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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]

Globally, freshwater fishes are among the taxa most vulnerable to climate change but are generally understudied in tropical island ecosystems where climate change is predicted to alter the intensity, frequency and duration of extreme flow events. These changes may impact stream ecosystems and native and non‐native biota in complex ways.We compiled an extensive dataset of fish assemblages collected at 119 sites across the Caribbean island of Puerto Rico from 2005 to 2015. We coupled these data with stream flow indices and dam height to understand associations between flow and fish assemblage structure. Sixteen percent of sites contained exclusively non‐native species, 34% contained exclusively native species, and 50% contained native and non‐native species.We built generalised linear models and conducted all subsets model selection to identify extreme flow variables explaining variation in native and non‐native species richness and biomass. We also built models to determine the combined effects of extreme flows and the presence of non‐native species on native species richness and biomass.Extreme flows and dam height were important in explaining variations in native and non‐native species richness and biomass. Model averages showed native biomass decreased by 0.42 kg/ha with a 1‐m increase in dam height, by 0.05 kg/ha with 1 cm/s increase in maximum mean daily high flow and by 3.45 kg/ha with each additional day increase in maximum high flow duration, and increased by 2.06 kg/ha with each additional day increase in mean high flow duration. Model averages predicted that non‐native biomass increased by 1.32 kg/ha with a 1‐m increase in dam height and by 0.01 kg/ha with each additional day increase in mean high flow duration, and decreased by 0.36 kg/ha with each additional day increase in maximum high flow duration. Model averages also predicted an increase in native and non‐native biomass of 0.71 gage and 0.06 kg/ha, respectively, with each additional day increase in maximum low flow duration.The combined effects of non‐native species presence and extreme flows changed the relationship between maximum high and low flow durations and native biomass. Model averages showed that native biomass increased by 1.83 kg/ha with each additional day increase in maximum high flow duration and decreased by 2.52 kg/ha with each additional day increase in maximum low flow duration when non‐native species were present.Native fishes may be able to better cope with longer maximum durations of low flows than expected when non‐native fishes are absent. In mixed fish assemblages, extended maximum durations of high flows may act as a control of non‐native species and dampen their negative effect on native species, but longer maximum durations of low flows may heighten the negative effects of non‐native fishes.Our results are informative for tropical island ecosystems globally and can guide the management and conservation of native fishes, particularly when faced with the dual threats of climate change and non‐native species. Managers may consider increasing efforts to conserve native fishes in Caribbean rivers by maintaining connectivity and habitat complexity while preventing non‐native species introductions. [ABSTRACT FROM AUTHOR]

Despite suffering dramatic declines due to habitat loss and overexploitation, tortoises and freshwater turtles in Southeast Asia remain understudied. Sustainable forest management offers a promising approach for advancing the conservation of threatened turtle populations. This study examines the effect of reduced impact logging (RIL), a sustainable forestry method, on 2 freshwater turtle species. We examined detectability patterns and habitat relationships for the threatened Malayan flat‐shelled turtle (Notochelys platynota) and the non‐threatened Malayan soft‐shelled turtle (Dogania subplana) in 8 streams within a commercial forest reserve between March and July 2019, in Sabah, Malaysian Borneo. Using single‐species occupancy models, we identified covariates associated with the detection and occupancy probabilities of these species across a post‐harvest recovery gradient (1–21 years since logging). Covariates used in the models were obtained directly from the field or from open‐source remote sensing data. Results for soft‐shelled turtles were inconclusive. In contrast, we found a negative association between monthly rainfall and flat‐shelled turtle detectability. The occupancy probability of flat‐shelled turtles was positively associated with greater distance from logging roads and higher stream flow accumulation. Occupancy probability for flat‐shelled turtles and soft‐shelled turtles was relatively high throughout the reserve (0.79 ± 0.1 [SD] and 0.57 ± 0.22, respectively). These results, suggest that appropriately managed forests, could serve as invaluable conservation areas for imperiled freshwater turtle species in the region. [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]

The generation of streamflow is linked with different factors such as water level, rainfall intensity, meteorological variables, and many more. In this study, we have developed a new hybrid approach (named LASSO-HF-SAA) by integrating the least absolute shrinkage and selection operator (LASSO) and Hampel filter (HF) with three data-driven models i.e. support vector machine (SVM), artificial neural network (ANN) and autoregressive distributed lag (ARDL). Firstly, LASSO selects meteorological variables important in daily streamflow prediction. Next, the HF detects and correct outliers in the variables to handle the randomness and noise of data. Thirdly, the HF-corrected data is fed to SVM, ANN, and ARDL models to obtain the predictions of the proposed LASSO-HF-SVM, LASSO-HF-ANN, and LASSO-HF-ARDL models. The performance of these models is checked using performance indices and the Diebold-Mariano (DM) test. The proposed hybrid approach is illustrated on the streamflow data of the Kabul River (Nowshera station) of Pakistan. Based on Nash-Sutcliffe efficiency (NSE), it is revealed that the prediction accuracy of the LASSO-HF-SVM hybrid model (NSE = 0.52) is better than SVM (NSE = 0.43), HF-SVM (NSE = 0.49) and LASSO-SVM (NSE = 0.47) models in testing phase. Similar findings are for the proposed LASSO-HF-ARDL and LASSO-HF-ANN hybrid models. Overall, the suggested LASSO-HF-ARDL hybrid model has shown winning performance compared to all models in the study. The root mean squared error (RMSE) and NSE of the proposed LASSO-HF-ARDL model is 443.5m3/s and 0.68 on the test data. The DM test confirms that the prediction accuracy of the proposed hybrid models is better than their respective single, HF-based, and LASSO-based models versions of SVM, ANN, and ARDL models respectively. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Water Resources Journal / Revue Canadienne des Ressources Hydriques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

