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Article

Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment, the New Um Al-Khair Dam Case Study, Jeddah, Saudi Arabia

by
Mohamed Hafedh Hamza
1,2,* and
Afnan Mohammed Saegh
1
1
Department of Geography and Geographic Information Systems, Faculty of Arts and Humanities, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2
Geomatics Section, Department of Geology, Faculty of Science of Tunis, University of Tunis El Manar, Tunis 1068, Tunisia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(2), 1074; https://doi.org/10.3390/su15021074
Submission received: 30 November 2022 / Revised: 26 December 2022 / Accepted: 28 December 2022 / Published: 6 January 2023
(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)
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Abstract

:
Recent years have seen an increase in floods with severe damage due to the intensity and frequency of rains. One of the periodic hydrological problems affecting Jeddah city, the second-biggest city in Saudi Arabia, is unexpected flash flooding. In dam breaks, water that has been retained is released uncontrollably. This study is related to a flood simulation methodology after a possible break of the New Um Al-Khair Dam, a dam built in 2012 outside residential areas, to replace the Old Um Al-Khair Dam built inside a residential area, which broke in January 2011. In fact, we simulated the impact on flood wave propagation in the study area through the use of GIS techniques coupled with hydrological/hydraulic modeling tools and the development of a flood inundation model. Planning a good emergency response in the future is possible by analyzing a supposed disaster. Based on the likelihood that there will be a flood and the corresponding inundation depth, a flood risk matrix is created as a quantitative tool to estimate flood damage, which is crucial to decision-makers. Negligible, low, moderate, high, and very high-risk categories are assigned according to that flood risk matrix. The results indicated a low to very high risk for 5 years, 50 years and 100 years return periods and a negligible to very high risk for a 200 years return period. To estimate the extent of damage, a quantitative summary of the results has been outlined graphically in order to visualize the scope of the inundation areas.

1. Introduction

A global increase in natural disasters has been attributed to climate change. Natural flood disasters have become more frequent and intense in recent years as rainfall intensity and frequency have increased [1]. The extension of urbanization, coupled with the exceptionally heavy rains that have recently occurred, have caused increasingly severe and frequent flash food events in different cities around the world [2]. Hydraulic studies established in Saudi Arabia showed that flash inundations occurred nine times between 1993 and 2021. The city of Jeddah is located on the west coast of Saudi Arabia along the Red Sea. With minimal impact from the Red Sea, Jeddah has a desert climate. Between December and March, this region usually experiences a cool season with temperatures ranging between 21 and 30 °C. In this area of Saudi Arabia, the hot season begins in April, with high humidity and daytime temperatures between 25 and 45 °C. It is common for the humidity to reach over 80 percent in the summer months. In November 2009 and January 2011, flash floods caused heavy human casualties (132 deaths), as well as major damage to infrastructure. The estimated economic damage was about 3 billion US dollars [3]. Floods are a natural phenomenon that occurs as part of the hydrological cycle, where water flows from the highest points to the lowest points, trying to fill every space on the earth’s surface. Due to the inability of drainage channels to store rainwater, floods can occur as a result of surface runoff caused by precipitation. There is a connection between excess precipitation in the watershed and its intensity [4]. In spite of the fact that most floods are small, monster floods do occasionally occur [5]. In reality, not all floods cause harm to humans. As far as the environment and the economy are concerned, normal floods are considered blessings [6], while on the other hand, high flood levels are detrimental [7].
Dams are structures constructed across rivers to collect water; they can be an efficient way to manage water resources [8]. It is an important topic for storing and distributing water in the future [9]. A dam’s benefits are classified by the US Federal Emergency Management Agency FEMA as hydroelectricity, flood control, control of sediment and hazardous materials, navigation, fishing, and clean and renewable energy water storage [10]. Another main use and benefit of dams are providing irrigation water to agriculture [11]. Despite the numerous advantages of dams, they are always at risk from severe dam break floods [12,13,14]. In dam breaks, water that has been retained is released uncontrollably [15]. Until 2020, 3861 dam failures have been recorded worldwide [16]. In order to determine the risky locations and the intensity of dam break floods, it is crucial to evaluate and model dam failure scenarios [17]. These details can facilitate planning for emergency preparedness and land use. Choosing the right location for the dam is also crucial to its protection [18]. In Jeddah, Old Jeddah’s Um Al-Khair dam was built more than 40 years ago and has so far worked to protect part of the urban area downstream. In conjunction with the city’s expansion midway through the 1980s, villas and buildings were built on the dam reservoir area. The floods of 2011 proved that building in this location was a fatal mistake. The houses in the neighborhood were severely damaged. In the aftermath of this flood, the resulting inappropriate modifications to the structure of the dam caused damage to its downstream side during the next flood in 2011 [19]. To prevent flooding in this district and further downstream, authorities built a large new dam east of the old one in 2012. The soil under the new dam is of good quality, the density is about 2 t/m3, and the standard penetration test (SPT) value varies from 8 to 25 [20].
An analysis of the suspected disaster related to the dam break can help to develop a more effective contingency plan in the future. Modeling urban floods uses both hydrologic and hydraulic models. Geographic information systems (GIS) are used in the present urban study as they are used in other urban studies related to planning and management [21]. GIS techniques coupled with hydraulic/hydrologic modeling software are used to study flood hazards in Jeddah, as was done in many previous studies around the world [22,23,24,25,26,27]. HEC-HMS and HEC-RAS, two open-source programs from the US Army Corps of Engineers, are employed. Watershed can be modeled using HEC-HMS, despite a hydrological flood analysis being performed with HEC-RAS [28]. The Watershed Modeling System (WMS) is also used in our study. Developed by Aquaveo, it is a computer simulation and modeling program for watersheds. Modeling watershed hydrology mathematically is its main purpose [29]. WMS is a comprehensive, visually based hydrologic modeling system created to benefit from watershed data created or saved in GIS. It can build, read, and write GIS data layers. However, it is not a full GIS itself nor an extension made with GIS macros or programming languages [30].
Possible effects and damage of the dam-break flood are investigated, and results are simulated to show dam-break effects on the region [31]. Several steps are involved in performing an analysis and flood disaster simulation of a possible dam break for the New Um Al-Khair Dam: Analyzing rainfall patterns, delineating catchments, analyzing land uses and coverings, and modeling hydraulic/hydrological systems. Creating dam break simulations and flood risk maps were among the main objectives of this work.

