T (2023) Flood hazard potential reveals global floodplain settlement patterns. Nat Commun 14, 2801. https://doi.org/10.1038/s41467-023-38297-9 Grigg NS (2023) Comprehensive Flood Risk Assessment: State of the Practice. Hydrology. 2023; 10(2):46. https://doi.org/10.3390/hydrology10020046 Gunjyal N, Rani S, Asgari Lajayer B, Senapathi V, Astatkie T (2023) A review of the effects of environmental hazards on humans, their remediation for sustainable development, and risk assessment. Environ Monit Assess. 1;195(6):795, PMC10234584. doi: 10.1007/s10661-023-11353-z. Hama Aziz KH, Mustafa FS, Omer KM, Hama S, Hamarawf RF, Rahman KO (2023) Heavy metal pollution in the aquatic environment: efficient and low-cost removal approaches to eliminate their toxicity: a review. RSC Adv. 12;13(26):17595-17610. doi: 10.1039/d3ra00723e. PMID: 37312989; PMCID: PMC10258679. Hashemyan F, Khaleghi M, Kamyar M (2015) Combination of HEC-HMS and HEC-RAS Models in GIS in Order to Simulate Flood (Case Study: Khoshke Rudan River in Fars Province, Iran). Res. J. Recent Sci. Res. J. Recent Sci., 4, 122–127. Jerome Glago, F. (2021). Flood Disaster Hazards; Causes, Impacts and Management: A State-of-the-Art Review. IntechOpen. doi: 10.5772/intechopen.95048 Jia H, Chen F, Du E (2021) Adaptation to Disaster Risk-An Overview. Int J Environ Res Public Health. 25;18(21):11187, PMC8582783. doi: 10.3390/ijerph182111187. Jonkman SN, Curran A, Bouwer LM (2024) Floods have become less deadly: an analysis of global flood fatalities 1975–2022. Nat Hazards 120, 6327–6342. https://doi.org/10.1007/s11069-024-06444-0 Jungudo M (2023) The Impact of Climate Change in Egypt. In: Ani, K.J. (eds) Resource Conflict and Environmental Relations in Africa. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-7343-7_11 Kabata-Pendias A, Pendias H (2011) Trace Elements in Soils and Plants. 4th ed. pp. 28–534. USA: CRC Press, Taylor and Francis Group, LLC. Karim F, Armin MA, Ahmedt-Aristizabal D, Tychsen-Smith L, Petersson L (2023) A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling. Water, 15(3), 566. https://doi.org/10.3390/w15030566 Kordi-Karimabadi F, Fadaei-Kermani E, Ghaeini-Hessaroeyeh M, Farhadi H (2025) Integrating numerical models with deep learning techniques for flood risk assessment. Sci Rep 15, 8913 (2025). https://doi.org/10.1038/s41598-025-93465-9 Kumar V, Sharma A, Kaur P, Singh Sidhu GP, Bali AS, Bhardwaj R, Thukral AK, Cerda A (2019) Pollution assessment of heavy metals in soils of India and ecological risk assessment: A state-of-the-art. Chemosphere; 216:449–462. doi: 10.1016/j.chemosphere.2018.10.066 Nile Basin Initiative (NBI) (2012) Nile River Basin Status Report. Climate Change and its implications for the Nile Basin. Chapter 8. Available at http://nileis.nilebasin.org/system/files/Nile%20SoB%20Report%20 Chapter%208%20-%20Climate%20Change.pdf Ozcan N, Altundag H (2013) Speciation of heavy metals in street dust samples from sakarya I. Organized industrial district using the bcr sequential extraction procedure by ICP-OES. Bull. Chem. Soc. Ethiop., 27:205–212. doi: 10.4314/bcse.v27i2.5. Peker İB, Gülbaz S, Demir V, Orhan O, Beden N (2024) Integration of HEC-RAS and HEC-HMS with GIS in Flood Modeling and Flood Hazard Mapping. Sustainability; 16(3):1226. https://doi.org/10.3390/su16031226 Pizzorni M, Innocenti A, Tollin N (2024) Droughts and floods in a changing climate and implications for multi-hazard urban planning: A review, City and Environment Interactions, 24: 100169, https://doi.org/10.1016/j.cacint.2024.100169. Rentschler J, Avner P, Marconcini M, Su R, Strano E, Vousdoukas M, Hallegatte S (2023) Global evidence of rapid urban growth in flood zones since 1985. Nature, 622(7981), 87 92. https://doi.org/10.1038/s41586-023-06468-9 Rizk S, Elhaddad B (2023) Environmental impact of sewage water disposable sites using remote sensing and geochemical studies in West Girga, Sohag, Egypt. Episodes; 46:575-589. https://doi.org/10.18814/epiiugs/2023/023009 Shi P (2019) Hazards, Disasters, and Risks. Disaster Risk Science. 5:1–48, PMC7123175.. doi: 10.1007/978-981-13-6689-5_1. Siam MS, Elthahir EAB (2017) Climate Change Enhances Interannual Variability of the River Nile Flow. Nature Climate Change 7: 350–354. https://www.nature.com/articles/nclimate3273 Singh R, Singh PK, Madheshiya P, Khare AK, Tiwari S (2024) Heavy metal contamination in the wastewater irrigated soil and bioaccumulation in cultivated vegetables: Assessment of human health risk, Journal of Food Composition and Analysis, 128, 106054, https://doi.org/10.1016/j.jfca.2024.106054. Sung K, Jeong H, Sangwan N, Yu DJ (2018) Effects of flood control strategies on flood resilience under sociohydrological disturbances. Water Resources Research, 54, 2661–2680. https://doi.org/10.1002/2017WR021440 Tabari H (2020) Climate change impact on flood and extreme precipitation increases with water availability. Sci Rep 10, 13768. https://doi.org/10.1038/s41598-020-70816-2 Tabasi N, Fereshtehpour M, Roghani B (2025) A review of flood risk assessment frameworks and the development of hierarchical structures for risk components. Discov Water 5, 10. https://doi.org/10.1007/s43832-025-00193-2 Tessier APGC, Campbell PG, Bisson MJAC (1979) Sequential extraction procedure for the speciation of particulate trace metals. Analytical Chemistry 51(7): 844-851. Vinet F, Bigot V, Petrucci O, Papagiannaki K, Llasat MC, Kotroni V, Boissier L, Aceto L, Grimalt M, Llasat-Botija M, et al （2019）Mapping Flood-Related Mortality in the Mediterranean Basin. Results from the MEFF v2.0 DB. Water; 11(10):2196. https://doi.org/10.3390/w11102196 Wang L, Cui S, Li Y, Huang H, Manandhar B, Nitivattananon V, Fang X, Huang W (2022) A review of the flood management: from flood control to flood resilience, Heliyon, 8(11): e11763, https://doi.org/10.1016/j.heliyon.2022.e11763. Winsemius HC, Aerts JCJH, Van Beek LPH, Bierkens MFP, Bouwman A, Jongman B, Kwadijk JCJ, Ligtvoet W, Lucas PL, Van Vuuren DP（2016）Global drivers of future river flood risk. Nat. Clim. Chang. 6, 381–385. Yamani K, Hazzab A, Sekkoum M, Slimane T (2016) Mapping of Vulnerability of Flooded Area in Arid Region. Case Study: Area of Ghardaïa-Algeria. Model. Earth Syst. Environ., 2, 147. Youssef AM, Omer AA, Ibrahim MS, Ali MH, Cawlfield JD (2011) Geotechnical investigation of sewage wastewater disposal sites and use of GIS land-use maps to assess environmental hazards: Sohag, Upper Egypt. Arab J Geosci 4:719–733. doi:10.1007/s12517-009-0069-6 Zhang Ke, Shalehy MdH, Ezaz GT, Chakraborty A, Mohib KM, Liu L (2022) An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis, Environmental Modelling & Software, 148, 105279, https://doi.org/10.1016/j.envsoft.2021.105279. Zipperer WC, Robert N, Michael A (2020) Urban development and environmental degradation. Oxford Research Encyclopedia of Environmental Science. Retrieved 01 Sep. 2020. https://oxfordre.com/environmentalscience/view/10.1093/acrefore/9780199389414.001.0001/acrefore-9780199389414-e-97. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Environmental Earth Sciences → Version 1 posted Editorial decision: Revision requested 12 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviewers invited by journal 23 Aug, 2025 Editor assigned by journal 05 Jul, 2025 Submission checks completed at journal 05 Jul, 2025 First submitted to journal 03 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7035710","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480038136,"identity":"3d9e5c22-ab4e-480f-8076-f0d1740e0c12","order_by":0,"name":"Bosy A. El-Haddad","email":"","orcid":"","institution":"Sohag University","correspondingAuthor":false,"prefix":"","firstName":"Bosy","middleName":"A.","lastName":"El-Haddad","suffix":""},{"id":480038137,"identity":"69450131-bf89-466d-8202-d46d35ee888f","order_by":1,"name":"Ahmed M. Youssef","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDAD9vkPGx8wMBwgQQvPgeTDBqRqSUuTIEoL/7Qzho95/tTJ8TCcMavmqbkjx8/A/PDRDTxaJG7nGBvzth025mHsMbvNc+yZsWQDm7FxDj5rbueYSfM2HEjcz8wD1MJ2OHHDAR42aXxa5EFagA6r72HjMSvm+UeEFgOwFjbmBB4etjRmoAsJazG8nVZsOLftsGGPBPNhybl9h40lmwn4Re528sYHb/7UyfNIMDZ+ePPtsBw/e/PDx3i9z8BhAGcy8YBIZrzKQYD9AZzJ+IOg6lEwCkbBKBiJAADdx0tUnsNpngAAAABJRU5ErkJggg==","orcid":"","institution":"Sohag University","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"M.","lastName":"Youssef","suffix":""},{"id":480038138,"identity":"dfa21a0a-0e6e-4254-ad99-c77bc31ff4e2","order_by":2,"name":"Shaymaa Rizk","email":"","orcid":"","institution":"Sohag University","correspondingAuthor":false,"prefix":"","firstName":"Shaymaa","middleName":"","lastName":"Rizk","suffix":""}],"badges":[],"createdAt":"2025-07-03 08:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7035710/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7035710/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12665-026-12873-w","type":"published","date":"2026-03-21T15:59:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86141387,"identity":"fa11320d-0c3b-4463-92ba-73c2a9de407b","added_by":"auto","created_at":"2025-07-07 08:26:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1289655,"visible":true,"origin":"","legend":"\u003cp\u003ea) Location of the study area in Egypt, b) location map of the study area, the drainage basins and drainage wadis surround it, c) close view of the study area showing wastewater site, cities, roads, canal, River Nile, and soil samples, d) close up view of the wastewater sites showing wastewater facility, wastewater farm areas, and wastewater pons as black spots, and e, f) field photographs showing the leakage of wastewater towards the urban area of Al-Kola village, making 5 thousand homes under environmental risk.