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This study presents a comprehensive comparison between traditional hydrological models and advanced machine learning (ML) techniques in predicting streamflow dynamics. Traditional models, namely the Soil and Water Assessment Tool (SWAT) and Génie Rural à 4 Paramètres Journalier (GR4J), are juxtaposed against ML models, including Random Forest (RF), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Both SWAT and GR4J demonstrated commendable performance, with GR4J displaying marginally superior predictive accuracy, evidenced by its tighter RMSE values. In the realm of ML, RF exhibited exceptional prowess in integrating diverse climatic features, especially in a scenario integrating comprehensive meteorological data. ANN showcased consistent performance across different input scenarios, emphasising its robustness. LSTM and BiLSTM, tailored for time series data, underscored the importance of precipitation's temporal dynamics in streamflow predictions. A notable revelation is the significance of choosing appropriate input data, with certain scenarios outperforming others based on the amalgamation of meteorological parameters. The flow duration curve (FDC) analysis further highlighted the model capabilities, with RF and BiLSTM excelling in capturing extreme flows, while traditional models resonated more with medium flow regimes. This research offers vital insights for hydrologists and decision-makers, aiding in informed model selection for streamflow predictions. [ABSTRACT FROM AUTHOR]

Floods are a widespread natural disaster that occur in most areas of the world, except for the polar regions. To minimize the damage caused by floods, effective management strategies and policies must be implemented. Petra and Wadi Musa areas are prone to floods, which happen every 2–3 years and result in significant harm to both lives and properties. To address this issue, a composite hazard and vulnerability index is commonly utilized to evaluate flood risk and guide policy formation for flood risk reduction. These tools are efficient and cost-effective in generating accurate results. Accordingly, the present study aims to determine the morphological and hydrometeorological parameters that affect flash floods in Petra catchment area and to identify high-risk zones using GIS, hydrological, and analytical hierarchy (AHP) modeling. Nine factors, including Elevation (E), Landuse/Landcover LULC, Slope (S), Drainage density (DD), Flood Control Points (FCP) and Rainfall intensity (RI), which make up the six risk indices, and Population Density (PD), Cropland (C), and Transportation (Tr), which make up the three vulnerability indices, were evaluated both individually and in combination using AHP in ArcGIS 10.8.2 software. These parameters were classified as hazard and vulnerability indicators, and a final flood map was generated. The map indicated that approximately 37% of the total area in Petra catchment is at high or very high risk of flooding, necessitating significant attention from governmental agencies and decision-makers for flood risk mitigation. The AHP method proposed in this study is an accurate tool for flood mapping that can be easily applied to other regions in Jordan to manage and prevent flood hazards. [ABSTRACT FROM AUTHOR]

This study investigates the intricate role of spatial variability in rainfall (SVR) concerning flood characteristics and its impact on refining flood prediction models. A spatial variability index was used to classify rainfall events into two categories: spatially homogeneous (Class A) and heterogeneous (Class B). The analysis of historical flood events suggests that the SVR influences flood peaks. This research introduces a novel approach to assess SVR's role in calibrating hydrological models, subsequently improving model selection. By separately calibrating Class A and B events within both lumped and distributed models, the models yield superior results compared to the conventional approach. For the catchments considered, the lumped models demonstrated heightened performance for Class A events, while the distributed models outperformed in Class B events. This study underscores not only the influence of SVR on flood dynamics but also the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy. [ABSTRACT FROM AUTHOR]

The Hindu Kush Karakoram Himalaya region, an important global water unit, is the origin of 10 major river basins. The Upper Indus basin, a part of this region, is a primary water source for the downstream population and is primarily fed by snow and glacier melt. Four semi-distributed conceptual hydrological models were used to understand this basin's hydrological responses. As the observed data was unavailable, global gridded precipitation and discharge data from the Global Flood Awareness System (GloFAS) were used to set up the model at the catchment outlet. Degree-day factor (DDF)-based snow and glacier modules are incorporated into the model structure to measure melt contribution. The results showed very good performance of all four models with Nash-Sutcliffe efficiency > 0.75, with the GR4J model exhibiting superior performance. The dominance of snow and glacier melt during summer monsoon affects the peak discharge and offers insights for investigations at the sub-basin scale. [ABSTRACT FROM AUTHOR]

Hydrological simulation in karstic areas is a hard task due to the intrinsic intricacy of these environments and the common lack of data related to their geometry. Hydrological dynamics of karstic sites in Mediterranean semiarid regions are difficult to be modelled mathematically owing to the existence of short wet episodes and long dry periods. In this paper, the suitability of an open-source SWAT method was checked to estimate the comportment of a karstic catchment in a Mediterranean semiarid domain (southeast of Spain), which wet and dry periods were evaluated using box-whisker plots and self-developed wavelet test. A novel expression of the Nash–Sutcliffe index for arid areas (ANSE) was considered through the calibration and validation of SWAT. Both steps were completed with 20- and 10-year discharge records of stream (1996–2015 to calibrate the model as this period depicts minimum gaps and 1985–1995 to validate it). Further, SWAT assessments were made with records of groundwater discharge and relating SWAT outputs with the SIMPA method, the Spain's national hydrological tool. These methods, along with recurrent neural network algorithms, were utilised to examine current and predicted water resources available to supply urban demands considering also groundwater abstractions from aquifers and the related exploitation index. According to the results, SWAT achieved a "very good" statistical performance (with ANSE of 0.96 and 0.78 in calibration and validation). Spatial distributions of the main hydrological processes, as surface runoff, evapotranspiration and aquifer recharge, were studied with SWAT and SIMPA obtaining similar results over the period with registers (1980–2016). During this period, the decreasing trend of rainfalls, characterised by short wet periods and long dry periods, has generated a progressive reduction of groundwater recharge. According to algorithms prediction (until 2050), this declining trend will continue reducing groundwater available to meet urban demands and increasing the exploitation index of aquifers. These results offer valuable information to authorities for assessing water accessibility and to provide water demands in karstic areas. [ABSTRACT FROM AUTHOR]

The physical river characteristics datasets described here provide spatially consistent information to support hydrological and inundation modelling at a 1 km × 1 km resolution across the United Kingdom, on the British National Grid. The datasets of physical river characteristics provide gridded datasets (outflow drainage directions, catchment areas, widths of bankfull rivers and depths of bankfull rivers) and a comma‐separated table of NRFA (National River Flow Archive) gauging station locations. These datasets are derived from a range of sources: outflow drainage directions, catchment area and bankfull river widths are derived from existing higher resolution datasets, whereas bankfull river depths were harder to source and instead are derived from sparse historical measurements. The new gridded datasets provide a derived value for each UK land cell on the British National Grid (BNG). The comma‐separated NRFA gauging station locations table provides the most appropriate locations of 1,499 river flow gauging stations on the 1 km resolution grids, together with the approximate error in the 1 km × 1 km gridded delineation of the upstream catchment area. This article explains how UK‐wide 1‐km grids of these variables were estimated, their format and how to use them. The data are available from the Environmental Information Data Centre (EIDC). [ABSTRACT FROM AUTHOR]

