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Groundwater discharge is critical for maintaining river flow during dry seasons, especially in lowland areas. Despite its significance, groundwater resources have often been overlooked highlighting the need for comprehensive studies amidst growing pressure to develop new water resources. This study focuses on the Soyang River Basin, South Korea, including its ungauged northern regions, the nearby DMZ (Demilitarized Zone), using the physically based Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model. A three-year simulation was conducted to examine variable aquifer depth distribution patterns by assuming an inverse relationship between surface elevation and aquifer bottom depth. Three case studies (i.e., equal distribution, linear regression, and logarithmic regression) were evaluated and compared. The method to identity optimal aquifer depth distributions to enhance groundwater simulation accuracy in regions with significant topographical variation was incorporated. Groundwater levels at six monitoring sites showed that altitude-based variable aquifer depths outperformed the equal distribution case. The results showed strong agreement between simulated and observed values, particularly in the linear regression case with an R-squared statistic of 0.858 and Nash–Sutcliffe Efficiency index of 0.789, indicating that linear regression-based aquifer depth estimation can significantly improves long-term runoff modeling and groundwater simulation accuracy. The logarithmic regression case had the lowest relative peak error in peak flow. These findings highlight the importance of adjusting aquifer depth distributions in physically based hydrologic models to better reflect real-world conditions. Overall, this study contributes to advance groundwater modeling by integrating variable aquifer depth distributions into a physically based hydrologic model for large scale watersheds. [ABSTRACT FROM AUTHOR]

In this research, we demonstrate the effectiveness of a convolutional neural network (CNN) model, integrated with the ERA5-Land dataset, for accurately simulating daily streamflow in a mountainous watershed. Our methodology harnesses image-based inputs, incorporating spatial distribution maps of key environmental variables, including temperature, snowmelt, snow cover, snow depth, volumetric soil water content, total evaporation, total precipitation, and leaf area index. The proposed CNN architecture, while drawing inspiration from classical designs, is specifically tailored for the task of streamflow prediction. The model's performance, assessed during both the training and testing phases, demonstrates high accuracy, reflected quantitatively in metrics such as RMSE, MAPE, R2, and NSE. Notably, the model exhibits enhanced accuracy in predicting lower flow rates, particularly in autumn and winter, as evidenced by an average RMSE of 2.02 m3/s for flows below 13.8 m3/s. In contrast, the model's precision decreases in high flow rate scenarios, predominantly in spring and early summer. The implementation of forward feature selection (FFS) has further optimized the model, pinpointing total evaporation and volumetric soil water as key parameters, thus enabling a more efficient model with accuracy comparable to the initial, more complex version. This research underscores the practical utility of an image-based approach using CNN models for streamflow prediction. Moreover, the adoption of the freely available and universally accessible ERA5-Land dataset highlights its effectiveness as a valuable and cost-efficient tool for streamflow prediction. [ABSTRACT FROM AUTHOR]

Against the background of global warming, the losses caused by hydrological extreme events are becoming more serious. Understanding how to quantitatively attribute the trends of river discharge and extreme events will play an important role in climate change adaptation. The main objective of this study is to analyze recent trends in river discharge and annual maximum daily runoff in Chinese catchments and attribute them to the ongoing climate change, where possible. For that, 20 catchments in China, which are not influenced or influenced only slightly by water management, are chosen as study areas. Then, we use the long-term observational climate dataset GSWP3-W5E5 from ISIMIP3a as factual climate and a detrended climate dataset based on it as counterfactual climate to drive the hydrological model for quantification of climate change contribution to trends in mean and extreme runoff. Our analysis shows that the trends in annual discharge over the period 1961–2019 in eight catchments (all located in western China) represented by five stations in the Upper Yellow and Upper Yangtze, Kaqun station in the Tarim-Yeerqiang, Changdu station in the Lancangjiang and Xindi station in the Heihe can be attributed to climate change. As well, it is shown that climate change enhanced annual maximum daily runoff in the Upper Yellow and Upper Yangtze River basins. The results provide a new understanding of the degree to which observed changes in mean and extreme runoff were induced by the observed changes in climate, which may improve adaptation to climate change in China. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Land cover is a crucial factor in controlling rainfall–runoff processes in mountain basins. However, various anthropogenic activities, such as converting natural vegetation to agricultural or urban areas, can affect this cover, thereby increasing the risk of flooding in cities. This study evaluates the hydrological behavior of two mountain basins in Loja, Ecuador, under varying land use scenarios. El Carmen small basin (B1), located outside the urban perimeter, and Las Pavas small basin (B2), within the urban area, were modeled using HEC-HMS 4.3 software. The results highlight the significant influence of vegetation degradation and restoration on hydrological processes. In degraded vegetation scenarios, peak flows increase due to reduced soil infiltration capacity, while baseflows decrease. Conversely, the conserved and restored vegetation scenarios show lower peak flows and higher baseflows, which are attributed to enhanced evapotranspiration, interception, and soil water storage. The study underscores the importance of ecosystem management and restoration in mitigating extreme hydrological events and improving water resilience. These findings provide a foundation for decision-making in urban planning and basin management, emphasizing the need for comprehensive and multidisciplinary approaches to develop effective public policies. [ABSTRACT FROM AUTHOR]

Accurate and efficient streamflow simulations are necessary for sustainable water management and conservation in arid and semi-arid contexts. Conceptual hydrological models often underperform in these catchments due to the high climatic variability and data scarcity, leading to unstable parameters and biased results. This study evaluates the stability of the HBV model across seven sub-catchments of the Oum Er Rabia river basin (OERB), focusing on the HBV model regionalization process and the effectiveness of Earth Observation data in enhancing predictive capability. Therefore, we developed a nested cross-validation framework for spatiotemporal stability assessment, using optimal parameters from a donor-single-site calibration (DSSC) to inform target-multi-site calibration (TMSC). The results show that the HBV model remains spatially transferable from one basin to another with moderate to high performances (KGE (0.1~0.9 NSE (0.5~0.8)). Furthermore, calibration using KGE improves model stability over NSE. Some parameter sets exhibit spatial instability, but inter-annual parameter behavior remains stable, indicating potential climate change impacts. Model performance declines over time (18–124%) with increasing dryness. As a conclusion, this study presents a framework for analyzing parameter stability in hydrological models and highlights the need for more research on spatial and temporal factors affecting hydrological response variability. [ABSTRACT FROM AUTHOR]

