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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]

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]

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]

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]

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]

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]

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.)

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]

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]

Flood wave propagation involves complex flow variable dependencies. Continuous in situ hydrograph peak magnitude and timing data provide the most relevant information for understanding these dependencies. New acoustic instruments can produce experimental evidence by extracting usable signals from noisy datasets. This study presents a new screening protocol to smoothen streamflow data from unwanted influences and noise generated by flow perturbations and observational fluctuations. The protocol combines quantitative (statistical fitness parameters) and qualitative (domain expert judgments) evaluations. It is tested with 18 smoothing methods to identify optimal data conditioning candidates. Sensitivity analyses assess the validity, generality, and scalability of the procedures. The goal of this analysis is to set a mathematical foundation for empirical results that can lead to unified, general conclusions on principles or protocols for unsteady flows propagating in open channels, formulating practical guidance for future data acquisition and processing, and using in situ data to better support data-driven modeling efforts. [ABSTRACT FROM AUTHOR]

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]

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

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]

Flash droughts, characterized by a rapid onset and severe intensity, pose a serious threat to water resource management. Extensive research has indicated that drought has lagged impacts on streamflow. Nevertheless, the hydrometeorological conditions by which streamflow dynamics respond to drought within the São Francisco River Basin (SFRB) remain ambiguous. To bridge this gap, we conducted a study on long-term streamflow responses to flash drought in the SFRB from 1991 to 2020, combining the Standardized Antecedent Precipitation Evapotranspiration Index (SAPEI) and quantile streamflow with a trend analysis. This study employed the SAPEI, a daily drought index, to identify flash droughts and assess the response of streamflow to the identified events across the SFRB. Our findings reveal insights into the direct response of streamflow to flash drought conditions, stimulated by the application of the SAPEI for analysis of flash drought events. The interannual flash droughts fluctuated over the years, with the middle part of the SFRB experiencing frequent, longer flash droughts, while the south part encountered shorter but less frequent events. About 55% of the study area is trended towards drying conditions. A comparative analysis of the SAPEI and streamflow identified a synchronized response to the onset of flash drought events, but the recovery timescale for the SAPEI and streamflow varied among the events. This study enhances understanding of the flash-drought–streamflow relationship in the SFRB and provides theoretical support for addressing drought risks under climate change. [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]

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]

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]

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]

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]

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]

[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]

Debris flow activity in the Geysernaya River basin was mapped. Large-scale debris flow processes due to the collapse of fragments of the valley's left wall were observed in this area three times over the past 40 years. Three independent debris flow areas of the river basin were identified and described: the upper reaches of the river, the Levaya Geysernaya basin; the middle reaches of the river, below the dammed lake that formed in 2014; and the lower downstream part, below the dammed lake that existed in 2007–2014. Debris flow processes are typical of most tributaries of the Geysernaya River. Moreover, debris flows can be aseasonal in the left tributaries, because they are formed under constant heating and active gas–hydrothermal influence within the thermal fields. The landslide dam formed in 2014 is resistant to erosion, and an independent outburst of the dammed lake is unlikely. With further erosion of the 2007 dam and expansion of the breach in the dam body, collapse of its sides and debris flow in the lower part of the Geysernaya River valley should not be ruled out. Risks of rockfall, landslide, and debris flow processes, most likely on the left wall of the valley, should be taken into consideration in planning the development of recreation facilities. [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]

Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period. [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]

Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite‐sample performance of the proposed methodology. A series of Monte‐Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non‐Gaussian and heavy‐tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non‐central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods. Plain Language Summary: This paper proposes a functional quantile autoregressive model of order one, which is used to predict the entire distribution of the realizations of river flow time series curve. The proposed model allows modeling the conditional quantiles of the response variable as a function of its past values of it. The proposed method for historical river flow curves is an excellent alternative to existing mean regression methods at the 0.5 quantile level (median regression). Also, as an advantage over existing methods, it offers a more thorough explanation of the connection among previous and future realizations of river flow curves at various quantile levels, providing a more extensive understanding of the relationship. Moreover, this feature of the proposed method allows for the effortless generation of pointwise prediction intervals for future realizations of river flow curves. The numerical results obtained by Monte Carlo experiments and empirical data analyses exhibit that, compared with existing methods, the proposed method produces competitive or even better forecasting results. The results also indicate that the future realizations of the river flow measurements are well covered by the prediction intervals constructed by the proposed method. Key Points: Predicting the mean and extreme values of the river flow curve is important for various applications in water resources managementThe FQAR(1) allows predicting the entire distribution of future realizations of the river flow curve as a function of its past values of itNumerical results based on river flow measurements collected from the Australia Continent confirmed the potential of the FQAR(1) [ABSTRACT FROM AUTHOR]

Climate change is increasing the proportion of river networks experiencing flow intermittence, which in turn reduces local diversity (i.e., α‐diversity) but enhances variation in species composition among sites (i.e., β‐diversity), with potential consequences on ecosystem stability. Indeed, the multiscale theory of stability proposes that regional stability can be attained not only by local processes but also by spatial asynchrony among sites. However, it is still unknown whether and how scale‐dependent changes in biodiversity associated with river flow intermittence influence stability across spatial scales. To elucidate this, we here focus on multiple metacommunities of French rivers experiencing contrasting levels of flow intermittence. We clearly show that the relative contribution of spatial asynchrony to regional stability was higher for metacommunities of intermittent than perennial rivers. Surprisingly, spatial asynchrony was mainly linked to asynchronous population dynamics among sites, but not to β‐diversity. This finding was robust for both truly aquatic macroinvertebrates and for taxa that disperse aerially during their adult stages, implying the need to conserve multiple sites across the landscape to attain regional stability in intermittent rivers. By contrast, metacommunities of truly aquatic macroinvertebrates inhabiting perennial rivers were mainly stabilized by local processes. Our study provides novel evidence that metacommunities of perennial and intermittent rivers are stabilized by contrasting processes operating at different spatial scales. We demonstrate that flow intermittence enhances spatial asynchrony among sites, thus resulting in a regional stabilizing effect on intermittent river networks. Considering that climate change is increasing the proportion of intermittent rivers worldwide, our results suggest that managers need to focus on the spatial dynamics of metacommunities more than on local‐scale processes to monitor, restore, and conserve freshwater biodiversity. [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]