A new daily water balance model is developed and tested in this paper. The new model has a similar model structure to the existing probability distributed rainfall runoff models (PDM), such as HyMOD. However, the model utilizes a new distribution function for soil water storage capacity, which leads to the SCS (Soil Conservation Service) curve number (CN) method when the initial soil water storage is set to zero. Therefore, the developed model is a unification of the PDM and CN methods and is called the PDM–CN model in this paper. Besides runoff modeling, the calculation of daily evaporation in the model is also dependent on the distribution function, since the spatial variability of soil water storage affects the catchment-scale evaporation. The generated runoff is partitioned into direct runoff and groundwater recharge, which are then routed through quick and slow storage tanks, respectively. Total discharge is the summation of quick flow from the quick storage tank and base flow from the slow storage tank. The new model with 5 parameters is applied to 92 catchments for simulating daily streamflow and evaporation and compared with AWMB, SACRAMENTO, and SIMHYD models. The performance of the model is slightly better than HyMOD but is not better compared with the 14-parameter model (SACRAMENTO) in the calibration, and does not perform as well in the validation period as the 7-parameter model (SIMHYD) in some areas, based on the NSE values. The linkage between the PDM–CN model and long-term water balance model is also presented, and a two-parameter mean annual water balance equation is derived from the proposed PDM–CN model.

Groundwater quality assessment is crucial for ensuring human welfare and promoting sustainable economic development. This study evaluates the effectiveness of linear Principal Component Analysis (PCA) and nonlinear Kernel PCA (KPCA) in developing a reliable Groundwater Quality Index (GWQI) for Qena, Egypt. Using ten hydrochemical parameters from seventy-three groundwater samples, we compare the performance of four kernel functions within the KPCA framework. The PCA-based GWQI classified 71.0% of samples as suitable for irrigation, closely aligning with the Wilcox Diagram classification (76.7%). In contrast, KPCA with linear, polynomial, sigmoid, and radial basis function kernels yielded suitability rates of 58.9%, 52.1%, 63.0%, and 58.9%, respectively. These values are consistent with USSL (53.4%) and Na% (53.4%) classifications. Notably, the sigmoid kernel in KPCA demonstrated stronger correlations with Key hydrochemical parameters, effectively capturing nonlinear data structures. These findings underscore the importance of accounting for nonlinearity in groundwater quality assessment and demonstrate the potential of KPCA to improve GWQI accuracy. This comparative analysis highlights KPCA’s superiority over PCA for nonlinear datasets, providing enhanced tools for groundwater management and more reliable quality evaluations.

Synthetic time series created from historical streamflow data are thought of as substitute events with a similar likelihood of recurrence to the real event. This technique has the potential to greatly reduce the uncertainty surrounding measured streamflow. The goal of this study is to create a synthetic streamflow model using a combination of Markov chain and Fourier transform techniques based on long-term historical data for the Nile River. First, the Markov chain’s auto-regression is applied, in which the data’s trend and seasonality are discovered and eliminated before applying the Pearson III distribution function. The Pearson III distribution function is substituted by a discrete Fourier transform (DFT) technique in the second approach. The applicability of the two techniques to simulate the streamflow between 1900 and 1999 is evaluated. The ability of the generated series to maintain the four most important statistical properties of the samples of monthly flows, i.e., the mean, standard deviation, autocorrelation lag coefficient, and cumulative distribution, was used to assess the quality of the series. The results reveal that the two techniques, with small differences in accuracy, reflect the monthly variation in streamflow well in terms of the three mentioned parameters. According to the coefficient of determination (R2) and normalized root mean square error (NRMSE) statistics, the discrete Fourier transform (DFT) approach is somewhat superior for simulating the monthly predicted discharge.

Good management of flash floods requires accurate estimation for both the hazard degrees and flood risk maps. Morphological parameters greatly affect the strength of flood’s hydrograph and accordingly the flash flood hazard degree. This study uses decision-making tools such as Multi Criteria Analysis (MCA) and/or Multi-Attribute Utility Theory (MAUT) to estimate the Gulf of Suez’s flash flood hazard degree, Sinai Peninsula, Egypt. The MAUT was applied successfully in estimating the hazard degrees of 56 basins in the coastal zone of the Suez Gulf. Based on the resulted correlation between all hydro-morphological parameters, only six equal weight parameters were selected and used to calculate the hazard degree. The results showed that, about 40 % of the 56 basins have high or moderately high hazard degree. Values of the correlation factors between the hydro-morphological parameters and the resulted hazard degrees are ranged between 0.813 for basin’s Sinuosity and 0.011 for Centroid Stream Slope. The weights of those parameters were non-linearly optimized to make the correlation of all chosen parameters with the hazard degree higher than or equal to the threshold value (0.6). Accordingly, percentage of basins with high or moderately high hazard degree have decreased to 32 %. Capability and validation of current model have been achieved using the damage information of some flood events in the last few decades. The comparison between current model’s scenarios and flood events showed that, all the affected basins that had high or moderated high hazard degree are highly coincide with the real cases.