Recent drought events have significantly affected navigation through the Rhine River and the transportation of goods due to low water levels. It has become imperative to analyze the conditions in which these events occur to establish actions to develop adaptive measures and avoid monetary losses. The main focus of this study is to calibrate the hydrological model for extremely low water levels and to test its performance during the 2018 drought event in the Rhine River basin. WRF-Hydro was developed to complement the land-atmosphere interactions with the meteorological model Weather Research and Forecasting (WRF), and it has been mainly used to study flood events. In this study, we simulated the Rhine River basin's streamflow using the meteorological ERA5 reanalysis dataset as input data. The calibration period is 2016–2017, during which the influence of several parameters on the streamflow was evaluated and contrasted with the daily observed discharge values at gauging stations along the river. Land use cover and terrain slope were used to create spatially distributed parameter maps, thus avoiding the calibration process of testing a range of values, substantially reducing computational demands. During calibration, the importance of modeling realistic outflow values of Lake Constance became apparent due to its significant contribution to the upper Rhine basin. However, WRF-Hydro's lake module resulted in an overly strong dampening of streamflow. Lake Constance was, therefore, represented without the lake module, resulting in more realistic hydrographs and statistical scores. Overall, the calibration and validation process demonstrated that WRF-Hydro is capable of reproducing the variability of the discharge along the Rhine and capturing low water levels observed during the 2018 drought event. These results suggest that WRF-Hydro is a suitable model for analyzing recent and future drought events in the Rhine River basin under different climate conditions. [ABSTRACT FROM AUTHOR]

In this study, two hydrodynamic models, TELEMAC-2D and HEC-RAS 2D, were compared for their Rain-on-Grid (RoG) technique with a particular focus on runoff generation processes in a small and steep catchment. Curve number (CN) method was applied in both the models to simulate two single storm events up to 20 h of duration, whereas the Green-Ampt Redistribution (GAR) method was additionally applied in HEC-RAS 2D for a multi-peak flood event with sustained flow between the peaks. CN and GAR methods were compared for this flood event, and a sensitivity analysis of the GAR parameters was also done. Moreover, the two models were compared for their calibration process, computational time, mesh size and shape, and model availability, in general, as well as the results including inundated areas, water depth, and velocity. The results indicate that both the models are capable of reproducing short duration single storm floods. NSE and R2 for both models ranged from 0.70 to 0.90 and from 0.93 to 0.95. However, the models struggled to reproduce the long-duration multipeak flood event. The sensitivity analysis showed that the results are not very sensitive to the two GAR parameters which are responsible to influence the flow of the second peak in the flood event. Neither the CN nor the GAR infiltration method successfully replicated such events because the hydraulic models permanently lose infiltrated water from the domain. The returned sub-surface flow significantly contributes to river flow during these flood events; however, none of the model incorporates a return flow algorithm. [ABSTRACT FROM AUTHOR]

Debris flows and floods generated by rainfall runoff occur in rocky mountainous landscapes and burned steeplands. Flow type is commonly identified post‐event through interpretation of depositional structures, but these may be poorly preserved or misinterpreted. Prior research indicates that discharge magnitude is commonly amplified in debris flows relative to floods due to volumetric bulking and increased frictional resistance. Here, we use this flow amplification to develop a metric (Q*) to separate debris flows from floods based on the ratio of observed peak discharge to the theoretical maximum water discharge from rainfall runoff. We compile 642 observations of floods and debris flows and demonstrate that Q* distinguishes flow type to ∼92% accuracy. Q* allows for accurate identification of debris flows through simple channel cross‐section surveys rather than through qualitative interpretation of deposits, and therefore should increase the performance of models and engineered structures that require accurate flow‐type observations. Plain Language Summary: Steep channels in rocky landscapes and recently burned areas carry a wide range of flow types, ranging from water floods to highly destructive debris flows composed of rock, sediment, other incidental debris, and water. Typically, the flow type that recently occurred in a channel is identified by interpreting the structure of deposits left behind by the flow. However, these deposits can be challenging to interpret, provided they are preserved at all. Prior research has shown that the shift in flow physics occurring in debris flows has the effect of amplifying the magnitude of the flow beyond that which is possible in floods. In this study, we take advantage of this flow amplification to develop a quantitative metric that allows for flow type to be identified without interpreting depositional structures. We demonstrate the metric to be highly accurate. This methodology should increase the accuracy of predictive models trained on such observations, which can help to better understand where these hazards exist and protect communities and infrastructure at risk. Key Points: To distinguish debris flows from floods, a metric Q* equal to the ratio of peak discharge to rainfall‐runoff discharge is developedQ* separates floods (Q* ≤ 1) from debris flows (Q* > 1) to ∼92% accuracy and is robust to uncertainties in the key parametersThe Q* metric should reduce misidentification of flow type and increase the performance of empirical‐hazard models and hazard mitigation [ABSTRACT FROM AUTHOR]

Streamflow records are biased toward large streams and rivers, yet small headwater streams are often the focus of ecological research in response to climate change. Conventional flow measurement instruments such as acoustic Doppler velocimeters (ADVs) do not perform well during low‐flow conditions in small streams, truncating the development of rating curves during critical baseflow conditions dominated by groundwater inflow. We revisited an instantaneous solute tracer injection method as an alternative to ADVs based on paired measurements to compare their precision, efficiency, and feasibility within headwater streams across a range of flow conditions. We show that the precision of discharge measurements using salt dilution by slug injection and ADV methods were comparable overall, but salt dilution was more precise during the lowest flows and required less time to implement. Often, headwater streams were at or below the depth threshold where ADV measurements could even be attempted and transects were complicated by coarse bed material and cobbles. We discuss the methodological benefits and limitations of salt dilution by slug injection and conclude that the method could facilitate a proliferation of streamflow observation across headwater stream networks that are highly undersampled compared to larger streams. [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]

Large-sample hydrology datasets have become increasingly available, contributing to significant scientific advances. However, in Europe, only a few such datasets have been published, capturing only a fraction of the wealth of information from national data providers in terms of available spatial density and temporal extent. We present "EStreams", an extensive dataset of hydro-climatic variables and landscape descriptors and a catalogue of openly available stream records for 17,130 European catchments. Spanning up to 120 years, the dataset includes streamflow indices, catchment-aggregated hydro-climatic signatures and landscape attributes (topography, soils, geology, vegetation and landcover). The catalogue provides detailed descriptions that allow users to directly access streamflow data sources, overcoming challenges related to data redistribution policies, language barriers and varied data portal structures. EStreams also provides Python scripts for data retrieval, aggregation and processing, making it dynamic in contrast to static datasets. This approach enables users to update their data as new records become available. Our goal is to extend current large-sample datasets and further integrate hydro-climatic and landscape data across Europe. [ABSTRACT FROM AUTHOR]