Abstract The physical river characteristics datasets described here provide spatially consistent information to support hydrological and inundation modelling at a 1 km × 1 km resolution across the United Kingdom, on the British National Grid. The datasets of physical river characteristics provide gridded datasets (outflow drainage directions, catchment areas, widths of bankfull rivers and depths of bankfull rivers) and a comma‐separated table of NRFA (National River Flow Archive) gauging station locations. These datasets are derived from a range of sources: outflow drainage directions, catchment area and bankfull river widths are derived from existing higher resolution datasets, whereas bankfull river depths were harder to source and instead are derived from sparse historical measurements. The new gridded datasets provide a derived value for each UK land cell on the British National Grid (BNG). The comma‐separated NRFA gauging station locations table provides the most appropriate locations of 1,499 river flow gauging stations on the 1 km resolution grids, together with the approximate error in the 1 km × 1 km gridded delineation of the upstream catchment area. This article explains how UK‐wide 1‐km grids of these variables were estimated, their format and how to use them. The data are available from the Environmental Information Data Centre (EIDC).

Hydrological models can benefit from soft calibration, a process by which the proper simulation of hydrological variables is proved while or before addressing hard calibration. Soft calibration reduces the probability of obtaining a statistically accurate but unrealistic model. However, it requires soft data, which is often hard to acquire or unavailable. This work presents HydRoVars, an R tool developed to facilitate the estimation of data which can be implemented in a soft calibration procedure. It allows us to estimate two key hydrological indices (the runoff coefficient and baseflow index) and weather-related variables at the catchment scale for one or numerous basins. The runoff coefficient is calculated automatically from precipitation and streamflow datasets. Groundwater contribution is estimated through a semi-automatic process based on a baseflow filter which considers hydrogeological properties. Modellers would benefit from incorporating soft calibration in their calibration procedures, and this tool might help to estimate these relevant hydrological variables in their modelled area. The tool has been tested in 19 subbasins of the Tagus River basin (Spain) located in different geological regions. In the test cases, we demonstrate the usefulness of this tool to improve the model representation and gain an understanding of the catchments' hydrology. HIGHLIGHTS An R tool has been developed to collect weather and hydrological variables.; It allows estimating the runoff coefficient, and the baseflow index at the catchment scale.; Runoff coefficient can be automatically obtained for multiple basins.; Guidance is provided for a realistic groundwater contribution estimation.; Its usefulness has been tested in the Upper Tagus River basin (Spain).;

This study integrated climate projections from five global climate models (GCMs) into the soil and water assessment tool to evaluate the potential impact of climate alterations on the Rietspruit River sub-basin under two representative concentration pathways (RCP4.5 and 8.5). The model's performance was evaluated based on the coefficient of determination (R2), percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE), probability (P)-factor and correlation coefficient (R)-factor. Calibration results showed an R2 of 0.62, NSE of 0.60, PBIAS of 20, P-factor of 0.86 and R-factor of 0.91, while validation produced an R2 of 0.64, NSE of 0.61, PBIAS of 40, P-factor of 0.85 and R-factor of 1.22. Precipitation is predicted to increase under both RCPs. Maximum temperature is projected to increase under both RCPs, with a major increase in the winter months. Minimum temperatures are projected to decrease under RCP4.5 in the near (−0.99 °C) and mid (−0.23 °C) futures, while the far future is projected to experience an increase of 0.14 °C. Precipitation and temperature changes correspond to increases in streamflow by an average of 53% (RCP4.5) and 47% (RCP8.5). These results indicate a need for an integrated approach in catchment water resource management amid potential climate and land use variations. HIGHLIGHTS An ensemble of five best-performing GCMs (MiroC5, CanESM2, SHMI-ESM, CSIRO and NorESM2) was employed.; P-factor and R-factor were used to assess model uncertainty particularly due to anthropogenic land use changes.; During winter, maximum and minimum temperatures are projected to increase and decrease, respectively.; The projected increase in streamflow seems to be aggravated by continued land use changes.;

This paper describes a case study of continuous hydrologic modeling in the Brahmani River basin in India using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC–HMS). The soil moisture algorithm (SMA) was employed as loss method for continuous simulations in the thirteen sub-basins. The sensitivity analysis showed that soil storage, tension storage and soil percolation are most sensitive parameters. The model performance was assessed for calibration period of 15 years (1990–2004) and validation period of 12 years (2005–2016), using statistical and visual evaluation techniques. During the calibration period, the observed and simulated values showed a good fit, with R2 ranging from 0.67 to 0.74, NSE from 0.66 to 0.72, and PBIAS from −8.5 to −9.5%. Similar performances are obtained during the validation period, with R2 ranging from 0.62 to 0.70, NSE from 0.60 to 0.68, and PBIAS from −2.5 to 21.4%. The results of the SMA procedure employed in the HEC-HMS model give valuable information for flood control and water supply management in the Brahmani Basin and similar other regions in Eastern India. [ABSTRACT FROM AUTHOR]

The paper investigates the effects of climate change on riverine ecosystems in the Upper Arno River basin, adopting a hybrid methodology relating the changes in streamflow to its related water ecosystem services. Twenty hydrological models were run using the Soil and Water Assessment Tool (SWAT), under their respective climate model and emission scenario. The streamflow variable obtained was analysed according to the method of Indicators of Hydrologic Alteration (IHA). The development of spatial maps of IHA allowed us to represent the spatial of the results according to the three river subbasins studied, with a focus on the presence of hotspots. Using IHA allowed to capture of the streamflow quantity and dynamics, through groups of magnitude, timing and rates, where the potential effect could be then linked to the concept of water ecosystem services. Finally, the results obtained were presented as a proxy that can be integrated into the existing Driver, Pressure, State, Impact, Response (DPSIR) management framework to eventually move towards an ecosystem‐based management methodology. The main results, induced from the Mann–Kendall trend analysis of the IHA, suggested that provisioning and regulating services are to be affected by climate change where the quantity of flows shows a decreasing trend under several emission scenarios, in addition to an intensification of the extreme events. This shift is accompanied by a change in the dynamics of flow, which holds the potential of affecting habitat suitability. With the emergence of studies directed towards ecosystem‐based management, the study refers to well‐established hydrological modelling to evaluate climate change as a driver and provide screening results to ensure a proactive response planning that converges with the idea of ecosystems. [ABSTRACT FROM AUTHOR]

