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The middle and lower reaches of the Yangtze River, a primary region for freshwater lakes in China, have undergone significant transformations throughout the Holocene. These changes, driven by factors such as sea-level rise, climate change, and human activities, have led to the progressive elevation of water levels in this area. As a result, a floodplain has emerged, characterized by the formation of numerous shallow lakes along the river course. However, the pattern of water-level changes in the main channel of the Yangtze River during the Holocene remains unclear. This gap in knowledge poses challenges for understanding sediment transport dynamics, the interactions between the river and its adjacent lakes, and the prevention and control of flood disasters in the Yangtze River basin. To shed light on these issues, our study compiled data on the surface elevation and water depth of 81 lakes in the mid-lower reaches of the Yangtze River basin. Additionally, we analyzed historical water-level records from the 1900s to the 1970s at eight gauging stations from Shashi to Jiangyin along the river's main stream. Our findings reveal that, particularly along the Jingjiang section, the basal elevation of most lakes is lower than the Yangtze River's water level during the dry season. Conversely, the water level of the main stream exceeds that of both the floodplain and the lakes enclosed by the Jingjiang embankment. In the tidal reach, especially within the Taihu Lake basin, the basal elevation of lakes typically falls below sea level. Meanwhile, lakes located along the section from Chenglingji to Wuhu exhibit basal elevations that correspond with the Yangtze River's annual average and dry season water levels. Given the widespread presence of lakes along the middle and lower reaches of the Yangtze River, our study introduces a new proxy for reconstructing the mean water level of the mid-lower Yangtze River in the Holocene. By analyzing sediments from Nanyi Lake and Chenyao Lake in the lower Yangtze River, we attempted to reconstruct the water level of the Yangtze River's main channel since 8 ka BP. [ABSTRACT FROM AUTHOR]

Constructing and operating cascade reservoirs significantly contribute to comprehensive basin water resource management, while altering natural hydrological regimes of rivers, which imposes negative impacts on riverine ecology. The main aim of this study is to synergistically optimize the objectives of increasing hydropower generation and alleviating hydrological regime alteration for cascade reservoirs. This study first proposed a dynamic time warping scenario backward reduction (DTW-SBR) framework to extract streamflow scenarios from the historical streamflow series regarded as benchmarks for calculating deviation degrees of hydrological regimes. Then a multi-objective long-term operation model considering the hydrological regime and hydroelectricity was formed for minimizing the deviation degrees of hydrological regimes at the downstream section (O1) and maximizing the hydropower generation of cascade reservoirs (O2). The non-dominated sorting genetic algorithm-II (NSGA-II) combined with the long-term conventional operation (CO) rules of cascade reservoirs was adopted to produce the Pareto-front solutions to derive the recommended policies for guiding the long-term operation of cascade reservoirs. The six large reservoirs in the middle reaches of the Jinsha River, China with a 10-day runoff dataset spanning from 1953 to 2015 constitute a case study. The results showed that nine streamflow scenarios were extracted for calculating the O1 by the DTW-SBR framework, which could reflect the intra- and inter- annual variability of hydrological regimes at the Panzhihua hydrological station. The Pareto-front solutions obtained by the NSGA-II revealed competitive relationships between the O1 and O2. As compared to the long-term CO rules of cascade reservoirs, the O1 value could be reduced by up to 42,312 (corresponding rate of 10.51%) and the O2 value could be improved by up to 1752 × 108 kW·h (corresponding rate of 5.14%). Based on the inclination to be dominated by different objectives, three typical operation schemes, A, B and C, were chosen from the Pareto-front solutions; Scheme A could be considered as the recommended solution, which simultaneously reduced the O1 value by 23,965 with the rate of 5.95% and increased the O2 value by 1752 × 108 kW·h with the rate of 5.14%, as compared to the long-term CO rules. This study can provide references on boosting the synergies of hydropower production and hydrological regime restoration for the long-term ecological operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

Aquifers are complex systems that present significant challenges in terms of characterization due to the lack or absence of watershed-scale hydrogeological information. An alternative to address the need to characterize watershed-scale aquifer behavior is recession flow analysis. Recession flows are flows sustained by groundwater release from the aquifer. Aquifer behavior can be characterized using recession flow records available from gauging stations, and therefore an indirect measure of aquifer behavior is obtained through watershed-scale recession flow records and analysis. This study seeks to identify the minimum time period necessary to characterize the behavior of groundwater storage systems in watersheds with different geological, morphological, and hydrological characteristics. To this end, various watersheds in south-central Chile underwent recession flow analysis, with eight time periods considered (2, 3, 4, 5, 10, 15, 20, and 25 years). The results indicate that 25 years of records are sufficient for the characterization of watershed-scale aquifer behavior, along with the representation of the groundwater storage-release (S-Q) process in watersheds with different geological, morphological, and hydrological characteristics. Additionally, the results show that an initial characterization of the groundwater system behavior in watersheds with different geological characteristics can be carried out with two years of records. This information could be important for practical engineering and the study of groundwater systems in watersheds with limited hydrological and hydrogeological information. [ABSTRACT FROM AUTHOR]

Recently, there has been an increase in the number of natural disasters caused by extreme events, which are enhanced by climate change and anthropogenic interference. Therefore, understanding the hydrological behavior in areas with high vulnerability to floods and water scarcity is essential to capably manage water resources. In this context the study aimed to analyze the streamflow trend in the Piranga river basin, as well as to evaluate the determining factors in the streamflow variation regime in the watershed. For this reason, historical series of seven stream gauging stations were analyzed, adopting the base period of studies from 1975 to 2018. In order to identify the trend in maximum, average and minimum streamflow data, the Mann–Kendall, Pettitt and Spearman correlation tests were used. To understand the possible causes of streamflow trends, precipitation data, land use and occupation, and water demand were analyzed. It was observed that all stations showed some significant trend of streamflow reduction, especially in the dry season, having reduced from 10 to 35% comparing to the historical series average. On an annual scale, significant trends of reduction in average and minimum streamflow were detected. The change in streamflow behavior was not related to the distribution of precipitation over the years in the watershed. The cause of streamflow reduction may be related to the increase in water demand and with changes in land use and occupation, mainly characterized by the increase in planted forest, forest formation and urban areas and the reduction of areas destined to agriculture. The methodology proposed in this study can be adapted to other watersheds in the world, aiming to assist in the planning of water resources. [ABSTRACT FROM AUTHOR]

The prediction of water plant flow should establish relationships between upstream and downstream hydrological stations, which is crucial for the early detection of flow anomalies. Long Short-Term Memory Networks (LSTMs) have been widely applied in hydrological time series forecasting. However, due to the highly nonlinear and dynamic nature of hydrological time series, as well as the intertwined coupling of data between multiple hydrological stations, the original LSTM models fail to simultaneously consider the spatiotemporal correlations among input sequences for flow prediction. To address this issue, we propose a novel flow prediction method based on the Spatiotemporal Attention LSTM (STA-LSTM) model. This model, based on an encoder–decoder architecture, integrates spatial attention mechanisms in the encoder to adaptively capture hydrological variables relevant to prediction. The decoder combines temporal attention mechanisms to better propagate gradient information and dynamically discover key encoder hidden states from all time steps within a window. Additionally, we construct an extended dataset, which preprocesses meteorological data with forward filling and rainfall encoding, and combines hydrological data from multiple neighboring pumping stations with external meteorological data to enhance the modeling capability of spatiotemporal relationships. In this paper, the actual production data of pumping stations and water plants along the East-to-West Water Diversion Project are taken as examples to verify the effectiveness of the model. Experimental results demonstrate that our STA-LSTM model can better capture spatiotemporal relationships, yielding improved prediction performance with a mean absolute error (MAE) of 3.57, a root mean square error (RMSE) of 4.61, and a mean absolute percentage error (MAPE) of 0.001. Additionally, our model achieved a 3.96% increase in R2 compared to the baseline model. [ABSTRACT FROM AUTHOR]

