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Different types of droughts, with a great number of indices which try to explain/categories them, exist in literature. Certain regions do not necessarily have the same drought pattern changes for different types of droughts. The socio-economic drought index WEI + of one country/region or catchment area is defined as a quotient of water abstraction and water sources availability (after deducting ecological demand), both with calculated returns. Water demand (or water use/abstraction) can be expressed as the total water demand (usually) or water demand for a certain purpose (water supply, irrigation, or industry). Both water demand and water availability can be examined at different time levels: mean annual, maximum (minimum) monthly, max (min) daily, etc. The reasons why these indices differ over time and across one region are different. Four larger and twenty smaller catchment areas in central Serbia were analyzed, the results of which are presented. River flows on hydrological stations (monthly basis data) have been used as water source availability. Available data for the 60-year period (1957–2016) has been included in the analyses. They have been split into two 30-year periods and differences between them have been considered. The adopted time levels are: 1) mean annual, and 2) 97%monthly flow which corresponds with maximum monthly demand. It has been shown that annual WEI + is still low, while 97%monthly is often higher than 0.5. An excessively high WEI + points to potential Environmental hazards, so Ecological demand must be considered. Due to climate change, and the increase in water demand, Ecological demand issues have become very debatable in dry areas worldwide. After presenting the methodology and results, the paper discusses all these issues. [ABSTRACT FROM AUTHOR]

Accurate and reliable monthly runoff forecasting is crucial for water resource management, but the increasing non-stationarity of runoff series poses new challenges to the development of forecasting models. To overcome these problems, this study proposes a novel climate-driven hybrid model based on two-stage decomposition and. optimization, called ICEEMDAN-SVMD-ESPSO-SVR. Firstly, the original runoff is decomposed into a set of components by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and high-frequency components identified through entropy analysis are further decomposed into multiple subcomponents by successive variational mode decomposition (SVMD). Secondly, the input variables are selected from the candidate set consisting of runoff, subcomponents and key climate factors by using correlation coefficient and mutual information, respectively. Thirdly, support vector regression (SVR) is employed to forecast each sub-component, and particle swarm optimization algorithm using eagle strategy (ESPSO) is used to select each model's parameters. Finally, the forecasted values of all sub-components are aggregated as the final result. Monthly runoff data from three different hydrological stations in China's Poyang Lake Basin are employed to evaluate the performance of the proposed hybrid model and other comparable models. Results indicate that the proposed hybrid model outperforms single models and other combination models based on single-stage decomposition, and the correlation coefficient method is more suitable for input variable selection in climatedriven models, thus confirming the effectiveness of two-stage decomposition, the ESPSO algorithm, and input variable selection in enhancing modeling accuracy. Therefore, the proposed hybrid model is a feasible and promising new method for monthly runoff forecasting. [ABSTRACT FROM AUTHOR]

ABSTRACT The reliability of seasonal snow cover information is constrained by limitation of in situ observations and uncertainties in remote sensing data and model simulations in alpine region, thus posing important challenges to understanding the climate system and water resource management in alpine region. Here, the assimilation of daily cloud‐free Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference snow index (NDSI) product into an intermediate complexity snow mass and energy balance model—Flexible Snow Model (version FSM2_MO)—was implemented. The aim is to improve the model simulations of seasonal snow cover (snow‐covered extent; SCE, snow depth; SD, snow water equivalent; SWE, and snowmelt runoff; SMR) in the alpine region (a case of the upper‐middle reaches of the Heihe River basin, Northwest China). The results indicate comprehensive improvement in the simulation of SCE, SD, and SMR in the study area through data assimilation, with the ability to significantly reduce prior biases of the FSM2_MO. Based on the independent daily cloud‐free Advanced Very High Resolution Radiometer (AVHRR) SCE product, the updated SCE simulation (i.e., data assimilation) showed a reduction in mean absolute error (MAE) from 10.46% to 7.16%, root mean square error (RMSE) from 16.14% to 12.26%, and an increase in Pearson's correlation coefficient (CC) from 0.18 to 0.67 compared with the open loop simulation (i.e., without assimilation). The evaluation results of SD observation data showed that data assimilation improved SD simulation compared with the open loop run (OL). And utilising the monthly discharge observations at the Yingluoxia hydrological station, data assimilation slightly improved the SMR simulation. The updated SMR simulation achieved a CC of 0.91, Nash‐Sutcliffe efficiency coefficient (NSE) of 0.73, and Kling‐Gupta efficiency coefficient (KGE) of 0.76. Moreover, the Landsat 8‐derived snow cover map and Sentinel‐1‐derived SD also indicated that the updated simulation effectively filled in the missing snow cover and removed the superfluous snow cover predicted by the OL simulation in terms of spatial distribution. [ABSTRACT FROM AUTHOR]

The lack of long-term meteorological observations largely hinders a comprehensive assessment of the changes in extreme precipitation in the high-mountainous region of the Yarlung Zanbo (YZ) river basin. In this study, the YZ basin was used as an example to clarify extreme changes based on a newly generated precipitation dataset in Tibetan Plateau (TP) basins with complex terrain and limited observations. Results showed that divergent changes in extreme precipitation were observed in the YZ basin. Extreme precipitation indices showed inconsistent changes in the YZ basin for this period. The duration-based consecutive wet day (CWD) indices (5 days/10 year) showed significant increasing trends, while the other eight indices showed insignificant change. It generally mutated in 1989, which showed increased trends for 1961–1988, but decreased trends for 1989–2020. Mean annual extreme precipitation increased from the upper stream to the downstream sub-basin of the YZ basin, and generally decreased from low altitudes to high altitudes. There were opposite tendencies of extreme precipitation between 1961 and 1988 and 1989–2020 across sub-basins. Extreme precipitation showed decreasing trends for the upper sub-basins of the Lhatse (LZ), Rikaze (RKZ), Lhasa (LS) and region between LZ and Nuxia hydrological station (LZ-NX), with an increasing trend in the region between NX and Pasighat (NX-BXK) for 1961–1988, with the opposite occurring during 1989–2020. The annual change in extreme precipitation was mostly influenced by the Arctic Oscillation (AO), East Asian Summer Monsoon (EASM), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) in the YZ basin for 1961–2020. However, the influence of large-scale climatic oscillation on extreme precipitation varied at different sub-basins and periods. We expect these findings will improve our understanding of the extreme precipitation characteristics in the TP basins. [ABSTRACT FROM AUTHOR]

Understanding the changes of sediment concentration in rivers, especially under heavy rains, is of great significance to accurately evaluate the effectiveness of soil and water conservation measures in the Loess Hilly Area. On July 26, 2017, an extremely heavy rain (maximum rain intensity of 66.6 mm/h, the highest rainfall of 256.8 mm in 10 h, and a return period of 100 years) occurred over the 821 km2 Xiaoli River watershed in the Wuding River watershed. In order to understand the sediment concentration during the heavy rain event, 10 dam‐controlled catchments in the center of the rainstorm (3 in the Xiaoli River watershed and 7 in its adjacent watersheds) without drainage and damage were selected. All the retained runoff and sediment in the check dams were measured by the cross‐sectional survey according to the flood marks left by the heavy rain. Combined with the observed runoff and sediment transport data by the hydrological station at the outlet of the Xiaoli River watershed as well as the deposited sediment investigated in the watershed during the heavy rain event, the change of sediment concentration and the role of check dam were analyzed. Results showed that under heavy rain, all the sediment concentrations of runoff in the dam‐controlled catchments ranged from 344 to 552 kg/m3, suggesting that the sediment retaining effect of slope measures (woodland, grassland, and terrace) is limited and cannot prevent the occurrence of hyperconcentrated flows. The gully measures (check dams) in the Xiaoli River watershed can hold a large amount of sediment under heavy rain and make the runoff no longer a hyperconcentrated flow at the watershed outlet. In order to control the transport of hyperconcentrated flows to the downstream, more attention should be paid to the construction of check dams while carrying out vegetation ecological construction in the Loess Hilly Area. [ABSTRACT FROM AUTHOR]