The purpose of this study is to establish basic policies for managing the impacts of climate change on water resources using the integration of machine learning and land cover modeling. We predicted future changes in land cover within the water management and assessed its vulnerability to climate change. After confirming this vulnerability, we considered measures to improve climate resilience and presented future water resource parameters. We reviewed the finances available to promote climate projects, noting the major river management funds. The future project will serve as a stepping stone to promote climate resilience projects addressing water resource challenges exacerbated by future climate change. The study examined the results of analyzing changes in land cover maps due to climate change and assessed vulnerability in water management areas until 2050. According to the analysis results, the regulations for our study areas were set lower than those for other water management zones, resulting in a high rate of urbanization. Therefore, the climate resilience project in the water management area should be implemented first, despite the need for a long-term view in adapting to climate change. [ABSTRACT FROM AUTHOR]

Hydrologic models are the primary tools that are used to simulate streamflow drought and assess impacts. However, there is little consensus about how to evaluate the performance of these models, especially as hydrologic modeling moves toward larger spatial domains. This paper presents a comprehensive multi-objective approach to systematically evaluating the critical features in streamflow drought simulations performed by two widely used hydrological models. The evaluation approach captures how well a model classifies observed periods of drought and non-drought, quantifies error components during periods of drought, and assesses the models' simulations of drought severity, duration, and intensity. We apply this approach at 4662 U.S. Geological Survey streamflow gages covering a wide range of hydrologic conditions across the conterminous U.S. from 1985 to 2016 to evaluate streamflow drought using two national-scale hydrologic models: the National Water Model (NWM) and the National Hydrologic Model (NHM); therefore, a benchmark against which to evaluate additional models is provided. Using this approach, we find that generally the NWM better simulates the timing of flows during drought, while the NHM better simulates the magnitude of flows during drought. Both models performed better in wetter eastern regions than in drier western regions. Finally, each model showed increased error when simulating the most severe drought events. [ABSTRACT FROM AUTHOR]

Water resources in arid and semi-arid regions are constrained by several critical problems, including scarcity of water resources and extensive use of groundwater. Climate change and population growth will certainly result in a decline in water tables and degradation of groundwater quality. The purpose of this study is to create rainwater retention ponds in the Najran Valley located at the southern border of Saudi Arabia. Over the past decade, various researchers have reported a sharp decrease in the availability of water in the area. In addition to urban sprawl, excessive groundwater extraction is the primary cause of the decrease. Even though the valley receives a sufficient amount of rainfall throughout the year, there is no adequate rainwater management system in place. As a result of this combined water management issue, the authorities are seeking additional water storage options in addition to the Najran dam reservoir. To evaluate the flood characteristics of the region, an extensive GIS-based hydrological study is conducted. To estimate flood volumes, the critical flood-prone areas are identified and their catchments are calculated. To store rainwater generated by these catchments during rainfall events, a variety of suitable locations have been proposed for retention ponds. The construction of retention ponds would have numerous environmental benefits in addition to solving the problem of water scarcity. As a result of the study, the authorities will be able to implement a management strategy that maximizes the use of the region's water resources. [ABSTRACT FROM AUTHOR]

Uncertainty analysis is crucial before hydrological simulation to address model vulnerability in snow-covered mountainous river basins, ensuring effective soil conservation and water resource management planning. The present study is conducted on Sutlej River Basin, which is located in Western Himalayas and originated from Manasarovar Lake at Tibetans platue in China. The Soil and Water Assessment Tool (SWAT) model is calibrated; the parameter sensitivity and uncertainty of the SWAT were quantified using four optimization methods such as (i) sequential uncertainty fitting (SUFI-2), (ii) particle swarm optimization (PSO), (iii) generalized likelihood uncertainty estimation (GLUE), and (iv) parameter solution (ParaSol). The model is performed with calibration period (1982–2000) and validation period (2001–2013), using monthly observed streamflow at the Bhakra gauging station. The statistical performance criteria such as Nash–Sutcliffe efficiency coefficient (NSE) and coefficient of determination (R2), values showing all four models, performed very well in the model calibration and validation. During the calibration period, SUFI-2 and ParaSol algorithms show higher performance with NSE and R2 values of 0.82 and 0.83. In the validation period, SUFI-2 shows high accuracy with NSE and R2 values of 0.77 and 0.78, while ParaSol also performed well with NSE and R2 values of 0.71 and 0.76. These results highlighted the superior performance of SUFI-2 and ParaSol compared to the other two (PSO, GLUE) techniques. Further, the values of p-factor and r-factor reveal that ParaSol method performed poor during both the calibration period (p-factor: 0.25, r-factor: 0.32) and the validation period (p-factor: 0.26, r-factor: 0.38). These results suggest that while ParaSol was effective in optimizing parameter sets, it fails in providing accurate estimates of uncertainty during both model calibration and validation, whereas other three methods perform very well in the model uncertainty analysis. This study fills a significant research gap by offering guidance on adjusting sensitive parameters, reducing uncertainty in streamflow simulation, and addressing the challenges specific to hydrological modeling in mountainous river basins, which is different from the previous studies conducted in plain river basins. [ABSTRACT FROM AUTHOR]

Copyright of Tikrit Journal of Engineering Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the hydrological dynamics of the Pativilca Basin in the Southern Hemisphere using the SWAT (Soil and Water Assessment Tool) model. Seventy-seven watersheds across a mountainous region were analyzed using elevation data, land cover, soil type, and gridded meteorological products (RAIN4PE and PISCO) for hydrological simulations. Watershed delineation, aided by a Digital Elevation Model, enabled the identification of critical drainage points and the definition of Hydrological Response Units (HRUs). The model calibration and validation, performed using the SWAT-CUP with the SUFI-2 algorithm, achieved Nash–Sutcliffe Efficiency (NSE) values of 0.69 and 0.72, respectively. Cluster analysis categorized the watersheds into six distinct groups with unique hydrological and climatic characteristics. The results showed significant spatial variability in the precipitation and temperature, with pronounced seasonality influencing the daily flow patterns. The higher-altitude watersheds exhibited greater soil water storage and more effective aquifer recharge, whereas the lower-altitude watersheds, despite receiving less precipitation, displayed higher flows due to runoff from the upstream areas. These findings emphasize the importance of incorporating seasonality and spatial variability into water resource planning in mountainous regions and demonstrate the SWAT model's effectiveness in predicting hydrological responses in the Pativilca Basin, laying the groundwork for future research in mountain hydrology. [ABSTRACT FROM AUTHOR]

