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Hydro-Meteorology and Its Application in Hydrological Modeling

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (1 September 2023) | Viewed by 16992

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Special Issue Editors

Dr. Khalil Ur Rahman

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Guest Editor

State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Interests: hydro-meteorology; hydrology; hydrological modeling; satellite precipitation estimation; drought assessment
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Amro Mohamed Elfeki

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Guest Editor

1. Irrigation and Hydraulics Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
2. Currently Working at Department of Water Resources, Faculty of Environmental Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
Interests: water resources modelling; stochastic hydrology; flood hydraulics; environmental water resources engineering

Prof. Dr. Jarbou A. Bahrawi

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Guest Editor

Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah 80208, Saudi Arabia
Interests: hydrology; stochastic analysis and simulation of hydrologic processes; flood and drought analysis; uncertainty analysis of hydrologic events

Dr. Muhammad Shahid

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Guest Editor

Department of Civil Engineering, University of Engineering and Technology (UET), Lahore, Pakistan
Interests: climate change; hydro-meteorology; remote sensing; watershed modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Shuai Chen

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Guest Editor

College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, China
Interests: agro-hydrological modelling; water resources; agricultural water management; soil and water pollution

Special Issue Information

Dear Colleagues,

Hydro-meteorology, the integration of hydrology and meteorology, is a vast field focusing on analyzing the transfer of water and energy fluxes between the earth and the lower atmosphere. With the recent advancements in science and engineering, the field of hydro-meteorology has significantly evolved, merging the two fields in consideration of the fundamental knowledge of meteorology with hydrology to study the energy and water cycles at local, regional and global scales. With the passage of time and advancements in remote sensing techniques (i.e., after the advent of radars and satellites), substantial achievements have been made due to the shift from “data scarcity” issues to a “data rich” environment. The remotely sensed data retrieved from satellites are now incorporated into numerical and hydrological models to study several hydrological variables (i.e., precipitation, soil moisture, evaporation and evapotranspiration, vegetation dynamics, flood inundation, drought assessment, etc.).

Earth observation satellites have advanced over the last decade, leading to the collection of enormous quantities of data related to the Earth’s surface processes for various applications. The scope of this Special Issue focuses mainly on hydro-meteorological data and its applications in a wide range of water resources and Earth ecosystems. The stakeholders of this Special Issue are researchers, practitioners, and engineers working in the field of water resources, environmental issues and ecosystems. The main purpose of this Issue is to publish state-of-the-art research on advanced measurement techniques, data assimilation methods, software applications, high-resolution data analysis, forecasting, and modeling in the field of hydro-meteorology in order to enhance our understanding of the hydrological–meteorological interaction of the Earth’s surface processes and improve our ability to develop models that capture observed phenomena and patterns in the data in a way that is not possible using current and previous measuring devices.

This Special Issue will update the existing literature on the current knowledge of the hydrological–meteorological interaction and its global impact on the Earth’s surface processes and ecosystems. It will also focus on newly established techniques, modeling, and forecasting in hydro-meteorology, and how forecasts contribute to decision-making. This Issue will discuss a range of practical applications related to floods, droughts, flow control, environmental impacts, and water resources. The topics of research include (but are not limited to) those listed below:

Precipitation estimation from satellite precipitation products;
Application and assessment of satellite precipitation products in hydrological modelling;
Extreme events;
Improved evapotranspiration estimation;
Monitoring of soil moisture using satellite products;
Flood modeling;
Agricultural water management and conservation;
Quantification of uncertainty in hydrological modelling;
Climate change impact: mitigation and adaptation;
Applications of machine learning (ML) in hydro-meteorology.

Dr. Khalil Ur Rahman
Prof. Dr. Amro Mohamed Elfeki
Prof. Dr. Jarbou A. A. Bahrawi
Dr. Muhammad Shahid
Dr. Shuai Chen
Guest Editors

Manuscript Submission Information

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Keywords

Precipitation estimation from satellite precipitation products
Application and assessment of satellite precipitation products in hydrological modelling
Extreme events
Improved evapotranspiration estimation
Monitoring of soil moisture using satellite products
Flood modeling
Agricultural water management and conservation
Quantification of uncertainty in hydrological modelling
Climate change impact: mitigation and adaptation
Applications of machine learning (ML) in hydro-meteorology.