Monthly runoff prediction is crucial for water resource allocation and flood prevention. Many existing methods use identical deep learning networks to understand monthly runoff patterns, neglecting the importance of predictor selection. To enhance predictive accuracy and reliability, this study proposes an RFECV–SSA–LSTM forecasting approach. It iteratively eliminates predictors derived from SSA decomposition and PACF using recursive feature elimination and cross-validation (RFECV) to identify the most relevant subset for predicting the target flow. LSTM modeling is then used to forecast flows 1–7 months into the future. Furthermore, the RFECV–SSA framework complements any machine-learning-based runoff prediction method. To demonstrate the method's reliability and effectiveness, its outputs are compared across three scenarios: direct LSTM, MIR–LSTM, and RFECV–LSTM, using monthly runoff historical data from Yangxian and Hanzhong hydrological stations in the Hanjiang River Basin, China. The results show that the RFECV–LSTM method is more robust and efficient than the direct LSTM and MIR–LSTM counterparts, with the smallest number of outliers for NSE, NRMSE, and PPTS under all forecasting scenarios. The MIR–LSTM approach exhibits the worst performance, indicating that single-metric-based feature selection may eliminate valuable information. The SSA time–frequency decomposition is superior, with NSE values remaining stably around 0.95 under all scenarios. The NSE value of the RFECV–SSA–LSTM method is greater than 0.95 under almost all forecasting scenarios, outperforming other benchmark models. Therefore, the RFECV–SSA–LSTM method is effective for forecasting highly nonlinear runoff series, exhibiting high accuracy and generalization ability. [ABSTRACT FROM AUTHOR]

Exploring the variation characteristics of actual evapotranspiration (ETa) and its response to climate change in the arid inland region of China is of great significance for strengthening regional water resources management and maintaining ecological environment security and stability. Taking the Dulan River Basin as the research area, based on the meteorological data from the Wulan Station and hydrological data from the Shanggaba Station from 1981 to 2020, the variation characteristics of ETa at the annual scale were analyzed. The ETa estimation model and joint distribution model of P and potential evapotranspiration (ET0) was constructed based on climate factors, and the uncertainty of ETa response to climate change was explored with the water balance method, multiple linear regression, marginal distribution function, Copula function, and Monte Carlo algorithm. The results showed that the multi-year mean value of ETa in the study area was 261.6 mm, and the interannual process showed an insignificant upward trend, and had no abrupt change during the period. There were two obvious main cycles, which were 19-year periodic changes on the 30-year time scale and 6-year periodic changes on the 9-year time scale. The ETa estimation model based on precipitation (P) and ET0 had good simulation accuracy. The optimal marginal distributions of P and ET0 were Pearson-III (P-III) distribution and Generalized Extreme Value (GEV) distribution, respectively. The Copula joint distribution probability density of P and ET0 was a symmetric saddle-shaped distribution. ETa showed an inverted 'S' distribution with the change in joint guarantee rate of P and ET0, ranging from 116.9 mm to 498.6 mm. ETa was an interval range under a certain joint guarantee rate. The research results can provide support for the assessment of ETa, and help to further understand the driving mechanism of climate change on ETa in the arid inland region of China. [ABSTRACT FROM AUTHOR]

Hydrological drought and river disconnectivity worsen from dams and human activities. This research investigates how dam constructions exacerbate hydrological drought and river disconnectivity in Apalachicola River system. Data from two hydrological stations (1928-2022), a hydrographic survey, high-resolution LiDAR, and a triangular irregular network (TIN) were used to identify three connectivity flow (141.5 m3/s, 169.8 m3/s, and 226.4 m3/s) categories, each impacting floodplain differently. A relative elevation model (REM) was also developed for floodplain connectivity assessment. Findings show a significant increase in the frequency, duration, and magnitude of connectivity low flow events from pre-dam to post-dam periods, especially at the 169.8 m³/s connectivity flow threshold. A 20th percentile variable threshold shows increasing hydrological drought. Drought increases have led to decreased connectivity between tributaries and the main river system, and an increase in the moderately disconnected floodplain area from 37.1% to 46.0%. Adaptive management strategies are needed to address drought-induced floodplain disconnectivity [ABSTRACT FROM AUTHOR]

Analysing non-stationarity in runoff and sediment load is crucial for effective water resource management in the Dongting Lake basin amid climate change and human impacts. Using the Mann-Kendall test, Generalized Additive Models for Location, Scale, and Shape framework, and Random Forest models, we evaluated non-stationarity and its drivers in the annual runoff and annual sediment load series at eight hydrological stations from 1961 to 2021. These stations include three inflow sites at the Jingjiang Three Outlets (Ouchi, Songzi, and Hudu Rivers), four inflow sites in the Four Rivers basin (Xiang, Zi, Yuan, and Li Rivers), and one outflow site at Chenglingji. Results revealed a significant decrease in annual runoff at the Three Outlets and Chenglingji, while the Four Rivers basin showed no significant trend. The non-stationary models with multiple physically-based covariates better captured non-stationarity compared to single covariate models. Annual rainfall was a key contributor to annual runoff in the Four Rivers basin, while reservoir storage capacity played a more dominant role in the Three Outlets. At Chenglingji station, both factors significantly influenced annual runoff. For annual sediment load, reservoir storage capacity emerged as the most critical factor across all regions. These findings provide a basis for improving runoff and sediment regulation in the Dongting Lake basin. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the current air2stream model does not consider the lag change of water temperature, this paper proposes a new method that combines empirical mode decomposition, power spectral density and a dynamic time warping algorithm to determine the optimal time lag parameters, and then constructs the air2stream with the optimal time lag (a2swtl). Finally, the effectiveness and stability of the new method and model were verified based on the measured data from 2020–2022 at three hydrological stations in the middle and lower reaches of the Yangtze River that are close to natural river conditions. The results show that the new method overcomes the problem of low accuracy in determining the time lag of the existing correlation coefficient method. Compared with the original model, a2swtl has better stability without introducing additional observation data, and the average prediction accuracy is improved by about 6.33%, 8.36% and 15.4% at the respective stations. [ABSTRACT FROM AUTHOR]

The article discusses the impact of global warming on glacierized environments, leading to cryospheric disasters such as glacial lake outburst floods and ice-rock avalanches. It highlights the need for robust early-warning systems to mitigate these events, especially in developing countries like Pakistan, India, and China. The proposed solution is a digital-twin platform that can simulate and monitor cryosphere changes in real-time, revolutionizing the prediction and mitigation of cryospheric disasters. The article emphasizes the importance of interdisciplinary collaborations and international cooperation in developing such systems. [Extracted from the article]