Precipitation partitioning in arid and semiarid environments is not well understood due to scanty precipitation, its temporal distribution, and the lack/absence of adequate measurements of the hydrometeorological components. Simulation methods have the potential to bridge the data gap, thereby providing a window to estimate the water balance components. The present investigation evaluates the water balance components of a typical watershed situated in the southeastern Mediterranean for the period 1979 through 2019 using daily meteorological data and a grid spacing of 250 m. Generated runoff results were commensurate with corresponding values obtained using the SWAT model. Computed groundwater recharge is also compatible with recharge values calculated using the chloride mass balance method. Results show that average runoff and groundwater recharge for the entire period was ⁓24 mm a−1 and 19 mm a−1, giving a precipitation ratio of 9.5% and 7.5%, respectively. Substantial interannual variability in the water balance components was observed during the study period which reflected the significant precipitation fluctuations typifying the Eastern Mediterranean. Results show that the period extending from 1998/1999 through 2018/2019 witnessed an 18% drop in annual precipitation, while surface runoff and groundwater recharge experienced a reduction of ⁓34% and ⁓67%, respectively. Although groundwater recharge is a complex function of numerous meteorological and geological factors, the NDVI can provide an excellent indicator of groundwater recharge in marginal Mediterranean environments. This is highly beneficial in areas where climate records are scanty or absent. The presented results emphasize the significant impacts of global warming and aridification on the future availability of water resources in the semiarid marginal climates in the Eastern Mediterranean and point out clearly that water resources in this area will become scarcer, leading to multiple security threats at national and regional levels. [ABSTRACT FROM AUTHOR]

Flood risk management is a critical task which necessitates flood forecasting and identifying flood source areas for implementation of prevention measures. Hydrological models, multi-criteria decision models (MCDM) and data-driven models such as the Artificial Neural Networks (ANN) have been used to identify flood source areas within a watershed. The aim of this study was to compare the results of hydrological modeling, MCDM and the ANN approaches in order to identify and prioritize flood source areas. The study results show that the classification results of the hydrological model and the ANN have a significant correlation. The correlation between the TOPSIS method with the hydrological model indicate no meaningful correlation. Since the ANN model has simulated the HEC-HMS classifications very accurately, it can be a good substitute for the hydrological models in watersheds with limited data. [ABSTRACT FROM AUTHOR]

This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time-series prediction across diverse catchments and compares its results against SWAT predictions. We found strong evidence of RF's better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [NSE] > 0.5), whereas SWAT generally yielded unsatisfactory results (NSE < 0.5) and tended to overestimate daily streamflow by up to 27% (PBIAS). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth. [ABSTRACT FROM AUTHOR]

The accessibility and deployment of complex hydrological models remain significant challenges in water resource management and research. This study presents a comprehensive workflow for converting Python-based hydrological models into web APIs, addressing the need for more accessible and interoperable modeling tools. The workflow leverages modern web technologies and containerization to streamline the deployment process. The workflow was applied to three distinct models: a GRACE downscaling model, a synthetic time series generator, and a MODFLOW groundwater model. The implementation process for each model was completed in approximately 15 min with a reliable internet connection, demonstrating the efficiency of the approach. The resulting APIs provide standardized interfaces for model execution, progress tracking, and result retrieval, facilitating integration with various applications. This workflow significantly reduces barriers to model deployment and usage, potentially broadening the user base for sophisticated hydrological tools. The approach aligns hydrological modeling with contemporary software development practices, opening new avenues for collaboration and innovation. While challenges such as performance scaling and security considerations remain, this work provides a blueprint for making complex hydrological models more accessible and operational, paving the way for enhanced research and practical applications in hydrology. [ABSTRACT FROM AUTHOR]

Evaluating flood risk though numerical simulations in areas where hydrometric and bathymetric data are scarcely available is a challenge. This is, however, of paramount importance, particularly in urban areas, where huge losses of human life and extensive damage can occur. This paper focuses on the 2–3 June 2023 event at Léogâne in Haiti, where the Rouyonne River partly flooded the city. Water depths in the river have been recorded since April 2022, and a few discharges were measured manually, but these were not sufficient to produce a reliable rating curve. Using a uniform-flow assumption combined with the Bayesian rating curve (BaRatin) method, it was possible to extrapolate the existing data to higher discharges. From there, a rainfall–runoff relation was developed for the site using a distributed hydrological model, which allowed the discharge of the June 2023 event to be determined, which was estimated as twice the maximum conveying capacity of the river in the measurement section. Bathymetric data were obtained using drone-based photogrammetry, and two-dimensional simulations were carried out to represent the flooded area and the associated water depths. By comparing the water depths of 21 measured high-water marks with the simulation results, we obtained a Kling–Gupta Efficiency (KGE) and Nash–Sutcliffe Efficiency (NSE) values of 0.890 and 0.882, respectively. This allows us to conclude that even when only scarce official data are available, it is possible to use field data acquired by low-cost methodologies to build a model that is sufficiently accurate and that can be used by flood managers and decision makers to assess flood risk and vulnerability in Haiti. [ABSTRACT FROM AUTHOR]

