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The study focuses on the uncertainty quantification and bias correction of hydrological projections using Bayesian applications. The climate change impact assessment on streamflow has been done using Soil and Water Assessment Tool (SWAT) model in Bharathapuzha river basin, India. The uncertainty quantification has been done by using Generalised Likelihood Uncertainty Estimation (GLUE) algorithm and the ensemble spread in the streamflow projections is quantified as the total uncertainty. A Hierarchical Bayesian Algorithm is adopted in the current study to remove the systematic bias in the projections of extreme streamflow. The approach established a probabilistic correction to the projected streamflow based on the biases in daily scale hindcast streamflow simulations with the corresponding observed historical streamflow data. The procedure is applied to the ensemble streamflow predictions for the Bharathapuzha catchment and over 10 times reduction in RMSE is observed in the bias corrected streamflow. The skill of the procedure in correcting the streamflow across different terciles is studied using the concept of reliability and significant improvement is observed in the reliability of high and medium flow ranges. The average width of the ensemble streamflow simulation band for the period 2021–2030 is seen to reduce from 5560 cumec to 2188 cumec after the correction procedure is applied. Highlight: The parametric uncertainty in the streamflow projection is accounted by calibrating the hydrological model using the GLUE procedure. A Hierarchical Bayesian approach has been adopted for bias correction of the streamflow projections. A higher reliability is observed in the bias-corrected streamflow, especially in high and medium flow ranges. The average width of the ensemble streamflow simulation band has reduced from 5560 cumec to 2188 cumec after the bias correction. [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]

Comprehensive assessments of hydrological components are crucial for enhancing operational water supply simulations. However, hydrological models are often evaluated based on their surface flow simulations, while the validation of subsurface and groundwater components tends to be overlooked or not well documented. In this study, we evaluated the outputs of two hydrological models, the Large Basin Runoff Model (LBRM) and the Weather Research and Forecasting – Hydrological modeling extension package (WRF-Hydro), for potential implementation in operational water balance forecasting in the Great Lakes region. We examined the simulated hydrological variables including surface (e.g. snow water equivalent, evapotranspiration, and streamflow), subsurface (e.g. soil moisture at different layers), and groundwater components with observed or reference data from ground-based stations and remotely sensed images. The findings of this study provide valuable insights into the capabilities and limitations of each model. These findings contribute to more informed water management strategies for the Great Lakes region. [ABSTRACT FROM AUTHOR]

Hydrological models are critical for water resources planning and management. The precision and reliability of the simulations hinge greatly on the accessibility and quality of available input data. Particularly in developing nations, the major challenge in modeling is the scarcity of fine-scale spatiotemporal input data, specifically precipitation. Remotely sensed weather data has been increasingly used in recent years. However, they possess bias compared to ground observations due to the nature of indirect measurement and may affect the simulated water balance. To address these limitations, we explored data assimilation techniques to improve the Global Precipitation Measurement product (IMERG) precipitation using limited ground observations. Multiple assimilation methods are applied by incorporating Linear scaling Correction Factor (CF) and Power Transformation Function methods (PF). The assimilated IMERG precipitation from the most effective method identified, is utilized in an eco-hydrological model, and the resulting stream flow simulations are validated against observed flow data. The findings indicate that assimilated precipitation enhances the monthly flow statistics in both CF and PF methods and also in conditional merged precipitation. An ensemble of hydrological simulations, outperformed those based on raw IMERG precipitation. Additionally, the hydrological simulations are compared with observed gauge precipitation data and the widely used Climate Forecast System Reanalysis (CFSR) dataset in data-limited watersheds. The simulations utilizing the assimilated IMERG dataset (NSE = 0.52) are comparable to gauge precipitation-based simulations (NSE = 0.61) and significantly superior to CFSR-based simulations (NSE=-0.2). These results highlight the potential of utilizing assimilated remote sensing data for hydrological modeling in data-limited watersheds, leading to improved simulation accuracy and reliability. [ABSTRACT FROM AUTHOR]

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]

Low Impact Development (LID) measures have proven to be effective in reducing the runoff volume and the peak of runoff at small spatial scales. Assessment of the hydrological impacts of LID measures at river basin scales is currently limited by the absence of adequate physically-based process models that can simulate LID processes at such scales. In this study, an attempt has been made to develop LID modules to simulate the water balance and hydrological processes based on the Multi-Layer Green-Ampt (MLGA) infiltration method, which is an advancement to the existing LID modeling techniques. The performance of the MLGA-based LID modules was benchmarked with the process-intensive numerical tool HYDRUS 1D for about 5000 soil samples across the soil textural classes. The results indicated that the MLGA-based model simulates the hydrological processes in close agreement with HYDRUS for soils with moderate-to-high hydraulic conductivity values, which are amenable for LID implementation. [ABSTRACT FROM AUTHOR]

This study assesses the performance of the Weather Research and Forecasting-Hydrological modeling system (WRF-Hydro) in the simulation of street-scale flood inundation. The case study is the Hackensack River Watershed in New Jersey, US, which is part of the operational Stevens Flood Advisory System (SFAS), a one-way coupled hydrodynamic-hydrologic system that currently uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to simulate streamflow. The performance of the 50-m gridded WRF-Hydro model was assessed for potential integration into the operational SFAS system. The model was calibrated with the dynamically dimensioned search algorithm using streamflow observations. The model performance was assessed using (i) streamflow observations, (ii) USGS HWMs, and (iii) crowdsourced data on street inundation. Results show that WRF-Hydro outperformed the HEC-HMS model. WRF-Hydro over and underestimated flood inundation extent due to the inaccuracy of the synthetic rating curves and the modeling structure errors. An agreement was noticed between WRF-Hydro and crowdsourced data on flood extent. [ABSTRACT FROM AUTHOR]

Flooding represents the greatest natural threat to the UK, presenting severe risk to populations along coastlines and floodplains through extreme tidal surge and hydrometeorological events. Climate change is projected to significantly elevate flood risk through increased severity and frequency of occurrences, which will be exacerbated by external drivers of risk such as property development and population growth throughout floodplains. This investigation explores the entire flood hazard modelling chain, utilising the nonparametric bias correction of UKCP18 regional climate projections, the distributed HBV-TYN hydrological model and HEC-RAS hydraulic model to assess future manifestation of flood hazard within the Broadland Catchment, UK. When assessing the independent impact of extreme river discharge and storm surge events as well as the impact of a compound event of the two along a high emission scenario, exponential increases in hazard extent over time were observed. The flood extent increases from 197 km2 in 1990 to 200 km2 in 2030, and 208 km2 in 2070. In parallel, exponential population exposure increases were found from 13,917 (1990) to 14,088 (2030) to 18,785 (2070). This methodology could see integration into policy-based flood risk management by use of the developed hazard modelling tool for future planning and suitability of existing infrastructure at a catchment scale. [ABSTRACT FROM AUTHOR]

