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Article

Integrated Hydrological Modeling for Watershed Analysis, Flood Prediction, and Mitigation Using Meteorological and Morphometric Data, SCS-CN, HEC-HMS/RAS, and QGIS

by
Heba El-Bagoury
1 and
Ahmed Gad
2,3,*
1
Geography Department, Faculty of Arts, Port Said University, Port Said 42511, Egypt
2
Geosciences Department, College of Science, United Arab Emirates University, Al Ain 15551, United Arab Emirates
3
Geology Department, Faculty of Science, Ain Shams University, Cairo 11566, Egypt
*
Author to whom correspondence should be addressed.
Water 2024, 16(2), 356; https://doi.org/10.3390/w16020356
Submission received: 18 December 2023 / Revised: 13 January 2024 / Accepted: 19 January 2024 / Published: 21 January 2024
(This article belongs to the Special Issue The Interrelationship between Climate Change, Human Activities and Hydrological Processes, Volume II)
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Abstract

:
Flooding is a natural disaster with extensive impacts. Desert regions face altered flooding patterns owing to climate change, water scarcity, regulations, and rising water demands. This study assessed and predicted flash flood hazards by calculating discharge volume, peak flow, flood depth, and velocity using the Hydrologic Engineering Centre-River Analysis System and Hydrologic Modelling System (HEC-HMS and HEC-RAS) software. We employed meteorological and morphological data analyses, incorporating the soil conservation service (SCS) curve number method for precipitation losses and the SCS-Hydrograph for runoff transformation. The model was applied to two drainage basins (An-Nawayah and Al-Rashrash) in southeastern Cairo, Egypt, which recently encountered several destructive floods. The applied model revealed that 25-, 50-, and 100-year storms produced runoff volumes of 2461.8 × 103, 4299.6 × 103, and 5204.5 × 103 m3 for An-Nawayah and 6212 × 103, 8129.4 × 103, and 10,330.6 × 103 m3 for Al-Rashrash, respectively. Flood risk levels, categorised as high (35.6%), extreme (21.9%), and medium (21.12%) were assessed in low- and very-low-hazard areas. The study highlighted that the areas closer to the Nile River mouth faced greater flood impacts from torrential rain. Our findings demonstrate the effectiveness of these methods in assessing and predicting flood risk. As a mitigation measure, this study recommends the construction of five 10 m high dams to create storage lakes. This integrated approach can be applied to flood risk assessment and mitigation in comparable regions.