Introduction Actual evapotranspiration, one of the most important components of the hydrological cycle, causes 60% of precipitation to return to the atmosphere. This value increases up to 90% in dry and semi-arid areas. In recent years, with population growth, increasing water demand, and climate change, the importance of this phenomenon has doubled. Despite its importance, actual evapotranspiration remains largely unknown and its estimation by direct measurement methods is costly and time-consuming. In this regard, the Penman-Monteith method, the most accepted method for estimating reference evaporation and transpiration, also requires a lot of meteorological data. Despite the weakness of the conceptual model, using experimental methods to estimate evaporation and transpiration is still common due to its simplicity. In addition, examining the conceptual model and statistical methods without prejudice makes it possible to identify the influential variables and create experimental relationships compatible with the conceptual model. Materials and Methods The case study, with an area of 13 ha, is located in the southwest of Sicily (Italy), about five km from Castelvetrano. The landscape is flat and the soil type is relatively homogeneous. The main crops are olives (70% coverage), vineyards (24%), fruit trees (2.6%), and other garden products (3.4%). The plants are about 3.5 m tall and are arranged in a regular grid of five m by eight m (density of 250 plants per ha). The climate of the region is Mediterranean and the soil texture class, according to the USDA classification, is silty clay loam. In this research, one-hour meteorological data from 23 meteorological variables from the Sicily meteorological-agricultural station and actual evapotranspiration data extracted from the Eddy covariance method for the statistical period of 2009-2016 were used. Linear regression methods were used to investigate the relationships between the 24 variables and actual evapotranspiration. Integrated hierarchical clustering was used also to classify the 24 variables. Results and Discussion Evaluating the relationships between the 24 variables (23 meteorological variables and actual evapotranspiration) using the linear regression method led to the extraction of relationships between parameters in the form of a 24×24 matrix. In other words, to predict each variable (as a dependent variable), 23 relationships with other parameters (as independent parameters) were extracted. Then, the priority of the independent variables to predict each dependent variable was determined based on the correlation coefficient (R). The average of the 23 numerical ranks of an independent variable to predict other dependent variables indicates the degree of competence of that variable to predict other variables. The result of integrated hierarchical clustering with a 70% correlation is seven clusters. The members of cluster number one are different temperature variables (instantaneous, minimum, maximum, and average), cluster number two, rainfall variable (the only single member cluster), cluster number three, humidity variables (instantaneous, minimum, maximum, and average), cluster number four, pressure variables (station pressure and sea level pressure), cluster number five, variables of total solar radiation and evaporation and transpiration (the only cluster with non-identical members), cluster number six, different wind speed variables (instantaneous, minimum, maximum and average at the height of two and 10 m) and cluster number seven showed the wind direction variables. The summary of the classification results generally shows that the meteorological variables are independent except for the variables of the same name (such as temperature variables) all Variables with the same name were placed in a cluster, and the only variables with different names that were located in a cluster were total solar radiation and evaporation and transpiration. The representative of each cluster is the best predictor (based on rank) among the members of that cluster to predict other variables. Based on this, average temperature variables, rainfall, average relative humidity, sea level pressure, total solar radiation, maximum wind speed at a height of two m, and wind direction at a height of 10 m were determined as representatives of seven clusters. Also, the best predictor of these representatives was determined from inside and outside the cluster members. Based on the regression analysis, the best predictor of actual evapotranspiration with a correlation coefficient of more than 70% in total solar radiation. Instantaneous and minimum relative humidity variables with a correlation coefficient of about 50% (inverse relationship) took second and third place respectively to estimate actual evapotranspiration. The fourth and fifth ranks also belong to the average and maximum humidity with a correlation coefficient of about 49%. The independent variable of the duration of wetness of the leaves with a correlation coefficient of 40% has taken the sixth place. The characteristics of wind speed, temperature, wind direction, total annual rainfall, and pressure have the next ranks to estimate the actual evapotranspiration, respectively, with correlation coefficients of less than 30%. Conclusion In general, the high correlation between total solar radiation and actual evapotranspiration in a cluster indicates the key role of this meteorological variable in estimating evaporation and transpiration and is a justification for using methods based on energy balance to estimate this parameter. The high correlation between the estimation of actual evapotranspiration with the total solar radiation, considering the dependence of this variable on other climatic and hydrological variables, can be a useful point for use in watersheds lacking data and information. Also, the state of relative humidity ranks first and second respectively to predict other variables and actual evapotranspiration, indicating the key role of this variable in the case study. Contrary to some research about the key role of precipitation in the estimation of evaporation and transpiration in the Mediterranean climate, in this research no acceptable correlation was observed between the independent variable of precipitation and the dependent variable of actual evapotranspiration, although this issue may be related to the form of the equation. In this regard, instantaneous, minimum, average, and maximum relative humidity were ranked after total solar radiation. On the other hand, the total solar radiation in the estimation of the actual evapotranspiration with a correlation coefficient (71%) compared to the independent variable of relative humidity is in the first rank of the predictors, although it is ranked after the average and minimum relative humidity in the estimation of 23 meteorological variables. However, total solar radiation and relative humidity (average and minimum) were identified as two independent variables that are effective in estimating meteorological variables, especially actual evapotranspiration, and it is suggested that more research be done in watersheds with different climatic variations to discover the internal relationships of actual evapotranspiration and other climatic variables. [ABSTRACT FROM AUTHOR]

The interaction between the forest canopy and precipitation is a fundamental process for understanding the hydrological cycle in forests. Physical models have been applied to estimate canopy water interception, and their efficiency has been tested based on metrics used to assess hydrological models. For eucalyptus plantations in Brazil, more studies are needed on the canopy rainfall interception model. Thus, we calibrated the Gash model using two complete hydrological years of observation in a eucalyptus plantation in southeastern Brazil. The model's parametrization was conducted using 17 trees individually in different planting spacings (3 m × 2 m, 3 m × 3 m, and 3 m × 5 m). The average values of the model's parameters were taken to represent the forest, and the average parameters for each planting spacing were used to assess the model's performance according to the planting spacings. We used NSE, KGE, and Pbias statistical metrics to assess the model's performance. For individual trees and rainfall events, the model showed an average NSE and Pbias of 0.59 and 18.2%, respectively, meaning a "satisfactory" performance for eight trees and "poor" performance for nine trees. When the model was averaged for the entire forest and individual rainfall events were considered, the metrics were improved, being 0.643 for NSE and 8.2% for Pbias, indicating a "good" model performance, which was strengthened by an average KGE of 0.746. Regarding the model for the planting spacings, the best results were found for the 3.0 m × 2.0 m spacing ("a good performance"). For the other spacings, Pbias was higher than 15%, leading to inferior performance, but with the NSE and KGE compatible with "good" performance. The practical implications of our findings are significant, as they can be used to enhance the accuracy of models for a better understanding of the hydrological cycle in eucalyptus forests in Brazil, thereby contributing to more effective forest management and conservation. [ABSTRACT FROM AUTHOR]

In a context of climate change, flash‐floods are expected to increase in frequency. Considering their devastating impacts, it is primordial to safeguard the exposed population and infrastructure. This is the responsibility of crisis managers but they face difficulties due to the rapidity of these events. The focus of this study was to characterize the extent of the link between hydrologists and crisis managers. It also aimed to determine the limiting and the fostering factors to an effective integration of forecasting in crisis management during flash‐floods. This was achieved through an extensive and methodological study of available literature in selected platforms. The models encountered were characterized on multiple levels including the physical, geographical and crisis management level. The results revealed a limited link between the two involved parties with limiting factors such as the complexity of the modeling approach, the insufficient projection in the implications of operationality of the models proposed and the financial aspect. On the other hand, acknowledging the threat of flash‐floods and conducting cost–benefit‐analysis were pinpointed as fostering factors. This study showed to reconsider the forecasting methods employed, particularly, the integration of machine learning, and the needs of end‐user in these applications in a crisis management context. [ABSTRACT FROM AUTHOR]