Streamflow in source region of Yellow River (SRYR) matters with regard to the adjacent and downstream water resources. Intraseasonal oscillation (ISO) in the streamflow in SRYR is of great significance to the sub-seasonal prediction of streamflow in SRYR, but is unknown. Here, we first report a 30–60-day ISO in the streamflow in SRYR, which is regulated by the atmospheric 30–60-day ISO at mid-high latitude over North Eurasia. The 30–60-day ISO in atmosphere is featured by a Rossby wavetrain, and the wave energy propagates southward onto the TP, which causes anomalous wind response over TP. The leading anomalous high (low) with anti-cyclonic (cyclonic) wind anomalies over the TP favors dry (wet) air in lower troposphere in SRYR, via enhancing the water vapor divergence (convergence). Dry (wet) air always results in strong (weak) evaporation from the Yellow River, which causes the later streamflow valley (peak) and thereby the 30–60-day ISO in the streamflow in SRYR. [ABSTRACT FROM AUTHOR]

[Objective] This study aims to explore the variation characteristics of streamflow components and baseflow index (BFI) in the Tuweihe River Basin, to clarify the impact of environmental factors on their variations, to identify the dominant factors, and to provide scientific basis for watershed water resources management. [Methods] Tuweihe River Basin in the middle reaches of the Yellow River was taken as the study area. The changes of surface runoff, baseflow and baseflow index (BFI) during the period 1958-2017 were analyzed. The correlation analysis and elasticity coefficient methods were employed to qualitatively and quantitively analyze the influence of environmental factors on them. [Results] The surface runoff and baseflow significantly decreased at a rate of 4.55 and 9.73 mm/decade (p<0.01), respectively, while the BFI showed a significant upward trend (p<0.05). Surface runoff (or baseflow, BFI) was highly sensitive to the changes in potential evapotranspiration, normalized difference vegetation index (NDVI), and volumetric soil water. The changes in NDVI and potential evapotranspiration contributed to the majority of the changes in surface runoff (-34% and -29%), baseflow (-39% and -29%), and BFI (33% and 31%). [Conclusion] The direct and potential hydrological effects of vegetation restoration play an important role in reducing the streamflow components. The changes of streamflow components as well as their relations to environmental changes provide a theoretical basis for the sustainability of water resources and vegetation. [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]

Low-flow period properties, including timing, magnitude, and duration, influence many key processes for water resource managers and ecosystems. We computed annual low-flow period duration and timing metrics from 1951 to 2020 for 1032 conterminous United States (CONUS) streamgages and analyzed spatial patterns, trends through time, and relationships to climate. Results show northwestern and eastern CONUS streamgages had longer and more inter-annually consistent low-flow period durations, while central CONUS periods were shorter and more variable. Low-flow periods most often occurred in summer months but start and end dates occurred later in north-central and mountainous western CONUS, which have the greatest number of low flows during cold seasons. Low-flow periods are becoming longer in southeastern and northwestern CONUS but shorter in much of the rest of CONUS. Temperature was correlated with low-flow period duration in southeastern and northwestern CONUS, and precipitation was correlated with duration everywhere, but most strongly in eastern CONUS. [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]

Upper and lower percentiles of Flow Duration Curves (FDCs) of daily streamflow data were investigated to develop frequency curves. Upper percentiles with exceedance probability of 1, 5 and 10% (Q1, Q5, Q10) were used for high flows, and lower percentiles with non-exceedance probability of 10, 5 and 1% (Q90, Q95, Q99) for low flows. Median value (Q50) was covered to represent the average conditions of streamflow. A mixed frequency analysis based on the total probability theorem taking zero values into account was applied for the lower percentiles of FDC. Case studies were performed for three intermittent Streamflow Gauging Stations (SGSs) from Kucuk Menderes River Basin in western Turkey. An overall assessment of results shows that the best-fit probability distribution function does not change from one SGS to another considerably for low flows while each SGS has its own probability distribution function for high flows. Upper and lower percentiles, and median value were calculated at various return periods by using the identified probability distribution functions. The calculated values were plotted in the form of frequency curves of high flow percentiles and low flow percentiles. The frequency curves have a practically significant potential use in hydrological analysis, water resources management and hydraulic design under high and low flow conditions. They are yet open to further development for regionalization and their applicability can be extended to ungauged sites in river basins. [ABSTRACT FROM AUTHOR]

The Standardized Streamflow Index (SSI) has frequently been used to quantify drought by comparing periods of streamflow against a river's historical values. This study expands upon previous SSI methodologies by creating a more flexible, regionalized version of the metric for Iowa, a Midwestern state located in the central United States. Five drought regions were developed for Iowa that largely correspond to the state's Major Land Resource Areas. Several United States Geological Survey gauges were identified within each drought region and streamflow data were used to calculate daily water yields from 1960 to the present. SSI values calculated for both individual river sites and the entire drought region provide insights into the relative dryness of Iowa watersheds over the past several decades. The SSI methodology can be used to evaluate river dryness with limited data records and water yields of specific streams can be compared to each other and across the overall region. This study demonstrates the potential for developing versions of the SSI that enable real‐time calculations at a daily scale at locations with limited historical streamflow data. The new SSI metric may be a valuable tool for decision‐makers as state and federal agencies continue to identify and manage drought. [ABSTRACT FROM AUTHOR]