1. Introduction

Hydrological events (floods), in addition to other water-related disasters, such as storms and droughts, have contributed to 80–90% of the worldwide natural catastrophes in the previous decade. Floods are considered the most destructive natural disasters on the Earth and have large-scale impacts, particularly in arid/semi-arid areas, causing irreversible catastrophic destruction, large number of human fatalities, and substantial economic losses [1,2]. Recently, severe flash floods have been observed in numerous countries, with an observed increase in their frequency, intensity, and damage attributed to climate change [3].
Flash flooding is a frequent climate change-related phenomenon that is particularly noteworthy because of its rapid onset, which makes it difficult to anticipate adequate warning times [4]. The simultaneous occurrence of flash floods and debris flows raises significant safety concerns because they can compound the risks associated with each distinct generative activity [5]. Given the global occurrence of flooding episodes in many forms, including coastal, riverine, urban, and flash floods, assessing flood hazards is a constantly relevant and significant topic of research [6,7,8,9].
The risk reduction and sustainable management of natural resources necessitate periodic assessment to avoid adverse natural and anthropogenic effects [10,11,12]. Water resource management and flood hazard assessments are becoming increasingly critical in arid and semi-arid areas because of climate change and urbanization accompanied by land cover/land use changes [9,13,14,15,16,17]. Effective water management and disaster mitigation measures are greatly hindered by weather variability, particularly severe events, including extreme rainfall, storm events, and long-ongoing droughts [14,18]. The intensification of the hydrological regimes caused by climate change has substantially impacted intensity, spatial range, duration, and frequency of flood events [8,19,20]. However, as climate change causes more hydro-meteorological extremes, common hydrological modeling methodologies are frequently insufficient for delivering reliable flood predictions and alternatives to mitigation.
Floods can be quantified in a variety of methods, including simple empirical procedures [21], the rational method [22], the flood frequency method [23], simplified conceptual models [24], multi-criteria decision analysis [25,26], and numerical and geographic information system (GIS)-based hydrodynamic modelling [7,16,27,28]. Hydrodynamic models are frequently employed in thorough flood dynamics simulations and are primarily associated with flood forecasting, mapping, and scenario analysis [29]. The morphological characteristics of the drainage network, soil properties, geological conditions, and climatic parameters that regulate rainfall input are crucial hydrological factors for estimating surface runoff in a basin [30,31,32]. With advancements in computing power, parallel computing has become increasingly accessible. This, coupled with the availability of high-quality input data and an abundance of remote sensing data for calibration purposes, has led to the widespread use of hydraulic models. Currently, hydrodynamic models include the Iber, Hydrologic Engineering Center-River Analysis System (HEC-RAS), Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS), Mike 21, TUFLOW, SOBEK, and BASEMENT [6].
The HEC-HMS model has proven to be extremely useful and has been used in numerous hydrological studies because of its ability to simulate runoff during both short and long duration events as well as its ease of use [33]. It is a distributed model that allows for the subdivision of a watershed into multiple sub-basins, making it helpful for understanding the flow of water within a drainage basin. Derdour et al. [34] utilized the HEC-HMS hydrological model to simulate runoff in the semi-arid zone of southwest Algeria, applying the soil conversation service (SCS) curve number (CN) to determine the loss rate and the SCS unit hydrograph model to simulate the runoff rate. In their study, Bermúdez et al. [35] presented a technique for downsizing weather data derived from climate models by utilizing a downscaling method using the HEC-HMS model in combination with Iber+. They applied this method to simulate flood inundation in Betanzos, Spain, considering the effects of climate change. The HEC-RAS specializes in hydraulic modeling and river channel analysis, providing detailed insights into river flow dynamics, sediment transport, and floodplain inundation. Quiroga et al. [36] observed that the 2D HEC-RAS flood simulation demonstrated effective performance when compared to the flood extent measured using satellite images. Sarchani et al. [37] conducted a study on the flooding resulting from an intense rainfall event in a small uncalibrated basin located in northwest Crete. Their analysis focused on assessing the uncertainties in combined 1D/2D HEC-RAS modeling, specifically pertaining to variations in the Manning roughness coefficient within the floodplains, using high-resolution digital elevation models. Ongdas et al. [38] demonstrated the strong performance of HEC-RAS in generating flood hazard maps for the Yesil (Ishim) River in Kazakhstan under several simulated flood scenarios. Zhang et al. [39] integrated the HEC-HMS and HEC-RAS to develop a flood risk measurement model and warning mechanism for the Brahmaputra River floodplain in Bangladesh.
Flood management can be broadly defined as improving our ability to handle flood hazards with a focus on ensuring that development initiatives do not increase vulnerability to floods [40]. Many structural and non-structural strategies can be implemented to mitigate the effects of urban flooding [17,41,42,43]. To recognise, forecast, and minimise the adverse effects of flooding and plan and manage responses within drainage basins, it is essential to integrate different progressive and innovative hydrological models and predictive tools [44,45]. By integrating SCS-CN, HEC-HMS/RAS, and QGIS, this study established a novel approach to flood prediction and mitigation. The aim of this study was to improve the accuracy of flood predictions for extreme events, specifically 100-year floods, to better support decisions in flood management and infrastructure planning. This methodology was applied to a case study southeast of Cairo, Egypt, where key factors such as discharge volume, peak flow, loss volume, and generated flood depth and velocity maps were determined. Furthermore, this study identified optimal sites for flood management and mitigation measures.