Baseflow is the part of streamflow that is mainly replenished by groundwater. The protection of the biological environment and the growth of its water resources greatly depend on the spatial and temporal evolution of baseflow. Therefore, the Baizhiao (BZA) and Shaduan (SD) catchments of the Jiaojiang River Basin (JRB) in the Zhejiang province of China were selected as study areas. The ABCD model and Eckhardt method were used to calculate baseflow and baseflow index (BFI). The temporal and spatial evolution patterns of baseflow were analyzed through statistical analysis and the Mann–Kendall test. The results showed that the ABCD model performs well in simulating overall hydrological processes on the monthly streamflow at BAZ and SD stations with NSE (Nash–Sutcliffe Efficiency) values of 0.82 and 0.83 and Pbias (Percentage Bias) values of 9.2% and 8.61%, respectively. The spatial–temporal distribution of the BFI indicates the higher baseflow contribution in upstream areas compared to downstream areas at both stations. The baseflow and BFI had significant upward trends at the BZA and SD stations in the dry season, while their trends were not uniform during the wet period. These findings are essential guidance for water resource management in the JRB regions. [ABSTRACT FROM AUTHOR]

Despite their popularity, the construction and operation of hydropower reservoirs pose challenges to water resources. This study investigated the impacts of cascading dams on streamflow in Tanzania's Rufiji River Basin. The SWAT model was developed to represent the entire Rufiji River Basin. The model simulated the streamflow for 41 years, from 1982 to 2022, and developed two main scenarios: with-dam and without-dam. To capture the influence of all dams, the results were emphasized from 2000 to 2022, when all three dams were operating. Calibration and validation were applied at the Rufiji-Stiegler and Kilombero-Swero stations with good performance. The results show that cascading dams annually decrease the streamflow by 1% at Rufiji-Stiegler station. In contrast, individually, the Mtera Dam displayed a 5% decrease while the Kidatu and Kihansi Dams exerted a 1% increase on the annual streamflow downstream at Rufiji-Stiegler. During 2000–2022, the Rufiji River Basin showed an annual reduction in streamflow contribution of 104.97 m3/s. Therefore, the reservoir's operation significantly impacts the downstream streamflow. The findings are expected to guide policymakers, water resource managers, and environmentalists in mitigating potential adverse effects while optimizing the benefits of hydropower generation and water regulation within the region. [ABSTRACT FROM AUTHOR]

The lack of some hydrological data or short hydrological sequences makes it difficult to predict the power generation capacity of small hydropower stations, and this paper summarises three prediction methods: ARIMA model, LSTM model, and large and small hydropower station correlation method, in order to effectively solve this problem. Taking Ni'ao Gia Ga power station in Gannan area as an example, with the actual power generation data of the power station in 2022 as the test standard, and based on the power generation and water inflow data from 2015 to 2021, ARIMA model, LSTM model and large and small hydropower station correlation method are successively used to predict the power generation capacity of small hydropower stations in this area. The results show that the correlation method of large and small hydropower stations using the flow of large hydropower stations as the relevant factor can effectively predict the amount of water coming from small hydropower stations, and then get the prediction results of power generation with higher accuracy, which is obviously better than the ARIMA model and the LSTM model, and it can be popularised and applied in similar areas. [ABSTRACT FROM AUTHOR]

Satellite rainfall estimates (SRE) offer spatial-temporal rainfall representations in regions with limited ground-based gauge rainfall measurements. However, differences exist between SRE and gauge measured rainfall, which needs assessment and reduction. This study presents a method to correct errors in SRE to make their use in agro-hydrological applications and models meaningful. The main scientific objective is the determination of effective window sizes for SRE bias correction. To conclude on effective window sizes, the crop water requirement satisfaction index (WRSI) for gauged rainfall, uncorrected SRE and bias corrected SRE were estimated and propagation effects of SRE errors on respective WRSI estimates were assessed. WRSI indicates how much of the crop water needs are satisfied by rainfall. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) SRE was bias corrected using gauged rainfall data from 20 stations in the Lake Victoria basin of Kenya from 2012 to 2018. The results show that the error in WRSI can serve to determine effective window sizes for SRE bias correction rather than using SRE bias error itself. This proposed correction method resulted in improved estimates of WRSI. [ABSTRACT FROM AUTHOR]

Reservoir sedimentation affects lifespan, hydropower generation, water resource use, and ecosystem sustainability. In this study, using measured data from the worldrenowned Three Gorges Reservoir (TGR), sediment transport capacity was analysed from 2002 to 2020 at four TGR gauging stations (Cuntan, Qingxichang, Wanxian, and Miaohe). The result indicated that: (1) a significant decrease in sediment transport capacity occurred in the TGR before and after dam construction, with an overall reduction of 82% in the perennial backwater zone. Notably, Qingxichang, Wanxian, and Miaohe experienced reductions of 40%, 91%, and 59%, respectively. Since the initial operation phase, their maximum monthly sediment transport capacity has consistently occurred in July. (2) Changes in the water level in front of the dam, the incoming flow, and sediment influxes were the primary factors driving the annual variations in sediment transport capacity. Among the factors affecting the variation of sediment transport capacity along the TGR, the influence of the cross-sectional morphology was more pronounced than that of the distance from the dam. Sediment transport capacities in the wide and shallow sections were considerably lower than those in the narrow and deep sections. (3) A sediment transport amplitude index (Fd) was proposed to assess the sediment dynamics within the TGR, which effectively characterized the sedimentation conditions based on the relative importance of these influencing factors. These findings provide a theoretical basis for operational strategies in the TGR and offer insights into the patterns of sediment transport capacity changes in reservoir areas before and after dam construction in other river-type reservoirs. [ABSTRACT FROM AUTHOR]

This hydrological study investigated a combined rating methodology tested on a 14,090 km2 area in Southwest Florida. The approach applied the Hydrological Simulation Program-Fortran (HSPF) over a 23-year period and was validated by 28 stream gauging stations. The regional hydrological complexity includes natural and agricultural areas, as well as extensive phosphate mining and urbanizing areas. This application is a novel and efficient methodology for generating stage–storage–discharge relationships using a geographic information system (GIS), empirical equations, and spreadsheets for 148,000 isolated and connected alluvial wetlands within the model domain. The validation metrics used to evaluate the applied methodology for populating the stage–storage–discharge relationship demonstrated the model effectiveness in simulating a range of hydrological events across various regions. For discharge prediction, the Nash–Sutcliffe efficiency values surpassed 0.7 at most stations, with an average of 0.67, and the average R squared was 0.74. This methodology, when applied, achieved a root-mean-square error of 4 m3/s for discharge prediction and 0.47 m for stage prediction. However, limitations emerged in simulating baseflow (low flows), highlighting the need for integrated modeling approaches to accurately capture groundwater–surface water interactions. The research provides an improved means for modeling regional water resources and lays the groundwork for enhanced hydrological modeling in watersheds with complex alluvial and isolated wetland systems. [ABSTRACT FROM AUTHOR]

The probabilistic analysis of streamflow and drought event durations plays a crucial role in the efficient and sustainable management of existing water resources in the region. This approach involves the collection of historical hydrological data from river gauging stations, the use of statistical and probabilistic models, and the assessment of hydrological projections at different return periods to provide valuable information for society to understand the potential impacts of extreme events. The analysis is carried out on the Sinú River in Colombia, with consideration given to both the presence and absence of the Hydropower Plant Urrá I. The results reveal that, under natural conditions, a higher number of return periods correspond to less extreme drought flows and longer temporal durations. However, when the hydropower plant is operational, the occurrence and duration of drought are influenced by the regulations implemented during energy generation. The results of this analysis can guide water resource management policies, considering the operation of the hydroelectric plant, thereby enabling decisions that enhance the resilience and sustainability of the river's hydrological conditions and communities that depend on it. [ABSTRACT FROM AUTHOR]

