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Climate change is causing stream flow regimes to change in many regions globally, including southwestern Australia (SWA) where many perennial streams have switched to intermittent flow regimes. In drier landscapes, ecological refuges and evolutionary refugia will become critical for conserving freshwater biodiversity. This study aimed to determine the contribution of dry season refuges to species persistence and community recovery in a forested headwater catchment where perennial streams have been exposed to severe permanent drying, causing them to become intermittent. That is, in streams where formerly no drought refuges were present. Macroinvertebrate assemblages were sampled in dry season refuges within recently intermittent streams. Refuges included small spring‐fed pools, perched pools and subterranean refuges associated with granite inselbergs. Dry streambeds were searched for organisms aestivating in situ, and sediments were collected from each intermittent stream for rehydration. Mantel tests were used to compare the contribution of refuges to species assemblages in the early wet season when intermittent streams had begun to flow. Analysis of similarities was used to compare patterns between dry and wet season assemblages of intermittent streams and the single remaining perennial stream in the catchment, to infer the contribution of the perennial stream to community recovery in intermittent streams. Refuge types had very different assemblages: spring‐fed refuges supported several locally endemic species, but perched pools were dominated by opportunistic colonists. Several taxa were found aestivating in dry sediments, primarily adult Coleoptera and larval Chironomidae. Inselberg springs supported small populations of endemic Trichoptera and Ephemeroptera and provided subterranean refuge for an endemic amphipod. However, dry season refuges did not significantly contribute to community recovery. Rather, assemblages in early winter flows were similar to those inhabiting the perennial stream, showing that recolonisation from perennial streams is likely the primary process of community recovery in these recently intermittent streams. Newly formed dry season refuges supported some locally endemic species, but also colonising species that were formerly absent (or rare) when all streams were perennial. However, continued loss of perennial streams in SWA will result in landscape‐wide reductions in diversity (as colonisation sources are lost), because there are no known evolutionary refugia in this landscape for relictual species to retreat into. Granite inselbergs may become evolutionary refugia for a few species, as perennial streams are lost. Refuges will likely play a pivotal role in persistence of species able to use them; however, the existence of refuges is also under threat from prolonged climatic drying, including refuges newly formed by stream dry. Thus, identification and protection of future evolutionary refugia is a matter of urgency in regions facing drier climates, as it is ultimately evolutionary refugia that will become critical reservoirs of freshwater biodiversity as perennial streams and refuges are lost. [ABSTRACT FROM AUTHOR]

We introduce CAMELS-INDIA (Catchment Attributes and MEteorology for Large-sample Studies – India), the hydrometeorological time series, and catchment attributes for 472 catchments in Peninsular India. Peninsular India covers 15 intrastate river basins defined by the Central Water Commission (CWC), where river flow and water level datasets are available for several gauge stations through the open-source India Water Resources Information System (India-WRIS). However, many of these gauge stations lack reliable metadata, and data are not in an analysis-ready format for large-sample hydrological studies. Therefore, we utilized 472 gauge stations and their catchment boundaries, characterized as stations with reliable metadata, from the 'Geospatial dataset for Hydrologic analyses in India (GHI)' (Goteti, 2023). For each of these catchments, the CAMELS-INDIA provides a catchment mean time series of meteorological forcings for 41 years (1980–2020) and around 211 catchment attributes representing hydroclimatic and land cover characteristics extracted from multiple data sources (including ground-based observations, remote sensing-based products, and reanalyses datasets). The CAMELS-INDIA follows the same standards of the previously developed CAMELS datasets for the USA, Chile, Brazil, Great Britain, Australia, Switzerland, Germany, and Denmark to facilitate comparisons with catchments of those countries and inclusion in global hydrological studies. Notably, the CAMELS-INDIA includes available observed streamflow and catchment mean time series of 19 meteorological forcings, including precipitation, maximum, minimum, and average temperature, long-wave and short-wave radiation flux, U and V-components of wind, relative humidity, evaporation rates from canopy and soil surface, actual and potential evapotranspiration, and soil moisture of four layers (covering depth up to 3 m below ground) for detailed hydrometeorological studies. We also derived catchment attributes representing human influences, including the number of dams and their utilization, total volume contents of dams in catchments, population density, and increase in urban and agricultural land covers to facilitate studies to understand human influences on catchment hydrology. Furthermore, the dataset includes predicted streamflow time series from a regionally trained Long-Short Term Memory (LSTM)-based hydrological model, which can fill gaps in observed streamflow data or serve as a benchmark for testing and developing new hydrological models. We envision that CAMELS-INDIA will provide a strong foundation for a community-led effort toward gaining new hydrological insights from hydrologically distinct Indian catchments and solving pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unraveling regional-scale hydrologic functioning, and climate change impact assessment of catchments across India. The CAMELS-INDIA dataset is available at https://doi.org/10.5281/zenodo.13221214 (Mangukiya et al., 2024). [ABSTRACT FROM AUTHOR]

Antecedent hydrological conditions are recorded through the evolution of dissolved lithium isotope signatures (δ7 ${\delta }^{7}$Li) by juxtaposing two storm events in an upland watershed subject to a Mediterranean climate. Discharge and δ7 ${\delta }^{7}$Li are negatively correlated in both events, but mean δ7 ${\delta }^{7}$Li ratios and associated ranges of variation are distinct between them. We apply a previously developed reactive transport model (RTM) for the site to these event‐scale flow perturbations, but observed shifts in stream δ7 ${\delta }^{7}$Li are not reproduced. To reconcile the stability of the subsurface solute weathering profile with our observations of dynamic stream δ7 ${\delta }^{7}$Li signatures, we couple the RTM to a distribution of fluid transit times that evolve based on storm hydrographs. The approach guides appropriate flux‐weighting of fluid from the RTM over a range of flow path lengths, or equivalently fluid residence times. This flux‐weighted RTM approach accurately reproduces dynamic storm δ7 ${\delta }^{7}$Li‐discharge patterns distinguished by the antecedent conditions of the watershed. Plain Language Summary: Storm events often cause characteristic shifts in stream solute chemistry. Interpreting these signals offers insight into the water‐rock interactions occurring within watersheds. Here, we use lithium stable isotopes and reactive transport modeling to relate how long water spends in a catchment, or how deep water infiltrates through a catchment, to the extent of chemical weathering. We show that the first significant storm after a dry season exports more chemically evolved water, while a wet season storm releases less evolved, shallower, and younger water. Our results indicate that stream flow δ7 ${\delta }^{7}$Li in small watersheds offers a sensitive record of hydrological conditions prior to the storm, reflecting subtle shifts in the efficiency of the Critical Zone to generate, transport, and ultimately export solutes. Key Points: Stream lithium stable isotope ratios (δ7Li) recorded at high frequency over storm events are sensitive to antecedent conditionsA reactive transport model cannot produce observed shifts in stream chemistry through variations in flow rate aloneFlux‐weighting of model fluid outputs based on time‐varying fluid transit time distributions describes stream δ7Li over storm hydrographs [ABSTRACT FROM AUTHOR]

