Spatial Prediction of Flood Susceptibility Zones

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Spatial prediction of flood susceptibility zones using an index-based approach and multi‐criteria decision support systems: Challenge of scale in susceptibility assessments

Abstract. Flood susceptibility and risk assessment are very important for suitable urban development. However, there are no available flood maps in Saudi Arabia. Consequently, traditional methods such as watermarks on buildings and media reports are used for the identification of flood hazard zones. The present study introduces a methodology for the identification of flood susceptibility zones using an index-based approach and multi‐criteria decision support systems. The methodology was applied in Riyadh Province, central region of Saudi Arabia, where recurring flood events have been recorded. Moreover, the methodology was validated using historical flood records and re-applied in the Riyadh city to assess the effect of scale on the results. The methodology incorporates 10 conditioning factors: flow accumulation, distance from the drainage network, elevation, land use/cover, rainfall intensity, geology, slope, runoff, soil type, and drainage density. The factors were assigned weights by the analytic hierarchy process and decision support system. The susceptibility model generates a flood susceptibility map with five vulnerability classes based on their means, 3.4 % (13033.6 km2) and 14.4 % (54658.6 km2) of the study area were classified as very high and high, respectively, while 27.9 % (106277.7 km2), 36.5 % (138784.3 km2), and 17.8 % (67743.7 km2) of the area were classified as moderate, low, and very low, respectively. The very high and high susceptibility zones are mostly located in the extended northern, northeastern, and northwestern parts of Riyadh Province owing to the combination of under 5 % lowlands with slopes, dense flow accumulation, high rainfall intensity, built up areas, and high runoff depth. Several floods have been reported in the last few years by the local authorities. In contrast, the very low and low susceptible zones are located in the western parts of Riyadh Province. Records of historical flood events support the developed flood susceptibility map as a high number of recorded flood events have occurred in the highly susceptible areas, which is an additional indication of accuracy. According to the developed flood susceptibility map, the Riyadh city and its surrounding areas fall within very high, high, and moderate susceptibility zones. The flood susceptibility map was found to be in very good agreement with historical flood events. This suggests that the developed flood susceptibility index successfully predicts areas susceptible to flood with no scale effect. The developed flood susceptibility mapping method could be of great help to planners and local governments in choosing suitable locations to implement developments. Moreover, the flood susceptibility map can be used as the basic data to assist flood mitigation and land use planning.

Key words: Flood susceptibility; Analytic Hierarchy Process; Multi‐criteria decision support systems; Central region of Arabia

  1. Introduction

Flood is a natural hazard that occurs all over the world, affecting an average of 99 million people per year between 2000 and 2008 (Opolot, 2013). It is, therefore, critical to control floods through proper management. Owing to their high intensity and sudden onset, flash floods usually cause serious local disasters, and thus, flash flood prevention is a significant challenge in many countries. The high frequency of the flood occurrence in Saudi Arabia has made this disaster one of the most serious environmental problems; they are among the most catastrophic natural extreme events that present a potential threat to both lives and properties. The frequent rise in the number of flood events is mainly due to rapid urbanization and civilization along the rivers and deforestation (Bronstert, 2003; Christensen and Christensen, 2003). Hence, flood susceptible areas should be declared in order to avoid investments in these areas and to be able to have a fast emergency response under various circumstances. In western Saudi Arabia, flood discharge from the wadi basins that drain toward the Red Sea can become dangerous and presents a threat to the coastal cities, towns, villages, and engineering structures. On the national level, a precise flood assessment is considered important for Makkah Province, Saudi Arabia, because the unexpected nature of rainfall often produces hazardous flash floods (Dawod et al., 2013). A recent flood event that occurred in the Makah, Jizan, Al-Baha, Riyadh, Jeddah, and Abha regions among others in Saudi Arabia (KSA) reflect the flash flood risks in arid/semi-arid regions. Therefore, developing flood warning systems in any region could be considered as one of the most effective ways to reduce the loss of life and property damage (Negri et al., 2005).

Geographical Information Systems (GIS) are powerful tools that manage large amounts of data involved in multiple criteria decision analysis. The analytic hierarchy process (AHP) involves a multi-criteria decision-making (MCDM) approach introduced by Saaty (1977, 1980, 1990, 1994, 2008). Saaty (1990) noted that the process includes structuring of factors that are selected in a hierarchy, starting from the overall aim to the criteria, sub-criteria, and alternatives. The AHP is a type of GIS-MCDM that combines and transforms spatial data (input) into decisions (output) using geographical data. The decision maker’s preferences and the manipulation of data and preferences according to the specified decision rules are referred to as factors and constraints, respectively (Mahmoud and Alazba, 2015a). Multicriteria decision analysis (MCDA) provides the methodology and techniques for analyzing complex decision problems, which often involve incommensurable data or criteria. The use of GIS and MCDA has proven successful in natural hazard analysis (Rashed and Weeks, 2003; Kiker et al., 2005; Phua and Minowa, 2005; Dassanayake et al., 2005; Gamper et al., 2006; Fernandez and Lutz, 2009; Soussa et al., 2010; Forkuo, 2011; Mouri et al., 2011; Parker et al., 2011; Dang et al., 2011; Feizizadeh and Blaschke, 2013; Bajabaa et al., 2014; Ahn and Merwade, 2015; Singh et al.,2017) and other geo-environmental studies (Dai et al., 2001; Svoray et al., 2005; Kolat et al., 2006; Fedeski and Gwilliam, 2007; Dong et al., 2008; Tudes and Yigiter, 2010; Youssef et al., 2011; Ju et al., 2012; Mahmoud and Tang, 2015; Mahmoud et al., 2015a; Mahmoud et al., 2015b; Mahmoud and Alazba, 2015b; Mahmoud et al., 2016; Singh et al.,2017). Fuzzy logic has been successfully integrated with GIS-MCDA in various applications (Amiri et al., 2012; Sinha et al., 2012; Donevska et al., 2012; Feizizadeh, 2013; Najafi et al., 2014; Jelokhani and Malczewski, 2015), especially in flood susceptibility mapping (Ouma and Tateishi, 2014; Dos Santos and Tavares, 2015; Kazakis et al., 2015). Combining an AHP with fuzzy set theory permits greater flexibility in the assessment of results and the subsequent decision-making process. A fuzzy-AHP retains many advantages of conventional AHPs, in particular the relative ease with which it handles multiple criteria and combinations of qualitative and quantitative data (Mahmoud and Alazba, 2016a). An AHP provides a hierarchal structure, facilitates decomposition and pairwise comparison, reduces inconsistency, and generates priority vectors.

