Hydrology

The Mediterranean coastal aquifer system of the Grombalia basin (NE Tunisia) offers immense potential as a source of fresh water for agriculture, industry, and drinking water supply. Nonetheless, due to its intricate hydrogeological characteristics and the prevailing issue of groundwater salinity, comprehending its groundwater system behavior becomes crucial for the effective and sustainable management of this aquifer system. Based on the hydrogeological characterization of the Grombalia basin, a novel 3D hydrogeological conceptual model was developed to enhance the understanding of its complex aquifer system. The integration of insights from geological, hydrogeological, hydrodynamic, and hydrochemical components facilitated the construction of the hydrogeological conceptual model. Although the model’s validity faced initial uncertainties due to spatial interpolation of lithological sequences, this study’s thorough and encompassing hydrogeological investigation overcame these limitations. As a result, a more informed comprehension of the aquifer system complexities was achieved. This study reveals that the basin is underlain by an extensive, cohesive Mio–Plio–Quaternary aquifer system. The model demonstrates vertical and lateral hydrogeological continuity between the Quaternary and underlying Mio–Pliocene deposits, enabling groundwater flow and exchange between these layers. Over-abstraction of the Mio–Plio–Quaternary aquifer system has led to a significant drop in piezometric levels and raised the risk of seawater intrusion. These findings emphasize the critical necessity of taking into account the interconnections among hydrogeological units to ensure sustainable groundwater management. The developed conceptual model offers a key tool for understanding the hydrodynamic functioning of the Grombalia aquifer system with a view toward guiding future groundwater management strategies. The application of this approach in the Grombalia basin suggests its potential applicability to other regional aquifers facing comparable challenges. Full article

Estimating intensity−duration−frequency (IDF) curves requires local historical information of precipitation intensity. When such information is unavailable, as in areas without rain gauges, it is necessary to consider other methods to estimate curve parameters. In this study, three methods were explored to estimate IDF curves in ungauged areas: Kriging (KG), Inverse Distance Weighting (IDW), and Storm Index (SI). To test the viability of these methods, historical data collected from 31 rain gauges distributed in central Chile, 35° S to 38° S, are used. As a result of the reduced number of rain gauges to evaluate the performance of each method, we used LOOCV (Leaving One Out Cross Validation). The results indicate that KG was limited due to the sparse distribution of rain gauges in central Chile. SI (a linear scaling method) showed the smallest prediction error in all of the ungauged locations, and outperformed both KG and IDW. However, the SI method does not provide estimates of uncertainty, as is possible with KG. The simplicity of SI renders it a viable method for extrapolating IDF curves to locations without data in the central zone of Chile. Full article

Microbially induced calcium carbonate precipitation (MICP) is an innovative biocementation technique that facilitates the formation of calcium carbonate within a pore network. Initially gaining prominence in the field of geotechnical engineering, MICP has attracted significant attention since its inception (the last three decades) and expanded its reach across various engineering disciplines. Examples include rock mechanics, geology and the oil and gas industry fields through the generation of rock-like specimens, and plugging of fractures, in civil and architectural engineering and material science for concrete repair, protection, and for self-healing of building materials, and in environmental engineering for the study of biomimetic materials. In response to this burgeoning interest, the current paper aims to present a comprehensive review of the main biochemical mechanisms underlying MICP (bacterial ureolytic activity, reactions duration and settling times, and chemical solution properties), their direct relevance to altering hydraulic and mechanical properties, both at the microscale and macroscale responses, and the precipitation mechanisms, particularly in relation to water resources and hydrology applications. Four main categories of relevant applications are identified, namely, the groundwater and soil remediation, the applications related to the generation of a low hydraulic conductivity barrier, those related to gaining cohesion, and the applications related to fluid flow studies in artificially generated porous media. Moreover, this comprehensive review not only aims to identify the existing applications of MICP within hydrological fields but also strives to propose novel and promising applications that can further expand its utility in this domain. Along with the investigation of the potential of MICP to revolutionize water resources and hydrology, it is imperative to delve deeper into its environmental implications to ensure sustainable and ecologically responsible implementation. Full article