Reliable streamflow forecasting is a determining factor for water resource planning and flood control. To better understand the strengths and weaknesses of newly proposed methods in streamflow forecasting and facilitate comparisons of different research results, we test a simple, universal, and efficient benchmark method, namely, the naïve method, for short-term streamflow prediction. Using the naïve method, we assess the streamflow forecasting performance of the long short-term memory models trained with different objective functions, including mean squared error (MSE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and mean absolute error (MAE). The experiments over 273 watersheds show that the naïve method attains good forecasting performance (NSE > 0.5) in 88%, 65%, and 52% of watersheds at lead times of 1 day, 2 days, and 3 days, respectively. Through benchmarking by the naïve method, we find that the LSTM models trained with squared-error-based objective functions, i.e., MSE, RMSE, NSE, and KGE, perform poorly in low flow forecasting. This is because they are more influenced by training samples with high flows than by those with low flows during the model training process. For comprehensive short-term streamflow modeling without special demand orientation, we recommend the application of MAE instead of a squared-error-based metric as the objective function. In addition, it is also feasible to perform logarithmic transformation on the streamflow data. This work underscores the critical importance of appropriately selecting the objective functions for model training/calibration, shedding light on how to effectively evaluate the performance of streamflow forecast models. [ABSTRACT FROM AUTHOR]

This study accounted for the importance of daily expansion flow data in compensating for insufficient flow data in a watershed. In particular, the 8-day interval flow measurement data (intermittent monitoring data) could cause uncertainty in the high- or low-flow conditions that have been used to estimate the flow duration curve (FDC) and the load duration curve (LDC) used in Total Maximum Daily Load (TMDL) evaluation in Korea. Thus, this study developed a method to expand the 8-day interval flow data (missing data) to daily flow data in order to evaluate the Total Maximum Daily Load (TMDL) appropriately in a watershed. We employed the machine learning technique (the gradient descent method provided by the Google TensorFlow package) to develop a regression for expanding the 8-day interval flow data. The method was applied in the Nakdong River basin located in Korea to collect the 8-day interval and daily flow data from a number of gauging stations. The results of the expanded daily flow were evaluated through the RMSE, MAE, IOA, and NSE, and the valid expanded daily flow data were obtained for the 29 TMDL gauging stations (IOA 0.84~0.99, NSE −0.18~0.99). A good performance in the creation of daily flow data (continuous data) from the 8-day interval flow data (intermittent data) was shown using the proposed method. In addition, the Web GIS-based pollutant load assessment system was developed to evaluate the TMDL; it included the daily data expansion method and provided the pollution load characteristics objectively and intuitively. This system will help decision makers, such as environmental regulators, researchers, and the general public, and support their decision making for pollution source management with accessible and efficient tools for understanding and addressing water quality issues. [ABSTRACT FROM AUTHOR]

This study examines temporal variations in water quality parameters of Godavari River. The present study examines the concentrations of seven water quality parameters, namely total alkalinity (Alk), calcium (Ca), chlorine (Cl), total hardness (Hard), magnesium (Mg), sodium (Na), and sulfate (SO4), using data from 1981 to 2005 at seven gauging stations, that is, Mancherial, Pathagudem, Nowrangpur, Jagdalpur, Perur, Konta, and Polavaram. A seasonal analysis (pre‐monsoon, monsoon, and post‐monsoon) is conducted to detect trends in water quality parameters. The study also uses normality tests Kolmogorov–Smirnov (K–S) and Shapiro–Wilk (S–W) which reveals nonnormal distribution in the majority of datasets, necessitating non‐parametric analysis. The concentration of Alk, Ca, and Hard is found to be significantly increasing trend at some stations during all three seasons, along with significantly decreasing trend found in the concentrations of Cl, Na, and SO4 using non‐parametric Mann–Kendall's (M–K) and Sen's Slope test. The concentration of Mg depicts mixed trends at different stations which may be due the variation in agricultural practices or industrial activities surrounding the stations. It is evident that none of the parameter exhibits statistically significant trends across all seasons at any station. The results of Sen's Slope test are found to be in good agreement with Mann–Kendall's test statistics. A second order auto‐regressive model (AR (2)) is successfully employed to predict and forecast the water quality parameters in present and future time steps. The authenticity of AR (2) model is assessed using Nash‐Sutcliffe (NSE) efficiency and coefficient of determination (R2). The values of NSE and R2 are found to be more than 0.85 for all parameters during all the three seasons, along with, the values of mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results of coefficient of variation (CV) and persistence‐criterion (PC) also depicts that AR (2) can successfully be utilized in forecasting the water quality parameters. The research contributes insights for effective water resource management in Godavari River basin. [ABSTRACT FROM AUTHOR]

Design flood estimation is an essential step in many water engineering design tasks such as the planning and design of infrastructure to reduce flood damage. Flood frequency analysis (FFA) is widely used in estimating design floods when the at-site flood data length is adequate. One of the problems in FFA with an annual maxima (AM) modeling approach is deciding how to handle smaller discharge values (outliers) in the selected AM flood series at a given station. The objective of this paper is to explore how the practice of censoring (which involves adjusting for smaller discharge values in FFA) affects flood quantile estimates in FFA. In this regard, two commonly used probability distributions, log-Pearson type 3 (LP3) and generalized extreme value distribution (GEV), are used. The multiple Grubbs and Beck (MGB) test is used to identify low-flow outliers in the selected AM flood series at 582 Australian stream gauging stations. It is found that censoring is required for 71% of the selected stations in using the MGB test with the LP3 distribution. The differences in flood quantile estimates between LP3 (with MGB test and censoring) and GEV distribution (without censoring) increase as the return period reduces. A modest correlation is found (for South Australian catchments) between censoring and the selected catchment characteristics (correlation coefficient: 0.43), with statistically significant associations for the mean annual rainfall and catchment shape factor. The findings of this study will be useful to practicing hydrologists in Australia and other countries to estimate design floods using AM flood data by FFA. Moreover, it may assist in updating Australian Rainfall and Runoff (national guide). [ABSTRACT FROM AUTHOR]

The paper provides an overview of the methods for hydrometric streamflow computation in the regime and real-time variants developed at the State Hydrological Institute and intended for inclusion in the technologies for automated processing and generalization of hydrological observations, which were included in the Roshydromet regulatory documents put into effect in 2017–2022. The results of further development of the methodological basis for water discharge calculations in the conditions of variable backwater are given on the example of the reach of the Ob River from Barnaul to Kamen-on-Ob. To control the reliability of streamflow computation at gauging sections, it is proposed to use the computation and analysis of dynamic (short-term) channel water balances as the most effective tool. The effectiveness of using the improved methods for streamflow computation and coordination is shown through examples. [ABSTRACT FROM AUTHOR]

The Western Ghats in the humid tropical zone of Kerala State, India have several drinking water, irrigation and hydroelectric projects, which exclusively depend on streamflow. The seasonal monsoon rainfall imposes several limitations in estimating the streamflows. The rugged topography of these highland areas with thick forest vegetation restricts the installation of conventional gauging stations. Considering these factors, an attempt is made to estimate the streamflow from these watersheds using the Soil and Water Assessment Tool model. Eight representative watersheds with stream gauging stations were selected, of which six were used for initial calibration, validation of the model and identifying the model parameters to be regionalised. The regionalised model was then applied to the other two watersheds, and the estimated data were compared with the observed. Global average, regression method and common parameter transfer approaches were adopted to regionalise the parameters. The calibrated model parameters are averaged in the global average method. The regression function in the data analysis add‐on in the Excel platform determined the functional relationship between the watershed characteristics and the model calibration parameters. The parameter transfer helped recognise two standard sensitive parameters. It is found that by providing the topography, land use and soil data, the streamflow from the ungauged watersheds of the highland belt in the wet humid tropical zone of Kerala can be satisfactorily simulated. [ABSTRACT FROM AUTHOR]