2. Study Area

2.1. Geographical Setting

With a population of around 4,781,000 in 2022, Jeddah is the second-largest city in Saudi Arabia. Its population constitutes approximately 14% of the total population of the country. This city is situated along the Red Sea coast on the west coast of Saudi Arabia. As well as being a business and money center, the city has a thriving economy and travel hub. Jeddah has a total area of 1765 km2 in its urban area, while its municipal area covers 5460 km2 [32]. Elevations range from 0 m to 265 m above mean sea level. During the winter, from November to March, the plain lowlands are especially prone to flooding due to their 0–2% slope. An important area and district of Jeddah called Um Al-Khair, a densely inhabited neighborhood, is where this study is being carried out. It is located east of the Al-Haramain highway between latitudes 21°31′ and 21°32′ North and longitudes 39°16′ and 39°17′ East. The dam of Um Al-Khair was partially broken at the time of the flash floods of January 2011. It was demolished after some months by the authorities to be replaced by the New Um Al-Khair Dam. This new dam was constructed in 2012 about 0.9 km east of the old dam. Both dams are located in the catchment of Wadi Mraikh [33]. Figure 1 shows the localization of the study area in Saudi Arabia and the region of Jeddah. In the reservoir area of this new dam built in 2012, construction is not permitted due to severe municipal restrictions (Figure 2). New Um al-Khair Dam is a serpentine-shaped dam with a length of about 1.1 km, located between 41.5 and 50 m above sea level. The dam has a trapezoidal cross-section, is 1 m wide, and rises to a maximum height of 9.5 m. Both faces are sloped 3H:1V, with an 8-m wide crest. A cross-section of the dam consists of an 8 m × 9.5 m rectangle and two equal-area triangles with a base of 19.5 m and a height of 9.5 m (Figure 3). At one meter width, the dam covers 261.3 m2 [20]. Photos taken in 2022 showing the New Um Al-Khair are presented in Figure 4. Figure 5 shows the localizations of the old and the new ones. A comparison between two satellite images taken in 2001 and 2010 shows the progression of urbanization in the reservoir area of the old dam. During this period of 10 years, new neighborhoods arose. These neighborhoods have suffered the brunt of the floods of 2011 (Figure 6).

2.2. Climate Conditions

Infrequent rainfall, a scorching desert atmosphere during the day, and only a small dip in temperature at night are common in Saudi Arabia [34]. Except for the hilly southwestern regions of Saudi Arabia, there is essentially no rainfall [35]. Temperature and humidity are significantly influenced by variations in elevation and a subtropical high-pressure system. Additionally, some regions have various climates, particularly near the Red Sea and Arabian Gulf shores [36]. Jeddah, located by the Red sea, is classified as having hot weather on average. Although it has very little rain on a regular basis, when it does, it is tremendously heavy. It is with highly unpredictable and intense rainfall. It receives an average of 52.5 mm of rainfall each year [37]. November, December, and January are the three wettest months of the year, with an average monthly rainfall of 10.45 mm. There is an average monthly rainfall of 3.27 mm for February, March, and April, while the average monthly rainfall for the other months is 1.02 mm [38]. In Jeddah, winters are short, warm, dry, windy, and largely clear, whereas summers are long, hot, steamy, arid, and partly cloudy. During the eight months between April and December, the weather is wettest. The hottest days in Jeddah are in September, and the most humid days are in February. High rates of evaporation are observed in Jeddah [39].