Floods are one of the most common natural calamities and the disastrous action of flood becomes stringent when the floods are caused by dam failures. It is quite difficult to predict the failure of dam however the technique of dam break analysis provides an effective way to understand the flood wave generated due to assumed failure of dam. The present work focuses on dam break analysis of Ajwa dam constructed on river Surya, a tributary of river Vishwamitri. The work is divided into two parts. The first part consists of hydrological modeling of Vishwamitri basin using SWAT model and estimates the flood for 20-year return period and further calibrating and validating the model in SWATCUP 2019 using SUFI2 algorithm. The maximum flood calculated from SWAT model is taken as lateral inflow in dam break model setup. The breach parameters have been adopted as per the guidelines provided by Central Water Commission. Time of arrival of dam break flood at important locations downstream of Ajwa dam has been identified. The results reflect the potential of such a study to encompass the emergency action plans to safeguard the public and property in case of the possible flood event due to dam break. [ABSTRACT FROM AUTHOR]

Floods stand out as one of the most devastating environmental threats worldwide resulting in the tragic loss of human lives and significant damage to our essential infrastructure. This study focuses on creating floodplain maps for the two important reaches of Swat River in the Khyber Pakhtunkhwa Province of Pakistan, namely, Kalam–Khwazakhela and Khwazakhela–Chakdara reaches. The Advanced Land Observing Satellite Phased Array Type L-band Synthetic Aperture Radar (ALOS PALSAR) 12.5 m Digital Elevation Model (DEM) data has been used for this purpose. Furthermore, the sensitivity of the flood model was assessed for the Flood 2022 event, considering simulated flood depth, extent, and velocity in relation to various terrains derived from the 12.5-m ALOS PALSAR data. To estimate extreme flows for different return periods (2, 5, 10, 25, 50, and 100 years), the Log Normal (LN), Log Pearson III (LP3), and Generalized Extreme Value (GEV) distributions were employed for the frequency analysis. The GEV distribution turned out to be the best fit for modeling the Swat River for both the Chakdara and Khwazakhela gauge stations. To predict flood levels for the peak floods identified through frequency analysis and for the specific return periods the Hydrologic Engineering Center's River Analysis System (HEC-RAS) 2D simulations were performed. Subsequently the outcomes of this model were used to create floodplain maps using Geographic Information System Professional (ArcGIS Pro) software customized for various return periods. The analysis revealed a linear increase in flood inundation area with longer return periods. The floodplain maps developed hold significant importance for the governing authorities of the Swat region. These maps serve as essential tools for implementing proactive measures against potential infrastructure damage, thereby protecting against economic losses and enhancing public safety. This research effectively combines hydrological modeling and geospatial technology offering practical solutions for managing flood risks. It serves as a valuable guide for making well-informed decisions and promoting sustainable development in flood-prone areas. [ABSTRACT FROM AUTHOR]

This study uses a new dataset on gauge locations and catchments to assess the impact of 21st-century climate change on the hydrology of 221 high-mountain catchments in Central Asia. A steady-state stochastic soil moisture water balance model was employed to project changes in runoff and evaporation for 2011–2040, 2041–2070, and 2071–2100, compared to the baseline period of 1979–2011. Baseline climate data were sourced from CHELSA V21 climatology, providing daily temperature and precipitation for each subcatchment. Future projections used bias-corrected outputs from four General Circulation Models under four pathways/scenarios (SSP1 RCP 2.6, SSP2 RCP 4.5, SSP3 RCP 7.0, SSP5 RCP 8.5). Global datasets informed soil parameter distribution, and glacier ablation data were integrated to refine discharge modeling and validated against long-term catchment discharge data. The atmospheric models predict an increase in median precipitation between 5.5% to 10.1% and a rise in median temperatures by 1.9 °C to 5.6 °C by the end of the 21st century, depending on the scenario and relative to the baseline. Hydrological model projections for this period indicate increases in actual evaporation between 7.3% to 17.4% and changes in discharge between + 1.1% to –2.7% for the SSP1 RCP 2.6 and SSP5 RCP 8.5 scenarios, respectively. Under the most extreme climate scenario (SSP5-8.5), discharge increases of 3.8% and 5.0% are anticipated during the first and second future periods, followed by a decrease of -2.7% in the third period. Significant glacier wastage is expected in lower-lying runoff zones, with overall discharge reductions in parts of the Tien Shan, including the Naryn catchment. Conversely, high-elevation areas in the Gissar-Alay and Pamir mountains are projected to experience discharge increases, driven by enhanced glacier ablation and delayed peak water, among other things. Shifts in precipitation patterns suggest more extreme but less frequent events, potentially altering the hydroclimate risk landscape in the region. Our findings highlight varied hydrological responses to climate change throughout high-mountain Central Asia. These insights inform strategies for effective and sustainable water management at the national and transboundary levels and help guide local stakeholders. [ABSTRACT FROM AUTHOR]

The knowledge of factors that influence the waters present in the hydrographic basins is extremely important for efficient management, considering its multiple uses. In this regard, the present work aims to apply mathematical modeling tools, expressed by the HRI-Hydro Tools, the hydrological model of large basins and WARM-GIS Tools, to carry out the physical characterization, hydrological modeling, and the quantitative water balance, for the Jaguarão River hydrographic basin (JRhb). In two scenarios, monthly-based, the reference flow adopted was Q90. Further the water stress index (WSI) was applied for each section of the JRhb drainage network, with emphasis on the analysis of the influence of multiple uses of water in the basin, especially for irrigation. The water withdrawal index (WWI) was used to develop a diagnosis of the watershed level of scarcity, taking into account the annual water commitment, to improve the quantitative representation of water for the JRhb. The results of the hydrological modeling performed showed that, in general, the model presented a good performance both in the calibration and in the validation of the studied periods. The WSI representation showed that in the current scenario (realistic scenario) the basin has been suffering great water stress in the months that comprise the summer season (December, January, February, and March). In addition, the application of the WSI exposed the great influence of water use in the production of irrigated rice in this territory. This result was supported by WWI, which revealed sub-basins that were in a vulnerable state, requiring intensive management and major mitigation efforts. [ABSTRACT FROM AUTHOR]

