Hydrological Stations Research Articles

Spatial–temporal variations of sediment transport rate and driving factors in Shule River Basin, northwest China

The sediment content and transport rate of rivers are crucial indicators reflecting soil erosion, water quality, and water resource management in a region. Studying changes in river sediment transport rates within a basin is essential for evaluating water quality, restoring water ecosystems, and implementing soil and water conservation measures. This study focused on the Shule River Basin and utilized various methods such as moving average, cumulative anomaly, Mann–Kendall mutation test, Mann–Kendall (M–K) trend test, Sen’s slope estimation, Correlation analysis, wavelet analysis, R/S analysis, ARCGIS10.7 interpolation, non-uniformity coefficient, and concentration to analyze data from hydrologic stations at Changmapu (CMP), Panjiazhuang (PJZ), and Dangchengwan (DCW). The research examined the temporal and spatial characteristics of sediment transport rates and identified key driving factors. Findings revealed significant increases in annual sediment transport rates at CMP and PJZ by 12.227 and 4.318 kg/s (10a)−1, respectively, while DCW experienced a decrease of 0.677 kg/s (10a)−1. The sediment transport rate of the three stations had a sudden change around 1994. The average annual sediment transport rates displayed distinct cycles, with CMP, PJZ, and DCW showing cycles of 51a, 53a, and 29a respectively. Additionally, while CMP and PJZ exhibited a continuous upward trend in sediment transport rates, DCW showed a consistent decline. The annual average sediment transport rates of CMP, PJZ, and DCW were 1305.43 kg/s, 810.06 kg/s, and 247.80 kg/s, respectively. These research findings contribute to enhancing the comprehension of sediment dynamics in the arid region of northwest China and offer a theoretical basis for the restoration and management of ecological environments in similar areas in the future.

Human Activities Impacts on Runoff and Ecological Flow in the Huangshui River of the Yellow River Basin, China

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.

Identification of the Runoff Evolutions and Driving Forces during the Dry Season in the Xijiang River Basin

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.

The pulse of Yarlung Tsangpo River: Ultra-high-resolution insights into the flow and sediment

Traditional monitoring techniques of flow and sediment are inadequate for the sparsely populated, the rivers in the Qinghai-Tibetan Plateau region which are experiencing amplified climate change. Inspired by the Euchiloglanis fish, this study developed a unique self-contained hydrodynamic observation platform to withstand the high velocities of mountain rivers typically encountered in mountain rivers. The platform, equipped with sensors, was successfully deployed in the Yarlung Tsangpo River, collecting ultra-high-resolution data on flow and sediment over a period of nearly two months. Performance assessments showed that the median value of discharge per unit width measured and suspended sediment concentration by this system were basically consistent with those daily measurements from the hydrological station. Meanwhile, the superiority of the system was reflected in the more significant daily average change, especially at the flooding period. The data unveiled significant minute-level hydrological fluctuations and a complex interplay of factors affecting the river’s flow and sediment transport. A wavelet coherence analysis revealed asynchrony changes in short-period flow and sediment variables. Persistent short-period and coherent long-period signals are significantly displayed during flood events. The platform promises future applications, such as automated benthic observations and replacing traditional ’lead-line’ methods. Despite acknowledging system limitations of underwater attitude, burying tendency, and real-time data transmission capabilities, the study highlighted the crucial role of this system in understanding and managing river dynamics, particularly amid escalating environmental threats.

Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023

Abstract. 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: https://doi.org/10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024).

Daily Runoff Prediction Based on FA-LSTM Model

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R2) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R2 and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R2 and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction.

Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data

Streamflow simulation is crucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models and machine learning algorithms are the mainstream tools for streamflow simulation. However, their inherent limitations, such as time-consuming and large data requirements, make achieving high-precision simulations challenging. This study developed a hybrid approach to simultaneously improve the accuracy and computational efficiency of streamflow simulation, which integrates Block-wise use of the TOPMODEL (BTOP) model into the eXtreme Gradient Boosting (XGBoost), i.e., BTOP_XGB. In this approach, BTOP generates simulated streamflow using the Latin hypercube sampling algorithm instead of the time-consuming calibration algorithms to reduce computational costs. Then, XGBoost combines BTOP simulated streamflow with multi-source data to reduce simulation errors. In which, serval input variable selection algorithms are employed to choose relevant inputs and remove redundant information for model. The hybrid approach is validated and compared with a standalone model at three hydrological stations in the Jialing River basin, China. The results show that the performance of BTOP_XGB is significantly better than the BTOP and XGBoost models. The NSE of BTOP_XGB at Beibei, Xiaoheba, and Luoduxi stations increases by 54%, 21%, and 83%, respectively. Meanwhile, the computational time of BTOP_XGB is saved by >90% compared to the original calibrated BTOP. BTOP_XGB is less affected by parameter sample sizes and data amounts, demonstrating the robustness of the hybrid model. This study simplifies the complexity of the hydrological model and enhances the stability of machine learning, jointly improving the reliability of streamflow simulation. The hybrid approach provides a potential shortcut for streamflow simulation over basins with large areas or limited observed data.

Rapid identification of back-flow in three distributaries of Jingjiang Reach, middle Yangtze River, China

Unusual back-flow events occurred in 2015 and 2017 at the near-mainstream ends of the three distributaries of the Jingjiang Reach, middle Yangtze River, China. It is presently challenging to precisely quantify back-flow discharges at river distributary ends where normal-flow discharges are measured chronically. This problem is more complicated at the ends of the three Jingjiang distributaries, each of which is more than 100 km long, and thus favors the generation of different back-flow forms. We use empirical formulae based on the Saint-Venant Equation and the Fall Index Method to quantify impacts on flow discharges at the two ends of each distributary of three key hydrodynamic variables: (1) water discharge at Zhicheng hydrological station, (2) total discharge from the four southwest tributary rivers, and (3) water levels at the two ends of the distributaries. We use the variable control approach to identify the contribution ratios of natural and human driving factors to flow discharges at the two ends of the distributaries during five historical stages. The empirical formulae provide reasonable results, with high values of correlation coefficients (R2 = 0.84 ∼ 0.98) obtained, demonstrating that the total water discharge from the four southwest tributary rivers is negligible. These formulae perform well in reproducing observed flow discharges at the two ends of the distributaries, especially the maximum discharges and cumulative volumes for normal-flow and back-flow events. Three charts are devised for different flow conditions at the two ends, from which back-flow events can be rapidly identified. Before impoundment of the Three Gorges Dam (TGD), riverbed adjustment caused by the lower Jingjiang Cutoff Projects and the Gezhou Dam contributed the most to flow discharge changes (≥50 %) at the near-mainstream ends of the distributaries. Afterwards, precipitation dominated (>60 %). In the future, back-flow events are expected only to occur at Mituosi (i.e., Taipingkou) and Nanxian hydrological stations, for which optimal reservoir operation and water resource utilization could remedy the back-flow crisis. Use of climate models and more advanced technologies such as remote sensing and GIS are recommended to enhance the reliability of predictions.

Trends of water composition and discharge in the Ramganga River, Ganga Basin over the last 40 years signal enhanced nitrate flux

Studying long-term trends in river water chemistry is critical to assessing the impacts of human activities and climate change on water resources. This is of particular importance in the Indian subcontinent such as the Ganga River Basin – home to nearly 500 million people. As previous studies are mostly focused on seasonal to annual scale variabilities, the long-term riverine changes in the hydrochemistry of the rivers draining the Ganga Basin remain poorly constrained. Here we present the river chemistry and discharge data of Ramganga Basin, a major sub-catchment of Ganga River, for three hydrological stations between 1981 and 2020. Our results indicate that the total concentration of dissolved solids (TDS) within the Ramganga basin varies from 123 to 1170 mg/L and does not show a systematic spatiotemporal trend. However, we found that the fluxes of certain dissolved inorganic constituents from Ramganga Basin have significantly increased in the last 40 years with NO3– having the highest trend followed by Ca2+, Cl−, K+, SiO2, F−, and PO43−. The concentration-discharge (C–Q) analysis suggests a dominant dilution behaviour for most of the major solutes in the basin at longer timescales. We find that the cations and anions riverine loads are primarily dominated by Ca2+ and HCO3– ions, respectively, controlled by rock weathering processes, whereas the fluxes of NO3–, PO43−, and SO42− are controlled by anthropogenic processes. In summary, the long-term dataset for the Ramganga River revealed significant variability in river water chemistry, solute fluxes, and C–Q relationships over the last 40 years controlled by natural and anthropogenic processes.