The Ottawa River Watershed is a vast area that stretches across Ontario and Quebec and holds great importance for Canada’s people, economy, and collective history, both in the present and the future. The river has faced numerous floods in recent years due to climate change. The most significant flood occurred in 2019, surpassing a 100-year flood event, and serves as a stark reminder of how climate change impacts our environment. Considering the limitations of machine learning (ML) models, which heavily rely on historical data used during training, they may struggle to accurately predict such “non-experienced” or “unseen” floods that were not encountered during the training process. To tackle this challenge, our study has utilized a combination of numerical modeling and ML to create an integrated methodology. Indeed, a comprehensive dataset of river flow discharge was generated using a numerical model, encompassing a wide range of potential future floods. This significantly improved the ML training process to generalize the accuracy of results. Utilizing this dataset, a novel ML model called the Expanded Framework of Group Method of Data Handling (EFGMDH) has been developed. Its purpose is to provide decision-makers with explicit equations for estimating three crucial hydrodynamic characteristics of the Ottawa River: floodplain width, flow velocity, and river flow depth. These predictions rely on various inputs, including the location of the desired cross-section, river slope, Manning roughness coefficient at different river sections (right, left, and middle), and river flow discharge. To establish practical models for each of the aforementioned hydrodynamic characteristics of the Ottawa River, different input combinations were tested to identify the most optimal ones. The EFGMDH model demonstrated high accuracy throughout the training and testing stages, achieving an R² value exceeding 0.99. The proposed model’s exceptional performance demonstrates its reliability and practical applications for the study area. Full article

The present work aims to highlight the possibility of improving model performance by assimilating soil moisture information in the calibration and validation process. The Soil and Water Assessment Tool (SWAT) within QGIS, i.e., QSWAT, was used to simulate the hydrological processes within the test basin, i.e., Vosvozis River Basin (VRB) in NE Greece. The model calibration and validation were conducted via SWAT-CUP for a four-year period from 2019 to 2022, in three different ways, i.e., using the traditional calibration process with river flow measurements, using satellite-based soil moisture only in the calibration, and finally incorporating satellite-based soil moisture datasets and calibrating using simultaneously flow and soil moisture information. All modeling approaches used the same set of input data related to topography, land cover, and soil information. This study utilized the recently released global scale daily downscaled soil moisture at 1 km from the Soil Moisture Active Passive (SMAP) mission to generate soil moisture datasets. Two performance indicators were evaluated: Nash Sutcliffe (NS) and coefficient of determination (R²). Results showed that QSWAT successfully simulated river flow in VRB with NS = 0.61 and R² = 0.69 for the calibration process using river flow measurements at the outlet of VRB. However, comparing satellite-based soil moisture, NS and R² were considerably lower with an average derived from the 19 subbasins (NS = 0.55, R² = 0.66), indicating lower performance related to the simulation of soil moisture regime. Subsequently, introducing satellite-derived soil moisture as an additional parameter in the calibration process along with flow improved the acquired average soil moisture results of the 19 subbasins (NS = 0.85, R² = 0.91), while preserving the satisfactory performance related to flow simulation (NS = 0.57, R² = 0.66). Our work thus demonstrates how assimilating available satellite-derived soil moisture information into the SWAT model may offer considerable improvement in the description of soil moisture conditions, keeping the satisfactory performance in flow simulation. Full article