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

This article presents comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) model for rainfall-runoff (R-R) process. Aim of the present work is to forecast runoff one day ahead at Shivade station of Upper Krishna Basin, India, using 17 years of daily rainfall and discharge data. The R-R modelling can be exercised using various traditional methods which generally require exogenous data in the form of basin parameters. Unavailability of such data becomes major impediment in applying these models at many basins. In such situations, soft computing techniques like ANNs have been extensively applied to model R-R process. Though ANN is now an established tool in hydrology, compared to HEC-HMS, its results are viewed with suspicion owing to its data-driven nature rather than a model-driven nature. In this study, ANN model performed reasonably well, with a higher correlation coefficient (0.87) and the lowest Root Mean Square Error (136.28 m3/s) when compared with HEC-HMS (0.76, 139.8 m3/s) respectively. Novelty of the present work lies in model development using restricted basin data. Both models showed less accuracy in predicting extreme events. Finally, it is concluded that ANN model can be used as a supplementary technique along with HEC-HMS for this phenomenon. [ABSTRACT FROM AUTHOR]

High-resolution temperature forecasting plays a crucial role in various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. The existing low-resolution forecast data may not accurately capture the fine-grained temperature patterns required for localized predictions. These forecasts may contain biases that need to be corrected for accurate results. Therefore, there is a need for an effective framework that can downscale low-resolution forecast data and correct biases to generate high-resolution temperature forecasts. The paper proposes a machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting. The framework utilizes low-resolution forecast data from the European Centre for Medium-Range Weather Forecasts and real-time 1km analysis product data from the National Meteorological Administration, to generate high-resolution 1km forecast temperature data. The framework consists of four modules: data acquisition module, data preprocessing module, downscaling and correction model module, and post-processing and visualization module. Through experiments, it demonstrated that the framework has superior performance and potential in meteorological data downscaling and correction and can be used to achieve real-time high-resolution temperature forecasting, which has important significance for various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. [ABSTRACT FROM AUTHOR]

Widespread flooding in Africa has devastating repercussions on communities, and sometimes leading to loss of life, displacement of populations, and significant damage to infrastructure and agriculture. Despite this, there are limited studies that investigate the behaviour of high time frequency river flows for the major river systems of Africa to inform adaptation and mitigation strategies for improved resilience of society. This paper fills this gap by assessing the occurrence time of annual maximum daily discharge for five of the longest rivers of Africa using a statistical modelling approach. This is the first of such a study covering all of the five longest rivers of Africa in one paper. Annual maximum daily discharge time for each river was modeled by mixtures of von Mises distributions, fitted by a Markov chain Monte Carlo algorithm. Data on mean daily discharge was obtained from the Global Runoff Data Centre database for the Niger, Zambezi, Okavango, Limpopo and Orange rivers in Africa. Estimates were inferred for the location parameter of the major mode, location parameter of the minor mode, concentration parameter of the major mode, concentration parameter of the minor mode, mean time, mean resultant, circular variance, circular skewness, and circular kurtosis. The developed models reveal distinctive temporal patterns of peak discharge events in each river, which can have significant implications for flood management, water resource planning, hydrological modeling, risk assessment and infrastructure design. [ABSTRACT FROM AUTHOR]

The frequency change of 100‐year flood events is often determined by fitting extreme value distributions to annual maximum discharge from a historical base period. This study demonstrates that this approach may significantly bias the computed flood frequency change. An idealized experiment shows frequency bias exceeding 100% for a 50‐year base period. Further analyses using Monte Carlo simulations, mathematical derivations, and hydrological model outputs reveal that bias magnitude inversely relates to base period length and is weakly influenced by the generalized extreme value distribution's shape parameter. The bias, persisting across different estimation methods, implies floods may exceed local defenses designed based on short historical records more often than expected, even without climate change. We introduce a frequency bias adjustment method, which significantly reduces the projected rise in global flood occurrence. This suggests a substantial part of the earlier projected increase in flood occurrence and impacts is not attributable to climate change. Plain Language Summary: We report a previously overlooked bias in a common method for projecting the frequency of extreme floods, like 100‐year events. This method typically uses a short period of historical data for two primary purposes: first, to define local thresholds for extreme floods, such as 100‐year events, and second, to serve as a base for projecting future changes in flood occurrences. Our study, utilizing random data, demonstrates that this method can lead to inaccurate projections, increasing the number of expected extreme floods in future years and decreasing them in historical baseline years, even when no change is indicated by the data. Additionally, we find that the longer the historical period used, the smaller the bias. By simulating the characteristics of real hydrological model projections with artificial data, we are able to estimate a bias similar to that observed in these projections, and thus propose a simple method for its mitigation. Our findings indicate that previous studies might have overstated the impact of climate change on the projected increase in flood frequency and affected people. Additionally, using short historical records to design flood defenses can lead to more frequent flooding than anticipated, even without climate change. Key Points: Using a short historical period both to define extreme flood thresholds and as a base for future projections leads to significant biasThis bias shows an inverse relationship with the length of the base period and is slightly influenced by certain data characteristicsWe propose a practical adjustment method and suggestions to reduce biases in estimating future flood frequency changes [ABSTRACT FROM AUTHOR]

Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.1° × 0.1° spatial resolution) integrating satellite (CMORPH and IMERG) and reanalysis (ERA5-Land) derived rainfall erosivity estimates with gauge rainfall erosivity observations collected from approximately 6,200 locations across the globe. We used a machine learning-based Gaussian Process Regression (GPR) model to assimilate multi-source rainfall erosivity estimates alongside geoclimatic covariates to prepare a unified high-resolution mean annual rainfall erosivity product. It has been shown that the proposed rainfall erosivity product performs well during cross-validation with gauge records and inter-comparison with the existing global rainfall erosivity datasets. Furthermore, this dataset offers a new global rainfall erosivity perspective, addressing the limitations of existing datasets and facilitating large-scale hydrological modelling and soil erosion assessments. [ABSTRACT FROM AUTHOR]

Accurate estimate of groundwater recharge is crucial for prediction of groundwater table dynamics and dependent eco-hydrological processes. Despite its importance, benchmark data for groundwater recharge at fine (~ daily) temporal resolution is lacking. We present a first-of-a-kind daily groundwater recharge per unit specific yield (RpSy) data over periods of 2–38 years at 485 groundwater monitoring wells in the US. The RpSy data for these locations are calculated from the daily groundwater table time series using the water table fluctuations (WTF) method. Although direct validation of the data is not possible, since it is the first of its kind, we compare the RpSy data with the monthly USGS product to identify similarities and differences. The RpSy dataset may serve as a benchmark for validating the temporal consistency of recharge products and daily simulation results from land surface and integrated hydrologic models. [ABSTRACT FROM AUTHOR]