Limited availability of hydrometeorological data and lack of data sharing practices have added to the challenge of hydrological modelling of large and transboundary catchments. This research evaluates the suitability of latest near real-time global precipitation measurement (GPM)-era satellite precipitation products (SPPs), IMERG-Early, IMERG-Late and GSMaP-NRT, for hydrological and hydrodynamic modelling of the Brahmaputra Basin. The HEC-HMS modelling system was used for the hydrological modelling of the Brahmaputra Basin, using IMERG-Early, IMERG-Late, and GSMaP-NRT. The findings showed good results using GPM SPPs for hydrological modelling of large basins like Brahmaputra, with Nash–Sutcliffe efficiency (NSE) and R2 values in the range of 0.75–0.85, and root mean square error (RMSE) between 7000 and 9000 m3 s−1, and the average discharge was 20611 m3 s−1. Output of the GPM-based hydrological models was then used as input to a 1D hydrodynamic model to assess suitability for flood inundation mapping of the Brahmaputra River. Simulated flood extents were compared with Landsat satellite-captured images of flood extents. In critical areas along the river, the probability of detection (POD) and critical success index (CSI) values were above 0.70 with all the SPPs used in this study. The accuracy of the models was found to increase when simulated using SPPs corrected with ground-based precipitation datasets. It was also found that IMERG-Late performed better than the other two precipitation products as far as hydrological modelling was concerned. However, for flood inundation mapping, all of the three selected products showed equally good results. The conclusion is reached that for sparsely gauged large basins, particularly for trans-boundary ones, GPM-era SPPs can be used for discharge simulation and flood inundation mapping. [ABSTRACT FROM AUTHOR]

This study employs a comprehensive multi-objective efficiency index (EI) to assess urban flood mitigation strategies. The EI enables the simple interpretation of a mitigation strategy's efficiency with a value range between −1 (low efficiency) and 1 (high efficiency), which represents a practical communication tool for decision makers, engineers, and researchers. This was tested at the study site of Feldbach (Austria) with an integrated 1D–2D urban flood model and a distributed hydrological model. A total of 112 scenarios were analysed for six mitigation strategies, which were built from three future challenge scenarios, two observed heavy storm events, and two hydrological pre-conditions. For the given study site, the analysis identifies mitigation strategies implemented in rural boundary areas as the most effective. A novel aspect of this study is the consideration of the urban water balance change, highlighting its impact on the EI. The analysis highlights the importance of analysing each relevant process separately to determine the EI in order to understand why a mitigation strategy is more or less efficient. [ABSTRACT FROM AUTHOR]

During the last two decades, a great number of global products about evapotranspiration ($E$ E) and terrestrial water storage ($S$ S) have been released. This has led to numerous combinations for describing catchment water balance, and hence determining an appropriate combination has become an important issue. The main objective of this study is to evaluate various global products of $E$ E and $S$ S at the catchment scale and determine the most appropriate combination, taking two Korean catchments as examples. For $E$ E , we evaluated global evapotranspiration (Global ET), Global Land Evaporation Amsterdam Model (GLEAM), and Penman–Monteith–Leuning (PML). For $S$ S , six individual datasets of Gravity Recovery and Climate Experiment (GRACE) were evaluated, with Global Land Data Assimilation System (GLDAS) products used for comparison. We found that the performance is sensitive to the choice of $E$ E product while various $S$ S products displayed minimal differences in results. Based on four evaluation criteria, the combination of PML and GRACE-SH-GFZ is suggested as the most suitable pair. For the period between 2003 and 2013, these data exhibit noteworthy trends of increasing $E$ E , offset by decreasing $S$ S. Warming climate is suspected to be behind these trends. Our research presents an approach that allows for the estimation of monthly streamflow exclusively with global data products, which is an advancement in hydrological analysis and particularly useful for regions that lack in-situ data networks. This approach provides a new perspective in the application of global datasets for the assessment of water balance and could significantly improve predictions in ungauged basins. [ABSTRACT FROM AUTHOR]

This study explored how the water balance components of the Middle Tapi Basin (MTB) might alter between 2010 and 2100 as an outcome of climate change. We used an approach were Statistically downscaled future predicted data for temperature and rainfall for five different Global circulation models were employed under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The baseline and future scenarios' monthly analyses of multiple water balance indicators also showed that RCP 4.5 and RCP 8.5 will see an increase in maximum temperature of 5.2% and 9.5%, respectively, by the end of the century. The baseline period contribution from monsoonal rainfall and streamflow was also 89.3% and 90.6%, but in future scenarios, the figures are likely to reduce to 75.2% and 57.8% for RCP 4.5 and 70.8% and 54.7% for RCP 8.5. While the average inflow estimates at the Ukai dam tend to rise by 19.2% and 46.8% till the distant future scenario for RCP 4.5 and RCP 8.5, respectively. In future scenarios, the percentage of groundwater flow and surface runoff contributions will be lesser due to the higher evapotranspiration rate and revaporisation of water to the root zone. Even though the monsoonal rainfall tends to reduce, the flow duration curves for the future scenarios exhibited a consistent increase in water availability compared to their respective historical counterparts, possibly due to more frequent and severe rainfall events. [ABSTRACT FROM AUTHOR]

Accurately quantifying runoff sources and understanding hydrological processes in glaciated mountain basins is essential for effective water resource management in the face of climate change. This study aimed to determine the contributions from various runoff sources in glaciated basins within the inner Tien-Shan mountains of Kyrgyzstan by utilizing integrated land surface, glacier energy-mass balance, and river routing models. To account for local topographic influences on solar radiation and cloud transmissivity processes, gridded meteorological forcing data were downscaled. The integrated models were then evaluated against observed discharge, glacier mass balance, and snow water equivalent, with a focus on the Kara-Batkak glacier reference site. Shortwave radiation correction was particularly important for improving the accuracy of model simulations. The results indicate that the peak glacier melt contribution occurs in July and August, with some basins reaching up to 54%. On an annual basis, the average contribution from glaciers across the basins was found to be 19%, while the ratios of snowmelt and rainfall were 58% and 23%, respectively. This study highlights the utility of integrated modelling approaches in understanding and quantifying runoff components in data-scarce high mountain regions. The incorporation of observed glacier data proved to be crucial for accurately representing hydrological processes under current climatic conditions. These findings underscore the significance of considering glacier dynamics and their influence on water resources to inform effective water management strategies in glaciated mountain basins. [ABSTRACT FROM AUTHOR]