The issue of water scarcity, exacerbated by climate change and demographic increase, has become a growing concern in many regions throughout the world. Understanding hydrological behaviour to promote resilient and sustainable water management is paramount. Hydrological models that integrate natural processes and anthropogenic alterations of the basin's hydrology are a powerful tool to support decision-making. We developed a SWAT+ hydrological model including stakeholder expert knowledge on water management and introducing a novel calibration and validation approach suitable for heterogeneous basins in space and / or time. We also assessed spatio-temporal patterns and trends of streamflow during the first two decades of the 21st century in the Catalan River Basin District, in the western Mediterranean, using a wide variety of indicators to fully characterize the hydrological regime. We calibrated and validated the model using data from 50 gauging stations, verifying the usefulness of the new calibration and validation strategy. Co-development with stakeholders and the integration of expert knowledge, most notably on reservoir operations, helped improve model performance. Results revealed a generalized streamflow reduction, as well as increased dominance of streamflow flashiness and zero-flows recurrence. We also observed differences in seasonal trends, with autumn being the most affected season. These results provide insights into how climate change and anthropogenic pressures are going to keep affecting water resources availability in the future, thus raising the need for sustainable management practices in the Catalan River Basin District, as well as other regions vulnerable to water scarcity. [ABSTRACT FROM AUTHOR]

The Lower Yellow River (LYR) is a vital water resource for agriculture, industry, and domestic use in the surrounding areas. Understanding the delayed response of local streamflow response to remote reservoir operations is crucial for effective water management and flood control. In this work, we utilize historical hydrological data and statistical analysis techniques to investigate the time-lagged response of streamflow in the LYR to water regulation by the Xiaolangdi Reservoir. The results demonstrate that there is a time lag of 1.98 days, 2.86 days, and 3.93 days between the record of water regulation at Xiaolangdi Reservoir and the arrival time at Gaocun, Aishan, and Lijin stations, respectively. Time lag correction is proven to be crucial when establishing the relationship between the daily streamflows in the LYR and those at Xiaolangdi station. Further analysis reveals that the travel distance of streamflow is the dominant factor determining the lag time, with a time lag coefficient of 0.57 days per hundred kilometers. It is expected that the findings in this study could offer a fundamental basis for decision-makers in water resource management. [ABSTRACT FROM AUTHOR]

Climate is probably the most important factor affecting river discharge and flow dynamics. Low flows in rivers during the warm period cause stress to aquatic ecosystems and pose a challenge to sustainable water management. Previous research has shown that the average minimum discharge of the 30 driest continuous days, known as Q30, is a suitable measure for ecological flow estimation in Lithuania. This study aims to examine whether large-scale atmospheric processes, so-called teleconnections, can have an impact on Q30 during the warm period. Hydrological data for 1961–2020 from 25 water gauging stations were used to search for hydrological response signals with five selected climate indices (NAO, SCA, POL, EA/WR, and EA). Pearson correlation and Wilcoxon–Mann–Whitney test approaches were applied. The results suggested that the EA/WR and NAO had the strongest influence on Q30 in the studied region during the warm period. The positive phases of the indices tended to cause a greater decrease in Q30 values due to the prevailing easterly edge of the anticyclonic circulation over the studied region determined by the EA/WR and NAO indices, while the negative phases of the mentioned indices caused an increase and greater dispersion of Q30. [ABSTRACT FROM AUTHOR]

The complex topology of river networks and the numerous factors influencing streamflow make it challenging to forecast streamflow in large river basins. Improving the accuracy of streamflow forecasting in these basins is of great importance for water resource planning and management. In this paper, considering the spatial variability of impacts of human activities on streamflow, a streamflow forecasting method with a hybrid physical process-mathematical statistic is proposed to obtain better results by realizing the complementary advantages of conventional methods and the streamflow into Hongze Lake in China is forecasted by the proposed method. Firstly, the physical process-based Soil and Water Assessment Tool (SWAT) is used to forecast the streamflow of tributaries with few water storage projects such as reservoirs in the catchment area. Secondly, considering the correlation between mainstream streamflow and tributaries streamflow as well as the correlation between mainstream streamflow and its previous streamflow, streamflow forecasting models based on the vine copula function are constructed to forecast the mainstream streamflow and its confidence intervals, where there are many water storage projects in the catchment area. Then, the total streamflow into Hongze Lake and its confidence intervals are obtained by coupling the outputs of the above two parts. Finally, statistical indicators are chosen to evaluate the forecasting effects in terms of the credibility of deterministic forecasting as well as the reliability and acuity of probabilistic forecasting. The results demonstrate that the proposed method outperforms existing SWAT and long and short-term memory (LSTM) neural network methods in terms of forecasting performance. Consequently, it presents an effective alternative for addressing complex hydrological forecasting tasks in large river basins. [ABSTRACT FROM AUTHOR]

In late 2023 the Surface Water and Ocean Topography (SWOT) satellite mission will release unprecedented high-resolution measurements of water surface elevation (WSE) and water surface slope (WSS) globally. SWOT's exciting Ka-band near-nadir wide-swath interferometric radar (InSAR) technology could transform studies of surface water hydrology, but remains highly experimental. We examine Airborne SWOT (AirSWOT) data acquired twice over Canada's Peace-Athabasca Delta (PAD), a large, low-gradient, ecologically important riverine wetland complex. While noisy and susceptible to "dark water" (low-return) data losses, spatially averaged AirSWOT WSE observations reveal a broad-scale water-level decline of ~44 cmn (σ =271 cm) between 9 July and 13 August 2017, similar to a ~56 cm decline (σ=33 cm) recorded by four in situ gauging stations. River flow directions and WSS are correctly inferred following filtering and reach-averaging of AirSWOT data, but ~10 km reaches are essential to retrieve them. July AirSWOT observations suggest steeper WSS down an alternate flow course (Embarras River–Mamawi Creek distributary) of the Athabasca River, consistent with field surveys conducted the following year. This signifies potential for the Athabasca River to avulse northward into Mamawi Lake, with transformative impacts on flooding, sedimentation, ecology, and human activities in the PAD. Although AirSWOT differs from SWOT, we conclude SWOT Ka-band InSAR observations may detect water level changes and avulsion potentials in other low-gradient deltas globally. [ABSTRACT FROM AUTHOR]

Streamflow prediction is an important matter for the water resources management and the design of hydraulic structures that can be built on rivers. Recently, it has become a widely studied research field where data obtained from stream gauge stations can be utilized for creating estimating models by resorting to different methods such as machine and deep learning techniques. In this study, we performed monthly streamflow predictions by using the following data-driven methods of machine learning: linear regression, support vector regression, random forest and deep learning (DL) models to compare the performances of ML's and DL's techniques. A general workflow that can be applied to similar regions is presented. An estimating model containing six-input combinations and time-lagged streamflow data is improved by means of the autocorrelation function (ACF) and partial autocorrelation function (PACF). Furthermore, moving average is used as a smoothing technique to make the dataset more stable and reduce the effects of noise data. A comparative evaluation has been conducted to determine the performances of the above-mentioned methods. In this study, we proposed four different DL models and compared them with existing techniques. For the comparison of the results, we used evaluation criteria such as Nash–Sutcliffe efficiency (NSE), mean square error (MSE) and percent bias (PBIAS). The experimental results indicate that our bidirectional gated recurrent units (BiGRU) model outperforms both ML algorithms and existing solutions with 0.971 NSE, 0.001 MSE and − 1.536 PBIAS scores. [ABSTRACT FROM AUTHOR]

This study utilized a max-stable process (MSP) model with a dependence structure defined via a non-Euclidean distance metric, with the goal of modelling extreme flood data on a river network. The dataset was composed of mean daily discharge observations from 22 United States Geological Survey streamflow gaging stations for river basins in Missouri and Arkansas. The analysis included the application of the elastic-net penalty to automatically build spatially varying trend surfaces to model the marginal distributions. The dependence model accounted for the river distance between hydrologically connected gaging sites and the hydrologic distance, defined as the Euclidean distance between the centers of site's associated drainage areas, for all stations. Modelling the marginal distributions and spatial dependence among the extremes are two key components for spatially modelling extremes. Among the 16 covariates evaluated for marginal fitting, 7 were selected to spatially model the generalized extreme value (GEV) location parameter (for each gaging station's contributing drainage basin, its outlet elevation, centroid x coordinate, centroid elevation, area, average basin width, elevation range, and median land surface slope). The three covariates selected for the GEV scale parameter included the area, average basin width, and median land surface slope. The GEV shape parameter was assumed to be constant throughout the entire study area. Comparisons of estimates obtained from the spatial covariate model with their corresponding "at-site" estimates resulted in computed values of 0.95, 0.95, 0.94 and 0.85, 0.84, 0.90 for the coefficient of determination, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency for the GEV location and scale parameters, respectively. Brown–Resnick MSP models were fit to independent multivariate events extracted from a set of common discharge data, transformed to unit Fréchet margins while considering different permutations of the non-Euclidean dependence model. Each of the fitted model's log-likelihood values indicated improved fits when using hydrologic distance rather than Euclidean distance. They also demonstrated that accounting for flow-connected dependence and anisotropy further improved model fit. In this study, the results from both parts were illustrative; however, further research with larger datasets and more heterogeneous systems is recommended. [ABSTRACT FROM AUTHOR]

