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Understanding the hydrological impacts of climate change is essential for robust and sustainable water management. This study assessed the hydrologic conditions under changing climate in the Jinshajiang River basin, the source region of the Yangtze River, using the hydrological model SWAT with the historical observations and the future climate simulations under two Shared Socioeconomic Pathways (SSP2‐4.5 and SSP5‐8.5). For the historical period, with an increasing trend of precipitation, evapotranspiration, and snowmelt, streamflow increases in upstream region but keeps decreasing in the downstream catchment. For future scenarios, a warmer and wetter climate is projected for the basin throughout the 21st century, leading to an overall increase in mean and extreme streamflow. The streamflow magnitude increases more significantly in the far future than in the near future, and more significant under SSP5‐8.5 than SSP2‐4.5. The projected remarkable increase in precipitation causes the transition in changing trend of streamflow compared with the historical period. The projected warming leads to a continuing decline in snowfall and snow water equivalent, followed by an earlier snowmelt and higher peak streamflow, especially at the upstream catchment. Ultimately, reservoirs in the basin are expected to gain more inflows, however, with greater variability including higher likelihoods of flood and drought events, which impose potential challenges on reservoir operations. These outcomes indicate the importance of adaptive water resources management in the melting water contributed basin to sustain and enhance its services under global warming. [ABSTRACT FROM AUTHOR]

A flow map is a type of thematic visualization that depicts the movement of objects across a geographical space using a tree layout resembling a natural river system. In this paper, we introduce an innovative and automated approach called REA-FM, which leverages the power of the maze-solving algorithm to extract rivers from digital elevation models (DEMs). This enables the creation of flow maps that originate from a single source and extend to multiple destinations. Initially, REA-FM represents the mapping space of a flow map using a DEM. Subsequently, a maze-solving algorithm is adapted to extract flow paths from the destinations to the origin within the DEM data, with constraints on search directions, direction weights, and search ranges based on quality criteria specific to flow maps. To obtain comprehensive flow maps, the maze-solving algorithm is employed iteratively, considering the importance of each flow path, as determined by their respective lengths. These obtained paths are finally rendered smoothly with varying widths using Bézier curves, thereby enhancing the visual aesthetics of the flow map. A comparative evaluation with existing approaches demonstrates that REA-FM can generate natural-looking flow maps with reduced total length and improved node distribution, eliminating node overlaps and edge crossings. Furthermore, the effectiveness of REA-FM is validated through three extension experiments involving heterogeneous mapping spaces and areas with obstacles. Parameter analysis confirms that REA-FM offers intuitive control over the layout of flow maps. Project website: [ABSTRACT FROM AUTHOR]

Widespread floods affecting multiple subbasins in a river basin have implications for infrastructure, agriculture, environment, and groundwater recharge. However, the crucial linkage between widespread floods and their drivers remains unexplored for Indian subcontinental river basins. Here, we examine the occurrence and drivers of widespread flooding in seven Indian subcontinental river basins during the observed climate (1959–2020). The peninsular river basins have a high probability of widespread flooding, compared to the transboundary basins of the Ganga and Brahmaputra. Favorable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and precipitation seasonality determine the probability of widespread floods in Indian river basins. The widespread floods are associated with large atmospheric circulations that cause precipitation in a large part of a river basin. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India. [ABSTRACT FROM AUTHOR]

Amidst growing concerns over climate-induced extreme weather events, precise flood forecasting becomes imperative, especially in regions like the Chaersen Basin where data scarcity compounds the challenge. Traditional hydrologic models, while reliable, often fall short in areas with insufficient observational data. This study introduces a hybrid modeling approach that combines the deep learning capabilities of the Informer model with the robust hydrological simulation by the WRF-Hydro model to enhance runoff predictions in such data-sparse regions. Trained initially on the diverse and extensive CAMELS dataset in the United States, the Informer model successfully applied its learned insights to predict runoff in the Chaersen Basin, leveraging transfer learning to bridge data gaps. Concurrently, the WRF-Hydro model, when integrated with The Global Forecast System (GFS) data, provided a basis for comparison and further refinement of flood prediction accuracy. The integration of these models resulted in a significant improvement in prediction precision. The synergy between the Informer's advanced pattern recognition and the physical modeling strength of the WRF-Hydro significantly enhanced the prediction accuracy. The final predictions for the years 2015 and 2016 demonstrated notable increases in the Nash–Sutcliffe Efficiency (NSE) and the Index of Agreement (IOA) metrics, confirming the effectiveness of the hybrid model in capturing complex hydrological dynamics during runoff predictions. Specifically, in 2015, the NSE improved from 0.5 with WRF-Hydro and 0.63 with the Informer model to 0.66 using the hybrid model, while in 2016, the NSE increased from 0.42 to 0.76. Similarly, the IOA in 2015 rose from 0.83 with WRF-Hydro and 0.84 with the Informer model to 0.87 using the hybrid approach, and in 2016, it increased from 0.78 to 0.92. Further investigation into the respective contributions of the WRF-Hydro and the Informer models revealed that the hybrid model achieved the optimal performance when the contribution of the Informer model was maintained between 60%-80%. [ABSTRACT FROM AUTHOR]

Rainfall intensity and land use changes and from natural vegetation to cultivated fields both impact on river streamflows and sediment dynamics. Assessing the magnitude of these flow and sediment changes is important for water management. However, many rivers are still under-studied/monitored and data sparse. Fully distributed rainfall–runoff models, such as the Jena Adaptable Modelling System (JAMS/J2000), allow for simulation of streamflow, its flow components and potential sediment yield given land use and hydrological changes. This study demonstrates the use of this method in the Bot River, a data-scarce catchment in South Africa. A hydrological model was implemented, and sediment yield was estimated under three land use and two hydrological flow component scenarios. The model reported Nash-Sutcliffe efficiencies (E2) of 0.64 and 0.32 (for streamflow) for model calibration and validation periods, respectively. Modelled mean annual sediment yield increased from 423 t/km2/year in 1990 to 490 t/km2/year in 2018, attributable to land use changes. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the performance of a hydrological model by using satellite image classification (SIM) to extract land use (LU) information. Four methods, namely naive Bayes (NB), classification and regression trees (CART), support vector machine (SVM), and random forest (RF), are assessed for SIM using satellite images. CART demonstrated the highest overall accuracy (OA) of 0.996, followed by RF (OA = 0.994), SVM (OA = 0.797), and NB (OA = 0.543). The Soil and Water Assessment Tool (SWAT) model was employed in this study to construct and refine the hydrological model for simulating the streamflow in the basin. For this purpose, the ground-based layer was replaced by LU generated from the classification algorithms. Integrating the four classification approaches yielded a significant improvement in Nash-Sutcliffe efficiency of the SWAT model, increasing it from 0.37 to approximately 0.76. These findings highlight the effectiveness of using satellite image classification in enhancing the efficiency of hydrological models. [ABSTRACT FROM AUTHOR]