2. Materials and Methods

2.1. The Case Study

The selected case study area is situated between latitudes 29°14′ and 29°33′ N and longitudes 31°15′ and 31°58′ E (Figure 1a), covering an area of ~985.5 km2. It encompasses the drainage basins of Wadi Al-Rashrash and Wadi An-Nawayah, which discharge into the western side of the Nile River through artificial outfalls in the villages of Qababat and Al-Wedi and extend eastward to the water divide in northern Galalah.
Recently, the risk of urban flooding in Egypt has markedly increased, mostly owing to extensive urbanization and the impacts of climate change [46]. The choice of Wadi Al-Rashrash and Wadi An-Nawayah drainage basins as a case study area to assess and predict flash flood hazards was strategic. The location of these drainage basins that had recently experienced devastating floods was the most important factor in their choice. Other factors taken into consideration were past floods, topography, land use, geologic setting, soil types, and its proximity to the Nile River to confirm their relevance. Over the past nine decades, this region has experienced an increasing number of catastrophic floods, including notable events in 1967, 1982, 1988, and the most devastating event in March 2020. This disaster was initiated by an atmospheric depression, leading to the rapid inundation of the Al-Nawiya and Al-Rashrash basins at speeds of approximately 60 km/h. This resulted in numerous casualties, including the destruction of approximately 300 houses in the towns of Daismi and Al-Wedi, collapse of five bridges and brick factories, flooding of agricultural lands, loss of livestock, and disruption of both the water supply and electrical infrastructure.
Topographically, the area varies in elevation from 6 m in the western floodplain to 844 m above sea level (ASL) in northern Galalah in the east (Figure 1a). The study area is divided into three topographic provinces as follows. The floodplain in this study has a width of ~9 km and elevations ranging from 6 to 70 m ASL. It is composed of dark-colored clay to sandy soils that are used for agriculture, with clear agricultural reclamation areas extending into the second province. The Bahada Plain and Wadi Fill represent the transitional zone between the floodplain and mountain escarpment, comprising sediments composed of sand, gravel, boulders, and rock fragments. These sediments are highly susceptible to weathering and cover an estimated area of 157.90 km2, accounting for approximately 16.02% of the total study area. The Eocene Plateau Province dominates most of the study area, spanning elevations between 250 and 844 m ASL. This plateau consists of heavily fractured and weathered limestone that forms steep and irregular escarpments.
The stratigraphic section includes sedimentary sequences spanning from the Middle Eocene to recent times [47] (Figure 1b). The Middle Eocene rocks are represented by the Mokattam Formation, which is composed of limestone, marl, and chalky limestone, extensively dissected by faults and joints. These rocks cover approximately 833.3 km2 of the study area and are known for their susceptibility to water erosion, resulting in narrow, deep waterways and a rugged surface appearance. The Oligocene outcrops, located in the the southeastern Wadi Al Rashrash Basin, consist of dark grey and brown gravels, boulders, and hard chert nodules. These deposits contain fossils from a riverine environment, suggesting transportation by ancient river flows during the Oligocene rainy periods. The Oligocene deposits occupy approximately 1.72 km2 of the study area. The Pliocene deposits comprise alternating layers of sandstone and coquinal limestone, represented by the Kom El Shelul Formation, overlying conglomerates that represent undifferentiated Pliocene sediments. These deposits span an average area of approximately 82.67 km2 and serve as significant aquifers of groundwater. The Holocene deposits from the Quaternary era consist of Wadi deposits and Nile silt. Wadi deposits that fill dry wadies include sand, silt, and gravel, varying in thickness across the area. They cover approximately 45.16 km2 of the total area. Nile silt, characterized by dark yellowish and grey silt and mud with heavy metals and fine sand originating from the Eocene Plateau through dry wadies, was also present. Structurally, the study area is intersected by numerous faults and joints, with predominant NW-SE and NE-WS trends, with a few running nearly E-W [47].

2.2. Data

To depict and create a hydrodynamic model for the study region, various spatial and non-spatial datasets were employed. These datasets originated from a range of sources and, in certain cases, underwent additional processing and refinement to meet the necessary standards for the software used in flood prediction and mitigation. Shuttle Radar Topography Mission (SRTM) data with a cell size of 30 m × 30 m were obtained from the USGS Earth Explorer [48], in decimal degree and datum WGS84. The detected gaps in the SRTM data were filled using the preprocess sink tool in the HEC-HMS (version 4.11; 2023) model before any procedures were applied. The accuracy of the digital elevation model (DEM) was verified using topographic maps with a scale of 1:50.000 and satellite images. The DEM was employed to create a runoff curve number (CN) model, elevation, drainage density, and slope indicators. A record of 32 years (1990–2021) of daily rainfall was obtained for the Giza Meteorological Station using the USGS Climate Engine dataset [49] (Table S1). Land use data were obtained from the unsupervised classification of Sentinel 2A and classified using ArcGIS (ArcGIS; version 10.8.4.1; 2020) into four types of land use: urban (16), agriculture (23), sand sheets (71), and desert land (74), following the land use classification of Anderson et al. [50] (Figure 2a). Land use data were used for the CN model and land use indicators. Soil data were extracted from a reconnaissance soil map of the El Faiyum-Cairo area (sheet VI, United Arab Republic–High Dam soil survey project in cooperation with the United Nations Special Fund) using ArcGIS (ArcGIS; version 10.8.4.1; 2020) (Figure 2b). The soil type affects the runoff capability; therefore, it was used to calculate the CN.

2.3. Data Processing and Modeling

2.3.1. Sub-Basin Delineation

Watershed delineation is fundamental in hydrological and environmental studies. Using GIS technology and the HEC-HMS model, watersheds can be automatically estimated by employing DEM. In this study, the HEC-HMS (version 4.11; 2023) model was utilized to delineate sub-basins in a series of steps: (1) creating a new project in the HEC-HMS model, (2) generating a new basin model from the component option and downloading DEM data, and (3) utilizing GIS (ArcGIS; version 10.8.4.1; 2020) to define the coordinate system, fill sinks, preprocess drainage, identify streams, manage break points, and delineate elements (sub-basins).

2.3.2. Curve Number (CN) Determination

The efficacy of the SCS-CN approach in the rainfall–runoff relationship has been frequently recognized, particularly in locations with diverse land use and soil types [34,39,51]. The SCS-CN approach is a practical and effective tool for assessing precipitation losses and their effects on runoff, which varies across our study area due to morphological factors such as soil type and land cover.
The CN was calculated to estimate water leakages in soil and was generated automatically based on land use and hydrological soil groups. This process was conducted using the Water Management System software (WMS; version 10.1; 2016, United States Army Corps of Engineers) by converting the GIS format of land use, soil, and sub-basin layers into Arc coverage format and then intersecting the sub-basins with the land use and soil maps using the GIS Attribute option in the WMS software (Figure S1). According to SCS [52], the high CN values (>90) indicate minimal or no infiltration, and low values (<50) indicate impervious surfaces.