The effective management of water resources in regions with a high potential for water resources, such as the Tocantins/Araguaia Basin in Brazil, is crucial in the face of current climate change and urban and agricultural expansion. In this context, this study evaluates the WRF-Hydro hydrological model to simulate the flow of the Manuel Alves Pequeno, Vermelho, and Manuel Alves Grande rivers in the MATOPIBA region (encompassing areas from the states of Maranhão, Tocantins, Piauí, and Bahia), an agricultural frontier and the most key area in terms of grain production in Brazil. The aim is to analyze the hydrological parameters of soil infiltration, surface retention depth, land surface roughness, and Manning's channel roughness. The simulations are conducted at a spatial resolution of 3 km with a channel network of 100 m, covering a period of heavy rainfall from 13 March to 1 June 2018. For model validation, observational data from three river gauge stations of the National Water and Sanitation Agency are used, with assessments based on the Nash-Sutcliffe efficiency index, standard deviation of observations, root mean square error, percentage bias, and correlation coefficient, resulting in values of 0.69, 0.56, 4.99, and 0.83, respectively. In particular, the adjustment of the infiltration factor and surface roughness parameter has a greater contribution to improving the statistical results than the adjustment of the other two hydrological parameters. Additionally, the quality of discharge simulation at each river gauge station is correlated with the temporal distribution of simulated precipitation compared to observed data in the drainage network. Highlighting WRF-Hydro's potential as a fine-scale model easily coupled with numerical weather prediction, this study significantly advances regional river dynamics evaluation, crucial for strategic water resource management. [ABSTRACT FROM AUTHOR]

The complexity of hydrological data requires an understanding of its spatiotemporal evolution for effective modeling and prediction. In this study, the underlying spatiotemporal, chaotic, and nonlinear dynamics of rainfall and streamflow data at different timescales in Ceará were assessed by applying chaos theory concepts. This assessment included phase space reconstruction (PSR), correlation dimension, the largest Lyapunov exponent (LLE), nonlinear methods such as recurrence plots, and recurrence quantification analysis techniques. The results indicated that as the timescale increased, the required dimension for PSR decreased for most stations, indicating a shift in the dynamics of the variables. The presence of chaos was confirmed in 78% of the rainfall stations at a monthly timescale by the correlation dimension and positive values of LLE. For streamflow, 73% of monthly data showed indications of chaos using both methods. Values of LLE ranged from 0.02 to 0.24 and − 0.32 to 3.4 for monthly data of rainfall and streamflow, respectively. Most methods showed that the northwestern area of Ceará exhibited higher complexity. Furthermore, the extent of data complexity varied temporally, with the monthly timescale being more complex than the annual timescale. The study revealed that long-term predictions for streamflow may be ineffective for water resources in the region. The results suggested a potential for rainfall predictions up to six years in advance. These findings have implications for developing an integrated water management plan in the region and provide insight into how hydrological variables evolve over time and space. [ABSTRACT FROM AUTHOR]

Sustaining a steady supply of water to urban communities is of importance in a period that is characterised by rapid urban population growth, a global pandemic, and a changing climate that threatens the availability of the resource from its sources. Water supply to the City of Mutare is from three sources, Small Bridge Dam, Odzani, and the Pungwe River. The Pungwe source provides better quality water resources equivalent to the combined quantity supplied by the other two. It becomes an important source for the city, but climate change threatens the availability of water resources in the southern African region. Thus, it is imperative to quantitatively assess the impacts of a changing climate on water resources to enable the development of sustainable management alternatives. Using two carbon emission scenarios Representative Concentration Pathways (RCPs) 4.5 and 8.5, the study assesses the future availability of water resources from the Pungwe River to the City of Mutare using the Pitman hydrological model applied in an uncertainty framework. Available historical streamflow observations at gauging station F14 indicate a Q95 flow of about 2 Mm3/year. Projected future water resources at the end of the 21st century show a slight increase of up to 2.38% under the low carbon emission scenario (RCP4.5) and a decrease of up to 9.73%under the high carbon emission scenario (RCP8.5). These model‐generated results are useful to water managers to plan for catchment management strategies that would ensure continuous urban water supply, and the identification and development of possible future alternative water sources. [ABSTRACT FROM AUTHOR]

Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Most of the RFFA techniques are based on the annual maximum (AM) flood model; however, research has shown that the peaks-over-threshold (POT) model has greater flexibility than the AM model. There is a lack of studies on POT-based RFFA techniques. This paper presents the development of POT-based RFFA techniques, using regularised linear models (least absolute shrinkage and selection operator, ridge regression and elastic net regression). The results of these regularised linear models are compared with multiple linear regression. Data from 145 stream gauging stations of south-east Australia are used in this study. A leave-one-out cross-validation is adopted to compare these regression models. It has been found that the regularised linear models provide quite accurate flood quantile estimates, with a median relative error in the range of 37 to 47%, which outperform the AM-based RFFA techniques currently recommended in the Australian Rainfall and Runoff guideline. The developed RFFA technique can be used to estimate flood quantiles in ungauged catchments in the study region. [ABSTRACT FROM AUTHOR]

For many catchments, there is insufficient field data to calibrate the hydrological models that are needed to answer water resources management questions. One way to overcome this lack of data is to use remotely sensed data. In this study, we assess whether Landsat‐based surface water extent observations can inform the calibration of a lumped bucket‐type model for Brazilian catchments. We first performed synthetic experiments with daily, monthly, and limited monthly data (April–October), assuming a perfect monotonic relation between streamflow and stream width. The median relative performance was 0.35 for daily data and 0.17 for monthly data, where values above 0 imply an improvement in model performance compared to the lower benchmark. This indicates that the limited temporal resolution of remotely sensed data is not an impediment for model calibration. In a second step, we used real remotely sensed water extent data for calibration. For only 76 of the 671 sites the remotely sensed water extent was large and variable enough to be used for model calibration. For 30% of these sites, calibration with the actual remotely sensed water extent data led to a model fit that was better than the lower benchmark (i.e., relative performance >0). Model performance increased with river width and variation therein. This indicates that the coarse spatial resolution of the freely‐available, long time series of water extent used in this study hampered model calibration. We, therefore, expect that newer higher‐resolution imagery will be helpful for model calibration for more sites, especially when time series length increases. Plain Language Summary: Hydrological models are important for water resources management. The parameters for these models are estimated in a calibration process. Usually, calibration is based on streamflow data from gauging stations. However, for many catchments there are no streamflow data and therefore the calibration of hydrological models is difficult. In this study, we tested whether satellite data that shows the area that is covered by water can be used to calibrate the parameters of a hydrological model for Brazilian catchments. First, we tested if satellite data would be useful if the water extent was perfectly correlated to streamflow and available for every day, month, or month for half of the year due to cloud cover. For two thirds of the catchments, daily observations would be helpful for model calibration, but both monthly data sets were also informative. When we used actual satellite images to calibrate the model for a subset of 76 large rivers, only 30% of them benefitted from these data. This is probably due to inaccuracies in the water extent from satellite images and its coarse spatial resolution. We expect that newer higher‐resolution satellite data will be more useful for model calibration, especially when they become available for longer time periods. Key Points: Synthetic (i.e., perfect) daily water extent time series were informative for model calibration for two thirds of the Brazilian study catchmentsReduction of the temporal resolution to monthly time series did not limit the value of the synthetic water extent data for model calibrationActual remotely sensed water extent data was helpful for calibration for only one third of the subset of 76 catchments with large rivers [ABSTRACT FROM AUTHOR]