Reliable hydrological simulations require accurate precipitation data. However, data uncertainties due to the indirect nature of satellite estimates can propagate through hydrological models and lead to simulation errors. This study assesses the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products, comparing them directly with ground-based precipitation data and evaluating their performance in rainfall–runoff modelling across Great Britain. Three IMERG V06 products (IMERG-Early, IMERG-Late, and IMERG-Final) are examined. Utilizing the simple water balance model (SWBM), the analysis covers 250 basins, revealing that the SWBM performs well in over 50% of the basins. Runoff estimations show that European Observation (E-OBS) ground-based data yield the highest Nash-Sutcliffe efficiency (NSE) score (0.91), followed by IMERG-Final (0.85), IMERG-Late (0.82), and IMERG-Early (0.73). The findings underscore IMERG's utility in hydrological modelling for ungauged or poorly gauged basins. [ABSTRACT FROM AUTHOR]

Electrical resistivity tomography (ERT), a geophysical imaging method, is commonly used on flood embankments (dykes or levees) to characterize their internal structure and look for defects. These surveys often use a single line of electrodes to enable 2-D imaging through the embankment crest, an approach that enables rapid and efficient surveying compared to 3-D surveys. However, offline variations in topography can introduce artefacts into these 2-D images, by affecting the measured resistivity data. Such topographic effects have only been explored on a site-specific basis. If the topographic effects can be assessed for a distribution of embankment geometries (e.g. slope angle and crest width) and resistivity variations, it would allow for targeted correction procedures and improved survey design. To investigate topographic effects on ERT measurements, we forward-modelled embankments with different trapezoidal cross-sections sat atop a flat foundation layer with contrasting resistivity values. Each was compared to a corresponding flat model with the same vertical resistivity distribution. The modelling workflow was designed to minimize the effect of forward modelling errors on the calculation of topographic effect. We ran 1872 unique embankment forward models, representing 144 geometries, each with 13 different resistivity contrasts. Modelling results show that offline topography affects the tested array types (Wenner–Schlumberger, Dipole–Dipole and Multiple–Gradient) in slightly different ways, but the magnitudes are similar, so all are equally suitable for embankment surveys. Three separate mechanisms are found to cause topographic effects. The dominant mechanism is caused by the offline topography confining the electrical current flow, increasing the measured transfer resistance from the embankment model. The two other mechanisms, previously unidentified, decrease the measured transfer resistances from the embankment model compared to a layered half-space but only affect embankments with specific geometries and resistivity distributions. Overall, we found that for typical embankment geometries and resistivity distributions, the resistivity distribution has a greater control on the magnitude of the topographic effect than the exact embankment geometry: the subsurface resistivity distribution cannot be neglected. 2-D inversions are suitable when both the embankment is more resistive than the foundations and when the embankment's cross-sectional area is greater than 4 m2 m−2 (area scaled to an embankment with a height of 1 m). Topographic corrections, 3-D data acquisition or 3-D forward models are required when these conditions are not met. These are demonstrated using field data from an embankment at Hexham, Northumberland, UK. Improving the accuracy of the resistivity values in ERT models will enable more accurate ground models, better integration of resistivity data with geotechnical data sets, and will improve the translation of resistivity values into geotechnical properties. Such developments will contribute to a better characterized and safer flood defence network. [ABSTRACT FROM AUTHOR]

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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.)

This study assesses river discharges derived using remote sensing and hydrologic modeling approaches throughout the CONUS. The remote sensing methods rely on total water storage anomalies (TWSA) from the GRACE satellite mission and water surface elevations from altimetry satellites (JASON-2/3, Sentinel-3). Surface and subsurface runoff from two Land Surface Models (NOAH, CLSM) are routed using the Hillslope River Routing model to determine discharge. The LSMs are part of NASA's Global Land Data Assimilation System (GLDAS). Differences in key physical processes represented in each model, model forcings, and use of data assimilation provide an intriguing basis for comparison. Evaluation is performed using the Kling Gupta Efficiency and USGS stream gauges. Results highlight the effectiveness of both satellite-derived discharge methods, with altimetry generally performing well over a range of discharges and TWSA capturing mean flows. LSM-derived discharge performance varies based on hydroclimatic conditions and drainage areas, with NOAH generally outperforming CLSM. CLSM-derived discharges may be impacted by the use of data assimilation (GLDAS v2.2). Low correlation and high variability contribute to lower KGE values. GLDAS models tend to perform poorly in snow dominated, semi-arid and water-regulated systems where both the timing and magnitude of the simulated results are early and overestimated. [ABSTRACT FROM AUTHOR]

Regional- and continental-scale hydrologic models are increasingly important forecasting tools, yet they rely on highly variable channel parameters (e.g. width, depth, hydraulic resistance) that remain unquantified for millions of stream reaches across the country. Existing hydrologic models utilize over-simplified channel geometries that directly impact the accuracy of streamflow estimates, with cascading effects for water resource and hazard prediction. Here, we present a hydraulic geometry dataset, termed 'HyG', derived from discharge field measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). The HyG dataset includes (1) at-a-station hydraulic geometry parameters, (2) at-a-station hydraulic resistance (Manning's n) calculated from the Manning equation, (3) daily discharge percentiles, and (4) regionalized downstream hydraulic geometry parameters based on HUC4 (Hydrologic Unit Code 4), derived from a total of 4,051,682 individual measurements from 66,841 total gages. The regionalized HyG dataset can be used directly to improve channel representations in models over CONUS. The original HyG relationships can also be regionalized for finer scales if required. [ABSTRACT FROM AUTHOR]