2.3.3. Hyfran Plus and Depth of Rainfall

The determination of the maximum precipitation depth for various return periods (25, 50, and 100 y) was conducted using the Hyfran Plus program (version 1.2; 2008). The analysis involved inputting 32 years of daily rainfall data and fitting of normal (N), log-normal (LN), Gumbel (G), Generalized Extreme Value (GEV), and Log-Pearson type III (LP-III) probability distribution methods, in accordance with the standard procedure, considering mean, standard deviation, skewness coefficient, and coefficient of variation of the rainfall data and their standard errors [53,54]. Gado et al. [55] performed a comprehensive evaluation and comparison of various common probability distributions based on different parameter estimation methods for rainfall extremes over Egypt. They recommended LP-III as the best-fit model for 23% of the total stations in Egypt, including Giza station, for daily annual extreme rainfalls. LP-III is used to estimate extremes in many natural processes and is one of the most used probability distributions in hydrology [53,56]. In the current study, the selection of LP-III distribution as the best-fit model was based on a comparison between the results of the calculated methods of estimation of the maximum rainfall depths, depending on non-exceedance probability, standard deviation, and visual inspection of the probability plots and how well they hold to the 0.05 confidence interval to ensure the appropriateness of the selected distribution and the results of Chi-squared test as adequacy criteria. The LP-III distribution was fitted by computing base-10 logarithms of the rainfall data at selected exceedance probability, and the distribution parameters (shape, scale, and location) were estimated using the Method of Moments (MOM), considering mean, standard deviation, and skewness. This method is widely accepted for fitting probability distributions to rainfall data due to its statistical efficiency, uncertainty quantification, and credible intervals [57]. Further details of distribution functions can be obtained elsewhere [54,57].

2.3.4. HEC-HMS Model

The HEC-HMS software (version 4.11; 2023) was used to create hydrographs of the studied watersheds. Water quantity and inflow rate calculations were based on the SCS unit runoff hydrograph approach, utilizing a hypothetical storm for meteorological data. SCS-Hydrograph is an effective approach for transforming estimated direct runoff into hydrographs that model the temporal distribution of a runoff event. Because it serves to estimate flash flood hazards using discharge volume, peak flow, flood depth, and velocity calculations, this method is consistent with the objectives of this study. The most important inputs include area and slope, concentration and lag time, CN values, and rainfall amounts [58]. The hydrologic parameters for each sub-basin were entered using the HEC-HMS sub-basin editor, with relevant information such as the sub-basin area and the loss rate method (SCS-CN method). The mathematical model was applied throughout a 24 h design storm period, and the delay and concentration time were estimated using the SCS approach based on the analysis of extracted watersheds. The SCS Unit Hydrograph equation is as follows:
T L = L 0.8 × S + 1 0.7 1900   × y 0.5
where T L is the basin lag time (h) and represents the time from the centroid of rainfall to the occurrence of peak flow; L is the length from the sub-basin outlet to divide along the longest drainage path (ft); y is the sub-basin slope (m/m %); and S is the maximum potential retention (in) (S = 1000/CN − 10).

2.3.5. 2D HEC-RAS Model

The HEC-RAS software (version 6.4.1; 2023, U.S. Corps Army Engineering) was used for the calculation and analysis of the floodplain. The software supports 1D and 2D HEC-RAS hydraulic simulation. In the present study, a 2D HEC-RAS hydraulic simulation was applied using the hydrograph data for different return period (25, 50, and 100) year outputs from the HEC-HMS model and DEM data in two steps: (1) defining the geometric boundary, the cell size of the mesh, and up and downstream; and (2) running unsteady flow analysis by entering hydrograph data and choosing the diffusion wave equation, which is utilized to compute the flow field in the 2D mesh. The diffusion wave equation makes the model faster and minimizes model instability. Further details of the equations used for calculating the flood depth and velocity using HEC-RAS can be obtained elsewhere [37,59]. Figure 3 presents a summary of the modeling process and flood hazard assessment.