We investigate whether geomorphic signatures of permafrost are embedded in planforms of river meanders, and we inquire as to how physical factors unique to permafrost environments are able to affect their dynamics. By exploiting satellite imagery, a data set of 19 freely‐meandering Arctic rivers is compared against an independent data set of 23 freely‐meandering streams flowing through temperate and tropical regions. Suitable dimensionless metrics are defined to characterize morphometric properties of meanders in terms of the spatio‐temporal distribution of curvature and channel width. Results show the absence of marked contrasts in the amplitude of bend‐curvature between the two data set. Differently, we find a permafrost signature in the channel width response, which manifests itself through larger values of the average bend‐width and by peaks of width fluctuations. Field data suggest that permafrost meanders tend to widen for increasing bend sinuosity, likely promoting a shift of their morphodynamic regime as final cutoff is approached. Plain Language Summary: One of the most striking impacts of climate warming in the Arctic region is permafrost thaw. Arctic rivers typically flow through perennially‐frozen floodplains, thus they are particularly susceptible to ground thawing. In order to understand the response of Arctic rivers to climate variability, basic knowledge about key differences with respect to non‐permafrost streams is needed. Despite recent studies which have emphasized the slower yearly movement rates distinguishing Arctic streams, we still do not understand whether permafrost‐affected rivers show distinctive features in their morphology due to specific physical mechanisms. By exploiting satellite imagery, we show that permafrost leaves a signature in the shape of meandering Arctic rivers. Specifically, their average bend‐width increases as sinuosity develops, while the amplitude of width oscillations is larger than that displayed by their non‐permafrost kin. Key Points: Permafrost is found to leave a morphological signature in the spatio‐temporal signal of channel width at the bend scalePermafrost meander bends show larger amplitude of width oscillations and widen as sinuosity increasesBend curvature as a standalone indicator does not provide evidence of any significant permafrost‐fingerprint [ABSTRACT FROM AUTHOR]

Mars has experienced drastic environmental evolution in terms of pH, redox, and desiccation. NASA's Curiosity rover discovered that the sediments of the Pahrump Hills member of the Murray formation in Gale Crater contained different redox states of Fe oxides. To interpret the observations, understanding the factors that control iron redox states within early Gale lakes on Mars is needed. Here, we present the results of a one-dimensional geochemical model of a closed-basin lake that considers the Fe(II) photo-oxidation reaction. Depending on the source of Fe(II) (groundwater vs. river flow), lake pH, advection rate, and atmospheric composition, we defined four types of chemical profiles in the lake; namely, Fe(II) dominated, Fe(III) dominated, carbonate precipitating, and redox-stratified profiles. The Fe(III) dominated profile corresponds to the end member case in which photo-oxidation effectively oxidizes the supplied Fe(II). The redox-stratified profile, interpreted as an intermediate condition between Fe(III) dominated profile and Fe(II) dominated profile, appears in broad parameter set when Fe(II) is supplied from the groundwater, typically with a low to moderate Fe(II) input flux at acidic to alkaline pH (pH 5–9) conditions; however, the low water flux due to the nature of the groundwater may be unable to explain the measured abundance of Fe oxides in the sediments. Another endmember case, the Fe(II) dominated profile, which would also be expected to generate mixed valence Fe oxides, occurs when the pH is acidic (pH 4–5); nevertheless, the acidic pH is inconsistent with the mineral assemblages of the sediments. Carbonate precipitating profiles were limited to the case in which Fe(II) is supplied by groundwater with high Fe input flux and alkaline pH of 8–9. These results may imply either the Fe(II) input flux was high enough for the redox-stratified case or Fe(II) photo-oxidation was suppressed by turbidity or by dusty/cloudy atmosphere, or possibly that Fe(II) oxides found in the Pahrump Hills member are, at least partly, of detrital origin. Although numerous other factors (ex., effect of lake depth, variable pH throughout the lake water, detailed chemical speciation) remain unconsidered, the diverse profiles in Fe redox resulting from Fe(II) photo-oxidation reaction sheds light on interpreting the variety and the evolution of redox condition on early Mars. [ABSTRACT FROM AUTHOR]

When field observations are lacking or are difficult to obtain, streamflow intermittency is usually assessed from the frequency of zeroes in streamflow time series, using some (often arbitrary) definition of the zero-count threshold signaling an intermittent flow regime. This research proposes alternative and robust indicators for differentiating between perennial and intermittent flow regimes from discharge time series. We perform inductive hydrologic classification of 136 rivers and streams in Greece and Cyprus at daily and monthly scales, using selected hydrologic signatures. PCA is used to reduce the dimensionality of the input data, followed by classification using hierarchical clustering to group rivers into classes of similar hydrological characteristics. Two superclusters are defined, interpreted as corresponding to perennial and intermittent river regimes respectively. A subset of monthly hydrological signatures explaining most of the variance in the flow record is used with univariate clustering to cluster the data into two groups with maximum homogeneity. The resulting cluster membership is validated by assessing similarity with the clusters from the multivariate hydrologic classifications. Seven signatures are identified (entropy of flow volumes, entropy of flow distribution, CV of mean annual flow, index of flow variability, variability of mean monthly flows, average maximum monthly flow, and 10th percentile of monthly flows) as the most useful in differentiating between perennial and intermittent river regimes; cluster separation is also assessed visually on scatterplots between signature pairs. Overall, the entropy-based signatures outperform the others. Finally, threshold values of the proposed hydrologic signatures are identified for streamflow intermittency assessment. [ABSTRACT FROM AUTHOR]

Flood hazard assessment is essential for climate-change management planning. River flow frequency and size affect basin flood regime. This study evaluates the flood hazard of delta region of a large Indian river basin, the Mahanadi River basin, for possible future flood scenarios. Based on flood severity and frequency, basin flood regime change is examined. Four different flood scenarios are considered such as reference scenario (1955–2001), present scenario (1981–2011) and two possible future scenarios, by modifying the peak flood series with percentage change approach. Flood hazard was assessed based on the results of hydrodynamic modelling. Based on flood estimations at head of delta region, flood intensity is increasing in delta region of Mahanadi basin. Floods of higher return period (61 years) in the reference scenario happens to be a lower return period flood (11 years) in possible future scenario. In recent years, high floods have maximum water level profiles equal to or higher than 5- and 10-year return period floods of hypothetical future flood scenarios. Flood inundation evaluation under multiple land-uses shows that economically significant land-uses including farmland, built-up land, and aquaculture are more vulnerable to future floods. In the Mahanadi delta, 'high' and 'very high' flood depth coverage increases and 'low' depth diminishes. Inundation area for 'very high' flood depth is increasing, indicating agricultural vulnerability in the delta region. [ABSTRACT FROM AUTHOR]

