Search Results (157)

High-quality precipitation data are vital for hydrological research. In regions with sparse observation stations, reliable gridded data cannot be obtained through interpolation, while the coarse resolution of satellite products fails to meet the demands of small watershed studies. Downscaling satellite-based precipitation products offers an effective solution for generating high-resolution data in such areas. Among these techniques, machine learning plays a pivotal role, with performance varying according to surface conditions and algorithmic mechanisms. Using the Qinghai Lake Basin as a case study and rain gauge observations as reference data, this research conducted a systematic comparative evaluation of nine machine learning algorithms (ANN, CLSTM, GAN, KNN, MSRLapN, RF, SVM, Transformer, and XGBoost) for downscaling IMERG precipitation products from 0.1° to 0.01° resolution. The primary objective was to identify the optimal downscaling method for the Qinghai Lake Basin by assessing spatial accuracy, seasonal performance, and residual sensitivity. Seven metrics were employed for assessment: correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), standard deviation ratio (Sigma Ratio), Kling-Gupta Efficiency (KGE), and bias. On the annual scale, KNN delivered the best overall results (KGE = 0.70, RMSE = 17.09 mm, Bias = −3.31 mm), followed by Transformer (KGE = 0.69, RMSE = 17.20 mm, Bias = −3.24 mm). During the cold season, KNN and ANN both performed well (KGE = 0.63; RMSE = 5.97 mm and 6.09 mm; Bias = −1.76 mm and −1.75 mm), with SVM ranking next (KGE = 0.63, RMSE = 6.11 mm, Bias = −1.63 mm). In the warm season, Transformer yielded the best results (KGE = 0.74, RMSE = 23.35 mm, Bias = −1.03 mm), followed closely by ANN and KNN (KGE = 0.74; RMSE = 23.38 mm and 23.57 mm; Bias = −1.08 mm and −1.03 mm, respectively). GAN consistently underperformed across all temporal scales, with annual, cold-season, and warm-season KGE values of 0.61, 0.43, and 0.68, respectively—worse than the original 0.1° IMERG product. Considering the ability to represent spatial precipitation gradients, KNN emerged as the most suitable method for IMERG downscaling in the Qinghai Lake Basin. Residual analysis revealed error concentrations along the lakeshore, and model performance declined when residuals exceeded specific thresholds—highlighting the need to account for model-specific sensitivity during correction. SHAP analysis based on ANN, KNN, SVM, and Transformer identified NDVI (0.218), longitude (0.214), and latitude (0.208) as the three most influential predictors. While longitude and latitude affect vapor transport by representing land–sea positioning, NDVI is heavily influenced by anthropogenic activities and sandy surfaces in lakeshore regions, thus limiting prediction accuracy in these areas. This work delivers a high-resolution (0.01°) precipitation dataset for the Qinghai Lake Basin and provides a practical basis for selecting suitable downscaling methods in similar environments. Full article

The Mediterranean region is currently experiencing the effects of a climate crisis, marked by an increase in the frequency and intensity of drought events. Climate variability has led to prolonged periods of drought, even in areas not traditionally classified as arid. These events have significant impacts on water resources, agricultural productivity, and socioeconomic systems. This study investigates the evolution of meteorological, hydrological, and socioeconomic droughts using the Standardized Precipitation Index (SPI) at time scales of 3, 12, and 24 months in a Mediterranean region identified as particularly vulnerable to climate change. Observational data from local meteorological stations were used for the 1991–2020 baseline period. Future climate projections were derived from the MPI-ESM model under the RCP 4.5 and RCP 8.5 scenarios, extending to the year 2080. Data were aggregated on a 0.50° × 0.50° spatial grid and bias-corrected using linear scaling. The Kolmogorov–Smirnov test was applied to assess the statistical compatibility between observed and projected precipitation data. Results indicate a substantial decline in annual precipitation, with reductions of up to 20% under the RCP 8.5 scenario for the period 2051–2080, compared to the reference period. The frequency of severe and extreme drought events is projected to increase by 30–50% in several grid meshes, especially during summer. Conversely, altered weather patterns in other areas may increase the likelihood of flood events. This study identifies the grid meshes most vulnerable to drought, highlighting the urgent need for adaptive water management strategies to ensure agricultural sustainability and reduce the socioeconomic impacts of climate-induced drought. Full article