Two outstanding questions for the future of the Greenland Ice Sheet are (a) how enhanced meltwater draining beneath the ice will impact the behavior of large tidewater glaciers, and (b) to what extent tidewater glacier velocity is driven by changes at the terminus versus changes in sliding velocity due to meltwater. We present a two‐way coupled framework to simulate the nonlinear feedbacks of evolving subglacial hydrology and ice dynamics using the Subglacial Hydrology And Kinetic, Transient Interactions (SHAKTI) model within the Ice‐sheet and Sea‐level System Model (ISSM). Through coupled simulations of Helheim Glacier, we find that terminus effects dominate the seasonal velocity pattern up to 15 km from the terminus, while hydrology drives the velocity response upstream. With increased melt, the hydrology influence yields seasonal acceleration of several hundred meters per year in the interior, suggesting that hydrology will play an important role in future mass balance of tidewater glaciers. Plain Language Summary: Water draining under glaciers and ice sheets affects the friction between the ice and the bed, and controls how fast the ice can slide into the ocean, contributing to sea‐level rise. We present a framework for simulating the feedbacks between hydrology and ice flow. We investigate the relative influence of changes at the terminus of the glacier where it meets the ocean, versus changes in meltwater drainage, in determining how fast the glacier moves. Our modeling of Helheim Glacier in southeast Greenland highlights the importance of terminus effects up to 15 km from the terminus, and hydrology farther upstream, with increased melt yielding higher inland acceleration. These results suggest that meltwater will play an increasingly important role in the future behavior of glaciers. Key Points: We couple a subglacial hydrology model with an ice flow model to simulate the relationship between sliding velocity and effective pressureTerminus effects at Helheim Glacier drive velocity up to 15 km upstream, but seasonal hydrology controls velocity patterns further inlandIncreased melt accelerates ice inland of the main trunk, implying importance of hydrology in tidewater glacier future mass balance [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]

Large samples of hydrometeorological time series and catchment attributes are critical for improving the understanding of complex hydrological processes, hydrological model development and performance benchmarking. CAMELS (Catchment Attributes and Meteorological time series for Large Samples) datasets have been developed in several countries and regions around the world, providing valuable data sources and testbeds for hydrological analysis and new frontiers in data-driven hydrological modelling. Regarding the lack of samples from low-land, groundwater-dominated, small-sized catchments, we develop an extensive repository of a CAMELS-style dataset for Denmark (CAMELS-DK). This CAMELS addition is the first containing both, gauged and ungauged catchments as well as detailed groundwater information. The dataset provides dynamic and static variables for 3330 catchments from various hydrogeological datasets, meteorological observations, and a well-established national-scale hydrological model. The dataset is enhanced with streamflow observations in 304 of those catchments. The spatially dense and full spatial coverage, supplying data for 3330 catchments, instead of only gauged catchments, together with the addition of simulation data from a distributed, process-based model enhance the applicability of such CAMELS data. This is especially relevant for the development of data-driven and hybrid physical informed modelling frameworks. We also provide quantities related to human impact on the hydrological system in Denmark, such as groundwater abstraction and irrigation. The CAMELS-DK dataset is freely available at https://doi.org/10.22008/FK2/AZXSYP (Koch et al., 2024). [ABSTRACT FROM AUTHOR]

Watershed hydrological modeling methods are currently the predominant approach for flood forecasting. Digital elevation model (DEM) data, a critical input variable, significantly influence the accuracy of flood simulations, primarily due to their resolution. However, there is a paucity of research exploring the relationship between DEM resolution and flood simulation accuracy. This study aims to investigate this relationship by examining three watersheds of varying scales in southern Jiangxi Province, China. Utilizing the Liuxihe model, a new-generation physically based distributed hydrological model (PBDHM), we collected and collated data, including DEM, land use, soil type, and hourly flow and rainfall data from monitoring stations, covering 22 flood events over the last decade, to conduct model calibration and flood simulation. DEM data were processed into seven resolutions, ranging from 30 m to 500 m, to analyze the impact of DEM resolution on flood simulation accuracy. The results are as follows. (1) The Nash–Sutcliffe efficiency coefficients for the entire set of flood events were above 0.75, demonstrating the Liuxihe model's strong applicability in this region. (2) The DEM resolution of the Anhe and Dutou watersheds lost an average of 7.9% and 0.8% accuracy when increasing from 30 m to 200 m, with further losses of 37.9% and 10.7% from 200 m to 300 m. Similarly, the Mazhou watershed showed an average of 8.4% accuracy loss from 30 m to 400 m and 20.4% from 400 m to 500 m. These results suggest a threshold where accuracy sharply declines as DEM resolution increases, and this threshold rises with watershed scale. (3) Parameter optimization in the Liuxihe model significantly enhanced flood simulation accuracy, effectively compensating for the reduction in accuracy caused by increased DEM resolution. (4) The optimal parameters for flood simulation varied with different DEM resolutions, with significant changes observed in riverbed slope and river roughness, which are highly sensitive to DEM resolution. (5) Changes in DEM resolution did not significantly impact surface flow production. However, the extraction of the water system and the reduction in slope were major factors contributing to the decline in flood simulation accuracy. Overall, this study elucidates that there is a threshold range of DEM resolution that balances data acquisition efficiency and computational speed while satisfying the basic requirements for flood simulation accuracy. This finding provides crucial decision-making support for selecting appropriate DEM resolutions in hydrological forecasting. [ABSTRACT FROM AUTHOR]

Large‐scale hydrologic modeling is important for understanding changes in water resources and flood hazard across a broad range of climatic and hydrologic conditions. Parsimonious models, although simple, allow for an efficient way to model river systems across multiple decades to even centuries. Therefore, this study aims to assess the ability of the distributed Hillslope Link Model (HLM) TETIS to simulate streamflow observations across the contiguous United States (CONUS) from 1981 to 2020. To obtain model parameters across this domain, we partition the study area into 234 HydroSHEDS level 5 basins and calibrate the model to a single representative location near the outlet of each basin using dynamical dimension search for 100 realizations. Performance is then assessed at 5046 US Geological Survey streamgages with respect to the Kling Gupta Efficiency (KGE) and bias. Our simulations result in a median KGE of 0.43, with 89% of the sites having a value above the reference of 1 − √2 (~ ‐0.41). Furthermore, there is a dependence of the model performance on climate regions, with the model performing better in basins in cold and temperate regions than in arid ones. While the parameters are estimated based on daily precipitation inputs, it is shown that the model performs well even when forced with hourly precipitation, highlighting the robustness of the selected parameters to different inputs. Finally, the soil related parameters show dependence on soil properties, providing a basis for future model improvement. Overall, this study highlights the model's flexibility in performing across a vast domain with different runoff generation mechanisms. [ABSTRACT FROM AUTHOR]