Published Papers (11 papers)

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Research

29 pages, 10573 KiB

Open AccessArticle

A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction

by Levent Latifoğlu and Mehmet Özger

Sustainability 2023, 15(19), 14046; https://doi.org/10.3390/su151914046 - 22 Sep 2023

Cited by 1 | Viewed by 918

Abstract

Drought, as a natural disaster, has significant negative consequences and directly impacts living organisms. Drought forecasting commonly relies on various drought indices, with the Standardized Precipitation Index (SPI) being widely used. In this study, we propose a novel approach to estimate SPI values at 3- and 6-month lead times with high accuracy. This novel method introduces a phase transfer entropy (pTE) technique that analyzes time-shifted data matrices and the connectivity of SPI-3 and SPI-6 data. By maximizing the information flow between these data points, the most suitable time index (t − n) for input data in forecasting models is determined. This approach, not previously explored in the literature, forms the basis for predicting SPI values effectively. Machine learning algorithms, in combination with the Tunable Q Factor Wavelet Transform (TQWT) optimized by the Grey Wolf Optimization (GWO) algorithm, are employed to predict SPI values using the identified input data. The TQWT method generates subband signals, which are then estimated using Artificial Neural Networks (ANN), Support Vector Regression (SVR), and the Gaussian Process Regression Model (GPR). To evaluate the performance of the proposed GWO-TQWT-ML models, the subband data derived from the SPI is also estimated using ANN, GPR, and SVR models with the Empirical Mode Decomposition and Variational Mode Decomposition methods. Additionally, non-preprocessed SPI data is estimated independently using ANN, GPR, and SVR models. The results demonstrate the superior performance of the pTE-GWO-TQWT-ML models over other methods. Among these models, the pTE-GWO-TQWT-GPR model stands out with the best prediction performance, surpassing both the pTE-GWO-TQWT-ANN and pTE-GWO-TQWT-SVR models. The pTE-GWO-TQWT-GPR model yielded determination coefficient (R²) values for SPI-6 data as follows: 0.8039 for one-input, 0.9987 for two-input, and 0.9998 for three-input one ahead prediction, respectively; 0.9907 for two-input two ahead prediction; and 0.9722 for two-input three ahead prediction. For SPI-3 data, using the pTE-GWO-TQWT-GPR model, the R² values were as follows: 0.6805 for one-input, 0.9982 for two-input, 0.9996 for three-input one ahead prediction, 0.9843 for two-input two ahead prediction, 0.9535 for two-input three ahead prediction, 0.9963 for three-input two ahead prediction, and 0.9826 for three-input three ahead prediction. Overall, this study presents a robust method, the pTE-GWO-TOWT-GPR model, for the time series estimation of SPI data, enabling high-performance drought prediction. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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21 pages, 6293 KiB

Open AccessArticle

Streamflow Response to Climate and Land-Use Changes in a Tropical Island Basin

by Can Cao, Rui Sun, Zhixiang Wu, Bangqian Chen, Chuan Yang, Qian Li and Klaus Fraedrich

Sustainability 2023, 15(18), 13941; https://doi.org/10.3390/su151813941 - 20 Sep 2023

Cited by 1 | Viewed by 836

Abstract

The effects of climate change and of land use/cover change (LUCC) on streamflow as demonstrated by hydrological models are pressing issues on the frontiers of global environmental change research. The Nandu River Basin (NRB) as the largest of three river basins on the tropical Hainan Island, China, is subjected to an analysis of streamflow response to climate and to land-use change. It is based on the Soil and Water Assessment Tool (SWAT) coupled with climate change signals extracted from the global climate model data in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and with land-use change scenarios modeled by Cellular Automata (CA)—Markov. The results are summarized as follows: (1) Climate change contributed more to streamflow change than land-use change in the NRB, with contributions of 97.57% and 2.43%, respectively. Precipitation and temperature were the most important climate variables, contributing 92.66% and 4.91% to streamflow change. (2) In the tropical island basin from 1990 to 2015, LUCC regulated the hydrological processes in the NRB and affected hydrological processes by increasing evapotranspiration and decreasing surface runoff and subsurface flow, which resulted in decreasing streamflow. (3) Under the climate change and land-use change scenarios of the near-term period (2021–2040), the annual streamflow decreased as during the reference period (1995–2014); particularly, it decreased most (−6.16%) on the SSP126 path. These results present a case study for understanding the hydrological cycle of tropical island basins and to provide a theoretical basis for water resources management and regional sustainable development of tropical islands. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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33 pages, 7245 KiB