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/d57865bb430489bbcdf7fcb0.png"},{"id":86142089,"identity":"01fd253a-0b84-4d78-8401-fad53371a2b7","added_by":"auto","created_at":"2025-07-07 08:34:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":687542,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e) Soil-type map of the area, b) Land-use class, and c) Curve number grids of the area.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/7aa6ae063c5e25be6479bdb9.png"},{"id":86143494,"identity":"923b4bd8-21f6-4a81-a89c-eb1a288666da","added_by":"auto","created_at":"2025-07-07 08:42:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":449768,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart showing HEC-HMS and HEC RAS modeling steps.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/06a3143c561e94d617878f37.png"},{"id":86141391,"identity":"751cc946-e538-4e8e-b298-543304393d7a","added_by":"auto","created_at":"2025-07-07 08:26:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89607,"visible":true,"origin":"","legend":"\u003cp\u003ePeak discharge for different basin outlets S1 to S4, at different rainfall depth scenarios 20, 30, 40, 50, and 60 mm.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/8341fdff0a00ca36081c42d1.png"},{"id":86142088,"identity":"1e240a8a-ce44-4da9-abef-cefde5457342","added_by":"auto","created_at":"2025-07-07 08:34:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":258288,"visible":true,"origin":"","legend":"\u003cp\u003eHydrographs for Q\u003csub\u003e20\u003c/sub\u003e, Q\u003csub\u003e30\u003c/sub\u003e, Q\u003csub\u003e40\u003c/sub\u003e, Q\u003csub\u003e50\u003c/sub\u003e, and Q\u003csub\u003e60\u003c/sub\u003e for different basins outlet a-d) S1-S4 outlets. Two rainfall values, 20 and 60mm, were used for HEC-RAS simulations. Flood propagation maps (water depth and water velocity) for both 20mm and 60mm rainfalls in the study area are shown in\u0026nbsp;Figures 6 and 7. The flood propagation map indicates that the flows in these different scenarios cannot be accommodated within the wadis section and exceed the right and left, covering the wastewater areas. \u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/47528d47965f87dffb9d33c0.png"},{"id":86141393,"identity":"a6ebc7c8-2db8-42b5-9d5f-29c56422d5b8","added_by":"auto","created_at":"2025-07-07 08:26:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":251882,"visible":true,"origin":"","legend":"\u003cp\u003eFlood propagation water depth maps for: \u003cstrong\u003ea)\u003c/strong\u003e the first scenario “Q\u003csub\u003e20\u003c/sub\u003e\u003csup\u003e”\u003c/sup\u003e, \u003cstrong\u003eb\u003c/strong\u003e) the second scenario “Q\u003csub\u003e60\u003c/sub\u003e\u003csup\u003e”\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/f19a5e35ee98d1a886a392d2.png"},{"id":86141403,"identity":"137de074-8f2e-41b3-a3ad-430f2954f76e","added_by":"auto","created_at":"2025-07-07 08:26:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":234154,"visible":true,"origin":"","legend":"\u003cp\u003eFlood propagation water velocity maps for: \u003cstrong\u003ea)\u003c/strong\u003e the first scenario “Q\u003csub\u003e20\u003c/sub\u003e\u003csup\u003e”\u003c/sup\u003e, \u003cstrong\u003eb\u003c/strong\u003e) the second scenario “Q\u003csub\u003e60\u003c/sub\u003e\u003csup\u003e”\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/8e58c5732ea2bee651b0304d.png"},{"id":86142096,"identity":"b6071f84-e99d-4f3f-8a4f-e02f93bbb8bd","added_by":"auto","created_at":"2025-07-07 08:34:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":244197,"visible":true,"origin":"","legend":"\u003cp\u003eFlood hazard level propagation maps for: \u003cstrong\u003ea)\u003c/strong\u003e the first scenario “Q\u003csub\u003e20\u003c/sub\u003e\u003csup\u003e”\u003c/sup\u003e, \u003cstrong\u003eb\u003c/strong\u003e) the second scenario “Q\u003csub\u003e60\u003c/sub\u003e\u003csup\u003e”\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/f7938a6b6fe54b8bdac6f40d.png"},{"id":86141397,"identity":"94e8df41-1ab0-4376-9e88-a8b2f2c15542","added_by":"auto","created_at":"2025-07-07 08:26:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":62154,"visible":true,"origin":"","legend":"\u003cp\u003eArea percentage of each hazard level for Al-Kola Basins for the first and second scenarios.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/18908697a3e041939d9c6923.png"},{"id":105223441,"identity":"61d26ea1-7539-4917-9659-f5b04d849f0d","added_by":"auto","created_at":"2026-03-23 16:06:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4290267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7035710/v1/2516f52c-5fbb-498d-a08b-aaa05f328228.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEnvironmental Hazards of Surface Water Resources due to heavy metals from the wastewater sites: A case study: Integration of HEC-RAS, HEC-HMS, and GIS in Flood Hazard Mapping in Scarcity Rainfall Region, Sohag, Egypt\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHazards of natural, technological, or anthropogenic origin (floods, landslides, earthquakes, wildfires, droughts, and wastewater) may cause loss of life, property damage, and health hazards (Shi 2019; Gunjyal et al., 2023). These hazards can disrupt the existing societal order and living conditions due to hazardous events, resulting in physical, economic, and social damages that exceed a society\u0026rsquo;s adaptive capacity (Jia et al., 2021; Jonkman et al., 2024). Floods are among the worst natural disasters, displacing people and costing money (Vinet et al., 2019; Jonkman et al., 2024). Between 2000 and 2019, flood occurrences increased by 23% above the previous annual average of 163 flood events, resulting in economic losses exceeding $ 600 billion, which accounted for 22% of global disaster losses (Devitt et al., 2023). Climate change and global warming, land-use modifications, urbanization, and higher population density in flood-prone areas, new hydraulic structures, and river section changes have increased flood risk globally in recent years (Tabari 2020; Barati et al., 2023). Rentschler et al. (2023) found an 85% rise in urban flood-prone regions between 1985 and 2015 using satellite images, putting one-fourth of the world\u0026apos;s population at danger. Future changes in severe weather, such as significant precipitation events, may impact the frequency and intensity of flooding (Winsemius et al., 2016). Bl\u0026ouml;schl et al. (2019) found that flood patterns in Europe varied, with some areas experiencing an increase of 11% and others a decrease of 23%. Understanding the relationship between flood risk and climate and societal changes is a major fundamental challenge for improving communities\u0026apos; resilience to water-related disasters by enabling cost-effective flood risk management and mitigation (Bosseler et al., 2021). According to the Emergency Events Database (EM-DAT) (Delforge et al., 2025), 9,865 events causing natural disasters have been recorded globally between 2000 and 2023 (www.emdat.be). These events were found in Africa, the Americas, Asia, Europe, and Oceania. Approximately 40% of these disasters were recorded as flood-related problems that are occurring more frequently due to global warming and climate change (Jerome Glago, 2021). Population growth, urbanization, and industrialization have led to the destruction of vegetation, soil loss, and an increase in artificial and impermeable surfaces in urban areas (Zipperer et al., 2020). The destructive effect of floods is more severe in areas with inadequate permeable surfaces and scarce underground drainage channels (Sung et al., 2018; Peker et al., 2024; Tabasi et al., 2025). Floods can significantly contribute to the transfer of contaminated elements and pollute surface waters (Balaram et al., 2023). However, it is challenging to prevent flood disasters (Wang et al., 2022). Therefore, studies that determine potential flood impact areas and disaster damage using techniques such as geographic information systems, remote sensing, and hydrological modeling are crucial for managers and decision makers (Amatebelle et al., 2025).