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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]

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]

The measurement of soil moisture is important for many practical applications. We describe the theoretical design of a simple, noncontact, electromagnetic probe that complements many existing soil moisture measurement techniques. The approach uses a low‐frequency (i.e., 50–150 MHz) antenna operating in proximity of the soil. The presence of the soil affects the antenna input impedance, which in turn depends on the distance between the soil and antenna and the complex dielectric constant of the soil. The latter strongly depends on the soil wetness, which suggests that bulk soil moisture integrated over a depth of roughly 1 m can be inferred from antenna impedance measurements. This is in contrast with many current higher‐frequency techniques that penetrate only a few centimeters into the soil and provide only near‐surface values of soil wetness. Our work suggests that under ideal conditions bulk soil moisture can be mapped with an accuracy on the order of 1% over horizontal scales spanning a few tens of meters to a few kilometers using simple low‐frequency antennas. Plain Language Summary: Soil moisture information to depths of 1 m is critical for a variety of geophysical applications, including the accurate forecasting of precipitation, river flow levels and floods, and drought prediction and monitoring. While there are a large number of remote sensing techniques available that measure surface moisture values, relatively few if any can access deeper so‐called root‐zone depths. We present theoretical results for a noncontact technique that uses a simple, thin‐wire, loop antenna operating at 50–150 MHz and a height of approximately 1 m above the soil surface to infer bulk soil moisture properties integrated over a depth of roughly 1 m with an accuracy on the order of 1% under ideal conditions. In principle, the technique can be applied using a moving vehicle or drone to map soil moisture over horizontal length scales that bridge the gap between in‐situ measurements and regional‐scale satellite retrievals. Key Points: Soil moisture is a critical quantity relevant for many geophysical applicationsA wide variety of techniques can be used to infer soil moisture, each with its own set of advantages and disadvantagesA simple, noncontact method using a low‐frequency antenna operating near the soil surface can be used to map bulk soil moisture [ABSTRACT FROM AUTHOR]

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

Convergent estuaries have been shortened by dam‐like structures worldwide. Here, we evaluate 31 long‐term water level stations and use a semi‐analytical tide model to investigate how landward‐funneling and a dam influence tidal and storm surge propagation in the greater Charleston Harbor region, South Carolina, where three rivers meet: the Ashley, Cooper, and Wando. Results show that the phase speed and amplification of the principal tidal harmonic (M2) is larger than other long waves such as storm surge (∼1–4 days) and setup‐setdown (∼4–10 days). Further landward, all waves attenuate, but, as they approach the dam on the Cooper River, a frequency dependent response in amplitude and phase progression occurs. A semi‐analytical tidal model shows that funneling and the presence of a dam amplify tidal waves through wave interference from partial and full reflection, respectively. The different phase progressions of the reflected waves interact with the incident wave to increase or decrease the summed overall wave amplitude. Using a friction‐convergence parameter space, we demonstrate that dominant tides in 23 estuaries and the tidal, storm surge, and setup‐setdown waves in the Cooper River can be delineated into three regimes that describe landward amplification or attenuation associated with funneling, a dam, or both. The regime of each tidal constituent is consistent but can change with the duration and height of each storm surge event; dam associated wave interference can attenuate long‐duration events, while the most intense events (short duration, high water) are amplified by dams more than funneling and greatly increase flood exposure. Plain Language Summary: Most ports and cities are located along estuaries and deltas where flood hazards are increasing partially due to human modifications of channel geometry and land use. Dams, salt barrages, and surge barriers are common in estuaries. They modify estuarine geometry, regulate river flow, protect against flooding, and prevent salt intrusion. Many estuaries are naturally convergent, wide near the sea and narrower landward. Dams are barriers which shorten an estuary. Like ocean swell at a seawall, tides reflect off dams and often increase tidal range. Here we investigate how dams and landward changes in estuarine geometry influence tides and storm surges. Using measurements from the greater Charleston Harbor, SC, we find that constructing a dam either increases or decreases water levels, depending primarily on estuary convergence and surge event duration, as well as flow resistance and river flow. The presence of dams results in higher water levels, and thus flood exposure, when convergence is weak and storm surge at the sea has a short duration and high water levels. Further, channelization that extends inland, resulting from dredging of shipping channels, increases the magnitude and seaward extent of dam effects with increasing flood risk. Key Points: Estuarine geomorphic funneling and dams produce partial and full reflections, respectively, with magnitudes that are frequency dependentThree convergence regimes emerge: dominant tides have near peak amplification, overtides attenuate, and long duration surges mildly amplifyDams reflect and amplify long waves—increasing flood exposure—the most in weakly convergent estuaries with low friction [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]