Hydrological modeling can be challenging due to significant data requirements and computational complexities. Hydrological models must be sufficiently complex to describe physical processes yet simple enough to use. This paper describes the development of a simplified watershed-scale input–output model to simulate runoff quantity and quality during a storm event. This work builds upon an existing semi-distributed rainfall–runoff model by adding calculations for pollutant concentrations based on simplified mass balance equations. The model was tested against various watershed examples of increasing complexity. The results show the change in peak flow and pollutant concentration in different areas of the watershed, demonstrating the model's ability to account for the dynamics of runoff movement through the watershed. This paper advances watershed management by addressing data scarcity through the development of a simplified hydrological model that effectively incorporates spatial variability within a watershed while requiring minimal data input. [ABSTRACT FROM AUTHOR]

Hydrological modeling plays a vital role in water-resource management and climate-change studies in hyper-arid regions. In the present investigation, surface runoff was estimated by a Soil and Water Assessment Tool (SWAT) model for Wadi Al-Aqul, Saudi Arabia. The Sequential Uncertainty Fitting version 2 (SUFI-2) technique in SWAT-CUP was adopted for the sensitivity analysis, calibration, and validation of the SWAT model's components. The observational runoff data were scarce and only available from 1979 to 1984; such data scarcity is a common problem in hyper-arid regions. The results show good agreement with the observed daily runoff, as indicated by a Pearson Correlation Coefficient (r) of 0.86, a regression (R2) of 0.76, and a Nash–Sutcliffe coefficient (NSE) of 0.61. Error metrics, including the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), were notably low at 0.05 and 0.58, respectively. In the daily validation, the model continued to perform well, with a correlation of 0.76 and regression of 0.58. As a new approach, fitted parameters of daily calibration were incorporated into the monthly simulation, and they demonstrated an even better performance. The correlation coefficient (regression) and Nash–Sutcliffe were found to be extremely high during the calibration period of the monthly simulation, reaching 0.97 (0.95) and 0.73, respectively; meanwhile, they reached 0.99 (0.98) and 0.63 in the validation period, respectively. The sensitivity analysis using the SUFI-2 algorithm highlighted that, in the streamflow estimation, the Curve Number (CN) was found to be the most responsive parameter, followed by Soil Bulk Density (SOL_BD). Notably, the monthly results showed a higher performance than the daily results, indicating the inherent capability of the model in regard to data aggregation and reducing the impact of random fluctuations. These findings highlight the applicability of the SWAT model in predicting runoff and its implication for climate-change studies in hyper-arid regions. [ABSTRACT FROM AUTHOR]

Elucidating the impact of climate change on watershed hydrological model parameters is a fundamental scientific issue for analyzing parameter transferability and estimating future water balance components. Based on meteorological data from three global climate models (CNRM, IPSL and MRI) under the CMIP6 framework, the HBV hydrological model was used to simulate monthly runoff changes in the Ganjiang River basin from 2015 to 2100. The sensitivity and uncertainty of typical parameters controlling runoff simulation processes under climate change were quantified and evaluated. The major findings of this research are as follows: ① In scenarios where future precipitation either increases or decreases, the sensitivity of model parameters is generally higher relative to the stable precipitation scenario, with the soil module parameters (which calculate soil evapotranspiration and water content) being most sensitive. ② The amount and intra- annual distribution of precipitation could influence parameters′ sensitivity. ③ If future climate change would significantly alter the proportions of runoff components (fast and slow flow), particularly under future scenarios of increased precipitation, more attention should be paid to the response module parameters. [ABSTRACT FROM AUTHOR]

Hydrological analyses carried out based on precipitation in the Brazilian Legal Amazon (BLA) are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, data series with short periods and many gaps, especially at the daily scale, are a limitation in this region. In order to improve precipitation analysis, a non-parametric stochastic model based on Artificial Neural Networks (ANNs) was used to estimate daily precipitation in the BLA. For this purpose, 22 rain gauge stations were adopted and organized, taking into account the complete series and the seasonal periods (rainy and dry).The results obtained showed a good performance of the model, with ranges of MSE (0.0022–0.2665), MAPE (0.0083–1.5343) and RMSE (0.0017–0.0214), which characterize an acceptable estimate for the estimation daily precipitation, especially in those with a wetter climate and more frequent precipitation during the year, as is the case in those located in the Amazon Biome. However, in regions that suffer from droughts, such as the Amazon-Cerrado ecotone areas, the results were less satisfactory due to the greater recurrence of zeros in the historical series. The seasonal division into dry and rainy periods did not provide better estimates for the model, except for some rain gauge stations located at latitudes close to the equator. However, this study could support future research on the estimation of daily precipitation in the region. [ABSTRACT FROM AUTHOR]