\u003c/p\u003e\n\u003cp\u003eIf the cross-sectional capacity is exceeded at various points along a wadi route, floods may cause severe damage in basins and downstream areas. It is essential to obtain timely and reliable flood data and accurate information about flood risks by interpreting flood maps to avert potential post-event disasters and reduce damage in flood-susceptible areas (Grigg 2023). The GIS environment can accurately extract hydrological factors based on the digital elevation model (DEM), including catchment and networks characteristics (Chowdhury\u0026nbsp;2023). The first step in obtaining various flood maps and risk maps is to perform flood modeling, including hydrological and hydraulic modeling (Yamani et al., 2016). Flood hazard mapping is essential for risk reduction and risk management. Hydrodynamic and hydraulic models have been used to create flood danger maps for decades, aiding land use planning. These models use physically based equations and detailed terrain definitions to identify flood-prone areas and estimate flood depth and flow velocity for different return periods (D\u0026apos;Angelo et al., 2022; Karim et al., 2023), which are important for flood hazard assessment. The US Army Corps of Engineers developed the HEC-HMS and HEC-RAS software, which are abbreviations for the Hydrologic Engineering Center-Hydrologic Modeling System and the Hydrologic Engineering Center-River Analysis System, respectively (Hashemyan et al., 2015). They are used in various hydrological simulations. Worldwide, several researchers employ HEC-HMS and HEC-RAS to understand both hydrological and hydraulic processes, including simulating urban floods, agricultural floods, flash floods in arid and semi-arid areas, flood frequency assessments, flood warning systems, reservoir spillway capacity evaluations, and stream restoration planning (Zhang et al., 2022). HEC-HMS enables us to predict flood peak discharges and runoff volumes. In addition, various studies integrate HEC-HMS and HEC-RAS in a GIS environment to understand the flood susceptibility areas, for flood prediction, and establish inundation maps. Numerical models are essential for developing a hydraulic model and applying hydraulic analysis using the HEC-RAS program, which is suitable for performing hydraulic calculations (Demir et al., 2021). In recent years, the integration of geographic information systems (GIS) with hydrological and hydraulic modeling has significantly enhanced numerical flood modeling (Abdessamed and Abderrazak 2019). The HEC-HMS and HEC-RAS are numerical models that utilize mathematical equations to calculate water flow rates and evaluate flood risks and other related hazards qualitatively (Kordi-Karimabadi et al., 2025). Flood modeling is a technical method for acquiring high-accuracy information regarding key flood factors, including runoff, storage, and velocity.\u003c/p\u003e\n\u003cp\u003eHeavy metals (HMs) pose a significant threat to the environment and human health globally (Awad \u0026nbsp;et al., 2021). The Ministry of Environmental Protection and the Ministry of Land and Resources, PRC, report that several heavy metals pollution sources and their high soil levels harm soil quality, fertility, food safety, and human health (Angon et al., 2021).\u0026nbsp; Effluent discharges and wastewater activities in the Al-Kola area, Sohag, Egypt, have resulted in increased pollution levels for over 30 years (Youssef et al., 2011). High levels of heavy metals characterize wastewater operations in the Sohag desert zones (Rizk and Elhaddad 2023). Geochemical components of heavy metals in this region are little studied. In general, overall heavy metal content does not indicate environmental toxicity. A low mobile quantity may be more damaging than a high immobile amount, depending on chemical form, species, and solid-phase features, which greatly impact redistribution (Briffa et al., 2020). Precipitation, ion exchange, water compounds, stability, and plant uptake govern these types (Hama Aziz et al., 2023). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study focuses on simulating the occurrence of floods in the Al-Kola area basins, Sohag, Egypt. This area was impacted by a dyke failure in 2002, which held the untreated wastewater ponds, leading to the movement of pollutants to urban and agricultural areas and the irrigated canals, causing severe health problems. For about 30 years, the wadi\u0026apos;s low-lying areas were occupied by wastewater ponds and woody farms covering an area of 11 km\u003csup\u003e2\u003c/sup\u003e. Geochemical analysis indicated that heavy metals contaminate the soil and water in the ponds. The question that arises is, what if a severe rainstorm occurred \u0026ldquo;due to climate change\u0026rdquo;, could these polluted soils and water move and contaminate the surface water elements (irrigated canals and the River Nile)? Based on that, an integration of HEC-HMS, HEC-RAS, and GIS models was carried out in flood modeling to evaluate the contamination elements in the surrounding areas. To overcome the scarcity of rainfall records, various assumptions were made based on the past flood event in 1994 as a reference point (Q\u003csub\u003er\u003c/sub\u003e)(about 40mm). To conduct the hydrological model, five scenarios were used: Q\u003csub\u003e20\u003c/sub\u003e (20 mm), Q\u003csub\u003e30\u003c/sub\u003e (30 mm), Q\u003csub\u003e40\u003c/sub\u003e (40 mm), Q\u003csub\u003e50\u003c/sub\u003e (50 mm), and Q\u003csub\u003e60\u003c/sub\u003e (60 mm), to calculate peak discharge and runoff volume at each rainfall value. Moreover, two scenarios, Q20 (20 mm) and Q60 (60 mm), were used for hydraulic 2D flow modeling using HEC-RAS. Manning\u0026rsquo;s \u0026ldquo;n\u0026rdquo; values and curve number maps were estimated based on field work and soil and land use maps of the area. The inundation depth and velocity were calculated using the HEC-RAS model. Subsequently, a flood hazard map was developed based on water depth and velocity using a GIS model. The impact of flood distribution on transporting polluted soils towards critical elements in the downstream areas of the wadis was evaluated and discussed.\u003c/p\u003e"},{"header":"2. Al-Kola Basins","content":"\u003cp\u003eThe Al Kola basins are part of the East Limestone Plateau basins, located in Sohag Governorate, Egypt. These basins drain their waters towards the west, where agricultural areas of the Nile floodplain, urban centers, the irrigated canal, and the River Nile (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The River Nile flows from south to North through the Upper Egypt governorates, one of which is the Sohag city. It finally reaches the Mediterranean Sea. The current basin's area is about 100 km\u003csup\u003e2\u003c/sup\u003e. The main wadis that dissect the area are Wadi Deir El-Hadied, Wadi Naziza, and Wadi El-Kiman (Fig. ). All wadis originate from the eastern limestone plateau. Close to the River Nile, these wadies move through a low desert zone, which was occupied by the wastewater facilities (wastewater plant, ponds, and farm lands). The slope of the wadis in the low desert zone ranges between 0.008 and 0.015with an average of 0.012 m/m. To the west, there is the new floodplain of the Nile occupied by urban and agricultural activities. The Nag Hamady canal runs from south to north and the River Nile to the west. In addition, the Aswan-Cairo highway runs through the area parallel to the River Nile. Another highway crosses the area, which is Sohag \u0026ndash; Red Sea \u0026ndash; Cairo. The altitude of the basin area ranges between 44 m above sea level (at the River Nile in the west) and 500m above sea level to the east, above the limestone plateau. In the study area, where an arid climate prevails, heavy storms sometimes form flash floods.\u003c/p\u003e \u003cp\u003eIn this study, Al-Kola basins were modeled using HEC-HMS hydrological processes. Then, the critical sites located in these basins were evaluated using HEC-RAS models (inundation models and the impact of wastewater sites on the downstream elements) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the east of Sohag governorate, there have been many flood events in the past that caused loss of life and property. Two historical events are documented here: one flood event in 1994 and another in 2016, both of which caused severe damage to people's property and infrastructure. The region's critical importance in terms of floods due to the failure of the wastewater pond dike is emphasized by the 2002 flood, which was recorded in a study as a flood causing a serious environmental problem (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee and f). Climate change has had a severe impact on the globe, particularly in the Arab regions. Understanding the impact and the inundation areas can help avoid serious environmental problems to the surface water (irrigation canals and the River Nile). And mitigate severe damage to urban and agricultural areas.\u003c/p\u003e \u003cp\u003eIn addition, since 2000, the wadi courses and the old flood plain in the area have been occupied by a wastewater disposal site (plant, farm, woods and shrubs, and wastewater depressions). Geochemical investigations of the soil samples collected from the area indicated that these soils are contaminated with heavy metals. To the west of the study area, there are critical elements, including agricultural activities, urban centers, irrigation canals, and the River Nile.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. HEC-HMS Model Data and Base Model Setup\u003c/h2\u003e \u003cp\u003eTo construct the HEC-HMS model, this research required the use of both geographical and meteorological data. In Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the characteristics of all of the input data that were utilized are presented. Data from the ALOS PALSAR satellite, which was obtained from the Alaska Satellite Facility (ASF) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asf.alaska.edu/\u003c/span\u003e\u003cspan address=\"https://asf.alaska.