This study uses an integrated approach, bringing together geographic information system (GIS), remote sensing, and rainfall–runoff modeling, to assess the urbanization impact on flash floods in arid areas. Runoff modeling was carried out as a function of the catchment characteristics and the maximum daily rainfall parameters. Land-use types were extracted from the supervised classification of SPOT-5 (2010) and Landsat-8 (2015) satellite images and were validated during field checks. Catchment morphometric characteristics were carried out using the correlated Topaz and Arc-Hydro tools. Maximum floods of the catchment were evaluated by coupling GIS and remote sensing with Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) hydrologic modeling. Peak discharges were estimated, and the abstraction losses were computed for different return periods. The model results were calibrated according to actual runoff event. The research shows that rapid urbanization adversely affects hydrological processes, since the sprawl on the alluvial channels is significant. This reduces infiltration into the underlying alluvium and increases runoff, leading to higher flood peaks and volumes even for short duration low intensity rainfall. To retain a considerable amount of water and sediments in these arid areas, construction of small dams at the fingertip channels at the outlet of the lower order sub-basins is recommended.
INTRODUCTION
Flash flood assessment is a vital issue for establishing properly sustainable development in arid areas. Flash floods are defined as the fast flooding of water, often combined with debris transport, that usually takes place in high gradient streams (Jarrett 1990; Borga et al. 2007). Floods result in a variety of hazardous phenomena associated with consequential problems (Cooke et al. 1982; El Alfy 1998; Tooth 2000; Chin & Gregory 2001; Oba 2001; Herschy 2002; Foody et al. 2004; Ozturk et al. 2013). Reliable prediction of runoff in arid areas is difficult to obtain for ungauged basins, since there is a lack of hydro-meteorological data and the gauging of flash floods is very rare (Rodier 1985; Sorman & Abdulrazzaq 1993; Al-Qurashi et al. 2008; El Bastawesy et al. 2009, 2012; Ozturk et al. 2013).
Flash flood frequencies and magnitudes are controlled by the interaction of different variables including rainfall, catchment antecedent conditions, and spatial distribution of land-uses. Urbanization in basin areas can be added as an additional significant controlling factor for the development of flash floods. It can increase the risk of flooding due to increased peak discharge and volume, and decreased time to peak (Campana & Tucci 2001; Liu et al. 2004; Nirupama & Simonovic 2007; Saghafian et al. 2008; Al-Ghamdi et al. 2012). Urban watersheds, on an average, lose 90% of storm rainfall to runoff (Shang & Wilson 2009).
In the absence of hydro-meteorological data, runoff modeling could be a promising approach to enhance the livability of an area (Gheith & Sultan 2002; Foody et al. 2004). For predicting hydrologic responses in ungauged catchments, Soil Conservation Service curve number (SCS-CN) models are widely used (SCS 1972). This method had since been adapted to measure infiltration losses in arid areas with similar climatic and physiographic conditions (Reich 1963; Graf 1988; Walters 1990; Al-Khalaf 1997). These SCS models are considered to be as good, or even better than, more complex models in terms of predicting rainfall–runoff in gauged and ungauged catchments (Michaud & Sorooshian 1994; Al-Khalaf 1997; Wagener et al. 2004; Dawod et al. 2011; Dawod & Koshak 2011; Hublart et al. 2015). The Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) is designed to simulate the precipitation–runoff processes of watershed systems (USACE 2010). The input data for this HEC-HMS model setup include digital elevation model (DEM), rainfall, flow gage data, soil types, land-use/land-cover data, etc. The resultant hydrographs are used to study water availability, urban drainage, flow forecasting, future urbanization impact, reservoir spillway design, flood damage reduction, floodplain regulation, and systems operation.
This study uses an integrated approach, bringing together remote sensing, geographic information system (GIS), and hydrological models, which are utilized to assess the impact of urbanization on flash floods in arid areas. The analyses presented in this study are concerned with the estimation of extreme flows, which form the basis for subsequent flood level and mapping stages of the study area. Comparing flood peaks after development to flood peaks before development, over a range of return periods, can be used as a function of urbanization impact (Kibler et al. 2007). This study will aid in the decision-making for future planning and the results of this work can help to prioritize areas where flood control measures should be directed, and effectively augment management plans for the appropriate development of water resources.
STUDY AREA
Location map of the study area, multi-spectral SPOT-5 image (2.5 m pixels).