Floods regularly cause substantial damage worldwide. Changing flood characteristics, e.g., due to climate change, pose challenges to flood risk management. The spatial extent of floods is an important indicator of potential impacts, as consequences of widespread floods are particularly difficult to mitigate. The highly uneven station distribution in space and time, however, limits the ability to quantify flood characteristics and, in particular, changes in flood extents over large regions. Here, we use observation-driven routed runoff simulations over the last 70 years in Europe from a state-of-the-art hydrological model (the mesoscale Hydrologic Model – mHM) to identify large spatiotemporally connected flood events. Our identified spatiotemporal flood events compare well against an independent flood impact database. We find that flood extents increase by 11.3 % on average across Europe. This increase occurs over most of Europe, except for parts of eastern and southwestern Europe. Over northern Europe, the increase in flood extent is mainly driven by the overall increase in flood magnitude caused by increasing precipitation and snowmelt. In contrast, the increasing trend in flood extent over central Europe can be attributed to an increase in the spatial extent of heavy precipitation. Overall, our study illustrates the opportunities to combine long-term consistent regional runoff simulations with a spatiotemporal flood detection algorithm to identify large-scale trends in key flood characteristics and their drivers. The detected change in flood extent should be considered in risk assessments as it may challenge flood control and water resource management. [ABSTRACT FROM AUTHOR]

Accurate land-use and land-cover (LULC) mapping is crucial for effective watershed management and hydrological modeling in arid regions. This study examines the use of high-resolution PlanetScope imagery for LULC mapping, change detection, and hydrological modeling in the Wadi Ham watershed, Fujairah, UAE. The authors compared LULC maps derived from Sentinel-2 and PlanetScope imagery using maximum likelihood (ML) and random forest (RF) classifiers. Results indicated that the RF classifier applied to PlanetScope 8-band imagery achieved the highest overall accuracy of 97.27%. Change detection analysis from 2017 to 2022 revealed significant transformations, including a decrease in vegetation from 3.371 km2 to 1.557 km2 and an increase in built-up areas from 3.634 km2 to 6.227 km2. Hydrological modeling using the WMS-GSSHA model demonstrated the impact of LULC map accuracy on simulated runoff responses, with the most accurate LULC dataset showing a peak discharge of 1160 CMS at 930 min. In contrast, less accurate maps showed variations in peak discharge timings and magnitudes. The 2022 simulations, reflecting urbanization, exhibited increased runoff and earlier peak flow compared to 2017. These findings emphasize the importance of high-resolution, accurate LULC data for reliable hydrological modeling and effective watershed management. The study supports UAE's 2030 vision for resilient communities and aligns with UN Sustainability Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action), highlighting its broader relevance and impact. [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 assessment of extreme hydrological events relies on the assumption that time series are independent and identically distributed. However, empirical evidence contradicts this assumption, indicating the presence of nonstationarity, including trends, step changes, or both, in the river records of the Argentinean Central Andes. This study proposes the use of generalized additive models in location, scale and shape (GAMLSS) to model the annual maximum and minimum flow of seven rivers. Firstly, the presence of trends and step changes was assessed. Subsequently, the models were formulated considering time and various climatic indices as covariates. The findings reveal declining trends and negative jumps over the past 50 to 70 years. The location parameters of the models exhibit linear or smoothing dependence with time or climate indices covariates. Different modes of climate variability are associated with hydrological extremes. The nonstationary models provide a novel complementary framework for water resource planning in the region. [ABSTRACT FROM AUTHOR]

A new pre-processing methodology for gridded Satellite Precipitation Products (SPPs) is developed to improve the performance of Machine Learning (ML) algorithms for runoff prediction. The developed approach was applied to capture the rainfall patterns, and to select relevant input data. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modelling. The methodology was tested in a semiarid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPMIMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. The results demonstrate the effectiveness of the proposed approach using all employed SPPs. In terms of Nash-Sutcliffe efficiency, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and of ELM by 15%, which highlights how pre-processing techniques significantly enhance ML models with SPP data. [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]

This study assessed the small-scale hydropower potential (SHP) of the Gidabo River catchment. The Soil and Water Assessment Tool (SWAT) hydrological model was used to estimate the streamflow in the river networks. The model was calibrated and validated at Messa and Apposto gauging stations, and the results for the coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) revealed that the observed and simulated streamflow results agree fairly well at the calibration and validation locations. Thus, the simulated streamflow at the sub-catchment outlets was used to determine design discharge at 50%, 75%, and 95% flow dependability. The potential assessment was conducted under a scenario of 500, 1000 and 2000 m horizontal intervals along the stream networks. The study revealed that the river catchment has the potential to develop run-of-river-type plants at different locations with 50%, 75%, and 95% dependability, though it varies from place to place. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the runoff response of Zishui Basin under future climate change scenarios, and to provide scientific support for the sustainable development of the basin and the decision-making of flood control and drought control. [Methods] Based on three CMIP6 global climate models, the SDSM downscaling model and SWAT hydrological model were constructed to predict the changes of temperature and precipitation in the Zishui Basin during 2030—2089 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 climate scenarios, and to further explore the response of runoff to climate change. [Results] Zishui Basin will present a significant warming and humidification trend in the future, and this trend will increase with the increase of discharge scenario. Spatially, the temperature rise of the whole basin is the largest in the vicinity of Xinning and Shaoyang stations. The increasing areas of precipitation mainly concentrate in the middle and lower reaches with Lengshuijiang as the center, while the decreasing area is located west of Dongkou station. In this context, the future annual runoff of Taojiang station and Shaoyang station will have the same variation trend with the watershed precipitation, and the amount of available water resources in the upstream area above Shaoyang station is more likely to decrease. Water resources in the basin will face the risk of more shortage in the dry season, and the flood season tends to advance, and the evenness of water resources allocation in the main flood season will increase. [Conclusion] Under the background of climate warming and humidification, the management of water resources in Zishui Basin will encounter more severe challenges, and the protection of water resources and the integrated management of Zishui Basin should be strengthened. [ABSTRACT FROM AUTHOR]

Modeling streamflow is essential for understanding flow inundation. Traditionally, this involves hydrologic and numerical models. This research introduces a framework using agent-based modeling (ABM) combined with data-driven modeling (DDM) and Artificial Intelligence (AI). An agent-driven model simulates streamflow and its interactions with river courses and surroundings, considering hydrologic phenomena related to precipitation, water level, and discharge as well as channel and basin characteristics causing increased water levels in the Medio River. A five-year dataset of hourly precipitation, water level, and discharge measurements was used to simulate streamflow. The model's accuracy was evaluated using statistical metrics like correlation coefficient (r), coefficient of determination (R2), root mean squared error (RMSE), and percentage error in peak discharge (Qpk). The ABM's simulated peak discharge (Qpk) was compared with the measured peak discharge across four experimental scenarios. The best simulations occurred in scenario 3, using only rainfall and streamflow data. Data management and visualization facilitated input, output, and analysis. This study's ABM combined with DDM and AI offers a novel approach for simulating streamflow and predicting floods. Future studies could extend this framework to other river basins and incorporate advanced sensor data to enhance the accuracy and responsiveness of flood forecasting. [ABSTRACT FROM AUTHOR]