Flood occurrences are complex since they depend on interactions between many geological and morphological characteristics of the basins, including rock types, elevation, slope, sediment transport, and flood plain area (Melesse and Abtew, 2016). Moreover, hydrological phenomena, such as rainfall, runoff, evaporation, and surface and groundwater storage have a great impact on flood occurrences (Farquharson et al., 1992; Flerchinger and Cooly, 2000; Şen, 2004; Nouh, 2006). Various researchers have used different factors for flood susceptibility mapping, such as digital elevation model (DEM), curvature, geology, river/stream power index (SPI), rainfall, land use/cover (LULC), soil type, topographic wetness index (TWI), and slope. However, these factors have rarely been studied together because of the non-availability of data. Hence, a systematic study of these factors led to a better delineation of flood susceptibility in an area, which is then followed up on the ground through detailed hydrogeological and geophysical investigations. Fernandez and Lutz (2010) developed a GIS-aided urban flood hazard zoning in the Tucumán Province in Argentina, using multicriteria decision analysis. The model incorporates five parameters: distance to the drainage channels, topography, ground water table depths, and urban land use.

There are very few studies on the application of spatial MCA in the field of flood risk analysis and management. For example, Chen et al. (2011) integrated AHP and GIS to provide preferred options for flood risk analysis in two cities in Taiwan. The factors contributing to flooding in this study were categorized into two sections: the first section includes factors linked to flooding caused by the failure of drainage systems, such as rainfall, topography, capacity of stormwater drainage systems, existence of water gates or pumping stations, mobile pumps, and management practices. The second section deals with flooding caused by broken dikes. The factors linked to flooding caused by broken dikes are the material and conditions of the levees, angle between the bank and main flow direction, bank slope, river channel slope, rainfall, topography, whether the river is tide-dominated, drainage systems, existence of water gates or pumping stations, mobile pumps, and management practices. Another study conducted by Tehrany et al. (2013) to identify the flood susceptibility areas in the Kelantan River basin used an advanced rule-based decision tree and ensemble statistical method. In their study, 10 conditioning factors were used in flood susceptibility mapping: DEM, curvature, geology, river, SPI, rainfall, LULC, soil type, TWI, and slope.

More recently, Kazakis et al. (2015) attempted to assess the flood hazard areas in the Rhodope–Evros region in Greece using a multi-criteria index. The authors developed the methodology for seven parameters: flow accumulation, distance from the drainage network, elevation, land use, slope, rainfall intensity, and geology. The relative importance of each parameter for the occurrence and severity of flood has been connected to the weight values. These values were calculated following an AHP, based on their weight, information about different parameters is superimposed for flood hazard mapping. The reliability of the application is confirmed by the historical flood records. Similarly, Elkhrachy (2015) used an AHP approach to generate a flash flood map for the Najran city, Saudi Arabia. AHP is used to determine the relative impact weight of the flood causative factors to get a composite flood hazard index. The causative factors in this study are runoff, soil type, surface slope, surface roughness, drainage density, distance to main channel, and land use.

This paper introduces a methodology to identify the flood susceptibility zones using an index-based approach and multi‐criteria decision support systems. The methodology was applied in Riyadh Province, the central region of the Kingdom of Saudi Arabia (18 % of KSA area), where recurring flood events have occurred. In addition, the reliability of the adapted methodology was verified by using historical flood records. Moreover, after validation, the methodology was re-applied in the vicinity of Riyadh City (4.85 % of Riyadh Province area and 0.89 % of the total area of KSA) to test the effect of scale in the adapted methodology.

  1. Study area

Riyadh Province (Fig. 1(a)) is the second largest province in Saudi Arabia. It has an area of 380497.8 km² and a population of 6,777,146 (2010), making it the second largest province in terms of both area (behind the eastern region) and population (behind Makkah region). It is situated in the center of the Arabian Peninsula (24°38′’ N and longitude 46°43′ E) on a large plateau. Its relief ranges from 200 to 1200 m above the mean sea level, constituting a part of the Nejd Plateau. This plateau extends to the Tuwayq Mountains on its western edge, to the Awanid Scarp on the northern edge, to the Kharj Rise on its southern edge, and to the Dahna sand belt on its eastern edge. However, this whole tableland is broken by long protruding cliffs that are formed by the fault scarps near the Awanid Mountains and by the Hit Scarp between Riyadh and Kharj. Its capital is the city of Riyadh, which is also the national capital. The recently completed and ongoing constructions boast of having some of the most ambitious architectural designs in the kingdom. For example, the King Abdullah Financial District Metro Station and several other forthcoming projects. Riyadh is the capital and the largest city of Saudi Arabia. It is also the capital of Riyadh Province. The average high temperature in July is 42.6 °C. Winters are warm with cold, windy nights. The monthly average relative humidity ranges from 15 % (during summer) to 51 % (during winter). The mean annual relative humidity is 32.5 %. The overall climate is arid, and the city experiences an annual rainfall of 41–230 mm/yr. The construction of 57 dams in the region for groundwater recharge and rainwater harvesting, the increase in water consumption, and the expansion in the agriculture sector have significantly affected the relative humidity. Such factors may cause microclimatic changes. Flash floods occur periodically in Riyadh Province due to several factors including rugged topography and geological structures. The northern parts of Riyadh Province have the highest potential risk of flood generation with a large flash floods record. Which always cause traffic jams, and suspend studies in schools and universities for day due to its serious damage.