In the 465,000 km2 Canadian Prairies ecozone, robust hydrological input data for hydraulic model applications are uncommon because of the sparse monitoring network and the intermittently connected stream network. New hydrological datasets can offer a valuable advancement for making water management decisions and designing infrastructure in this water stressed region. The Prairie Hydrology Design and Analysis Product (PHyDAP) was created to address existing limitations, and provides a comprehensive regional dataset for use in hydraulic modelling applications. PHyDAP is a collection of outputs from a physically based hydrological modelling framework, run for periods ranging from 38 to 150 years, according to three climate forcing datasets. The dataset includes vertical and lateral fluxes (rainfall, snowmelt, upland runoff, and open water evaporation) and basin streamflow, at hourly or 3-hourly intervals for the >4000 small basins of approximately 100 km2 that span the region. This contribution describes the motivation for this work and methodology used to derive the data product, summarizes the data and its accessibility, and provides an overview of potential use cases. [ABSTRACT FROM AUTHOR]

We evaluate trends in terrestrial water storage over 1950–2100 in CMIP6 climate models against a new global reanalysis from assimilating GRACE and GRACE-FO satellite observations into a hydrological model. To account for different timescales in our analysis, we select regions in which the influence of interannual variability is relatively small and observed trends are assumed to be representative of the development over longer periods. Our results reveal distinct biases in drying and wetting trends in CMIP6 models for several world regions. Specifically, we see high model consensus for drying in the Amazon, which disagrees with the observed wetting. Other regions show a high consensus of models and observations suggesting qualitatively correctly simulated trends, e.g., for the Mediterranean and parts of Central Africa. A high model agreement might therefore falsely indicate a robust trend in water storage if it is not assessed in light of the observed developments. This underlines the potential use of maintaining an adequate observational capacity of water storage for climate change assessments. [ABSTRACT FROM AUTHOR]

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems. [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 spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales. [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]

Revealing the spatiotemporal variation in baseflow and its underlying mechanisms is critical for preserving the health and ecological functions of alpine rivers, but this has rarely been conducted in the source region of the Yangtze River (SRYR). Our study employed the Soil and Water Assessment Tool (SWAT) model coupled with two-parameter digital filtering and geostatistical approaches to obtain a visual representation of the spatiotemporal heterogeneity characteristics of the baseflow and baseflow index (BFI) in the SRYR. The SWAT model and multiple linear regression model (MLR) were used to quantitatively estimate the contribution of climate change and human activities to baseflow and BFI changes. The results underscore the robust applicability of the SWAT model within the SRYR. Temporally, the precipitation, temperature, and baseflow exhibited significant upward trends, and the baseflow and BFI showed contrasting intra-annual distribution patterns, which were unimodal and bimodal distribution, respectively. Spatially, the baseflow increased from northwest to southeast, and from the watershed perspective, the Tongtian River exhibited higher baseflow values compared to other regions of the SRYR. The baseflow and BFI values of the Dangqu River were greater than those of other tributaries. More than 50% of the entire basin had an annual BFI value greater than 0.7, which indicates that baseflow was the major contributor to runoff generation. Moreover, the contributions of climate change and human activities to baseflow variability were 122% and −22%, and to BFI variability, 60% and 40%. Specifically, precipitation contributed 116% and 60% to the baseflow and BFI variations, while the temperature exhibited contributions of 6% and 8%, respectively. Overall, it was concluded that the spatiotemporal distributions of baseflow and the BFI are controlled by various factors, and climate change is the main factor of baseflow variation. Our study offers valuable insights for the management and quantitative assessment of groundwater resources within the SRYR amidst climate change. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of multi-decadal climatic variability on the temporal evolution of root zone storage capacities (Sr,max) and its implications for streamflow predictions in the Meuse basin. Through a comprehensive analysis of 286 catchments across Europe and the US that are hydro-climatically comparable to the Meuse basin, we construct inter-decadal distributions of past deviations in evaporative ratios (IE) from expected values based on catchment aridity (IA). These distributions of ΔIE were then used to estimate inter-decadal changes in Sr,max and to quantify the associated consequences for streamflow predictions in the Meuse basin. Our findings reveal that, while catchments do not strictly adhere to their specific parametric Budyko curves over time, the deviations in IE are generally very minor, with an average ΔIE=0.01 and an interquartile range (IQR) of -0.01 to 0.03. Consequently, these minor deviations lead to limited inter-decadal changes in Sr,max , mostly ranging between -10 and +21 mm (-5 % to +10 %). When these changes (ΔSr,max) are accounted for in hydrological models, the impact on streamflow predictions in the Meuse basin is found to be marginal, with the most significant shifts in monthly evaporation and streamflow not exceeding 4 % and 12 %, respectively. Our study underscores the utility of parametric Budyko-style equations for first-order estimates of future Sr,max in hydrological models, even in the face of climate change and variability. This research contributes to a more nuanced understanding of hydrological responses to changing climatic conditions and offers valuable insights for future climate impact studies in hydrology. [ABSTRACT FROM AUTHOR]

Alpine basins are important water sources for human life, and reliable hydrological modeling can enhance the water resource management in alpine basins. Recently, hybrid hydrological models, coupling process-based models and deep learning (DL), have exhibited considerable promise in hydrological simulations. However, a notable limitation of existing hybrid models lies in their failure to incorporate spatial information within the basin and describe alpine hydrological processes, which restricts their applicability in hydrological modeling in large alpine basins. To address this issue, we develop a set of hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing embedded neural networks (ENNs) to parameterize and replace different internal modules. The proposed models are tested on three large alpine basins on the Tibetan Plateau. A climate perturbation method is further used to test the applicability of the hybrid models to analyze the hydrological sensitivities to climate change in large alpine basins. Results indicate that proposed hybrid hydrological models can perform well in predicting runoff processes and simulating runoff component contributions in large alpine basins. The optimal hybrid model with Nash–Sutcliffe efficiencies (NSEs) higher than 0.87 shows comparable performance to state-of-the-art DL models. The hybrid model also exhibits remarkable capability in simulating hydrological processes at ungauged sites within the basin, markedly surpassing traditional distributed models. In addition, the results also show reasonable patterns in the analysis of the hydrological sensitivities to climate change. Overall, this study provides a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves our understanding about the hydrological sensitivities to climate change in large alpine basins. [ABSTRACT FROM AUTHOR]