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]

The Thoubal river catchment (TRB) and Iril river catchment (IRB), which are two major adjoining subbasins of the Manipur river basin in India, were selected for this study. A GIS-based semi-distributed hydrological model soil and water assessment tool (SWAT) was used to derive the hydroclimatic parameters such as surface run-off, evapotranspiration. The present study aims to calibrate and validate the SWAT model using streamflow and satellite soil moisture data, as well as to examine the hydroclimatological responses in the two adjacent basins, and compare the relative changes in treating the Iril-Thoubal river basin jointly and as two separate entities. The model was calibrated and validated using the SUFI-2 tool. During calibration and validation, both IRB and TRB showed good model performance. This study shows that in a poorly gauged river basin with scarce data, remotely sensed soil moisture data may be effectively applied for the calibration and validation of hydrological models. The novel aspect of this study is the efficient calibration and validation of a hydroclimatic model for subtropical river sub-basins with limited hydrological ground data reinforced with open-source satellite-derived soil moisture datasets. [ABSTRACT FROM AUTHOR]

Accurate prediction of stream flow is essential for effective water resource management, particularly in regions with complex hydrological dynamics. The Mille River, which flows through this basin and has a watershed area of 4,862.3 km², plays a crucial role in the region's water resources. However, selecting the most suitable hydrological model for this task remains a challenge due to varying model performances. This study addresses the problem of identifying the most effective hydrological model for predicting stream flow in the Mille River by evaluating three models: Soil and Water Assessment Tool (SWAT), Hydrologiska Byråns Vattenbalansavdelning (HBV-IHMS), and Artificial Neural Network (ANN). The objectives are to compare these models’ abilities to simulate daily stream flow and to determine their accuracy. The significance of this evaluation lies to guide water management decisions by identifying which model provides the most reliable predictions. The methodology involves using performance metrics, Nash-Sutcliffe Efficiency (NSE) values, to assess the accuracy of each model. The results show that SWAT achieved the highest NSE values of 0.81 and 0.82, indicating the best performance in simulating stream flow. ANN followed with NSE values of 0.79 and 0.81, while HBV-IHMS had lower NSE values of 0.73 and 0.74. Although all models demonstrated potential, SWAT and ANN outperformed HBV-IHMS in capturing the complex hydrological processes of the Mille River. The findings from this study indicate that the choice of model significantly impacts the accuracy and reliability of streamflow predictions. The SWAT and ANN models show strong potential for use in operational streamflow forecasting in the Mille River and similar basins. In conclusion, further research is recommended to refine model calibration and validation processes. Incorporating additional data sources, such as remote sensing and climate projections, could further enhance model accuracy and provide a more comprehensive understanding of the region's hydrological dynamics. [ABSTRACT FROM AUTHOR]

Accurate inflow forecasting is an essential non-engineering strategy to guarantee flood management and boost the effectiveness of the water supply. As inflow is the primary reservoir input, precise inflow forecasting may also offer appropriate reservoir design and management assistance. This study aims to generalize the machine learning model using the support vector machine (SVM), which is support vector regression (SVR), to predict the discharges of the Euphrates River upstream of the Haditha Dam reservoir in Anbar province West of Iraq. Time series data were collected for the period (1986-2024) for the river's daily, monthly, and seasonal flow. Different kernel functions of SVR were applied in this study. The kernels are linear, Quadratic, and Gaussian (RBF). The results showed that the daily time scale is better than the monthly and seasonal performance. In contrast, the linear kernel outperformed the other SVR kernel with a time delay of one day based on the value of the coefficient of determination (R2 = 0.95) and the root mean square error (RMSE = 53.29) m3/sec for predicting daily river flow. The results showed that the proposed machine learning model performed well in predicting the daily flow of the Euphrates River upstream of the Haditha Dam reservoir; this indicates that the model might effectively forecast flows, which helps improve water resource management and dam operations. [ABSTRACT FROM AUTHOR]

Communities around the world rely on snowmelt to meet water demands, and periods of lower than normal snow accumulation, snow droughts, can decrease water supplies. Leveraging 172 minimally disturbed and seasonally snow-covered watersheds, we developed an approach to examine the effects of cool & dry, warm & dry, and warm & wet snow droughts on streamflow timing and magnitude by hydrologic region. Our results showed all types of snow droughts in all regions correlate with lower annual streamflow, lower maximum and minimum flows, and lower runoff ratios, with more numerous low flow days and earlier streamflow timing. However, departures from non-snow drought conditions differed substantially between drought types and regions. Consecutive snow droughts further reduced runoff ratios and increased low flow days, likely due to additional subsurface storage depletion. With warm snow drought occurrence expected to increase, we discuss impacts for water management systems whose design specifications may not reflect the changing hydroclimate. [ABSTRACT FROM AUTHOR]

In recent years, the escalating effects of climate change on surface water bodies have underscored the critical importance of analyzing streamflow trends for effective water resource planning and management. This study conducts a comprehensive regional investigation into the streamflow rate trends of 18 rivers across the United Kingdom (UK). An enhanced Mann–Kendall (MK) test was employed to meticulously analyze both rainfall and streamflow trends on monthly and annual scales. Additionally, the Innovative Trend Analysis (ITA) method was applied to elucidate the variability of streamflow rates, providing a more nuanced understanding of hydrological changes in response to climatic shifts. MK test reveals statistically significant positive trends in streamflow rates, particularly for rivers in south-central Scotland and northern England. Specifically, in January, rivers such as the Tay at Ballathie, Tweed at Peebles, and Teviot at Ormiston showed Z-scores above 2. Annually, similar positive trends were observed, with the Tay at Ballathie (Z = 3.42) and Nith at Friars Carse (Z = 3.35) exhibiting the highest increases in streamflow rates. The ITA method showed no relevant trends for the lowest values of streamflow, except for the Thames at Kingston, while considerable variability was observed for the highest streamflow rates, with several rivers showing positive trends and, however, some England rivers, like Bure at Ingworth, Test at Broadlands, and Trent at Colwick, showing negative trends. From this perspective, a more in-depth analysis of the extreme streamflow trends was carried out. In particular, the flood frequency of the maximum annual streamflow was assessed, based on the fitting of the Generalized Extreme Value (GEV) distribution on the annual maxima. Increasing location parameter (μ) and return period trends were observed for several rivers across the UK. In particular, the Tay at Ballathie (Scotland) showed the most marked increase, with μ that ranged from about 730 m3/s to more than 900 m3/s. At the same time, slight decreasing trends were observed for the Trent River (μ from 378 m3/s to 341 m3/s). The critical comparison of the MK test, ITA, and GEV distribution fitting revealed both agreements and discrepancies among the methods. While the analyses generally aligned in detecting significant trends in streamflow rates, notable discrepancies were observed, particularly in rivers with negligible trends. These inconsistencies highlight the complexity of hydrological responses and the limitations of individual methods. Overall, the study provides a comprehensive view of how streamflow dynamics are evolving in UK rivers, highlighting regional variations in the impact of climate change. This understanding can improve water resource management strategies by integrating diverse analytical approaches. [ABSTRACT FROM AUTHOR]

