Floods are one of the most dangerous water-related risks. Numerous sources of uncertainty affect flood modeling. High-resolution land-cover maps along with appropriate Manning’s roughness values are the most significant parameters for building an accurate 2D flood model. Two land-cover datasets are available: the National Land Cover Database (NLCD 2019) and the Land Use/Land Cover for Environmental Systems Research Institute (LULC 2020-ESRI). The NLCD 2019 dataset has national coverage but includes references to Manning’s roughness values for each class obtained from earlier studies, in contrast to the LULC 2020-ESRI dataset, which has global coverage but without an identified reference to Manning’s roughness values yet. The main objectives of this study are to assess the accuracy of using the LULC 2020-ESRI dataset compared with the NLCD 2019 dataset and propose a standard reference to Manning’s roughness values for the classes in the LULC 2020-ESRI dataset. To achieve the research objectives, a confusion matrix using 548,117 test points in the conterminous United States was prepared to assess the accuracy by quantifying the cross-correspondence between the two datasets. Then statistical analyses were applied to the global maps to detect the appropriate Manning’s roughness values associated with the LULC 2020-ESRI map. Compared to the NLCD 2019 dataset, the proposed Manning’s roughness values for the LULC 2020-ESRI dataset were calibrated and validated using 2D flood modeling software (HEC-RAS V6.2) on nine randomly chosen catchments in the conterminous United States. This research’s main results show that the LULC 2020-ESRI dataset achieves an overall accuracy of 72% compared to the NLCD 2019 dataset. The findings demonstrate that, when determining the appropriate Manning’s roughness values for the LULC 2020-ESRI dataset, the weighted average technique performs better than the average method. The calibration and validation results of the proposed Manning’s roughness values show that the overall Root Mean Square Error (RMSE) in depth was 2.7 cm, and the Mean Absolute Error (MAE) in depth was 5.32 cm. The accuracy of the computed peak flow value using LULC 2020-ESRI was with an average error of 5.22% (2.0% min. to 8.8% max.) compared to the computed peak flow values using the NLCD 2019 dataset. Finally, a reference to Manning’s roughness values for the LULC 2020-ESRI dataset was developed to help use the globally available land-use/land-cover dataset to build 2D flood models with an acceptable accuracy worldwide.

Stormwater drainage networks are designed to reduce the risk of rainwater damage to the served area. The purpose of optimizing a stormwater drainage system is to reduce overall construction costs and to meet hydraulic design requirements. Currently, designs that rely on software or manual calculations are limited by the available time and the designer’s capabilities. In fact, manual optimization for large networks consumes a lot of time and effort, and there is no guarantee that the optimal design is reached, also it is subject to human errors. In recent years, several researchers have focused on creating optimization design algorithms specifically for sewer and storm networks, such as genetic algorithm (GA), linear programming (LP), heuristic programming (HP),…etc. However, these studies were limited to covering one or two design parameters and constraints. Additionally, in some studies, the hydraulic performance of the designed network was not treated in a proper way, especially the water surface profile effects. So, the main objective of the study is to develop an effective hydraulic-based optimization algorithm (HBOA) that can dynamically get the optimal design with minimum total cost for a given storm network layout and meet all hydraulic requirements. To achieve this, a MATLAB code is created and coupled with SewerGEMS software that automatically simulates all expected optimization scenarios based on network hydraulic performance. The HBOA is validated economically and hydraulically using two benchmark examples from the literature. According to the economic validation, the total network cost generated by HBOA was the lowest when compared to the optimization methods found in the literature. During the hydraulic evaluation, it was observed that the optimization algorithm (GA-HP) used in the literature for the benchmark examples does not meet the hydraulic requirements where the networks are flooded, whereas HBOA meets the hydraulic requirements with minimal overall network cost. Also, the HBOA is applied to four real stormwater drainage networks that were already designed, constructed, and optimized manually. The four redesigned real cases using HBOA revealed a cost reduction of about 15% compared to the original designs, while consuming a few hours for the design and optimization processes. Finally, the developed HBOA is a robust, time-efficient, and cost-effective optimization and hydraulic design tool which could be used in the design of stormwater drainage networks with different design constraints with minimal human interference.