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The DEM data is a Synthetic Aperture Radar (SAR) with a spatial resolution of 12.5 m. The DEM data was employed to delineate the stream networks and sub-basins; in addition, the morphometric parameters were extracted. To calculate the time of concentration (T\u003csub\u003ec\u003c/sub\u003e) for the hydrological model, the curve number (CN) values were computed. To achieve this, an overlay of land-use data (digitized from the Landsat OLI 30m and verified using high-resolution Google Earth images) and soil map (extracted from the geologic map with a scale of 1:250,000 and verified by the field work) was performed. Data on land-use types and the Hydrologic soil group map were resampled to 30-m resolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The CN grid map was created using ArcGIS (spatial analysis tools, overlay, weighted overlay) by combining both land-use and soil-type maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The overlapping method computed CN values for each land use and soil type. CN maps were generated as raster data over the whole region. Next, calculate the average CN values within each subbasin to match the HEC-HMS input requirement for one average CN value per subbasin. Final step: compute time of concentration (T\u003csub\u003ec\u003c/sub\u003e) and lag time (T\u003csub\u003elag\u003c/sub\u003e) values for each subbasin were estimated using Equations (1\u0026ndash;3) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e\u0026#119879;\u003csub\u003e\u0026#119888;\u003c/sub\u003e = \u0026#119897;^0.8*(\u0026#119878;+1)^0.7/1140*\u0026#119884;^0.5 (1)\u003c/p\u003e \u003cp\u003eS = (1000/CN)\u0026thinsp;\u0026minus;\u0026thinsp;10, (0\u0026thinsp;\u0026lt;\u0026thinsp;CN\u0026thinsp;\u0026lt;\u0026thinsp;100) (2)\u003c/p\u003e \u003cp\u003eT\u003csub\u003el\u0026#119886;\u0026#119892;\u003c/sub\u003e = 0.6*\u0026#119879;\u003csub\u003e\u0026#119888;\u003c/sub\u003e (3)\u003c/p\u003e \u003cp\u003eWhere T\u003csub\u003elag\u003c/sub\u003e = lag time, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e = time of concentration, \u003cem\u003el\u003c/em\u003e\u0026thinsp;=\u0026thinsp;longest flow length, \u003cem\u003eY\u003c/em\u003e\u0026thinsp;=\u0026thinsp;basin slope (\u003cem\u003e%\u003c/em\u003e), \u003cem\u003eS\u003c/em\u003e\u0026thinsp;=\u0026thinsp;maximum potential retention, and CN\u0026thinsp;=\u0026thinsp;curve number.\u003c/p\u003e \u003cp\u003eRainfall data were gathered from historical records and from the observations by the Meteorological Service (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.visualcrossing.com/weather-history\u003c/span\u003e\u003cspan address=\"https://www.visualcrossing.com/weather-history\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). As the rain is scared in the area five rainfall scenarios were applied, each with a single value; the first scenario used the highest rainfall recorded at the time of the 1994 flood event (reference point \u0026ldquo;Q\u003csub\u003er\u003c/sub\u003e\u0026rdquo;), which was approximately 40 mm. Two scenarios before and two after the reference value, as follows Q\u003csub\u003e20\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;20mm, Q\u003csub\u003e30\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;30mm, Q\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;50mm, and Q\u003csub\u003e60\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;60mm, were applied. Q\u003csub\u003e50\u003c/sub\u003e and Q\u003csub\u003e60\u003c/sub\u003e were attributed to any unexpected rainstorm that occurs in the future due to climate change, assuming an increase of the maximum rainfall in 1994 by 25% and 50% (1.25*Q\u003csub\u003er\u003c/sub\u003e = 50mm; 1.5*Q\u003csub\u003er\u003c/sub\u003e = 60mm). To make these values valid, the rainstorm that occurred in Assiut in 1994 was 60mm. Many studies have documented the impact of climate change on Egypt (NBI 2012; Siam and Elthahir \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jungudo \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInput data used in the HEC-HMS and HEC-RAS models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinal Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntended Purpose (s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDEM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeospatial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eALOS PALSAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asf.alaska.edu/\u003c/span\u003e\u003cspan address=\"https://asf.alaska.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e(accessed on 01 February 2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBasin and stream delineation, and extraction of morphometric parameters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLULC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandsat OLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed on 01 February 2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCN grid map generation (from a combination of land-use and soil maps) was used to extract CN values for each sub-basin.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSoil type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaper map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModified from the Geological map and field work.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1- Conco 1985 (1:250,000)\u003c/p\u003e \u003cp\u003e2- Field reconnaissance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRainfall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeteorological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRain gauge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1- Historical records\u003c/p\u003e \u003cp\u003e2- \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.visualcrossing.com/weather-history/\u003c/span\u003e\u003cspan address=\"https://www.visualcrossing.com/weather-history/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed on 01 February 2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1- 40 mm in 1994\u003c/p\u003e \u003cp\u003e2- Records from 1970 to 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFive values were used 20, 30, 40, 50, and 60 mm.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMorphometric parameters for sub-basins (Area, maximum flow length (MFL), basin slope (BS), average curve number (CN\u003csub\u003eaverage\u003c/sub\u003e)), maximum potential retention (S), time of concentration (t\u003csub\u003ec\u003c/sub\u003e), and lag time (T\u003csub\u003elag\u003c/sub\u003e) characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub-basin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003eKm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMFL\u003c/p\u003e \u003cp\u003e(km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBS (m/m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCN\u003csub\u003eaverage\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003csub\u003ec\u003c/sub\u003e (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003csub\u003elag\u003c/sub\u003e (min)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. HEC-HMS Model and Parameters Adjustment\u003c/h2\u003e \u003cp\u003eHEC-HMS version 4.12 software offers several methods for simulating rainwater flow in a basin, and it utilizes parameter-based models. The model consists of three primary components: the basin module, the meteorological module, and the control specifications module. It allows them to choose from a variety of options. To carry out the simulations, various techniques are employed, including the \"SCS Curve Number\" method for loss calculation, the \u0026ldquo;SCS loss\u0026rdquo; method for the transformation method, and the \"Muskingum\" method for routing. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the curve number \u0026ldquo;CN\u0026rdquo;, time of concentration \u0026ldquo;T\u003csub\u003ec\u003c/sub\u003e\u0026rdquo;, and lag time \u0026ldquo;T\u003csub\u003elag\u003c/sub\u003e\u0026rdquo; values for each basin. In the meteorological part, data on precipitation were entered as a single value. SCS\u0026ndash;type II was used for the rainfall value distribution, in which 60% of the rainfall occurred in two hours, and the remaining 40% was distributed over the rest of the 24 hours. The control specifications section sets the simulation period's start and end timings, which in our case is the duration of the flood hydrograph. The flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes HEC-HMS modeling. Parameter modification was performed by comparing model simulations of flow rates for five scenarios with the relevant historical data of the 1994 flood. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e lists adjustment parameters for the HEC-HMS model. In the flood management plan, five scenarios were applied, as follows Q\u003csub\u003e20\u003c/sub\u003e (20 mm), Q\u003csub\u003e30\u003c/sub\u003e (30 mm), Q\u003csub\u003er\u003c/sub\u003e (40 mm), Q\u003csub\u003e50\u003c/sub\u003e (50 mm), and Q\u003csub\u003e60\u003c/sub\u003e (60 mm).