The complex contamination of groundwater near radioactive waste repositories by nitrates and actinides is a common problem for many nuclear fuel cycle facilities. One of the effective methods to remove nitrates and reduce actinide migration activity is bioremediation through the activation of native microbial communities by soluble electron donors and carbon sources. This work evaluated the effectiveness of using simple and complex electron donors to remove nitrate in the microbial community in an aquifer near the B2 storage of the Siberian Chemical Combine (Seversk, Siberia). The addition of sugar and milk whey led to the maximum efficiency of nitrate-ion removal and a decrease in the redox potential of the system, creating optimal conditions for the immobilization of actinide. Special attention was paid to the behavior of uranium, plutonium, neptunium, and americium under conditions simulating groundwater when sugar, acetate, and milk whey were added and when microbial metabolic products were formed. Neither microbial metabolites nor organic solutions were found to have a significant effect on the leaching of neptunium. At the same time, for plutonium, a decrease in yield was observed when rocks were treated with organic solutions were compared to groundwater treatment without them. Plutonium leaching is significantly affected by rock composition. In rocks with a low clay fraction content, its yield can reach 40%. At the same time, microbial metabolites can increase americium (Am) desorption from rocks with a low clay fraction content. Additionally, particle size analysis was performed using a step-by-step filtration approach, aiming to evaluate the risks that are associated with colloidal phase formation. It was shown that microbiological stimulation resulted in particle enlargement, substantially diminishing the presence of actinides in the form of dissolved or sub-50 nm nanoparticles. This outcome significantly reduced the potential for colloidal and pseudocolloidal transfer, thereby lowering associated risks. Full article

The topic of diffuse pollution is of particular interest from technical, scientific, and administrative management points of view. Diffuse pollution is defined as the contamination or chemical, physical, or biological alterations of environmental matrices caused by diffuse sources and not attributable to a single origin. In this study, various sources of diffuse pollution such as nitrates, pesticides, metals, and plastics were analysed. This was aimed at the implementation of a decision support system able to represent the state of environmental matrices degradation, with particular attention to water resources, and to make decisions evaluating similar environmental contexts. The potential of the developed system makes it possible to identify areas with the same environmental characteristics, referring to the various activities that create diffuse pollution and areas with the same pressure values on the environmental matrices. The system provides the political decision-maker with greater awareness of the environmental state, thus enabling him to apply more accurate land management policies. The created system, based on open-source software, which can be implemented with additional available data sources, is characterised by a data processing workflow that provides output information at the municipal level, so that it can be managed both by mayors and regional managers who are able to share the same information with all. Full article

The El-Minia district is a location of interest for future urban development. Using hydrochemistry and electrical resistivity studies, this work aimed to evaluate the groundwater potentiality and it’s suitable for various uses. The groundwater potential in the study area was evaluated based on 24 VESs (vertical electrical soundings), and its quality was determined based on the analyses of 57 groundwater samples. EC (salinity index), Na% (salt hazard), SAR (ratio of sodium adsorption), chloride risks, SSP (soluble sodium percentage), MH (magnesium hazard), and other indicators were used to determine whether the collected water samples were suitable for irrigation. Four layers in the study area are mentioned in the geoelectrical cross-sections that have been constructed. The first is made up of silt and clay from the Nile River, while the second is made up of sandy clay, which has a resistivity range of 15 to 32 Ohm.m and a range thickness of 2 to 68 m. Dry limestone makes up the third layer; its resistivity ranges from 1222 to 3000 Ohm.m and its thickness varies between 75 and 95 m. The Eocene aquifer in the research area is represented by the final layer, which has a thickness of more than 250 m and resistivity values that range from 602 to 860 Ohm.m. Most groundwater samples that were collected are safe for drinking; however, none of them are fit for home usage because of their extreme hardness. According to the SAR and US diagram, RSC, KR, and PI, most groundwater samples from the Pleistocene and Eocene aquifers are fit for irrigation. Full article