3. Methodology

The following procedures were used to simulate the New Um-Al-Khair dam break and analyze the flood disaster: delineation of the watershed, land use and land cover assessment, rainfall analysis, hydrological modeling of the catchment, flood inundation modeling, and flood risk assessment.

3.1. Watershed Delineation

There are numerous global open Digital Elevation Models (DEMs) available online, including those generated by NASA’s Shuttle Radar Topography Mission (SRTM), JAXA’s Advanced Land Observing Satellite (ALOS), and the European Space Agency’s Copernicus [40]. In our case, the study area watershed was delineated using an SRTM DEM with a 30 m resolution. In order to perform this operation, a variety of software can be used, including paid software such as the Aquaveo Watershed Modeling System (WMS), which allows for computer simulation and modeling of watersheds, ArcGIS, MapInfo Professional, or Global Mapper as well as open-source software, such as GRASS GIS or QGIS. The same DEM was used to extract the hydrographic network. Figure 7 depicts the demarcation of the watershed with its related hydrographic network. Table 1 displays the geomorphological catchment parameters retrieved using the WMS software.

3.2. Analysis of Land Use and Land Cover

The Land Use/Land Cover (LULC) classification system classifies human activities and physical features on the landscape throughout a given time period in accordance with recognized scientific and statistical techniques of analysis of pertinent sources [41]. A 10 m by 10 m resolution Sentinel-2 image provided by the European Space Agency, ESA, was used to extract and classify the land use and land cover map of the study area. A supervised classification was performed based on the maximum likelihood technique. This classification can be implemented using commercial software, such as ENVI and ERDAS Imagine, or free software, such as QGIS or GRASS GIS. The supervised classification process involves the selection of appropriate data types, locating representative training sites for the features to be identified, and selecting an image classification method [42]. Based on how comparable their spectral responses were, the software classified each pixel of the image into a land cover category. Data on land coverings was gathered through a field survey in conjunction with this categorization [43]. A stale image extracted from Google Earth Pro was used in this investigation as a reference. Using a portable GPS receiver, random ground truth points were gathered to evaluate categorization. An accuracy evaluation of the generated categorized map is performed by means of training sites. The categorization seems to be accurate since the calculated kappa coefficient was 80% (near-perfect agreement). The kappa coefficient is used to describe and test classification accuracy or reliability [44]. We identified four land use classes: Vegetation, Bareland, Rock, and Urban (Figure 8). Table 2 shows the composite CN value, as well as the CN values for each land use category within the watershed.

3.3. Rainfall Analysis

For the rainfall analysis, 2 stations in the watershed were used, namely: J1134 and 41,024 (Figure 9). Hydrological events are often analyzed using several different theoretical frequency distributions [45]. The rainfall data were used to conduct a frequency analysis. The following theoretical frequency distributions were tested: Gumbel Type 1, Generalized Extreme Value (GEV), 2-Parameter Log-Normal, 3-Parameter Log-Normal, and Pearson type III. There is a mean inconsistency between the predicted value and the measured value based on the Root Mean Square Error (RMSE) values for each distribution [46,47]. Table 3 shows each station’s RMSE value for each distribution type. Distribution types with the minimum RMSE value are the most suitable. Based on that RMSE criterion, 3-Parameter Log-Normal and Pearson type III are the most suitable distribution methods for fitting the data from stations J134 and 412,024, respectively. In the same table, values in boldface show the minimum RMSE and, consequently, the best distribution per station. Figure 10 shows the Maximum daily rainfall at the 2 stations as a time series (1970 to 2018). Figure 11 shows the curves of the different distribution methods for both stations and which is the best. A rainfall depth prediction is provided for each station in Table 4 based on 5 years, 10 years, 25 years, 50 years, 100 years, and 200 years return period.