In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using various types of RNNs, including long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and stacked long short-term memory (Stacked LSTM). It was observed that considering flow discharge, temperature, and precipitation as inputs to the models yields the best results. Additionally, wavelet transform was employed to enhance the accuracy of the RNNs. Among the RNNs, the GRU model exhibited the best performance in simulating monthly streamflow without using wavelet transform, with RMSE, MAE, NSE, and R2 values of 0.061 m3/s, 0.038 m3/s, 0.556, and 0.642, respectively. Moreover, in the case of using wavelet transform, the Bi-LSTM model with db5 mother wavelet and decomposition level 5 was able to simulate the monthly streamflow with high accuracy, yielding RMSE, MAE, NSE, and R2 values of 0.014 m3/s, 0.008 m3/s, 0.9983, and 0.9981, respectively. Uncertainty analysis was conducted for the two mentioned superior models. To quantify the uncertainty, the concept of the 95 percent prediction uncertainty (95PPU) and the p-factor and r-factor criteria were utilized. For the GRU, the p-factor and r-factor values were 82% and 1.28, respectively. For the Bi-LSTM model, the p-factor and r-factor values were 94% and 1.06, respectively. The obtained p-factor and r-factor values for both models are within the acceptable and reliable range. [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.)

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]

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]

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 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]

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]

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

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

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

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

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

The Antarctic photopsychrophile, Chlamydomonas priscui UWO241, is adapted to extreme environmental conditions, including permanent low temperatures, high salt, and shade. During long-term exposure to this extreme habitat, UWO241 appears to have lost several short-term mechanisms in favor of constitutive protection against environmental stress. This study investigated the physiological and growth responses of UWO241 to high-light conditions, evaluating the impacts of long-term acclimation to high light, low temperature, and high salinity on its ability to manage short-term photoinhibition. We found that UWO241 significantly increased its growth rate and photosynthetic activity at growth irradiances far exceeding native light conditions. Furthermore, UWO241 exhibited robust protection against short-term photoinhibition, particularly in photosystem I. Lastly, pre-acclimation to high light or low temperatures, but not high salinity, enhanced photoinhibition tolerance. These findings extend our understanding of stress tolerance in extremophilic algae. In the past 2 decades, climate change-related increasing glacial stream flow has perturbed long-term stable conditions, which has been associated with lake level rise, the thinning of ice covers, and the expansion of ice-free perimeters, leading to perturbations in light and salinity conditions. Our findings have implications for phytoplankton survival and the response to change scenarios in the light-limited environment of Antarctic ice-covered lakes. [ABSTRACT FROM AUTHOR]

This study analyzed 61 years of hydrological data from the Minhe and Xiangtang Hydrological Stations (1956–2016) to examine hydrological changes and ecological flow assurance rates in the Huangshui River Basin, China. Using the Mann–Kendall trend test, IHA/RVA method, and ecological flow calculation methods, the study revealed the following results: (1) After 1994, increased human activity in the Datong River led to a measured runoff decrease compared to natural runoff. Although human activities in the Huangshui River's main stream were present before 1972, after 1972, these activities intensified, resulting in a more pronounced decrease in the measured runoff. (2) Ecological flow analysis indicated that the main stream of the Huangshui River and the Datong River have ecological flow assurance rates of 100% for all but a few months, where the rates are 98%. The water volume is sufficiently abundant to meet ecological water demands. [ABSTRACT FROM AUTHOR]