Characteristics of river heatwaves in the Vistula River basin, Europe

Rivers worldwide are warming due to the impact of climate change and human interventions. This study investigated river heatwaves in the Vistula River Basin, one of the largest river systems in Europe using long-term observed daily river water temperatures from the past 30 years (1991–2020). The results showed that river heatwaves are increased in frequency and intensity in the Vistula River Basin. The total number of river heatwaves showed clear increasing trend with an average rate of 1.400 times/decade, the duration of river heatwaves increased at an average rate of 14.506 days/decade, and the cumulative intensity of river heatwaves increased at an average rate of 53.169 °C/decade. The Mann-Kendall (MK) test was also employed, showing statistically significant increasing trends in the total number, duration, and intensity of heatwaves for all rivers, including the main watercourse of the Vistula River and its tributaries, with few exceptions. Air temperature is the major controller of river heatwaves for each hydrological station, and with the increase of air temperatures, river heatwaves will increase in frequency and intensity. Another impacting factor is flow, and with the increase of flow, river heatwaves tend to decrease in number, duration and intensity. The results suggested that mitigation measures shall be taken to reduce the effect of climate change on river systems.

Restored vegetation dominates the decrease in surface and subsurface runoff on the Loess Plateau

Understanding the runoff generation response to environmental changes at the basin scale can provide insights into the evolution of hydrological processes. The objective of this study is to detect and attribute the changes in surface and subsurface runoff in the Chinese Loess Plateau, a region with significant environmental and hydrological changes in the past several decades, to understand how and to what extent the runoff generation can be changed. Taking 13 basins (comprising 56 hydrological stations) on the Chinese Loess Plateau as the study area, we identified rainfall-runoff events and established indicators (event surface runoff coefficient, event timescale, normalized event peak discharge, baseflow, and baseflow index) to characterize the runoff generation, and further attributed the changes in runoff generation characteristics before (1961–2000) and after (2001–2019) the revegetation project. In particular, we inversely estimated the parameters of Soil Conservation Service Curve Number (SCS-CN) to analyze the initial abstraction and potential maximum retention under changing environment. Surface and subsurface runoff significantly decreased, with the surface runoff coefficient and baseflow decreasing by 34 % ± 24 % and 26 % ± 23 %, respectively. The surface runoff hydrograph became flatter with a 75 % decrease in the normalized event peak discharge and 82 % increase in the timescale of surface runoff, respectively. Normalized Difference Vegetation Index (32 %) and population (34 %) dominated the increase in the potential maximum retention, with 84 % of stations experiencing growth exceeding 100 %. The reduction in surface runoff can be primarily attributed to the enhanced soil infiltration due to revegetation and other human activities. Higher evapotranspiration resulting from revegetation was likely the dominant factor leading to the decrease in subsurface runoff. This study detailed the impacts of revegetation on surface and subsurface runoff generation, which can enhance our understanding of hydrological changes.