Making accurate and reliable predictions for monthly runoff in data scarce basins is still a major challenge. In this study, a new model, the CL-NDM, is developed by combining Convolutional Neural Network-Long Short-term Memory (CNN-LSTM) and a nonlinear dynamic model. The CL-NDM can overcome the deficiency of observed data by fusing spatial and temporal dependencies in runoff sequences at different stations. First, phase space reconstruction is used to enlarge the dimensions of the runoff sequences and reconstruct the attractors of the runoff sequences. Then, the CNN-LSTM is employed to construct the mapping between non-delay and delay attractors. Finally, the prediction set of the target variable is obtained by embedding multiple times. The CL-NDM is performed for monthly runoff prediction at eleven hydrological stations in the Weihe River, China. Compared with the CNN, LSTM and CNN-LSTM models, which require a large amount of training samples, the CL-NDM behaves much better, especially in situations with small training sample sizes. The maximum increase in R is 74%, and the maximum NSE is as large as 0.8. The maximum improvement in RMSE and MAPE is 53% and 88%, respectively. The CL-NDM has stronger ability to capture peak value while LSTM, CNN-LSTM and CNN models show obvious time lag in the prediction of peak point. The improved nonlinear dynamical model may provide a valuable method for runoff prediction in data-scarce regions. [ABSTRACT FROM AUTHOR]

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods. [ABSTRACT FROM AUTHOR]

Accurate forecasting of mid to long-term runoff is essential for water resources management. However, the traditional model cannot predict well and the precision of runoff forecast needs to be further improved. Here, we proposed a novel data-driven model aimed at enhancing the performance of the Gated Recurrent Unit (GRU) through the integration of Robust Local Mean Decomposition (RLMD) and the Slime Mould Algorithm (SMA). The objective is to improve mid to long-term runoff prediction in three hydrographic stations: Heishiguan, Baimasi, and Longmenzhen, located within the Yiluo River Watershed in central China. The model leverages monthly runoff data spanning from 2007 to 2022 to achieve this objective. The results indicated that (1) the new data-driven model (RLMD -SMA-GRU) had the highest monthly runoff prediction accuracy. Both RLMD and SMA can improve the accuracy of the model (NSE = 0.9466). (2) The precision of the models in wet season outperformed in dry season. (3) The hydrological stations with large discharge and stable runoff sequence have better forecasting effect. The RLMD-SMA-GRU model has good applicability and can be applied to the monthly runoff forecast at watershed scale. [ABSTRACT FROM AUTHOR]

Channel sandbars are extremely sensitive to basin‐wide natural processes and human activities, and their modifications considerably affect channel stability and river ecosystem health. However, quantitatively detecting the three‐dimensional dynamics of channel sandbars at a high spatial resolution over a large area remains challenging. In this study, we propose a semi‐automated process to address this challenge by extracting sandbar contours from Sentinel‐2 images in the Google Earth Engine (GEE) platform. We then generate contour datasets for each sandbar based on the spatial relationship between contours and water levels at nearby hydrological stations. Subsequently, digital elevation models (DEMs) are created for each sandbar using the contour dataset, and non‐linear relations between sandbar areas and water levels are fitted through polynomial curves of degrees 3–6 to calculate changes in sandbar volumes. Finally, we comprehensively analysed the driving mechanisms of sandbar changes in different sub‐reaches. Our findings reveal the area and volume of sandbars in three of the four sub‐reaches increased significantly between 2018 and 2019; however, these values decreased significantly and continuously between 2019 and 2022. Sandbars located downstream near the dam experienced more severe erosion compared to those farther downstream. Although the morphodynamic processes of each sub‐reach were different, extremely low rainfall in 2019–2020 and the operation of the Xayaburi Dam are significant global factors leading to the shrinkage of the sandbars. Furthermore, abnormal erosion with a change rate of −9.62% in area and −8.95% in volume was observed in the sub‐reach between Vientiane and Khong Chiam hydrological stations between 2018 and 2019, with the susceptibility of the channel to flow scour and anthropogenic sand mining being significant factors. Although the sediment replenishment from tributaries diminishes the impact of upstream dams and droughts on the reach farther downstream, the threat to channel stability due to excessive sand mining should be given high priority. Our study highlights how satellite remote sensing, specifically utilizing time series images with fine spatio‐temporal resolutions, effectively detects three‐dimensional variations in sandbars and river geomorphic changes. Additionally, it demonstrates the significant post‐dam alteration of erosion patterns and deposition dynamics within the sandbar region of the lower Mekong River. [ABSTRACT FROM AUTHOR]

Continuous hydrological records at the desired site with spatial and temporal coverage are essential to water resources management and flood prevention. Hydrological gauging stations and observations are limited at transboundary Himalayan River basins in developing countries. This study is carried out to estimate discharge at ungauged sites from donor catchments based on calibrated discharge. In the Tamakoshi River basin, Spatial Process in HYdrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV)-light, and Hydrologic Engineering Centre Hydrologic Modelling System (HEC HMS) models were used for hydrological simulation. The ungauged discharge estimated at Benighat is based on daily, monthly, and annual bases from upstream gauged stations. Statistical indices were obtained as NSE >0.62, R² >0.77, and PBias <26% in the simulation of these models. The low flow prediction is more reliable than the high flow prediction. The deviation in predicted flow mostly appeared in high flow periods. All the hydrological models simulated ungauged streamflow were similar in the flow pattern, and the estimated discharge at the ungauged receiver site was greater than at the donor site. Comparative simulations are better alternatives to estimate ungauged discharge than individual simulations. This research will support future reference to generate streamflow data at ungauged sites and can help fill the data recording gap at similar mountain river basins. [ABSTRACT FROM AUTHOR]

Climate change is expected to significantly impact temperature and precipitation, as well as snow accumulations and melt in mid-latitudes, including in the Baltic region, ultimately affecting the quantity and seasonal distribution of streamflow. This study aims to investigate the changes in the magnitude and timing of annual maximum discharge for 30 hydrological monitoring stations across Latvia from 1950/51 to 2021/22. Circular statistics and linear mixed effects models were applied to identify the strength of seasonality and timing. Trend analysis of the magnitude and timing of flood peaks were performed by using the Theil–Sen method and Mann–Kendall test. We analyzed regional significance of trends across different hydrological regions and country using the Walker test. Results indicate strong seasonality in annual flood peaks in catchments, with a single peak occurring in spring in the study sub-period of 1950/51–1986/87. Flood seasonality has changed over recent decades (i.e., 1987/88–2021/22) and is seen as a decrease in spring maximum discharge and increase in winter flood peaks. Alterations in annual flood occurrence also point towards a shift in flow regime from snowmelt dominated to mixed snow–rainfall dominated, with consistent changes towards the earlier timing of the flood peak, with a more or less pronounced gradation from west to east. Analysis shows that a significant trend of decrease in the magnitude and timing of annual maximum discharge was detected. [ABSTRACT FROM AUTHOR]