Open AccessArticle

Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations

by Joško Trošelj, Han Soo Lee and Lena Hobohm

Sustainability 2023, 15(18), 13897; https://doi.org/10.3390/su151813897 - 19 Sep 2023

Cited by 1 | Viewed by 1278

Abstract

This study marks a significant step toward the future development of river discharges forecasted in real time for flash flood early warning system (EWS) disaster prevention frameworks in the Chugoku region of Japan, and presumably worldwide. To reduce the disaster impacts with EWSs, accurate integrated hydrometeorological real-time models for predicting extreme river water levels and discharges are needed, but they are not satisfactorily accurate due to large uncertainties. This study evaluates two calibration methods with 7 and 5 parameters using the hydrological Cell Distributed Runoff Model version 3.1.1 (CDRM), calibrated by the University of Arizona’s Shuffled Complex Evolution optimization method (SCE-UA). We hypothesize that the proposed ensemble hydrological parameter calibration approach can forecast similar future events in real time. This approach was applied to seven major rivers in the region to obtain hindcasts of the river discharges during the Heavy Rainfall Event of July 2018 (HRE18). This study introduces a new historical extreme rainfall event classification selection methodology that enables ensemble-averaged validation results of all river discharges. The reproducibility metrics obtained for all rivers cumulatively are extremely high, with Nash–Sutcliffe efficiency values of 0.98. This shows that the proposed approach enables accurate predictions of the river discharges for the HRE18 and, similarly, real-time forecasts for future extreme rainfall-induced events in the Japanese region. Although our methodology can be directly reapplied only in regions where observed rainfall data are readily available, we suggest that our approach can analogously be applied worldwide, which indicates a broad scientific contribution and multidisciplinary applications. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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14 pages, 10156 KiB

Open AccessArticle

Application and Research of Liuxihe Model in the Simulation of Inflow Flood at Zaoshi Reservoir

by Yanzheng Zhu, Yangbo Chen, Yanjun Zhao, Feng Zhou and Shichao Xu

Sustainability 2023, 15(13), 9857; https://doi.org/10.3390/su15139857 - 21 Jun 2023

Viewed by 764

Abstract

Floods occur frequently in China, and watershed floods are caused mainly by intensive rainfall, but the spatial distribution of this rainfall is often very uneven. Thus, a watershed hydrological model that enables a consideration of a heterogeneous spatial distribution of rainfall is needed. In this study, a flood forecasting scheme based on the Liuxihe model is established for the Zaoshi Reservoir. The particle swarm optimization (PSO) algorithm is used to optimize the model parameters for flood simulation, and the model’s performance is assessed by a comparison with measured flood data. The spatial distributions of rainfall selected for this study are non-uniform, with much greater rainfall in some areas than in others in some cases. Rainfall may be concentrated in the middle of the basin, in the reservoir area, or in the upstream portion of the basin. The Liuxihe-model-based flood inflow forecasting scheme for the Zaoshi Reservoir demonstrates an excellent simulation effect, with an average peak simulation accuracy of 96.3%, an average peak time of 1.042 h early, and an average Nash–Sutcliffe coefficient of 0.799. Under the condition of an uneven spatial distribution of rainfall, the Liuxihe model simulates floods well. The PSO algorithm significantly improves the model’s simulation accuracy, and its practical application requires only the selection of a typical flood for parameter optimization. Thus, the flood simulation effect of the Liuxihe model is ideal for the watershed above the Zaoshi Reservoir, and the scheme developed in this study can be applied for operational flood forecasting. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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20 pages, 4695 KiB