Ever since Alfred Russel Wallace's nineteenth-century observation that related terrestrial species are often separated on opposing riverbanks, major Amazonian rivers have been recognized as key drivers of speciation. However, rivers are dynamic entities whose widths and courses may vary through time. It thus remains unknown how effective rivers are at reducing gene flow and promoting speciation over long timescales. We fit demographic models to genomic sequences to reconstruct the history of gene flow in three pairs of avian taxa fully separated by different Amazonian rivers, and whose geographic ranges do not make contact in headwater regions where rivers may cease to be barriers. Models with gene flow were best fit but still supported an initial period without any gene flow, which ranged from 187 000 to over 959 000 years, suggesting that rivers are capable of initiating speciation through long stretches of allopatric divergence. Allopatry was followed by either bursts or prolonged episodes of gene flow that retarded genomic differentiation but did not fully homogenize populations. Our results support Amazonian rivers as key barriers that promoted speciation and the build-up of species richness, but they also suggest that river barriers are often leaky, with genomic divergence accumulating slowly owing to episodes of substantial gene flow. [ABSTRACT FROM AUTHOR]

Climate-driven changes in freshwater inputs have been shown to affect the structure and function of coastal ecosystems. We evaluated changes in the influence of river runoff on coastal systems of Northwestern Patagonia (NWP) over recent decades (1993–2021) by combined analysis of long-term streamflow time series, hydrological simulation, satellite-derived and reanalysis data on sea surface conditions (temperature, turbidity, and salinity). Significant decreases in minimum streamflow across a zone spanning six major river basins were evident at weekly, monthly, and seasonal scales. These changes have been most pronounced in mixed-regime northern basins (e.g., Puelo River) but appear to be progressing southward to rivers characterised by a nival regime. In the adjacent two-layer inner sea, reduced freshwater input corresponds with a shallower halocline and increased surface temperatures across northern Patagonia. Our results underscore the rapidly evolving influence of rivers on adjacent estuarine and coastal waters in NWP. We highlight the need for cross-ecosystem observation, forecasting, mitigation and adaptation strategies in a changing climate, together with corresponding adaptive basin management of systems that supply runoff to the coastal marine waters. [ABSTRACT FROM AUTHOR]

The present study analyzed sensitivity of flow parameters using SWAT and effect of climate change on surface water resources, considering uncertainty with Monte Carlo. For this purpose, output of nine-model related to fifth-climate change-report during baseline period 1971–2000 was weighted. Using Monte Carlo, 100 samples of probabilistic distribution of basin temperature and rainfall were generated. LARS-WG model was used for downscaling, then temperature and precipitation were calculated under RCP2.6 and RCP8.5 for future periods 2040–2069 and 2070–2099. SWAT was calibrated using observational-data and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) (NCEP CFSR) global climate datasets and sensitivity of parameters affecting flow was analyzed. Results showed that observed-data had better performance than NCEP CFSR. Finally, future runoff was calculated under RCP2.6 and RCP8.5 for 2040–2069 and 2070–2099. Results showed that average annual runoff decreased by 84, 80, 82 and 80%, for 2040–2069 (RCP2.6 and RCP8.5) and for 2070–2099 (RCP2.6 and RCP8.5) relative to baseline, respectively. [ABSTRACT FROM AUTHOR]

We present a mathematical model for agri-food industry residual streams flow management, which serves as a decision support tool for optimizing their valorization. The aim is to determine, under a cost-benefit analysis approach, the best strategy at a global level. The proposed mathematical model provides the optimal valorization scenario, namely the set of routes followed by agri-food industry residual streams that maximizes the total profit obtained. The model takes into account the complete stoichiometry of the residual stream at each step of the valorization route. Furthermore, the model allows for the calculations of different scenarios to support decision-making. The proposed approach is illustrated through a case study using a real-case network of a region. The case study bears evidence that the use of the model can lead to significant profit increases compared to those obtained with current practices. Moreover, notable profit improvements are obtained in the case study if the selling price of all the value-added products considered increases or if the processing cost of the animal feed producer decreases. Therefore, our model enables the detection of key factors that influence the optimal strategy, making it a powerful decision-support tool for optimizing the valorization of agri-food industry residual streams. [ABSTRACT FROM AUTHOR]

Accurate daily streamflow forecasts remain challenging in arid regions. A Bayesian model averaging (BMA) ensemble learning strategy was proposed to forecast 1-, 2-, and 3-day-ahead streamflow in Dunhuang Oasis, northwest China. The efficiency of BMA was compared with four decomposition-based machine learning and deep learning models. Satisfactory forecasts were achieved with all proposed models at all lead times; however, based on Nash-Sutcliffe efficiency values of 0.976, 0.967, and 0.957, BMA achieved the greatest accuracy for 1-, 2-, and 3-day-ahead streamflow forecasts, respectively. Uncertainty analysis confirmed the reliability of BMA in yielding consistently accurate streamflow forecasts. Thus, BMA could provide an efficient alternative approach to multistep-ahead daily streamflow forecasting. The incorporation of data decomposition techniques (e.g. variational mode decomposition) and deep learning algorithms (e.g. deep belief network) into BMA may provide worthy technical references for supervised learning of streamflow systems in data-scarce regions. [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 paper presents an integrated simulation of future hydrological droughts using different climate scenarios for the Piancó River basin. Streamflow and sediment yield are estimated using the Soil and Water Assessment Tool (SWAT) model, and hydrological droughts are calculated using the Streamflow Drought Index (SDI). The study incorporates two future climate scenarios: RCP 4.5 (optimistic) and RCP 8.5 (pessimistic). The SDI is determined based on a series of simulated future flows to identify drought events within the basin. The results show that the model calibration and validation demonstrated excellent agreement with observed data (R² = 0.83, NSE = 0.82 for calibration and R² = 0.89, NSE = 0.77 for validation). When comparing the scenarios, reductions in flow volume by −75.86% and sediment yield by −86.5% are noted, indicating a significant decrease in sediment contribution to the outlet. Interestingly, the most critical years related to drought events, as determined by the SDI, do not coincide across the pessimistic and optimistic scenarios. [ABSTRACT FROM AUTHOR]