During the dry season, river flow gradually diminishes, and surface water flow dries up. Therefore, the investigation of runoff during the dry season is of great practical significance for rational water allocation and water resource management. Based on hydrological station data from the Xijiang River Basin (XRB) from 1961 to 2020, this study examines the trend and periodic characteristics of dry-season runoff, identifies fluctuation and variability in dry-season runoff, and investigates the main circulation factor combinations influencing dynamic changes in dry-season runoff. The results indicate the following: (1) the characteristics of dry-season runoff variations are basically consistent across sub-basins in the XRB during the study period, with the minimum (21.96 × 108 m3) and maximum (54.67 × 108 m3) average monthly runoff occurring in February and October, respectively; (2) interannual-scale dry-season runoff exhibits periodicity of 3.53 years and 7.5 years; (3) using the Bayesian estimator of abrupt seasonal and trend change algorithm (BEAST), a seasonal abrupt point with a probability of 20.5% occurs in 1983, and the confidence interval for this abrupt point is from 1980 to 1986; (4) based on the cross wavelet approach, solar sunspots are identified as the primary circulation factor contributing to dry-season runoff in the XRB, exhibiting a significant 8–14 years resonance cycle of negative correlation with runoff during the high-energy phase from 1972 to 2006. These findings offer a new perspective on understanding the evolution of dry-season runoff and circulation factor variations, which are crucial for accurate prediction, early warning, and rational allocation of water resources during the dry season. [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]

This study investigates the problem of low efficiency and the lack of a water supply at the Hemmat Water Intake, in Iran, where severe sediment accumulation was observed at the intake mouth. The Flow-3D software was used to simulate the flow patterns under various scenarios of hydraulic regimentation works. The considered parameters include: (i) three alternative locations of the spur dike (i.e., a spur dike placed on the opposite side of the intake inlet and aligned with the upstream edge of the intake, to be regarded as a witness spur dike; a spur dike at a distance DS of 7 m downstream of the witness spur dike, which implies a dimensionless distance DS/bi1 of 1/3, with bi1 being the intake opening width; and a spur dike at a distance of 7 m upstream of the witness spur dike with a dimensionless distance, still, of 1/3); (ii) four spur dike lengths, LS/Br, with LS being the effective spur dike length and Br the approach river width; and (iii) five spur dike deviation angles of 75, 90, 105, 120, and 135 degrees (the deviation angle is the angle between the spur dike axis and the original river-bank line from which the spur dike extends). The results showed that, with the increase in the relative spur dike length (LS/Br), the velocity of the flow entering the water intake increases by 11%. A spur deviation angle of 135 degrees increases the flow depth at the intake inlet by 9% compared to a smaller deviation angle of 75 degrees. In addition, the spur dike increases the flow shear stresses at the intake inlet by up to 50%. Overall, the main flow of the river with the highest velocity and depth, and best directed towards the water intake, occurs for the placement of the longest spur dike (i.e., LS/Br = 0.46) in front of the inlet (i.e., witness spur dike) and for a spur dike deviation angle of 135 degrees. The spur dike increases the shear stress at the intake entrance by more than five times with respect to the case of its absence. In general, the presence of a spur dike on the opposite bank and with a deviation angle in the direction of the intake inlet well directs the main flow towards the canal intake. Moreover, it reduces the possibility of sedimentation in the canal inlet by increasing the flow velocity. Therefore, the results of this study could also be useful in increasing the hydraulic efficiency of lateral intakes by reducing the sedimentation phenomena. [ABSTRACT FROM AUTHOR]

Like many coastal lagoons in several countries, the "Navío Quebrado" lagoon (La Guajira, Colombia) is a very delicate and precious environment; indeed, it is a nationally recognized Flora and Fauna Sanctuary. Several factors, including climate change, are threatening its existence because of changes in the governing hydro-morphological and biological processes. Certainly, the first step to addressing this problem is to understand its hydrological behavior and to be able to replicate, via simulation, its recent history before inferring likely futures. These potential futures will be marked by changes in the water input by its tributary, the Camarones River, and by modified water exchange with the sea, according to a foreseen sea level rise pattern, as well as by a different evaporation rate from the free surface, according to temperature changes. In order to achieve the required ability to simulate future scenarios, data on the actual behavior have to be gathered, i.e., a monitoring system has to be set up, which to date is non-existent. Conceptually, designing a suitable monitoring system is not a complex issue and seems easy to implement. However, the environmental, socio-cultural, and socio-economic context makes every little step a hard climb. An extremely simple—almost "primitive"—monitoring system has been set up in this case, which is based on very basic measurements of river flow velocity and water levels (river, lagoon, and sea) and the direct participation of local stakeholders, the most important of which is the National Park unit of the Sanctuary. All this may clash with the latest groovy advances of science, such as in situ automatized sensors, remote sensing, machine learning, and digital twins, and several improvements are certainly possible and desirable. However, it has a strong positive point: it provides surprisingly reasonable data and operates at almost zero additional cost. Several technical difficulties made this exercise interesting and worthy of being shared. Its novelty lies in showing how old, simple methods may offer a working solution to new challenges. This humble experience may be of help in several other similar situations across the world. [ABSTRACT FROM AUTHOR]

