Estimating urban flooding potential near the outlet of an arid catchment in Saudi Arabia

Al-Zahrani, Muhammad A.; Al-Areeq, Ahmed M.; Sharif, Hatim O.

doi:10.1080/19475705.2016.1255668

Cited by 39 publications

(20 citation statements)

References 23 publications

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“…This divergence for the 2027 time period is attributed to the fact that this study assumes that all land use types, including urban areas, were constant as in 1979. For small catchments, the SCS-CN method [69] is the main approach used for estimating the infiltration capacity and runoff for various combinations of soil and land/use cover type [70]. The study highlighted that, as the urban development expanded over the decades, flood hydrograph peak flows and flood volume substantially increased by 1.4-2.6 times compared to the pre-urban conditions; the latter peak flow increase has also been documented in similar studies [6,40].…”

Section: Discussionmentioning

confidence: 99%

“…where CN (Curve Number) is the dimensionless index developed by the USDA Natural Resources Conservation Service [30] that ranges from 0 (maximum infiltration) to 100 (zero infiltration), whilst its value depends on land use, soil type and the Antecedent Moisture Conditions (AMC). The transformation of the effective precipitation into a runoff hydrograph is realized through the usage of the SCS unit hydrograph method [70], by computing the storm hydrograph from a 1 mm rainfall event as follows:…”

Section: Hydrologic and Sediment Yield Modellingmentioning

confidence: 99%

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Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

Myronidis

Ιωάννου²

2018

Water

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Urban expansion substantially alters the impervious areas in a catchment, which in turn affects surface runoff and sediment yield in the downstream areas. In this study, the Land Transformation Model (LTM) was used to forecast the urban land expansion in a catchment, whilst future land use maps were employed according to the Soil Conservation Service Curve Number method (SCS-CN) and the Modified Universal Soil Loss Equation (MUSLE) model, so as to examine the urbanization effects on runoff and sediment yield production respectively. Compared to pristine conditions, urban land is anticipated to increase from 6% in 1979 to 31% by 2027. The latter expansion pointed to an increase of peak discharge by 2.2-2.6 times and of flood volume by 1.6-2.1 times, with the sediment yield ranging between 0.47 to 1.05 t/ha for the upcoming 2027 period. Furthermore, the urban sprawl effects on all the latter variables were more profound during short duration storm events. Forecasting urban expansion through integrated artificial neural networks (ANN) and geographic information system (GIS) techniques, in order to calculate the associated design storm hydrograph and sediment yield, is of great importance, in order to properly plan and design hydraulic works that can sustain future urban development.have been negatively impacted by the degree of urbanization [5] and the hydrologic cycle and in stream process have been altered [6]. These changes could seriously impact the mountain rivers sediment production and transport [7] with significant changes in the bed morphology [8] and floods with a high content of transported sediments and debris floods can occur [9]. Moreover, the temporal and spatial scales of these phenomena may vary greatly during intense precipitation events with respect to normal conditions [10].In recent decades, a water budget or hydrologic model have been commonly utilized [11,12] to evaluate the effects of the recorded urban expansion on the hydrology of catchments, so as to model the effects of different land use change scenarios on runoff and sediment yield [13,14]. However, since land use planners and policy makers are more and more interested in predicting urban expansion and its effect on natural resources, much of the research in recent years has focused on the coupling of hydrologic models with sophisticated spatial land use change (LUC) models, that are able to project LUC changes in the near future. LUC models can be used as tools for studying the human effect on the Earth's surface and to forecast land change into the future [15].LUC models use a variety of techniques, including linear extrapolation, suitability mapping, genetic algorithms, neural networks, scenario analysis, expert opinions, public participation and agent-based modelling, in order to produce a prediction map for the near future [16]. However, there are three main stream approaches for LUC models: models which rely primarily on economic models and data [17][18][19], models that rely equally on multiple approaches and data to determine ca...

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Section: Discussionmentioning

confidence: 99%

Section: Hydrologic and Sediment Yield Modellingmentioning

confidence: 99%

Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

Myronidis

Ιωάννου²

2018

Water

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“…Therefore, a negative relationship exists between a flood event and vegetation density. Conversely, urban areas increase the storm water runoff due to the extensive impervious surfaces (Al-Zahrani et al 2016). The FR outputs indicated that a strong relationship exists between LULC and flooding.…”

Section: Fr and Woe Outcomesmentioning

confidence: 94%

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Tehrany

Shabani

Jebur

et al. 2017

Geomatics, Natural Hazards and Risk

205

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The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.

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“…Key benefits are that a SCS storm distribution contains storm depth information for events of all duration up to 24 hours, provides cumulative rainfall at any point in time and expresses runoff values through computationally simple means. Hypothetical storm type II distribution was selected for its resemblance to extreme precipitation events in this region (e.g., Sharif [29], Al-Zahrani [57]). Figure 6 shows the hydrograph and the areal average of the storm event that occurred on March 21, 2016 to the Oman Gulf from the eastern watersheds.…”

Section: Hydrologic Simulationsmentioning

confidence: 99%

Coastal Runoff in the United Arab Emirates—The Hazard and Opportunity

et al. 2019

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Properly quantifying the potential exposure of hyper-arid regions to climate extremes is fundamental to developing frameworks that can be used to manage these extremes. In the United Arab Emirates (UAE), rapid growth may exacerbate the impacts of climate extremes through urbanization (increased runoff), population and industrial development (more water demand). Water resources management approaches such as Managed Aquifer Recharge (MAR) application may help mitigate both extremes by storing more water from wet periods for use during droughts. In this study, we quantified the volumes of runoff from coastal watersheds discharging to the Gulf of Oman and the Arabian Gulf that could potentially be captured to replenish depleted aquifers along the coast and help reduce the adverse impacts of urban flooding. To this aim, we first downloaded and processed the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) rainfall data for a recent wide-spread storm event. The rainfall product was then used as input to hydrologic models of coastal watersheds for estimating the resulting runoff. A multi-criteria decision analysis technique was used to identify areas most prone to runoff accumulation. Lastly, we quantified the volumes of runoff that could potentially be captured from frequency storms of different return periods and how rapid urbanization in the region may increase these runoff volumes creating more opportunities for the replenishment of depleted aquifers. Our results indicate that the average runoff from watersheds discharging to the ocean ranges between 0.11 km3 and 0.48 km3 for the 5-year and 100-year storms, respectively. We also found that these amounts will substantially increase due to rapid urbanization in the coastal regions of the UAE. In addition to water supply augmentation during droughts, potential benefits of application of MAR techniques in the UAE coastal regions may include flood control, mitigation against sea-level rise through subsidence control, reduction of aquifer salinity, rehabilitation of ecosystems, cleansing polluted runoff and preventing excessive runoff into the Gulf that can contribute to red tide events.

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Estimating urban flooding potential near the outlet of an arid catchment in Saudi Arabia

Cited by 39 publications

References 23 publications

Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Coastal Runoff in the United Arab Emirates—The Hazard and Opportunity

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