Meteorological and hydrological hazards present challenges to people and ecosystems worldwide, but the limited length of observational data means that the possible extreme range is not fully understood. Here, a large ensemble of climate model data is combined with a simple grid-based hydrological model, to assess unprecedented but plausible hydrological extremes in the current climate across England. Two case studies are selected—dry (Summer 2022) and wet (Autumn 2023)—with the hydrological model initialised from known conditions then run forward for several months using the large climate ensemble. The modelling chain provides a large set of plausible events including extremes outside the range from use of observed data, with the lowest flows around 28 % lower on average for the Summer 2022 drought study and the highest flows around 42 % higher on average for the Autumn 2023 flood study. The temporal evolution and spatial dependence of extremes is investigated, including the potential time-scale of recovery of flows to normal and the chance of persistent extremes. Being able to plan for such events could help improve the resilience of water supply systems to drought, and improve flood risk management and incident response. [ABSTRACT FROM AUTHOR]

Hydropedology is an interdisciplinary field that studies the interactions between soil and water, recognizing that soils influence hydrological processes through their hydraulic properties, and serve as indicators of hydrological behaviour through their morphological properties that are shaped by water regimes. Given the practical implications of hydropedology and its integration into South Africa's latest soil classification system, an updated categorization of soil forms and 1 657 (1 629 + 28) families was necessary, organizing them into three overarching response groups based on their predominant hydrological responses: recharge, interflow, and responsive. Within these groups, recharge soils are further classified into deep, shallow, and slow subgroups, interflow soils encompass soil/bedrock, shallow, and slow categories, while responsive soils are subdivided into responsive shallow and responsive wet. This paper aims to enhance the reader's comprehension of hydrological responses and simplify the intricacies integrated into South Africa's official soil classification system. [ABSTRACT FROM AUTHOR]

Background: Post-fire studies show that water repellency is limited by moisture conditions, but no existing study has examined this limiting effect at a watershed scale. Aims: This study aimed to identify the soil moisture threshold value at which wildfire-induced hydrophobic condition transitions back to hydrophilic condition at a watershed scale. Methods: The effect of moisture thresholds on post-fire water-repellent soil and hydrological variables including infiltration, runoff volume and peak flow are examined, using the post-wildfire hydrological model of the upper Arroyo Seco watershed, California, following the August 2009 Station Fire. Key results: As the moisture threshold value increased from wilting point towards field capacity, the wildfire's impact on runoff was greatest near the wilting point, and decreased sharply as the threshold increased. The percentage error in peak flow exponentially decreased as the moisture threshold increased and the corresponding Nash–Sutcliffe efficiency increased. Soil moisture threshold values >0.2 m3/m3 were significantly less sensitive to Nash–Sutcliffe efficiency, infiltration depth and percentage error in peak flow and runoff volume. Conclusion: At the soil moisture threshold value of 0.25 m3/m3, transition from hydrophobic to hydrophilic conditions occurred. Identification of this watershed-scale soil moisture threshold value allows inclusion of the wildfire-induced hydrophobic transition back to hydrophilic condition in post-fire hydrological modelling of watersheds. At a watershed scale, the impact of wildfires on runoff following a precipitation event is greatest when the soil is dry and decreases sharply as soil moisture increases. Once the post-wildfire soil moisture reaches the threshold value of 0.25 m3/m3, flooding due to water repellency is no longer significant. [ABSTRACT FROM AUTHOR]

To advance understanding of the spatiotemporal variability of the streamflow in the Pearl River Basin (PRB), we used the Soil and Water Assessment Tool model to simulate the water fluxes from 2010 to 2020 and to identify the underlying controlling mechanisms. The streamflow in the PRB is highly variable and controlled by complex land surface processes and atmospheric impacts over the heterogeneous terrain. Two key factors primarily govern the streamflow: (1) the location of the active precipitation zone, which is determined by the interaction between the monsoon path and uplifting effect of the terrain, and (2) the redistributions resulting from land use and soil characteristics. We observe distinct patterns in the different water fluxes across the different regions. Specifically, surface flow exhibits the highest activity within the precipitation zone. Lateral flow and actual evapotranspiration (AET) have the greatest intensity in the forests and in agricultural regions, respectively, and the aquifer flow is more active in areas with coarse soil textures. The land surface processes of the AET and aquifer retention significantly govern the temporal variability of the streamflow, contributing to the precipitation and streamflow being out of phase in the PRB. Based on the underlying mechanisms driving streamflow variability, we classify the PRB into three substreams: a drought-prone upstream, a hydrologically active midstream, and a typhoon-affected downstream, and each substream exhibits distinct spatiotemporal characteristics of streamflow. We find that the time series and probability distributions of the streamflow at different tributaries within each substream are similar. Each probability distribution is multimodal and can be decomposed into three unimodal distributions representing dry, transitional, and wet conditions. Specifically, the PRB features a large and steep dry mode, a flat transitional mode, and a short wet mode. [ABSTRACT FROM AUTHOR]

This paper documents the development of PixelSWAT, a Graphical User interface (GUI) python toolbox developed with the motive of creating gridded watershed and stream features to run the Soil and Water Assessment Tool (SWAT) in a distributed discretization scheme thus allowing optimum utilization of gridded weather datasets. Additionally, the tool also aims to automate the preparation of SWAT weather input files from Network Common Data (NetCDF) files for any SWAT user along with the option to interpolate the weather files for each grid. A case study was conducted in the Mago basin of Tawang, Arunachal Pradesh, using gridded weather datasets for hydrological simulation. Three SWAT models were prepared – a conventional SWAT model; a 500 m and a 1000 m gridded watershed PixelSWAT models. Statistical indices Nash Sutcliffe (NSE), Coefficient of Determination (R2) and Percent Bias (PBIAS) showed that the PixelSWAT projects performed marginally better than the conventional model and also incorporated the weather data more meaningfully.

Study region: Rana watershed, located in the mid-Mahanadi River basin in the state of Odisha, India. Study focus: This study attempted to develop a generalizable machine learning (ML)-based streamflow prediction model implementing prediction selection algorithms to the physiographic characteristics, and hydro-meteorological data collected for Rana Watershed. New hydrological insights: The pertinent predictors identified were land use/ land cover (LULC), one and two-day lagged rainfall, one-day lagged PET, and one-day lagged streamflow and its categorized flow regime. The random forest algorithm, which outperformed the other five algorithms evaluated, was trained using identified predictors to develop a streamflow prediction model called “stRFlow”. The mean absolute error, root mean squared error, coefficient of determination, and Nash-Sutcliffe efficiency during training were 0.753 m3/s, 3.584 m3/s, 0.973, and 0.972 and testing were 2.829 m3/s, 10.503 m3/s, 0.855, and 0.851, respectively. The Kling-Gupta efficiency was found to be 0.96 and 0.92 during training and testing, respectively. There was an enhancement to model proficiency with the addition of LULC to temporal predictors. Moreover, the partial auto-correlation factor for the streamflow and examining the categorization of specific lagged flow regimes enhanced the predictive capacities of “stRFlow”. Results depict the potential of stRFlow and the framework in streamflow modeling in similar hydroclimatic regions with applicability for practical and reliable streamflow predictions globally.