(a) Aerial photograph of washed up road, Jazan area (29/8/2010), and (b) the road is submerged during a flood, Jazan area (10/8/2003).
(a) Aerial photograph of washed up road, Jazan area (29/8/2010), and (b) the road is submerged during a flood, Jazan area (10/8/2003).
MATERIALS AND METHODS
Several inputs of rainfall data, remote sensing data, and hydro-morphometrical measurements, along with DEM analyses and fieldwork, were used to estimate the various hydrological parameters (Scipal et al. 2005; Milzow et al. 2009). The available data of rainfall–runoff modeling were gathered and processed; such as those of precipitation, terrain analyses, land-use, and wadi hydrogeometric roughness characteristics, which were verified during on-site visits. These different data categories were integrated into GIS to develop a hydrological model for a typical arid land catchment.
Characteristics of the main and third order sub-basins and their channel networks
. | Sub-basin No. 1 . | Sub-basin No. 2 . | Sub-basin No. 3 . | Sub-basin No. 4 . | Sub-basin No. 5 . | Main basin . |
---|---|---|---|---|---|---|
Area (km2) | 15.02 | 4.07 | 3.43 | 2.81 | 0.97 | 36.70 |
Length (km) | 11.10 | 4.24 | 3.68 | 3.53 | 1.71 | 20.28 |
Perimeter (km) | 29.54 | 11.68 | 11.45 | 9.67 | 5.15 | 47.76 |
Minimum elevation (m asl) | 112 | 112 | 101 | 90 | 112 | 90 |
Maximum elevation (m asl) | 497 | 255 | 299 | 200 | 167 | 497 |
Mean elevation (m) | 212 | 147 | 150 | 100 | 123 | 159 |
Height range (m) | 385 | 143 | 198 | 110 | 55 | 407 |
Slope (m/m) | 0.15 | 0.10 | 0.12 | 0.04 | 0.05 | 0.11 |
Average overland flow (m) | 235 | 242 | 282 | 213 | 226 | 226 |
North-facing aspect | 0.53 | 0.66 | 0.54 | 0.39 | 0.64 | 0.51 |
South-facing aspect | 0.47 | 0.34 | 0.46 | 0.61 | 0.36 | 0.49 |
Shape factor | 3.96 | 2.48 | 2.95 | 2.67 | 2.02 | 2.81 |
Elongation ratio | 0.57 | 0.72 | 0.66 | 0.69 | 0.80 | 0.34 |
Sinuosity factor | 1.37 | 1.13 | 0.98 | 1.03 | 0.78 | 1.99 |
Maximum flow distance (m) | 11,104 | 4,236 | 3,684 | 3,226 | 1,703 | 20,313 |
Maximum flow slope (m/m) | 0.037 | 0.033 | 0.023 | 0.003 | 0.033 | 0.022 |
Maximum stream length (m) | 10,602 | 3,595 | 3,124 | 2,810 | 1,094 | 19,842 |
Maximum stream slope (m/m) | 0.026 | 0.009 | 0.023 | 0.003 | 0.017 | 0.016 |
Distance from centroid to stream (m) | 43.25 | 122.32 | 0.00 | 136.76 | 61.16 | 319.27 |
Centroid to stream distance (m) | 5,264 | 1,741 | 1,714 | 2,607 | 714 | 11,718 |
Centroid to stream slope (m/m) | 0.009 | 0.007 | 0.017 | 0.002 | 0.010 | 0.006 |
. | Sub-basin No. 1 . | Sub-basin No. 2 . | Sub-basin No. 3 . | Sub-basin No. 4 . | Sub-basin No. 5 . | Main basin . |
---|---|---|---|---|---|---|
Area (km2) | 15.02 | 4.07 | 3.43 | 2.81 | 0.97 | 36.70 |
Length (km) | 11.10 | 4.24 | 3.68 | 3.53 | 1.71 | 20.28 |
Perimeter (km) | 29.54 | 11.68 | 11.45 | 9.67 | 5.15 | 47.76 |
Minimum elevation (m asl) | 112 | 112 | 101 | 90 | 112 | 90 |
Maximum elevation (m asl) | 497 | 255 | 299 | 200 | 167 | 497 |
Mean elevation (m) | 212 | 147 | 150 | 100 | 123 | 159 |
Height range (m) | 385 | 143 | 198 | 110 | 55 | 407 |
Slope (m/m) | 0.15 | 0.10 | 0.12 | 0.04 | 0.05 | 0.11 |
Average overland flow (m) | 235 | 242 | 282 | 213 | 226 | 226 |
North-facing aspect | 0.53 | 0.66 | 0.54 | 0.39 | 0.64 | 0.51 |
South-facing aspect | 0.47 | 0.34 | 0.46 | 0.61 | 0.36 | 0.49 |