Accurately predicting suspended sediment concentrations (SSC) is vital for effective reservoir planning, water resource optimization, and ecological restoration. This study proposes a hybrid ensemble model—VMD-MGGP-NGO-BiLSTM-NGO—which integrates Variational Mode Decomposition (VMD) for signal decomposition, Multi-Gene Genetic Programming (MGGP) for feature filtering, and a double-optimized NGO-BiLSTM-NGO (Northern Goshawk Optimization) structure for enhanced predictive learning. The model was trained and validated using daily discharge and SSC data from the Tangnaihai Hydrological Station on the upper Yellow River. The main findings are as follows: (1) The proposed model achieved an NSC improvement of 19.93% over the Extreme Gradient Boosting (XGBoost) and 15.26% over the Convolutional Neural Network—Long Short-Term Memory network (CNN-LSTM). (2) Compared to GWO- and PSO-based BiLSTM ensembles, the NGO-optimized VMD-MGGP-NGO- BiLSTM-NGO model achieved superior accuracy and robustness, with an average testing-phase NSC of 0.964, outperforming the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) counterparts. (3) On testing data, the model attained an NSC of 0.9708, indicating strong generalization across time. Overall, the VMD-MGGP-NGO-BiLSTM-NGO model demonstrates outstanding predictive capacity and structural synergy, serving as a reliable reference for future research on SSC forecasting and environmental modeling. Full article

Our understanding of water and salt changes in the context of declining groundwater levels in the Tarim Basin remains limited, largely due to the scarcity of hydrological monitoring stations and field observation data. This study utilizes water and salt monitoring data from 474 apparent electromagnetic induction (ECa, measured by EM38-MK2 device) sites across seven oases, combined with groundwater level observation data from representative areas, to analyze the spatiotemporal changes in ECa within the oases of the Tarim Basin from 2000 to 2022. Specific results are shown below: Numerous algorithmic predictions show the ensemble learning algorithm with the smallest error explained 71% of the ECa spatial variability. The ECa was particularly effective at identifying areas where groundwater extends beyond a depth of 5 m, demonstrating increased efficacy when ECa readings exceed the threshold of 1100 mS/m. Our spatiotemporal analysis spanning the years 2000 to 2022 has revealed a significant decline in ECa values within the artificially irrigated zones of the oasis clusters. In contrast, the transitional ecotone between the desert and the oases in Atux, Aksu, Kuqa, and Luntai have experienced a significant increase in ECa value. The variations observed within the defined Zone B, where ECa values ranged from 800 mS/m to 1100 mS/m, and Zone A, characterized by ECa values exceeding 1100 mS/m, aligned with the periodic fluctuations in the groundwater drought index (GDI), indicating a clear pattern of correlation. This study demonstrated that ECa can serve as a valuable tool for revealing the spatial and temporal variations of water resources in arid zones. The results obtained through this approach provided essential references for the local scientific management of soil and water resources. Full article

Recent studies have incorporated tidal elevation into hydrological models, yet they have not focused on simulating or evaluating tidal processes within these frameworks. Integrating tidal dynamics improves the representation of terrestrial–coastal interactions, including groundwater fluctuations, vegetation dynamics, and sediment transport. This study evaluates the capability of MOHID-Land, a physically based hydrological model, to simulate macro-tidal conditions in an Amazonian estuary. MOHID-Land enables tidal simulation by incorporating water-level time series as boundary conditions. A sensitivity analysis was conducted to (i) evaluate two global tidal models as boundary conditions; (ii) verify the impact of hydrological processes on water levels; and (iii) assess the effect of different bathymetries on water dynamics. The model effectively simulated tidal oscillations with good accuracy across eight tidal stations, although the inner stations required improved bathymetry. The Reference, Atmosphere, Porous Media and Vegetation (AtPmVg), and Finite Element Solution (FES) version 2014 (FES2014) simulations yielded similar water levels and goodness-of-fit metrics. While MOHID-Land is robust, and water level modeling is insensitive to meteorological, soil, or vegetation parameters, the model is highly sensitive to bathymetry. This study enhances the understanding of the applicability of hydrological models in terrestrial–coastal modeling. Full article