Fig. 1. (a) Location of Riyadh Province in KSA. (b) Photos of flood taken on November 16, 2013, in Riyadh Province. Source: http://english.alarabiya.net/en/News/middle-east/2013/11/17/Video-Saudi-capital-flooded-with-heavy-rains.html

Figure 1(b) shows cars driving through a flooded street in northern Riyadh on November 17, 2013, after heavy rainfall overnight in the Saudi capital, caused floods and traffic jams, which forced the Saudi Education Ministry to suspend studies in schools and universities for one day. At least 15 people died and eight others were reported missing in the flash floods caused by heavy rains in Riyadh and other parts of the kingdom within 24 hours. The Civil Defense Department reported that they had received more than 7,000 calls for help from different regions following the rains. “We have rescued over 800 stranded people, while 450 vehicles have been pulled out from flooded areas,” it said in a statement. The department had urged the public to be cautious following weather forecasts for more rains. Deaths resulting from rain-related accidents were reported across the kingdom in different places. Flash floods had washed away the entire road surface and the ground had also caved in before the truck attempted to cross it.

  1. Materials and methods

In the present study, we used an index-based approach and multi‐criteria decision support systems to identify the flood susceptibility zones in Riyadh Province. In addition, the reliability of flood susceptibility mapping was verified by using historical flood records. The methodology adopted for the present study is shown in Fig. 2.

Fig. 2. Workflow chart

  1. Selection of criteria and data processing

The selection of the controlling factors varies from one study area to another based on different characteristics of each place. As one variable can have a high degree of impact in flooding in a specific area, it can be without any influence in another region (Kia et al., 2012). This study first categorized various factors related to flood events based on literature reviews, field survey, and historical records in Saudi Arabia. In the present study, 10 controlling factors were used in the flood susceptibility mapping: flow accumulation, distance from the drainage network, elevation, LULC, rainfall intensity, geology, slope, runoff, soil type, and drainage density. Because of the different scales on which the criteria were measured, the values contained in the criterion maps have to be converted to comparable units for SMCE. Therefore, the criteria maps were reclassified into five comparable units or susceptibility classes: 5 (very high), 4 (high), 3 (medium), 2 (low), and 1 (very low). The susceptibility classes were then used as the basis to generate a criteria map.

  1. Assessment of susceptibility level for criteria using AHP-DSS approach

The values for each susceptibility category were scaled from 1 to 9 and were based on the criteria proposed in various studies (Fernandez and Lutz, 2010; Kazakis et al., 2015; Elkhrachy, 2015). The susceptibility rankings for flow accumulation, distance from the drainage network, elevation, LULC, rainfall intensity, geology, slope, runoff, soil type, and drainage density are shown in Table 1. The selection of these parameters has been theoretically based on their relevance to flood hazards as documented in the literature. Flash floods in major cities are caused by superficial flow generation heightened by the high percentage of impermeable urban surfaces. Lower values are usually related to mountainous areas or with low water accumulation potential and also with more permeable soils (Jacinto et al., 2014). Soil type is a very important factor in determining the water holding and infiltration characteristics of an area, which in turn affect the flood susceptibility (Nyarko, 2002). Some soil types can influence the runoff, and hence, the flash flood occurrence potential. This is because flash floods can and do occur in regions with dry soils and drought conditions (Hill et al., 2010). Generally, clay soils may result in greater surface runoff than sandy soils. Higher rainfall intensity can result in more runoff because the ground cannot absorb the water quickly enough. Although prior ground saturation increases the flash flood risk, many flash floods occur when the ground is not saturated. Land cover and land use are other essential influences on flash floods (Alexakis et al., 2014). Topographic elements are one of the most effective elements in natural floods. Surface slope is a reliable indicator for flood susceptibility. When the slope increases, then the flow velocity will also increase. If the drainage network is dense at any area, it indicates a high flow accumulation path and is more likely to get flooded. Areas located close to the flow accumulation path are more likely to get flooded. There is a negative relationship between the occurrence of flooding and vegetation density. Rainfalls on the bare lands flow rapidly compared to the farmlands and forest areas. This indicates that the urban areas with impervious surfaces yield more storm runoff compared to similar areas covered by mass vegetation and forestry.