The uncertainty of hydrological forecasts is strongly related to the uncertainty of the rainfall field due to the nonlinear relationship between the spatio-temporal pattern of rainfall and runoff. Rain gauges are typically considered to provide reference data to rebuild precipitation fields. However, due to the density and the distribution variability of the rain gauge network, the rebuilding of the precipitation field can be affected by severe errors which compromise the hydrological simulation output. On the other hand, retrievals obtained from remote sensing observations provide spatially resolved precipitation fields, improving their representativeness. In this regard, the comparison between simulated and observed river flow discharge is crucial for assessing the effectiveness of merged precipitation data in enhancing the model's performance and its ability to realistically simulate hydrological processes. This paper aims to investigate the hydrological impact of using the merged rainfall fields from the Italian rain gauge network and the NASA Global Precipitation Measurement (GPM) IMERG precipitation product. One aspect is to highlight the benefits of applying the cellular automata algorithm to pre-process input data in order to merge them and reconstruct an improved version of the precipitation field. The cellular automata approach is evaluated in the Tanaro River basin, one of the tributaries of the Po River in Italy. As this site is characterized by the coexistence of a variety of natural morphologies, from mountain to alluvial environments, as well as the presence of significant civil and industrial settlements, it makes it a suitable case study to apply the proposed approach. The latter has been applied over three different flood events that occurred from November to December 2014. The results confirm that the use of merged gauge–satellite data using the cellular automata algorithm improves the performance of the hydrological simulation, as also confirmed by the statistical analysis performed for 17 selected quality scores. [ABSTRACT FROM AUTHOR]

The Alps are a key water resource for central Europe, providing water for drinking, agriculture, and hydropower production. Thus, understanding runoff generation processes of Alpine streams is important for sustainable water management. It is currently unclear how much streamflow is derived from old water stored in the subsurface and how much stems from more recent precipitation that reaches the stream via near-surface quick flow processes. It is also unclear how this partitioning varies across different Alpine catchments in response to hydroclimatic forcing and catchment characteristics. Here, we use stable water isotope time series in precipitation and streamflow to quantify the young water fractions (Fyw ; i.e., the fraction of water younger than approximately 2–3 months) and new water fractions (Fnew ; here, the fraction of water younger than 1 month) in streamflow from 32 Alpine catchments. We contrast these measures of water age between summer and winter and between wet and dry periods and then correlate them with hydroclimatic variables and physical catchment properties. New water fractions varied between 3.5 % and 9.6 %, with values of 9.2 % in rainfall-dominated catchments, 9.6 % in hybrid catchments, and 3.5 % in snow-dominated catchments (mean across all catchments of 7.1 %). Young water fractions were approximately twice as large (reflecting their longer timescale) and ranged between 10.1 % and 17.6 %, with values of 17.6 % in rainfall-dominated catchments, 16.6 % in hybrid catchments, and 10.1 % in snow-dominated catchments (mean across all catchments of 14.3 %). New water fractions were negatively correlated with catchment size (Spearman rank correlation, rS , of -0.38), q95 baseflow (rS=-0.36), catchment elevation (rS=-0.37), total catchment relief (rS=-0.59), and the fraction of slopes steeper than 40° (rS=-0.48). Large new water fractions, implying faster transmission of precipitation to streamflow, are more prevalent in small catchments, at low elevations, with small elevation differences, and with large fractions of forest cover (rS=0.36). New water fractions averaged 3.3 % following dry antecedent conditions, compared with 9.3 % after wet antecedent conditions. Our results quantify how hydroclimatic and physical drivers shape the partitioning of old and new waters across the Alps, thus indicating which landscapes transmit recent precipitation more readily to streamflow and which landscapes tend to retain water over longer periods. Our results further illustrate how new water fractions may find relationships that remained invisible with young water fractions. [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]