Global surface pressure, terrestrial water storage models, and seabed pressure grids provide valuable support for studying the mechanisms of the nonlinear motion behind GNSS stations. These data allow for the precise identification and analysis of displacement effects caused by environmental loads. This study analyzes GNSS coordinate time series data from 186 ITRF reference stations worldwide over a 10-year period, thoroughly examining the magnitude, spatial distribution, and impact of hydrological, atmospheric, and non-tidal oceanic loading on nonlinear motion. The results indicate that the atmospheric loading effects had a magnitude of approximately ±5 mm in the up (U) direction and ±1 mm in the east (E) and north (N) directions. Moreover, the impact of atmospheric loading on station displacements was more pronounced in high-latitude regions compared with mid- and low-latitude regions. Secondly, the hydrological loading showed a magnitude of approximately ±5 mm in the U direction and ±0.8 mm in the E and N directions, with inland areas causing larger displacements than coastal regions. Furthermore, the non-tidal oceanic loading induced displacements with magnitudes of approximately ±0.5 mm in the E and N directions and ±2 mm in the U direction, significantly affecting stations in the nearshore areas more than inland stations. Subsequently, this study analyzes the corrective effects of environmental loads on the coordinate time series. The average correlation coefficients between the E, N, and U directions and the coordinate time series were 0.35, 0.31, and 0.52, respectively. After removing the displacements caused by environmental loads, the root mean square (RMS) values of the coordinate time series decreased by 85.5% in the E direction, 77.4% in the N direction, and 89.8% in the U direction, with average reductions of 6.2%, 4.4%, and 16.7%, respectively. Lastly, it also comprehensively assesses the consistency between environmental loads and coordinate time series from the perspectives of the optimal noise model, velocity and uncertainty, and amplitude and phase. This study demonstrates that the geographic location of a station is closely related to the impact of environmental loads, with a significantly greater effect in the vertical direction than that in the horizontal direction. By correcting for environmental loads, the accuracy of the coordinate time series can be significantly enhanced. Full article

The Integrated Valuation of Ecosystem Services and Trade-Offs (InVEST) model with the distributed hydrological model Soil and Water Assessment Tool (SWAT) were implemented. The SWAT model quantifies and visualizes water production and groundwater reserves in the Mudanjiang River Basin, employing the groundwater runoff modulus method to calculate groundwater recharge in the basin. This study aims to assess the model’s applicability in cold basins and subsequently analyze groundwater distribution characteristics, water reserves, and the exploitable volume. It serves as a reference for the judicious allocation of groundwater resources and the preservation of the local aquatic ecosystem. The study indicates the following: (1) Utilizing the monthly runoff data from the Mudan River hydrologic station, SWAT simulation and calibration were conducted, yielding a determination coefficient (R²) of 0.75 and a Nash–Sutcliffe efficiency coefficient (NS) of 0.77, thereby satisfying fundamental scientific research criteria. The water yield predicted by the InVEST model aligns closely with the water resources bulletin of the research region. (2) The data from the water production module of the InVEST model indicate that the average annual water production during the research period was 6.725 billion m³, with an average annual water production depth of 148 mm. In 2018, characterized by ample water supply, the water output was at its peak, with a depth of 242 mm. In 2014, the water depth recorded was merely 16 mm. (3) Throughout the study period, the average annual flow of the Mudan River was 4.2 billion m³, whereas the groundwater reserve was 24.13 (10⁸ m³·a⁻¹). In 2013, the maximum groundwater reserve was 38.42 (10⁸ m³·a⁻¹), while the minimum reserve in 2014 was 2.36 (10⁸ m³·a⁻¹), suggesting that the region was predominantly experiencing sustainable exploitation. (4) The mean groundwater runoff modulus is 0.28 L/(s·km²), with a peak annual recharge of 15.4 (10⁸ m³·a⁻¹) in 2013 and a lowest recharge of just 3.2 (10⁸ m³·a⁻¹) in 2011. Full article

Lakes are a crucial component of inland water bodies, and changes in their water levels serve as key indicators of global climate change. Traditional methods of lake water level monitoring rely heavily on hydrological stations, but there are problems such as regional representativeness, data stability, and high maintenance costs. The satellite altimeter is an essential tool in lake research, with the Synthetic Aperture Radar (SAR) altimeter offering a high spatial resolution. This enables precise and quantitative observations of lake water levels on a large scale. In this study, we used Sentinel-3A SAR Radar Altimeter (SRAL) data to establish a more reasonable lake height inversion algorithm for satellite-derived lake heights. Subsequently, using this technology, a systematic analysis study was conducted with Qinghai Lake as the case study area. By employing regional filtering, threshold filtering, and altimeter range filtering techniques, we obtained effective satellite altimeter height measurements of the lake surface height. To enhance the accuracy of the data, we combined these measurements with GPS buoy-based geoid data from Qinghai Lake, normalizing lake surface height data from different periods and locations to a fixed reference point. A dataset based on SAR altimeter data was then constructed to track lake surface height changes in Qinghai Lake. Using data from the Sentinel-3A altimeter’s 067 pass over Qinghai Lake, which has spanned 96 cycles since its launch in 2016, we analyzed over seven years of lake surface height variations. The results show that the lake surface height exhibits distinct seasonal patterns, peaking in September and October and reaching its lowest levels in April and May. From 2016 to 2023, Qinghai Lake showed a general upward trend, with an increase of 2.41 m in lake surface height, corresponding to a rate of 30.0 cm per year. Specifically, from 2016 to 2020, the lake surface height rose at a rate of 47.2 cm per year, while from 2020 to 2022, the height remained relatively stable. Full article