A wide range of flow-type mass movements occur in nature. Depending on the solid fraction of these flows, they can be characterized as stream flows (flash floods), hyper-concentrated flows (debris floods), debris flows, and dry debris avalanches. A key scientific challenge in mitigating these hazards is estimating the impact force that they exert on protection structures. In this study, a new framework (Nfric - Fr2 - α relationship) is proposed to characterize and unify the impact behavior for a wide spectrum of flow-type mass movements. The friction number Nfric characterizes the ratio of grain-contact to fluid-viscous stresses for the wide range of flow types. Solid–fluid interaction regulates the pore fluid pressure, thereby governing the rheology of the flows and the degree of static loading exerted on a barrier. The Froude number Fr2 (in squared form) macroscopically characterizes the flow inertia relative to the earth's gravitational field. Finally, the dynamic pressure coefficient α is used to quantify the impact force in a dimensionless manner. As compared to existing guidelines, which recommend a wide range of α without considering the flow composition, the newly proposed framework in this study estimates the dynamic impact force by considering the effects of solid–fluid interaction. Findings from this study could further enhance the design of flow-type mass movement mitigation structures. [ABSTRACT FROM AUTHOR]

Water diversion for hydropower plants is one of the leading causes of habitat alteration and biotic homogenization. The impacts of small hydropower plants on fish communities are usually assessed using taxonomic composition and structure indicators. Size-based indicators are useful tools to evaluate the effects of environmental factors and anthropogenic perturbations on riverine ecosystems. In this study, we investigated the impact of water diversion on fish community body size structure in 16 small hydropower plants in the upper Ter River basin (NE Iberian Peninsula). We tested the use of multiple size-based metrics under different sampling intensities (one- vs. four-pass electrofishing) and compared control vs. diverted stream stretches. Results revealed that water diversion had a negative effect on fish community average and median length, while size spectrum and size diversity metrics showed no responses to the impact. Altitude was positively related with body size metrics and was the strongest natural driver affecting them. Moreover, we found that all the size-based metrics exhibited consistent values under different sampling efforts. Our findings suggest that size-based metrics could be useful indicators for bioassessment of river flow alteration and that one-pass electrofishing was robust enough to characterize the stream fish community size structure in our study. [ABSTRACT FROM AUTHOR]

The significant rainfall in October 2018 in northern Iran caused devastating flooding that caused considerable economic losses and claimed at least nine lives. Following the flooding, several bridges have suffered different levels of damage. Immediately after the event, the authors conducted a field investigation to study the performance of RC bridges during the flood. This paper presents the failure modes of flood-affected bridges and provides reasons for every failure case. The results revealed that scour around foundations, pier tilting, flexural failure of piers, and superstructure unseating were the most significant failure modes observed. Pile-supported bridges performed notably better than those with shallow foundations. In addition, observations have revealed that structural integrity plays a crucial role in the general stability of bridges during flooding. Pier ductility is another significant parameter that affects the performance of bridges during flood events. Finally, fragility curves were developed based on the empirical data set provided from the post-flood status of the bridges in the affected region. The river flow velocity (Vs), maximum flow discharge (Qp), and Qp normalized to the 10-year average river discharge (Qm) were considered intensity measures of the fragility analysis. The correlation analyses revealed that despite Vs and Qp/Qm , the flow discharge was not an appropriate intensity measure for generating fragility curves. [ABSTRACT FROM AUTHOR]

The Turkish straits system (TSS), consisting of the Marmara Sea, Dardanelles and Bosphorus, transfers the Black Sea water and the Mediterranean water. To our best knowledge, finding the lag time of the Bosphorus to Dardanelles and estimating upper layer discharge of the Dardanelles using Bosphorus upper layer discharge have not been studied before in the existing literature. With this motivation, continuous wavelet transform (CWT) was applied to the upper layer discharge data of the Dardanelles and Bosphorus as a pre-processing instrument and the noise in the data was eliminated by separating them into sub-series. The wavelet coherence (WTC) and the cross-wavelet transform (XWT) were applied to detect the lag time of the Bosphorus to the Dardanelles. The upper layer flow lag time of the Bosphorus to the Dardanelles is calculated to be about 5 days. In addition, there are not sufficient studies to predict Dardanelles flow by using Bosphorus flow. In order to fill this gap in the literature, the wavelet-ANN (WANN) and stand-alone ANN were employed to estimate the upper layer discharges of the Dardanelles from Bosphorus. Performances of model were estimated by RMSE and Pearson's R2. ANN and WANN models were developed to predict discharges with lead times of up to 7 days (168 h). Using the wavelet transform as a pre-processing tool significantly enhanced estimations in comparison to the stand-alone ANN model. While stand-alone ANN model cannot make accurate predictions for any lead time, the WANN model can make correct forecasts of Dardanelles upper layer discharges for up to 3 days (72 h). [ABSTRACT FROM AUTHOR]

The aim of this work is to analyze the changes in streamflow drought and flood distribution in Polish rivers through data decomposition and trend analysis. The study dataset constituted daily streamflow at 250 gauges located across Poland (eastern Europe) from January 1, 1965, to December 31, 2020. Hydrological extremes were identified by the means of the objective threshold method, and basic parameters such as occurrence frequency, duration times, and event volumes were calculated along with minimal and maximal flow information, aggregated on monthly and annual scales. The seasonal trend decomposition using Loess (STL) method was used to decompose long-term changes from raw datasets. Results indicated that the application of the STL decomposition significantly improved the ability to detect changes in the streamflow time series, which is crucial for further research on climate-induced changes in different parts of the world. The identified trends indicate a decrease in flooding and an increase in streamflow drought magnitudes across Poland, except for mountainous areas in southeastern Poland, where drought magnitudes increase during summer and decrease in winter, while flooding increases in late spring and early autumn. [ABSTRACT FROM AUTHOR]