Table 1. Susceptibility levels for different factors for flood susceptibility mapping

  1. Assignment of criteria weights using AHP-DSS approach

The criteria were assigned weights by applying the pairwise ranking and rank sum methods. The final weight calculation requires the computation of the principal eigenvector of the pairwise comparison matrix to produce a best-fit set of weights. The WEIGHT module in the IDRISI software was used for this calculation. The weighting procedure is based on AHP. The expected value method was used to calculate the weight,

The rank sum method was used to calculate the weight,

The accuracy of the pairwise comparisons was assessed by calculating the consistency index (CI). This index determines the inconsistencies in the pairwise judgments and is a measure of deviation from consistency based on the comparison matrices. It is expressed as

CI=λ-nn-1

(3)

Where, λ is the average value of the consistency vector and n is the number of columns in the matrix (Garfì et al., 2009; Saaty, 1990; Vahidnia et al., 2008). The consistency ratio (CR) was then calculated as follows:

CR=CI/RI

(4)

Where RI is the random index, which depends on the number of elements being compared (Garfì et al., 2009). Table 2 presents the RIs of the matrices in the order 1-15, as derived by Saaty (1980).

Table 2. Random Indices (RI) for n = 1, 2… 15 (Saaty, 1980)

The pairwise rating procedure has several advantages. First, the ratings are independent of any specific measurement scale. Second, the procedure, by its very nature, encourages discussion, leading to a consensus on the weights to be used. In addition, the criteria that are omitted from initial deliberations are quickly uncovered through the discussions that accompany this procedure. To provide a systematic procedure for comparison, a pairwise comparison matrix was created by setting out one row and one column for each factor in the problem (Table 3). The rating was then calculated for each cell in the matrix. Because the matrix is symmetrical, the ratings are provided for half of the matrix and then inferred for the other half.

Table 3. Factors of flood susceptibility: Analytical Hierarchy Process.

  1. Consistency check

The consistency ratio of the matrix, which shows the degree of consistency achieved when comparing the criteria or the probability that the matrix rating was randomly generated, was 0.06, which indicates acceptable consistency (Saaty, 1977). The values for different thematic are shown in Table 4.

Table 4. Normalized flood susceptibility factors: Analytical Hierarchy Process

  1. Results and discussions
    1. Development of flood conditioning factor database

Thematic maps in Fig. 3 illustrate the spatial distribution of the factors in the study area that has been analyzed in the developed method.

  1.       Elevation and slope

A DEM with 30m resolution (Fig. 3(a)) developed at KACST was used to generate the slope map for Riyadh (Fig. 3(b)). Sinks and flat areas were removed to maintain the continuity of the flow of water to the catchment outlets. This conditioning factor and its derivatives play a major role in recognizing the areas susceptible to flood occurrence (Pradhan, 2009). Mostly, the flood prone areas can be assumed to be low-elevation and flat areas. The elevation was divided into five categories. An area with low elevation (0–250 m) falls into the very high category, while an area at a high elevation was classified as a very low susceptibility area. Similarly, steeper slopes make rapid flows, but floods tend to occur on gentle slopes. Therefore, low slope and low elevation have been assigned the highest rating. The slope was divided into five categories. The areas having 0–2 % slope fall into the very high category because of the flat terrain and relatively high susceptibility to flood occurrence. The areas having 2–5 % were considered as high for susceptibility to flood occurrence. The areas having a slope of 5–15 % lead to a relatively moderate runoff, and hence are categorized as moderate for flood occurrence. The areas having a slope of 15–35 % were considered as low susceptibility zone. Areas having a slope higher than 35 % were categorized as very low susceptibility zones.

Fig. 3. Controlling factors for flood susceptibility: (a) DEM, (b) slope, (c) flow accumulation, (d) drainage density, (f) distance from the drainage network, (g) land use/cover, (h) rainfall intensity, (i) geology, (j) soil type, (k) runoff

  1. Flow accumulation

Flow accumulation (Fig. 3(c)) shows the accumulation paths and the amount of cells in the entire study area that contribute to the flow on a specific cell. Flow accumulation map could determine the convergence zone for surface runoff. Flow accumulation has been considered one of the most important factors in the alignment in relevant studies. Flow accumulation represents the drainage network and its water accumulation potential. Therefore, an increase in flow accumulation reflects an increase in flood susceptibility and higher susceptibility to be flooded (Lehner et al., 2006). Accumulation values are representative of the entire province, and even though they are represented in a spatially continuous grid, the range of values is very wide, making the small value visually imperceptible due to their small flow accumulation values. Buildings and other infrastructure concentrated around the high flow accumulation areas are naturally more vulnerable to flooding. In Fig. 3(c), there is a high accumulation rate around the blue and green pixels, while the back surrounding stands for a low accumulation rate. Areas where flow accumulations are found are usually more vulnerable to flooding. Such areas are convergence points for surface run-off. In the present study, the flow accumulation values vary within the range 0–1510000. These values spots were divided into five zones from very low (0–250) to very high (3430–151000). High values of accumulated flow indicate areas of concentrated flow and consequently higher flood hazard possibility.

  1.       Drainage density

Drainage density is defined as the closeness of channel spacing within a basin (Horton, 1932). It is an indication of basin permeability. Soil permeability and the underlying rock type affect the runoff in a watershed; impermeable ground or exposed bedrock leads to an increase in surface water runoff, and therefore, more frequent storm events (Mahmoud and Alazba, 2016a, b). Regions with high relief will also have a higher drainage density than other drainage basins if the other characteristics of the basin are the same. Higher densities (also implied by a high bifurcation ratio) indicate a greater flash flood risk (Mahmoud and Alazba, 2016a, b). A drainage density map (Fig. 3(d)) was prepared using a density analysis tool in ArcGIS software. This was done by dividing the total length of all the streams in a drainage basin by the total area of the drainage basin. The study area has been classified into five classes. If the drainage network is dense at any given area, it is a good indicator of the high flow accumulation path and the given area is more likely to get flooded. These classes have been categorized as “very high” (1–1.83 km/km2), “high” (0.71–1 km/km2), “moderate” (0.34–0.71 km/km2), “low” (0.25–0.34 km/km2), and “very low” (0–0.25 km/km2) susceptibility classes. A low drainage density area causes more infiltration (Srivastava and Bhattacharya, 2006) and lower surface runoff as compared to a high drainage density region. It means that areas having high density are more likely to get flooded because of the high surface runoff (Kumar et al., 2007).