Study on the regularities of flow‐sediment transport and sedimentation in the lower Yellow River

AbstractThe training effectiveness of the lower Yellow River (LYR) depends on the understanding of the regularity of flow‐sediment transport and riverbed sedimentation. The measured data of daily flow discharge and sediment transport rate at the five hydrological stations (Xiaolangdi [Xld], Huayuankou [Hyk], Gaocun [Gc], Aishan [As], and Lijin [Lj]) in the LYR during the period from 1960 to 2017 are used to investigate the regularity of flow‐sediment transport and sedimentation in the LYR. The Xld station is used as the inlet control station, and the LYR is divided into four segments using four other stations, and the whole year is divided into three periods, namely, the dry season, the flood period, and the nonflood period of the wet season. On this basis, the relationships between the sediment transport rates at the four stations (Hyk, Gc, As, and Lj) and the rates at their respective closest upstream stations are analyzed in each of the three periods. According to the incoming sediment coefficient of the Xld station, the flow and sediment processes in the three periods are classified, and the refined equations for the relationship between the sediment transport rates at the downstream station and its upstream station are established. The results show that the calculated amount and process of erosion and deposition in each period and each segment of the LYR using the equations are in good agreement with the measured values. The relationship equations established in this study can conveniently predict the amount of erosion and deposition in different periods and different segments of the LYR in the future, which is of great significance to the rapid decision of the impact of the construction and operation of hydraulic projects in the upper and middle reaches of the Yellow River on the sedimentation in the LYR.

Streamflow in the United States: Characteristics, trends, regime shifts, and extremes

Long-term streamflow observations contain essential information for understanding hydrological changes and managing water resources. A continental-scale dataset or analysis of temporal streamflow change is still lacking across hydrologic gauges in the Conterminous United States (CONUS). Here, we compiled 70 years of streamflow records from 1951 to 2021 at ~ 8000 hydrologic stations across the CONUS and characterized temporal trends, regime shifts, and extreme events using a Bayesian time series analysis algorithm. We found that the occurrences of sudden streamflow changes (e.g., regime shifts and extreme events) have been increasing with time across the CONUS. In addition, we derived 181 streamflow indicators that are valuable for hydrological and biological applications, such as the duration and frequency of high or low streamflow events. The Mississippi River Basin, especially the middle and lower parts, was a hot spot of high-frequency high-flow events. Overall, we anticipate the dataset generated here offers valuable information for understanding and quantifying changes in water resources across the CONUS.

Global-scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

Determination of River Ecological Flow Thresholds and Development of Early Warning Programs Based on Coupled Multiple Hydrological Methods

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.

Past, present and future changes in the annual streamflow of the Lancang-Mekong River and their driving mechanisms

The rapid development of the Greater Mekong Subregion (GMS) makes it essential to understand the major mechanisms controlling the streamflow, especially for the Lancang-Mekong River (abbr. Mekong River). We used instrumental annual streamflow data (1960–2007) from Chiang Saen hydrological station and several gridded hydroclimatic datasets to explore the hydroclimatic evolution of the Mekong River, together with its driving mechanisms. We found that changes in the Mekong streamflow are consistent with precipitation changes, and the Mekong is thus a precipitation-dominated river that is susceptible to the effects of ongoing climate change. The instrumental record of Mekong annual streamflow is closely related to hydroclimatic changes, especially those related to moisture, within the area from the Hengduan Mountains to the Chiang Saen Station. Based on these gridded records, we extended the Mekong annual streamflow record to cover 1891–2021 using nested multiple linear regression fitting. The fitted streamflow explained up to 57.6 % of the instrumental changes and it indicates that the major 2019 drought was not unique over the past century. Despite extremely low precipitation and high temperatures, it is likely that the effects of drought can be mitigated via hydraulic engineering regulation. Climatological analyses showed that the Mekong annual streamflow is driven by the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), which is consistent with observed quasi-interannual cycles of 3–4 years. A multi-model ensemble of CMIP6 revealed that the Mekong annual streamflow will experience an upward trend in the future, accompanied by the more extreme impacts of ENSO and the IOD.

Effects of Climate Change and Human Activities on Runoff in the Upper Reach of Jialing River, China

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.

The Influence of Snow Cover Variability on the Runoff in Syr Darya Headwater Catchments between 2000 and 2022 Based on the Analysis of Remote Sensing Time Series

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.

Homogeneity Detection and Adjustment of Sea Surface Salinity along the Coast of the Northern South China Sea

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.

A dataset of riverine nitrogen yield across watersheds in the Conterminous United States

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.