Climate change significantly impacts water cycle processes and water resource management in the upper Yellow River (UYR), China. Understanding the influence of meteorological factors on low-flow changes is crucial, but the optimal number of antecedent days and the specific contributions of different factors remain unclear. In this study, we use a structural equation model and path analysis to dissect the direct and indirect effects of selected meteorological factors (daily precipitation, P; average temperature, AT; average wind velocity, AWV; average relative humidity, ARH; and total radiation, TR) on four low-flow indices in the UYR. We employ data from 1958 to 2017, collected from six meteorological stations and eight hydrological stations above Lanzhou hydrological station. Our findings reveal that: (1) meteorological factors have varying direct and indirect impacts on low-flow changes at corresponding and cumulative scales. For instance, at the corresponding scale, P, AT, AWV, ARH, and TR have direct impacts of 42%, 54%, 74%, 79%, and 59%, respectively. At the cumulative scale, these values change to 67%, 59%, 67%, 64%, and 60%, respectively. (2) Cumulative effects of meteorological factors enhance the significance and goodness of fit of the analysis model, decreasing residual path coefficients and elevating the contribution of independent variables to the model. (3) The dominant components of meteorological factors affecting low-flow changes differ between corresponding and cumulative scales, explaining the variations in direct and indirect impacts. These insights are valuable for sustainable water resource management in drought-prone regions with water scarcity. [ABSTRACT FROM AUTHOR]

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Runoff forecasting is crucial for water resource management and flood safety and remains a central research topic in hydrology. Recent advancements in machine learning provide novel approaches for predicting runoff. This study employs the Competitive Adaptive Reweighted Sampling (CARS) algorithm to integrate various machine learning models into a data-driven rainfall–runoff simulation model. We compare the forecasting performance of different machine learning models to improve rainfall–runoff prediction accuracy. This study uses data from the Maduwang hydrological station in the Bahe river basin, which contain 12 measured flood events from 2000 to 2010. Historical runoff and areal mean rainfall serve as model inputs, while flood data at different lead times are used as model outputs. Among the 12 flood events, 9 are used as the training set, 2 as the validation set, and 1 as the testing set. The results indicate that the CARS-based machine learning model effectively forecasts floods in the Bahe River basin. Under the prediction period of 1 to 6 h, the model achieves high forecasting accuracy, with the average NSE ranging from 0.7509 to 0.9671 and the average R2 ranging from 0.8397 to 0.9413, though the accuracy declines to some extent as the lead time increases. The model accurately predicts peak flow and performs well in forecasting high flow and recession flows, though peak flows are somewhat underestimated for longer lead times. Compared to other machine learning models, the SVR model has the highest average RMSE of 0.942 for a 1–6 h prediction period. It exhibits the smallest deviation among low-, medium-, and high-flow curves, with the lowest NRMSE values across training, validation, and test sets, demonstrating better simulation performance and generalization capability. Therefore, the machine learning model based on CARS feature selection can serve as an effective method for flood forecasting. The related findings provide a new forecasting method and scientific decision-making basis for basin flood safety. [ABSTRACT FROM AUTHOR]

Frequent soil erosion disasters in the middle reaches of the Yellow River (MRYR) have a profound effect on the sediment load of the river. This paper addresses the intertwined effects of human activities and climate change on river runoff and sediment load. Therefore, runoff and sediment loads from hydrological stations along the main and tributary rivers within the MRYR were used. The Mann–Kendall (M–K) trend test and the double mass curve analysis, among other analytical tools, were used to examine the erosion patterns of these rivers from 1950 to 2022, as well as the main factors driving these changes. The results showed that the runoff depth of the Yan River tended to decrease, and there was a significant decrease in the mainstream and nine other tributaries, with a significant decrease in the sediment transport modulus for both the mainstream and tributaries. In the main river, human activities contributed between 69.99% and 94.69% to the runoff and between 88.52% and 98.49% to the sediment load, while in the tributaries, the contribution of human activities was greater. The annual runoff and annual sediment load in the MRYR showed a decreasing trend, with a discernible impact of human activities. The results of this research are of great significance for erosion control and the restoration of the ecological balance in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Climate change and human activities combine to alter river hydrology, thereby threatening the health of river ecosystems. Quantifying the impacts of climate change and human activities on ecological flow assurance is essential for water resource management and river ecological protection. However, fewer studies quantify the impacts of climate change and human activities on ecological flow assurance based on a complete set of frameworks. The present study introduces an integrated assessment framework designed to quantify the impacts of climate change and human activities on ecological flow security. The framework includes the following steps: (1) natural river runoff reconstruction utilizing a semi‐distributed hydrological model (SWAT), (2) calculation of the most suitable ecological stream flow of the watershed ecosystem by using the non‐parametric kernel density estimation method, (3) calculation of the safety and security levels under minimum ecological flow and appropriate ecological flow conditions in the watershed and (4) quantification of the influences of climate change and human activities on the security of ecological flow in the watershed through the application of a quantitative attribution method. The impact of climate change and human activities on the ecological flow assurance level was analysed using three hydrological stations in Xiangtan, Hengyang and Laobutou, which are the main tributaries of the Xiangjiang River Basin, as a case study. The findings indicated a substantial decrease in ecological flow assurance levels across the basin during the period of human impact (1991–2019). The quantitative assessment results suggest that human activities predominantly drive the degradation of ecological flow assurance throughout the period of human impact, accounting for 57.05% of the total impact. Extensive gradient reservoir scheduling and anthropogenic water withdrawals were the main factors contributing to the degradation of ecological flow assurance in the study basin. The methodology and findings presented in this study offer insights into the evolutionary characteristics and driving forces behind ecological flow security in a dynamic environment. Furthermore, they establish a scientific foundation for local water resource management and river ecosystem protection. [ABSTRACT FROM AUTHOR]

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Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office 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 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]

Jiamusi is situated in Heilongjiang Province, China, in the center of the Sanjiang Plain. The 1980s' overplanting of paddy fields resulted in a decrease in groundwater levels, scarcity of groundwater resources, and frequent earth collapses. Examining and safeguarding the groundwater resources in this region has emerged as a crucial subject. In light of this, this paper uses the remote sensing water balance method and the SWAT distributed hydrological model to calculate groundwater resources in the Jiamusi area. It also conducts scientific experiments by examining various factors, including rainfall, the degree of water supply, soil type, and land use. The measured monthly runoff of Jiamusi City's Tongjiang and Fuyuan City's hydrology stations was utilized to establish the model parameters for the SWAT model. A preliminary assessment of the distribution features of shallow groundwater in the Jiamusi area is conducted using the two methodologies mentioned above, and the following results are reached: (1) Tongjiang Hydrological Station and Fuyuan Hydrological Station both had good runoff modeling results, with R2 and NS values of 0.81, 0.77, and 0.77, 0.75, respectively. (2) The SWAT model works well for assessing groundwater resources. Between 2010 and 2016 (two preheating years), Jiamusi's average groundwater recharge was 61.03 × 108 m3, with a recoverable amount of 27.4 × 108 m3. (3) Based on the remote sensing water balancing approach, the average exploitable quantity of groundwater recharge in the Jiamusi area between 2008 and 2016 is 23.94 × 108 m3, while the average recharge in the area is 53.2 × 108 m3. (4) The Jiamusi metropolitan area is the core of the groundwater phreatic reservoir water reserves, which progressively decline in both the northeast and southeast directions. It falls to the southwest as Fuyuan City's center. The Songhua River's main stream area near Tongjiang City has the least volume of water reserves in the phreatic layer, and the area's groundwater reserves converge to the southeast and northwest, where surface water makes up the majority of the water resources. [ABSTRACT FROM AUTHOR]