The expansion of urban areas and the increasing frequency and magnitude of intense rainfall events are anticipated to contribute to the widespread escalation of urban flood risk across the globe. To effectively mitigate future flood risks, it is crucial to combine a comprehensive examination of intense rainfall events in urban areas with the utilization of detailed hydrodynamic models. This study combines extreme value analysis techniques applied to rainfall data ranging from sub-hourly to daily durations with a high-resolution flood modelling analysis at the building level in the centre of Thessaloniki, Greece. A scaling procedure is employed to rainfall return levels assessed by applying the generalised extreme value (GEV) distribution to annual maximum fine-temporal-scale data, and these scaling laws are then applied to more reliable daily rainfall return levels estimated by means of the generalised Pareto distribution (GPD), in order to develop storm profiles with durations of 1 h and 2 h. The advanced flood model, CityCAT, is then used for the simulation of pluvial flooding, providing reliable assessments of building-level exposure to flooding hazards. The results of the analysis conducted provide insights into flood depths and water flowpaths in the city centre of Thessaloniki, identifying major flowpaths along certain main streets resulting in localised flooding, and identifying around 165 and 186 buildings highly exposed to inundation risk in the study area for 50-year storm events with durations of 1 h and 2 h, respectively. For the first time in this study area, a detailed analysis of extreme rainfall events is combined with a high-resolution Digital Terrain Model (DTM), used as an input into the advanced and fully featured CityCAT hydrodynamic model, to assess critical flowpaths and buildings at high flood risk. The results of this study can aid in the planning and design of resilient solutions to combat urban flash floods, as well as contribute to targeted flood damage mitigation and flood risk reduction. Full article

In this study, we analyzed the quality and the potential noncarcinogenic health risk of nitrate in groundwater in the El Milia plain, Kebir Rhumel Basin, Algeria. Moran’s I and the ordinary kriging (OK) interpolation technique were used to examine the spatial distribution pattern of the hydrochemical parameters in the groundwater. It was found that the hydrochemical parameters Ca, Cl, and HCO₃ showed strong spatial autocorrelation in the El Milia plain, indicating a spatial dependence and clustering of these parameters in the groundwater. The groundwater quality was evaluated using the entropy water quality index (EWQI). The results showed that approximately 86% of the total groundwater samples in the study area fall within the moderate groundwater quality category. The spatial map of the EWQI values indicated an increasing trend from the south-west to the northeast, following the direction of groundwater flow. The highest EWQI values were observed near El Milia city in the center of the plain. This spatial pattern suggests variations in groundwater quality across the study area, with potentially higher risks near the city center. The potential noncarcinogenic health risks associated with nitrate contamination in groundwater for adults and children through the drinking water pathway were assessed using the hazard quotient (HQ). The results revealed that approximately 5.7% of the total groundwater samples exceeded the HQ limit for adults, indicating potential health risks. Moreover, a higher percentage, 14.28%, of the total groundwater samples exceeded the HQ limit for children, highlighting their increased vulnerability to noncarcinogenic health hazards associated with nitrate contamination in the study area. Taking timely action and ensuring strict compliance with regulations in groundwater management are crucial for protecting public health, preserving the environment, addressing water scarcity, and achieving sustainable development goals. Full article

Droughts are projected to increase in intensity and frequency with the rise of global mean temperatures. However, not all drought indices equally capture the variety of influences that each hydrologic component has on the duration and magnitude of a period of water deficit. While such indices often agree with one another due to precipitation being the major input, heterogeneous responses caused by groundwater recharge, soil moisture memory, and vegetation dynamics may lead to a decoupling of identifiable drought conditions. As a semi-arid basin, the Limpopo River Basin (LRB) is a severely water-stressed region associated with unique climate patterns that regularly affect hydrological extremes. In this study, we find that vegetation indices show no significant long-term trends (S-statistic 9; p-value 0.779), opposing that of the modeled groundwater anomalies (S-statistic -57; p-value 0.05) in the growing season for a period of 18 years (2004–2022). Although the Mann-Kendall time series statistics for NDVI and drought indices are non-significant when basin-averaged, spatial heterogeneity further reveals that such a decoupling trend between vegetation and groundwater anomalies is indeed significant (p-value < 0.05) in colluvial, low-land aquifers to the southeast, while they remain more coupled in the central-west LRB, where more bedrock aquifers dominate. The conclusions of this study highlight the importance of ecological conditions with respect to water availability and suggest that water management must be informed by local vegetation species, especially in the face of depleting groundwater resources. Full article