Non-stationary sediment load analysis is critical for river engineering design and water resource management. Traditional sediment load frequency analysis methods usually assume stationarity, which can lead to inconsistent results in a changing environment because they cannot account for factors such as time variations. Here, we use generalized additive models for location, scale and shape (GAMLSS) to establish non-stationary models with time, precipitation and streamflow as covariates (named Model 1 and Model 2, respectively), and compare their fitting effects with stationary models (parameters unchanged: Model 0). In this study, the sediment load of the Jinsha River Basin in southwest China was analyzed. Outcomes indicate that: (1) the research area's sediment load decreased significantly, with a significant change point in 2002 (p < 0.1); (2) the goodness of fit indices (global fitting deviation: GD, AIC criterion and SBC criterion) based on Model 2 are smaller than the values of the other two models. The other two models' sediment load quantile design values are within Model 2's range. (3) Compared with Model1, precipitation and streamflow as covariates in Model 2 are more able to capture the non-stationary features of sediment load frequency. Furthermore, Model 2 can more accurately forecast future changes in sediment load when external physical factors are considered. The findings of this research can serve as a scientific foundation for decision makers to carry out water conservancy planning and design and river management and development. [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]

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]

Data‐driven artificial intelligence (DDAI) prediction has gained much attention, especially in recent years, because of its power and flexibility compared to traditional approaches. In hydrology, streamflow forecasting is one of the areas that took advantage of utilizing DDAI‐based forecasting, given the weakness of the old approaches (e.g., physical‐based approaches). Since many different techniques and tools have been used for streamflow forecasting, there is a new way to explore them. This manuscript reviews the recent (2011–2023) applications of DDAI in streamflow prediction. It provides a background of DDAI‐based techniques, including machine learning algorithms and methods for pre‐processing the data and optimizing or enhancing the machine learning approaches. We also explore the applications of DDAI techniques in streamflow forecasting. Finally, the most common tools for utilizing DDAI techniques in streamflow forecasting are presented. [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]

Multi-channel dilution analysis (MCDA) is a new calibration method that performs multiple dilutions of a standard solution as it makes its way from an autosampler to an analytical instrument. MCDA is based on the standard dilution analysis (SDA) method that combines the traditional standard additions and internal standardization calibration methods, correcting for both matrix interferences and fluctuations in signal levels associated with variations in the sample environment. Analysts operate the instrument in the normal manner, as all dilutions are performed automatically by splitting the sample stream entering the instrument into multiple channels of different tubing diameters and lengths. This setup results in a signal "stairstep" as portions of solution are measured at different points in time. A calibration curve is prepared from the plateau regions of the signal stairstep. MCDA has been exhibited using inductively coupled plasma optical emission spectrometry, which is a workhorse for the determination of trace metals in solution. However, MCDA is applicable for any analyte of interest in any sample type, as long as the selected measurement technique accepts samples as a flowing liquid stream. MCDA is applied to the analysis of three certified reference materials by inductively coupled plasma optical emission spectrometry (ICP-OES). Percent recoveries for a suite of analytes range from 87--106%, with relative standard deviations on the order of 1%. MCDA simplifies the analysis process, increasing sample throughput by significantly decreasing the time required for solution preparation. [ABSTRACT FROM AUTHOR]

Recharge to and flow within the Columbia River Basalt Group (CRBG) groundwater flow system of northeastern Oregon were characterized using isotopic, gas, and age‐tracer samples from wells completed in basalt, springs, and stream base flow. Most groundwater samples were late‐Pleistocene to early‐Holocene; median age of well samples was 11,100 years. The relation between mean groundwater age and completed well depth across the eastern portion of the study area was similar despite differences in precipitation, topographic position, incision, thickness of the sedimentary overburden, and CRBG geologic unit. However, the lateral continuity in groundwater age was disrupted across large regional fault zones indicating these structures are substantial impediments to groundwater flow from the high‐precipitation uplands to adjacent lower‐precipitation and lower‐elevation portions of the study area. Recharge rates calculated from the age‐depth relations were <3 mm/yr and independent of the modern precipitation gradient across the study area. The age‐constrained recharge rates to the CRBG groundwater system are considerably smaller than previously published estimates and highlight the uncertainty of prevailing models used to estimate recharge to the CRBG groundwater system across the Columbia Plateau in Oregon and Washington. Age tracer and isotopic evidence indicate recharge to the CRBG groundwater system is an exceedingly slow and localized process. Article impact statement: Recharge to the Columbia River Basalt groundwater system in northeast Oregon is considerably smaller than previously published estimates and models. [ABSTRACT FROM AUTHOR]

The length and frequency of extreme fire weather has increased across the globe in recent decades, with potential deleterious consequences to streamflow quantity, timing and quality. Changes in the hydrologic regime following wildfire can have substantial downstream consequences, affecting communities and ecosystems through flooding, erosion, loss of habitat and degraded water quality. While there are many studies that address post‐wildfire hydrology across the globe, there are few studies in the snow‐dominated regions. The 2017 Elephant Hill wildfire in south‐central BC burned across or adjacent to four watersheds with long‐term streamflow gauges providing a rare opportunity to evaluate hydrologic change. Several approaches were used to identify patterns of change following the wildfire, all of which suggest increased post‐fire flows. The before‐after‐control‐impact design showed significant increases in annual, spring and summer water yield from the small (49 km2) Arrowstone Creek watershed (30%, 21% and 86%, respectively). Significant increases in spring water yield were observed in the larger (5318 km2) Bonaparte River watershed (48%). Annual and summer water yield increased in the Bonaparte River (31% and 58%, respectively) but these changes were not statistically significant. In both the Bonaparte River and Arrowstone Creek, the onset of spring freshet (26 days earlier in both) was significantly advanced, however, the timing of maximum snowmelt discharge was significantly advanced (27 days earlier) only in Arrowstone Creek. Smaller changes were also observed in the reference watersheds; however, these were not statistically significant. The difference in results between the small and large watershed, as well as the effects of weather and watershed attributes, highlight the need for continued research into the relationships between wildfire and hydrologic regime across diverse landscapes. [ABSTRACT FROM AUTHOR]