Floods pose recurrent threats to numerous countries, carrying substantial social, economic and at times, catastrophic implications, especially in extreme scenarios. In addition, coastal areas are strongly threatened by extreme weather events and climate change impacts. Given these pressing challenges, it becomes imperative to conduct thorough assessments of future flood impacts, particularly in areas of significant environmental and socio-economic importance. This study addresses this need by focusing on the potential consequences of flooding in the Loira River, situated in the municipality of Marín, Pontevedra, Northwestern Spain. For this purpose, the free software IBER has been used, these is a free software that solves the 2D depth-averaged shallow water equations (SWEs) by using a finite volume scheme, with the domain being discretized with both structured and unstructured triangular or quadrilateral elements. The hydrological model has been established from the maximum flow for different return periods (T10, T100 and T500). Field observations, including channel widths, bridge configurations, and various parameters, further inform our analysis. Our findings reveal critical insights, including a maximum height differential of 0.48 m, particularly significant at the upstream bridge, exacerbating flooding risks in adjacent plains. Moreover, velocities along riverbanks reach hazardous levels, notably during the 500-year return period, necessitating urgent protective measures for local residents. The hydrological model also indicates that small-scale forestation has no significant effect on flood prevention. This research underscores the utility of advanced hydrological modelling in informing risk management strategies and underscores the necessity of pre-emptive hydrological analyses to mitigate future flood risks effectively.

This research addresses the growing complexity and urgency of climate change’s impact on water resources in arid regions. It combines advanced climate modelling, machine learning, and hydrological modelling to gain profound insights into temperature variations and precipitation patterns and their impacts on the runoff. Notably, it predicts a continuous rise in both maximum and minimum air temperatures until 2050, with minimum temperatures increasing more rapidly. It highlights a concerning trend of decreasing basin precipitation. Sophisticated hydrological models factor in land use, vegetation, and groundwater, offering nuanced insights into water availability, which signifies a detailed and comprehensive understanding of factors impacting water availability. This includes considerations of spatial variability, temporal dynamics, land use effects, vegetation dynamics, groundwater interactions, and the influence of climate change. The research integrates data from advanced climate models, machine learning, and real-time observations, and refers to continuously updated data from various sources, including weather stations, satellites, ground-based sensors, climate monitoring networks, and stream gauges, for accurate basin discharge simulations (Nash–Sutcliffe efficiency – NSE RCP2.6 = 0.99, root mean square error – RMSE RCP2.6 = 1.1, and coefficient of determination R 2 RCP2:6= 0.95 of representative concentration pathways 2.6 (RCP)). By uniting these approaches, the study offers valuable insights for policymakers, water resource managers, and local communities to adapt to and manage water resources in arid regions.

En France, le Ministère de la Transition Ecologique avec Vigicrues fournis une information sur le risque de crues à venir dans les prochaines 24 h (Vigilance Crue) sur plus de 20000km de cours d’eau. En complément de cette information, des prévisions de débits ou de hauteurs d’eau sont produites sur plus de 500 stations hydrométriques. Pour les besoins de la prévision des crues, PLATHYNES, une plateforme multi-modèles a été développée. L’article permettra de revenir sur l’origine de ce projet et détaillera ses trois principaux modèles distribués. Ils permettent sur le pourtour méditerranéen français mais aussi dans d’autres contextes hydroclimatiques de fournir des prévisions de crues lors d’épisodes pluvieux très intenses.Le mode global – semi-distribué de PLATHYNES sera également présenté, il permet notamment de créer des modèles simples de propagation débit-débit.Enfin dans le cadre de son plan stratégique, le réseau Vigicrues ambitionne de fournir sur les stations présentant les enjeux les plus importants, des prévisions plus fiables et avec une échéance d’au moins 24 h. En outre, une information de vigilance crues devra être publiée à terme, sur l’ensemble des cours d’eau de métropole. Pour atteindre ces objectifs ambitieux l’outil PLATHYNES de par sa polyvalence constitue un atout important.

The spatial scale mismatch between runoff generation and runoff routing is an acceptable compromise but a common issue in challenging hydrological modelling. Moreover, there is hardly any report available on whether unit hydrograph (UH) that is consistent with the spatial scale of runoff generation can be computed. The objective of this study was to explore the influence of spatial heterogeneity of runoff generation on the UH for flood prediction. To this end, a novel GIS-based dynamic time-varying unit hydrograph (DTDUH) was proposed based on the time-varying unit hydrograph (TDUH). The DTDUH can be defined as a typical hydrograph of direct runoff which gets generated from one centimeter of effective rainfall falling at a uniform rate over the saturated drainage basin uniformly during a specific duration. The DTDUH was computed based on the saturated areas of the watershed instead of the global watershed. The saturated areas were extracted based on the TWI. Finally, the Longhu River basin and Dongshi River basin were selected as two case studies. Results showed that the proposed method exhibited consistent or better performance compared with that of the linear reservoir routing method, and performed better than the TDUH method. Specifically, the DTDUH method indicated good performances for the flood events with low antecedent soil moisture, and it performed consistently with the TDUH when the global watershed is nearly saturated. The proposed method can be used for the watersheds with sparse gauging stations and limited observed rainfall and runoff data, as is the same with the TDUH method. Simultaneously, it is well applicable for the humid or mountain watershed where the saturation-excess rainfall is the dominant. [ABSTRACT FROM AUTHOR]

Low-density precipitation measurements impair the ability of hydrological models to estimate surface water resources accurately. Remote sensing techniques and climate models can help to improve the estimation of the space-time rainfall variability. However, they alone are not good enough to be used in surface models built to support water management. In this research, we test the improvement of rainfall field estimation by using hydrological modelling based on the premise that a higher hydrological performance generally implies that precipitation is more consistent with streamflow observations and evaporation estimates in the basin. The SWAT model was forced with two satellite and rain gauge blending techniques and with the traditional IDW deterministic interpolation method from stations. The three simulated stream-flows were compared separately against observed records. We do not only focus the comparison on one hydrological performance metric but also conduct a deeper evaluation using several hydrological signatures and statistics. We included the bias, the temporal correlation, the relation of general variability, and an analysis of the Flow Duration Curves (we found that low and medium segments were estimated correctly, whereas the high segments were underestimated). We conclude that either combination technique has its advantages over the other and that both outperform the performance achieved by the IDW in most of the defined criteria, with an overall 10% improvement and with individual streamflow gauge performance enhancement up to 50%. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the rainfall-runoff relationship in the Wadi Mekerra watershed located in northwest Algeria, in order to propose effective solutions for flood protection. The methodology is based on the processing of meteorological and physical data in the geographical environment and on the extraction of data using remote sensing techniques (Landsat satellite imagery) and GIS. Using this database, we were able to develop a hydrological flood model for Mekerra watershed using the hydrological modeling software HEC-HMS. In this study the frequency storm was used for the meteorological model, the SCS curve numbers was used to determine the hydrological losses of the study area and the SCS unit hydrograph method have been applied to simulate the runoff rate. After calibration and validation, the simulated peak discharges were very close with observed values. The Nash-Sutcliffe efficiency coefficient was 0.869, indicates that the hydrological modeling results are satisfactory and accepted for simulation of rainfall-runoff. [ABSTRACT FROM AUTHOR]