3.4. Hydrological Modelling of the Dam Catchment (Watershed)

One of the simplest and most primitive ways to model rainfall runoff is using Soil Conservation Services and Curve Numbers (SCS-CN) technique. This method can be used to estimate surface runoff from a catchment [48]. It was developed in 1954 by the Soil Conservation Service (which has now been renamed the Natural Resources Conservation Service), which is one of the services of the United States Department of Agriculture (USDA). A detailed description of the SCS-CN technique can be found in the National Engineering Handbook [49]. Worldwide, SCS-CN is frequently used, and this includes arid regions [50].
To simulate the hydrologic response in a watershed, three sets of the HEC-HMS main components need to be set up: the components of the basin model, the components of the meteorological model, and the input data or time series data utilized for a particular hyetograph. Figure 12 shows the adopted rainfall-runoff methodology using WMS & HEC-HMS tools, modified based on Marko’s study in 2013 at Wadi Qows in Jeddah [4]. The meteorologic model’s input is used to run a simulation that determines the precipitation-runoff response in the basin model. The basin model depicts the actual watershed, which includes sub-basins, routing reach, junction, reservoir, source, sink, and diversion elements, whereas the meteorologic model depicts actual or fictitious weather conditions. The time period and time step of the simulation run are specified by the control specifications. One data set is therefore used to configure a “run” for the basin model, precipitation model, and control requirements. HEC-HMS was used to extract the hydrographs for various return periods 5, 10, 25, 50, 100, and 200 years (Figure 13).

3.5. Dam Breach and Hydraulic Modelling

For the analysis of dam breaches, the hydraulic modeling open-source tool HEC-RAS is frequently used [51]. Through the use of one and two-dimensional river and flood simulations, it is possible to represent both steady flows and unsteady flows. Analyzing the breach of the New Um Al-Khair Dam was performed using HEC-RAS. HEC-RAS is able to calculate the water surface for steadily varying steady flows in a river reach or a network of channels as a whole [52,53]. Unsteady flow routing can be performed in 1D, 2D, or combined 1D/2D with the HEC-RAS model. By including a component of a 2D flow area in the flood model, HEC-RAS achieves 2D flow modeling. Assuming incompressible flow, uniform density, and hydrostatic pressure, the HEC-RAS model uses Shallow Water (SW) equations. In flood modeling, shallow water equations are approximated by Navier-Stokes equations. Here are the SW equations [54]:
∂ H ∂ t + ∂ ( h u ) ∂ x + ∂ ( h v ) ∂ y + q = 0
where: H : elevation of the water surface, h : depth of the water, t: time, u and v: the x and y directions’ respective velocity components, and q: the flux between source and sink.
These are the 2D momentum conservation equations for SW:
∂ u ∂ t + u ∂ u ∂ x + v ∂ u ∂ y = − g ∂ H ∂ x + v t { ∂ 2 u ∂ x 2 + ∂ 2 u ∂ y 2 } − c f u
∂ v ∂ t + u ∂ v ∂ x + v ∂ v ∂ y = − g ∂ H ∂ y + v t { ∂ 2 v ∂ x 2 + ∂ 2 v ∂ y 2 } − c f v
where: g : Gravitational constant (9.81 m/s2), vt: horizontal eddy viscosity, and cf: bottom friction coefficient.
For the simulation of the dam break of the New Um Al-Khair Dam, a Digital Surface Model (DSM) with a 10 m resolution related to the urban area of Jeddah city was used (Figure 14). Dam break analysis is dependent on breach parameters. The dam break parameters were extracted and presented in Table 5.

3.6. Flood Inundation and Flood Risk Assessment

Managing flood risks requires detailed information about flood inundation derived from numerical modeling. The risk matrix is employed throughout the project planning phase to evaluate risks [55]. There are two axes in a risk matrix, one for probability (or likelihood) and the other for impact (or severity or consequences) [56]. There is wide use of risk matrices in risk management [57]. In order to assess flood risk due to dam breaks, risk matrices are commonly developed. The flood risk matrix was developed using the information and characteristics of the floodable areas of the study area. The flood characteristics and their possible impact on flood risk were examined using two variables: the first variable is related to flood-affected places with certainty, a return period of 5, a return period of 50, and a return period of 100 years, as well as with rareness. The second variable is the relative consequences in terms of depth [58]. A risk matrix has two axes: consequence severity “y” and probability/type of event “x” [59]. The used risk matrix is modified and adapted to our study area, starting from the one used in a previous study in Saudi Arabia’s Medina city [60]. As shown in Table 6, this matrix considers five different risk classes (negligible, low, moderate, high, and very high), while the matrix used in Medina city is only based on three risk levels. The y-axis of the matrix is related to five types of the likelihood of occurrence: Almost certain, very likely (1 in 5 years), possible (1 in 50 years), unlikely (1 in 100 years), and rare. The x-axis refers to consequences in terms of flood depth which are classified into five classes: low (<0.1 m), minor (0.1 to 1 m), major (1 to 2 m), severe (1 to 2 m), and catastrophic (>2 m). Each cell in the resulting table is colored to reflect the corresponding risk level described in the table legend. Negligible/Low-likelihood and rare/low-consequence risks are acceptable, while very high/high-likelihood and very high/high-consequence risks are unacceptable. Between the “lower tolerable limit” and “upper tolerable limit” is the ALARP (“As Low As Reasonably Practicable”) zone, where risk reduction is not practicable, or its costs are unreasonably high [61].