High-resolution numerical ocean models can be used to help interpret sparse observations in the nearshore and to help understand the impacts of climate change and extreme events on these dynamically complex coastal areas. However, these high-resolution ocean models require inputs with comparably high resolution, which is particularly difficult to achieve for freshwater discharge. Here, we explored a simple rain-based hydrological model as input into a high-resolution (≳ 13 m) model of Quatsino Sound – a fjord system located on the northwestern coast of Vancouver Island, British Columbia, Canada. Through a series of sensitivity tests using an application of the Finite Volume Community Ocean Model (FVCOM version 4.1), we found that model performance was hindered by the lack of knowledge of ungauged rivers and streams. In this case study, including the only major gauged river implied ignoring 538 other watersheds of various sizes and accounted for only about a quarter of the total estimated freshwater discharge. We found that including at least 60 % and ideally closer to 75 %–80 % of total freshwater fluxes gave similar model performance to including all possible 539 freshwater sources; in our model simulations, this percentage of freshwater flux meant including rivers with watersheds greater than 20–50 km2 , corresponding to 7–19 total rivers. Further sensitivity tests also indicated that knowing the main outpour locations into the nearshore ocean is an important factor, but it is not as important as the total freshwater discharge included. Overall, this study illustrates the complexities of studying the land–ocean connection and offers a simple and accessible tool to help address a common problem in nearshore modelling. [ABSTRACT FROM AUTHOR]

The present study aims to examine the changing hydro-geomorphological properties of the Mayurakshi River located in a plateau fringe region in Jharkhand and West Bengal. The field investigation of 14 cross-sectional sites has been conducted during the post-monsoon season of 2021 to determine important geomorphic parameters such as channel depth, width, bank height, hydraulic radius, and wetted perimeter. Post-processed kinematic survey and electromagnetic current meter tool have been used to measure the river bed profile and channel velocity, respectively, to obtain more accurate locational information. The primary objective of this study is to compare field-observed discharge data with the discharge volume predicted by a few hydraulic and hydrological models in order to determine the models that are best suited for further application in a scientifically accepted and cost-effective manner. The results demonstrate that there is a noticeable rise in flow velocity from upstream (0.22 m/s) to downstream of the river (0.85 m/s), which is also supported by the results of Manning's equation and kinematic wave parameter. The most correct relationship is then presented by minimizing the deviations between the calculated and analytical discharges at three separate stages. The results show that the proposed Manning's technique is quite accurate with the observed data, as determined by the mean absolute percentage error (60.16%), root mean square error (30.53%), and normalized root mean square error (0.05) indices. [ABSTRACT FROM AUTHOR]

The study gives an estimate of the effect of four methods most often used in hydrological models to calculate the potential evapotranspiration—Penman–Monteith, Priestley–Taylor, Oudin, and Hargreaves, which differ in complexity and meteorological data requirements—on the efficiency and the results of simulating the dynamics of water balance components. The methodological base of the hydrological simulation was the widely known HBV conceptual model, in which the actual evapotranspiration is associated with the current state of a storage, which simulates the dynamics of soil moisture content. The objects of the study were 18 catchments with areas from 2.4 to 755 km2, located within the former Primorskaya Water-Balance station, the data of which were used in calculations of potential evapotranspiration and simulations of runoff. The comparison of simulation results with observations showed that the model values of evapotranspiration at the use of physically sound methods are in better agreement with the Budyko's procedure of determination of potential evapotranspiration. The analysis of model sensitivity to the input potential evaportranspiration showed that the use of physically based methods to calculate the potential evapotranspiration has no general effect on the efficiency of runoff hydrograph simulation of the studied objects. At the same time, in the case when empirical models of evapotranspiration are used, the uncertainty in the results of calculation of potential evapotranspiration should be compensated for by model parameters that are less physically substantiated, which, in turn, can lead to distortion of the dynamics of water balance components and runoff genetic components. [ABSTRACT FROM AUTHOR]

Many dammed reservoirs in dry climate conditions witness high sediment inflow rates due to higher soil erodibility, yet there are limited actual sediment influx measurements. Therefore, this study first applies the Soil and Water Assessment Tool (SWAT) hydrologic model to simulate reservoir sedimentation inflow to the Haditha Reservoir. Next, utilizing sediment inflows estimated by the SWAT model, the Trap Efficiency Function (TEF) is employed to estimate its remaining storage capacity and its useful life at multiple reservoir water levels. Calibration (1986–1997) and validation (1998–2007) of the SWAT model were conducted at three streamflow gaging stations and one sediment station located upstream of the reservoir. Results show that the SWAT model performed better during calibration than during the validation period for all streamflow and sediment gaging stations. In addition, modeled streamflow and sediment predictions were relatively more accurate on a monthly scale than on a daily scale. Simulated daily sediment inflow to the reservoir demonstrates slightly lower accuracy than daily streamflow, where the Coefficient of Determination (R2) and Nash-Sutcliffe Efficiency values are 0.34 and 0.32 in the case of sediment load, compared to 0.39 and 0.33 for streamflow, respectively. Reservoir storage capacity for the period (1986–2005) shows a continuous decrease with time at all reservoir water levels, which indicates an increase in sediment accumulation. According to measurements taken between 1986 and 2005, sediment accumulation has reduced the reservoir's capacity by approximately 15% at a water level of 112 m (the lowest water level in the reservoir). During the same period, the storage capacity loss at 147 m (the design working water level in the reservoir) was calculated to be 35%. Over 19 years of operation (1986–2005) at the 147-m water level, the total sediment buildup in the reservoir is estimated at 3.2 million tons. Notably, about one-third of this sediment was deposited in the five-year span from 2000 to 2005. [ABSTRACT FROM AUTHOR]

Traditional hydrological model calibration using limitedly available streamflow data often becomes inadequate, particularly in dry climates, as the flow regimes may abruptly vary from arid conditions to devastating floods. Newly available remote-sensing-based datasets can be supplemented to overcome such inadequacies in hydrological simulations. To address this shortcoming, we use multi-variable-based calibration by setting up and calibrating a lumped-hydrological model using observed streamflow and remote-sensing-based soil moisture data from Soil Moisture Active Passive Level 4. The proposed method was piloted at the Maduru Oya River Basin, Sri Lanka, as a proof of concept. The relative contributions from streamflow and soil moisture were assessed and optimised via the Kling–Gupta Efficiency (KGE). The Generalized Reduced Gradient non-linear solver function was used to optimise the Tank Model parameters. The findings revealed satisfactory performance in streamflow simulations under single-variable model validation (KGE of 0.85). Model performances were enhanced by incorporating soil moisture data (KGE of 0.89), highlighting the capability of the proposed multi-variable calibration technique for improving the overall model performance. Further, the findings of this study highlighted the instrumental role of remote sensing data in representing the soil moisture dynamics of the study area and the importance of using multi-variable calibration to ensure robust hydrological simulations of river basins in dry climates. [ABSTRACT FROM AUTHOR]