3. Results and Discussion

3.1. Morphometric Parameters

The morphometric variables of a basin can describe the distribution of runoff, peak flood levels, erosion estimates, sediment yields, and consequences of floods [42]. The results of the morphometric characteristics are recorded in Tables S2 and S3 and show that Wadi An-Nawayah and Al-Rashrash are divided into 21 and 27 sub-basins, respectively (Figure 4). Wadi An-Nawayah sub-basins have an area that ranges from 2.1 to 38.1 km2, whereas that of Wadi Al-Rashrash ranges from 0.2 to 72.5 km2.
The results of the basin slope also refer to the maximum value of 0.34 m/m and minimum value of 0.07 m/m for Wadi An-Nawayah and that of Wadi Al-Rashrash sub-basins ranges from 0.05 to 0.3 m/m. The higher the value of the basin slope, the higher the risk of flash floods. Large stream slopes cause a faster loss of storage, resulting in a steeper recession limb in the hydrograph and a smaller time base. In small catchments where overland flow is essential, the basin slope is crucial. Under such circumstances, a steeper catchment slope leads to a higher peak discharge [60,61]. The drainage densities of the studied sub-basins range from 0.01 to 1.53 and 0.09 to 2.69 km/km2 for Wadi An-Nawayah and Al-Rashrash, respectively. A high density typically allows for rapid runoff disposal. Consequently, hydrographs with larger peaks and shorter periods are predicted for sub-basins with higher drainage densities [61,62]. The elongation ratio ranges from 0.26 to 0.52 and 0.26 to 0.55 for Wadi An-Nawayah and Al-Rashrash sub-basins, respectively. According to Schumm [63], most sub-basins in the study area are more elongated, whereas others are less elongated. The existence of considerable areas of high-relief impermeable limestone with steep ground surfaces contributes to the wide range of morphometric parameters in these basins.
The calculated CN values of the different soil groups and land use classes are mostly higher than 90 (Tables S2 and S3), with average values of 93.6 and 95.4 for Wadi An-Nawayah and Al-Rashrash, respectively. Low CNs indicate a low runoff potential, whereas larger numbers indicate an increased runoff potential. A higher CN value allows for rapid runoff and increases the peak of the hydrograph [64]. Furthermore, the varied CN values were influenced by the various geological structures in each sub-basin.

3.2. Rainfall Data Analysis

Rainfall is a key factor that contributes to the triggering of flash floods and is a mandatory parameter in hydrological model studies. The precipitation for a specific return period must be calculated, where the return period represents the average duration in years between the peak of a flood of a certain size and another flood of equal or greater magnitude [65]. Rainfall records for 32 years were used for rainfall frequency analyses. Figure S2 illustrates the fitting of the theoretical frequency distributions with the LP-III as the best-fitting distribution method. The predicted rainfall depths for different hypothetical return periods are listed in Table 1. The predicted rainfall depths were 19.3, 23.1, and 27.3 mm for the hypothetically proposed storms of 25, 50, and 100 years, respectively. Rainfall depth is known to be affected by soil, surface geology, and land use [59]. Its efficiency increases when it falls on an impermeable surface, which frequently occurs in urban and rocky areas. This generates substantial potential for a sensitive runoff response because most rain precipitation is converted into surface-flowing water, raising the possibility of flooding [59].

3.3. Lag Time Calculation

Lag time is considered one of the most essential inputs in the HEC-HMS model and is calculated using Equation (1). The results are presented in Tables S4 and S5, indicating values ranging from 13.5 to 107.2 in Wadi An-Nawayah and from 4.4 to 135.2 in Wadi Al-Rashrash. The lag time results in this study were categorized into three classes for each wadi, as illustrated in Figure 5. Lag time is affected by several factors, such as basin size, soil, land use, geology, slope, width, and drainage density [66].

3.4. HEC-HMS Model

The precipitation volume over a 24 h period was used to generate hydrographs for each sub-basin within the channels of Wadi An-Nawayah and Wadi Al-Rashrash, considering both the rainfall amount and moisture loss during the specified period (Figures S3–S8). Consequently, hydrographs were created for each wadi for return periods of 25, 50, and 100 years (Figure 6). Tables S6 and S7 present the extracted values of the peak flow, discharge volume, and loss volume.

3.4.1. Peak Flow Discharge

The HEC-HMS model generated various results, including peak flow discharge, which is a key indicator of water flow during peak conditions and indicates the highest runoff flow rate. At the outlet of each sub-basin, the hydrograph and peak discharge for each return period were calculated and analyzed. In Wadi An-Nawayah, the peak flow discharge rates were 276.20, 375.70, and 491.80 m3/s for return periods of 25, 50, and 100 years, respectively (Figure 6a). The minimum peak discharge values were 0.90, 3.40, and 4.40 m3/s and the maximum values were 31.80, 48.90, and 56.70 m3/s (Tables S6 and S7). For Wadi Al-Rashrash, the peak flow discharge rates were 675.70, 880.90, and 1112.80 m3/s for return periods of 25, 50, and 100 years, respectively (Figure 6b). The minimum peak discharge values were 0.20, 0.70, and 1.40 m3/s, and the maximum values were 80.40, 102.70, and 127.60 m3/s (Tables S6 and S7). Figure 7 shows the peak flow discharge in the Wadi An-Nawayah and Al-Rashrash sub-basins. The peak discharge values increased with the return period. The morphometric parameters of the basins contribute to their peak discharge, leading to flash floods characterized by a high peak and short duration [16].