The centuries-old history of dam construction, from the Saad el-Kafara Dam to global expansion in the 1950s, highlights the importance of these structures in water resource management. The Jucazinho Dam, built in 1998, emerged as a response to the scarcity of water in the Agreste region of Pernambuco, Brazil. After having less than 1% of its water storage capacity in 2016, the dam recovered in 2020 after interventions by the local water utility. In this context, the reliability of influent flow prediction models for dams becomes crucial for managers. This study proposed hydrological models based on artificial intelligence that aim to generate flow series, and we evaluated the adaptability of these models for the operation of the Jucazinho Dam. Data normalization between 0 and 1 was applied to avoid the predominance of variables with high values. The model was based on machine learning and employed support vector regression (SVM), random forest (RF) and artificial neural networks (ANNs), as provided by the Python Sklearn library. The selection of the monitoring stations took place via the Brazilian National Water and Sanitation Agency's (ANA) HIDROWEB portal, and we used Spearman's correlation to identify the relationship between precipitation and flow. The evaluation of the performance of the model involved graphical analyses and statistical criteria such as the Nash–Sutcliffe model efficiency coefficient (NSE), the percentage of bias (PBIAS), the coefficient of determination (R2) and the root mean standard deviation ratio (RSR). The results of the statistical coefficients for the test data indicated unsatisfactory performance for long-term predictions (8, 16 and 32 days ahead), revealing a downward trend in the quality of the fit with an increase in the forecast horizon. The SVM model stood out by obtaining the best indices of NSE, PBIAS, R2 and RSR. The graphical results of the SVM models showed underestimation of the flow values with an increase in the forecast horizon due to the sensitivity of the SVM to complex patterns in the time series. On the other hand, the RF and ANN models showed hyperestimation of the flow values as the number of forecast days increased, which was mainly attributed to overfitting. In summary, this study highlights the relevance of artificial intelligence in flow prediction for the efficient management of dams, especially in water scarcity and data-scarce scenarios. A proper choice of models and the ensuring of reliable input data are crucial for obtaining accurate forecasts and can contribute to water security and the effective operation of dams such as Jucazinho. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation estimates are a critical source of information for understanding and predicting hydrological processes at regional or global scales. Given the potential variability in the accuracy and reliability of these estimates, comprehensive performance assessments are essential before their application in specific hydrological contexts. In this study, six satellite-based precipitation products (SPPs), namely, CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were evaluated for their utility in hydrological modeling, specifically in simulating streamflow using the Variable Infiltration Capacity (VIC) model. The performance of the VIC model under varying flow conditions and timescales was assessed using statistical indicators, viz., R2, KGE, PBias, RMSE, and RSR. The findings of the study demonstrate the effectiveness of VIC model in simulating hydrological components and its applicability in evaluating the accuracy and reliability of SPPs. The SPPs were shown to be valuable for streamflow simulation at monthly and daily timescales, as confirmed by various performance measures. Moreover, the performance of SPPs for simulating extreme flow events (streamflow above 75%, 90%, and 95%) using the VIC model was assessed and a significant decrease in the performance was observed for high-flow events. Comparative analysis revealed the superiority of IMERG and CMORPH for streamflow simulation at daily timescale and high-flow conditions. In contrast, the performances of CHIRPS and PERSIANN were found to be poor. This study highlights the importance of thoroughly assessing the SPPs in modeling diverse flow conditions. [ABSTRACT FROM AUTHOR]

Climate change will cause alterations in the hydrological cycle, a topic of great relevance to the scientific community due to its impacts on water resources. Investigating changes in hydrological characteristics at the watershed level in the context of climate change is fundamental for developing mitigation and adaptation strategies against extreme hydrological events. This study aimed to analyze the impacts of climate change on flow and sediment production in the Puyango-Tumbes watershed. Projected climate data from CMIP6 were used, corrected through a bias adjustment process to minimize discrepancies between model data and historical observations, ensuring a more accurate representation of climate behavior. The analysis combined two representative climate change scenarios (SSP2-4.5 and SSP5-8.5) with two land use and land cover (LULC) scenarios: (a) an optimistic scenario with reduced anthropogenic effects (LULC_1985) and (b) a pessimistic scenario reflecting future impacts (LULC_2015). The SWAT model estimated future flow and sediment production for two periods (2035-2065 and 2070-2100), following model calibration and validation against the reference period 1981-2015 at three hydrometric stations: Pindo, Puyango, and El Tigre, located in Ecuador and Peru. The simulations revealed a significant increase in sediment generation under the pessimistic scenario SSP5-8.5, followed by SSP2-4.5, while lower sediment yields were observed in the optimistic scenarios. Even in the best-case scenario (optimistic SSP2-4.5), sediment yields remained substantially higher than the reference conditions. Additionally, higher flows were anticipated in some scenarios, with the El Tigre station in the lower watershed being the most affected area. These findings underscore the high probability of more frequent flooding events due to increased sediment yields and flow variability. The results highlight the urgent need for implementing adaptation measures, such as improved land use management and hydrological infrastructure, to enhance social resilience and mitigate the impacts of climate change in the watershed.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across key hydrological processes. By critically evaluating these techniques against traditional models, we highlight their superior ability to capture complex, nonlinear relationships and adapt to diverse environments. We further explore AI applications in precipitation forecasting, evapotranspiration estimation, groundwater dynamics, and extreme event prediction (floods, droughts, and compound events), showcasing their timely potential in addressing critical water-related challenges. A particular emphasis is placed on Explainable AI (XAI) and transfer learning as essential tools for improving model transparency and applicability, enabling broader stakeholder trust and cross-regional adaptability. The review also addresses persistent challenges, including data limitations, computational demands, and model interpretability, proposing solutions that integrate emerging technologies like quantum computing, the Internet of Things (IoT), and interdisciplinary collaboration. This review charts a strategic course for future research and practice by bridging AI advancements with practical hydrological applications. Our findings highlight the importance of embracing AI-driven approaches for next-generation hydrological modeling and provide actionable understandings for researchers, practitioners, and policymakers. As hydrology faces escalating challenges due to human-induced climate change and growing water demands, the continued evolution of AI-integrated models and innovations in data handling and stakeholder engagement will be imperative. In conclusion, the findings emphasize the critical role of AI-driven hydrological modeling in addressing global water challenges, including climate change adaptation, sustainable water resource management, and disaster risk reduction.

Suspended sediment loads (SSL) transported from the watershed of the Diyala River (WODR) are the most important and dangerous forms of sediment as they drift to the stream flow of the Diyala River and are then transferred to the reservoirs of Hemren Dam (HD) and Derbendikhan Dam (DD), which are located in the study area, affecting the capacity storage of the reservoirs and reducing electrical energy production. Therefore, it is necessary to apply a hydrological model that can simulate the SSL distribution in the WODR to enable decision-makers to develop an appropriate plan to solve the sediment problem. In WODR, the data of SSL are very rare, as sediment measurements have not been conducted for more than 40 years. Due to the lack of historical data for sediment values for the study area and the need to reduce uncertainty, sediment measurements were conducted from November 2022 to April 2023. The motivation of the present study is to study and address the limitations imposed on the Soil and Water Assessment Tool (SWAT) model during the estimation of SSL in the WODR that have scarce data and whose quality is inaccurate. The observed monthly flow data from two gauging stations, HD and DD, from January 2000 to April 2023 and suspended sediment concentration, which was measured in the field from November 2022 to April 2023, were used for calibration and validation of the model, respectively, using the Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm and SWAT-Calibration Uncertainty Procedures (CUP). Statistically, using the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), and percent of bias (Pbias) the performance of the model was evaluated, with good agreement between observed and simulated values for both stream flow and SSL. The results showed that the values of the SSL in the WODR from January 2000 to April 2023 were equal to 115.240 t/ha/yr. Sub-basins 5 and 12 have the highest SSL values of 15.125 t/ha/yr and 9.098 t/ha/yr, respectively, and the most important factor in SSL formation is the slope of the land, with a correlation coefficient (R2=0.94).