Debris‐flow volumes can increase along their flow path by entraining sediment stored in the channel bed and banks, thus also increasing hazard potential. Theoretical considerations, laboratory experiments and field investigations all indicate that the saturation conditions of the sediment along the flow path can greatly influence the amount of sediment entrained. However, this process is usually not considered for practical applications. This study aims to close this gap by combining runout and hydrological models into a predictive framework that is calibrated and tested using unique observations of sediment erosion and debris‐flow properties available at a Swiss debris‐flow observation station (Illgraben). To this end, hourly water input to the erodible channel is predicted using a simple, process‐based hydrological model, and the resulting water saturation level in the upper sediment layer of the channel is modelled based on a Hortonian infiltration concept. Debris‐flow entrainment is then predicted using the RAMMS debris‐flow runout model. We find a strong correlation between the modelled saturation level of the sediment on the flow path and the channel‐bed erodibility for single‐surge debris‐flow events with distinct fronts, indicating that the modelled water content is a good predictor for erosion simulated in RAMMS. Debris‐flow properties with more complex flow behaviour (e.g., multiple surges or roll waves) are not as well predicted using this procedure, indicating that more physically complete models are necessary. Finally, we demonstrate how this modelling framework can be used for climate change impact assessment and show that earlier snowmelt may shift the peak of the debris‐flow season to earlier in the year. Our novel modelling framework provides a plausible approach to reproduce saturation‐dependent entrainment and thus better constrain event volumes for current and future hazard assessment. [ABSTRACT FROM AUTHOR]

Remotely sensed evapotranspiration (ETRS) shows promise for enhancing hydrological models, especially in regions lacking in situ streamflow observations. However, model calibration studies showed conflicting results regarding the ability of ETRS products to improve streamflow simulation. Rather than relying on model calibration, here we produce the first randomized experiment that explores the full streamflow–ET skill distribution, and also the first probabilistic assessment of the value of different global ETRS products for streamflow simulation. Using 280,000 randomized SWAT (Soil and Water Assessment Tool) model runs across seven catchments and four ETRS products, we show that the relationship between ET and streamflow skills is complex, and simultaneous improvement in both skills is only possible in a limited range. Parameter sensitivity analysis indicates that the most sensitive parameters can have opposite contributions to ET and streamflow skills, leading to skill trade-offs. Conditional probability assessment reveals that models with good ET skills are likely to produce good streamflow skills, but not vice versa. We suggest that randomized experiments such as ours should be performed before model calibration to determine whether using ETRS is worthwhile, and to help in interpreting the calibration results. [ABSTRACT FROM AUTHOR]

The evaluation of model performance is an essential part of hydrological modeling. However, leveraging the full information that performance criteria provide requires a deep understanding of their properties. This Technical Note focuses on a rather counterintuitive aspect of the perhaps most widely used hydrological metric, the Nash–Sutcliffe efficiency (NSE). Specifically, we demonstrate that the overall NSE of a dataset is not bounded by the NSEs of all its partitions. We term this phenomenon the "divide and measure nonconformity". It follows naturally from the definition of the NSE, yet because modelers often subdivide datasets in a non-random way, the resulting behavior can have unintended consequences in practice. In this note we therefore discuss the implications of the divide and measure nonconformity, examine its empirical and theoretical properties, and provide recommendations for modelers to avoid drawing misleading conclusions. [ABSTRACT FROM AUTHOR]

The Nash–Sutcliffe efficiency (NSE) is now the most widely used criterion for measuring the goodness of fit between the hydrological model simulation and corresponding observation. Because there is substantial sampling uncertainty regarding hydrological simulation and observation, the NSE is a random variable. A probability density function (PDF) of NSE variable was derived based on the assumption of the simple linear regression model between the observation and simulation from the hydrological model. To avoid a subjective interpretation of the hydrological model performance, the confidence interval of the NSE variable was determined by its PDF. Because the difference in NSE variables (Φ) between two time periods or two hydrological models is often used for comparing their performances and can also be taken as a random variable, hypothesis testing should be implemented to determine whether the difference is adequate or whether the difference is a chance variation. Because the PDF of the difference can be derived based on the joint PDF of the every NSE random variables, a procedure of the hypothesis testing for the difference between two NSE variables is then proposed for comparing the performances between the hydrological models or between different time periods for a hydrological model. The proposed hypothesis testing has been applied in the abcd and dynamic water balance model (DWBM) hydrological models as case studies to illustrate the procedure of assessing the performances of a hydrological model and justifying its superiority to another model according to both the estimated NSE value and its confidence level due to the sampling uncertainty. Therefore, the proposed hypothesis testing can provide hydrological model end-users with rational model assessment. [ABSTRACT FROM AUTHOR]

At a regional scale, decision-making in water resources management is based on the analysis of long-term, continuous observations of hydrometeorological variables including streamflow. Quite often, constraints such as paucity of observed flow data hinder scientific planning in ungauged watersheds of developing countries. In this context, we present an improved framework for predicting streamflow data in an ungauged tropical watershed (Jambhira) in eastern India utilizing coupled data analytics and the Soil and Water Assessment Tool (SWAT) for flow regionalization. In this study, the choice of different dimensionality reduction methods such as principal components analysis (PCA) and Isomap during the clustering stage, as well as the choice of inverse distance weighting (IDW) and physical similarity (PS) approaches for regionalization of SWAT model parameters for the ungauged watershed, are investigated. As a novel aspect, in this case study, we included certain seasonal climate characteristics and changed land-use composition of the study watersheds as pertinent features for clustering of homogeneous watersheds. The proposed framework is then evaluated for its possible limitations, namely, whether the integrated approaches yield the best estimates of hydrological model parameters for ungauged watersheds, and if they guarantee good performance across different flow regimes. The regionalization results for the pseudoungauged watershed (Govindpur, India) indicate that coupled PS-SWAT approach is the most efficient with R2 and Nash–Sutcliffe efficiency (NSE) values ranging from 0.75 to 0.83 and from 0.74 to 0.77, respectively. The hydrological model of the ungauged watershed (Jambhira) is developed using the regionalization framework, and using the simulated flows, flow duration curve (FDC) analysis is also performed. Analysis of regime-based flow characteristics reveals a declining trend in the magnitude of low and high flows by 4% and 22.2%, respectively, in Jambhira. [ABSTRACT FROM AUTHOR]