3.4.2. Discharge Volume

The recorded discharge volumes in Wadi An-Nawayah for return periods of 25, 50, and 100 years were 2461.80, 4299.60, and 5204.50 × 10−3 m3, respectively. The minimum discharge volumes were 11.90, 23.80, and 30.00 × 10−3 m3, and the maximum values were 312.90, 558.6, and 679.50 × 10−3 m3. On the other hand, the values for the same return periods in Wadi Al-Rashrash were 6212.00, 8129.40, and 10,330.60 × 10−3 m3, respectively. The minimum discharge volumes were 5.20, 6.50, and 8.00 × 10−3 m3, and the maximum values were 900.90, 1151.60, and 1434.10 × 10−3 m3. Figure 8 shows the discharge volumes in the Wadi An-Nawayah and Al-Rashrash sub-basins. The results indicate that sub-basins with high discharge volumes have CN values exceeding 90, linked to small values of loss volume, considering factors such as the area, slope, and length of the sub-basin.

3.4.3. Loss Volume

For return periods of 25, 50, and 100 years, the recorded precipitation loss volumes in Wadi An-Nawayah were 3167.58, 3664.20, and 3843.70 × 10−3 m3, respectively. The minimum discharge volumes were 15.60, 17.00, and 17.50 × 10−3 m3, and the maximum values were 421.80, 480.60, and 505.50 × 10−3 m3. The values for the same return periods in Wadi Al-Rashrash were 3505.50, 5495.60, and 6337.00 × 10−3 m3, respectively. The minimum discharge volumes were 1.90, 1.90, and 2.00 × 10−3 m3, whereas the highest values were 721.90, 769.10, and 840.20 × 10−3 m3.
The surface-appearing limestone rocks have a considerable impact on hydrological settings. This limits the rate of infiltration and increases runoff, resulting in increased flooding. Consequently, heavy rainstorms can cause rapid and significant peak floods [60].

3.5. 2D HEC-RAS Model

The discharge peaks for each sub-basin, as well as land use maps, were used as an input for the 2D HEC-RAS model, allowing the simulation of the flow pathway distribution and variability. The model then created flood depth and velocity maps to assess the flooded areas and identify hazard locations.

3.5.1. Flood Depth

The 2D HEC-RAS model can simulate the variability along the flow path. Though the topography of the Wadi basins allows water to move in any direction, flow resistance is influenced by the type of land use [67]. The flood depth maps generated using the 2D HEC-RAS are presented in Figure 9. Flood depth values were categorized into five classes based on the classification provided by Mihu-Pintilie et al. [67] (Table 2). Flood levels between 2 and 5 m significantly affected the largest area across different return periods (25, 50, and 100 years), covering approximately 36% of the total area, compared with other flood depth classes. Depths exceeding 5 m constituted approximately 22% of the entire area at various return periods (25, 50, and 100 years).

3.5.2. Flood Velocity

Flood velocities in the study area were categorized into five classes (Figure 10): 0–2, 2–4, 4–6, 6–8, and >8 m/s. Across various computed return periods (25, 50, and 100 years), the 0–2 m/s velocity class exhibited the highest frequency, followed by the 2–4 m/s class, with occasions of >8 m/s logged in specific localities of the flooded area, which may be attributed to floodplain roughness and hydrotechnical activities within the study region. Flood velocity was not considered as it remained a constant factor throughout all return periods.

3.6. Flood Hazard Assessment

A flash flood hazard map is essential for indicating expected floods of different magnitudes. Flood risk assessment depends on detectable factors, such as flood degree, velocity, or water depth, reflecting the susceptibility of built-up areas to potentially damaging hydrological events. The flooding risk was assessed using the flood extent and depth simulated by the 2D HEC-RAS model for various return periods (25, 50, and 100 years) (Figure 11). Flood risk was classified according to floodwater depth into five classes: very low flood hazard, low, medium, high, and extreme flood hazard [36] (Table 2). Flood risk is positively proportional to the peak discharge value and inversely proportional to lag time [51].