Shape factor | 3.96 | 2.48 | 2.95 | 2.67 | 2.02 | 2.81 |
Elongation ratio | 0.57 | 0.72 | 0.66 | 0.69 | 0.80 | 0.34 |
Sinuosity factor | 1.37 | 1.13 | 0.98 | 1.03 | 0.78 | 1.99 |
Maximum flow distance (m) | 11,104 | 4,236 | 3,684 | 3,226 | 1,703 | 20,313 |
Maximum flow slope (m/m) | 0.037 | 0.033 | 0.023 | 0.003 | 0.033 | 0.022 |
Maximum stream length (m) | 10,602 | 3,595 | 3,124 | 2,810 | 1,094 | 19,842 |
Maximum stream slope (m/m) | 0.026 | 0.009 | 0.023 | 0.003 | 0.017 | 0.016 |
Distance from centroid to stream (m) | 43.25 | 122.32 | 0.00 | 136.76 | 61.16 | 319.27 |
Centroid to stream distance (m) | 5,264 | 1,741 | 1,714 | 2,607 | 714 | 11,718 |
Centroid to stream slope (m/m) | 0.009 | 0.007 | 0.017 | 0.002 | 0.010 | 0.006 |
Wadi Al-Burfi, Jazan area: (a) digital elevation model; (b) slope %; (c) flow direction; (d) flow accumulation; (e) sub-catchment boundaries; (f) stream network orders; (g) estimated morphometric parameters of the third order sub-basins.
Wadi Al-Burfi, Jazan area: (a) digital elevation model; (b) slope %; (c) flow direction; (d) flow accumulation; (e) sub-catchment boundaries; (f) stream network orders; (g) estimated morphometric parameters of the third order sub-basins.
Rainfall analyses were based on reviewing and processing 46 years of historic records for the SA-101 station located at the outlet of the studied basin (PME 2011). Five hypothetical design storms were developed for the events with 5, 10, 25, 50, and 100 year return periods. An intensity duration frequency (IDF) relationship is a mathematical relationship between the rainfall intensity i, the duration d, and the return period T. IDF-curves allow for the estimation of the return period of an observed rainfall event, or conversely, of the rainfall intensity corresponding to a given return period for different aggregation times. The equations of Gumbel's method were used to perform the best probability distribution and the calibrated equation for IDF curves.


RESULTS AND DISCUSSION
Statistics of the 24 hr rainfall depths (mm) at Abu Arish metrological station
Number of observations . | 46 . | . | . |
---|---|---|---|
Minimum (Max) | 6.90 | Median | 38.5 |
Maximum (Min) | 126.00 | Coefficient of variation (Cv) | 0.54 |
Mean | 41.10 | Skewness coefficient (Cs) | 1.42 |
Standard deviation (σ) | 22.30 | Kurtosis coefficient (Ck) | 5.82 |
Number of observations . | 46 . | . | . |
---|---|---|---|
Minimum (Max) | 6.90 | Median | 38.5 |
Maximum (Min) | 126.00 | Coefficient of variation (Cv) | 0.54 |
Mean | 41.10 | Skewness coefficient (Cs) | 1.42 |
Standard deviation (σ) | 22.30 | Kurtosis coefficient (Ck) | 5.82 |
Twenty-four hour rainfall depths estimated for different return periods at Abu Arish metrological station
Return period (years) . | 5 . | 10 . | 25 . | 50 . | 100 . |
---|---|---|---|---|---|
24 hr rainfall height (mm) | 57.18 | 70.23 | 86.76 | 98.95 | 111.13 |
Return period (years) . | 5 . | 10 . | 25 . | 50 . | 100 . |
---|---|---|---|---|---|
24 hr rainfall height (mm) | 57.18 | 70.23 | 86.76 | 98.95 | 111.13 |
Annual rainfall of Saudi Arabia (National Center for Atmospheric Research 2008).
Twenty-four hour rainfall depths and cumulative probability (Gumbel) at Abu Arish.
Twenty-four hour rainfall depths and cumulative probability (Gumbel) at Abu Arish.