The community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) that is widely used in studies from uncoupled land surface hydrometeorology and ecohydrology to coupled weather and climate predictions. In this study, we developed NMP-Hydro, a hydrological model written in CSharp(C#). NMP-Hydro was developed by faithfully translating the FORTRAN version Noah-MP from the uncoupled WRF-Hydro 3.0, and was coupled with a river routing model. NMP-Hydro exhibits the capacity of parallel execution on Windows systems, utilizing the multi-core CPUs commonly available in today's personal computers. The code of NMP-Hydro has been rigorously tested to ensure that it produces a high-degree of consistency with the simulation output of the original WRF-Hydro. High-resolution (6 km) simulations were conducted and assessed over a grid domain covering the entire Yellow River Basin and the most part of North China. The spatial maps and temporal variations of many state variables simulated by NMP-Hydro and WRF-Hydro/Noah-MP demonstrate consistent results, with occasionally minor discrepancies. The river discharge for the Yellow River under various scheme combinations of six Noah-MP parameterizations exhibits general close agreement with the natural river discharge at the Lanzhou station. NMP-Hydro can be regarded as a reliable replica of Noah-MP in WRF-Hydro 3.0, but it can leverage the modern, powerful, and user-friendly features brought by the C # language to significantly improve the efficiency of the model users and developers. [ABSTRACT FROM AUTHOR]

The assessment of trends in hydroclimatic characteristics averaged over the watershed area and changes in the fields of the hydrological cycle components in the 21st century in a large river basin of the Nizhnekamskoye Reservoir was carried out on the basis of a model approach combining regional space distributed hydrological model and global climate models. It is shown that a slight decrease by 8% is expected in the area-averaged specific runoff by the end of the century according to the Representative Concentration Pathway 8.5 scenario. However, in various parts of the watershed both a slight increase by 3–6% and a significant decrease in the mean annual runoff by 22% can occur, depending on physiographic conditions. Moreover, climatic changes can increase contrasts in the ecological status of the territory: warm regions in the steppe zone become even hotter and more arid, and the moisture availability of wetter territories in the centre and east of the basin increases even more. [ABSTRACT FROM AUTHOR]

Societal risks from flooding are evident at a range of spatial scales and climate change will exacerbate these risks in the future. Assessing flood risks across broad geographical regions is a challenge, and often done using streamflow time-series records or hydrologic models. In this study, we used a national-scale hydrological model to identify, assess, and map 16 different streamflow metrics that could be used to describe flood risks across 34,987 HUC12 subwatersheds within the Mississippi-Atchafalaya River Basin (MARB). A clear spatial difference was observed among two different classes of metrics. Watersheds in the eastern half of the MARB exhibited higher overall flows as characterized by the mean, median, and maximum daily values, whereas western MARB watersheds were associated with flood indicative of high extreme flows such as skewness, standardized streamflow index and top days. Total agricultural and building losses within HUC12 watersheds were related to flood metrics and those focused on higher overall flows were more correlated to expected annual losses (EAL) than extreme value metrics. Results from this study are useful for identifying continental scale patterns of flood risks within the MARB and should be considered a launching point from which to improve the connections between watershed scale risks and the potential use of natural infrastructure practices to reduce these risks. [ABSTRACT FROM AUTHOR]

This study conducts a comparative analysis of four hydrological models – Génie Rural à 4 paramètres Journalier (GR4J), Hydrologiska Byråns Vattenbalansavdelning – light (HBV-light), Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS), and Water and Evaluation and Planning System (WEAP) – to evaluate their effectiveness in simulating surface water responses in humid Mediterranean climates. The Longaví basin, located in the Maule River basin of central Chile, was selected for its characteristic climate. The models used lumped and semi-distributed approaches to simulate the basin’s hydrological cycle. The study area was divided into three zones, considering climate, soil properties, and altitude. Results showed that GR4J, HEC-HMS, and WEAP performed well during the winter months (June to October), while HBV-light was more effective in summer (November to May). WEAP, with its extensive parameterization, effectively captured streamflow variability during the rainy season. The differential split-sample test indicated that GR4J and WEAP offered the best fit for analysing climatic variability. These models are highly recommended for climate change impact studies, given their strong performance in capturing seasonal hydrological responses across similar regions. [ABSTRACT FROM AUTHOR]

The paper presents a historical overview of the development of hydrological models in the former USSR and Russia since the 1930s. An overwhelming number of hydrological papers were published in Russian, and they are not sufficiently well known to the world hydrological community. The purpose of the review is to fill the gap and present the main achievements, some of which were pioneering for their time. The period under consideration is divided into three sub-periods. Pre-computer, the 1930–1950s were associated, first of all, with the development of models of unsteady water flow in river channels and linear flood-routing models; in the 1960–1980s, many theoretical and practical foundations for modelling the processes of the hydrological cycle and the formation of river runoff were developed; and in the Russian era, following 1991, several notable advancements in the considered field were also made. [ABSTRACT FROM AUTHOR]

Hydrological models are vital for water management to determine in-stream flow, irrigational water, domestic water supply, and biodiversity conservation. This study formulates a hydrological model with a novel approach for streamflow and sediment load in the QGIS-supported Soil and Water Assessment Tool for the Halda River catchment, a unique ecological habitat for natural carp spawning and freshwater sources. The daily simulation uses an innovative stage-discharge relationship technique from available 15-day interval flow data. The model evaluation parameters R2 values 0.80 and 0.62, and NS values 0.81 and 0.61 for calibration and validation of streamflow suggested excellent agreement in the seasonal cycle and most of the monsoon peak flow. The streamflow/precipitation ratio indicates a significant influence of groundwater through infiltration. The baseflow shows a decreasing trend. The sediment load based on suspended sediment concentration at a downstream location is 1,625 tons/day. On the contrary, the model prediction is 30 times lower. The scattered sediment load data support the model estimate by considering relatively lower intervention or land use change in its upstream. This model provides a baseline for daily flow and sediment load for scenario modeling (e.g., climate change, land use change) for environmental flow estimation of the fish habitat, freshwater supply, irrigation, and salinity intrusion. [ABSTRACT FROM AUTHOR]