4. Flash Flood Mitigation

Water resources in Egypt face significant pressure owing to population growth. Consequently, flood mitigation measures are essential not only for protection but also for the sustainable utilization of this freshwater resource [60,68]. Based on the findings of this modeling on the potential for flash flooding, implementation of mitigation measures for flash flood control are strongly recommended to mitigate potential flash flood hazards in the study areas. Five artificial barriers (dams), each with an elevation of approximately ten meters, were proposed using QGIS and to be strategically placed across the stream, as depicted in Figure 12. The primary objective is to create storage lakes behind these dams, reserving a portion of the water drained behind them. This reservoir formation should reduce the severity of torrential flows and discharge levels. Figure 13 illustrates the amount of water retained behind each dam intended for various purposes such as agriculture, industry, and other long-term development.
The construction of dams for flood mitigation can contribute to a positive economic impact by reducing damage to resources, agricultural land, and human settlements. It can also assist in managing agricultural and domestic water usage. The feasibility of the proposed dam is based on technical considerations, including the suitability of dams, materials of construction, and compliance with safety standards. A comprehensive cost–benefit analysis should be conducted to assess the economic advantages of dam construction relative to potential flood damage. Dam construction can change the local environment. Changes in flow and storage can affect aquatic habitats, plants, and animals. Environmental studies should be conducted to understand and mitigate environmental impacts.

5. Limitations

The findings and mitigation measures presented here are particular to the An-Nawayah and Al-Rashrash basins, Egypt. However, it should be emphasized that there are certain limits in terms of the transferability or generalization of the integrated model to other areas of the world with different climatic conditions, morphological aspects, and hydrological settings. Hydrological models often contain a set of assumptions such as channel geometry and representation of hydraulic structures that must be evaluated for local conditions.
This study provides flood risk assessments based on 25-, 50-, and 100-year designed storms using historical data and assumes that future climate and land use will follow similar trends. However, changing climate and land use patterns may require a more dynamic approach considering long-term changes in rainfall, land use, and other relevant factors, which can introduce additional uncertainties.

6. Conclusions

This study proposed an integrated modeling for flood assessment, prediction, and mitigation. This methodology combines meteorological and morphometric data with GIS, SCS-CN, HEC-HMS, 2D HEC-RAS, and QGIS techniques. Detailed morphometric analysis combined with an analysis of rainfall records for 32 years was used for the hydrological modeling of two drainage basins (Wadi An-Nawayah and Al-Rashrash) situated in southeastern Cairo, Egypt. The key points of this study indicated that the morphometric parameters, surface geology, soil, and land use of the basins contribute to the peak discharge and discharge volume of the basin, leading to flash floods characterized by a high peak and short duration. This study revealed that 25-, 50-, and 100-year return periods lead to specific runoff volumes for each basin. Flood risk levels were categorized, and the areas closer to the mouths of the Nile River were identified as facing greater flood impacts from torrential rainfall. According to the flood hazard maps, flood levels between 2 and 5 m significantly impacted approximately 36% of the study area across the different return periods. Floods with velocities of 0–2 m/s had the highest frequencies. These findings demonstrate the effectiveness of the integrated modeling approach for assessing and predicting flood risks. Five dams were proposed for strategic placement across the stream to reduce the severity of torrential flows and discharge levels. The flood hazard and mitigation maps generated in this study are cost effective, show an appropriate scale, and consider many flood parameters such as hazard degree, water depth, velocity, and water extent area. These maps serve as initial tools for planning decisions, determining suitable land uses, and guiding extension forms.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16020356/s1, Table S1: Giza station records of maximum rainfall data from 1990 to 2021. Table S2: Morphometric characteristics of Wadi An-Nawayah sub-basins. Table S3: Morphometric characteristics of sub-basins of Wadi Al-Rashrash. Table S4: Lag time values of Wadi An-Nawayah sub-basins. Table S5: Lag time values of sub-basins of wadi Al-Rashrash. Table S6: Runoff and loss volumes and peak discharges in sub-basins of W. An-Nawayah during return periods (25, 50, and 100 years). Table S7: Runoff and loss volumes and peak discharges in sub-basins of W. Al-Rashrash during return periods (25, 50, and 100 years). Figure S1: Calculation of CN values of sub-basins using WMS software. Figure S2: The rainfall theoretical frequency distribution fitting of Log-Pearson type 3. Figure S3: Hydrographs of sub-basins of W. An-Nawayah during 25-year return period. Figure S4: Hydrographs of sub-basins of W. An-Nawayah during 50-year return period. Figure S5: Hydrographs of sub-basins of W. An-Nawayah during 100-year return period. Figure S6: Hydrographs of sub-basins of W. Al-Rashrash during 25-year return period. Figure S7: Hydrographs of sub-basins of W. Al-Rashrash during 50-year return period. Figure S8: Hydrographs of sub-basins of W. Al-Rashrash during 100-year return period.

Author Contributions

Conceptualization, H.E.-B. and A.G.; methodology, H.E.-B. and A.G.; software, H.E.-B.; validation, H.E.-B. and A.G.; investigation, H.E.-B. and A.G.; data curation, H.E.-B. and A.G.; visualization, H.E.-B. and A.G.; writing—original draft preparation, H.E.-B. and A.G.; writing—review and editing, H.E.-B. and A.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article or supplementary material.

Conflicts of Interest

The authors declare no conflicts of interest.