  1.       Distance from the drainage Network

Areas located close to the drainage network and flow accumulation path have a higher susceptibility to be flooded (Islam and Sado, 2000). To delineate the flood-prone zones according to that, Riyadh Province was classified into very high, high, moderate, low, and very low susceptibility classes using the buffer distance as the factor (Fig. 3(f)). The classes of this factor have been assigned by processing the records of historical floods in the entire study area. The drainage network was first buffered at a distance of 200 m to indicate the area along the study area that are very highly susceptible to flood hazard based on the previous flood intensity. The drainage network was again buffered at a distance of 500, 1000, 2000, and >2000 m to indicate the areas that are high, moderately, low, and very low susceptibility zones, respectively. It appears that the areas near the river network (200 m) are highly prone to flood hazard, whereas the effect of this parameter decreases in distances >2000 m. This finding is in good agreement with Kazakis et al. (2015).

  1.       Land use/cover

A Landsat image for the year 2015, which has a spatial resolution of 30 m, was incorporated with the collected data, and was ultimately used in categorizing LULC (Fig. 3(g)). Training samples were collected during field surveys to create spectral signatures for the supervised classification. The LULC map was classified into seven main classes: irrigated cropland, forest, shrubland, sparse vegetation, builtup areas, bare soil, and water bodies. Georeferenced ground truthing points were collected using a GPS unit and were used to validate the LULC map. Validation analysis was performed using the Kappa Agreement Index (KIA), where a value exceeding 0.8 indicates a high classification performance (Jensen, 2005). The overall kappa statistic was 0.946, indicating that the classification of the LULC map was accurate. LULC classes have been assigned rates for flood susceptibility. Urbanization can generate greater runoff volumes due to increased percentage of impermeable surfaces and compacted soils, and faster runoff can be attributed to road grids and storm sewer networks. As a result, when compared to rural conditions, built up areas flood faster and more frequently; therefore, they were assigned a very high susceptibility for flood occurrence. In fact, in an urban environment, flood conditions can occur with much less rainfall than that necessary for rural conditions.

  1.       Rainfall intensity

Rainfall intensity (Fig. 3(h)) is identified using the modified Fournier index (MFI). MFI is the sum of the average monthly rainfall intensity at each rain gauge station (Kazakis et al., 2015). Susceptibility to flash flooding greatly increases with the higher rainfall intensity. Riyadh has a very dry climate, which makes the summer heat bearable. Rainfall at Riyadh is influenced by the Mediterranean winter, which results from the frontal system moving towards east along the Mediterranean Sea from the Atlantic Ocean, which then travels inland, reaching the Najd Plateau (Mahmoud and Alazba, 2016a). One of the major causes of floods in the study area is the occurrence of extremely heavy rainfall over a short period and low water absorptive capacity of the soil type, leading to an increased overland flow. This excess overland flow is controlled by the topography and converges on the area channel network, generating a flood flow. Therefore, despite the total rainfall amount being relatively small in Saudi Arabia, a rainfall event can be very intense, causing problems of flooding. The records of the hydrological station at Riyadh show that the MFI ranged from 41–230 mm/yr. MFI values were classified into five classes according to their importance in flood occurrence (Table 1).

  1.       Geology

The geology of Riyadh Province consists of a great thickness of continental and shallow marine limestone deposits (Mahmoud and Alazba, 2016a). The study area is characterized by a complex geological and structural setting. The geological formations in the area (Fig. 3(i)) comprise of seven geological units: Cenozoic rocks, Mesozoic–Jurassic and Cretaceous, Mesozoic–Triassic, Plutonic rocks, Precambrian (Archean+Proterozoic), Quaternary, and Upper Paleozoic (Dev, Car, Per). Mesozoic–Jurassic and Cretaceous units extend over a large area in Riyadh Province (33.6 % of the total area). Moreover, this formation was classified as a moderate susceptibility zones. However, Mesozoic–Triassic and Cenozoic deposits occupied the lowest area, which is about 5.7 % and 10.8 % of the total area, respectively. These features were assigned a low susceptibility for flood occurrence due to their high permeability. Plutonic rocks represent 13.8 % of the total area, and due to its extremely low porosity, they were assigned very high susceptibility for flood occurrence. Precambrian (Archean+Proterozoic) basement rocks occupy 15.6 % of the total area. They are highly weathered and clay-rich; therefore, the permeability is low and these areas are highly susceptible to floods. Quaternary rocks are the most recent geological period in Earth’s history; they occupy 12.6 % of the study area, and due to their high porosity, these rocks were assigned moderate flood susceptibility. Upper Paleozoic (Dev, Car, Per) rocks occupy 8 % of the total area; they have high porosity and permeability. Therefore, their flood susceptibility was considered to be very low.