Ephemeral ponds (EPs) are seasonally flooded isolated wetlands that provide a variety of hydroecological benefits, including the provision of breeding habitat for several amphibian and invertebrate species. However, the lack of their explicit representation in hydrological models limits a comprehensive understanding of their interaction with surrounding landscapes and their vulnerability in the context of human interventions and climate change. The purpose of this research was to improve the isolated wetland module of the Soil Water Assessment Tool (SWAT) to better represent EP hydrology. The changes include (1) representation of groundwater and hypodermic flow as the only inflows from the pond drainage surface, due to the intermittent and negligible presence of inflow from surface runoff in forested ponds, (2) revision of how evapotranspiration within EPs is represented and (3) implementation of distinct volume‐area‐depth relationships for ponds based on their geometrical shape. The accuracy of these improvements was assessed against that of a previous isolated wetland formulation in replicating water depth observations of 10 EPs of a portion of the Kenauk forest (68 km2) in the Canadian Shield of the Outaouais region (Québec, Canada). The comparison results show that the revised SWAT model presented here significantly improves the distinct filling and drying water cycle of EPs (average root mean square error of 0.1 m of the revised model vs. 0.23 m for the original model). Besides, the new module allowed to identify that hypodermic flow, evapotranspiration and seepage to the underlying soil are the main EP source and sinks. The new module also allowed to explicitly quantify the differences in filling/drying pattern of the EPs of the Kenauk forest and unlike the original model structure, the new module was able to closely replicate the interannual variation of spring and annual hydroperiod duration. [ABSTRACT FROM AUTHOR]

Floods are among the most devastating and financially burdensome natural disasters in Europe. The combined impact of climate change and land use change is expected to exacerbate and intensify the destructive consequences of river floods. In this study, we analysed the effects of wetland restoration on peak and base flows and on water quality in the Kylldal catchment of the Kyll River in the German Middle Mountains using the Soil and Water Assessment Tool+ (SWAT+). Monthly median daily discharge increases varied between 3% and 33% in the studied (micro)catchments. The higher median flow rates show that discharge peaks were attenuated and distributed over a longer period, making both extreme peak flows and low flows less common. Peak flows tended to decrease, with the largest effects between late fall and early spring when peak flow values decreased by up to 18%. The annual maximum peak flows in each of the three micro-catchments decreased by 12–24% on average. The occurrence of daily average flow rates larger than 1 m3 s−1 was up to 45% lower after wetland restoration. Low flows increased by up to 21% and 13% in the summer and fall, respectively, which suggests that drought risk also decreases after wetland restoration. Average nitrogen exports decreased by 38–50% in the project areas and by 20% at the catchment level. Average phosphorus exports decreased by 52–67% in the project areas and by 25% at the catchment level. The study highlights the potential of wetland restoration for improving hydrological services, mitigating flood risks, and enhancing water quality. Restoring and maintaining freshwater ecosystems and their natural sponge functions is crucial for effectively managing water resources and addressing the challenges posed by climate change and land use changes. [ABSTRACT FROM AUTHOR]

The objective of the study is to explore and assess the potential of Nature based Solutions (NbS) to mitigate the peak discharge rate in Banas River catchment, India. Dynamic modelling using the HEC-HMS modelling system to simulate the peak discharge rate of runoff was conducted. A systematic literature review was conducted to assess how NbS could be introduced in HEC-HMS and then be applied to a case study located in northern India to assess the potential impacts on peak flow. It was found that the overall peak discharge rate of runoff at the sink reduced from 1069.9 m3/s to 1033 m3/s on application of Nature based Solutions. Next, scenarios were built for four pilot sub-catchments where the effect of Nature based Solution was represented as surface roughness modulators. A reduction in the peak discharge by 24.9%, 34.16%, 81.02% and 27.73% was observed. The findings suggest that the application of NbS has the potential to reduce surface runoff, peak flows, and flood volumes in the Banas River catchment area and in other catchments with similar conditions. [ABSTRACT FROM AUTHOR]

This research addresses the growing complexity and urgency of climate change's impact on water resources in arid regions. It combines advanced climate modelling, machine learning, and hydrological modelling to gain profound insights into temperature variations and precipitation patterns and their impacts on the runoff. Notably, it predicts a continuous rise in both maximum and minimum air temperatures until 2050, with minimum temperatures increasing more rapidly. It highlights a concerning trend of decreasing basin precipitation. Sophisticated hydrological models factor in land use, vegetation, and groundwater, offering nuanced insights into water availability, which signifies a detailed and comprehensive understanding of factors impacting water availability. This includes considerations of spatial variability, temporal dynamics, land use effects, vegetation dynamics, groundwater interactions, and the influence of climate change. The research integrates data from advanced climate models, machine learning, and real-time observations, and refers to continuously updated data from various sources, including weather stations, satellites, ground-based sensors, climate monitoring networks, and stream gauges, for accurate basin discharge simulations (Nash-Sutcliffe efficiency - NSERCP2.6 = 0.99, root mean square error - RMSERCP2.6 = 1.1, and coefficient of determination RRCP2:6² = 0.95 of representative concentration pathways 2.6 (RCP)). By uniting these approaches, the study offers valuable insights for policymakers, water resource managers, and local communities to adapt to and manage water resources in arid regions. [ABSTRACT FROM AUTHOR]

This study investigates the attribution of climate change to trends in river discharge during six decades from 1955 until 2014 in 12 selected river catchments across six Central Asian countries located upstream of the main rivers. For this purpose, the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model) was firstly calibrated and validated for all study catchments. Attributing climate change to streamflow simulation trends was forced by factual (reanalysis) and counterfactual climate data (assuming the absence of anthropogenic influence) proposed in the framework of the ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) or ESM without anthropogenic forcing that were firstly tested and then compared. The trend analysis was performed for three variables: mean annual discharge and high flow (Q5) and low flow (Q95) indices. The results show that trends in the annual and seasonal discharge could be attributed to climate change for some of the studied catchments. In the three northern catchments (Derkul, Shagan, and Tobol), there are positive trends, and in two catchments (Sarysu and Kafirnigan), there are negative streamflow trends under the factual climate, which could be attributed to climate change. Also, our analysis shows that the average level of discharge in Murghab has increased during the historical study period due to climate change, despite the overall decreasing trend during this period. In addition, the study reveals a clear signal of shifting spring streamflow peaks in all catchments across the study area. [ABSTRACT FROM AUTHOR]