4. Results and Discussion

4.1. Inundation Mapping Due to Dam Break

For different return periods ranging from 5 to 200 years (5, 10, 25, 50, 100, and years), flood inundation maps were extracted and visualized (Figure 15). The outcomes of the examination of the various maps for different return periods, including peak flows, flooded areas, and maximum water depths, are listed in Table 7. When this figure and this table are examined together, the following results and analysis can be drawn: Figure 15a shows the flood inundation map corresponding to a 5-year return period with a peak flow of 70 m3/s. The expected maximum water depth is 1.62 m. The predictable flooded area is 3.2 km2. This map shows only areas of shallow to medium water depth. Medium-depth water can be found southwest of the dam, directly below the dam wall, and in the areas just southwest of Jeddah’s Al-Haramain express train station. The flood inundation map corresponding to a return period of 10 years (Figure 15b) corresponds to a peak flow of 105.3 m3/s, a predicted flooded area of 5.4 km2, and a maximum water depth of 2.05 m. Areas with shallow to deep water are indicated on this map. High-depth water is present in the area just southwest of Jeddah’s Al-Haramain express train station and also in the area located just southwest of the dam wall. Just east and southeast of Al-Salam mall, one of the biggest malls in the city of Jeddah, mid-water depth zones may be seen. For a return period of 25 years, the peak flow is 144.4 m3/s, the flooded area is 6.8 km2, and the maximum water depth is 2.46 m (Figure 15c). Zones with high water depth are similar to those described for the return period of 10 years. Moreover, there is another medium-depth area that appears along the eastern border of the Al-Sharafiyyah district and another one north of the Al-Baghdadiyya Al-Sharqiyyah district. With a peak flow of 169.6 m3/s, a forecast flooded area of 6.8 km2, and a maximum depth of 2.73 m, the flood inundation in Figure 15d corresponds to a return period of 50 years. High water depth areas can be found in four key places: the region just southwest of the dam wall, the region situated just southwest of Jeddah’s Al-Haramain express train station, the region along eastern Al-Sharafiyyah’s border, and a third region north of the Al-Baghdadiyya Al-Sharqiyyah district that continues along the main road that connects Al-Sharafiyyah district to Al-Baghdadiyya Al-Gharbiyyah one. A 100-year return period leads to a peak flow of 191.2 m3/s, a flooded area of 8.4 km2, and a maximum water depth of 2.92 m (Figure 15e). With a 200-year return period, the peak flow is 210.3 m3/s, the flooded area is 9.1 km2, and the maximum water depth is 3.15 m (Figure 15f). For the last two return period cases (e and f), the high-depth areas are identical to case (d) with a larger increase in depth.
It is expected that at low return periods (5, 10, and 25 years), there are significant effects after dam failure only if the dam reservoir is full before the occurrence of these events, and it is for this reason that we have taken them into consideration in our analysis.