Forecasting river flow is an important stage in reservoir operation, urban water management, and water resource optimization. The goal of this research is to forecast daily river flows for two intermittent and ephemeral rivers. Based on the antecedent river flow, the forecasting approach used stochastic (AR, ARIMA, and SARIMA) and machine learning (ML) techniques. The ML methods consist of three shallow learning models (NARnet, OS-ELM, and NAR-GMDH) and three deep learning models (LSTM, CNN-GRU, and stacked-LSTM). The precision of all the models in the ephemeral river was higher the intermittent river considering both base flow (R2ave = 0.94 vs. 0.87) and peak flow. The study was also extended to forecasting peak river flow values, demonstrating in the superiority of deep learning (RMSEave = 7.6 m3/s) over the shallow (RMSEave = 8.9 m3/s) and stochastic (RMSEave = 9.1 m3/s). Applied models acted similarly in forecasting peak flow in both rivers due to substantial variations in the floods. Moreover, the results demonstrated that the deep learning group models surpass stochastic and shallow learning group models in terms of five evaluation criteria, including RMSE, MAE, mean bias error, correlation coefficient, and agreement index for both rivers. In general, the results indicate that the CNN-GRU outperforms the other models in terms of river flow forecasting and is suggested as a viable model for learning the complicated behavior of streamflow. [ABSTRACT FROM AUTHOR]

Cyclonic storms (i.e., hurricanes) are powerful disturbance events that often cause widespread forest damage. Storm‐related canopy damage reduces rainfall interception and evapotranspiration, but impacts on streamflow regimes are poorly understood. We quantify streamflow changes in Puerto Rico following Hurricane Maria in September 2017, and evaluate whether forest cover and storm‐related canopy damage account for the differences. Streams are particularly vulnerable to flooding in early post‐disturbance stages during hurricane season, so we focus on 3 months (Oct–Dec) following the hurricane. To discern changes in rainfall responses, we partitioned streamflow into baseflow and quickflow using a digital filter. We collected 2010–2017 streamflow and rainfall data from 18 watersheds and compared the relative magnitude of post‐ to pre‐hurricane double mass curve slopes of baseflow and quickflow volumes against rainfall. Several watersheds displayed higher post‐hurricane quickflow and baseflow, however, the response was variable. The magnitude of quickflow increase was greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low initial forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow generally increased, but increases were greater in highly forested watersheds and smaller in highly damaged watersheds. These results suggest that post‐storm baseflow increases were due to recharge of hurricane‐related rainfall, as well as forest transpiration interruption and soil disturbance enhancing recharge of post‐hurricane rainfall, while increases to quickflow are related to loss of canopy rainfall interception and higher soil saturation decreasing infiltration. Our research demonstrates that forest damage from disturbance lowers quickflow and elevates baseflow in highly forested watersheds, and elevates quickflow and lowers baseflow in less‐forested watersheds. Less‐forested watersheds may be closer to the forest cover loss threshold needed to elicit a streamflow response following disturbance, suggesting higher flooding potential downstream, and a lower storm‐related forest disturbance threshold than in heavily forested watersheds. [ABSTRACT FROM AUTHOR]

Despite hosting ∼16% of the global freshwater and almost 50% of water resources in South America, Brazilian catchment‐scale relationships between drivers and streamflow are still poorly understood. Here, we used streamflow signatures and attributes of 735 catchments from the Catchment Attributes for Brazil data set to investigate the dominant hydrological processes for the catchments. We also assess how catchments group based on hydrologic behavior similarities and analyze which climatic/landscape attributes control the streamflow variability. To classify and group the catchments, we used the k‐means method optimized by the Elbow approach, along with a Principal Component Analysis. Uncertainty on catchment grouping was checked by k‐fold cross‐validation. Then, we used a recursive feature elimination using the random forest technique to assess the most influential catchment attributes to the hydrological signatures. Our results revealed six similarity groups, which followed mainly an aridity gradient ranging from the wettest to the driest, but also seasonality. The climate is the primary driver of hydrological behavior for the water‐limited groups, highlighting the influence and importance of the atmospheric demand in several Brazilian catchments. High soil storage capacity in energy‐limited catchments associated with high precipitation led to high discharge all year due to the subsurface fluxes' contribution. Our findings may be useful to improve streamflow predictability and hydrological behavior identification by further understanding hydrological similarities and their signatures due to catchment landscape characteristics. Further, by employing an easily reproducible methodology and clear metrics to weigh uncertainty, our study provides a significant step toward establishing a catchment‐scale common classification system. Key Points: We analyzed catchment‐scale relationships between streamflow and its drivers by clustering the catchments based on hydrological similaritySix catchment groups emerged following mainly an aridity gradient, from the wettest to the driest, and seasonalityClimate controls the hydrological behavior of water‐limited catchments, while landscape characteristics control energy‐limited catchments [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]

Image‐based algorithms have become a powerful tool for estimating flow velocities in rivers. In this study, we generalize the space‐time image velocimetry (STIV) framework for reach‐scale application rather than along a cross section. The new algorithm provides information on both the magnitude and orientation of velocity vectors, and we refer to the algorithm as two‐dimensional STIV, or 2D‐STIV. The workflow involves setting up a grid, using centreline tangent vectors as initial estimates of flow direction, and then extracting space‐time images (STIs) along search lines radiating from each grid node. The autocorrelation function is used to infer the inclination of streak lines present in STIs, which represents the advection of water surface features. Information on flow direction is obtained by evaluating various candidate search lines and identifying that which yields the highest velocity. This search can be performed exhaustively or via optimization. We applied the new 2D‐STIV algorithm to three test cases, one simulated data set and two natural channels, and compared image‐derived velocities to modelled or measured values. We also applied two established particle image velocimetry (PIV) algorithms to the same data sets. 2D‐STIV performed as well as the two PIV algorithms for simulated images. For a natural river with distinct water surface features, 2D‐STIV was effective for much of the channel but also led to a more patchy, irregular velocity field than the two PIV algorithms. For a site lacking obvious surface features, exhaustive 2D‐STIV led to velocity estimates uncorrelated with field data while the optimization‐based version produced erratic flow directions. 2D‐STIV also required greater image sequence durations, higher frame rates, and generally longer computational run times. Overall, ensemble PIV was the most reliable algorithm. [ABSTRACT FROM AUTHOR]