Open AccessArticle

Assessment of Variability in Hydrological Droughts Using the Improved Innovative Trend Analysis Method

by Muhammad Shehzad Ashraf, Muhammad Shahid, Muhammad Waseem, Muhammad Azam and Khalil Ur Rahman

Sustainability 2023, 15(11), 9065; https://doi.org/10.3390/su15119065 - 03 Jun 2023

Cited by 3 | Viewed by 1259

Abstract

The use of hydro-climatological time series to identify patterns is essential for comprehending climate change and extreme events such as drought. Hence, in this study, hydrological drought variability based on the standard drought index (SDI) using DrinC was investigated at ten (10) hydrological stations in the Upper Indus River Basin (UIRB) of Pakistan on a monthly timescale for a period of 1961–2018. Moreover, the applicability of the improved innovative trend analysis by Sen Slope method (referred hereafter as the IITA) method was evaluated in comparison with innovative trend analysis (ITA) and Mann–Kendall (MK). The findings demonstrated a significant decreasing trend in the hydrological drought from October to March; on the other hand, from April through September, a significant increasing trend was observed. In addition to that, the consistency of the outcomes across the three trend analysis methods was also observed in most of the cases, with some discrepancies in trend direction, such as at Kharmong station. Conclusively, consistency of results in all three trend analysis methods showed that the IITA method is reliable and effective due to its capability to investigate the trends in low, median, and high values of hydrometeorological timeseries with graphical representation. A degree-day or energy-based model can be used to extend the temporal range and link the effects of hydrological droughts to temperature, precipitation, and snow cover on a sub-basin scale. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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14 pages, 3349 KiB

Open AccessArticle

Impact of Urbanization on Groundwater and Surface Temperature Changes: A Case Study of Lahore City

by Huzaifah Zahran, Muhammad Zeeshan Ali, Khan Zaib Jadoon, Hammad Ullah Khan Yousafzai, Khalil Ur Rahman and Nadeem Ahmed Sheikh

Sustainability 2023, 15(8), 6864; https://doi.org/10.3390/su15086864 - 19 Apr 2023

Cited by 2 | Viewed by 1675

Abstract

The over-exploitation of groundwater resources is a significant concern due to the potential risks associated with the depletion of this valuable freshwater source. Future planning must consider changes in groundwater availability and urban expansion which are critical for understanding urban growth patterns. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the Land surface temperature (LST) analysis has been performed to find the spatial spread of urbanization and its impact on surface temperature. The Gravity Recovery and Climate Experiment (GRACE) data for groundwater storage monitoring and Landsat data for land cover and LST mapping have been used. The GRACE-based Groundwater Storage (GWS) anomaly has been correlated with Tropical Rainfall Measuring Mission (TRMM)-based precipitation data. The GWS is further cross validated with the groundwater monitoring stations in the study area and the correlation of 0.7 is found. The time series analysis of GWS and the land cover maps with a decadal interval from 1990 to 2020 has been developed to find the impact of groundwater change due to urbanization. The results demonstrate a rapid increase in groundwater depletion and urbanization rates over the past decade. The LST spatial pattern is increasing similarly with the study area’s urban expansion, indicating the temperature rise due to urbanization. The study highlights the limitation of effective policies to regulate groundwater extraction in urban areas and the importance of proper planning to ensure the long-term sustainability of freshwater resources. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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15 pages, 6376 KiB

Open AccessArticle

Occurrence and Distribution of Long-Term Variability in Precipitation Classes in the Source Region of the Yangtze River

by Naveed Ahmed, Lianqi Zhu, Genxu Wang, Oluwafemi E. Adeyeri, Suraj Shah, Shahid Ali, Hero Marhaento and Sarfraz Munir

Sustainability 2023, 15(7), 5834; https://doi.org/10.3390/su15075834 - 28 Mar 2023