4.2. Flood Risk Assessment Based on the Developed Flood Risk Matrix

Flood risk assessment is based on the developed flood risk matrix previously presented in Table 6. Figure 16 shows the distribution of flood risk following the breach of the New Umm Al-Khair Dam for four potential return periods (5, 50, 100, and 200 years). In Figure 17, the percentage of the area inundated by specific depths versus the depth of inundation is shown for the four different return periods. For a return period of 5 years, there are three classes of risk: low, medium, and high (Figure 16a). The total flood risk area covers an area of 3.576 km2. The high-risk level is located in the southwest region of the dam, immediately below the dam wall, covering an area of 0.31 km2 (8.7% of the total area). There are four degrees of risk: low, medium, high, and extremely high for a return period of 50 years, with a total area of 7.67 km2 (Figure 16b). Areas of high-risk degree cover 1.131 km2. They are located on the southwest corner of the dam, directly below the dam wall. The Al-Nakheel district, located 1 km west of the dam, has a little section that is very high risk. Another area that is larger and has the same risk level is one that includes a portion of the Al-Haramain express train station. The very high-risk zone covers a total area of 0.097 km2. There are also other high-risk areas in the Al-Haramain train station and the area around the eastern border of Al-Sharafiyyah, as well as the area north of Al-Baghdadiyya Al-Sharqiyyah, which continues along the main road connecting the Al-Sharafiyyah and the Al-Baghdadiyya Al-Gharbiyyah districts. As for the 50 years return period, there are the same four risk levels associated with the 100 years return period, encompassing an area of 8.392 km2 (Figure 16c). The geographical distribution of the risk is very similar to that recorded for the 50 years return period, with the appearance of a new, very high-risk zone juxtaposing the dam wall. Also, a slight extension of the high-risk area at Al-Haramain station (compared to the previous case), as well as the appearance of a very small new high-risk area on the road joining the districts of Al -Sharafiyyah and Al-Baghdadiyya Al-Gharbiyyah. Finally, for a return period of 200 years, there are five classes of risk: negligible, low, medium, high, and very high, with a total area of 9.09 km2 (Figure 16d). The area of a very high-risk degree covers 0.39 km2. There is an important extension of these very high-risk areas, and it is about 16.66 times larger compared to the previous case. They are mainly located below the dam wall, at Al-Haramain station, as well as on the road joining the districts of Al -Sharafiyyah and Al-Baghdadiyya Al-Gharbiyyah. In comparison with previous return periods, we observed that negligible risk has appeared for the first time (water depth less than 0.1 m). This class covers an area of 1.01 km2, scattered over several regions and concentrated to the east of the Al-Baghdadiyya Al-Gharbiyyah district.
Using quantitative results, flood risk can be assessed comprehensively [62]. Transferring the results to a global summary for use in cost-benefit analysis and financial calculations requires the examination of quantitative data [63]. An overview of the quantitative flood risk results is presented in Figure 18. In that graph, risk assessment proceeds clockwise, starting with (a) and ending with (c). Figure 18a illustrates the peak flow, in m3/s, at various return periods. Figure 18b shows the area flooded by a particular discharge in relation to the return period. Finally, the maximum water depth reached by the peak flow during the corresponding return period is shown in Figure 18c. From the risk maps, a map of the degree of damage can be created to illustrate the magnitude of risk in the flooded areas graphically. Additionally, the risk results can be translated into an economic value based on nearby properties and lands, and the damages can subsequently be calculated. This will be taken into account by decision-makers who are required to adopt the necessary mitigation and protection measures to be taken in vulnerable areas. To minimize flood losses, managers must decide how to efficiently manage the flood hazards in their community [64]. Decision-makers in flood management should produce creative and useful lessons to minimize damages brought on by floods [65].

5. Summary and Conclusions

We discussed in this paper the flash flood risk due to a possible break of one of the dams of the city of Jeddah, the New Um Al-Khair Dam. With the use of GIS and the hydraulic/hydrologic modeling tools WMS, HEC-HMS, and HEC-RAS, floods and flood risk have been mapped for various return periods. An SRTM DEM with a 30 m resolution was used to define the watershed and to extract the corresponding hydrographic network. Using the WMS program, the geomorphological catchment parameters were extracted. The land use and land cover map of the research region were extracted using remote sensing spatial analysis from a Sentinel-2 image. Four land use classes: Vegetation, Bareland, Rock, and Urban, were ultimately identified by supervised classification using the maximum likelihood method. Two meteorological stations were used in the analysis of rainfall. Several different frequency theoretical frequency distributions were tested, and a rainfall depth prediction was provided for each station for different return periods. A rainfall-runoff methodology using WMS & HEC-HMS tools was adopted. HEC-HMS was used to extract the hydrographs for various return periods. For dam breach analysis, HEC-RAS was used, and the principal dam breach parameters were extracted (average breach width, slope break, duration, and crest elevation).
For various return periods from 5 to 200 years, inundation maps were retrieved and displayed in the study area. These maps were examined to determine peak flows, inundated areas, and maximum water depths. The geographical distribution of the different water depth classes was determined, which will make it possible to predict the intensity of the damage that could be caused. For communities in floodplains and flood hazard zones, they are meant to support emergency preparedness programs.
A risk flood matrix considering five different risk classes (negligible, low, moderate, high, and very high) was employed. Flood risk assessment maps based on this matrix were extracted for four return periods (5, 50, 100, and 200 years). The areas, the geographic distribution, and the proportions of the different risk levels in relation to the flood depth water after the breach of the New Umm Al-Khair Dam were determined. These maps of flood risk highlight potential negative effects that a flood event may have on cities. Consequences include social, economic, environmental, and cultural problems.
Based on the extracted flood risk maps and with the help of the Civil Protection’s actions, it will be feasible to prevent an increase in fatalities from flooding by evacuating the population in a sufficiently quick and effective manner. On the other hand, the municipal authorities, especially those in charge of planning, must forbid construction in places with a high or extremely high risk of flooding and take and implement the most effective precautions in these regions. Furthermore, it would always be interesting to update the altimetric data associated with the high-resolution DSM from time to time in order to continuously update the risk map, given Jeddah’s rapid urbanization.
Climate change research at high spatial resolution can provide valuable insights into potential flood hazards. A study should be undertaken in the future to examine the impact of climate change on extreme precipitation rates and flood intensities at the scale of local cities. It will be crucial for the long-term sustainability of regional economies and communities in Saudi Arabia, as well as for regional habitability.