The traditional treatment of uncertainty in hydrological modelling primarily attributes it to model parameters, but rarely systematically considers meteorological input errors, especially in quantifying the impact of meteorological input errors on parameter uncertainty. This study developed a Bayesian‐based integrated approach to quantitatively investigate uncertainties in meteorological inputs (precipitation and temperature) and model parameters as well as the variation in parameter uncertainty due to meteorological input errors. Additionally, we analysed the propagation from these uncertainties to runoff response in snowmelt and non‐snowmelt periods. The applicability and advantages of this approach were presented by applying of the Soil and Water Assessment Tool to the Shitoukoumen Reservoir Catchment. Differential Evolution Adaptive Metropolis‐Markov Chain Monte Carlo was applied for the straightforward Bayesian inference the uncertainties of meteorological inputs and model parameters. On this basis, multilevel factorial analysis technology was used to quantitatively investigate the specific impact of the model parameters' individual and interactive effects due to meteorological input errors. Finally, the impact of meteorological input errors and model parameter uncertainty on the model performance were analysed and quantified systematically. The results showed that the meteorological input errors could affect the random characteristics of multiple model parameters. Moreover, meteorological input errors could further affect the model parameters' effects on annual average runoff. Overall, the above results have significant implications in enhancing hydrological model to simulate/predict runoff and understanding hydrological processes during different periods. [ABSTRACT FROM AUTHOR]

This study investigated the effects of nonpoint source (NPS) pollution reduction and pollutant dynamics in a highland agricultural watershed in Korea. We employed the SWAT model to simulate hydrological processes and pollution transport within the watershed. The model incorporates future climatic scenarios derived from downscaled climate projections to assess their impacts on NPS pollution and pollutant reduction methods. These changes lead to heightened surface runoff and erosion rates, resulting in elevated sediment and nutrient concentrations. The projection indicates an anticipated increase in the annual average temperature by 1.3 to 2.1 °C by the mid-century, under scenarios SSP126 and SSP585. Additionally, precipitation levels are projected to increase by 31 to 61 mm from the baseline to the end of the century. Variations in hydrological components such as evapotranspiration, streamflow, and soil moisture are expected to range from +3.2 to +17.2%, −9.1 to +8.1%, and 0.1 to 0.7%, respectively, during the years 2040 and 2080. Fluctuations in TN, SS, and TP loading are estimated to range from −4.5 to +2.3%, −5.8 to +29.0%, and +3.7 to +17.4%, respectively. This study emphasizes the importance of adaptive management options for stakeholders and the need for adaptive management options to reduce nonpoint source pollution and protect water quality to maintain sustainable water supplies and conserve the environment in this watershed. [ABSTRACT FROM AUTHOR]

Natural hazard is recognized to be a threat to human society that is becoming more critical under the perspective of climate change. Italy is one of the most exposed European countries to hydrogeomorphological hazard, as proven by the annual casualties and economic losses. Among the most recent events, the one happened in the Misa river catchment in 2022 is of particular interest for the great amount of rainfall poured in just one night, which led to extensive erosion. The mountainous part of the catchment was hit by several landslides, and the resulting sediments and debris, transported downstream by rivers, increased the amount of damages. This event was interpreted as directly caused by climate change, as it was linked to a tropical storm not common at this latitude. This work proposes an analysis of this exceptional event from a hydrological point of view. The 2022 event was compared with other relevant events from the past, in order to evaluate the peak discharge, the sediment erosion and transport processes in the area for different scenarios. The analysis included the modelling at catchment scale with two physically-based models: SWAT, which provides results on channel processes, and SMART-SED, working at the catchment scale. The two scales of analysis, together with field surveys, gave a better understanding of the evolution of the shallow processes during the events. Finally, this work intends to highlight the relevance of hydrological modelling for the management of hazard in different scenarios at catchment scale. [ABSTRACT FROM AUTHOR]

Study region: Two typical mountainous Haihe River Basins: The Xiaojue Basin and the Yehe River Basin Study focuses: The mountainous Haihe River Basin has suffered from a water crisis duo to large-scale restoration. Quantifying the hydrological impacts of vegetation restoration is necessary for local water management. In this study, we used the Soil and Water Assessment Tool (SWAT) hydrological model, in combination with two vegetation restoration scenarios, to analyze the effects of vegetation restoration on runoff and its runoff components (surface runoff, baseflow, and total runoff) New hydrological insights for the region: Our results indicated a general decrease in surface runoff and an increase in baseflow as a result of vegetation restoration. At the seasonal timescale, total runoff and surface runoff decreases mainly concentrated in the flood season, and slightly increased in the non-flood season, and baseflow showed the opposite trend during the flood-season in the Xiaojue and Yehe River Basin. The magnitude of change in runoff components in the Yehe River Basin was higher than that in the Xiaojue Basin, and the Yehe River Basin has a larger water yield capability. This study advances our knowledge on the way in which vegetation affects runoff and offers recommendations for water resources management

ABSTRACTWe compared the flood forecasts issued by a model used by operational services in France (GRP) and by a model developed to improve the simulation of floods resulting from intense rainfall (GR5H_RI). We selected 10,652 flood events from 19 years of hourly data available for 229 French catchments. The models were combined with a state-updating procedure to produce forecasts at 3, 6, 12 and 24 h lead times. Results indicate that the GR5H_RI model performs better on average than the GRP model at all lead times, particularly for forecasting flash floods (rise time

Accurately quantifying runoff sources and understanding hydrological processes in glaciated mountain basins is essential for effective water resource management in the face of climate change. This study aimed to determine the contributions from various runoff sources in glaciated basins within the inner Tien-Shan mountains of Kyrgyzstan by utilizing integrated land surface, glacier energy-mass balance, and river routing models. To account for local topographic influences on solar radiation and cloud transmissivity processes, gridded meteorological forcing data were downscaled. The integrated models were then evaluated against observed discharge, glacier mass balance, and snow water equivalent, with a focus on the Kara-Batkak glacier reference site. Shortwave radiation correction was particularly important for improving the accuracy of model simulations. The results indicate that the peak glacier melt contribution occurs in July and August, with some basins reaching up to 54%. On an annual basis, the average contribution from glaciers across the basins was found to be 19%, while the ratios of snowmelt and rainfall were 58% and 23%, respectively. This study highlights the utility of integrated modelling approaches in understanding and quantifying runoff components in data-scarce high mountain regions. The incorporation of observed glacier data proved to be crucial for accurately representing hydrological processes under current climatic conditions. These findings underscore the significance of considering glacier dynamics and their influence on water resources to inform effective water management strategies in glaciated mountain basins.