Viewed by 1242

Abstract

Various precipitation-related studies have been conducted on the Yangtze River. However, the topography and atmospheric circulation regime of the Source Region of the Yangtze River (SRYZ) differ from other basin parts. Along with natural uniqueness, precipitation constitutes over 60% of the direct discharge in the SRYZ, which depicts the decisive role of precipitation and a necessary study on the verge of climate change. The study evaluates the event distribution of long-term variability in precipitation classes in the SRYZ. The precipitation was classified into three precipitation classes: light precipitation (0–5 mm, 5–10 mm), moderate precipitation (10–15 mm, 15–20 mm, 20–25 mm), and heavy precipitation (>25 mm). The year 1998 was detected as a changing year using the Pettitt test in the precipitation time series; therefore, the time series was divided into three scenarios: Scenario-R (1961–2016), the pre-change point (Scenario-I; 1961–1998), and the post-change point (Scenario-II; 1999–2016). Observed annual precipitation amounts in the SRYZ during Scenario-R and Scenario-I significantly increased by 13.63 mm/decade and 48.8 mm/decade, respectively. The same increasing trend was evident in seasonal periods. On a daily scale, light precipitation (0–5 mm) covered most of the days during the entire period, with rainy days accounting for 83.50%, 84.5%, and 81.30%. These rainy days received up to 40%, 41%, and 38% of the annual precipitation during Scenario-R, Scenario-I, and Scenario-II, respectively. Consequently, these key findings of the study will be helpful in basin-scale water resources management. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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18 pages, 4964 KiB

Open AccessArticle

Stochastic Rational Method for Estimation of Flood Peak Uncertainty in Arid Basins: Comparison between Monte Carlo and First Order Second Moment Methods with a Case Study in Southwest Saudi Arabia

by Nassir S. Al-Amri, Hatem A. Ewea and Amro M. Elfeki

Sustainability 2023, 15(6), 4719; https://doi.org/10.3390/su15064719 - 07 Mar 2023

Cited by 5 | Viewed by 1382

Abstract

The flood peak is commonly estimated using the rational method for the design of hydraulic structures. The method is mainly used in a deterministic context. However, there is often uncertainty in flood predictions, which should be incorporated in the design of mitigation schemes. This research proposes a methodology to cope with uncertainty in the rational method via the application of a stochastic framework. Data from 158 storms, recorded in the period 1984–1987 in 19 subbasins in the southwestern part of Saudi Arabia, were used to implement the proposed methodology. A tri-variate log-normal probability density function was used to model the joint relationship between the rational method parameters. The model considered the parameters as random variables. The uncertainty in the rainstorms was represented by intensity or depth; the uncertainty in basin delineation (due to the use of different digital elevation model resolution) was represented by the basin area; and the uncertainty in the land use/land cover was represented by the runoff coefficient. The Monte Carlo method was used to generate realizations of the peak flow and runoff volume with 95% and 99% confidence levels from the input parameters. Although the correlation between the parameters was weak, the model was capable of simulating the rational model parameters and estimating the peak flow and runoff volume relatively well, and the generated realizations fell within the confidence levels, except for a few marginal cases. The model can be used to generate peak flows and the associated confidence limits in ungauged basins from the statistics of the input parameters using the equations developed in this study. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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22 pages, 7365 KiB

Open AccessArticle

Application of Environmental Isotopes and Hydrochemistry to Identify the Groundwater Recharge in Wadi Qanunah Basin, Saudi Arabia

by Milad Masoud, Maged El Osta, Abdulaziz Alqarawy and Hesham Ezzeldin

Sustainability 2023, 15(3), 2648; https://doi.org/10.3390/su15032648 - 01 Feb 2023