Knowledge of the behaviour of marine‐based ice sheets during times of climatic warming, such as the last deglaciation, provides important information to understand how ice sheets respond to external forcing. We analysed swath bathymetric and acoustic sub‐bottom profiler data from Wrigley Gulf on the western Amundsen Sea shelf, West Antarctica, to identify glacial features and reconstruct past changes in the extent of the West Antarctic Ice Sheet (WAIS) and ice flow directions. Glacial bedforms mapped within a bathymetric cross‐shelf trough include features showing cross‐cutting and overprinting relationship and indicate changes in ice‐flow orientation. Here, we distinguish at least two phases of different ice‐flow patterns on the Wrigley Gulf shelf. During the earlier phase, seaward ice stream flow on the inner shelf was deflected towards the east due to the existence of an ice dome on the middle‐outer continental shelf. Retreat of grounded ice towards the centre of this dome is indicated by the asymmetric cross profile of recessional moraines mapped on the middle shelf. The later glaciation phase was characterized by fast, NNW‐directed ice flow across the shelf along a broad front and subsequent stepwise landward retreat, which is evident from the common occurrence and orientation of mega‐scale glaciation lineations and grounding zone wedges on the middle‐inner shelf. It is uncertain whether the two phases of glaciation recorded on the seafloor occurred during the last and penultimate glacial periods or at different times of the last glaciation. Reliable chronological constraints from sediment cores and additional geomorphological information are needed to understand the cause of the changes in WAIS dynamics reflected by the two ice‐flow phases. [ABSTRACT FROM AUTHOR]

Rivers are vital and complex natural systems that provide a wide range of ecosystem services. This study presents a methodology for assessing the riverine provisioning and supporting ecosystem services, whose applicability has been demonstrated over the Budhabalanga River Basin of India. The Soil and Water Assessment Tool (SWAT) is used to generate streamflow time series at various ungauged sites, and then the streamflow is characterized for the evaluation of provisioning services. Further, the diversity and abundance of macroinvertebrates, along with the Lotic-invertebrate Index for Flow Evaluation (LIFE), is used to study the riverine supporting ecosystem services. The streams show intermittent behavior and strong seasonality for low flows, which limits the water availability, particularly during pre-monsoon season. The Baseflow Index (BFI) is greater than 0.6, indicating that groundwater contributes more than 60% of the total streamflow. Interestingly, despite the high BFI, the streams did not conform to the prevailing opinion that a greater baseflow contribution results in a later commencement of the low-flow period in the hydrological year. Furthermore, the study depicts significant variations in the diversity and abundance of the macroinvertebrates across the various sampling sites. However, the LIFE score across the sites remained consistent within a narrow range, i.e., 8 to 9, suggesting a steady supply of supporting ecosystem services. The results of the study can help the policymakers towards an informed decision making and the simplistic methodology proposed in this study can be replicated in other river basins for identifying vulnerable watersheds and prioritizing management actions. [ABSTRACT FROM AUTHOR]

The construction and operation of large‐scale reservoirs alter the natural river flow regimes and destroy the original suitable reproduction and living conditions of aquatic organisms. Especially during the spawning season, the fluctuating water flow process is particularly important for fish reproduction. A bi‐objective optimization model considering both the environmental flow demand for fish spawning and the stability of hydropower output is established in this study and solved by one of the most widely used algorithms for multi‐objective problems, the Non‐Dominated Sorted Genetic Algorithm‐II. The derived Pareto Front can be used to guide reservoir operation during fish spawning seasons. The model proposed in this paper is applied to the Yangqu Reservoir, currently under construction on the upper Yellow River in China. The results show that the stability of hydropower output can still be improved by 4.80% to 10.56%, even if the environmental flow demand is taken into account. Even for the scheme preferring ecological recovery, the stability of hydropower output can be increased by more than 0.85%. Highlights: Fish spawning demand and hydropower stability are considered simultaneously.Derived Pareto Front can be used for reservoir operation in fish spawning seasons.Room still exists for improving hydropower stability meeting ecological demands. [ABSTRACT FROM AUTHOR]

Machine learning models' performance in simulating monthly rainfall–runoff in subtropical regions has not been sufficiently investigated. In this study, we evaluate the performance of six widely used machine learning models, including Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), Gaussian Process Regression (GPR), LASSO Regression (LR), Extreme Gradient Boosting (XGB), and the Light Gradient Boosting Machine (LGBM), against a rainfall–runoff model (WAPABA model) in simulating monthly streamflow across three subtropical sub-basins of the Pearl River Basin (PRB). The results indicate that LSTM generally demonstrates superior capability in simulating monthly streamflow than the other five machine learning models. Using the streamflow of the previous month as an input variable improves the performance of all the machine learning models. When compared with the WAPABA model, LSTM demonstrates better performance in two of the three sub-basins. For simulations in wet seasons, LSTM shows slightly better performance than the WAPABA model. Overall, this study confirms the suitability of machine learning methods in rainfall–runoff modeling at the monthly scale in subtropical basins and proposes an effective strategy for improving their performance. [ABSTRACT FROM AUTHOR]

Changes in water quality are closely linked to seasonal fluctuations in streamflow, and a thorough understanding of how these variations interact across different time scales is important for the efficient management of surface water bodies such as rivers, lakes, and reservoirs. The aim of this study is to explore the potential connection between streamflow, rainfall, and water quality and propose an optimised ensemble model for the prediction of a water quality index (WQI). This study modelled the changes in five water quality parameters such as ammonia nitrogen (NH3-N), phosphate (PO43−), pH, turbidity, total dissolved solids (TDS), and their associated WQI caused by rainfall and streamflow. The analysis was conducted across three temporal scales, weekly, monthly, and seasonal, using a generalised additive model (GAM) in Toowoomba, Australia. TDS, turbidity, and WQI exhibited a significant nonlinear variation with the changes in streamflow in the weekly and monthly scales. Additionally, pH demonstrated a significant linear to weakly linear correlation with discharge across the three temporal scales. For the accurate prediction of WQI, this study proposed an ensemble model integrating an extreme gradient boosting (XGBoost) and Bayesian optimisation (BO) algorithm, using streamflow as an input across the same temporal scales. The results for the three temporal scales provided the best accuracy of monthly data, based on the accuracy metrics R2 (0.91), MAE (0.20), and RMSE (0.42). The comparison between the test and predicted data indicated that the prediction model overestimated the WQI at some points. This study highlights the efficiency of integrating rainfall, streamflow, and water quality correlations for WQI prediction, which can provide valuable insights for guiding future water management strategies in similar catchment areas, especially amidst changing climatic conditions. [ABSTRACT FROM AUTHOR]