IDF curves computed by Gumbel method at Abu Arish area (2–100 years).
Calculations of the weighted CN in the study area (2010 and 2015)
. | . | Land-use/cover . | Area (A) km2 . | Curve number (CN) . | A*CN . | W.CN . |
---|---|---|---|---|---|---|
2010 | 1 | Harrat (basalt) | 17.12 | 86 | 1472.32 | |
2 | Sand and gravel | 9.12 | 76 | 693.12 | ||
3 | Vegetated area | 7.17 | 78 | 559.26 | ||
4 | Fine sand and silty soil | 3.29 | 82 | 269.78 | ||
Σ | 36.7 | 2,994.48 | 81.59 | |||
2015 | 1 | Harrat (basalt) | 16.64 | 86.00 | 1,431.04 | |
2 | Sand and gravel | 6.32 | 76 | 480.32 | ||
3 | Vegetated area | 6.14 | 78 | 478.92 | ||
4 | Fine sand and silty soil | 3.08 | 82 | 252.56 | ||
5 | Urban-up area | 4.52 | 95 | 429.40 | ||
Σ | 36.70 | 3,072.24 | 83.71 |
. | . | Land-use/cover . | Area (A) km2 . | Curve number (CN) . | A*CN . | W.CN . |
---|---|---|---|---|---|---|
2010 | 1 | Harrat (basalt) | 17.12 | 86 | 1472.32 | |
2 | Sand and gravel | 9.12 | 76 | 693.12 | ||
3 | Vegetated area | 7.17 | 78 | 559.26 | ||
4 | Fine sand and silty soil | 3.29 | 82 | 269.78 | ||
Σ | 36.7 | 2,994.48 | 81.59 | |||
2015 | 1 | Harrat (basalt) | 16.64 | 86.00 | 1,431.04 | |
2 | Sand and gravel | 6.32 | 76 | 480.32 | ||
3 | Vegetated area | 6.14 | 78 | 478.92 | ||
4 | Fine sand and silty soil | 3.08 | 82 | 252.56 | ||
5 | Urban-up area | 4.52 | 95 | 429.40 | ||
Σ | 36.70 | 3,072.24 | 83.71 |
(a) Land-use map of the study area classified according to: (a) SPOT-5 Satellite image (2010); (b) Landsat-8 Satellite image (2015).
(a) Land-use map of the study area classified according to: (a) SPOT-5 Satellite image (2010); (b) Landsat-8 Satellite image (2015).
Estimated morphometric and storm time parameters of the third order sub-basin and the main basin
. | . | Area (A) km2 . | Watershed length (L) km . | Watershed slope (%) . | Average overland slope . | Lag time (Tl) min. . | Time of concentration (Tc) min. . | Time to peak (Tp) min. . |
---|---|---|---|---|---|---|---|---|
2010 | Sub-basin No. 1 | 15.02 | 11.10 | 15.38 | 0.037 | 82 | 85 | 170 |
Sub-basin No. 2 | 4.07 | 4.24 | 10.07 | 0.033 | 47 | 42 | 143 | |
Sub-basin No. 3 | 3.43 | 3.68 | 11.66 | 0.045 | 39 | 34 | 125 | |
Sub-basin No. 4 | 2.81 | 3.53 | 4.36 | 0.017 | 62 | 47 | 149 | |
Sub-basin No. 5 | 0.97 | 1.71 | 5.39 | 0.033 | 31 | 21 | 117 | |
Main basin | 36.70 | 20.28 | 10.49 | 0.022 | 161 | 165 | 251 | |
2015 | Sub-basin No. 1 | 15.02 | 11.10 | 15.38 | 0.037 | 77 | 78 | 157 |
Sub-basin No. 2 | 4.07 | 4.24 | 10.07 | 0.033 | 44 | 38 | 123 | |
Sub-basin No. 3 | 3.43 | 3.68 | 11.66 | 0.045 | 36 | 31 | 115 | |
Sub-basin No. 4 | 2.81 | 3.53 | 4.36 | 0.017 | 57 | 43 | 136 | |
Sub-basin No. 5 | 0.97 | 1.71 | 5.39 | 0.033 | 29 | 19 | 108 | |
Main basin | 36.70 | 20.28 | 10.49 | 0.022 | 149 | 150 | 235 |
. | . | Area (A) km2 . | Watershed length (L) km . | Watershed slope (%) . | Average overland slope . | Lag time (Tl) min. . | Time of concentration (Tc) min. . | Time to peak (Tp) min. . |
---|---|---|---|---|---|---|---|---|
2010 | Sub-basin No. 1 | 15.02 | 11.10 | 15.38 | 0.037 | 82 | 85 | 170 |
Sub-basin No. 2 | 4.07 | 4.24 | 10.07 | 0.033 | 47 | 42 | 143 | |
Sub-basin No. 3 | 3.43 | 3.68 | 11.66 | 0.045 | 39 | 34 | 125 | |
Sub-basin No. 4 | 2.81 | 3.53 | 4.36 | 0.017 | 62 | 47 | 149 | |