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModeling process and adjustment factors applied in the current study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModeling Process\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit Applied\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFitting Values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLoss Method\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSCS Curve\u003c/p\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInitial Abstraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMillimeter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic (0.2S)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnitless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariable for each subbasin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImpervious surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTransform Method\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSCS unit Hydrograph\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etime of concentration-Tc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariable for each subbasin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStorage coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRouting Method\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuskingum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLag time- T\u003csub\u003elag\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariable for each subbasin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. HEC-RAS Model\u003c/h2\u003e \u003cp\u003eThe HEC-RAS (Hydrological Engineering Centre River Analysis System) version 6.7 software, developed by the US Army Corps, is a widely used tool for flood modeling in hydrodynamic simulations and is freely available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hec.usace.army.mil/software/hec-ras/\u003c/span\u003e\u003cspan address=\"https://www.hec.usace.army.mil/software/hec-ras/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For simulating water flow, the HEC-RAS model can perform simulations in both one-dimensional steady flow and two-dimensional unsteady flow. Throughout the stream network, flow analysis can be done by using geometric and hydraulic calculation processes. Users of the 2D HEC-RAS model have the option of selecting from one of three sets of equations: the 2D diffusion wave equations, the shallow water equations, and the Navier\u0026ndash;Stokes equations. In the current model, the 2D diffusion wave equations were taken into consideration. These equations continue to be valid even when assumptions of shallow water breakdown are made, such as when crossing a hydraulic jump. When using HEC-RAS for hydraulic modeling, the initial step includes the collection of high-resolution digital elevation model (DEM) data. 12.5 meters is the resolution of the DEM data that was utilized in this investigation. For the purpose of hydraulic modeling, the DEM was clip to the cover the study region, which consisted of the Al-Kola basins. Following the completion of the second step, the size of the calculation mesh was chosen to be 25 meters, which was consistent with the DEM. As part of the research, surface Manning \"n\" values were calculated by using CN data in conjunction with field work. The data on the unsteady flow were gathered from the HEC-HMS model. Following that, the various wadi slopes were identified inside the GIS environment, and the model was executed with the boundary conditions set as the inflow hydrograph and the normal depth. The flowchart of the HEC-RAS modeling process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Flood Hazard Maps\u003c/h2\u003e \u003cp\u003eDuring floods, the most prevalent method by which people sustain injuries or lose their lives is due to their unconscious actions. Most of the time, people are ignorant of the strength of rushing water, and as a result, they put themselves and the people around them at risk when they unknowingly cross current pathways. Within its \"Risk to People\" guideline, the United Kingdom's Department of Environment, Food, and Rural Affairs (DEFRA) defines flood danger levels. This guidance outlines the actions that individuals should take and those that they should avoid when confronted with flood conditions. To determine the amount of flood danger, the human guide highly recommends using the hazard rating approach, as described in Eq.\u0026nbsp;(4).\u003c/p\u003e \u003cp\u003eHR\u0026thinsp;=\u0026thinsp;d * (v\u0026thinsp;+\u0026thinsp;n)\u0026thinsp;+\u0026thinsp;DF (4)\u003c/p\u003e \u003cp\u003ewhere HR is the hazard rating value; \u003cem\u003ed\u003c/em\u003e is the inundation depth (m); \u003cem\u003ev\u003c/em\u003e is the flow velocity (m/s); DF is the debris factor; and \u003cem\u003en\u003c/em\u003e is a constant, which is 0.5. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a list of debris factors that are suitable for a variety of inundation depths and velocities, as well as the predominant land use. The four intervals in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e are used to establish the flood danger rate levels, which are determined according to the flood. One must first determine the debris factor that is chosen from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, taking into account the change in water velocity and/or inundation depth, and then enter the value of this factor into Eq.\u0026nbsp;(4). To carry out hydraulic modeling, it is necessary to specify the flood velocity (v) and flood depth (d) expressions in Eq.\u0026nbsp;(4) for each cell on the surface. As a result, HR values are calculated for each pixel, and the HR map is created by carrying out the same procedure for each element.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelection of the debris factor based on depth, velocity, and different land use types.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth and Velocity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare land\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePasture/Arable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWoodland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.00\u0026ndash;0.25 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.25\u0026ndash;0.75 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed\u0026thinsp;\u0026gt;\u0026thinsp;0.75 m and/or v\u0026thinsp;\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFlood hazard rate threshold value, flood hazard level, and flood hazard description based on Defra, Environment Agency Flood Risks to People (2006).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR threshold\u003c/p\u003e \u003cp\u003e(\u003cem\u003eHR\u003c/em\u003e\u0026nbsp;=\u0026nbsp;\u003cem\u003ed\u003c/em\u003e\u0026nbsp;\u0026times; (\u003cem\u003ev\u003c/em\u003e\u0026nbsp;+ 0.5) +\u0026nbsp;\u003cem\u003eDF\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood Hazard Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaution: Flood zone with shallow flowing water or deep standing water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.75\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDangerous for some (e.g., children): flood zone with deep or high-speed water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.25\u0026ndash;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDangerous for most people: flood zone with high-speed water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtreme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDangerous for all: Extreme danger: flood zone with deep, high-speed water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe HEC-HMS model was run using rainfall data from five scenarios, with values of 20 mm, 30 mm, 40 mm, 50 mm, and 60 mm. The peak flow rates obtained for the Bains outlets S1 to S4 are given in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Additionally, the peak discharge at the different rainfall values (20, 30, 40, 50, and 60 mm) and for various basins' outlets is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Flood hydrographs of the model simulations at the output points for different basins are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The computed peak flow rates for the five scenarios, as calculated using the HEC-HMS model for the other basins affected in the area.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePeak discharge for Q\u003csub\u003e20\u003c/sub\u003e to Q\u003csub\u003e60\u003c/sub\u003e at outlets of different basins.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePeak Flow Rate Calculated by Using\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBasin\u0026rsquo;s outlet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003ePeak Flow Rate for Various Scenarios\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003csub\u003e20\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ\u003csub\u003e30\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ\u003csub\u003e40\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ\u003csub\u003e60\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eHEC-HMS\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eS1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eS2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e154.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eS3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e201.