Cited by 1 | Viewed by 1541

Abstract

The current study focuses on the Wadi Qanunah basin, which is considered one of Makkah Al-Mukarramah’s most important watersheds. It is located in the southwestern part of the Al Qunfudhah governorate. The identification and characterization of the recharging sources for the quaternary aquifer is one of the most important goals of this study. In this context, different methods will be applied for the identification of the different factors impacting groundwater. Such methods will be based on the integration of geographic information system (GIS) and modern hydrochemical methods ranging from graphical plots, bivariate and multivariate analysis to geochemical modeling. The salinity of the groundwater studied varied from fresh to brackish, according to the seasonal influx of dilute runoff and the dissolution of the weathered rocks, as well as the cementing materials within the aquifers’ matrix. Ionic ratios indicated that ion exchange, silicate weathering and evaporation played a significant role in the enrichment of the groundwater with major constituents including calcium, sodium, magnesium, sulphate and chloride. Furthermore, four factors accounted for 73.92% of the total variance, calculated using SPSS’s statistical program. These factors accounted for leaching and dissolution, silicate and carbonate weathering, anthropogenic effects and evaporation. The δ18O vs. δD, TDS vs. δ18O and δ18O vs. d-excess relationships revealed that local rainfall is the main recharging source for groundwater; some samples were affected by evaporated rainfall, while others with lower salinity (<1000 mg/L) were diluted through seepage from the underlying fractured basement aquifer. Netpath geochemical modeling was applied to calculate the amount of evaporation or dilution which had affected an initial body of water as it moves from the upstream to downstream. The output of this program is consistent with what has been proved by stable isotopes, where the groundwater extracted from the final water is a mixture of an enriched recent precipitation with depleted older water. This study is an attempt to shed light on the assessment of groundwater and the extent at which it is affected by various factors in order to benefit from it in a way that ensures its sustainability. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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15 pages, 2361 KiB

Open AccessArticle

Evaluation of the Time of Concentration Models for Enhanced Peak Flood Estimation in Arid Regions

by Nassir Alamri, Kazir Afolabi, Hatem Ewea and Amro Elfeki

Sustainability 2023, 15(3), 1987; https://doi.org/10.3390/su15031987 - 20 Jan 2023

Cited by 1 | Viewed by 1785

Abstract

The uncertainties in the time of concentration (T_c) model estimate from contrasting environments constitute a setback, as errors in T_c lead to errors in peak discharge. Analysis of such uncertainties in model prediction in arid watersheds is unavailable. This study tests the performance and variability of T_c model estimates. Further, the probability distribution that best fits observed T_c is determined. Lastly, a new T_c model is proposed, relying on data from arid watersheds. A total of 161 storm events from 19 gauged watersheds in Southwest Saudi Arabia were studied. Several indicators of model performance were applied. The Dooge model showed the best correlation, with r equal to 0.60. The Jung model exhibited the best predictive capability, with normalized Nash–Sutcliffe efficiency (NNSE) of 0.60, the lowest root mean square error (RMSE) of 4.72 h, and the least underestimation of T_c by 1%. The Kirpich model demonstrated the least overestimation of T_c by 4%. Log-normal distribution best fits the observed T_c variability. The proposed model shows improved performance with r and NNSE of 0.62, RMSE of 4.53 h, and percent bias (PBIAS) of 0.9%. This model offers a useful alternative for T_c estimation in the Saudi arid environment and improves peak flood forecasting. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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22 pages, 71577 KiB

Open AccessArticle

Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment, the New Um Al-Khair Dam Case Study, Jeddah, Saudi Arabia

by Mohamed Hafedh Hamza and Afnan Mohammed Saegh

Sustainability 2023, 15(2), 1074; https://doi.org/10.3390/su15021074 - 06 Jan 2023

Cited by 6 | Viewed by 3200

Abstract

Recent years have seen an increase in floods with severe damage due to the intensity and frequency of rains. One of the periodic hydrological problems affecting Jeddah city, the second-biggest city in Saudi Arabia, is unexpected flash flooding. In dam breaks, water that has been retained is released uncontrollably. This study is related to a flood simulation methodology after a possible break of the New Um Al-Khair Dam, a dam built in 2012 outside residential areas, to replace the Old Um Al-Khair Dam built inside a residential area, which broke in January 2011. In fact, we simulated the impact on flood wave propagation in the study area through the use of GIS techniques coupled with hydrological/hydraulic modeling tools and the development of a flood inundation model. Planning a good emergency response in the future is possible by analyzing a supposed disaster. Based on the likelihood that there will be a flood and the corresponding inundation depth, a flood risk matrix is created as a quantitative tool to estimate flood damage, which is crucial to decision-makers. Negligible, low, moderate, high, and very high-risk categories are assigned according to that flood risk matrix. The results indicated a low to very high risk for 5 years, 50 years and 100 years return periods and a negligible to very high risk for a 200 years return period. To estimate the extent of damage, a quantitative summary of the results has been outlined graphically in order to visualize the scope of the inundation areas. Full article

(This article belongs to the Special Issue Hydro-Meteorology and Its Application in Hydrological Modeling)

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