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<p>La disponibilidad del recurso hídrico es de vital importancia para la planificación hidrológica, este depende de múltiples causas como la variabilidad climática y la cobertura vegetal. La precipitación, temperatura y evapotranspiración han sufrido cambios en diferentes partes de España. Para la cuenca del río Júcar además de otras cuencas en España se ha producido un significativo cambio en el valor promedio de las aportaciones hidrológicas desde 1980. El presente estudio se centra en analizar cómo han afectado los cambios en las variables climáticas a las aportaciones hidrológicas y en qué grado explican las reducciones registradas en las mismas. Adicionalmente se considera la posibilidad de que otros factores como cambios en la cobertura vegetal también puedan haber influido en la reducción de escorrentía. Para modelar la hidrología de la cuenca del Júcar se utilizó el modelo lluviaescorrentía HBV. El modelo fue calibrado para periodo anterior a la fecha de cambio considerada (1950-1979) y se simuló y adaptó para los años posteriores a 1980 (1980-2007) ajustando parámetros que puedan ser explicados por cambios en las condiciones de la cuenca. La zona más susceptible es la cabecera del Júcar donde se genera la mayor cantidad del recurso hídrico y se presenta la mayor disminución en las aportaciones. En la cuenca media del Júcar se presenta una menor disminución en las aportaciones, sin embargo en la cuenca baja del Júcar se registra un incremento en las aportaciones. Los cambios en las variables hidrológicas (precipitación y temperatura) explican una parte de los cambios en las aportaciones. Otras causas posibles como los cambios en la cobertura vegetal pueden completar la explicación del cambio en las aportaciones. Para probarlo se ha recalibrado el modelo hidrológico alterando solo uno o dos parámetros relacionados con esta condición.</p>

Abstract The article provides concise details about a hydraulic phenomenon known as the undular jump, that occurs under different conditions within different hydraulic constructions and can negatively influence on their operation and conditions. Therefore, the methods of modelling undular jump characteristics using physical and mathematical model are presented. Based on physical model of undular jump, the experimental data were obtained. Moreover, three mathematical models were considered and analyzed. The article gives the comparison of experimental data and data obtained by mathematical models. The advantages and disadvantages of considered mathematical models were specified. The novelty of work was in developing and improving mathematical model with taking into account the energy losses that are allowed to obtain the characteristics of undular jump with high accuracy.

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement).

Under arid conditions, where water availability is the limiting factor for plant survival, water balance models can be used to explain vegetation dynamics. [...]

The aim is to investigate the consistency or variability of catchment response over time and space and evaluate the predictive error caused by the impacts of climate variability on streamflow. For this purpose, both data- and top-down model-based analyses of the dynamic relation between rainfall and runoff for selected sub-catchments have been undertaken. Data analysis techniques (e.g. trend analysis, deconvolution and baseflow filtering) were used to assess the temporal and spatial variation in the hydrologic response characteristics for each site. The lumped conceptual rainfall-runoff model IHACRES CMD (Catchment Moisture Deficit) version is applied to the sub-catchments to assess the adequacy of the model response in representing the impact of weather patterns on streamflow. Several performance criteria have been used to evaluate the performance of the model in each calibration period using a multi-criteria approach. The IHACRES-3S (3 Storage) model is applied to assess low flow behaviour and capture the timing in the switch between baseflow and no flow periods. Rainfall-runoff model performance characteristics of each sub-catchment are quite related to their incident rainfall regime. Sub-catchments which are located in a lower rainfall regime show poor to average model performance. The reduction in performance in R^2 is due to the poor fitting to the peaks for both large and small streamflow events, with the model underestimating the highest flow peaks, and overestimating smaller peaks. Further work will be needed to assess observed data reliability and improve model performance in order to separate the impacts of climate variations and land use change on hydrological response. An appropriate model structure having a variable partitioning between quick and slow flow components is under consideration and techniques are being used to identify problematic periods and events with high error in the observational data.

Biodegradation of organic matter, including petroleum-based fuels and biofuels, can create undesired secondary water-quality effects. Trace elements, especially arsenic (As), have strong adsorption affinities for Fe(III) (oxyhydr)-oxides and can be released to groundwater during Fe-reducing biodegradation. We investigated the mobilization of naturally occurring As, cobalt (Co), chromium (Cr), and nickel (Ni) from wetland sediments caused by the introduction of benzene, toluene, ethylbenzene, and xylenes (BTEX) and ethanol mixtures under iron- and nitrate-reducing conditions, using in situ push-pull tests. When BTEX alone was added, results showed simultaneous onset and similar rates of Fe reduction and As mobilization. In the presence of ethanol, the maximum rates of As release and Fe reduction were higher, the time to onset of reaction was decreased, and the rates occurred in multiple stages that reflected additional processes. The concentration of As increased from <1 μg/L to a maximum of 99 μg/L, exceeding the 10 μg/L limit for drinking water. Mobilization of Co, Cr, and Ni was observed in association with ethanol biodegradation but not with BTEX. These results demonstrate the potential for trace-element contamination of drinking water during biodegradation and highlight the importance of monitoring trace elements at natural and enhanced attenuation sites.

The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.