To maximize crop production, reference evapotranspiration (ET₀) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET₀ and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET₀ estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET₀ using fewer climatic data are of critical importance. To estimate ET₀ with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET₀ with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R² = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET₀ accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET₀ using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R² Multan = 0.97, R² Skardu = 0.99, R² ISB = 0.98, R² Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET₀ estimation and suggest applying it in other parts of the world to validate model performance. Full article

We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of an evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources. Full article

Drought is a significant natural hazard with widespread socioeconomic and environmental impacts. This study investigated the long-term drought characteristics in a Mediterranean oak forest ecosystem using the Standardized Precipitation Evapotranspiration Index (SPEI) at various time scales and seasons. The analysis was based on a long-term time series dataset obtained from a meteorological station located at the University Forest of Taxiarchis in Greece. The dataset encompassed a substantial time span of 47 years of continuous monitoring, from 1974 to 2020. To accomplish the goals of the current research, the SPEI was calculated for 3, 6, 12, and 24-month periods, and drought events were identified. The Mann-Kendall (M-K) test was used to analyze the trends in drought severity and evaluate the trends significance. The results showed that shorter time scales (SPEI3 and SPEI6) were more efficient for identifying short-term droughts, while longer time scales (SPEI12 and SPEI24) were better for identifying less frequent but longer-lasting drought episodes. The analysis consistently revealed positive trends across all seasons and time scales, indicating an overall transition towards wetter conditions. Nearly all the data series for SPEI12 and SPEI24 exhibited statistically significant upward trends (wetter conditions) at a 95% confidence level. However, more intense events were detected during the recent decade using the seasonal analysis. Additionally, as the time scale expanded, the magnitude of these trends increased. The findings contributed to a better understanding of drought dynamics in Mediterranean oak forests and provided valuable information for forest management and climate change adaptation planning. Full article

The objective of this study was to develop and calibrate a photovoltaic-powered soil moisture sensor (SMS) for irrigation management. Soil moisture readings obtained from the sensor were compared with gravimetric measurements. An automated SMS was used in two trials: (i) okra crop (Abelmoschus esculentus) and (ii) chili pepper (Capsicum frutescens). All sensors were calibrated and automated using an Arduino Mega board with C++. The soil moisture data were subjected to descriptive statistical analysis. The data recorded by the equipment was correlated with the gravimetric method. The determination coefficient (R²), Pearson correlation (r), and root mean square error (RMSE) were adopted as criteria for equipment validation. The results show that our SMS achieved an R² value of 0.70 and an r value of 0.84. Notably, there was a striking similarity observed between SMS and gravimetric data, with RMSE values of 3.95 and 4.01, respectively. The global model developed exhibited highly efficient outcomes with R² (0.98) and r (0.99) values. The applicability of the developed SMS facilitates irrigation management with accuracy and real-time monitoring using digital data. The automation of the SMS emerges as a real-time and precise alternative for performing irrigation at the right moment and in the correct amount, thus avoiding water losses. Full article

This study investigated the hydrological properties of litter in different vegetation cover types, including Eucalyptus sp. plantation, Agroforestry, and Restoration Forest. The research focused on evaluating litter accumulation, composition, water holding capacity, and effective water retention. The results revealed variations in litter accumulation among the stands, and especially Eucalyptus sp., which had a higher proportion of branches compared to leaves. The water holding capacity of the litter differed among the stands. Agroforest and Restoration Forest showed higher litter water capacities than Eucalyptus sp. The composition and decomposition stage of the litter fractions influenced their water retention capabilities, with leaves exhibiting superior water retention. In contrast, branches had lower water absorption due to their hydrophobic nature. Despite these differences, the effective water retention, which indicates the ability of litter to intercept precipitation, was similar among the stands. The findings highlight the importance of considering litter composition and species-specific characteristics in understanding the hydrological functions of litter. This knowledge contributes to effective conservation and management strategies for sustainable land use practices and water resource management. Further research is recommended to expand the study’s scope to include a wider range of forest types and natural field conditions, providing a more comprehensive understanding of litter hydrological functions and their implications for ecosystem processes. Full article