Sufficient water is pivotal in maintaining the stability of boundaries in sandy river systems. However the current methodologies employed for computing the water demand for sediment transport in rivers frequently neglect this component. This research utilizes data spanning 1960 to 2020 from seven principal hydrological stations located in the lower Yellow River to establish the correlation between key factors pertaining to the sediment transport capacity of flow. A closed equation system was established based on the principles of river dynamics to solve unknown hydraulic parameters. Finding a suitable hydraulic geometric relationship equation as a supplementary equation is a key step in constructing a closed equation system. The findings indicate that sediment transport water demands are 71.79, 133.24, 226.89, 286.12, and 313.6 × 108 m3, respectively, when sediment inflow is at 1, 2, 4, 6, and 8 × 108 t, with a bankfull discharge of 4000 m3/s. As the sediment inflow diminishes and the unit water demand for sediment transport increases, the sediment transport efficiency of the lower Yellow River reduces. The outcomes of this research can serve as a foundation for the joint operation of the Yellow River's main and branch reservoirs, as well as for designing water resource allocation schemes within the basin. [ABSTRACT FROM AUTHOR]

This study examines potential changes in the summer rainy season of a watershed, using a cumulative rainy deficit curve to define the onset and demise of the rainy season. Historical daily rainfall data from 41 gauge stations within the Xiang River Basin in 1960–2017 were analyzed. Results indicate that the climatology of the onset date is Julian day 72 and the climatology of the demise date is Julian day 236. There is, however, a difference between the two segments, e.g., 1960–1989 and 1990–2017. The median rainy season duration in 1990–2017 is 138 days. This is larger than that of 1960–1989, which is 120 days. There is a statistically significant correlation between the onset date of the rainy season and the duration of the rainy season. Daily soil moisture estimates were derived from satellite remote sensing data. The averaged soil moisture during the rainy season is much higher than that of the non-rainy season. To examine the difference in quick flow between rainy and non-rainy season, baseflow separation was applied to daily streamflow time series. Result reveals the median of weekly quick flow in rainy season is 1316 cms, which is 5.7 times that of the weekly quick flow in non-rainy season. Different mechanisms that drive spring and summer rainfall, as well as potential use of the results in water management, are discussed. [ABSTRACT FROM AUTHOR]

The subject of this study is an approximately 300 km long Middle Danube River reach that spans from river kilometer (rkm) 1581 in Hungary to (rkm) 1255 in Serbia. The observed drying of floodplains in Hungary some thirty years ago initiated the hydrological studies. However, problems related to the navigation route maintenance of the Danube River and those in the water supply of irrigation and drainage canal networks are now present in the whole free-flowing middle Danube region. The study aims at investigating the correlation between the observed water level decrease and recorded incision of the river bed at gauging stations and the indirect estimation of the long-term sediment transport along the sand-bed reach based on the surveillance cross-sections' data collected during regular monitoring surveys on the navigable Danube. It starts with hydrological analyses of the 70-year-long time series of water level and discharge yearly data and continues with morphological and correlation studies. It ends with the estimation of sediment transport. The decreasing trend in water levels and the increasing trend in cross-sectional areas are persistent. There is a linear correlation between the two. Longitudinal changes in sediment transport indicate the existence of both degrading and aggrading riverbed reaches. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) are critical infrastructures responsible for processing wastewater before discharging effluent to rivers and other potential uses. WWTPs use large, connected deep tunnels for storing sanitary and wet-weather flows for treatment. However, wastewater in those systems cannot exceed safe tunnel levels in order to prevent overflows of untreated wastewater into the environment. Further, WWTPs are among the 16 national lifeline infrastructure sectors in which the utilization of sensor technology has increased, making the sectors vulnerable to all forms of cyber threats. Considering these challenges, the work presented in this manuscript uncovers the role of AI at WWTPs by focusing on two problems: tunnel water-level prediction and detection of security threats. This is done by proposing an AI framework: P2O (prediction, protection, and optimization). The prediction module forecasts the tunnel water level using deep-learning models based on the current wastewater flow in the tunnel and other inputs from the sensors and gauges. The protection module focuses on classifying the intentionality of an anomaly, i.e., whether an attack is adversarial in nature or merely an outlier, using recurrent neural network models. Last, the optimization module aims to provide actionable recommendations to pump operators using a genetic algorithm. The experimental results of P2O indicate that the prediction module can predict the tunnel water level with 85% accuracy, and the protection module can detect about 97% of intentional attacks on WWTPs. AI models within P2O are evaluated; the experimental results are presented and discussed. This manuscript presents P2O , which is a novel AI framework that can predict about 85% of wastewater overflow incidences and about 95% of intentional cyberattacks on a WWTP, as indicated in the experiments. The deployment of P2O at a WWTP is essential, especially considering the adverse effects of overflowing wastewater on the environment (i.e., rivers and other water bodies). Moreover, cyberattacks on WWTPs can be subtle, making them challenging to detect; on average, most of them are noticed within one week to one month after the attack. This makes national infrastructure vulnerable to external and internal threats, influencing the well-being of water bodies and overall national security. P2O provides a real-time monitoring interface and can recommend optimal actions in different scenarios (i.e., outliers) for pump operators and process engineers at WWTPs. [ABSTRACT FROM AUTHOR]

Stream gauging stations provide critical streamflow measurements for hydrological applications; however, they may not accurately capture total catchment discharge due to unmonitored regional groundwater flow. Here, we evaluate the effectiveness of streamflow data from gauging stations worldwide to represent total catchment discharge through a modified hydrological model that includes baseflow signatures to constrain groundwater flow processes. We find that approximately 70% of gauging stations present biased estimations of total catchment discharge (bias >10%). This result implies that hydrology‐related processes may not be fully understood, and misleading conclusions may be drawn owing to the low streamflow measurement effectiveness. By influencing subsurface hydrological processes, catchment factors, including catchment area, topography, climate, and geological features, are linked to the effectiveness of streamflow measurements. Our findings highlight the importance of accurate streamflow measurement effectiveness for obtaining a reliable understanding of catchment hydrological processes to support sustainable water resource management. Plain Language Summary: The outflow of water from catchments plays a critical role in supporting downstream ecosystems and human society. This catchment outflow includes surface and subsurface discharge. However, a portion of the subsurface discharge may not directly flow into river networks and thus remains unaccounted for in streamflow records at gauging stations. The extent to which these unmonitored subsurface flows exist across catchments and their contribution to the total catchment discharge remains unknown. To address this knowledge gap, we have developed a novel method that simulates the movement of both surface and subsurface flows, enabling us to evaluate the representativeness of streamflow records in capturing the total catchment discharge. Our findings indicate that approximately 70% of gauging stations worldwide inadequately capture the total catchment discharge (bias >10%). This observation highlights the importance of unmonitored subsurface discharge as a significant component of catchment water yield. Such insights enhance our understanding of catchment hydrological processes, supporting the development of sustainable water resource management strategies. Key Points: A modified hydrological model was developed by including baseflow signatures to constrain groundwater flow processesApproximately 70% of gauging stations presented biased estimations of total catchment dischargeThe catchment hydrological response based on gauging station measurements is highly likely to be overestimated or underestimated [ABSTRACT FROM AUTHOR]

The articles published in this Special Issue contribute to closing critical knowledge gaps in understanding the impacts of climate change on the hydrological cycle and the issues related to water resource management in various contexts. B Modeling climate change impacts on the hydrological cycle: b While global climate models predict the potential changes that may occur at the global scale, the impacts of such global changes on the hydrological cycle at both the regional and local scales are still not well understood. Keywords: climate change modeling; climate downscaling; hydroclimate modeling; flood modeling; sea-level rise; coastal erosion; storm surge; water resource monitoring and management; sustainable irrigation EN climate change modeling climate downscaling hydroclimate modeling flood modeling sea-level rise coastal erosion storm surge water resource monitoring and management sustainable irrigation 2342 4 07/18/23 20230701 NES 230701 Global warming can alter the hydrological cycle in various forms such as increased cloudiness and latent heat fluxes, leading to more intensive and frequent precipitation extreme events (e.g., droughts, storms, and floods). [Extracted from the article]