Unknown basal characteristics limit our ability to simulate the subglacial hydrology of rapidly melting contemporary ice sheets. Sediment-based landforms generated beneath Late Pleistocene ice sheets, together with detailed digital elevation models, offer a valuable means of testing basal hydrology models, which describe the flow and dynamics of water in the subglacial system. However, to date no work has evaluated how well process-based subglacial hydrology models represent the hypothesized conditions associated with glaciofluvial landform formation in the palaeo setting. Previous work comparing model output to geomorphological evidence has typically done so using models that do not resolve subglacial processes and instead express likely subglacial water pathways. Here, we explore the ability of the Glacier Drainage System model (GlaDS), a process-based subglacial hydrology model, to represent the genesis conditions associated with a specific glaciofluvial landform termed "murtoos". Distinctive triangular landforms found throughout Finland and Sweden, murtoos are hypothesized to form 40–60 km from the former Fennoscandian Ice Sheet margin within a "semi-distributed" system at the onset of channelized drainage in small cavities where water pressure is equal to or exceeds ice overburden pressure. Concentrating within a specific ice lobe of the former Fennoscandian Ice Sheet and using digital elevation models with a simulated former ice surface geometry, we forced GlaDS with transient surface melt and explored the sensitivity of our model outcomes to parameter decisions such as the system conductivity and bed topography. Our model outputs closely match the general spacing, direction, and complexity of eskers and mapped assemblages of features related to subglacial drainage in "meltwater routes". Many of the predictions for murtoo formation are produced by the model, including the location of water pressure equal to ice overburden, the onset of channelized drainage, the transition in drainage modes, and importantly the seasonal sequence of drainage conditions inferred from murtoo sedimentology. These conclusions are largely robust to a range of parameter decisions, and we explore seasonal and inter-annual drainage behaviour associated with murtoo zones and meltwater pathways. Our results demonstrate that examining palaeo basal topography alongside subglacial hydrology model outputs holds promise for the mutually beneficial analyses of palaeo and contemporary ice sheets to assess the controls of hydrology on ice dynamics and subglacial landform evolution. [ABSTRACT FROM AUTHOR]

Distribution and change of freshwater resources is often simulated with global hydrological models. However, owing to process representation limitations and forcing data uncertainties, these model simulations have shortcomings. Combining them with observations via data assimilation, for example, with data from the Gravity Recovery and Climate Experiment (GRACE) mission or streamflow measured at in situ stations is considered to improve the realism of the simulations. We assimilate gridded total water storage anomaly (TWSA) from GRACE into the WaterGAP Global Hydrology Model (WGHM) over the Mississippi River basin via an Ensemble Kalman Filter. Our results agree with previous studies where assimilating GRACE observations nudges TWSA simulations closer to the observations, reducing the root mean square error (RMSE) by 21% compared to an uncalibrated model. However, simulations of streamflow show degeneration at more than 90% of all gauge stations for metrics such as RMSE and correlations; only the annual phase of simulated streamflow improves at half the stations. Therefore, for the first time, we instead assimilated streamflow observations into the WGHM, which improved simulated streamflow at up to nearly 80% of the stations, with normalized RMSE showing improvements of up to 0.1, while TWSA was well‐simulated in all metrics. Combining both approaches, that is, jointly assimilating GRACE‐derived TWSA and streamflow observations, leads to a trade‐off between a good fit of both variables albeit skewed to the GRACE observations. Overall, we speculate that our findings point to limitations of process representation in WGHM hindering consistent flux simulation from the storage history, especially in dry regions. Plain Language Summary: The distribution of freshwater on Earth can be simulated by hydrological models like the WaterGAP Global Hydrology Model (WGHM). Changes of the total water storages can also be derived from satellite gravimetry, for example, with the Gravity Recovery and Climate Experiment (GRACE) mission. Model and observations do not necessarily agree and are both prone to errors. We combined the model and observations over the Mississippi River basin taking the errors into account by applying a data assimilation technique. This led to a more realistic simulation of the total water storage changes. Since simulated streamflow was found to degenerate at nearly all gauge stations, we assimilated streamflow observations (instead of GRACE data) into the WGHM. This turned out to improve simulated streamflow at up to nearly 80% of the stations. The assimilation of both data sets (GRACE and streamflow) together leads to a compromise between the TWSA and the streamflow simulations, with the GRACE data affecting the simulations more than the streamflow data. Our findings are expected to aid reducing the weaknesses of the WGHM model equations and thus contribute to improved quantification of Earth's freshwater distribution in the future. Key Points: GRACE assimilation improves the WGHM storage representation, but degrades the simulated streamflow at up to 99% of the gauge stationsAssimilating streamflow data instead improves the streamflow simulations at up to 79% of the validation stationsJoint assimilation leads to a trade‐off between a good fit of simulated storages and streamflow albeit skewed to the GRACE observations [ABSTRACT FROM AUTHOR]

Climate change is one of the most critical factors impacting hydrological dynamic systems. This study investigated how climate change influences the hydrological dynamics within the Siliana watershed in northwestern Tunisia, employing the Soil and Water Assessment Tool (SWAT) model. The analysis compared streamflow patterns for the future period (2046–2072) with a baseline period (1979–2005). Simulations were carried out using four combinations of regional and global climate models from EURO-CORDEX, based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The results indicate a projected annual precipitation decrease of 22% with RCP4.5 and 27% with RCP8.5, accompanied by a temperature rise of up to 7 °C under RCP8.5. Streamflow is anticipated to decrease by 44% under RCP4.5 and 69% under RCP8.5. Extreme events show intensified high flows of shorter durations and increased low flows. Analysis using the Standardized Precipitation Evapotranspiration Index (SPEI) revealed longer and more intense droughts. Under the RCP8.5 scenario, 24% of the watershed faces extreme drought, while 76% experiences severe drought conditions. These findings highlight notable changes in hydrological indicators, emphasizing the urgent need for adaptive strategies in water resource management within the Siliana Basin to mitigate the effects of climate change. [ABSTRACT FROM AUTHOR]

Canada‐wide, statistically downscaled simulations of global climate models from the Sixth Coupled Model Inter‐comparison Project (CMIP6) have been made available for 26 models using a new multivariate approach and an improved observational target dataset. These new downscaled scenarios comprise daily simulations of precipitation, maximum temperature, and minimum temperature at 1/12° resolution across Canada. Simulations from each of the 26 downscaled global climate models span a historical period (1950–2014), and three future Shared Socio‐economic Pathways (SSPs) representing low (SSP1 2.6), moderate (SSP2 4.5) and high (SSP5 8.5) future emissions from 2015 to 2100. Results from an evaluation of the multivariate downscaling method over Canada yield improved performance in replicating multivariate and compound climate indices compared to previously used univariate downscaling methods. This Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS‐M6) dataset is intended to facilitate climate impacts assessments, hydrologic modelling, and analysis tools for presenting climate projections. [ABSTRACT FROM AUTHOR]