  1.       Soil type

The soil map of the study area (Fig. 3(j)) was prepared from the published soil map obtained from the Ministry of Agriculture. The study area covered by six different soil types: arenosols, lithosols, miscellaneous land units, regosols, solonchaks, and yermosols. Arenosol (1.4 % of the study area) is a sandy soil with little profile development, which is characterized by high permeability. Therefore, it was classified as very low susceptibility for flood occurrence due to the high rate of infiltration. Lithosols, which cover about 12.6 % of the total area, are usually coarse-textured with a very low clay content and minimal organic matter accumulation at the surface. Lithosols are strongly acidic and have a low water holding capacity due to the coarse texture, abundant stone content, and shallow depth. However, the infiltration rates can be high (Mahmoud and Alazba, 2016). This soil type is mainly found in the central region of the study area. Moreover, the areas with this soil type were categorized as low flood susceptibility. Miscellaneous land units consist of dunes, salt flats, and rock debris or desert detritus. Miscellaneous land units are found along the southeastern and northwestern parts of the study area. These units represent 10.3 % of the total area, and are classified as high susceptibility zones. Regosols, which constitute 62.6 % of the total area, have a surface layer of rocky material and their texture is mainly coarse. This soil was classified as low susceptibility zones due to its coarse texture and high infiltration rate. Solonchaks (salty soil) and Yermosols occupy 11.2 and 1.9 % of the total area, respectively. Solonchaks have a moderate infiltration rate, and therefore, were classified as moderate susceptibility zones. The infiltration rate of Yermosols is moderately low due to their clay content. Therefore, this soil was considered to constitute very high susceptibility zones.

  1.       Runoff

Mahmoud and Alazba (2016a) identified the potential runoff coefficient (CN) for Riyadh Province. According to their study, the highest runoff coefficient is in the southeastern and southwestern parts of the capital, which contain very important infrastructures such as airports and administration offices, with a runoff value range of 0.4–1. To the north and east, runoff coefficient is much lower than other places owing to the domination of agriculture land and runoff harvesting structure. Based on this coefficient, the spatial and quantitative runoff depth distributions (Fig. 3(k)) have been identified in and around Riyadh using the Riyadh runoff coefficient and surplus rainfall (Mahmoud and Alazba, 2016a). The surface runoff in Riyadh varies from as low as 9 to a maximum of 179 mm/yr. Its value tends to increase in urban areas due to the soil and land cover properties, which lead to periodic flash floods in Riyadh Province, Saudi Arabia. These flash floods can also be attributed to several factors, including rugged topography, heavy rainfall events, and geological structures. Each year, it causes much damage to people’s lives and properties. The highest runoff depth was recorded in the southeastern and southwestern parts of the capital, which contain very important infrastructures like airports and administrative offices, with surface runoff value ranging from 71 to 179 mm/yr. To the north and east, the runoff volume is much lower than other places because they are occupied by agricultural land and have a runoff harvesting structure. However, these areas recently witnessed unforeseen flash floods that caused huge damages to infrastructure and major roads. The likelihood of a flood risk increases as the amount of runoff at a location increases. Therefore, the runoff depth map was classified into five classes. These classes have been categorized as “very high susceptibility” (144–179 mm/yr), “high susceptibility” (74–144 mm/yr), “moderate susceptibility” (47–74 mm/yr), “low susceptibility” (25–47 mm/yr), and “very low susceptibility” (0–25 mm/yr).

  1. Flood susceptibility zones in Riyadh Province (large-scale)

A susceptibility model has been developed in the model builder of ArcGIS 10.2. The suitability model generates a flood susceptibility map based on the integration of 10 thematic maps: flow accumulation, distance from the drainage network, elevation, LULC, rainfall intensity, geology, slope, runoff, soil type, and drainage density using a weighted overlay process (WOP), using both vector and raster databases. Using a weighted linear combination, the criteria were combined by applying a weight to each factor, followed by a summation of the results to yield a suitability map. This was undertaken using the weight module of the Idrisi software used for this calculation and the final weight is presented in Table 5.

Table 5. Weight (percent of influence)

Based on an AHP analysis taking into account the 10 thematic maps, the spatial extents of the flood susceptibility zones were identified using MCE. Different spatial analysis tools were used in the model to solve the spatial problems in the process of identifying the potential areas. The susceptibility model generated a flood susceptibility map (Fig. 4) with five vulnerability classes: very high, high, moderate, low, and very low.  According to their means (Table 6), 3.4 % (13033.6 km2) and 14.4 % (54658.6 km2) of the study area were classified as very high and high susceptibility areas, respectively, while 27.9 % (106277.7 km2), 36.5 % (138784.3 km2), and 17.8 % (67743.7 km2) of the area were classified as moderate, low, and very low susceptibility areas, respectively. The majority of the areas with very high to high flood susceptibility had slopes between 0 and 5 % and were in built up areas, shrublands, and irrigated croplands. The major soil type in the very high to high susceptibility zones were miscellaneous land units and Yermosols. The rainfall intensity ranged from 120 to 230 mm/yr. The main geological structures include plutonic and Precambrian (Archean+Proterozoic) rocks. Moreover, they have drainage density ranging from 0.71 to 1.83 km/km2, and within areas located close to the drainage network and flow accumulation path (200–500 distance).

Table 6. Classes of flood susceptibility and number of historical flood events

It can be seen that most of the very high and high susceptibility zones are mostly located in the northern, northeastern, and northwestern parts of Riyadh Province over an extended area as a consequence of the combination of lowlands with slopes under 5 % and the presence of dense flow accumulation, high rainfall intensity, built up areas, and high runoff depth. In this area, several neighborhoods with flood records have been reported in the last few years by the local authorities. Surface runoff depth in these areas, ranges from 110 to 179 mm/yr. A huge amount, which can damage the soil type, makes the soil layers unstable. An example of that is the Wadi Nimar valley, which is surrounded by steep banks and can, therefore, suddenly fill up with water in case of rainfall. Frequent flash floods in Riyadh caused power cuts in some parts of the city during the past few years. In addition, shops and markets had also been flooded. On the other hand, very low and low susceptibility zones are located in the western parts of Riyadh Province. There are some high to moderate zones with recorded floods situated in the central parts of the province. Records of historical flood events support the developed flood susceptibility map as a high number of recorded flood events fall within the highly susceptible areas. This is an additional indication of accuracy. In addition, the results revealed that Wadi Hanifah and Wadi Nisah have a moderate vulnerability to flooding, with high vulnerability restricted to the northeastern part of Riyadh Province.

