Missing data is a very frequent problem in climatology, it influences on the quality of results that will afford in hydrological studies, as well as water resources management. This paper proposes a new imputation algorithm, based on the optimization of some regression methods, which are hot deck, k-nearest-neighbors imputation, weighted k-nearest-neighbors imputation, multiple imputation, linear regression and simple average method. The choice of these methods was justified by qualitative and quantitative statistical tests analysis. However, the reliability of obtained results depends mainly on percentage of missing data, choice of neighboring stations and data missingness mechanism which should be missing at random. During the study it was found that the most of stations in Soummam watershed don't have a good correlation because the large loss in rainfall data or the geology of watershed which gives a relationship between station position and rainfall variability. For this case, principal component analysis is applied on a set of stations; it showed a positive impact of altitude, latitude and longitude on correlation index between selected stations. The graphical analysis of the normal law on RMSE values, which were obtained by applying the proposed technique in several random cases of missingness, that are 4%, 8%, 12% and 16% respectively, it confirmed the validity and the performance of this approach.

The monthly precipitations obtained during 51 years of measurement in 24 stations of Soummam watershed in Algeria were analyzed to describe rainfall trends and aridity state of the area using statistical modeling. The choice of distribution laws was justified by comparing fitting results of different distributions laws used in literature reviews. Hence, the p values proved that Generalized Extreme Value, Weibull (3) and Logistic the distribution law are more adequate to analyze rainfall frequencies in different part of the watershed. The diagnostic given by Q‐Q plot, P‐P plot and survival regression curve showed the period of wetness and dryness in the northeastern and the southwestern part of the watershed, respectively. Moreover, the study given by the De Martonne index explains the consequences of climate change by a new form of aridity in the watershed between 1994 and 2018.Recommendations for Resource Managers The annual rainfall of Soummam watershed has a moderate and irregular rainfall distribution between 1967 and 2018. Using distribution function on monthly rainfall in each bioclimatic floor to analyze the trend of rainfall frequency gives a spatio‐temporal description of climate in the area. Fitting by Kolmogorov‐Smirnov test allows us to choose generalized extreme value, Weibull (3) and Logistic for modeling monthly rainfall variability in each part of the watershed. The diagnostic obtained by P‐P plot, Q‐Q plot and survival regression curve proved a change of aridity in the northeastern and southwestern part of the watershed between 1994 and 2018.

Forecasting climate events is crucial for mitigating and managing risks related to climate change; however, the problem of non-stationarity in time series (NTS) arises, making it difficult to capture and model the underlying trends. This task requires a complex procedure to address the challenge of creating a strong model that can effectively handle the non-uniform variability in various climate datasets. In this work, we use a daily standardized precipitation index dataset as an example of NTS, whereby the heterogeneous variability of daily precipitation poses complexities for traditional machine-learning models in predicting future events. To address these challenges, we introduce a novel approach, aiming to adjust the non-uniform distribution and simplify the detection time lags using autocorrelation. Our study employs a range of statistical techniques, including sampling-based seasonality, mathematical transformation, and normalization, to preprocess the data to increase the time lag window. Through the exploration of linear and sinusoidal transformation, we aim to assess their impact on increasing the accuracy of forecasting models. A strong performance is effectively observed by using the proposed approach to capture more than one year of time delay across all the seasonal subsets. Furthermore, improved model accuracy is observed, notably with K-Nearest Neighbors (KNN) and Random Forest (RF). This study underscores RF’s consistently strong performance across all the transformations, while KNN only demonstrates optimal results when the data have been linearized.

This paper presents a comparison between the black box Nonlinear Auto-Regressive with eXogenous inputs-Neural Network (NARX-NN) and the conceptual Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) Rainfall-Runoff models. The two models were applied on a small urban watershed to assess its response to fourteen hourly real storm events. The differences between the steps engaged in each model to reach the hydrograph were presented in detail. The estimation of the best parameters is carried out using a weighted average function during the calibration phase. A statistical evaluation was conducted to assess the model’s performance thereafter; a critical comparison was made to illustrate the differences and discuss the steps involved. The results indicate that both models successfully reflect the urban basin runoff. However, the NARX-NN outperforms in the testing phase owing to their strength generalization feature. The NARX-NN model has more strength to produce the shape bending of the hydrograph. Consequently, this model is better to highlight the curvatures resulting from the local peaks of rainfall.

In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find more hydro-climatic parameters, which show good performance in the assessment of runoff in different climatic regions. Accordingly, a dynamic and reliable model is proposed to estimate inter-annual rainfall-runoff in five climatic regions of northern Algeria. This is a new improvement of Ol’Dekop’s equation, which models the residual values obtained between real and predicted data using artificial neuron networks (ANN_s), namely by ANN₁ and ANN₂ sub-models. In this work, a set of climatic and geographical variables, obtained from 16 basins, which are inter-annual rainfall (IAR), watershed area (S), and watercourse (WC), were used as input data in the first model. Further, the ANN₁ output results and De Martonne index (I) were classified, and were then processed by ANN₂ to further increase reliability, and make the model more dynamic and unaffected by the climatic characteristic of the area. The final model proved the best performance in the entire region compared to a set of parametric and non-parametric water balance models used in this study, where the R²_Adj obtained from each test gave values between 0.9103 and 0.9923.