Sub-basin No. 5 | 0.97 | 1.71 | 5.39 | 0.033 | 31 | 21 | 117 | |
Main basin | 36.70 | 20.28 | 10.49 | 0.022 | 161 | 165 | 251 | |
2015 | Sub-basin No. 1 | 15.02 | 11.10 | 15.38 | 0.037 | 77 | 78 | 157 |
Sub-basin No. 2 | 4.07 | 4.24 | 10.07 | 0.033 | 44 | 38 | 123 | |
Sub-basin No. 3 | 3.43 | 3.68 | 11.66 | 0.045 | 36 | 31 | 115 | |
Sub-basin No. 4 | 2.81 | 3.53 | 4.36 | 0.017 | 57 | 43 | 136 | |
Sub-basin No. 5 | 0.97 | 1.71 | 5.39 | 0.033 | 29 | 19 | 108 | |
Main basin | 36.70 | 20.28 | 10.49 | 0.022 | 149 | 150 | 235 |
Wadi Al-Burdi, Jazan area: (a) the selected third order sub-basins for the hydrologic modeling; (b) sub-Basin flow distances; (c) basin model.
Wadi Al-Burdi, Jazan area: (a) the selected third order sub-basins for the hydrologic modeling; (b) sub-Basin flow distances; (c) basin model.
Twenty-four hour computations in mm: (a) precipitation; (b) cumulative precipitation; (c) cumulative excess precipitation; (d) soil infiltration; (e) precipitation loss; (f) cumulative precipitation loss.
Twenty-four hour computations in mm: (a) precipitation; (b) cumulative precipitation; (c) cumulative excess precipitation; (d) soil infiltration; (e) precipitation loss; (f) cumulative precipitation loss.
Wadi Al-Maayen catchment observed vs. simulated stream flow hydrographs for the calibration (30/07/2012) using HEC-HMS.
Wadi Al-Maayen catchment observed vs. simulated stream flow hydrographs for the calibration (30/07/2012) using HEC-HMS.
Estimated rainfall, direct runoff and peak flows of the main basin
. | . | 5 years . | 10 years . | 25 years . | 50 years . | 100 years . |
---|---|---|---|---|---|---|
2010 | Rainfall max 24 hr (mm) | 57.18 | 70.23 | 86.76 | 98.95 | 111.13 |
Total precipitation (m3) | 2,098,500 | 2,577,400 | 3,184,100 | 3,631,500 | 4,078,500 | |
Total loss (mm) | 19.68 | 21.60 | 23.48 | 24.59 | 25.53 | |
Direct run off (mm) | 37.50 | 48.63 | 63.28 | 74.36 | 85.60 | |
Total run off (million m3) | 1,376,100 | 1,784,900 | 2,322,500 | 2,729,000 | 3,141,600 | |
Peak flow rate (m3/sec) | 63.7 | 83.7 | 110.1 | 130.0 | 150.3 | |
2015 | Total loss (mm) | 14.42 | 15.69 | 16.92 | 17.64 | 18.25 |
Direct run off (mm) | 42.76 | 54.54 | 69.84 | 81.31 | 92.88 | |
Total run off (million m3) | 1,569,300 | 2,001,600 | 2,563,000 | 2,983,900 | 3,408,800 | |
Peak flow rate (m3/sec) | 77.4 | 99.5 | 128.3 | 149.9 | 171.7 |
. | . | 5 years . | 10 years . | 25 years . | 50 years . | 100 years . |
---|---|---|---|---|---|---|
2010 | Rainfall max 24 hr (mm) | 57.18 | 70.23 | 86.76 | 98.95 | 111.13 |
Total precipitation (m3) | 2,098,500 | 2,577,400 | 3,184,100 | 3,631,500 | 4,078,500 | |
Total loss (mm) | 19.68 | 21.60 | 23.48 | 24.59 | 25.53 | |
Direct run off (mm) | 37.50 | 48.63 | 63.28 | 74.36 | 85.60 | |
Total run off (million m3) | 1,376,100 | 1,784,900 | 2,322,500 | 2,729,000 | 3,141,600 | |
Peak flow rate (m3/sec) | 63.7 | 83.7 | 110.1 | 130.0 | 150.3 | |
2015 | Total loss (mm) | 14.42 | 15.69 | 16.92 | 17.64 | 18.25 |
Direct run off (mm) | 42.76 | 54.54 | 69.84 | 81.31 | 92.88 | |
Total run off (million m3) | 1,569,300 | 2,001,600 | 2,563,000 | 2,983,900 | 3,408,800 | |
Peak flow rate (m3/sec) | 77.4 | 99.5 | 128.3 | 149.9 | 171.7 |
(a) Estimated peak flows for 5–100 year events Wadi Al-Burdi main basin: (a) before urbanization; (b) after urbanization.