Managing water in catchments with high agricultural demands, particularly during periods of low natural flows, is a challenging task. Environmental flow regimes, which are based on monthly minimum values, may not be adequate to address issues associated with rivers that have complex hydrological alteration patterns. The technical solution that provides values to create a flow regime can often be impractical for addressing large problems that planners need to solve, especially when demands are high and resources are limited. This work proposes a new approach to the problem by recognising established agricultural uses and demands that reduce river flow. The focus is on reducing the changes in the hydrological regime by proposing an alternative environmental flow regime that is compatible with agriculture. Deviation from the natural flow of these rivers has been minimized through various processes. Initially, this was achieved by reducing irrigation flows through more efficient demand calculations. The results provide solutions for various hydrological alteration problems, in three rivers, Riaza, Duratón, and Eresma; the inverted regime was corrected, in Cega, Tormes, and Adaja, whose main problem was the reduction of peak flows; controlled peak flows that are compatible with the available water and demands are proposed; and finally in the Agueda river, the reduction in monthly flows was increased on a monthly basis. A new strategy is proposed for considering environmental aspects in the management of rivers with high demands, which improves the fixed schemes for determining environmental flows used in Mediterranean basins. [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]

Hydrograph separation procedures aim to separate the baseflow and quickflow components of streamflow. Although widely used, these procedures are considered arbitrary, as they are mostly based on sheer geometrical or mathematical concepts and involve only streamflow data. Because the in-stream chemical concentrations are known to be valuable indicators of the actual flow-generating processes, we propose here a calibration approach for one of the most common hydrograph separation methods: the Recursive Digital Filter (RDF). The proposed approach uses continuous in-stream chloride concentrations, measured at the ORACLE-Orgeval Observatory (France). For this, we combine the RDF method with the chloride solute through the mass balance method (MB) and a multi-objective optimization function. Results show that the calibration approach allows a better estimation of the chemical concentration time series tested at flood event and inter-annual scale. [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]

Streamflow estimation plays an important role in water resources planning and management, and minimizes the impact of floods and droughts. Therefore, this study aims to estimate monthly streamflows in a semi-arid region of India by employing multiple data-driven models, which include standalone time-series models such as Thomas-Fiering (T-F), autoregressive moving average (ARMA), and the logistic smooth threshold autoregressive (LSTAR) models, artificial intelligence (AI) technique such as artificial neural network (ANN), and an advanced novel hybrid model such as LSTAR coupled with generalized autoregressive conditional heteroscedasticity (GARCH), i.e., LSTAR-GARCH model. Furthermore, capability of five models including standalone models (T-F, ARMA), a cascade feed-forward back propagation neural network (CFBPNN), LSTAR, and the hybrid LSTAR-GARCH model, was comparatively evaluated in estimating 40-year (June 1975 to May 2015) monthly streamflow of Jakham reservoir located in Rajasthan state of India. The models were calibrated using first 75% of the dataset, and their predictions were evaluated using four performance evaluation criteria: correlation coefficient (R), root mean square error (RMSE), modified Nash-Sutcliffe efficiency (MNSE), and modified index of agreement (MIA). The developed models were validated using remaining 25% dataset (120 months), and their performances were compared by plotting hydrographs and scatter plots between the observed and estimated values. The results suggested that the T-F model was satisfactory for generating synthetic monthly streamflow while the ARMA model accurately estimated the streamflows except at extremes. Among the stand-alone models, CFBPNN-4 model outperformed the other three ones with a higher MNSE value of 0.53, and a lower RMSE value of 30.78 million m3. The proposed hybrid LSTAR-GARCH model, which is based on a novel framework, outperformed the standalone ones in terms of R, MNSE, MIA, and RMSE (0.89, 0.78, 0.58, and 25.92 million m3, respectively). [ABSTRACT FROM AUTHOR]

The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability. [ABSTRACT FROM AUTHOR]

Water scarcity is becoming a common problem due to urbanization, industrialization, rapid population growth, and climate change. The situation is exacerbated in semi-arid and arid regions due to low or no rainfall and the resulting pronounced seasonality of river flows. Accurate assessment of reservoir operational performance plays a critical role in water resource sustainability and utilization. In this study, Water Evaluation and Planning (WEAP) models were developed to evaluate the operational performance of five reservoirs in the Yesilirmak Basin in Turkey. Since each reservoir is a single reservoir system, calibration of the WEAP model involved adjusting the approximate rule curves derived by the sequent peak algorithm and the allocation coefficient during hedging until the simulated reservoir storage states matched the observed ones as closely as possible. The agreement of the simulated storage capacities of the WEAP model with the measured volumes was evaluated using the coefficient of determination (R 2) and the Nash-Sutcliffe model efficiency coefficient (NSE). The results demonstrate that the WEAP model can be used to design and plan the operational strategies for the reservoirs in the Yesilirmak Basin. The performance of the reservoirs operated with the designed rule curves was evaluated using the reliability, resilience, vulnerability, and sustainability indices. For Cakmak, Corum and Yenihayat reservoirs, it is shown that these reservoirs are sufficient to meet the demand. The vulnerability of Alaca reservoir is only 1%, which is negligible. The vulnerability for Yedikir is 24% and the failure cannot be ignored. [ABSTRACT FROM AUTHOR]

A new version of a conceptual climate model of river runoff was used to calculate daily and monthly hydrographs for the Lena R. basin with the use of MERRA reanalysis data and data on runoff from R-ArcticNet archive over a 32-year period (1980–2011). The optimization of model parameters and corrections to precipitation allowed the authors to obtain a good quality of the calculated runoff hydrographs and to reveal a trend in the long-term runoff dynamics over the historical period since 1985 to 2011. The period 1985–2011 shows an abrupt increase in the surface air temperature, an increase in precipitation in the Arctic region since the mid-1980s, in particular, in the Lena River basin. The climate changes have led to changes in the hydrological regime of the river basin, and the question of assessing the long-term dynamics of water discharges over this period, in particular, using the conceptual model, gains in importance. [ABSTRACT FROM AUTHOR]