Given that the primary cause of flooding in Ontario, Canada, is attributed to spring floods, it is crucial to incorporate temperature as an input variable in flood prediction models with machine learning algorithms. This inclusion enables a comprehensive understanding of the intricate dynamics involved, particularly the impact of heatwaves on snowmelt, allowing for more accurate flood prediction. This paper presents a novel machine learning approach called the Adaptive Structure of the Group Method of Data Handling (ASGMDH) for predicting daily river flow rates, incorporating measured discharge from the previous day as a historical record summarizing watershed characteristics, along with real-time data on air temperature and precipitation. To propose a comprehensive machine learning model, four different scenarios with various input combinations were examined. The simplest model with three parameters (maximum temperature, precipitation, historical daily river flow discharge) achieves high accuracy, with an R2 value of 0.985 during training and 0.992 during testing, demonstrating its reliability and potential for practical application. The developed ASGMDH model demonstrates high accuracy for the study area, with a significant number of samples having a relative error of less than 15%. The final ASGMDH-based model has only a second-order polynomial (AICc = 19,648.71), while it is seven for the classical GMDH-based model (AICc = 19,701.56). The sensitivity analysis reveals that maximum temperature significantly impacts the prediction of daily river flow discharge. Full article

The identification and characterization of barriers to river continuity are essential for the preparation of an inventory of hydraulic infrastructure. To this end, it is necessary to define the main identifying and characterizing elements of hydraulic infrastructures and descriptors of ecological continuity, with information that can characterize them from the point of view of their impact on the watercourse. Several authors have defined decision criteria for the removal of existing hydraulic structures in watercourses and their application, reinforcing the environmental benefits of the elimination of these hydraulic structures. In the present work, we proposed to develop a methodology for the evaluation of barriers in the Selho River (Guimarães Municipality, Northwest Portugal), elaborating an Environmental Condition Index (ECI) based on hydromorphological, socioeconomical, and ecological criteria, which allowed the identification of 43 weirs, of which 95% revealed quality inferior to Good. Following the application of a decision support methodology for the removal of hydraulic structures, it was possible to determine that 16 of the 43 weirs evaluated could be subject to removal, 26 would be under conditioned removal, and only 1 would be able to remain unchanged. Full article

The evaluation of weather forecast accuracy is of major interest in decision making in almost every sector of the economy and in civil protection. To this, a detailed assessment of Bologna Limited-Area Model (BOLAM) seven days fine grid 3 h predictions is made for precipitation, air temperature, relative humidity, and wind speed over a large lowland agricultural area of a Mediterranean-type climate, characterized by hot summers and rainy moderate winters (plain of Arta, NW Greece). Timeseries that cover a four-year period (2016–2019) from seven agro-meteorological stations located at the study area are used to run a range of contingency and accuracy measures as well as Taylor diagrams, and the results are thoroughly discussed. The overall results showed that the model failed to comply with the precipitation regime throughout the study area, while the results were mediocre for wind speed. Considering relative humidity, the results revealed acceptable performance and good correlation between the model output and the observed values, for the early days of forecast. Only in air temperature, the forecasts exhibited very good performance. Discussion is made on the ability of the model to predict major rainfall events and to estimate water budget components as rainfall and reference evapotranspiration. The need for skilled weather forecasts from improved versions of the examined model that may incorporate post-processing techniques to improve predictions or from other forecasting services is underlined. Full article