Author Contributions

Conceptualization, M.H.H.; methodology, M.H.H.; system design and manufacturing, M.H.H.; software, M.H.H.; validation, M.H.H.; formal analysis, M.H.H.; investigation, M.H.H. and A.M.S.; data curation, M.H.H.; writing—original draft prepara-tion, M.H.H. and A.M.S.; writing—review and editing, M.H.H. and A.M.S.; visualization, M.H.H.; project administration, M.H.H.; funding acquisition, M.H.H. and A.M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. Location of the study area: (a): Jeddah in Saudi Arabia, (b): Study location in Jeddah (Source: Google Earth).
Figure 1. Location of the study area: (a): Jeddah in Saudi Arabia, (b): Study location in Jeddah (Source: Google Earth).
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Figure 2. The New Um Al-Khair Dam (an unbuilt area is shown in the reservoir area).
Figure 2. The New Um Al-Khair Dam (an unbuilt area is shown in the reservoir area).
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Figure 3. Cross section of the New Um Al-Khair Dam ([20]).
Figure 3. Cross section of the New Um Al-Khair Dam ([20]).
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Figure 4. The New Um Al-Khair Dam, as seen in photos taken in 2022: (a): Dam reservoir, (b): Close picture of the dam reservoir, (c): Dam outlet channel, (d): Dam outlet pipes.
Figure 4. The New Um Al-Khair Dam, as seen in photos taken in 2022: (a): Dam reservoir, (b): Close picture of the dam reservoir, (c): Dam outlet channel, (d): Dam outlet pipes.
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Figure 5. Location of the Old Um Al-Khair Dam and the New Um Al-Khair Dam in Jeddah.
Figure 5. Location of the Old Um Al-Khair Dam and the New Um Al-Khair Dam in Jeddah.
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Figure 6. Evolution of the area of the Old Um Al-Khair Dam between 2001 (a) and 2010 (b).
Figure 6. Evolution of the area of the Old Um Al-Khair Dam between 2001 (a) and 2010 (b).
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Figure 7. Mraikh watershed with its geomorphological stream network superimposed on the DEM.
Figure 7. Mraikh watershed with its geomorphological stream network superimposed on the DEM.
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Figure 8. Basin Landuse/Landcover.
Figure 8. Basin Landuse/Landcover.
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Figure 9. Location of the rainfall stations used in the study.
Figure 9. Location of the rainfall stations used in the study.
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Figure 10. Maximum daily rainfall time series in the two meteorological stations 41,025 and J134.
Figure 10. Maximum daily rainfall time series in the two meteorological stations 41,025 and J134.
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Figure 11. Stations J 134 and 41,204 frequency distributions fitted to maximum daily rainfall: top images are the fittings to the different distributions; bottom images represent the best distribution.
Figure 11. Stations J 134 and 41,204 frequency distributions fitted to maximum daily rainfall: top images are the fittings to the different distributions; bottom images represent the best distribution.
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Figure 12. Rainfall-runoff methodology using WMS & HEC- HMS tools (Modified from Marko [4]).
Figure 12. Rainfall-runoff methodology using WMS & HEC- HMS tools (Modified from Marko [4]).
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Figure 13. Storm hydrographs generated for various return periods (5, 10, 25, 50, 100, and 200 years).
Figure 13. Storm hydrographs generated for various return periods (5, 10, 25, 50, 100, and 200 years).
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Figure 14. Digital Surface Model (DSM) of the study area.
Figure 14. Digital Surface Model (DSM) of the study area.
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Figure 15. Mapping of the inundation extent caused by the breach of the New Umm-al-Khair Dam for six return periods: (a): 5 yrs, (b): 10 yrs, (c): 25 yrs, (d): 50 yrs, (e): 100 yrs and (f): 200 yrs.
Figure 15. Mapping of the inundation extent caused by the breach of the New Umm-al-Khair Dam for six return periods: (a): 5 yrs, (b): 10 yrs, (c): 25 yrs, (d): 50 yrs, (e): 100 yrs and (f): 200 yrs.
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Figure 16. Flood risk assessment maps based on the developed flood risk matrix for four return periods: (a): 5 yrs, (b): 50 yrs, (c): 100 yrs and (d): 200 yrs.
Figure 16. Flood risk assessment maps based on the developed flood risk matrix for four return periods: (a): 5 yrs, (b): 50 yrs, (c): 100 yrs and (d): 200 yrs.