Effective stakeholder engagement is essential for basin management, requiring structured approaches to foster collaboration and consensus. This paper applies the Issue Generating Assessment (IGA) method, which identifies core issues for stakeholder discussion, to basin management. Focusing on the Israeli part of the Hadera Basin, we identify the core issues that should be discussed by stakeholders using the IGA method. To this end 39 participants across 14 sectors evaluating three generic basin management strategies were asked to qualitatively explain their evaluations. By analyzing these explanations utilizing the IGA method, four core issues emerged: (1) Managing uncertainty: addressing climate change and land use impacts on stream flow; (2) Mutual impacts management: handling interactions between the stream and its surroundings; (3) Integration of uses: balancing various stream utilization priorities; (4) Defining natural system functions: determining the role of natural systems. For each core issue, we proposed questions to guide stakeholder discussions. The IGA method is thus found to be useful, and has the potential to foster meaningful dialogue in structured stakeholder meetings, thereby focusing discussions and allowing understandings among stakeholders to be reached as a basis for basin management plans. Such early understandings may contribute to the development of strategies for sustainable basin management. [ABSTRACT FROM AUTHOR]

In the present study, the Soil and Water Assessment Tool was applied to determine the impacts of changing Land Use and Land Cover (LULC), Geophysical Fluid Dynamics Laboratory – Earth System Model Version 2, and Representative Concentration Pathways (RCP) 8.5 climate scenario on the monthly streamflow in the Subarnarekha basin of India. The results showed increased flow due to a reduction in agricultural area, a rise in built-up area, and a reduction in water bodies due to LULC change. In addition, lower annual precipitation and increased projected temperature were observed under RCP8.5. Although annual precipitation is decreasing, some components of the water balance are slightly increasing. From 2013 to 2020, surface flow increased by 98.85 mm and water yield decreased by 13.33 mm. However, in the climate change scenario, surface flow increased by 142.85 mm. Water yield decreased by 21.88 mm, lateral flow slightly decreased by 7.06 mm, and a further significant decrease 68.37% was noted in groundwater flow. The downward trend in groundwater flow is a serious concern, and therefore, more surface water storage structures must be planned to increase groundwater recharge and capture the increased surface flow. The model performance was statistically tested for NSE (Nash–Sutcliffe efficiency), R2, and PBIAS (percent bias). During the calibration period and validation stages, NSE, R2, and PBIAS were found to be 0.72, 0.83, and − 15.20%, and 0.85, 0.82, and − 27%, respectively, with the 2013 LULC map. The decreased monthly water availability and declining trend of winter rainfall need to be taken care of while planning the cropping pattern of the basin. [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]

Large cells often rely on cytoplasmic flows for intracellular transport, maintaining homeostasis, and positioning cellular components. Understanding the mechanisms of these flows is essential for gaining insights into cell function, developmental processes, and evolutionary adaptability. Here, we focus on a class of self-organized cytoplasmic stirring mechanisms that result from fluid--structure interactions between cytoskeletal elements at the cell cortex. Drawing inspiration from streaming flows in late-stage fruit fly oocytes, we propose an analytically tractable active carpet theory. This model deciphers the origins and three-dimensional spatiotemporal organization of such flows. Through a combination of simulations and weakly nonlinear theory, we establish the pathway of the streaming flow to its global attractor: a cell-spanning vortical twister. Our study reveals the inherent symmetries of this emergent flow, its low-dimensional structure, and illustrates how complex fluid--structure interaction aligns with classical solutions in Stokes flow. This framework can be easily adapted to elucidate a broad spectrum of self-organized, cortex-driven intracellular flows. [ABSTRACT FROM AUTHOR]

In this study, we employ explainable machine learning (ML) models to predict daily streamflow ( Q flow ) by leveraging hydro-meteorological parameters. The predictive matrix incorporates crucial factors such as daily rainfall, temperature, relative humidity, solar radiation, wind speed, and the one-day lag value of Q flow . Notably, among these parameters, the one-day lag value of Q flow , along with rainfall, solar radiation, temperature, and relative humidity emerge as highly influential predictors. We apply various ML models, including bagging ensemble learning, boosting ensemble learning, Gaussian process regression (GPR), and automated machine learning (Auto ML). Following a rigorous evaluation, the bagging ensemble learning model stands out as the most effective with a correlation coefficient (R = 0.80) and root-mean-square error (RMSE = 218). Further, we compare the Q flow predicted using ML models with a process-based hydrological model (SWAT) that was executed using a similar set of climatic variables as the input parameters. In our case, the predictive strength of the ML model (R = 0.80; RMSE = 218) to estimate ( Q flow ) is greater than the SWAT (R = 0.82; RMSE = 281). In conclusion, by emphasizing the importance of explainable ML models and highlighting the significance of specific hydro-meteorological parameters, our study contributes to advancing the field of hydrology and water resource management. [ABSTRACT FROM AUTHOR]

Flow–duration curves (FDCs) provide a compact view of the historical variability of river flows, reflecting climate conditions and the main hydrologic features of river basins. The Surface Water and Ocean Topography (SWOT) satellite mission will enable the estimation of river flows globally, by sensing rivers wider than 100 m with a sampling recurrence from 3 to 21 days. This study investigated the lifetime mission potential for FDC estimation through the comparison between remotely-sensed and empirical FDCs. We employed the Global Runoff Data Center dataset and derived SWOT-like river flows by selecting gauging stations of rivers wider than 100 m with more than 10-year long daily river flow time series. Overall, 1200 gauged river cross-sections were examined. For each site, we created a set of 24 SWOT-simulated FDCs (i.e., based on different sampling recurrences, mean biases, and random errors) to be compared against their empirical counterparts through the Nash–Sutcliffe efficiency and the mean relative error. Our results show that climate and the sampling recurrence play a key role on the performance of SWOT-based FDCs. Tropical and temperate climates performed the best, whereas arid climates mostly revealed higher uncertainties, especially for high- and low-flows. [ABSTRACT FROM AUTHOR]