Although there is a general understanding of how afforestation impact on streamflow, there is also requirement for additional empirical data both on a small and large basins scale, primarily due to the significant variability found in global data sets. A multi‐method approach is proposed to understand the impact of plantation forest cover changes on stream flow, aiming to address individual approach weaknesses and enable cross‐validation. Focusing on Uruguay as a case study, the data based analysis indicated a significant decrease in streamflow within four highly afforested large basins of the north region, occurring approximately when the afforestation reached 15% of the total basin area. Also three of those basins showed a significant decreasing trend in streamflow after the change‐point. A stronger link between afforestation increase and lower runoff‐rainfall ratio was found in autumn‐winter season compared to spring–summer, due to higher soil water availability. The model residual approach effectively isolated land use and land cover effects in large basins with gradual afforestation, utilizing long data series. Consistently, statistically significant trends in the residual series indicated decreased streamflow in the after‐afforestation period in the four large basins. Finally a clear difference in the magnitude of the change emerged between the large and small basins, highlighting the influence of rainfall heterogeneity, landscape control and forest management on the scaling behaviour of streamflow and runoff ratio. [ABSTRACT FROM AUTHOR]

Sediment phenomenon is very important in hydraulic and water resources issues. The existence of this phenomenon causes many problems in water storage. Sediment simulation in rivers helps in controlling sediment as well as reducing damages. In this study, an attempt was made to estimate the suspended sediment load using the corresponding river flow rate in the Zohreh River, Iran using the newest intelligent simulation methods. This study seeks to couple the nonlinear support vector regression (SVR) with crowd intelligence optimization algorithms. For this purpose, support vector regression was optimized using four new crowd optimization algorithms including the ant colony optimizer (ACO), the ant lion optimizer (ALO), the dragonfly algorithm (DA), and the salp swarm algorithm (SSA). Simulation was done in the two phases of train and test. Due to the integration of the nonlinear support vector regression with the optimization algorithms, the model train phase requires more time than usual situations. Therefore, in the current study, taking into account the number of different iterations including 25, 50, 100 and 200 iterations to perform the optimization of the model and tried to find the best optimizer by considering the calculated error and the run time. It was generally found that the SVR model is accurate in estimating the suspended sediment load. Finally, according to the calculated error as well as the run time, the support vector regression model optimized with the salp swarm algorithm with 25 iterations was chosen as the best model. Also, the values of R2, NSE, and RMSE for the best model in the test phase were calculated as 1, 1, and 10.2 tons per day, respectively, and the algorithm run time was 252 s. [ABSTRACT FROM AUTHOR]

Most Andean lakes and lagoons are used as reservoirs to manage hydropower generation and cropland irrigation, which, in turn, alters river flow patterns through processes of storage and discharge. The Carhuacocha and Vichecocha lagoons, fed by glaciers, are important aquatic ecosystems regulated by dams. These dams increase the flow of the Mantaro River during the dry season, supporting both energy production and irrigation for croplands. Water quality in the Carhuacocha and Vichecocha lagoons was assessed between storage and discharge events by using the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) and multivariate statistical methods. The quality of both lagoons is excellent during the storage period; however, it decreases when they are discharged during the dry season. The most sensitive parameters are pH, dissolved oxygen (DO), and biochemical oxygen demand (BOD). This paper details the changes in water quality in the Carhuacocha and Vichecocha lagoons during storage and discharge events. [ABSTRACT FROM AUTHOR]

The Gudiyalchay River plays a crucial role in the environment and human activities of the Guba area in north-eastern Azerbaijan, supporting agriculture and the local water supply. Despite its significance, the river has received little scientific attention. The groundwater beneath the Gudiyalchay riverbeds, a vital source of drinking water and the second primary source of river recharge after snowmelt, remains insufficiently studied, with most monitoring data being outdated. With climate change intensifying, such research is critical to mitigating potential water risks. In this work, all available geological, hydrogeological, climatic, and hydrochemical data were collected to characterize the study area and analyze the seasonal fluctuations in river flow and total dissolved solid (TDS) values, with a focus on the interactions between the river and groundwater at the Khinaliq, Giriz, and Kupchal flow stations. The analysis shows that both river and groundwater TDS values are within acceptable drinking water limits, but continuous data collection is important to confirm this. Flow rate analysis and a literature review revealed that variations in flow rate are linked to seasonal changes, with the flow rate near the Giriz station indicating potential groundwater influence. Based on the literature review and analysis, a simplified hydrogeological diagram is created to provide a clearer understanding of the interactions between the river and groundwater systems. [ABSTRACT FROM AUTHOR]

The assessment of streamflow patterns and their complexity variations across multiple timescales within river basins is a crucial aspect of water resource management and policy formulation. In this study, the Hurst coefficient, Mann–Kendall nonparametric test method, streamflow pattern indices, and sample entropy (SampEn) analyses were used to investigate the streamflow pattern in the whole Yangtze River basin at annual, monthly, and daily scales. The results show that with the increase in the time resolution, the streamflow shows more complex changing characteristics and streamflow changes more obviously on the monthly timescale than on the annual one. The annual mean streamflow decreases only in some of the tributaries, while the monthly streamflow shows significant increasing trends in the dry season and significant decreasing trends in the late wet season in almost the whole basin. Results also show that the minimum extreme streamflow indices increase in almost the whole basin. The maximum indices show decreasing trends in most of the tributaries and the Yichang gauge in the main reach. The streamflow complexity in the tributaries is higher and the complexity increases from upstream to downstream along the main reach in the basin. Along the main reach, the average SampEn increases downstream of the reach with values of 0.05, 0.07, 0.10, 0.12, 0.14, and 0.14 at Shigu, Pingshan, Cuntan, Yichang, Hankou, and Datong, respectively. These findings are helpful for understanding the hydrological characteristics and water resource management in the Yangtze River basin. [ABSTRACT FROM AUTHOR]

Holistic environmental flow assessment includes evaluation of chemical, biological, hydrological, and morphological changes predicted from disrupting a river flow regime. Using available water chemistry together with biological and hydrological surveys, we report and assess environmental flows of the Alijanchay River, an important tributary of the Kura River, at four monitoring stations located in Azerbaijan. The river's natural flow regime has changed significantly due to the irrigation activities in the middle and lower reaches and further development is planned through construction of new reservoirs. Our methodology is based on the results of morphological, hydrological, and hydrobiological observations and analysis of the physical and chemical parameters of the river. Environmental flow was evaluated by six hydrological methods proposed in the literature, and a comparative analysis shows that its value has increased from 13.6 to 27.1% of the annual flow volume, consistent with increased pressure on this important surface water supply. Water Quality Indices (WQI) further show seasonal changes of water quality in this important water supply, impacting sustainable uses for drinking and agriculture. Parameters most affected by seasonal changes are turbidity, suspended solids, and dissolved oxygen. Further degradation of environmental flows of this important watershed in Azerbaijan are likely from the planned development. A more comprehensive holistic ecological flow can help support a sustainable plan for use of Alijanchay River basin water reserves, and, if resources are provided for other basins, can support development elsewhere. [ABSTRACT FROM AUTHOR]