This research report discusses the importance of environmental flows (e-flows) in sustainable water resources management and their inclusion in the United Nations Sustainable Development Goals (SDGs). The report compares e-flow estimates from the Global Environmental Flow Information System (GEFIS) with local assessments at 533 river sites and finds limited agreement due to differences in methods, data sources, and scale. The report recommends further investigation and improvements in global e-flow models to better represent on-ground realities and enhance sustainable development efforts. The field of e-flows aims to protect and restore aquatic biodiversity and ecosystem services by quantifying the flow regime of rivers, but the lack of harmonized data hinders global adoption. The Global Environmental Flow Information System (GEFIS) is a tool used for research, capacity building, and water resources planning studies, but it does not include specific flow requirements for freshwater ecosystems. The study compares various factors between GEFIS and local assessments and highlights the need for harmonization and further research. The study found that GEFIS estimates tended to overestimate mean annual runoff for medium to large rivers and underestimate it for small rivers and streams. There was little correlation between GEFIS estimates and local assessments, and GEFIS estimates were more conservative in terms of total annual e-flow than local assessments. The article recommends the use of an ensemble approach and the incorporation of routing processes in future global e-flow assessment tools. The document provides a list of references related to global environmental flow estimates and water availability, which [Extracted from the article]

Accurate streamflow estimation is crucial for proper water management for irrigation, hydropower, drinking and industrial purposes. The main aim of this study to adopt new data preprocessing techniques (e.g., EMD, EEMD and EWT) to capture the data noise and to enhance the prediction accuracy of machine learning methods for streamflow estimation which is a challenging task in high-altitude basins due to the influence of many external climatic and geographical parameters. The prediction accuracy of support vector regression (SVR), twin support vector machine (T), extreme learning machine (ELM), asymmetric Huber loss function-based ELM (AHELM) and ε-insensitive Huber loss function-based ELM (ε-AHELM) methods are investigated in monthly streamflow prediction. Among the standalone methods, the ε-AHELM performs superior to the SVR, TSVR, ELM, and AHELM in streamflow prediction; improvements in root mean square error are 6.9%, 4.9%, 6% and 4.2%, respectively. The study outcomes reveal that the preprocessing methods considerably improve the prediction accuracy of the implemented standalone models. Among the data preprocessing techniques, it is found that the EWT outperforms the EMD and EEMD techniques by reducing the prediction errors in the best ε-AHELM, EMD-ε-AHELM and EEMD-ε-AHELM models by 68–61.3%, 64.7–63.4% and 59.4–58.6%, respectively. The overall results of the study recommend the use of EWT-ε-AHELM in streamflow estimation. [ABSTRACT FROM AUTHOR]

Due to developing abstractions and their impacts on surface runoff, the recorded flow has been changed by human activities in most water gauging stations. Therefore, there is not found natural regime in the catchments. Accordingly, the objective of naturalization is to remove the effect of human activity factors and determine the actual amount of the river flow before the abstraction and the upstream development. Researchers have presented different methods that are mainly based on volume budget. In this way, this research presented the conventional methods as well as investigated their weak points. These new and innovative methods have been applied based on the available data. The methods have been planned based on the net consumption in which, the different types of water demands related to the upstream of each hydrometric station, are estimated for each month of a long-term series. Then, the amount of natural flow is determined by adding them to the observed flow. The accuracy and validation of the results are investigated by comparing the observed and calculated flow. As a case study, this method was utilized and implemented for Tireh and Marbareh sub-basins in Dez as well as Solgan and Beheshtabad sub-basins in the Karun basin. The results showed the role of the human activity factors decreasing the long-term outflow in the Tireh basin a 23.2%, in the Marbareh basin a 28.7%, in the Vanak watershed a 26%, and in the Beheshtabad basin a 9.5%. The results validation indicated the appropriate compatibility of the observational and estimated data for the control points (the stations). In this research, natural flow is obtained by presenting a practical method based on available information in the country. The proposed method has been in the preliminary stages. To verify and comprehend it, it should be used in future research on the interaction of surface and underground water and the use of new technologies such as remote sensing. [ABSTRACT FROM AUTHOR]

The thermal state of the northern rivers of Russia has undergone significant changes in the last 30–40 years due to climate warming, changes in water runoff. The purpose of this investigation is to study and explain only changes in heat flux, their long-term and spatial patterns in the northeast Asian part of Russia. Here, the heat flux of rivers is formed under the influence of climatic conditions, the altitude and latitude location of rivers, continuous permafrost, aufeis, glacial runoff and groundwater recharge, different water regime of rivers. An important feature of the research is the use of a large number of hydrological stations—145 and 231 gauging stations with data on water discharge and stream temperature, mainly from 1955 to 2012/2018. The abundance of data enabled to create unique maps of heat-flux and its deviations from the values of the base period. Empirical relationships between heat flux, on the one hand, and water runoff and water temperature, on the other, made it possible to improve the initial data, showed the leading role of water runoff in long-term fluctuations in heat-flux. However, climatic warming enhances the role of the temperature factor and transforms the relationship between water runoff and heat-flux, "shifting" them upwards. So far, these violations have not covered most of the gauging stations. The long-term "behavior" of heat flux is not unambiguous. Nevertheless, the period since the late 1980s is distinguished by higher values of heat flux. 20% of gauging stations demonstrate an increase in heat, compared to the previous period, by 10, 39% demonstrate an increase from 10 to 25%, 36% demonstrate >25%. Since the mid-second half of the 2000s, the heat flux of the rivers in the western part of the region has signs of stabilization and even a decrease. The reason is low water flow. A violation of the stationarity of the series in terms of dispersion (F-test) was found at 30% stations with reliable and long-term observations, mainly in the eastern part. Further changes in the heat flux of the regional rivers will depend on both changes in the river water flow and temperature. Within a year, almost all annual heat flux (90–95%) is generated in the summer months and has peaks in June and July. In the Vilyuy River basin, in the upper and middle part of the Kolyma River basin and on the coast of the Sea of Okhotsk, the proportion of summer months decreases to 65–90%. In addition to estimation of heat flux at the hydrological stations, the inflow of river waters into the mouths of the main rivers of the region and, in general, into the Arctic seas was assessed in various ways. [ABSTRACT FROM AUTHOR]

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The Amazon basin, the world's largest river basin, is a key global climate regulator. Due to the lack of an extensive network of gauging stations, this basin remains poorly monitored, hindering the management of its water resources. Due to the vast extension of the Amazon basin, hydrological modeling is the only viable approach to monitor its current status. Here, we used the Soil and Water Assessment Tool (SWAT), a process-based and time-continuous eco-hydrological model, to simulate streamflow and hydrologic water balance in an Amazonian watershed where only a few gauging stations (the Jari River Basin) are available. SWAT inputs consisted of reanalysis data based on orbital remote sensing. The calibration and validation of the SWAT model indicated a good agreement according to Nash-Sutcliffe (NS, 0.85 and 0.89), Standard Deviation Ratio (RSR, 0.39 and 0.33), and Percent Bias (PBIAS, −9.5 and −0.6) values. Overall, the model satisfactorily simulated water flow and balance characteristics, such as evapotranspiration, surface runoff, and groundwater. The SWAT model is suitable for tropical river basin management and scenario simulations of environmental changes. [ABSTRACT FROM AUTHOR]

Satellite remote sensing enables monitoring and detecting spatiotemporal changes in natural water cycle phenomena (e.g., precipitation and soil moisture), as well as the assessment of natural and human-induced changes on Earth. Improvements in satellite remote sensing techniques have allowed the development of several platforms that are able to capture multitemporal data with a wide range of spatial and temporal resolutions. Improving Remote Sensing Hydrological Variable Retrieval and Monitoring Indexes Several hydrological variables can be tracked using remote sensing technology. [Extracted from the article]