(a) Estimated peak flows for 5–100 year events Wadi Al-Burdi main basin: (a) before urbanization; (b) after urbanization.
The relation between the flood peak flow before and after urbanization.
The recommended management strategies for these flash floods include: (1) the construction of several small dams at the outlet of the third order sub-basins, (2) rainfall-sewage systems, and (3) conveyance culverts (in the upper part of the catchment) and levees alongside the wadis to transfer the flows into Jazan dam lake (Figure 1). Additionally, the considerable quantity of fresh water in this arid region could serve as a good water supply for the local inhabitants. Indeed, the total runoff volumes are computed to vary between 1.6 and 3.4 million m3 (Table 6). Regrettably, almost all of this water is lost due to the spreading of the meandering wadi channel and the consequent high evapotranspiration rates. It is recommended that this renewable water resource could be used to recharge groundwater through wells employing natural downward percolation or even through injection wells.
CONCLUSIONS
In arid areas, reliable prediction of runoff is difficult to achieve for ungauged basins. Peak discharge of flash floods in ungauged catchments could be estimated efficiently by coupling GIS and remote sensing tools with hydrologic modeling. This approach will help to further improve our understanding of flash flood processes in such arid regions. Hydrologic influences and urbanization have a major impact on the timing, location, and severity of flash flooding. As per the SCS tables and other relevant data, a composite CN of 81.59 is assumed for the 2010 land-use map. Urbanization is taking place rapidly in the southwestern part of the studied watershed. Accelerated urban expansion in 2015 caused changes in catchment land-use, increasing the composite CN to 83.71. The areas, lag time, and predicted peak discharge rates, along with the volumes of rainfall, runoff, and abstraction losses were computed. The calibration of the HEC-HMS model is an essential step to reduce prediction errors for a single storm event. The estimated and observed stream flow volumes of a single event are close enough to assume the applicability of the HEC-HMS model approach for the arid area. The susceptibility of the Wadi Al-Burdi basin to flash floods is high due to the scarcity of stream meanders, steep slopes, and low surface roughness. Rainfall estimations from different return periods are identified and probable maximum floods of the sub-basins are also estimated for different return periods between 2010 and 2015. Due to encroachment of the flood plain areas, the presence of several structures, and the absence of proper regulations for maintenance, the flood intensity is increased. Comparison of the two successive land-use maps (2010–2015) shows that urbanization increased by 8% of the total area. The model results show rapid urbanization adversely affects the hydrological processes, since the sprawl on the alluvial channels is significant. This reduces the infiltration into the underlying alluvium and increases the runoff, leading to higher flood peaks and volumes even for short duration low intensity rainfall, therefore increasing the potential threat to both lives and property. The construction of several small dams is recommended at the fingertip channels at the outlets of the third order sub-basins, in order to retain a considerable amount of water and sediments. Not only will these dams decrease the flood hazards, but they could also serve as a recharge source to the underlying alluvium aquifer. This flood water could be used to cover a considerable amount of the region's water supply, and be utilized to fulfill part of the increasing water demand due to the current high population growth and expected economic development in this arid area.
ACKNOWLEDGEMENTS
The author wishes to express his gratitude to the editor, the reviewers and Dr David Jalajel for their valuable comments and assistance in revising the manuscript. This project was financially supported by the Vice Deanship of Research Chairs at King Saud University.