Human activities are the main reasons for reducing sediment transport in the Yellow River, with thousands of check dams playing a crucial role. Quantifying the sediment reduction efficiency and analyzing the future development of check dams can help ecological construction and management in the Yellow River basin. This study developed a calculation method for sediment reduction by check dams and calculated the annual sediment reduction, and further estimated the sediment reduction efficiency at different periods. In addition, this study also analyzed the construction intensity and proposed construction strategies of check dams in the Yellow River basin. The results show that from 1952 to 2011, the cumulative sediment reduction of check dams in the middle reaches of the Yellow River was 8056 million tons, the annual sediment reduction varied from 0 to 403 million tons, with a multiyear average of 134 million tons. Among the major tributaries in the middle reaches of the Yellow River, the sediment reduction efficiency of check dams ranged from 0.51% to 76.88% due to the ecological management of slopes and the loss of sediment retention capacity by small‐ and medium‐sized check dams, the sediment reduction efficiency from 1980 to 1999 was higher than from 2000 to 2011. Following the analysis of the suitability of check dam construction, we calculated the potential and intensity of check dam construction in the Yellow River basin. The intensity of check dam construction exceeded 50% in the TDG‐LM region (Toudaoguai hydrological station to Longmen hydrological station) in the middle reaches of the Yellow River. We analyzed the siltation situation of large‐sized check dams and proposed strategies for the construction and management of check dams. [ABSTRACT FROM AUTHOR]

The Colorado River has experienced a significant streamflow reduction in recent decades due to climate change, resulting in pronounced hydrological droughts that pose challenges to the environment and human activities. However, current models struggle to accurately capture complex drought patterns, and their accuracy decreases as the lead time increases. Thus, determining the reliability of drought forecasting for specific months ahead presents a challenging task. This study introduces a robust approach that utilizes the Beluga Whale Optimization (BWO) algorithm to train and optimize the parameters of the Regularized Extreme Learning Machine (RELM) and Random Forest (RF) models. The applied models are validated against a KNN benchmark model for forecasting drought from one- to six-month ahead across four hydrological stations distributed over the Colorado River. The achieved results demonstrate that RELM-BWO outperforms RF-BWO and KNN models, achieving the lowest root-mean square error (0.2795), uncertainty (U95 = 0.1077), mean absolute error (0.2104), and highest correlation coefficient (0.9135). Also, the current study uses Global Multi-Criteria Decision Analysis (GMCDA) as an evaluation metric to assess the reliability of the forecasting. The GMCDA results indicate that RELM-BWO provides reliable forecasts up to four months ahead. Overall, the research methodology is valuable for drought assessment and forecasting, enabling advanced early warning systems and effective drought countermeasures. [ABSTRACT FROM AUTHOR]

This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018. The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow hydrological stations at 2737, 2805, and 2919 m a.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water contents, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. These devices are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates, and redistributed snow amounts during several blowing snow events. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high-altitude meteorological and snow processes and are collected to support the scientific community, local stakeholders, and the interested public, as well as operational warning and forecasting services. The data are publicly available from the GFZ Data Services repository: 10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024). [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to investigate the spatial and temporal changes of runoff and sediment load in the Kuye River Basin, to quantify the contribution of climate change and human activities to variation of runoff and sediment load, and to provide scientific basis for the rational use of water resources and the optimal allocation of soil and water conservation measures in the Kuye River Basin. [Methods] Based on the runoff, sediment load and rainfall data of Xinmiao, Wangdaohengta, Shenmu and Wenjiachuan hydrological stations in Kuye River Basin from 1960 to 2020, the spatial and temporal variation characteristics of runoff and sediment load were analyzed by the linear trend method, Mann-Kendall trend test and Pettitt mutation test, and the double mass curve was used to quantitatively analyze the contribution rate of climate change and human activities to runoff and sediment load variation in different periods. [Results] Annual runoff and sediment load in the Kuye River Basin showed a significant decreasing trend from 1960 to 2020 (p<0.01). The runoff reduction rates of Xinmiao, Wangdaohengta, Shenmu and Wenjiachuan hydrological stations were 2.4 million m³/a, 3.4 million m³/a, 8.1 million m³/a, 10.6 million m³/a, the reduction rates of sediment load were 0.438 million t/a, 0.693 million t/a, 1.603 million t/a and 2.721 million t/a, respectively. The annual runoff and sediment load of each hydrological station changed abruptly both in 1979 and 1996, so the runoff and sediment load sequences could be divided into the reference period (1960-1979, P1) and the change period (1980-1996, P2;1997-2020, P3). During the study period, the average annual runoff depth and sediment load modulus of different sub-basins showed a decreasing trend, but there were differences in space. [Conclusion] The increasing human activities (water conservancy and soil conservation measures, coal mining, etc.) are the dominant factors on variation of runoff and sediment load in the Kuye River Basin. Compared with the base period, the runoff and sediment load of each hydrological station decreased in both P2 and P3 periods, and reduction was more intense in P3 period. In the P2 and P3 periods, the contribution of human activities to the reduction of runoff sediment transport was higher than that of rainfall changes. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the change characteristics and driving factors of runoff and sediment load in the Weihe River Basin, and to provide scientific basis for water resource management and ecological environment construction in the Weihe River Basin. [Methods] Based on the measured precipitation, runoff and sediment load data of Huaxian and Zhuangtou hydrological stations in the Weihe River Basin from 1957 to 2019, the trend and the change-point year of annual precipitation, runoff and sediment load were analyzed by modern mathematical statistical. The contribution rates of runoff and sediment load reduction in Weihe River Basin were calculated. The main influencing factors of human activities on runoff and sediment load changes in the Weihe River were discussed. [Results] There was a strong decreasing trend in runoff and sediment load of Weihe River Basin from 1957 to 2019 (p<0.01), and the annual precipitation fluctuated randomly and had no obvious change trend. Abrupt year analyses showed that more rapid declines for runoff began in 1990 (p<0.01), and sediment load began in 1996 (p<0.01). The contribution rates of precipitation to the reduction of runoff and sediment load were 19% and 17%, respectively. The contribution rates of runoff and sediment load reduction caused by human activities were 81% and 83%, respectively. [Conclusion] Human activities are the main reason for the significant reduction of runoff and sediment load in the Weihe River Basin. [ABSTRACT FROM AUTHOR]

Accurate runoff prediction is crucial for watershed water resource management, flood prevention, and hydropower station scheduling. Data-driven models have been increasingly applied to runoff prediction tasks and have achieved impressive results. However, existing data-driven methods may produce unreasonable predictions due to the lack of prior knowledge guidance. This study proposes a multivariate runoff prediction model that couples knowledge embedding with data-driven approaches, integrating information contained in runoff probability distributions as constraints into the data-driven model and optimizing the existing loss function with prior probability density functions (PDFs). Using the main stream in the Yellow River Basin with nine hydrological stations as an example, we selected runoff feature factors using the transfer entropy method, chose a temporal convolutional network (TCN) as the data-driven model, and optimized model parameters with the IPSO algorithm, studying univariate input models (TCN-UID), multivariable input models (TCN-MID), and the coupling model. The results indicate the following: (1) Among numerous influencing factors, precipitation, sunshine duration, and relative humidity are the key feature factors driving runoff occurrence; (2) the coupling model can effectively fit the extremes of runoff sequences, improving prediction accuracy in the training set by 6.9% and 4.7% compared to TCN-UID and TCN-MID, respectively, and by 5.7% and 2.8% in the test set. The coupling model established through knowledge embedding not only retains the advantages of data-driven models but also effectively addresses the poor prediction performance of data-driven models at extremes, thereby enhancing the accuracy of runoff predictions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation 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.)