Regarding the terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (-FO) gravity satellite missions, a BEAST (Bayesian estimator of abrupt change, seasonal change and trend)+GMDH (group method of data handling) gap-filling scheme driven by hydrological and meteorological data is proposed. Considering these driving data usually cannot fully capture the trend changes of the TWSA time series, we propose first to use the BEAST algorithm to perform piecewise linear detrending for the TWSA series and then fill the gap of the detrended series using the GMDH algorithm. The complete gap-filling TWSAs can be readily obtained after adding back the previously removed piecewise trend. By comparing the simulated gap filled by BEAST + GMDH using Multiple Linear Regression and Singular Spectrum Analysis with reference values, the results show that the BEAST + GMDH scheme is superior to the latter two in terms of the correlation coefficient, Nash-efficiency coefficient, and root-mean-square error. The real GRACE/GFO gap filled by BEAST + GMDH is consistent with those from hydrological models, Swarm TWSAs, and other literature regarding spatial distribution patterns. The correlation coefficients there between are, respectively, above 0.90, 0.80, and 0.90 in most of the global river basins. [ABSTRACT FROM AUTHOR]

The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling. [ABSTRACT FROM AUTHOR]

This historical review addresses the issues of the evaluation and testing of hydrological models, with a focus on rainfall–runoff models. After a discussion of the general philosophies of hydrological modeling, nine different philosophies of model evaluation are considered, focusing on the period of modeling on digital computers since the 1960s. In addition, some discursions to discuss the definitions of calibration and validation, how much data is needed for model calibration, equifinality and uncertainty, probabilities and possibilities, the evaluation of model ensembles, and model benchmarking. The paper finishes with a final discursion on the philosophical problem of induction.This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes [ABSTRACT FROM AUTHOR]

AbstractDeep learning-based super-resolution methods have been successfully applied to digital elevation model (DEM) downscaling studies by designing structures and loss functions of the model. However, little attention has been paid to the design of super-resolution models that can maintain the hydrological characteristics of the DEM, which is important for hydrological studies. This study introduces a super-resolution model that integrates hydrologic knowledge (HKSRCGAN), with the aim to effectively maintain topographic features as well as the hydrologic connectivity of the DEM. The hydrological knowledge derived from surface flow direction and hydrological features are integrated into a deep learning algorithm to guide model training. The 30 m spatial resolution FABDEM is used to demonstrate the utility of the proposed method. Results show that the HKSRCGAN outperforms the bicubic interpolation, SRCNN, SRGAN, SRResNet and TfaSR methods in reducing topographic errors and maintaining hydrologic characteristics. In the test area, the entropy difference analysis shows that the DEM generated by HKSRCGAN is similar to the information contained in the reference DEM. Furthermore, super-resolution models integrating hydrological knowledge are valuable for modeling terrain primarily shaped by gravity and surface water flows. In the future, deep learning-based models integrating hydrologic knowledge are expected to be applied in DEM upscaling to maintain consistent hydrological characteristics. [ABSTRACT FROM AUTHOR]

Liparis loeselii is a rare and declining orchid species restricted to rich fens in the Northern Hemisphere. Habitat destruction, eutrophication, drainage and scrub encroachment have been suggested as reasons for the decline. However, which factors are most important is not well understood. Based on vegetation and environmental properties from extant, potential and historical L. loeselii sites, we 1) developed habitat suitability models from either Ellenberg Indicator Values, which were derived from known habitat preferences of co‐occurring plant species, or field‐measured environmental properties, and 2) identified the primary reasons for the observed decline of L. loeselii. We found nutrient status to be the most important predictor for L. loeselii occurrence, followed by hydrology proxies (Ellenberg reaction and Ellenberg moisture). Vegetation height and Ellenberg light were of minor importance. Effect partitioning based on sites, from which L. loeselii has gone locally extinct, pinpointed eutrophication and drainage to be the most likely primary drivers of the species' demise. Phosphorus limitation induced by discharge of calcium‐rich groundwater seems to be crucial for L. loeselii to sustain populations in landscapes dominated by intensive agriculture. [ABSTRACT FROM AUTHOR]

Climate change has several effects, including the changes of water discharge values and flood occurrences. In the present study five catchments are analysed using the European hydrology and climate data explorer. Two hydrological models and eight regional climate model simulations with three different representative concentration pathway (RCP) scenarios were selected for the investigation, focusing on the period 2071–2100 relative to 1971–2000. The catchments are located in different climate zones in Europe, therefore, it is possible to compare how climate and its changes determine mean and maximum water discharge. In addition, there is a special focus on the 10- and 50-year recurrence of floods. [ABSTRACT FROM AUTHOR]

We present a new calibration strategy guide for coupled surface–subsurface hydrologic models. The goal is to reduce bias in environmental modeling and make calibration processes more consistent. The strategy guide enhances model efficiency and accuracy by focusing on changes to channel, soil, landscape, and aquifer properties. This approach is particularly beneficial when simulating important hydrologic features like baseflow and peaks and across different watersheds. Developed using the Soil and Water Assessment Tool (SWAT+) and the new gwflow module in six diverse US watersheds, each with unique hydrologic characteristics, the guide encourages improved calibration through routine use. It addresses specific issues related to the temporal distribution of streamflow and offers a robust method for enhancing model performance across a wide range of hydrologic conditions. By employing parameter estimation and sensitivity analysis, the approach ensures rigorous and objective calibration of SWAT+ and other models that include land, soil, aquifer, and water processes, leading to more accurate hydrologic modeling. [ABSTRACT FROM AUTHOR]

Continuous hydrological simulation is a powerful approach for generating long-term series of river discharges used for hydrological analyses. This approach requires as inputs precipitation time series generated by a stochastic weather generator (WGEN) to simulate discharge time series. For small catchments where a lumped hydrological model is suitable, the weather generator needs to generate time series of mean areal precipitation (MAP). Here we assess the ability of an at-site hybrid WGEN to generate time series of MAP for a set of test areas ranging from 9 to 1,089 km 2 . The generator is composed of a model based on a Markov chain model used to generate time series of daily MAP, and a multiplicative random cascade used to disaggregate them to an hourly resolution. The work is carried out at several test locations in Switzerland with different precipitation regimes. The parameters of the model are estimated on the observed MAP time series extracted from CombiPrecip, a 1 km 2 resolution radar-gauge product of precipitation assimilating rain gauges and radar data. For each test location and each test area, 100-year time series are generated and compared with the observed MAP time series. Whatever the location and spatial scale considered, the performance of the WGEN is satisfactory. The model reproduces the observed standard statistics and extreme precipitation of observed MAP very well. At an hourly resolution, better results are obtained at larger spatial scales, while no difference is noticed at a daily resolution. The study shows that using this hybrid WGEN is possible to model and generate MAP for areas ranging from 9 to 1,089 km 2 . Moreover, this particular WGEN is easy to implement for end-user applications. The modelling approach is even more promising as high-resolution gridded precipitation data are expected to become increasingly available worldwide, offering a source of data to calibrate the hybrid model. [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]