Glaciers in the Pamir region are experiencing rapid melting and receding due to climate change, which has a significant implication for the Amu Darya river basin. Predominantly, surging glaciers, which undergo unpredictable advances, are potentially leading to the obstruction of high-altitude river channels and also glacial lake outburst floods. This study investigates surging glaciers in the Pamir region, specifically focusing on the Medvezhiy (M) and Russian Geographical Society (RGS) glaciers, using long-term reanalysis data records, unmanned aerial vehicles (UAVs), and satellite data during the last decades. Significant changes in regional climate were identified, including increasing precipitation rates (13.3 mm/year) and land surface temperature (0.1°C/decade). Between 1991 to 2023, the RGS glacier experienced a significant advancement around 1448.3 ± 30.0 m, while the Medvezhiy glacier showed a decrease of −703.5 ± 30.0 m. There is a substantial increase in the number (from 19 to 75) and area (from 4889.7 ± 0.6 m2 to 15345.5 ± 0.6 m2) of RGS lakes along with supra-glacier ponds based on a comparison of ArcGIS base map in 2011 and high-resolution UAV data in 2023. For M glacier, number of lakes increased from 4 to 22 but the lake area declined from 10715.2 ± 0.6 to 365.6 ± 0.6 m2. It was noted that the largest lake in 2011 with an area of 10406.4 ± 0.6 m2 at the southeastern portion of the glacier was not observed in 2023 due to outburst. Both the glaciers have substantially impacted the river flow (Abdukahor river) by obstructing a significant proportion of river channel in recent years and might cause outburst floods. These findings enhance the understanding of glacier dynamics and their impacts on the surrounding areas, emphasizing the urgent need for continued monitoring and appropriate management strategies, with a specific focus on surging glaciers and the associated risks. [ABSTRACT FROM AUTHOR]

To evaluate the seasonal changes in quality and quantity of river Rispana (a tributary of river Ganges—Dehradun region of India) water quality index (WQI) was calculated at four major locations by using the weighted arithmetic method. Sixteen samples in all were taken throughout the pre‐monsoon (April 2022), monsoon (July 2022), post‐monsoon (November 2022), and the Winter season (January 2023). The major parameters, namely, temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), and total dissolved solids (TDS), E‐coliform, sulphate, total alkalinity (TA), total hardness (TH), and biochemical oxygen demand (BOD) were considered for calculation of WQI. The WQI varied from minimum 44.53 (good) at site 1 – Kairwaan Gaon during monsoon season to maximum 302.69 (unfit for drinking) at site 4 – Mothrowala during the pre‐monsoon period. In 75% samples quality of water was unfit for domestic purpose and the contributing factors are DO, BOD, and Coliform which exceeds permissible limit in all samples. The major factors attributed for decline in the Rispana water quality are discharge of sewage, commercial sites effluent, and urban runoff. Pre‐monsoon, monsoon, post‐monsoon, and winter had average total coliform levels of 1849.6, 2419.6, 2419.6, and 1546.25, respectively, in MPN units. The average value of E‐coli for the four season vary from 1306.8 to 1888.1 MPN. The pollution load (physico‐chemical parameters and bacteriological factors) increases as the river Rispana flows to downstream in urban locations. Streamflow was observed as very lean except monsoon season (~1500 cfs) due to more extraction of Rispana's fresh water in upstream and unplanned changes in its catchment basin; thus establishes an extensive decline in availability of fresh water sources (Domestic use) in downstream Dehradun city area. In monsoon season, more streamflow (rain water influx) maintains the WQI also in good‐moderate category. The land use/land cover – LULC analysis in Rispana basin area, depicted 71% enhancement in urbanization (24.16%–41.55%) takes place in 15 years' span (2003–2017). The study establishes the potential causes (natural and anthropogenic) for Rispana deterioration and provides a baseline (quality index) for aquatic and ecological rejuvenation of its watershed by executing a proper management strategy with people participation. This work contributes discernments for effective water resource management to address the smart city objectives and sustainable development goal‐SDG:6, urban and municipal authorities must take cognizance for revival of this domestic water resource. [ABSTRACT FROM AUTHOR]

The aim of this research was to simulate the future water balance of the Silwani watershed, Jharkhand, India, under the combined effect of land use and climate change based on the Soil and Water Assessment Tool (SWAT) and Cellular Automata (CA)-Markov Chain model. The future climate prediction was done based on daily bias-corrected datasets of the INMCM5 climate model with Shared Socioeconomic Pathway 585 (SSP585), which represent the fossil fuel development of the world. After a successful model run, water balance components like surface runoff, groundwater contribution to stream flow, and ET were simulated. The anticipated change in land use/land cover (LULC) between 2020 and 2030 reflects a slight increase (3.9 mm) in groundwater contribution to stream flow while slight decrease in surface runoff (4.8 mm). The result of this research work helps the planners to plan any similar watershed for future conservation. [ABSTRACT FROM AUTHOR]

Hydrological droughts severely affect the demand of water for domestic water supply, irrigation, hydropower generation, and several other purposes. The pervasiveness and consequences of hydrological droughts necessitate a thorough investigation of their characteristics, which is hindered due to unavailability of continuous streamflow records at desirable resolutions. This study aims to assess the hydrological drought characteristics and their spatial distribution using high-resolution Global Flood Awareness System (GloFAS) v3.1 streamflow data for the period 1980 to 2020. Streamflow Drought Index (SDI) was used to characterize droughts at 3-, 6-, 9-, and 12-monthly timescales starting from June, i.e., the start of water year in India. GloFAS is found to capture the spatial distribution of streamflow and its seasonal characteristics. The number of hydrological drought years over the basin varied from 5 to 11 during the study duration, implying that the basin is prone to frequent abnormal water deficits. Interestingly, the hydrological droughts are more frequent in the eastern portion of the basin, i.e., the Upper Narmada Basin. The trend analysis of multi-scalar SDI series using non-parametric Spearman's Rho test exhibited increasing drying trends in the easternmost portions. The results were not similar for the middle and western portions of the basin, which may be due to presence of a large number of reservoirs in these regions and their systematic operations. This study highlights the importance of open-access global products that can be used for monitoring hydrological droughts, especially over ungauged catchments. [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]