In order to safeguard the health of river ecosystems and maintain ecological balance, it is essential to rationally allocate water resources. This study utilized continuous runoff data from 1967 to 2020 at the Zhouqu Hydrological Station on the Bailong River. Five hydrological methods, tailored to the hydrological characteristics of the Zhouqu hydrological cross-section, were employed. These methods included the improved dynamic calculation method, the NGPRP method, the improved monthly frequency computation method, the improved RVA method, and the Tennant method. Ecological flow calculations were conducted to determine the ecological flow, with analysis carried out through the degree of satisfaction, economic benefits, and the nonlinear fitting of the GCAS model. We established an ecological flow threshold and early warning program for this specific hydrological cross-section. Ecological flow values calculated using different methods for each month of the year were compared. The improved RVA method and Tennant method resulted in small values ranging from 4.05 to 36.40 m3/s and 7.65 to 22.94 m3/s, respectively, with high satisfaction levels and economic benefits, but not conducive to ecologically sound development. In contrast, the dynamic calculation method, NGPRP method, and improved monthly frequency calculation method yielded larger ecological flow values in the ranges of 21.79–97.02 m3/s, 23.90–137.00 m3/s, and 28.50–126.00 m3/s, respectively, with poor fulfillment and economic benefits. Ecological flow thresholds were determined using the GCAS model, with values ranging from 16.72 to 114.58 m3/s during the abundant water period and from 5.03 to 63.63 m3/s during the dry water period. A three-level ecological warning system was proposed based on these thresholds, with the orange warning level indicating optimal sustainable development capacity for the Zhouqu Hydrological Station. This study provides valuable insights into the scientific management of water resources in the Bailong River Basin to ensure ecological security and promote sustainable development. [ABSTRACT FROM AUTHOR]

This paper presents an innovative multivariate forecasting application for hydrological variables, using an online model based on backpropagation (BP) neural networks. The effectiveness of this approach is verified by using the Nonlinear Autoregressive with Exogenous Inputs (NARX) and the linear Autoregressive with Exogenous Inputs (ARX) methods. The proposed methodology is implemented in two hydrological stations in the department of Chocó, Colombia, located along the Atrato River. The BP neural network structure is applied to establish correlations between water level, flow, and precipitation. This structure is designed to handle the dynamic complexity of the hydrological variables and their interconnections. In addition, a short‐term water level prediction is developed for incorporation into a flood early warning system. Validation of the approach is performed by comparing various estimation errors using regression metrics such as root mean square error (RMSE), relative mean square error (MSRE), mean absolute error (MAE), relative mean absolute error (MARE), and Nash–Sutcliffe efficiency coefficient (NSE). The computation time and the accuracy of the estimated weights versus real data are also considered. The results show that the proposed structure is the most suitable for accurately predicting water levels. It is shown that the backpropagation neural network structure of the proposed model is more effective for water level prediction. This is observed when evaluating the real data compared to the estimated data, obtaining values corresponding to the regression metrics. In particular, for output 2, the RMSE is 0.0742. Compared to the NARX model (RMSE of 0.0985) and the ARX model (RMSE of 0.1265), it is observed that the backpropagation neural network model improves the estimation by 32.6 over the NARX model and by 70.4% over the ARX model. As for the NSE coefficient for output 2 of the model, the backpropagation neural network is the closest to 1 compared to the other two models. For output 2, the backpropagation model obtains a value of 0.9966, the NARX model 0.9958, and the ARX model 0.9914. Another evaluation metric applied to this model is the Kling–Gupta efficiency coefficient (KGE), which is used to evaluate the performance of hydrological models. For the output of the two hydrological stations located in the Atrato River, we observed that for the backpropagation model a value of 0.9992 was obtained, for the NARX model a value of 0.9988 was obtained, and for the linear ARX model with nonlinear structure a value of 0.9984 was obtained. This indicates that the backpropagation neural network model provides a better estimate, as its value is closer to 1. Furthermore, the computational time executed in the algorithm of the three proposed nonlinear models is calculated, where the backpropagation obtains a calculated value of 350.9 microseconds, for the NARX model the computational time is 5908.7 microseconds and for the ARX nonlinear model, the computational time is 5172.6 microseconds. The model put forward demonstrates its effectiveness in accurately predicting water levels at two key hydrological stations along Colombia's Atrato River. By incorporating intricate interactions among hydraulic structures and dynamic hydrological conditions, it enhances prediction accuracy. This not only aids in disaster risk management but also streamlines planning processes, particularly within flood early warning systems. [ABSTRACT FROM AUTHOR]

Cold areas are primary runoff generation areas of China′s major rivers. The accuracy of their runoff forecasts directly affects the scheduling of downstream water conservancy projects and the water resources management. This study aims at the problem that traditional conceptual hydrological models rarely consider the combined effects of snowmelt and soil freezing and thawing processes on the runoff process. Based on the Xin′anjiang model and using precipitation and air temperature as driving data, this study proposes a conceptual soil freeze- thaw module based on the equivalent soil temperature and soil freeze-thaw characteristic curve to describe the runoff process under the joint influence of snowmelt and soil freeze-thaw, and a Xin′anjiang model considering snowmelt runoff and soil freeze-thaw is developed. Taking the watershed above the Ganzi hydrological station in the Upper Yalongjiang River basin as a typical research area to carry out the daily streamflow simulation during the snowmelt period of 2017—2022. The results show that, compared with the HBV model, the Xin′anjiang model with snowmelt runoff and soil freeze- thaw processes can significantly improve the accuracy of streamflow simulation of the Ganzi station during the snowmelt period. The median Nash efficiency coefficient increases from 0. 69 to 0. 83, and the median correlation coefficient increases from 0. 88 to 0. 92. The Xin′ anjiang model improved in this study that considers snowmelt and soil freezing and thawing can provide support for hydrological forecasting in cold regions of China. [ABSTRACT FROM AUTHOR]

In recent years, the runoff of numerous rivers has experienced substantial changes owing to the dual influences of climate change and human activities. This study focuses on the Lixian hydrological station's controlled basin, located in the upper reaches of the Jialing River in China. The objective is to assess and quantify the impacts of human activities and climate change on runoff variations. This study analyzed runoff variations from 1960 to 2016 and employed the Soil and Water Assessment Tool (SWAT) model, the long short-term memory (LSTM) model, and eight Budyko framework formulations to assess factors influencing runoff. Additionally, it used the patch-generating land use simulation (PLUS) and SWAT models to simulate future runoff scenarios under various conditions. The results indicate the following. (1) The study area has witnessed a significant decline in runoff (p < 0.01), while potential evapotranspiration shows a significant upward trend (p < 0.01). Precipitation displays a nonsignificant decreasing trend (p > 0.1). An abrupt change point in runoff occurred in 1994, dividing the study period into baseline and change periods. (2) The Budyko results reveal that human activities contributed 50% to 60% to runoff changes. According to the SWAT and LSTM models, the contribution rates of human activities are 63.21% and 52.22%, respectively. Human activities are thus identified as the predominant factor in the decline in runoff. (3) Human activities primarily influence runoff through land cover changes. Conservation measures led to a notable increase in forested areas from 1990 to 2010, representing the most significant change among land types. (4) Future land use scenarios suggest that the highest simulated runoff occurs under a comprehensive development scenario, while the lowest is observed under an ecological conservation scenario. Among the 32 future climate scenarios, runoff increases significantly with a 10% increase in precipitation and decreases substantially with a 15% reduction in precipitation. These findings underscore the significant impact of human activities and climate change on runoff variations in the upper reaches of the Jialing River, highlighting the importance of incorporating both factors in water resource management and planning. [ABSTRACT FROM AUTHOR]