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Water 16 00356 g001
Figure 1. (a) Location, topographic provinces, and (b) geologic setting of the study area.
Figure 1. (a) Location, topographic provinces, and (b) geologic setting of the study area.
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Figure 2. (a) Land use and (b) soil maps of the study area.
Figure 2. (a) Land use and (b) soil maps of the study area.
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Figure 3. Summary of the modeling process and the flood hazard assessment.
Figure 3. Summary of the modeling process and the flood hazard assessment.
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Figure 4. Sub-basins of (a) Wadi An-Nawayah and (b) Wadi Al-Rashrash.
Figure 4. Sub-basins of (a) Wadi An-Nawayah and (b) Wadi Al-Rashrash.
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Figure 5. Lag times in (a) Wadi An-Nawayah and (b) Wadi Al-Rashrash.
Figure 5. Lag times in (a) Wadi An-Nawayah and (b) Wadi Al-Rashrash.
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Figure 6. Hydrographs of (a) Wadi An-Nawayah and (b) Wadi Al-Rashrash during return periods of 25, 50, and 100 years.
Figure 6. Hydrographs of (a) Wadi An-Nawayah and (b) Wadi Al-Rashrash during return periods of 25, 50, and 100 years.
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Figure 7. Peak flow discharge in Wadi An-Nawayah and Wadi Al-Rashrash during return periods of 25, 50, and 100 years.
Figure 7. Peak flow discharge in Wadi An-Nawayah and Wadi Al-Rashrash during return periods of 25, 50, and 100 years.
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Figure 8. Discharge volumes in Wadi An-Nawayah and Wadi Al-Rashrash during return periods of 25, 50, and 100 years.
Figure 8. Discharge volumes in Wadi An-Nawayah and Wadi Al-Rashrash during return periods of 25, 50, and 100 years.
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Figure 9. Flood depth simulated by 2D HEC-RAS model for return periods of 25, 50, and 100 years.
Figure 9. Flood depth simulated by 2D HEC-RAS model for return periods of 25, 50, and 100 years.
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Figure 10. Flood velocity simulated by 2D HEC-RAS model for return periods of 25, 50, and 100 years.
Figure 10. Flood velocity simulated by 2D HEC-RAS model for return periods of 25, 50, and 100 years.
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Figure 11. Flood hazard assessment map.
Figure 11. Flood hazard assessment map.
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Figure 12. Locations of proposed dams and storage lakes.
Figure 12. Locations of proposed dams and storage lakes.
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Figure 13. Retained water volume behind each dam.
Figure 13. Retained water volume behind each dam.
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Table 1. Depth of rainfall during return periods of 25, 50, and 100 years (LP3).
Table 1. Depth of rainfall during return periods of 25, 50, and 100 years (LP3).
Return Period (Years)Non-Exceedance ProbabilityRainfall Depth (mm)Standard Deviation
1000.990027.315.2
500.980023.17.17
250.960019.34.10
Table 2. Areas affected by the flood depth during different return periods.
Table 2. Areas affected by the flood depth during different return periods.
Hazard ClassesFlood Depth (m)Wadi An-NawayahWadi Al-Rashrash
25-Year (km2)50-Year (km2)100-Year (km2)25-Year (km2)50-Year (km2)100-Year (km2)
Very low0–0. 51.081.231.540.870.951.11
Low0.5–12.102.503.601.791.912.13
Medium1–22.032.252.603.474.206.00
High2–53.794.236.544.616.758.71
Extreme>51.101.671.993.835.007.72
Total10.1011.8916.2714.5618.8125.67
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El-Bagoury, H.; Gad, A. Integrated Hydrological Modeling for Watershed Analysis, Flood Prediction, and Mitigation Using Meteorological and Morphometric Data, SCS-CN, HEC-HMS/RAS, and QGIS. Water 2024, 16, 356. https://doi.org/10.3390/w16020356

AMA Style

El-Bagoury H, Gad A. Integrated Hydrological Modeling for Watershed Analysis, Flood Prediction, and Mitigation Using Meteorological and Morphometric Data, SCS-CN, HEC-HMS/RAS, and QGIS. Water. 2024; 16(2):356. https://doi.org/10.3390/w16020356

Chicago/Turabian Style

El-Bagoury, Heba, and Ahmed Gad. 2024. "Integrated Hydrological Modeling for Watershed Analysis, Flood Prediction, and Mitigation Using Meteorological and Morphometric Data, SCS-CN, HEC-HMS/RAS, and QGIS" Water 16, no. 2: 356. https://doi.org/10.3390/w16020356

APA Style

El-Bagoury, H., & Gad, A. (2024). Integrated Hydrological Modeling for Watershed Analysis, Flood Prediction, and Mitigation Using Meteorological and Morphometric Data, SCS-CN, HEC-HMS/RAS, and QGIS. Water, 16(2), 356. https://doi.org/10.3390/w16020356

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