Future changes in streamflow and sediment, influenced by anthropogenic activities and climate change, have a crucial role in watershed management. This study aimed to quantify the effects of anthropogenic and natural drivers on future streamflow and sediment changes in the tropical Sai Gon Dong Nai River basin using the Soil and Water Assessment Tool (SWAT) model. Specifically, the model incorporated thirty-six reservoirs and analyzed twenty future climate projected scenarios from four Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs) for 2023–2100. These models include BCC-CSM2-MR (China), CanESM5 (Canada), MIROC6 (Japan), and MRI-ESM2-0 (Japan). Our findings indicate that (1) dam operation and diversion lead to a 0.5% decrease in streamflow during the dry season and a 4.1% increase during the rainy season compared to those in scenarios without dams; (2) there is a 37.4% decrease in annual sediment across the entire basin under same climate conditions; and (3) rainfall is projected to decrease (24.6% – 6.2%), resulting in a decrease in streamflow (0.2 – 32.2%) and sediment (39.3 – 56.0%) compared to historical records. Streamflow is expected to decrease during the rainy season (16.7 – 23.1%) and increase during the dry season (14.5 – 25.4%). Further potential degradation of the environmental conditions and water mismanagement are caused by the synergies between too much and too little rainfall conditions. The anticipated reductions in future streamflow and sediment could adversely affect ecological streamflow, water security, and sediment dynamics in the Sai Gon Dong Nai River basin. Our approach effectively identifies future changes in streamflow and sediment due to the combined effects of climate change and reservoir operations, providing valuable insights for integrated water resource management in tropical regions. [ABSTRACT FROM AUTHOR]

The Yeşilırmak River Basin in northern Türkiye is crucial for the region's water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Yeşilırmak River basin (R2 = 0.98, RMSE = 0.18). The study's findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations. [ABSTRACT FROM AUTHOR]

A critical component of animal conservation in a changing world is an understanding of the physiological resilience of animals to different conditions. In many aquatic animals, hypoxia (low environmental oxygen levels) is a regular occurrence, but the likelihood and severity of hypoxia vary across habitats. Fast-flowing, stream-like habitats are never hypoxic, so long as flow is maintained. Do animals from such habitats retain the capacity to survive hypoxic conditions? We use aquatic frog tadpoles to test the effects of natural habitat on performance in hypoxia in an experimental framework, finding that stream-living tadpoles have reduced performance in hypoxia. Tadpoles also vary in lung presence, with some species able to breathe air during hypoxia. We found that among lunged tadpoles, air-breathing rates increase in hypoxia in pond-living species but not stream-living species. Lung presence was also found to influence hypoxia performance, as lungless, stream-living tadpoles were found to be especially vulnerable to hypoxia, while pond-living, lungless tadpoles appeared largely resilient to hypoxia. We consider the ramifications of our findings on conservation outlooks and strategies for frogs and their tadpoles, suggesting that stream-living tadpoles, and especially lungless, stream-living tadpoles, may be particularly at risk to factors that reduce stream flow. Thus, a primary goal for conservation and management of species with stream-living tadpoles should be the maintenance of year-round streamflow, which oxygenates waters and prevents hypoxia. [ABSTRACT FROM AUTHOR]

"In Dialogue with the Mahābhārata" by Brian Black is a book that explores the significance of dialogues in the epic Mahābhārata. The author provides commentary on key dialogues in the epic, offering insights into their philosophical and moral implications. The book is useful for scholars interested in the epic, as well as teachers and students looking to engage in discussions about complex episodes. Black argues that by studying the dialogues in the Mahābhārata, readers can learn to think critically about their own lives and gain a deeper understanding of existential and normative dimensions. [Extracted from the article]

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]

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