Hydrological studies in the White Nile State in Sudan

Salih, Abdelrahim; Hamid, Amna Ahmed

doi:10.1016/j.ejrs.2016.12.004

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The runoff (Q) factor was computed by the widely used Soil Conservation Service Curve Number (SCS-CN) method [71,72] developed in 1972 by the US Soil Conservation Service, which is now known as the Natural Resource Conservation (NRCS) curve number (CN) method. This method has been most commonly used in mapping potential zones for groundwater and water conservation in arid and semiarid areas [73,74] for rainwater harvesting and beak discharge estimation [65,75,76], and for flood susceptibility assessment [3,4,77]. CN in this study was used as a deterministic model to estimate runoff (Q) from previous rainstorm events for a specific return period and medium-to-high probability of occurrence.…”

Section: Hydrological Variables Extractionmentioning

confidence: 99%

Geospatial-Based Analytical Hierarchy Process (AHP) and Weighted Product Model (WPM) Techniques for Mapping and Assessing Flood Susceptibility in the Wadi Hanifah Drainage Basin, Riyadh Region, Saudi Arabia

Al-Ali

Salih

Hassaballa

2023

Water

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This paper aimed to map areas prone to flooding in the Wadi Hanifah drainage basin located in the Riyadh region, and identify the most important factors that contribute to flooding through examining the influence of ten topographical, hydrological, and environmental variables affecting flood occurrence. Remote sensing data from Landsat-8, Shuttle Radar Topography Mission (SRTM), and other ancillary datasets were used to map relevant variables. Two weighted overlay techniques were used, including: analytical hierarchy process (AHP) and weighted product model (WPM). A correlation matrix and optimum index factor (OIF) were employed to identify the relative importance of each factor. The two derived flood susceptibility maps were assessed through validation by comparing the locations of historical flood events to susceptibility zones. The results confirmed the validity of the WPM map. The results also showed that nearly 50% of the study area was dominated by the “moderate” flood susceptibility zone, while about 33% of the total land area was classified as a “high” flood susceptibility zone. The “slope” factor was found to be the most effective variable for flood occurrence, followed by the “geology” variable, while the “distance to the drainage network” was the least important variable. The results of the OIF indicated that the best combination of factors dictating the variability of all flood susceptibility areas were “geology”, “land use/cover (LULC)”, and “soil type”. The study findings are expected to be useful in understanding the effects of each factor on the spatial variation in flood occurrence and in improving flood control, and can be reapplied to other regions with similar climatic and environmental conditions worldwide.

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Section: Hydrological Variables Extractionmentioning

confidence: 99%

Geospatial-Based Analytical Hierarchy Process (AHP) and Weighted Product Model (WPM) Techniques for Mapping and Assessing Flood Susceptibility in the Wadi Hanifah Drainage Basin, Riyadh Region, Saudi Arabia

Al-Ali

Salih

Hassaballa

2023

Water

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“…It is also recognized as the Natural Resources Conservation (NRCS) curve number (CN) method. It has been commonly used to compute surface runoff (Q), map potential areas of groundwater recharge in arid and semi-arid regions [31,32], estimate peak discharge [33][34][35], and simulate and assess flooding [9,36,37]. The excess rainfall (Q) is related to the effective rainfall, as shown in Equation ( 1), through what is known as maximum potential retention value (S).…”

Section: Hydrologic Simulation Modelmentioning

confidence: 99%

Flash Flood Risk Assessment in the Asir Region, Southwestern Saudi Arabia, Using a Physically-Based Distributed Hydrological Model and GPM IMERG Satellite Rainfall Data

Salih,

Hassablla

2024

Atmosphere

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Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: ‘low risk’, ‘moderate risk’, ‘high risk’, and ‘very high risk’. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness.

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“…Taken as a whole, this suggests that the model works best in catchments where there is low-growing vegetation (grassland or (pastoral) agriculture), and not well in catchments where the vegetation is tall and/or dense (croplands and forests). The SRTM void filled elevation data, has previously been applied in hydrologic analyses for water harvesting studies (Sreedevi et al, 2009;Grum et al, 2016;Salih and Hamid, 2017;Ahmed and Diab, 2018;Abdekareem et al, 2022). The HRRTLE process reduces the spatial resolution of the SRTM dataset to 250 m to match the curve number dataset.…”

Section: Determination Of Catchment Types For Best Model Performance 390mentioning

confidence: 99%

HRRTLE (High Resolution Runoff and Transmission Loss Estimator): a novel tool for mapping connectivity of runoff in ephemeral stream networks to aid the siting of water harvesting structures

Delaney,

Blackburn,

Folkard

et al. 2024

Preprint

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Abstract. Water harvesting is predominantly carried out in arid and semi-arid regions. Site selection studies often rely on a methodology that calculates runoff using curve numbers to generate runoff maps. These maps, typically used as part of a multi-criteria selection process, identify areas conducive to the siting of water harvesting structures. However, traditional runoff maps do not account for transmission losses that occur along the surface flow path to the catchment outlet, and these losses can be significant in arid and semi-arid regions. Here we introduce a methodology that incorporates a curve number runoff method while also addressing transmission losses. Our approach, utilising three global datasets, was validated against observed runoff data from 28 catchments worldwide, and infers hydraulic characteristics of both overland and channel flow from curve number values. This involves leveraging the curve number dataset twice: initially for calculating runoff and subsequently for forecasting transmission losses. The outcomes include a runoff connectivity map, at a spatial resolution of 250 m × 250 m, presenting the runoff depth (in mm) for each pixel based on the direct runoff generated at that pixel and reaching the catchment outlet. This connectivity map aids planners in comprehending the dynamics of surface runoff towards a catchment outlet, assisting in identifying potential locations for future water harvesting structures. The process integrates 38 years of precipitation data, enabling predictions not only for average annual runoff but also for the return periods of various annual runoff volumes. Despite the simplicity of the model, a positive Nash-Sutcliffe efficiency value was observed in 11 out of the 28 catchments.

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Hydrological studies in the White Nile State in Sudan

Cited by 6 publications

References 12 publications

Geospatial-Based Analytical Hierarchy Process (AHP) and Weighted Product Model (WPM) Techniques for Mapping and Assessing Flood Susceptibility in the Wadi Hanifah Drainage Basin, Riyadh Region, Saudi Arabia

Geospatial-Based Analytical Hierarchy Process (AHP) and Weighted Product Model (WPM) Techniques for Mapping and Assessing Flood Susceptibility in the Wadi Hanifah Drainage Basin, Riyadh Region, Saudi Arabia

Flash Flood Risk Assessment in the Asir Region, Southwestern Saudi Arabia, Using a Physically-Based Distributed Hydrological Model and GPM IMERG Satellite Rainfall Data

HRRTLE (High Resolution Runoff and Transmission Loss Estimator): a novel tool for mapping connectivity of runoff in ephemeral stream networks to aid the siting of water harvesting structures

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