Fig. 4. Flood susceptibility map for Central Arabia

Surface runoff in Riyadh varies from as low as 9 to a maximum of 179 mm/yr. Its value tends to increase in urban areas due to soil and land cover properties, which lead to flash floods that occur periodically in Riyadh due to several factors, including rugged topography, heavy rainfall events, and geological structures. Each year, it causes much damage to people’s lives and properties. The southeastern parts are dominated by moderate and low susceptibility zones. This is attributed to the differences in spatial variability in factors important for delineating the flood susceptibility zones, including different soil types, rainfall intensity (below 100 mm/yr), and low elevation. The southwestern parts of Riyadh fall within the low and very low susceptibility zones due to the high elevation and other factors. According to the developed flood susceptibility map (Fig. 4), Riyadh City and its surroundings fall within very high, high, and moderate susceptibility zones. This finding agrees with the historical flood records. An example of these records is the flood event on November 16, 2013, in Riyadh City. Streets were flooded in northern Riyadh, and on November 17, 2013, following heavy rains in the Saudi capital, floods and traffic jams forced the Saudi Education Ministry to suspend schools and universities for a day. At least 15 people died and eight others were reported missing in the flash floods caused by heavy rains in Riyadh and other parts of the kingdom. Deaths resulting from rain-related accidents were reported across the kingdom at different places. Flash floods had washed away entire road surfaces and the ground had also caved in before a truck had attempted to cross it.

  1. Flood susceptibility map for Riyadh and its surroundings (small-scale)

The adapted methodology was reapplied in the Riyadh City, which represents a limited portion of Riyadh Province. An urban flood susceptibility model was developed and a flood susceptibility map was generated based on the integration of the 10 thematic maps using a weighted overlay process (WOP). The susceptibility model generated a flood susceptibility map (Fig. 5) with three vulnerability classes: very high, high, and moderate. Based on their means (Table 6), 29.6 % (4718.5 km2) and 29.5 % (4704.3 km2) of Riyadh City were classified as very high and high susceptibility zones, respectively, while 40.9 % (9037.5 km2) of Riyadh city was classified as moderate susceptibility zones. The majority of the areas with very high to high flood susceptibility were in built up areas.

Fig. 5. Flood susceptibility map for Riyadh’s neighborhood

This map is very useful to the Saudi Civil Defence Authority, where it is used in rescue operations during flash flood events. The southeastern and southwestern parts of the capital, which contain very important infrastructures such as airports and administrative offices, vary in flood susceptibility from very high to high susceptibility. This finding agrees with the result of the first flood susceptibility for Riyadh Province. However, to the west of Riyadh, high and medium flood susceptibility zones are common. These portions include Wadi Hanifah and Wadi Nisah. Wadi Hanifah is the most significant natural landmark in the region that gives rise to its basin and tributaries. A unique 120km long ecological region stretches from the Tuwaiq Escarpment to the open desert to the southeast of Riyadh (Fig. 5). The depth of the valley stream ranges between 10 and 100 m, and its width ranges from 100 to 1000 m, approximately. Moreover, Wadi Hanifah consists of dense urban settlements and is a highly populated area. It is located in a moderate to high flood susceptibility zone, while Wadi Nisah encompasses a low flood susceptibility zone. Wadi Hanifah represents a natural watershed for the floods and rainwater, covering an area of 4000 m2, and it has more than 40 tributaries. The most important among the Wadi’s tributaries are Al-Obaitah, Al-Imariyah, Safar, Al-Mahdiyah, Beir, Laban, Namar, Al-Awsat, and Laha in the west, as well as Al-Aysan and Al-Bathaa in the east. The amount of water poured into Wadi Hanifah is about 700,000 m3. Wadi Hanifah has five sections including bed, floodplain, horizontal alluvial terraces, valleys, and branches. Many small and large villages scatter along the banks of the valley. In general, the results show that Wadi Hanifah and Wadi Nisah have a moderate vulnerability to flooding, with high vulnerability in the northeast part of Riyadh Province.

The flood susceptibility map might be of great help to planners and engineers for shortlisting suitable locations to implement the requisite developments in and around Riyadh. The flood susceptibility map can be used as basic data to assist flood mitigation and land use planning. The methods used in the study have been proved to be valid for the generalized planning and assessment purposes as it does not depend on the site-specific scale. The runoff depth map of the area in and around Riyadh (Fig. 6) was developed using the same procedure used to develop the runoff depth map for the entire province to provide essential information about the amount of runoff in the flood susceptibility zones. As shown in Fig. 6, surface runoff in and around Riyadh varies from as low as 57 mm/yr to a maximum of 120 mm/yr. Its value ranges from 57 to 100 mm/yr in the very-high- to high-flood-susceptibility zones, which is mainly in the built up areas, and therefore, lead to flash floods that occur periodically in the surrounding areas. This is also supported by the number of the historical flood events that have occurred in the very high and high flood susceptibility zones (Fig. 6). The runoff values in the lower parts of Wadi Hanifah (100–120 mm/yr) imply that several roads are likely to be inundated during flood events. The reason behind floods being a major threat is multi-faceted. Rains have been relatively scarce in the area, and this has led to the under-development of a proper drainage system in the region. When rainfall occurs in the study area, water runs through the valleys towards the cities. With poor drainage systems, the continuous flow of water could easily lead to a flash flood.