Flood Frequency Analysis (FFA) is a process to relate the magnitude of extreme streamflows to their frequency of occurrence through distribution functions, and the results of FFA are useful in design flood estimation for various hydraulic structures. The FFA relies primarily on actual observed streamflow data (Qobts) and certain underlying assumptions that form the basis of FFA's philosophy. In today's world, Qobts time series are available for most river basins; however, it is an indubitable reality that Qobts do not satisfy the necessary requirements. Therefore, an alternative doctrine is needed to create such a dataset. This paper provides an overview of the limitations of utilising Qobts along with its drivers and suggests a surrogate mechanism to perform FFA in today's world.

The Stormwater Management Model (SWMM) is a widely used tool for assessing the hydrological performance of infiltration swales. However, validating the accuracy of SWMM simulation against observed data has been challenging, primarily because well-functioning infiltration swales rarely produce surface runoff, especially over short monitoring periods. This study addresses this challenge by using measured subsurface water storage levels for calibration and validation. The study evaluated three SWMM modules, namely, the snowpack, aquifer, and low-impact development (LID) modules, to simulate subsurface water storage levels of an infiltration swale located in a cold climate region during snow and snow-free periods. Global sensitivity analysis was used to identify influential parameters within these modules. The findings revealed that only a few parameters significantly influenced model outputs. Moreover, the aquifer module outperformed the LID module in simulating subsurface water storage due to limitations in setting the initial saturation of the LID module. Furthermore, simulation accuracy was better during snow-free periods due to challenges in simulating snow dynamics during snow periods with the snowpack module. The calibrated models offer valuable insights into the long-term hydrological performance of infiltration swales, enabling practitioners to identify events that trigger flooding in these systems. HIGHLIGHTS Groundwater data were used to evaluate the long-term performances of infiltration swales.; Sensitivity analysis identified key parameters in three selected modules in the Stormwater Management Model.; Aquifer and snowpack modules effectively predicted swale hydrological performances.; Groundwater monitoring reduces uncertainties in soil and snow processes in swales.;

Floods are a widespread natural disaster that occur in most areas of the world, except for the polar regions. To minimize the damage caused by floods, effective management strategies and policies must be implemented. Petra and Wadi Musa areas are prone to floods, which happen every 2–3 years and result in significant harm to both lives and properties. To address this issue, a composite hazard and vulnerability index is commonly utilized to evaluate flood risk and guide policy formation for flood risk reduction. These tools are efficient and cost-effective in generating accurate results. Accordingly, the present study aims to determine the morphological and hydrometeorological parameters that affect flash floods in Petra catchment area and to identify high-risk zones using GIS, hydrological, and analytical hierarchy (AHP) modeling. Nine factors, including Elevation (E), Landuse/Landcover LULC, Slope (S), Drainage density (DD), Flood Control Points (FCP) and Rainfall intensity (RI), which make up the six risk indices, and Population Density (PD), Cropland (C), and Transportation (Tr), which make up the three vulnerability indices, were evaluated both individually and in combination using AHP in ArcGIS 10.8.2 software. These parameters were classified as hazard and vulnerability indicators, and a final flood map was generated. The map indicated that approximately 37% of the total area in Petra catchment is at high or very high risk of flooding, necessitating significant attention from governmental agencies and decision-makers for flood risk mitigation. The AHP method proposed in this study is an accurate tool for flood mapping that can be easily applied to other regions in Jordan to manage and prevent flood hazards.

Floods are frequent natural disasters, and the management of floods involves predicting flood hydrographs and inundation maps. However, the uncertainty associated with these predictions is a significant challenge in flood management. This study focuses on the uncertainty associated with the selection of mathematical models to estimate flood hydrographs and their impact on inundation map predictions. The study involved setting up 27 hydrological models using different mathematical models for the loss, transform, and routing methods within the Hydrologic Engineering center – Hydrologic Modelling system (HEC-HMS) model. The results showed that the peak flow estimation depends on the models adopted for predicting the flood hydrograph. This uncertainty was translated to the flood inundation maps, where the inundated area varied across different flood events. The study also found that the hydrological conditions prior to the start of the event impact the hydrological model predictive uncertainty, and ensemble predictions by Bayesian model averaging can reduce the uncertainty. [ABSTRACT FROM AUTHOR]

The construction of dams and interbasin water transfers are major strategies to adapt to water scarcity. In the semi-arid Jaguaribe basin, Brazil, large strategic reservoirs supply water for urban centres, while farm dams serve multiple demands of rural communities. We used the Model of Water Availability in SemiArid Environments (WASA) to assess the water storage dynamics of strategic reservoirs impacted by: (i) water withdrawal from farm dams located upstream; (ii) interbasin water transfer to the Jaguaribe basin. Simulations show that intensive water use from smaller reservoirs reduces large reservoir volumes by less than 10%, whereas inflows from the São Francisco Integration Project to the basin can increase by 6% the period of time in which reservoir capacity is above the alert volume. The practice of saving water from small dams can be abandoned, enabling higher agricultural production without compromising the water supply of large urban centres. [ABSTRACT FROM AUTHOR]

Aim of the water balance study is to evaluate the distribution of hydrological responses towards water balance components for a watershed. The present study highlights the water balance study for a watershed of Mahanadi river basin of India. Impact estimation of Landuse Land Cover (LULC) on runoff using Soil and Water Assessment Tool (SWAT) model under varying levels of hydrological response unit (HRU) has also been determined. For the purpose, LULC maps are prepared and utilized in decadal intervals for the years 1994, 2004 and 2014. The sensitivity analysis was performed using local and global methods, where eight most sensitive parameters have been utilized for calibration and validation. The model showed good agreement between observed and estimated time scale and showed satisfactory results during calibration and validation. As per impact estimation of LULC during 1994 to 2014, it was noticed that significant percentage of runoff has been increased from 27.6 to 30.95%, respectively. It was also noticed that the annual average ground water contribution decreased with LULC, from 20.1% (1994) to 15.24% (2014). Sub-watershed (SWS-6) and SWS-2 have been identified as high priority for implementation of conservation measures and better water management. Because these sub-watersheds yielded maximum runoff. The HRU role in hydrological modelling revealed that a greater number of HRUs simulate runoff more precisely and more accurately useful for water management studies. [ABSTRACT FROM AUTHOR]