The Mann–Kendall (MK) trend test, Innovative Trend Analysis (ITA), double-ITA (D-ITA), triple-ITA (T-ITA), and Innovative Triangular Trend Analysis (ITTA) were used to analyze long-term trends in the annual and seasonal streamflow of the Tuotuohe and Zhimenda hydrological gauging stations in the Source Region of the Yangtze River (SRYR). The traditional MK test provides the average trends, while the other methods used in this study provide the graphical illustrations and trend stability (monotonic/non-monotonic). For example, the Tuotuohe station during summer using MK showed (0.05 m3/sec/year), and the ITA showed the monotonic increasing trend (1.12 m3/sec/year). In contrast, the ITTA showed unstable (monotonic and non-monotonic) trends while the highest trend magnitude was found from the 2nd to 5th sub-time series (6.6 m3/sec/year) followed by 1st to 5th sub-time series (5.51 m3/sec/year). The ITTA also showed the non-monotonic decreasing trend (-1.09 m3/sec/year) from 1st to 2nd sub-time series. The D-ITA showed the monotonic increasing trend from 1st to 2nd sub-time series (0.83 m3/sec/year). The non-monotonic increasing trend from 2nd to 3rd sub-time series (0.4 m3/sec/year) and T-ITA showed the non-monotonic trend for 1st to 2nd and 2nd to 3rd sub-time series (0.62 and 1.45 m3/sec/year, respectively), whereas the monotonic trend for 3rd to 4th sub-time series (14.94 m3/sec/year). Similarly, there are more instabilities and fluctuations in trend magnitudes found in the ITTA compared to D-ITA, T-ITA, and ITA. At the same time, the MK only provides the average trend values for a given time series. This showed that the ITTA method is better for understanding the trends and fluctuations in any basin, and the traditional MK test cannot detect these fluctuations. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) 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.)

Climate change is affecting the snow cover conditions on a global scale, leading to changes in the extent and duration of snow cover as well as variations in the start and end of snow cover seasons. These changes can have a paramount impact on runoff and water availability, especially in catchments that are characterized by nival runoff regimes, e.g., the Syr Darya in Central Asia. This time series analyses of daily MODIS snow cover products and in situ data from hydrological stations for the time series from 2000 through 2022 reveal the influences of changing snow cover on the runoff regime. All catchments showed a decrease in spring snow cover duration of −0.53 to −0.73 days per year over the 22-year period. Catchments located farther west are generally characterized by longer snow cover duration and experience a stronger decreasing trend. Runoff timing was found to be influenced by late winter and spring snow cover duration, pointing towards earlier snowmelt in most of the regions, which affects the runoff in some tributaries of the river. The results of this study indicate that the decreasing snow cover duration trends lead to an earlier runoff, which demands more coordinated water resource management in the Syr Darya catchment. Further research is recommended to understand the implications of snow cover dynamics on water resources in Central Asia, crucial for agriculture and hydropower production. [ABSTRACT FROM AUTHOR]

In this study, we applied the penalized maximum F test (PMF) method in the Relative Homogenization test V4 (RHtestV4) package without reference series to improve the reliability of monthly mean long-term sea surface salinity (SSS) data. The data were obtained from six coastal hydrological stations along the coast of the northern SCS, spanning from January 1960 to December 2018. Based on the detailed metadata, taking the influence of regional climate change factors into full account, the inhomogeneity of these SSS data was detected and adjusted. The findings indicate that all six coastal hydrological stations exhibited breakpoints, and among them, 22 breakpoints were identified in total, which were the causes of inhomogeneity in the monthly SSS time series. The primary factors contributing to these breakpoints were human-related and, specifically, related to changes in instruments. The average adjustment of monthly quantile matching (QM) of the salinity series ranged from around −4.25 to 3.33‰. The quality of the adjusted annual mean SSS time series was greatly improved. Notably, the annual mean SSS of the NZU and ZPO coastal hydrological stations in Guangdong Province exhibited a significant downward trend, indicating a trend of seawater freshening. Conversely, the WZU, BHI, HKO and QLN coastal hydrological stations in the Guangxi and Hainan coastal areas displayed an upward trend in SSS. This study fills the gap in current research on inhomogeneity detection and adjustment of SSS along the coast of the northern SCS. It also provides reliable and accurate first-hand information for research on climate change and marine science along the coast of the northern SCS. [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]

Earlier, hydrological simulation calibration and validation relied on flow observations at hydrological stations, but multisource observations changed the basin hydrological simulation from single-flow validation to multivariate validation, including evaporation, soil water, and runoff. This study used the Soil and Water Assessment Tool (SWAT) distributed hydrological model to simulate and investigate hydrological processes in the Jinghe River Basin in China. After a single-station, single-variable calibration using flow observation data at the Zhangjiashan Hydrological Station, multisource data were used to validate actual evaporation, soil water, and runoff. Using the flow station data from Zhangjiashan station for parameter calibration and validation, the simulated values of R2, NSE, and KGE were all above 0.64, the PBIAS was within 20%, and the values of all the metrics in the calibration period were better than those in the validation period. The results show that the model performed satisfactorily, proving its regional applicability. Qingyang, Yangjiaping, and Zhangjiazhan stations had R2, NSE, and KGE values above 0.57 and PBIAS within 25% during regional calibration, considering spatial variability. Additionally, simulation accuracy downstream increased. R2, NSE, and KGE were above 0.50, and PBIAS was within 25% throughout validation, except for Qingyang, where the validation period was better than the calibration period. The Zhangjiashan station monthly runoff simulation improved after regional calibration. Runoff validation performed highest in the multivariate validation of evaporation–soil water–runoff, followed by actual evaporation and soil water content in China. The evaluation results for each hydrological variable improved after additional manual calibration. Multivariate verification based on multisource data improved the hydrological simulation at the basin scale. [ABSTRACT FROM AUTHOR]