Fig. 6. Runoff depth in and around Riyadh

Since 1964, more than 319 people have been killed due to flash floods in the kingdom (Table 7). An example of these major floods, is the 2005 Riyadh flood, where heavy rains poured on the Riyadh region. The resulting flood claimed the lives of seven people. Another example is the 2010 Al-Riyadh flood on May 3, 2010. Riyadh city experienced heavy rainfall, accompanied by light winds gusting up to 24 km/hour, eventually resulting in floods and car crashes across the city. It had caused around 275 car crashes. Even though the King Khalid International Airport was not affected, which lies in the high flood susceptibility as shown in Fig. 5, many people missed their scheduled flights due to poor road conditions.

Table 7. Examples of major floods in Saudi Arabia from 1964 to 2013

  1. Validation of the flood susceptibility maps

To verify the accuracy of the flood susceptibility model, historical flood events were checked against the constructed flood susceptibility zones map. These validation results showed the database and methodology used for flood susceptibility mapping. This includes the rating of the factors and the relative importance weightages of the factors to yield accurate results. The flood susceptibility map is found to be in good agreement with the historical flood events. This is also supported by the number of historical flood events that have occurred in the high and very high flood hazard areas. In total, 68 flood events have occurred in very high flood susceptibility zones of Riyadh Province as compared to 33 historic flood events in the very high susceptibility zones. In addition, there have been 28 flood events in the medium susceptibility zones and only 9 events in the low susceptibility areas. This suggests that the developed flood susceptibility index successfully predicts the likelihood of an area to be flooded. In and around Riyadh, these validation results showed similar accuracy. In total, 17 flood events have occurred in the very high and high flood susceptibility zones and eight flood events had happened in the medium susceptibility zones. To quantify the scale effect, the results from the two models were compared with historical flood events and flood susceptibility. Riyadh and its surrounding areas were located in the very high, high, and medium flood susceptibility zones in the two models. Therefore, the contributions of the scale on the adapted methodology can be overlooked.

  1. Conclusion and recommendations

The main aim of the present study is to introduce a methodology to identify the flood susceptibility zones for Riyadh Province, Saudi Arabia. This is important and very useful for the Saudi Civil Defence Authority, where it can be used in rescue operations during flash flood events. The flood susceptibility map might be of great help to planners and engineers for choosing suitable locations to implement developments in Riyadh Province and its surroundings. It can also be used as basic data to aid flood mitigation and landuse planning. The methods used in the study have been proved to be valid for generalized planning and assessment purposes as it has not been developed on a site-specific scale. An index-based approach and multi‐criteria decision support systems have, thus, been developed. The methodology was applied in Riyadh Province, which is the central part of the Kingdom of Saudi Arabia, where recurring flood events have occurred. Moreover, the methodology was re-applied in Riyadh City and its surrounding areas to test the effect of scale in the adapted methodology. Based on literature reviews, field survey and historical records, 10 conditioning factors were used in flood susceptibility mapping: flow accumulation, distance from the drainage network, elevation, LULC, rainfall intensity, geology, slope, runoff, soil type, and drainage density. Because of the different scales on which the criteria were measured, the values contained in the criterion maps were converted into comparable units for SMCE. Therefore, the criteria maps were reclassified into five comparable units or susceptibility classes: 5 (“very high”), 4 (“high”), 3 (“medium”), 2 (“low”), and 1 (“very low”). The susceptibility classes were then used as the basis of generation of the criteria map. The factors were assigned weights by applying a pairwise ranking and by rank sum methods. The final weight calculation requires computation of the principal eigenvector of the pairwise comparison matrix.

Based on an AHP analysis taking into account 10 thematic layers, the spatial extents of the flood susceptibility zones were identified using MCE. Different spatial analysis tools were used in the model to solve the spatial problems in the process of identifying the potential areas. The susceptibility model generated a flood susceptibility map with five vulnerability classes: very high, high, moderate, low, and very low. According to their means, 3.4 % (13033.6 km2) and 14.4 % (54658.6 km2) of the study area was classified as very high and high, respectively, while 27.9 % (106277.7 km2), 36.5 % (138784.3 km2), and 17.8 % (67743.7 km2) of the area were classified as moderate, low, and very low susceptibility zones, respectively. The very high and high susceptibility zones are mostly located in the northern, northeastern, and northwestern parts of Riyadh Province over an extended area owing to the combination of lowlands with slopes under 5 % and also because of dense flow accumulation, high rainfall intensity, built up areas, and high runoff depth. Several floods have been reported in the last few years by the local authorities. In contrast, the very low and low susceptibility zones are located in the western part of Riyadh Province. Records of historical flood events support the developed flood susceptibility map because a large number of flood events have been recorded in the highly susceptible areas. This is an additional indication of accuracy. According to the developed flood susceptibility map, Riyadh City and its surrounding areas are very high, high, and moderate susceptibility zones. The reliability of the adapted methodology was verified by using historical flood records. The flood susceptibility map was found to be in very good agreement with the historical flood events. This is also supported by the number of the historical flood events that have occurred in the areas with high and very high susceptibility for flood hazard. This suggests that the developed flood susceptibility index successfully identifies the areas likely to be flooded. Results from the two models were compared with the historical flood events and flood susceptibility. Riyadh and its surrounding areas were placed in the very high, high, and medium flood susceptibility zones in the two models. Therefore, the contributions of the scale on the adapted methodology can be overlooked.

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