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Figure 17. Percentage of area inundated by specific depths to the total inundation area versus the specific inundation depth at different return periods 5, 50, 100, and 200 years.
Figure 17. Percentage of area inundated by specific depths to the total inundation area versus the specific inundation depth at different return periods 5, 50, 100, and 200 years.
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Figure 18. Quantitative summary of the study area’s risk results. From (a–c), the logic for risk assessment follows a clockwise path.
Figure 18. Quantitative summary of the study area’s risk results. From (a–c), the logic for risk assessment follows a clockwise path.
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Table
Table 1. Geomorphological parameters in the catchment area (watershed).
Table 1. Geomorphological parameters in the catchment area (watershed).
Geomorphological ParametersValue
Total Basin Area (km2)36.04
Total Basin Surface Area (km2)36.42
Total Basin Perimeter (km)40.04
Basin Length (km)7.78
Main Channel Length (km)10.92
Stream Frequency (number/km2)1.08
Total Basin Relief (m)195.0
Relief Ratio0.02
Slope Catchment Ratio 0.00048
Stream order 1 number 30
Stream order 2 number6
Stream order 3 number 2
Stream order 4 number1
Average Bifurcation Ratio3.33
Table
Table 2. Computed composite SCS-CN for the watershed.
Table 2. Computed composite SCS-CN for the watershed.
Land Cover FeaturesSCS-CNArea (km2)Composite CN
Vegetation604.8580
Bareland6513.09
Rocks9518.47
Urban 980.23
Table
Table 3. Calculated RMSE for stations J134 and 41,024 at various types of distribution (boldface numbers show the best distribution for each station).
Table 3. Calculated RMSE for stations J134 and 41,024 at various types of distribution (boldface numbers show the best distribution for each station).
Distribution TypeRMSE (J134)RMSE (41,024)
(mm)(mm)
Gumbel Type 112.874.03
Generalized Extreme Value (GEV)5.975.31
2-Parameter Log-Normal10.767.82
3-Parameter Log-Normal5.514.5
Pearson Type III9.614.01
Log-Pearson Type III106.42
Table
Table 4. Rainfall depth prediction for different return periods.
Table 4. Rainfall depth prediction for different return periods.
Station No.CoordinatesRainfall (mm) at Different Return Periods (Years)
Lat. (N)Long. (E)25102550100200
J13421 30 0039 12 0023.644.557.773.584.995.8106.4
41,02421 40.8 0039 09 0022.245.664.793.1117.3144.0173.5
Average 22.945.061.283.3101.1119.9139.9
Table
Table 5. Dam break parameters.
Table 5. Dam break parameters.
Dam Break ParametersMagnitude
Average Breach Width (Bave) (m)50
Slope Break (horizontal:vertical)1:4
Duration (hour)0.5
Crest Elevation (m)57.31
Table
Table 6. Flood risk matrix used in the study (modified from Abdulrazzak et al. [60]).
Table 6. Flood risk matrix used in the study (modified from Abdulrazzak et al. [60]).
Consequences in terms of Flood Depth
<0.10 m0.1–0.5 m0.5–1.0 m1.0–2.0 m>2.0 m
LowMinorMajorSevereCatastrophic
Likelihood of occurrenceAlmost Certain
(every time)
Very likely
(1 in 5 years)
Possible
(1 in 50 years)
Unlikely
(1 in 100 years)
Rare (exceptional circumstances)
Legend
Very High Risk
High Risk
Moderate Risk
Low Risk
Negligible Risk
Table
Table 7. Outcomes of the simulation of the various flood maps for different return periods.
Table 7. Outcomes of the simulation of the various flood maps for different return periods.
Return Period
(years)		Peak Flow Qp
(m3/s)		Flooded Area
(sq km)		Maximum Water Depth
(m)
570.03.61.62
10105.35.42.05
25144.46.82.46
50169.67.72.73
100191.28.42.96
200210.39.13.15
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Hamza, M.H.; Saegh, A.M. Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment, the New Um Al-Khair Dam Case Study, Jeddah, Saudi Arabia. Sustainability 2023, 15, 1074. https://doi.org/10.3390/su15021074

AMA Style

Hamza MH, Saegh AM. Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment, the New Um Al-Khair Dam Case Study, Jeddah, Saudi Arabia. Sustainability. 2023; 15(2):1074. https://doi.org/10.3390/su15021074

Chicago/Turabian Style

Hamza, Mohamed Hafedh, and Afnan Mohammed Saegh. 2023. "Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment, the New Um Al-Khair Dam Case Study, Jeddah, Saudi Arabia" Sustainability 15, no. 2: 1074. https://doi.org/10.3390/su15021074

APA Style

Hamza, M. H., & Saegh, A. M. (2023). Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment, the New Um Al-Khair Dam Case Study, Jeddah, Saudi Arabia. Sustainability, 15(2), 1074. https://doi.org/10.3390/su15021074

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