The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak‐sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash‐Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision‐making process, three primary flood‐inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation‐dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data‐driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics. Plain Language Summary: The formation of floods is often dynamically influenced by multiple driving factors. Traditional methods based on statistics or hydrological models struggle to clearly understand flood mechanisms due to their limitations. Although deep learning has become an effective tool for flood modeling, its black‐box nature makes it difficult to enrich understanding of flood processes through it. Our proposed interpretable deep learning offers a perspective to unveil the dynamic drivers of floods by extracting patterns from deep learning in a reverse deduction approach. We started with models that perform best on flood extremes and identified the dominant factors and variations of floods across 482 watersheds in the United States. These variations exhibit regional characteristics, with precipitation playing a more significant role and showing more pronounced trends in watersheds where floods are primarily driven by precipitation. We found that variables from 2 days before a flood could have a greater impact than those from the previous days. Furthermore, we revealed the combined impact of variables on flooding through deep learning, showcasing their dynamic changes. Interpretable deep learning explores a new avenue to derive new hydrological insights from inverse data, helping hydrologists better understand natural physical processes. Key Points: Proposed a framework for interpretable time series deep learning based on optimal performance to reveal the dynamic drivers of floodsUnveiled the changes in the dominant driving factors of floods and the roles of driving factors at different moments before a floodElucidated the dynamic responses of floods to the interactions among driving factors [ABSTRACT FROM AUTHOR]

This study assesses the impact of climate change on streamflow characteristics in the Lualaba River Basin (LRB), an important yet ungauged watershed in the Congo River Basin. Two conceptual hydrological models, HBV-MTL and GR4J, were calibrated using the reanalysis datasets and outputs of Generalized Circulation Models (GCMs) under CMIP6 during the historical period. The hydrological models were fed with outputs of GCMs under shared socioeconomic pathways (SSPs) 2-45 and 5-85, moderate- and high-radiative future scenarios. The results demonstrate that hydrological models successfully simulate observed streamflow, but their performance varies significantly with the choice of climate data and model structure. Interannual streamflow (Q) percentiles (10, 50, 90) were used to describe flow conditions under future climate. Q10 is projected to increase by 33% under SSP2-45 and 44% under SSP5-85, suggesting higher flow conditions that are exceeded 90% of the time. Q50 is also expected to rise by almost the same rate. However, a considerably higher Q90 is projected to increase by 56% under the moderate- and 80% under the high-radiative scenario. These indicate the overall higher water availability in this watershed to be used for energy and food production and the need for flood risk management. [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]

This study evaluates two reanalysis precipitation products (CRA40 and ERA5) over the Ganjiang River Basin with precipitation data from 37 ground rainfall gauges and surface-observed stream flow data from January 1998 to December 2008. Direct comparison with rain gauge observations shows that both CRA40 and ERA5 can capture the spatial and temporal characteristics of precipitation at the basin scale of the Ganjiang River and reflect most of the precipitation events, but there are pronounced differences in the quality of precipitation between them. ERA5 performs better on the daily scale, capturing precipitation changes more accurately over short periods of time, while CRA40 performs better on the monthly scale, providing more stable and long-term precipitation trends. The results of stream flow simulations using two reanalysis precipitation products driving the VIC hydrological model show that (1) CRA40 outperforms ERA5 with a better Nash–Sutcliffe Efficiency (NSE, 0.65 and 0.6) and higher CC (0.96 and 0.91) in daily and monthly scale stream flow simulations, and ERA5 has a good CC (0.86 and 0.93, respectively), but its NSE is poor (0.29 and 0.30, respectively); (2) both CRA40 and ERA5 generally overestimate basin stream flows, especially during the flood season (April–September), with ERA5's overestimation being more pronounced. This study is expected to provide a basis for the selection of reliable reanalysis products for Ganjiang River Basin precipitation and hydrological simulation. [ABSTRACT FROM AUTHOR]

Rainfall-induced hydrological processes and surface-water flow hydrodynamics may play a key role in initiating debris flows. In this study, a new framework based on an integrated hydrological and hydrodynamic model is proposed to estimate the intensity–duration (ID) rainfall thresholds that trigger debris flows. In the new framework, intensity–duration–frequency (IDF) analysis is carried out to generate design rainfall to drive the integrated models and calculate grid-based hydrodynamic indices (i.e., unit-width discharge). The hydrodynamic indices are subsequently compared with hydrodynamic thresholds to indicate the occurrence of debris flows and derive rainfall thresholds through the introduction of a zone threshold. The capability of the new framework in predicting the occurrence of debris flows is verified and confirmed by application to a small catchment in Zhejiang Province, China, where observed hydrological data are available. Compared with the traditional statistical approaches to derive intensity–duration (ID) thresholds, the current physically based framework can effectively take into account the hydrological processes controlled by meteorological conditions and spatial topographic properties, making it more suitable for application in ungauged catchments where historical debris-flow data are lacking. [ABSTRACT FROM AUTHOR]

Hydrological models serve as effective tools for managing water sources and assessing water components. Although basin hydrological models have been developed, selecting suitable software for modeling specific areas remains challenging. Hence, it's essential to opt for software capable of simulating the hydrological cycle with a simple structure and minimal input parameters. In northwest Iran, AWBM and ARCSWAT software were employed for simulation. AWBM is a simplistic program estimating runoff using precipitation and evaporation indicators, while ARCSWAT is a semi-distributed software for continuous flow simulation. ARCSWAT necessitates various inputs, including geological, land use, and slope maps, along with precipitation, temperature, relative humidity, and radiation statistics. The runoff simulation results during calibration and validation periods were assessed using two statistical indices: Nash Sutcliffe Efficiency (NSE) and the coefficient of determination (R2). A comparison of the statistical indicators revealed NSE values of 0.83 and 0.7 for calibration and validation periods, respectively, and R2 values of 0.83 and 0.8 for calibration and validation periods, respectively, for the semi-distributed SWAT model. In contrast, for the AWBM integrated model in the basin, NSE values were 0.63 and 0.48, and R2 values were 0.48 and 0.48 for calibration and validation periods, respectively, indicating better results in runoff simulation at the monthly time scale. [ABSTRACT FROM AUTHOR]