The Indus River, a lifeline for Pakistan, holds paramount significance for its geography, history, and economy. This research delves into a comprehensive analysis of the river's behavior by introducing a novel statistical framework. Combining the Gumbel Type 2 distribution and the Rayleigh distribution, a new generalized Gumbel Type 2 (GG2) distribution is derived, and used for modeling the data about the river's outflow at the Tarbela Dam during 2020–2021. Our study contributes to the understanding of the complex dynamics of the Indus River. The GG2 distribution, designed for extreme value events, adept at modeling positive-valued variables, were combined to model the complexed characteristics of the river's flow. Parameters are estimated using both classical and Bayesian methods, enhancing the accuracy and reliability of our findings. The Bayse estimators are not in the closed form expression, hence the Tierney-Kadane approximation technique is used. Through simulation study and analysis, the data set of overflows of the river Indus over Tarbela Dam, the Bayes estimators demonstrate superior performance in minimizing risk compared to classical estimators. It is shown graphically that that our proposed distribution performs better than its competitor distributions. The results not only deepen our understanding of the river's behavior but also offer insights crucial for infrastructure planning, flood control, and resource allocation. [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 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.)

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]

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This study examined the impact of climate change on streamflow in the Andit Tid watershed using climate models of dynamically downscaled Ethiopia's CORDEX. The Arc SWAT and ArcGIS 10.5 software assessed the spatial and temporal distribution of streamflow, incorporating geospatial data like land use maps, digital elevation models, soil maps, and climate data. The SWAT model was calibrated and validated using SWAT-CUP with the SUFI-2 algorithm. The Canadian Centre for Climate Modeling and Analysis, Canada (CCCma (RCA4) model was selected for future projections after validation. From 1991 to 2021, the average streamflow rate was 0.0374 m3/s (247 mm), with R2 values of 0.83 for calibration and 0.72 for validation. Hotspots with active gullies and slopes over 20% were identified mainly in cultivated lands. Future projections indicated a comparable streamflow rate to current conditions at 0.0322 m3/s (212.6 mm). A decline in streamflow is projected: 7.2% and 30.2% decreases in the near and far future under RCP 4.5, and 32.3% decreases and 5% increases under RCP 8.5 scenarios. These variations were attributed to differences in catchment characteristics and climate variability. Further research is needed to validate these findings by incorporating additional biophysical variables. This study provides insights into hydrological planning and management in the Andit Tid watershed and similar regions facing climate variability. [ABSTRACT FROM AUTHOR]

Estuaries, as connectors between land and ocean, have complex interactions of river and tidal flows that affect the transport of buoyant materials like floating plastics, oil spills, organic matter, and larvae. This study investigates surface-trapped buoyant particle transport in estuaries by using idealized and realistic numerical simulations along with a theoretical model. While river discharge and estuarine exchange flow are usually expected to export buoyant particles to the ocean over subtidal timescales, this study reveals a ubiquitous physical transport mechanism that causes retention of buoyant particles in estuaries. Tidally varying surface convergence fronts affect the aggregation of buoyant particles, and the coupling between particle aggregation and oscillatory tidal currents leads to landward transport at subtidal timescales. Landward transport and retention of buoyant particles is greater in small estuaries, while large estuaries tend to export buoyant particles to the ocean. A dimensionless width parameter incorporating the tidal radian frequency and lateral velocity distinguishes small and large estuaries at a transitional value of around 1. Additionally, higher river flow tends to shift estuaries toward seaward transport and export of buoyant particles. These findings provide insights into understanding the distribution of buoyant materials in estuaries and predicting their fate in the land-sea exchange processes. [ABSTRACT FROM AUTHOR]

Sedimentary landforms on Earth and other planetary bodies are built through scour, transport, and deposition of sediment. Sediment connectivity refers to the hypothesis that pathways of sediment transport do not occur in isolation, but rather are mechanistically linked. In dryland river systems, one such example of sediment connectivity is the transport of fluvially deposited sediment by wind. However, predictive tools that can forecast fluvial‐aeolian sediment connectivity at meaningful scales are rare. Here we develop a suite of models for quantifying the availability of river‐sourced sediment for aeolian transport as a function of river flow, wind regime, and land cover across 168 km of the Colorado River in Grand Canyon, USA. We compare and validate these models using topographic changes observed over 10 years in a coupled river sandbar‐aeolian dunefield setting. The models provide a path forward for directly linking fluvial hydrology with the management and understanding of aeolian landscapes. Plain Language Summary: Landscapes on Earth are built from sediment, which is deposited by a variety of processes, especially water and wind. In rivers located in dry regions, sediment is deposited by flowing water during river floods. Once this sediment dries, it is often then moved by wind across the landscape. Although we frequently observe this sediment connectivity between water and wind, we are not currently able to predict when and where windblown transport of river‐deposited sediment may occur. We used maps of sand in Grand Canyon along 168 km of the Colorado River, along with records of river flow, wind speed, and vegetation cover, to develop four models that predict how much sediment is available for wind to move. We compared the results of these models to actual windblown sediment transport at a study site in Grand Canyon, finding that when models predicted more sand was available at the site, we also observed more landscape changes there which were due to wind. Our research makes it possible to understand how changes in river flows affect the amount of sediment moved by wind. Key Points: We developed four methods for modeling the extent of exposed, bare sand available for windblown transport in river valleysOur models employ data on river flow, land cover, wind velocity, and vegetation to predict exposed, bare sand extent at corridor scalesWhere models predict increased areal extent of bare sand, we also observe a greater volume of topographic changes driven by wind [ABSTRACT FROM AUTHOR]

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

The present study involves the development of a new method, a range-dependent multivariate adaptive regression splines–genetic algorithm (RD-MARS-GA), for monthly streamflow prediction. The results of the RD-MARS-GA model are compared with standard MARS, Gaussian process regression, principal component regression, M5 model tree and random forest. The input data utilized in the study combine satellite and ground-based datasets. Monthly data from the Treene, Schaale, and Wandse rivers in Northern Germany were used as case studies. The satellite datasets used were sourced from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG GPM) and Moderate Resolution Imaging Spectroradiometer Evapotranspiration (MODIS). A comparison of the results reveals that the RD-MARS-GA can improve the forecasting accuracy of the MARS model across all watersheds at different temporal scales. The RD-MARS-GA model surpassed the other regression models and improved the root mean square accuracy of the MARS model by 12.8%, 26.7% and 18.5% in predicting one-month-ahead streamflow of the rivers Treene, Schaale, and Wandse, respectively. [ABSTRACT FROM AUTHOR]