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Using peak discharge estimation methods in urban flood modeling for WADI AL-AQIQ

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  • Ali Aldrees

    (Prince Sattam Bin Abdulaziz University)

Abstract

Several large floods have hit city areas in various regions of Saudi Arabia as a result of significant rainfall with significant effects on economic and social living conditions. Madinah is a city with a lot of severe floods during the past 40 years. Adding that quick and uncontrolled wadi production is a key source of desert floods. This research aims at estimating the hydrographic and peak discharge of Madinah's Wadi Al Aqiq flood and its principal sub-catches based on normal rain distribution. The maximum annual daily records are picked for rainfall distribution in the research region. The hydrological and hydrogeo-chemical properties of the city of Madinah Al-Aqiq have been researched. Soil water is exclusively provided by rainfall in the field of study. The concentration of total dissolved solids, main components and trace elements in groundwater increased dramatically following construction of an industrial sector in the south of town close to Wadi Al Aqiq, suggesting an important pollution mechanism in the studied region. Therefore, this study uses time series analysis methods as peak discharge estimation to analyze the dynamics of rainfall records of four stations in and around Wadi Al-Aqiq, Kingdom of Saudi Arabia (KSA).

Suggested Citation

  • Ali Aldrees, 2022. "Using peak discharge estimation methods in urban flood modeling for WADI AL-AQIQ," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3461-3484, March.
    • Handle: RePEc:spr:endesu:v:24:y:2022:i:3:d:10.1007_s10668-021-01574-y
    DOI: 10.1007/s10668-021-01574-y
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    References listed on IDEAS

    as
    1. Ali Aldrees, 2021. "Water management in Saudi Arabia: a case study of Makkah Al Mukarramah region," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13650-13666, September.
    2. Safa, Maryam & Sari, Puteri Azura & Shariati, Mahdi & Suhatril, Meldi & Trung, Nguyen Thoi & Wakil, Karzan & Khorami, Majid, 2020. "Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    3. I. Mansouri & M. Shariati & M. Safa & Z. Ibrahim & M. M. Tahir & D. Petković, 2019. "RETRACTED ARTICLE: Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1247-1257, March.
    4. Hu, Pengfei & Cao, Lihua & Su, Jingkai & Li, Qi & Li, Yong, 2020. "Distribution characteristics of salt-out particles in steam turbine stage," Energy, Elsevier, vol. 192(C).
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