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e265.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eS4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe DEFRA approach was employed to assess the levels of risk posed by various sites in the region (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). To transform the raster data of water depth values and water velocities that were acquired by flood modeling with HEC-RAS into a debris factor, the Spatial Analysis Tools (Reclassify function in the ArcGIS 10.8.2) were used. After that, Eq.\u0026nbsp;(5) was applied to this raster data with the assistance of the Spatial Analysis Tools\u0026mdash;Raster Calculator function, and hazard maps were created according to hazard classes. The hazard maps that are generated by the DEFRA approach are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. These maps are based on the link between flood depth and flow rates. There is a greater likelihood of hypothetical scenarios occurring on the right side of the research area. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the hazard level class percentage for each hazard zone level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the first scenario, assuming a rainfall of 20mm, based on the hazard level map, the hazard level class areas are 25.9, 16.2, 19.1, 18.5, and 24.3 km\u0026sup2; for No, low, moderate, significant, and extreme hazard zones, respectively. It was found that the extreme hazard is located along the narrow valleys situated in the central and eastern portions of the study area (the upstream and middle stream areas of the basins). However, the western part of the study area, where the flood plain of the wadis that occupied by the wastewater areas to the east and the urban areas, agricultural, and surface water elements (irrigation canal and the River Nile) are characterized by low to moderate hazard level for wadis 1, 2, and 4 and from moderate to significant hazard for wadi 4.\u003c/p\u003e \u003cp\u003eFor the second scenario, assuming a rainfall of 60mm, based on the hazard level map, the hazard level class areas are 24.5, 7.1, 11.3, 17.3, and 43.7 km\u0026sup2; for No, low, moderate, significant, and extreme hazard zones, respectively. It was found that the extreme hazard zone is located along the narrow valleys situated in the central and eastern portions of the study area (the upstream and middle stream areas of the basins). These extreme areas have a higher proportion than the extreme zone of the first scenario. However, the western part of the study area, where the flood plain of the wadis that occupied by the wastewater areas to the east and the urban areas, agricultural, and surface water elements (irrigation canal and the River Nile) are characterized by low to significant hazard level for wadis 2, and 4 and from significant to extreme hazard for wadis 1 and 4.\u003c/p\u003e \u003cp\u003eUnfortunately, verification of the hydraulic model could not be carried out because no recorded data were available. Despite this, when the historical flood spots in the region in 1994 are taken into consideration, it is clear that the model's findings provide water distributions in areas comparable to those based on the region's population. The fact that this scenario has occurred suggests that the past floods in the area may have been observed, and the water distributions indicate that the model's conclusions are consistent. The reasons for this are both the lower elevations of the regions on the west side, where agriculture, urban, and waste elements are present. The floodwater, based on the depth and velocity models for both 20mm and 60mm scenarios, reaches the surface water elements and could impact their quality. The floodwater can carry wastewater, contaminated soils, and heavy metals with it. Water levels of up to extreme hazards were observed in some low-elevation areas with the rainfall of 60mm.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Heavy metals mobility and contamination level\u003c/h2\u003e \u003cp\u003eTo determine the mode of occurrence of heavy metals and their effects on the environment based on their mobility (Ozcan and Altundag \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Awad et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Tessier et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) approach for trace element speciation in soil has been widely used to estimate the mobility of heavy metals and their environmental impacts. Five fractions are determined in the fractionation methods (F1, F2, F3, F4, and F5) as follows: exchangeable, carbonates, reducible, oxidizable, and residual, respectively. F1 to F4 represent the mobility of the heavy metals, whereas F5 is used for residual. For Cd, Co, Cu, Ni, Pb, and Zn, the mobility values are 89.9\u0026ndash;97.7%, 43.8\u0026ndash;73.3%, 80.2\u0026ndash;95.2%, 35.9\u0026ndash;76.0%, 80.7\u0026ndash;96.5%, and 58.2\u0026ndash;93.1%, respectively. With an average value of 81.3% for Zn, 43.3% for Ni;\u003c/p\u003e \u003cp\u003eDue to wastewater leaking from Al-Kola wastewater ponds, the soil samples had high concentrations of hazardous heavy metals, exceeding the global background value (Kabata-Pendias and Pendias, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Due to heavy metal concentration, Zn, Ni, Pb, Co, Cu, and Cd concentrations are maximum at 305.0, 43.3, 41.6, 24.5, 22.3, and 8.9 mg/kg. Higher concentrations of heavy metals in the studied region indicate anthropogenic origins. Recent studies have shown soil heavy metal contamination (Kumar et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the Contamination index (Bali and Sidhu \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which measures the proportion of the concentration of heavy metals to the concentration of the background level of each element (international soil samples). Results indicated that the contamination index ranged from 1.6\u0026ndash;2.7 for 1 to 3 for Co and 1.1\u0026ndash;2.6 for Ni, indicating moderate contamination; 0.77\u0026ndash;10.2 for Zn indicates low to very high contamination, 13.4\u0026ndash;37.5 for Cd showing very high contamination, and 0.93\u0026ndash;2.7 Pb and 0.18\u0026ndash;1.2 for Cu indicate low to moderate contamination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Flood simulation and its implications\u003c/h2\u003e \u003cp\u003eNumerous studies in both national and international literature utilize the combination of HEC-RAS and HEC-HMS. Due to the HEC-HMS model's complete automation, it has been extensively used in the literature over the last several years. The HEC-HMS model fills a significant gap in hydrological\u0026ndash;hydraulic modeling research, as its primary function is to simulate flow and analyze it in comparison to observed data. This research revealed that the findings of the HEC-HMS were in agreement with the floods observed in the past (1994 and 2016). Recent research has shown that the resolution of the digital elevation model (DEM) is a crucial component that directly affects operational sensitivity in flood simulation. To accomplish this goal, high-resolution digital elevation model data with a resolution of 12.5 meters were used in this research. Additionally, processing surface details of wadis and other features on a digital elevation model (DEM) enables the study area to be transformed into detailed topography. For this purpose, a 12.5 m digital elevation model (DEM) was acquired for the study. This digital surface model was created using remote sensing methods, which not only deliver speed and economy but also convey the most recent state of the terrain. The land use and soil data used for this purpose are the latest and most up-to-date, and roughness assignments are based on literature. Flood hydrograph peak flow rates obtained using HEC-HMS are consistent with those reported before in the 1994 and 2016 flood events. It can be seen that the HEC-HMS results coincide with the previous event results.\u003c/p\u003e \u003cp\u003eSince these findings demonstrate that the HEC-HMS and HEC-RAS models are trustworthy and usable models for flood modeling and flood hazard mapping, it may be concluded that these models deserve praise. Due to its modeling capabilities, the DEFRA technique is crucial for flood management studies, both before and during crises, as well as for prioritizing access to disaster victims (search and rescue) following floods. This is because the DEFRA approach also helps prioritize access to victims of disasters. Instead of being produced by constructions or characteristics that impact water flow hydrology, it seems that the dimensional insufficiency of wadi sections triggers floods to pass incoming flow. The water that exceeds its cross-section quickly spreads over the surrounding region, where wastewater ponds and farms are located. The velocity of the floodwater is great, and it has the potential to transport contaminants in a westerly direction toward essential components, such as metropolitan centers, agricultural regions, and surface water bodies. The area can be made more resistant to the risk of flooding by implementing structural measures, such as constructing a drainage channel on the appropriate slope. Additionally, flow rates and velocities can be more effectively regulated by lowering the base slope.