The flood related problems have amplified greatly in the Panchganga River (PR) basin. Thus, the present research work developed coupled 1D–2D hydrodynamic model by using Hydrological Engineering Centre-River Analysis System (HEC-RAS) for mapping of flood on the PR reach between Shinganpur to Ichalkaranji gauging stations. In this study, 1D unsteady model is calibrated and validated for flood events occurred in 2021 and 2019, respectively. The developed 1D model is then coupled with 2D floodplain model. The capabilities of 1D and coupled 1D–2D models are evaluated by RMSE, NSE, PBIAS and R2 values, these values lie in range; RMSE = 0.64 to 0.85, NSE = 0.33 to –0.35, PBAIS = –0.0011 to 0.089 and R2 = 0.70 to 0.97. The floodplain's Manning's n is selected on the basis of six available observed data locations in the Kolhapur City. The inundation map is developed for the Kolhapur City. The fairly accurate flooding depth and inundation extent is observed from inundation map developed in 1D–2D coupled HEC-RAS model. [ABSTRACT FROM AUTHOR]

More than half of water supply in the western United States is sourced from forested lands that are increasingly under wildfire risk. Studies have begun to isolate the effects of wildfire on streamflow, but they have typically used coarse temporal resolutions that cannot account for the numerous, interconnected watershed processes that control the responses to rainfall events. In this study, we employed a method to isolate fine‐scale (daily) effects of fire. Wildfire effects were estimated as the difference between measured post‐fire streamflow and unburned scenarios of post‐fire streamflow simulated by a hydrologic model calibrated to pre‐fire conditions. The method was applied to track hydrologic recovery after wildfires in six burned watersheds across the western United States: North Eagle Creek, NM (2012 Little Bear Fire), Lopez Creek, CA (1985 Las Pilitas Fire), City Creek, Devil Canyon Creek, East Twin Creek, and Plunge Creek, CA (2003 Old Fire). All six watersheds experienced prolonged increases of post‐fire streamflow, with the most consistent changes occurring during periods of low streamflow. Following 6 years of increased streamflow, Lopez Creek experienced 6 years of reduced streamflow before returning to the pre‐fire hydrologic regime. North Eagle Creek and the four watersheds affected by the Old Fire continued to have elevated streamflow 9 and 18 years post‐fire, respectively, without returning to the pre‐fire hydrologic regime. [ABSTRACT FROM AUTHOR]

Hydrological model is an important tool for flood simulation, and most of the models contain a certain number of parameters. There? fore, parameter calibration is one of the keys to improve the precision of hydrological model runoff simulation and ensure the accuracy of mod? el results. As the optimization standard of parameter calibration process, there is currently no detailed classification system to provide a clear selection basis for the selection of objective function. In order to explore the effects of different types of objective functions on the calibration results of hydrological model parameters and the precision of runoff simulation, this paper comprehensively considered various types of objectives, such as Nash efficiency coefficient, Kling-Gupta coefficient, flood relative error, etc., and constructed an objective function classification system that includes high flow target, low flow target, flood volume target, flood process target and weighted target. According to the requirements of the current hydrological forecast information standard, some criteria were provided for the selection of objective function. Further, the Xinanjiang model was established, and the optimization algorithms of SCE-UA and NSGA-II were introduced. A variety of objective function selection schemes were set up for different algorithms, and the influence of different objective function schemes and optimization algorithms on the precision of runoff simulation was analyzed by taking Muma River in Hanjiang River basin as an example. The results show that using a single objective function can improve the simulation accuracy of hydrological elements embodied by the objective function; Selecting the appropriate objective function is the key to improve the precision of runoff simulation. Under the requirements of existing norms, selecting flood process target and high flow target is the best choice to ensure the accuracy of hydrological model. Compared with Nash efficiency coefficient, Kling Gupta coefficient has better performance in flood volume simulation accuracy and is a better choice for flood process target. The simulation results of weighted single target method and multi-target method are different in the high-flow part. [ABSTRACT FROM AUTHOR]

Flood forecasting is crucial for flood prevention and mitigation in both watersheds and urban areas. In watersheds, the development of flood forecasting methods is relatively mature, while their application in urban areas is still in the research and exploration stage. In practice, high-precision hydrological and hydrodynamic modeling during large floods is constrained to varying degrees by power, timeliness, and transmission conditions, whereas the unit hydrograph method is simple, efficient, has fewer constraints, and plays an important role. To address the current situation, this study introduces an improved synthetic unit hydrograph method to evaluate its practical effectiveness in watershed and urban flood forecasting. Firstly, based on the Snyder's synthetic unit hydrograph method, the flooding process in Muyangxi watershed in Fujian Province was calculated and compared with the results of the Xin'anjiang model and the measured data; secondly, based on the Snyder's synthetic unit hydrograph method, the urban synthetic unit hydrograph was established by taking into account the impermeability of the sub-surface and the slope, which was then applied to the calculation of the flooding process line in the urban area of Zhengzhou, and the differences of the simulation results between this method and the mainstream SWMM model were compared. The differences between this method and the mainstream SWMM model simulation results are compared. The results show that the synthetic unit hydrograph method is close to the measured and hydrological model results in the selected watersheds, and the forecast pass rate is 91.7%, with no peak-present time difference; compared with the SWMM model results, the relative error of the peak flow of the most floods is within 10%, and the coefficient of certainty of the floods in 72.7% is above 0.9. The example application shows that compared with the hydrological and hydrodynamic modeling methods, the partial rise and fall process of the flood calculated by the synthetic unit hydrograph method is better than the model results, which are more in line with the actual process, and its calculation is concise. The synthetic unit hydrograph method requires fewer parameters, and has strong implementation and popularity. The research further verifies the superiority of the synthetic unit hydrograph method in flood forecasting and provides an effective method for flood process calculation. [ABSTRACT FROM AUTHOR]