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

Runoff simulation is essential for effective water resource management and plays a pivotal role in hydrological forecasting. Improving the quality of runoff simulation and forecasting continues to be a highly relevant research area. The complexity of the terrain and the scarcity of long-term runoff observation data have significantly limited the application of Physically Based Models (PBMs) in the Qinghai–Tibet Plateau (QTP). Recently, the Long Short-Term Memory (LSTM) network has been found to be effective in learning the dynamic hydrological characteristics of watersheds and outperforming some traditional PBMs in runoff simulation. However, the extent to which the LSTM works in data-scarce alpine regions remains unclear. This study aims to evaluate the applicability of LSTM in alpine basins in QTP, as well as the simulation performance of transfer-based LSTM (T-LSTM) in data-scarce alpine regions. The Lhasa River Basin (LRB) and Nyang River Basin (NRB) were the study areas, and the performance of the LSTM model was compared to that of PBMs by relying solely on the meteorological inputs. The results show that the average values of Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and Relative Bias (RBias) for B-LSTM were 0.80, 0.85, and 4.21%, respectively, while the corresponding values for G-LSTM were 0.81, 0.84, and 3.19%. In comparison to a PBM- the Block-Wise use of TOPMEDEL (BTOP), LSTM has an average enhancement of 0.23, 0.36, and −18.36%, respectively. In both basins, LSTM significantly outperforms the BTOP model. Furthermore, the transfer learning-based LSTM model (T-LSTM) at the multi-watershed scale demonstrates that, when the input data are somewhat representative, even if the amount of data are limited, T-LSTM can obtain more accurate results than hydrological models specifically calibrated for individual watersheds. This result indicates that LSTM can effectively improve the runoff simulation performance in alpine regions and can be applied to runoff simulation in data-scarce regions. [ABSTRACT FROM AUTHOR]

The rapid advancement of machine learning techniques has led to their widespread application in various domains, including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a state-of-the-art (SOTA) deep learning sequential model, leveraging a Temporal Convolutional Network (TCN), for snowmelt forecasting of the Hindu Kush Himalayan (HKH) region. To evaluate the performance of our proposed model, we conducted a comparative analysis with other popular models, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Transformer models. Furthermore, nested cross-validation (CV) was used with five outer folds and three inner folds, and hyperparameter tuning was performed on the inner folds. To evaluate the performance of the model, the Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), R square ( R 2 ), Kling–Gupta Efficiency (KGE), and Nash–Sutcliffe Efficiency (NSE) were computed for each outer fold. The average metrics revealed that the TCN outperformed the other models, with an average MAE of 0.011, RMSE of 0.023, R 2 of 0.991, KGE of 0.992, and NSE of 0.991 for one-day forecasts of streamflow. The findings of this study demonstrate the effectiveness of the proposed deep learning model as compared to traditional machine learning approaches for snowmelt-driven streamflow forecasting. Moreover, the superior performance of this TCN highlights its potential as a promising deep learning model for similar hydrological applications. [ABSTRACT FROM AUTHOR]

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

Knowing where and when rivers cease to flow provides an important basis for evaluating riverine biodiversity, biogeochemistry and ecosystem services. We present a novel modeling approach to estimate monthly time series of streamflow intermittence at high spatial resolution at the continental scale. Streamflow intermittence is quantified at more than 1.5 million river reaches in Europe as the number of no‐flow days grouped into five classes (0, 1–5, 6–15, 16–29, 30–31 no‐flow days) for each month from 1981 to 2019. Daily time series of observed streamflow at 3706 gauging stations were used to train and validate a two‐step random forest modeling approach. Important predictors were derived from time series of monthly streamflow at 73 million 15 arc‐sec (∼500 m) grid cells that were computed by downscaling the 0.5 arc‐deg (∼55 km) output of the global hydrological model WaterGAP, which accounts for human water use. Of the observed perennial and non‐perennial station‐months, 97.8% and 86.4%, respectively, were correctly predicted. Interannual variations of the number of non‐perennial months at non‐perennial reaches were satisfactorily simulated, with a median Pearson correlation of 0.5. While the spatial prevalence of non‐perennial reaches is underestimated, the number of non‐perennial months is overestimated in dry regions of Europe where artificial storage abounds. Our model estimates that 3.8% of all European reach‐months and 17.2% of all reaches were non‐perennial during 1981–2019, predominantly with 30–31 no‐flow days. Although estimation uncertainty is high, our study provides, for the first time, information on the continent‐wide dynamics of non‐perennial rivers and streams. Plain Language Summary: Even in wet climates, small streams can seasonally dry up. In drier areas, large rivers might not carry water for weeks or months. However, as streamflow observations are lacking for most drying rivers, we know little about when, where, and how long rivers experience such a streamflow intermittence that is crucial for both river life and human water supply. We developed and applied a novel approach to estimate, for the first time, the temporal dynamics of streamflow intermittence across European rivers and streams, including small ones. This approach combines the output of a global hydrological model with streamflow observations and other data. We refined the global model output available for 50 km cells to monthly streamflow in 500 m cells. We then applied a machine learning model to predict the number of days without water flow in each month during the period 1981–2019 for over 1.5 million river segments. We found that 17% of all European segments and 4% of all months at all segments experienced at least one day without flow. In the future, the model will be used to estimate the impact of climate change on streamflow intermittence. Key Points: Streamflow intermittence at more than 1.5 million European reaches was estimated for every month during 1981–201918.7% of the European river network length and 3.8% of all reach‐months are non‐perennial, predominantly with 30–31 no‐flow days15 arc‐sec monthly streamflow obtained by downscaling the output of a global hydrological model serves as input to random forest modeling [ABSTRACT FROM AUTHOR]

The impact of earthquake-induced landslides is a global concern, particularly in the Himalayas and its nearby region, leading to substantial socioeconomic losses. High seismicity along with intense rainfall and steep slopes make the Himalayan region more vulnerable to these landslides. Existing research on regional scale analysis has mainly focused on studying landslides triggered by rainfall or earthquakes separately, with limited studies considering the combined effects of both. The objective of this research is to develop a GIS-based data integration approach for mapping shallow translational landslide susceptibility at regional scale, considering the combined effect of earthquakes and rainfall. The methodology utilizes a combination of the pseudostatic approach and hydrological models, taking advantage of publicly available topographic, seismic, geological, and remote sensing data and requiring basic geotechnical properties obtained from conventional tests to determine factor of safety (FoS). The approach has been implemented along an important and strategic highway corridor in the Indian state of Sikkim, located in the Eastern Himalayan region. The FoS distribution maps developed using the proposed approach have been validated against the reported landslides triggered in the study area due to the 2011 moment magnitude (Mw) 6.9 Sikkim earthquake. The proposed approach performed well in terms of prediction, with a success rate of 74.75%. Finally, the FoS distribution maps are generated for different scenarios. These maps provide significant insights that can be utilized by local governments, transportation agencies, and policymakers to proactively identify high-risk zones and develop early warning systems to mitigate the risk. Practical Applications: The Himalayan region has experienced a surge in tourist activity and infrastructure development in the past decade. However, the steep slopes, coupled with high seismic activity and heavy monsoonal rainfall, make it naturally prone to landslides. Landslides triggered by earthquakes can further worsen the effects of the seismic activity by damaging infrastructure, including roads, leading to challenges in rescue operations, disruptions in the supply of essential commodities, and inconvenience to travelers. The occurrence of earthquake during the monsoonal season can significantly increase the likelihood of landslides. To address this issue, a methodology has been proposed to identify areas prone to landslides caused by combined action of rainfall and earthquakes, focusing on a strategic highway in the Eastern Himalayas. By considering different scenarios of rainfall and earthquakes, various maps have been created which highlights the high-risk zones. These maps serve as valuable guides, indicating where it's unsafe to construct infrastructure and which highway sections are more susceptible to landslides. Equipped with this information, policy makers can construct more robust roads, plan alternative routes, ensure better connectivity, and provide early warnings to minimize the disruptive effects of landslides on people and infrastructure. [ABSTRACT FROM AUTHOR]