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eIn this study, flood propagation and flood hazard mapping of the Al-Kola area in Sohag, Egypt, were examined using the DEFRA method, incorporating GIS, HEC-HMS, and HEC-RAS. Peak flows using HEC-HMS were obtained for five scenarios: Q\u003csub\u003e20\u003c/sub\u003e, Q\u003csub\u003e30\u003c/sub\u003e, Q\u003csub\u003e40\u003c/sub\u003e, Q\u003csub\u003e50\u003c/sub\u003e, and Q\u003csub\u003e60\u003c/sub\u003e floods. Additionally, in two scenarios, Q20 and Q60, the flood maps indicated that most of the area was highly affected by flood events during these scenarios. The results of the modeling scenarios showed that the average water levels in the study area increased from 1.48 m for the first scenario (rainfall\u0026thinsp;=\u0026thinsp;20 mm) to 2.29 m for the second scenario (rainfall\u0026thinsp;=\u0026thinsp;60 mm). In addition, considering that the average velocity in the region for the first scenario (rainfall\u0026thinsp;=\u0026thinsp;20 mm) is 2.41 m/s, and for the second scenario (rainfall\u0026thinsp;=\u0026thinsp;60mm), the average velocity is 3.74 m/s. The area studied was a wastewater area (wood farms and cultivated areas). It was determined that the majority of the flood-affected areas had an \u0026ldquo;Extreme\u0026rdquo; level of hazard for the first scenario and the second scenario, which ranged from 23.4 to 42%, respectively. This study makes the first contribution to the literature on coupled hydrological and hydraulic modeling to assess the impact of wastewater sites on surface water elements, agricultural areas, and urban areas in the Al-Kola area. The findings can be applied to inform management strategies for basins. To generalize and assess the trustworthiness of the results, the hydrologic and hydraulic models used in this research can be combined and applied to various flood-prone areas. It is feasible to utilize models to manage flood management and simulate real-time inundation. This will help to limit the amount of items that are lost and avoid any potential harm that may be caused by flooding. It is envisaged that flood effects in this region can be addressed by establishing a flood channel that can convey the flood water to the irrigation canal and prevent the flood water from carrying any pollutants with it.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBosy A. El Haddad, Ahmed M. Youssef, and Shaymaa Rizk designed the experiments, ran models, analyzed the results, and wrote and reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdessamed D, Abderrazak B (2019) Coupling HEC-RAS and HEC-HMS in Rainfall\u0026ndash;Runoff Modeling and Evaluating Floodplain Inundation Maps in Arid Environments: Case Study of Ain Sefra City, Ksour Mountain. SW of Algeria. Environ. Earth Sci., 78, 586.\u003c/li\u003e\n\u003cli\u003eAhmad W, Alharthy RD, Zubair M, Ahmed M, Hameed A, Rafique S (2021) Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk. Sci Rep 11, 17006. https://doi.org/10.1038/s41598-021-94616-4\u003c/li\u003e\n\u003cli\u003eAmatebelle CE, Owolabi ST, Ogundeji AA, Okolie CC (2025) A systematic analysis of remote sensing and geographic information system applications for flood disaster risk management. Journal of Spatial Science, 1\u0026ndash;27. https://doi.org/10.1080/14498596.2025.2476973\u003c/li\u003e\n\u003cli\u003eAngon PB, Islam MdS, KC S, Das A, Anjum N, Poudel A, Suchi SA (2024) Sources, effects and present perspectives of heavy metals contamination: Soil, plants and human food chain, Heliyon, 10(7), e28357, https://doi.org/10.1016/j.heliyon.2024.e28357.\u003c/li\u003e\n\u003cli\u003eAwad M, Liu Z, Skalicky M, Dessoky ES, Brestic M, Mbarki S, Rastogi A, El Sabagh A (2021) Fractionation of Heavy Metals in Multi-Contaminated Soil Treated with Biochar Using the Sequential Extraction Procedure. Biomolecules, 17;11(3):448. doi: 10.3390/biom11030448. PMID: 33802758; PMCID: PMC8002428.\u003c/li\u003e\n\u003cli\u003eBalaram V, Copia L, Kumar US, Miller J, Chidambaram S (2023) Pollution of water resources and application of ICP-MS techniques for monitoring and management\u0026mdash;A comprehensive review, Geosystems and Geoenvironment, 2(4): 100210, https://doi.org/10.1016/j.geogeo.2023.100210.\u003c/li\u003e\n\u003cli\u003eBali AS, Sidhu GPS (2021) Heavy metal contamination indices and ecological risk assessment index to assess metal pollution status in different soils, Editor(s): Vinod Kumar, Anket Sharma, Artemi Cerd\u0026agrave;, Heavy Metals in the Environment, Elsevier, p 87-98, https://doi.org/10.1016/B978-0-12-821656-9.00005-5.\u003c/li\u003e\n\u003cli\u003eBarati AA, Zhoolideh M, Azadi H, Lee J-H, Scheffran J (2023) Interactions of land-use cover and climate change at global level: How to mitigate the environmental risks and warming effects, Ecological Indicators, 146, 109829, https://doi.org/10.1016/j.ecolind.2022.109829.\u003c/li\u003e\n\u003cli\u003eBl\u0026ouml;schl G, Hall J, Viglione A, Perdig\u0026atilde;o RAP, Parajka J, Merz B, Lun D, Arheimer B, Aronica GT, Bilibashi A, （2019） Changing climate both increases and decreases European river floods. Nature 573, 108\u0026ndash;111. \u003c/li\u003e\n\u003cli\u003eBosseler B, Salomon M, Schl\u0026uuml;ter M, Rubinato M (2021) Living with Urban Flooding: A Continuous Learning Process for Local Municipalities and Lessons Learnt from the 2021 Events in Germany. Water, 13(19), Article 19. https://doi.org/10.3390/w13192769 \u003c/li\u003e\n\u003cli\u003eBriffa J, Sinagra E, Blundell R (2020) Heavy metal pollution in the environment and their toxicological effects on humans, Heliyon, 6(9): e04691, https://doi.org/10.1016/j.heliyon.2020.e04691.\u003c/li\u003e\n\u003cli\u003eChowdhury MdS (2023) Modelling hydrological factors from DEM using GIS, MethodsX, 10, 102062, https://doi.org/10.1016/j.mex.2023.102062.\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Angelo C, Passalacqua P, Fiori A, Volpi E (2022) Identification of flood‐prone areas with GeoFlood: Lessons learned from the Tiber River case study. Journal of Flood Risk Management, 15(2), e12795. https://doi.org/10.1111/jfr3.12795 \u003c/li\u003e\n\u003cli\u003eDelforge D, Wathelet V, Below R, Sofia CL, Tonnelier M, van Loenhout JAF, Speybroeck N (2025) EM-DAT: the Emergency Events Database, International Journal of Disaster Risk Reduction, 124, 105509, https://doi.org/10.1016/j.ijdrr.2025.105509.\u003c/li\u003e\n\u003cli\u003eDemir V, Beden N, \u0026Uuml;lke A (2021) Review and Comparison of Flood Modeling Methods. Eur. J. Sci. Technol. 1013\u0026ndash;1021\u003c/li\u003e\n\u003cli\u003eDevitt L, Neal J, Coxon G, Savage J, Wagener T (2023) Flood hazard potential reveals global floodplain settlement patterns. Nat Commun 14, 2801. https://doi.org/10.1038/s41467-023-38297-9\u003c/li\u003e\n\u003cli\u003eGrigg NS (2023) Comprehensive Flood Risk Assessment: State of the Practice. Hydrology. 2023; 10(2):46. https://doi.org/10.3390/hydrology10020046\u003c/li\u003e\n\u003cli\u003eGunjyal N, Rani S, Asgari Lajayer B, Senapathi V, Astatkie T (2023) A review of the effects of environmental hazards on humans, their remediation for sustainable development, and risk assessment. Environ Monit Assess. 1;195(6):795, PMC10234584. doi: 10.1007/s10661-023-11353-z. \u003c/li\u003e\n\u003cli\u003eHama Aziz KH, Mustafa FS, Omer KM, Hama S, Hamarawf RF, Rahman KO (2023) Heavy metal pollution in the aquatic environment: efficient and low-cost removal approaches to eliminate their toxicity: a review. RSC Adv. 12;13(26):17595-17610. doi: 10.1039/d3ra00723e. PMID: 37312989; PMCID: PMC10258679.\u003c/li\u003e\n\u003cli\u003eHashemyan F, Khaleghi M, Kamyar M (2015) Combination of HEC-HMS and HEC-RAS Models in GIS in Order to Simulate Flood (Case Study: Khoshke Rudan River in Fars Province, Iran). Res. J. Recent Sci. Res. J. Recent Sci., 4, 122\u0026ndash;127.\u003c/li\u003e\n\u003cli\u003eJerome Glago, F. (2021). Flood Disaster Hazards; Causes, Impacts and Management: A State-of-the-Art Review. IntechOpen. doi: 10.5772/intechopen.95048\u003c/li\u003e\n\u003cli\u003eJia H, Chen F, Du E (2021) Adaptation to Disaster Risk-An Overview. Int J Environ Res Public Health. 25;18(21):11187, PMC8582783. doi: 10.3390/ijerph182111187. \u003c/li\u003e\n\u003cli\u003eJonkman SN, Curran A, Bouwer LM (2024) Floods have become less deadly: an analysis of global flood fatalities 1975\u0026ndash;2022. Nat Hazards 120, 6327\u0026ndash;6342. https://doi.org/10.1007/s11069-024-06444-0\u003c/li\u003e\n\u003cli\u003eJungudo M (2023) The Impact of Climate Change in Egypt. In: Ani, K.J. (eds) Resource Conflict and Environmental Relations in Africa. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-7343-7_11\u003c/li\u003e\n\u003cli\u003eKabata-Pendias A, Pendias H (2011) Trace Elements in Soils and Plants. 4th ed. pp. 28\u0026ndash;534. USA: CRC Press, Taylor and Francis Group, LLC.