This study details the design and implementation of a real-time river monitoring station established on the Sakarya River, capable of instantaneously tracking water levels and flow rates. The system comprises an ultrasonic distance sensor, a GSM module (Global System for Mobile Communications), which enables real-time wireless data transmission to a server via cellular networks, a solar panel, a battery, and a microcontroller board. The river monitoring station operates by transmitting water level data collected by the ultrasonic distance sensor to a server via a communication module developed on a microcontroller board using an Arduino program, and then sharing these data through a web interface. The developed system performs regular and continuous water level readings without the need for human intervention. During the installation and calibration of the monitoring station, laboratory and field tests were conducted, and the obtained data were validated by comparison with data from the hydropower plant located upstream. This system, mounted on a bridge, measures water levels twice per minute and sends these data to the relevant server via the GSM module. During this process, precipitation data were utilized as a critical reference point for validating measurement data for the 2023 hydrological year, with changes in precipitation directly correlated with river water levels and calculated flow values, which were analyzed accordingly. The real-time river monitoring station allows for instantaneous monitoring of the river, achieving a measurement accuracy of within 0.1%. The discharge values recorded by the system showed a high correlation (r² = 0.92) with data from the hydropower plant located upstream of the system, providing an accurate and comprehensive database for water resource management, natural disaster preparedness, and environmental sustainability. Additionally, the system incorporates early warning mechanisms that activate when critical water levels are reached, enabling rapid response to potential flood risks. By combining energy-independent operation with IoT (Internet Of Things)-based communication infrastructure, the developed system offers a sustainable solution for real-time environmental monitoring. The system demonstrates strong applicability in field conditions and contributes to advancing technologies in flood risk management and water resource monitoring. Full article

The spatial distribution of rainfall intensity–duration–frequency (IDF) values, essential for hydrological applications, were estimated for Catalunya, Spain. From a larger database managed by the Meteorological Service of Catalunya and after rigorous quality control, 163 high-quality daily series spanning from 1942 to 2016, with an average length of 39.8 years and approximately one station per 200 km², were selected. A monofractal downscaling methodology was applied to derive rainfall intensities for sub-daily durations using the intensities from a reference 24 h duration as the basis, followed by spatial interpolations on a 1 km × 1 km grid. The scaling parameter values have been found to be higher in the northwestern mountainous areas, influenced by Atlantic climate, and lower in the central–western driest zones. A general negative gradient was observed toward the coastline, reflecting the increasing influence of the Mediterranean Sea. The IDF results are presented as spatial distribution maps, providing intensity–frequency estimates for durations between one hour and one day, and return periods between 2 and 200 years, with an estimated uncertainty below 12% for the 200-year return period, and lower for shorter return periods. These findings highlight the need to capture rainfall spatial variations for urban planning, flood control, and climate resilience efforts. Full article

Recent advances in hydrological modling have led to the generation of numerous global or regional runoff datasets, which have been widely used in hydrological analysis. However, it is not yet clear how their accuracy and reliabilities are. In this study, using observed gauge streamflow data at four stations (Hequ, Fugu, Wubu, and Longmen) in the middle reaches of the Yellow River as reference, we compare and evaluate the accuracy of three runoff gridded dataset products (GloFAS, GRFR v1.0, and WGHM) at four temporal scales: daily, monthly, annual, and wet/dry seasons. The results indicate the following: (1) As the temporal scale increases, the simulated streamflow accuracy of the three datasets gradually improves. The GloFAS dataset performs the best at daily scale, while the WGHM dataset outperforms the other two at monthly and annual scales. (2) The three datasets all tend to overestimate the total streamflow at the main stations. (3) Comparing the two hydrological scenarios of wet and dry seasons, all three datasets exhibit better performance during the wet season. (4) The capture of peak streamflow is influenced by dataset type, temporal scale, and station characteristics. In general, the three datasets perform better at stations with higher base streamflow, such as Longmen and Wubu stations. Additionally, this study discusses the possible reasons for their different performances, which can be mainly attributed to three aspects: the quality of meteorological input datasets, missing or simplified simulation processes, and incorrect model structure and parameterization. Future research will consider revising the datasets to obtain more accurate data sources and further enhance the accuracy of watershed streamflow simulations. Full article