Advances in geodetic altimetry instruments are providing more accurate measurements, thus enabling satellite missions to produce useful data for narrow rivers and streams. Altimetry missions produce spatially dense land and water surface elevation (WSE) measurements in remote areas where in situ data are scarce that can be combined with hydraulic and/or hydrodynamic models to simulate WSE and estimate discharge. In this study, we combine ICESat-2 (Ice, Cloud and land Elevation Satellite) land and water surface elevation measurements with a low-parameterized hydraulic calibration to simulate WSE and discharge without the need for surveyed cross-sectional geometry and a rainfall–runoff model. ICESat-2 provides an opportunity to map river cross-sectional geometry very accurately, with an along-track resolution of 0.7 m, using the ATL03 product. These measurements are combined with the inland water product ATL13 to calibrate a steady-state hydraulic model to retrieve unobserved hydraulic parameters such as river depth or the roughness coefficient. The low-parameterized model, together with the assumption of steady-state hydraulics, enables the application of a global search algorithm for a spatially uniform parameter calibration at a manageable computational cost. The model performance is similar to that reported for highly parameterized models, with a root mean square error (RMSE) of around 0.41 m. With the calibrated model, we can calculate the WSE time series at any chainage point at any time for an available satellite pass within the river reach and estimate discharge from WSE. The discharge estimates are validated with in situ measurements at two available gauging stations. In addition, we use the calibrated parameters in a full hydrodynamic model simulation, resulting in a RMSE of 0.59 m for the entire observation period. [ABSTRACT FROM AUTHOR]

An aspect correlated with climate change is certainly represented by the alternation of severe floods and relevant drought periods. Moreover, there is evidence that changes in climate and land cover are inducing changes in stream channel cross-sections, altering local channel capacity. A direct consequence of a significant change in the local channel capacity is that the relationship between the amount of water flowing at a given point in a river or stream (usually at gauging stations) and the corresponding stage in that section, known as a stage–discharge relationship or rating curve, is changed. The key messages deriving from the present work are: (a) the more frequent and extreme the floods become, the more rapid the changes in the stream channel cross-section become, (b) from an operational point of view, the collection and processing of field measurements of the stage and corresponding discharge at a given section in order to quickly and frequently update the rating curve becomes a priority. It is, therefore, necessary to define a control system for acquiring hydrological data capable of keeping river levels and discharges under control to support flood early warnings and water management. The proposed stage–discharge management system is used by the Civil Protection Service of the Marche Region (east-central Italy) for the monitoring of river runoff in the regional watersheds. The Civil Protection Service staff performs stage–discharge field measurements using water level sensors and recorders (e.g., staff gauges, submersible pressure transducers, ultrasound and radar sensors) and a current meter, acoustic doppler velocimeter, acoustic doppler current profilers, portable mobile radar profiler and salt dilution method equipment, respectively. Power functions are fitted to the stage–discharge field data. Furthermore, extrapolation is performed to cover the full range of flow measurements; in general, extrapolation is not an easy task because of sharp changes in the stream cross-section geometry for very high or very low stages. In the present work, we also focused attention on the application problems that occur in practice and the need for frequent updating. [ABSTRACT FROM AUTHOR]

The National Water Model (NWM) will provide the next generation of operational streamflow forecasts across the United States (U.S.) using the WRF‐Hydro hydrologic model. In this study, we propose a strategy to calibrate 10 parameters of WRF‐Hydro that control runoff generation during floods and snowmelt seasons, and due to baseflow. We focus on the Oak Creek Basin (820 km2), an unregulated mountainous sub‐watershed of the Salt and Verde River Basins in Arizona, which are the largest source of water supply for the Phoenix Metropolitan area. We calibrate the model against discharge observations at the outlet in 2008–2011, and validate it at two stream gauging stations in 2012–2016. After bias correcting the precipitation forcings, we sequentially modify the model parameters controlling distinct runoff generation processes in the basin. We find that capturing the deep drainage to the aquifer is crucial to improve the simulation of all processes and that this flux is mainly controlled by the SLOPE parameter. Performance metrics indicate that snowmelt, baseflow, and floods due to winter storms are simulated fairly well, while flood peaks caused by summer thunderstorms are severely underestimated. We suggest the use of spatially variable soil depth to enhance the simulation of these processes. This work supports the ongoing calibration effort of the NWM by testing WRF‐Hydro in a watershed with a large variety of runoff mechanisms that are representative of several basins in the southwestern U.S. [ABSTRACT FROM AUTHOR]

In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level data. EEMD is used to decompose water level signals from a tidal river into several intrinsic mode functions (IMFs). These IMFs are then used to reconstruct the ocean and stream components that represent the tide and river flow, respectively. The forecasting model is obtained through stepwise regression on these components. The ocean component at a location 1 h ahead can be forecast using the observed ocean components at the downstream gauging stations, and the corresponding stream component can be forecast using the water stages at the upstream gauging stations. Summing these two forecasted components enables the forecasting of the water level at a location in the tidal river. The proposed model is conceptually simple and highly accurate. Water level data collected from gauging stations in the Tanshui River in Taiwan during typhoons were used to assess the feasibility of the proposed model. The water level forecasting model accurately and reliably predicted the water level at the Taipei Bridge gauging station. [ABSTRACT FROM AUTHOR]

Forecasting upstream flow amount based on downstream flow values is a new way of managing flood risk. This kind of prediction of the flow, size, and intensity of rivers in the alluvial aquifers is, in most cases, a challenging task due to climate change, as well as anthropogenic impacts on river flows. The presented methodology allows for organizing and preparing measures for flood protection, the operational work of hydropower plants, and many other usages. The methodology consists of correlation, cross-correlation, and a rescaled adjusted partial sums (RAPS) method. Although all methods are known, they have not been used in a methodology like the one presented. Research is conducted on a real case study of the river Bednja, on five consecutive hydrological measurement stations in the northwestern part of Croatia, for an available time series of the average daily flows from 2007–2018. High correlations were observed between the five stations along the Bednja River, with values of the correlation coefficient between 0.85 and 0.97 for the average daily flows, and between 0.88 and 0.99 for the RAPS values. The presented analysis gives information about similarities and connections between all five hydrological stations, which is significant and can be used to predict flow intensity. [ABSTRACT FROM AUTHOR]

In times of population growth, climate change, and increasing water scarcity around the world, it is important to take an objective look at water, a fundamental resource for life. Hydrodynamic modeling makes possible the research of different aspects of the water cycle and the evaluation of different hydrological and hydrogeological forecasting scenarios in the short and medium terms. The present research offers a more detailed scope at the hydrodynamic processes and their space-time distributions on a UE pilot in the Júcar River Basin, providing a calibrated and validated hydrodynamic model of 121 km river reach for 45 years period (1974–2019) on a daily scale. The obtained information is about discharge and water depths along the Júcar River reach within the hydrogeological boundaries of the Mancha Oriental Aquifer (MOA). The river–aquifer interactions have been represented as dynamic boundary conditions expressed as a difference between observed discharges measured in 3 gauging stations. The obtained calibration error performance evaluations of observed and simulated values cover two periods, according to observed data availability from gauging station 08036 with resulting R2 for both discharges and water depths over 0.96. The model validation results were obtained for a different gauge 08132 and the determination coefficients R2 also perform very well with value of 0.90. The model developed might be useful for decision making in water resources management and can be used to generate simulated time series of water depths, levels, discharges, and velocities in reaches where gauging measurements are not available with a desired space-time resolution (from meter/second to kilometer/month). Estimation of critical discharge value (1.973 m3s−1) for system equilibrium, based on the balance between losing and gaining sub-reaches of the river, is also made with a statistical significance at 95% for hydrologic years 2007–2010, period influenced by restrictions in groundwater withdrawals. The results of the present research are important for the proper and objective management of the scarce water resources on a watershed scale in Júcar River Basin, a complex case study representing semiarid climate, growing anthropogenic pressures, and complex river–aquifer interactions. The used approach of dynamic representation of the river–aquifer interactions as distributed source boundary condition in the one-dimensional hydrodynamic model might be applied in another study case on similar scale. [ABSTRACT FROM AUTHOR]