Comprehensive large sample hydrological datasets, particularly the CAMELS datasets (Catchment Attributes and Meteorology for Large-sample Studies), have advanced hydrological research and education in recent years. These datasets integrate extensive hydrometeorological observations with landscape features, such as geology and land use, across numerous catchments within a national framework. They provide harmonised large sample data for various purposes, such as assessing the impacts of climate change or testing hydrological models on a large number of catchments. Furthermore, these datasets are essential for the rapid progress of data-driven models in hydrology in recent years. Despite Germany's extensive hydrometeorological measurement infrastructure, it has lacked a consistent, nationwide hydrological dataset, largely due to its decentralised management across different federal states. This fragmentation has hindered cross-state studies and made the preparation of hydrological data labour-intensive. The introduction of CAMELS-DE represents a step forward in bridging this gap. CAMELS-DE includes 1555 streamflow gauges with hydro-meteorological time series data covering up to 70 years (median length of 46 years and a minimum length of 10 years), from January 1951 to December 2020. It includes consistent catchment boundaries with areas ranging from 5 to 15,000 km 2 along with detailed catchment attributes covering soil, land cover, hydrogeologic properties and data about human influences. Furthermore, it includes a regionally trained Long-Short Term Memory (LSTM) network and a locally trained conceptual model that were used as quality control and that can be used to fill gaps in discharge data or act as baseline models for the development and testing of new hydrological models. Given the large number of catchments, including numerous relatively small ones (617 catchments < 100 km 2 ), and the time series length of up to 70 years (156 catchments), CAMELS-DE is one of the most comprehensive national CAMELS datasets available and offers new opportunities for research, particularly in studying long-term trends, runoff formation in small catchments and in analysing catchments with strong human influences. [ABSTRACT FROM AUTHOR]

Maximizing the impact of agricultural wastewater conservation practices (CP) to achieve total maximum daily load (TMDL) scenarios in agricultural watersheds is a challenge for the practitioners. The complex modeling requirements of sophisticated hydrologic models make their use and interpretation difficult, preventing the inclusion of local watershed stakeholders' knowledge in the development of optimal TMDL scenarios. The present study develops a seamless modeling approach to transform the complex modeling outcomes of Hydrologic Simulation Program Fortran (HSPF) into a simplified participatory framework for developing optimized management scenarios. The study evaluates seven conservation practices in the Pomme de Terre watershed in Minnesota, USA, focusing on sediment and phosphorus pollutant load reductions incorporating farmers' opinions to guide practitioners toward implementing cost-effective CPs. Results show reduced tillage and filter strips are the most cost-effective practices for non-point source pollution reduction, followed by conservation cover perennials. The integration of SAM with HSPF is crucial for sustainable field-scale implementation of conservation practices through enhanced involvement of amateur-modeling stakeholders and farmers directly connected to fields. [ABSTRACT FROM AUTHOR]

Data Assimilation (DA) of time-variable satellite gravity observations, e.g., from the Gravity Recovery and Climate Experiment (GRACE), GRACE-Follow On (GRACE-FO) and future gravity missions, can be applied to constrain the vertical sum of water storage simulations of Global Hydrological Models (GHMs). However, the state-of-the-art DA of these measured Terrestrial Water Storage (TWS) changes into models is often performed regionally, and if globally, at low spatial resolution. This choice is made to handle the considerably high computational demands of DA, and to avoid numerical problems, e.g., instabilities related to the inversion of covariance matrices. To fully exploit the potential of satellite gravity observations and the high spatial resolution of GHMs, we developed a Python-based open-source PyGLDA system that allows performing DA globally at a fine scale with high numerical efficiency. The main novelties of the system include (i) implementing a globe-scale patch-wise DA via domain localization and neighbouring-weighted global aggregation and (2) its great compatibility between basin-scale and grid-scale DAs. This PyGLDA system represents a considerable functional advancement on previous implementations with wide and flexible options offered to allow for various user-specific studies. The modular structure of PyGLDA provides users with various possibilities to interact with (and add/remove) individual water storage compartments, change the representation of observations, and, therefore, the ability to choose different GHMs. In this paper, we present a full description of this system and its application for the Danube River Basin as a regional case study and through a global DA. The DA demonstrations are performed using the monthly TWS fields of GRACE (2002–2010) and the W3RA water balance model at 0.1-degree/daily spatial-temporal resolution. [ABSTRACT FROM AUTHOR]

A recent study in headwater sub-basins of the Zambezi River concluded that a machine learning model (HydroForecast) outperformed a deterministic monthly time-step water balance model (Pitman) when observed streamflow data were used as part of the training data for the machine learning approach. This was partly attributed to non-stationary bias in the input rainfall data that has a greater impact on a model that has mass balance constraints. The current study investigated methods of adjusting the input rainfall data based on an analysis of the streamflow simulation errors and found that the results for the water balance model could be improved so that they compared more favourably with the machine learning model. The improvements were achieved without changing the long-term statistical characteristics of the original monthly rainfall time series. It is acknowledged that there is some evidence that differences in daily rainfall patterns might explain some of the simulation errors. [ABSTRACT FROM AUTHOR]

This study utilizes the Simulator of Hydrologic Unstructured Domains (SHUD) model and the China Meteorological Forces Dataset (CMFD) to investigate the hydrological dynamics of the Buha River watershed, a critical tributary of Qinghai Lake, from 1979 to 2018. By integrating high-resolution terrestrial and meteorological data, the SHUD model simulates streamflow variations and other hydrological characteristics, providing valuable insights into the region's water balance and runoff processes. Key findings reveal a consistent upward trend in precipitation and temperature over the past four decades, despite minor deviations in daily precipitation intensity and relative humidity data. The SHUD model demonstrates high accuracy on a monthly scale, with Nash–Sutcliffe Efficiency (NSE) values of 0.72 for the calibration phase and 0.61 for the validation phase. The corresponding Kling–Gupta Efficiency (KGE) values are 0.73 and 0.49, respectively, underscoring the model's applicability for hydrological forecasting and water resource management. Notably, the annual runoff ratios for the Buha River fluctuate annually between 0.11 and 0.21, with significant changes around 2007 correlating with a shift in Qinghai Lake's water levels. The analysis of water balance indicates a net leakage over long-term periods, with spatial alterations in leakage and replenishment along the river. Furthermore, snow accumulation, which increases with altitude, significantly contributes to streamflow during the melting season. Despite the Buha River basin's importance, research on its hydrology remains limited due to data scarcity and minimal human activity. This study enhances the understanding of the Buha River's hydrological processes and highlights the necessity for improved dataset accuracy and model parameter optimization in future research. [ABSTRACT FROM AUTHOR]