The critical role of macrophytes in aquatic environments cannot be overstated, but little attention has been paid to macrophytes in Moroccan rivers. This study aims to investigate the environmental factors that affect the presence and distribution of macrophytes in the Hydrological Basin of Sebou (HBS) in Morocco. The study focused on 39 hydrological stations, distributed across the five hydro‐ecoregions of the basin. The results show that the number of aquatic species is limited and significantly lower than that of riparian species, which are more diverse. Analysis of similarity (ANOSIM) revealed that there is a significant difference in the riparian species communities between the various hydro‐ecoregions (R = 0.1853, Bonferroni corrected α = 0.0022). However, when only aquatic species were considered, ANOSIM showed no significant difference (R = 0.05524, Bonferroni corrected α = 0.1453), and the results were confirmed by the PERMANOVA test. Furthermore, ANOSIM did not reveal a clear difference in the composition of aquatic species between stations with low and high nutrients (R = 0.01348, p = 0.4245). The Student's t‐test also showed no significant difference in the variation of riparian and aquatic species numbers between the two groups (all species combined: t = 0.1639, p = 0.8723; riparian species: t = 0.4740, p = 0.6434; aquatic species: t = 0.9869, p = 0.3417). These findings highlight the need for further research into environmental factors limiting aquatic species diversity and nutrient thresholds affecting macrophyte abundance. Understanding these elements is crucial for addressing species distribution constraints and elucidating nutrient dynamics that influence macrophyte populations, enriching ecosystem management and conservation strategies. [ABSTRACT FROM AUTHOR]

In this study, a novel nonlinear segmental runoff ensemble forecast model based on the Bayesian model averaging (BMA) algorithm (NLTM-BMAm(P-III)) is proposed based on multimodel ensemble prediction for forecasting monthly runoff and quantifying forecast uncertainty. Four nonlinear time series models were used as ensemble members, and runoff segmented intervals were divided based on P-III type hydrological frequency curves. On this basis, the BMA algorithm was used to obtain the weight sets of each interval after the Box‒Cox transformation. Finally, the mean and probability forecasts were obtained using the weighted average method and the Monte Carlo method. The model was applied to monthly runoff forecasts at eight hydrological stations in the Hei River Basin and two hydrological stations in the Wei River Basin; and compared with the whole-segment simple averaging model NLTM-SMA, the whole-segment Bayesian averaging model NLTM-BMA1 and the segmented Bayesian averaging model with normal distribution partitioning NLTM-BMAm(Normal). The results show that (1) the BMA algorithm yields more reliable forecasts than the SMA algorithm, (2) Segmentation criteria appropriate for the runoff distribution can improve the forecasting accuracy, which would otherwise be reduced, and (3) Compared with the NLTM-SMA and NLTM-BMA1 models, the NLTM-BMAm(P-III) model yields a higher CR value, demonstrating that the segmented ensemble forecasting model can improve the accuracy of probability prediction by considering the diversity of ensemble members. Additionally, the BMA algorithm has good applicability in the segmented ensemble model. The model provides a new method for medium- and long-term runoff forecasting. [ABSTRACT FROM AUTHOR]

Riverine nitrogen is a pivotal determinant influencing water quality in inland and coastal waters. Despite the recognized utility, no spatially-explicit data on riverine nitrogen yield is available for large parts of the world, thus hindering our ability to identify the contributors to riverine nitrogen and understand aquatic nitrogen cycling. To fill the data gap for the United States, here we (1) compiled 294,996 total nitrogen (TN), 225,827 nitrate (NO3−), 204,015 ammonium (NH4+), and 158,837 total organic nitrogen (TON) concentrations, with concurrent streamflow data, across the Conterminous United States (CONUS), (2) estimated riverine nitrogen loads for over 1,800 hydrological stations, (3) derived the spatial distribution of annual riverine nitrogen yield by leveraging river and catchment connectivity information contained in the National Hydrography Dataset plus (NHDPlus), and (4) characterized nonpoint-source TN loads by excluding point-source loads. This new spatial dataset quantifies spatial sources of nitrogen yield from point and non-point sources (e.g., up to 36% from point sources across the U.S.) and serves as ground-truthing to validate water quality models. [ABSTRACT FROM AUTHOR]

In this study, spanning from 1938 to 2020, the hydrological changes in the Volga River and their repercussions on the Caspian Sea level were examined. Analysis reveals a correlation between high Volga River runoff and increased atmospheric precipitation in its basin. However, in recent years (2005–2020), a significant decline in the runoff coefficient at the Verkhneye Lebyazhie hydrological station, attributable to climate warming surpassing global temperature anomalies, has been observed. This warming's impact on river flow and sea level was quantified, resulting in a 133 cm decrease in sea level from 1977 to 2020. Notably, while, historically, Caspian Sea level changes mirrored Volga River runoff fluctuations until 2005, since 2006, the sea level has markedly dropped, decoupling from river runoff variations. Comparison with recent studies suggests that altered wind characteristics over the Caspian Sea, influencing surface evaporation, may have significantly contributed to this rapid sea level decline in recent years. [ABSTRACT FROM AUTHOR]

The Three Gorges Project is the largest hydraulic hub project in the world, and its hydrological management has altered the hydrological environment of the reservoir area, affecting the carbon emission and absorption of the reservoir water. In this study, representative hydrological stations in the Three Gorges Reservoir area were selected as research sites to monitor the CO2 and CH4 fluxes of the reservoir water and nine environmental factors during the drainage and impoundment periods in 2022. The study aimed to explore the mechanisms of hydrological management and environmental factors on greenhouse gas emissions. The results showed that the mean CO2 fluxes of the reservoir water during the drainage and impoundment periods were (103.82 ± 284.86) mmol·m−2·d−1 and (134.39 ± 62.41) mmol·m−2·d−1, respectively, while the mean CH4 fluxes were (1.013 ± 0.58) mmol·m−2·d−1 and (0.571 ± 0.70) mmol·m−2·d−1, respectively, indicating an overall "carbon source" characteristic. Through the evaluation of the characteristic importance of environmental factors, it was found that the main controlling factors of CO2 flux during the drainage period were total phosphorus (TP) and chlorophyll a (Chl_a), while total nitrogen (TN) was the main controlling factor during the impoundment period. Dissolved organic carbon (DOC) was the main controlling factor of CH4 flux during the different periods. Based on these findings, a "source-sink" mechanism of CO2 and CH4 in the Three Gorges Reservoir water under reservoir regulation was proposed. This study is of great significance for revealing the impact of reservoir construction on global ecosystem carbon cycling and providing scientific support for formulating "emission reduction and carbon sequestration" plans and achieving "dual carbon" goals. [ABSTRACT FROM AUTHOR]

Human activities, particularly the regulation of hydropower stations, have profoundly altered river flow patterns. While studies have extensively assessed the impact of large or multi-year regulated hydropower stations on hydrological regimes using indicators of hydrological alteration (IHA) and range of variability approach (RVA), the impact of daily regulation hydropower stations has received comparatively less attention. This study aims to evaluate the influence of daily regulation hydropower stations on hydrological regime changes, focusing on the upper Yellow River region in China. Using daily runoff data from 1954 to 2020 at the Guide station, the study compares the impacts of multi-year regulated (Longyangxia) and daily regulation (Laxiwa and Nina) hydropower stations. The Mann-Kendall test showed that 27 out of 32 Indicators of Hydrological Alteration (IHAs) had significant trends under Longyangxia operation, which reduced to 18 IHAs with the inclusion of daily regulation stations. The Range of Variability Approach (RVA) revealed that only 46.87% of IHAs exhibited high alteration from the natural regime when daily regulation was considered, down from 75.00% with Longyangxia alone. This suggests that daily regulation can mitigate the negative impacts of multi-year regulation, potentially enhancing the river's eco-hydrological health. [ABSTRACT FROM AUTHOR]