Rainfall has a dominant role in rainfall-runoff models, with the rendering of these models depending on the data accuracy and on the way that rainfall is spatially allocated. The research proposes a methodological framework where a genetic algorithm (GA)-based method responsible for the spatial distribution of gauge observations at the basin scale is coupled with the HEC-HMS hydrological model to produce simulated discharges of high accuracy. The custom-developed GA is used to divide a 2D space, adhering to specific criteria, into polygonal geometries that represent gauge zones of influences, similar to the Thiessen polygon method concept. A collection of vectorial polygonal areas, equivalent in number to the employed monitoring stations, is produced with the areal weights to be used for distributing the rainfall across the case study basin and subsequently to force the hydrological simulations. The generated gauge weights are validated for a different temporal precipitation event. The final outputs expressed through a series of statistical measures, clearly demonstrate the effectiveness of the specific methodology (e.g. R2 and Nash–Sutcliffe are larger than 0.83 and 0.73). The methodology can foster accurate hydrological simulations, especially in cases where there is a limited number of rainfall stations and corresponding observations. HIGHLIGHTS Geometric-based rain distribution introduces uncertainties in hydrological models.; Genetic algorithms (GAs) can produce alternate rain distribution shapes.; Automated coupling of GA with the hydrological model at a well-defined case basin.; Validation of the GA gauge weight method performance at different temporal events.; New perspectives in watershed's hydrological modeling with limited gauges.;

Abstract For managing water resources and operating reservoirs in dynamic contexts, accurate hydrological forecasting is essential. However, it is difficult to track complex hydrological time series with highly non-linear and non-stationary characteristics. The intricacy of the issue is further increased by the risk and uncertainty that are brought about by the dependence of several factors on the hydrological system’s output. To hydrologically model river outflows, a hybrid GARCH time series model technique has been applied in this study. To improve the precision of the proposed model estimation, this hybrid model employs a controllable fuzzy logic system to explore the impact of various input variables and an Archimedean detail function to account for the uncertainty in the dependence of the variables. The prediction error in this model is minimized by utilizing weighting factors and problem analysis parameters that are calculated using the water cycle algorithm. It was found that the minimum root-mean-square error values for the training and testing modeling stages are RMSE = 1.89 m and 1.92 m, respectively, by looking at the hydrological modeling results for a watershed of the Karaj dam. For extended lead (i.e., a 6-month rainfall lag), the weakest forecasting capacity was found. The modeling of the copula function using a higher percentage of answers in the confidence band and a smaller bandwidth resulted in less uncertainty for the estimation of the suggested model, according to the uncertainty analysis.

Railway infrastructure plays a critical role in transportation networks, and ensuring its integrity and resilience is of utmost importance. Culverts are vital components of railway tracks, providing drainage and water management to prevent structural damage and disruptions. Identifying suitable locations for culverts requires careful evaluation and consideration of various factors. A study using GIS techniques was conducted on an existing commercial railway track in the Eastern Desert of Upper Egypt to assess the effectiveness of existing culverts in preventing flash floods. The culvert suitability index map revealed that areas with high slopes and large drainage areas were more susceptible to water accumulation, indicating the need for culverts. The study also highlighted areas where culverts could be installed without significantly impacting existing infrastructure. It was recommended to install culverts in 27 locations along the track where they intersect with watercourses. Existing culverts covered just 93 watercourses, while 5 specialized culverts needed to be relocated. The findings have significant implications for railway engineering, as using GIS techniques streamlines the process of culvert location evaluation, saving time and resources. The systematic approach ensures culverts are installed in the most appropriate locations, minimizing flooding risks and ensuring the safety and efficiency of railway operations. HIGHLIGHT The research aims to develop a GIS-based methodology for assessing culvert locations on Egyptian railways, specifically the Qena–Safaga Track. The methodology aims to improve decision-making and infrastructure development by enhancing efficiency and accuracy. It addresses traditional methods and uses GIS techniques for railway engineering.;

The water ecosystem food (WEF) nexus duly acknowledges the complex interdependencies among water, ecosystems, and food production, underscoring nature based solutions (NBS) as an efficacious strategy for navigating these interconnections. In this research, four different NBS (terraces, riparian forest, livestock management and agro ecological practices) were assessed in terms of their impact to WEF nexus. The Karst-SWAT and the one-dimensional integrated critical zone (1D-ICZ) models were used to simulate the impact of NBS on water quantity and quality as well as on soil ecosystem services of Koiliaris River Basin, which serves as an illustrative example of a basin that has experienced severe soil and biodiversity degradation. The Karst-SWAT model showed that a combination of NBS of terraces and riparian forest can reduce soil erosion and the sediment load by 97%. The 1D-ICZ model successfully simulated the soilplant-water system and showed that agro ecological practices affect biomass production, carbon and nutrient sequestration, soil structure and geochemistry [ABSTRACT FROM AUTHOR]

The hydrological model Soil Water Assessment Tool (SWAT) is widely used in water resources management worldwide. It is also used to simulate catchment hydrology in high‐mountainous regions where glaciers play an important role. However, SWAT considers glaciers in a simplistic way. Although some efforts were done to overcome this limitation, there is no official version available that considers glaciers adequately. This strongly impairs its applicability in glacierized catchments. In this technical note, we propose a novel version of the traditional SWAT, called SWAT‐GL, which introduces (1) a mass balance module and (2) a glacier evolution routine to represent dynamic glacier changes. Mass balance calculations are based on a conceptual degree‐day approach, similar to the snow routine implemented in SWAT. Glacier evolution is realized using the delta‐h (∆h) parameterization, which requires a minimum of data and is thus suitable in data‐scarce regions. The approach allows users to simulate spatially distributed glacier changes. Annual mass balance changes are translated to distributed ice thickness changes depending on the glacier elevation. We demonstrate how SWAT‐GL is technically integrated into SWAT and how glaciers are merged with the existing spatial units. Model code and test data are freely accessible to promote further model development efforts and a wide application. Ultimately, SWAT‐GL aims to make SWAT easily applicable in glacierized catchments without the need of additional tools. [ABSTRACT FROM AUTHOR]