\u003c/li\u003e\n\u003cli\u003eKarim F, Armin MA, Ahmedt-Aristizabal D, Tychsen-Smith L, Petersson L (2023) A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling. Water, 15(3), 566. https://doi.org/10.3390/w15030566 \u003c/li\u003e\n\u003cli\u003eKordi-Karimabadi F, Fadaei-Kermani E, Ghaeini-Hessaroeyeh M, Farhadi H (2025) Integrating numerical models with deep learning techniques for flood risk assessment. Sci Rep 15, 8913 (2025). https://doi.org/10.1038/s41598-025-93465-9\u003c/li\u003e\n\u003cli\u003eKumar V, Sharma A, Kaur P, Singh Sidhu GP, Bali AS, Bhardwaj R, Thukral AK, Cerda A (2019) Pollution assessment of heavy metals in soils of India and ecological risk assessment: A state-of-the-art. Chemosphere; 216:449\u0026ndash;462. doi: 10.1016/j.chemosphere.2018.10.066\u003c/li\u003e\n\u003cli\u003eNile Basin Initiative (NBI) (2012) Nile River Basin Status Report. Climate Change and its implications for the Nile Basin. Chapter 8. Available at http://nileis.nilebasin.org/system/files/Nile%20SoB%20Report%20 Chapter%208%20-%20Climate%20Change.pdf\u003c/li\u003e\n\u003cli\u003eOzcan N, Altundag H (2013) Speciation of heavy metals in street dust samples from sakarya I. Organized industrial district using the bcr sequential extraction procedure by ICP-OES. Bull. Chem. Soc. Ethiop., 27:205\u0026ndash;212. doi: 10.4314/bcse.v27i2.5.\u003c/li\u003e\n\u003cli\u003ePeker İB, G\u0026uuml;lbaz S, Demir V, Orhan O, Beden N (2024) Integration of HEC-RAS and HEC-HMS with GIS in Flood Modeling and Flood Hazard Mapping. Sustainability; 16(3):1226. https://doi.org/10.3390/su16031226\u003c/li\u003e\n\u003cli\u003ePizzorni M, Innocenti A, Tollin N (2024) Droughts and floods in a changing climate and implications for multi-hazard urban planning: A review, City and Environment Interactions, 24: 100169, https://doi.org/10.1016/j.cacint.2024.100169.\u003c/li\u003e\n\u003cli\u003eRentschler J, Avner P, Marconcini M, Su R, Strano E, Vousdoukas M, Hallegatte S (2023) Global evidence of rapid urban growth in flood zones since 1985. Nature, 622(7981), 87 92. https://doi.org/10.1038/s41586-023-06468-9 \u003c/li\u003e\n\u003cli\u003eRizk S, Elhaddad B (2023) Environmental impact of sewage water disposable sites using remote sensing and geochemical studies in West Girga, Sohag, Egypt. Episodes; 46:575-589. https://doi.org/10.18814/epiiugs/2023/023009\u003c/li\u003e\n\u003cli\u003eShi P (2019) Hazards, Disasters, and Risks. Disaster Risk Science. 5:1\u0026ndash;48, PMC7123175.. doi: 10.1007/978-981-13-6689-5_1.\u003c/li\u003e\n\u003cli\u003eSiam MS, Elthahir EAB (2017) Climate Change Enhances Interannual Variability of the River Nile Flow. Nature Climate Change 7: 350\u0026ndash;354. https://www.nature.com/articles/nclimate3273\u003c/li\u003e\n\u003cli\u003eSingh R, Singh PK, Madheshiya P, Khare AK, Tiwari S (2024) Heavy metal contamination in the wastewater irrigated soil and bioaccumulation in cultivated vegetables: Assessment of human health risk, Journal of Food Composition and Analysis, 128, 106054, https://doi.org/10.1016/j.jfca.2024.106054.\u003c/li\u003e\n\u003cli\u003eSung K, Jeong H, Sangwan N, Yu DJ (2018) Effects of flood control strategies on flood resilience under sociohydrological disturbances. Water Resources Research, 54, 2661\u0026ndash;2680. https://doi.org/10.1002/2017WR021440\u003c/li\u003e\n\u003cli\u003eTabari H (2020) Climate change impact on flood and extreme precipitation increases with water availability. Sci Rep 10, 13768. https://doi.org/10.1038/s41598-020-70816-2\u003c/li\u003e\n\u003cli\u003eTabasi N, Fereshtehpour M, Roghani B (2025) A review of flood risk assessment frameworks and the development of hierarchical structures for risk components. Discov Water 5, 10. https://doi.org/10.1007/s43832-025-00193-2\u003c/li\u003e\n\u003cli\u003eTessier APGC, Campbell PG, Bisson MJAC (1979) Sequential extraction procedure for the speciation of particulate trace metals. Analytical Chemistry 51(7): 844-851.\u003c/li\u003e\n\u003cli\u003eVinet F, Bigot V, Petrucci O, Papagiannaki K, Llasat MC, Kotroni V, Boissier L, Aceto L, Grimalt M, Llasat-Botija M, et al （2019）Mapping Flood-Related Mortality in the Mediterranean Basin. Results from the MEFF v2.0 DB. Water; 11(10):2196. https://doi.org/10.3390/w11102196\u003c/li\u003e\n\u003cli\u003eWang L, Cui S, Li Y, Huang H, Manandhar B, Nitivattananon V, Fang X, Huang W (2022) A review of the flood management: from flood control to flood resilience, Heliyon, 8(11): e11763, https://doi.org/10.1016/j.heliyon.2022.e11763.\u003c/li\u003e\n\u003cli\u003eWinsemius HC, Aerts JCJH, Van Beek LPH, Bierkens MFP, Bouwman A, Jongman B, Kwadijk JCJ, Ligtvoet W, Lucas PL, Van Vuuren DP（2016）Global drivers of future river flood risk. Nat. Clim. Chang. 6, 381\u0026ndash;385. \u003c/li\u003e\n\u003cli\u003eYamani K, Hazzab A, Sekkoum M, Slimane T (2016) Mapping of Vulnerability of Flooded Area in Arid Region. Case Study: Area of Gharda\u0026iuml;a-Algeria. Model. Earth Syst. Environ., 2, 147.\u003c/li\u003e\n\u003cli\u003eYoussef AM, Omer AA, Ibrahim MS, Ali MH, Cawlfield JD (2011) Geotechnical investigation of sewage wastewater disposal sites and use of GIS land-use maps to assess environmental hazards: Sohag, Upper Egypt. Arab J Geosci 4:719\u0026ndash;733. doi:10.1007/s12517-009-0069-6\u003c/li\u003e\n\u003cli\u003eZhang Ke, Shalehy MdH, Ezaz GT, Chakraborty A, Mohib KM, Liu L (2022) An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis, Environmental Modelling \u0026amp; Software, 148, 105279, https://doi.org/10.1016/j.envsoft.2021.105279.\u003c/li\u003e\n\u003cli\u003eZipperer WC, Robert N, Michael A (2020) Urban development and environmental degradation. Oxford Research Encyclopedia of Environmental Science. Retrieved 01 Sep. 2020. https://oxfordre.com/environmentalscience/view/10.1093/acrefore/9780199389414.001.0001/acrefore-9780199389414-e-97.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Environmental sustainability, Flood risk modeling, Heavy metals, Wastewater problems","lastPublishedDoi":"10.21203/rs.3.rs-7035710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7035710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOnly land application is accessible for sewage wastewater disposal in Upper Egypt's Nile Valley. The lowland desert zone, located between the farmed floodplain and the Eocene Limestone plateau, features wastewater disposal installations. These wastewater disposal sites are located in the mouths of wadis, which are vulnerable to flooding. They are located near farmed floodplains, reclaimed lands, residential areas, surface water systems, irrigation canals, and the River Nile. In this work, untreated wastewater forms enormous uncontrolled pools on the ground. Geochemical studies have revealed that wastewater ponds and the surrounding soil are contaminated with heavy metals and bacteria, posing a significant environmental risk. Flood modeling was developed for the Al-Kola Basins, Sohag, Egypt, to generate flood hazard and ecological risk maps by integrating GIS, HEC-HMS, and HEC-RAS techniques. Due to the scarcity of rainfall data, the last recorded rainfall event in 1994 was used as a reference to determine the water runoff rate using HEC-HMS. The study was done using a digital elevation model with 12.5 m resolution. Based on HEC-RAS modeling, it was found that the average water depth increased from 1.48 m to 2.29 m, the average velocity increased from 2.41m/s to 3.76m/s, and the water spread risen from 26\u0026ndash;37% of the entire basin area for the first scenario (rainfall\u0026thinsp;=\u0026thinsp;20mm) and the second scenario (rainfall\u0026thinsp;=\u0026thinsp;60mm), respectively. Our findings show that heavy metals contaminate the area due to anthropogenic activities, and floodwater can transport these polluted materials towards irrigation canals and the River Nile. Engineering recommendations were made to mitigate these critical environmental risks that could compromise human health.\u003c/p\u003e","manuscriptTitle":"Environmental Hazards of Surface Water Resources due to heavy metals from the wastewater sites: A case study: Integration of HEC-RAS, HEC-HMS, and GIS in Flood Hazard Mapping in Scarcity Rainfall Region, Sohag, Egypt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 08:26:00","doi":"10.21203/rs.3.rs-7035710/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-12T17:03:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-09T22:24:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294851538548274541381002080477822003665","date":"2025-08-23T21:58:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-23T19:36:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-05T04:06:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-05T04:05:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2025-07-03T07:59:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7e8e5f77-fb88-4e5c-87f7-2852cee09bac","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:03:17+00:00","versionOfRecord":{"articleIdentity":"rs-7035710","link":"https://doi.org/10.1007/s12665-026-12873-w","journal":{"identity":"environmental-earth-sciences","isVorOnly":false,"title":"Environmental Earth Sciences"},"publishedOn":"2026-03-21 15:59:36","publishedOnDateReadable":"March 21st, 2026"},"versionCreatedAt":"2025-07-07 08:26:00","video":"","vorDoi":"10.1007/s12665-026-12873-w","vorDoiUrl":"https://doi.org/10.1007/s12665-026-12873-w","workflowStages":[]},"version":"v1","identity":"rs-7035710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7035710","identity":"rs-7035710","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}