Data from pumping tests is a noisy process, and therefore, performing the pumping test numerous times will not get the same drawdown values. As a consequence, various pumping experiments lead to different values for aquifer parameter estimates. The data of pumping tests are usually analyzed using traditional methods (aquifer tests and AQtesolv software), which depend on trial and error technique. During these methods, non-unique values of hydraulic parameters are selected, which usually have a high level of uncertainty. Uncertainty must be taken into account in determining aquifer parameters, especially when using groundwater models for decision makers. The main goal of this study is to build a comprehensive tool for quantifying uncertainty associated with hydraulic parameter estimation from different pumping test conditions for fully penetrating wells in confined and semi-confined aquifers. To achieve the previous objective, a FORTRAN code was developed to apply the Markov Chain Monte Carlo (MCMC) algorithm using different likelihood functions (exponential, inverse, and log). This developed tool can be used to detect the most probable range of aquifer parameters that are consistent with pumping test data with a high degree of confidence. The tool was successfully used to several hypothetical cases to demonstrate the uncertainty in the quantification of aquifer parameters and compare the findings to the standard method's results. Also, the concept was verified numerically (using Modflow program) with satisfactory results using a hypothetical case with well-known aquifer parameters. Finally, the tool was applied for actual pumping test data with good results.

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R² of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R² to 75%, with an overall validation (for unseen catchments) R² of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R² of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth.

Flooding remains one of the most disruptive and costly natural hazards worldwide. Conventional approaches for estimating flood damage cost rely on empirical loss curves or historical insurance data, which often lack spatial resolution and predictive robustness. This study develops a data-driven framework for estimating flood damage costs across the contiguous United States, where comprehensive hydrologic, climatic, and socioeconomic data are available. A database of 17,407 flood events was compiled, incorporating approximately 38 parameters obtained from the National Oceanic and Atmospheric Administration (NOAA), the National Water Model (NWM), the United States Geological Survey (USGS NED), and the U.S. Census Bureau. Data preprocessing addressed missing values and outliers using the interquartile range and Walsh tests, followed by partitioning into training (70%), testing (15%), and validation (15%) subsets. Four modeling configurations were examined to improve predictive accuracy. The optimal hybrid regression–classification framework achieved correlation coefficients of 0.97 (training), 0.77 (testing), and 0.81 (validation) with minimal bias (−5.85, −107.8, and −274.5 USD, respectively). The findings demonstrate the potential of nationwide, event-based predictive approaches to enhance flood-damage cost assessment, providing a practical tool for risk evaluation and resource planning.

Intensity-duration-frequency (IDF) curves are essential for designing stormwater and flood mitigation systems. However, traditional IDF curves may no longer be reliable due to climate change. This study aims to assess the impact of climate change uncertainty on IDF curves for the entire of Egypt. Due to the significant lack of rainfall data in Egypt, historical rainfall data from three sources – GPM, PDIR, and Tutiempo – were collected and assessed for consistency using observed rainfall data. The Tutiempo dataset was to be the most reliable and was used as a reference for climate model evaluation based on statistical comparison. Eighteen climate models under RCP4.5 and RCP8.5 were downloaded and bias-corrected for 21 stations in Egypt. Rainfall analysis was achieved for the historical and future projected climate datasets. The results indicate a decline in annual maximum daily rainfall depths across all regions in Egypt for return periods of 100, 50, 10, and 5 years, with reductions ranging from 2% in coastal areas to 85% in the southwest desert. IDF curves for 21 rainfall stations in Egypt were created, including the uncertainty in climate change models, which offer decision-makers adaptable tools for planning and engineering applications.