Incomplete environmental datasets pose significant challenges in developing accurate predictive models, particularly in hydrological research. This study addresses data missingness by investigating gap imputation methodologies for datasets with 5–20% data absence, focusing on the Mureș River in Romania. Utilizing a novel approach, we applied various imputation techniques, including the ratio method, Kalman filtering, and machine learning algorithms (XGBoost, Gradient Boosting, Random Forest and CatBoost), while developing an innovative self-assessment metric for evaluating imputation performance without relying on external reference data. Through systematic analysis of hydrological station data from four monitoring points, we artificially introduced data gaps to rigorously test method applicability. The research demonstrates the feasibility of constructing a robust self-evaluation framework for selecting optimal imputation techniques, potentially enhancing data reliability and analytical precision in environmental and geospatial research. Our findings contribute a structured methodology for addressing data incompleteness, offering researchers a quantitative approach to improving dataset integrity and predictive modeling in complex environmental systems. Full article

It is very well-known that the reliable coupling relationship between water area and water level is very important in analyzing the risks of floods and droughts for big lakes, such as Dongting Lake, especially when remote sensing images are absent and in situ measurements cannot be carried out. To obtain this relationship, two types of mathematical models—polynomial regression (PR) based on the least square algorithm and machine learning regression (MLR) based on the BP (Backpropagation) neural network algorithm—are constructed using the water area data extracted from multiple temporal remote sensing images and water levels recorded at several representative hydrological stations for nearly 30 years. In this study, Dongting Lake is divided into three parts: East Dongting Lake (EDL), South Dongting Lake (SDL), and West Dongting Lake (WDL). This is because water slope exists on its surface, which is formed by several inflow rivers and the high and low terrain. To calculate the total water area of this lake, two ways are put forward by choosing the water levels: from EDL, SDL, and WDL in their turn; or from all three simultaneously. In other words, three univariate and one multivariate regression. For PR, there are perfect coefficients of determination (most nearly 0.95, the smallest being 0.76), which is in line with regression test relative errors (between 0.27% and 6.7%). For MLR, which was initially applied to this problem, the best node number (10 for the first way, 8 for the second way) in the hidden layer of the neural network is adaptively chosen, with coefficients of determination (similar to PR), together with training and testing error performances (between 1% and 10%). These results confirm the validity and reliability of them. The regression and prediction results on the two models are better than the documented way (only focus on the water level of EDL). These results can provide some references for researchers and decision makers in studying similar big Lakes. Full article

China’s Dongting Lake area is intertwined with rivers and lakes and possesses many water systems. As such, it is one of the most complicated areas in the Yangtze River Basin, in terms of the complexity of its flood control. Over time, siltation and reclamation in the lake area have greatly weakened the river discharge capacity of the lake area, and whether it can endure extreme floods remains an open question. As there is no effective scenario simulation model for the lake area, this study constructs a hydrological model for the Jingjiang–Dongting Lake system and verifies the model using data from 11 typical floods occurring from 1954 to 2020. The parameters derived from 2020 data reflect the latest hydrological relationship between the lake and the river, while meteorological data from 1954 and 1998 are used as inputs for various scenarios with the aim of evaluating the flood pressure of the lake area, using the water levels at the Chengglingji and Luoshan stations as indicators. The preliminary results demonstrate that the operation of the upstream Three Gorges Dam and flood storage areas cannot completely offset the flood pressure faced by the lake area. Therefore, the reinforcement and raising of embankments should be carried out, in order to cope with potential extreme flood events. The methodology and results of this study have reference value for policy formation, flood control, and assessment and dispatching in similar areas. Full article

Runoff simulation is of fundamental importance for hydrological research. This study evaluated the applicability of multiple hydrological models and their ensembles for simulating runoff in small and medium-sized river basins of southeastern coastal China, focusing on the Xixi tributary of Jinjiang River and the Songxi and Chongyang tributaries of Minjiang River in Fujian Province. Four lumped hydrological models were selected for analysis: GR4J, IHACRES, TVGM, and MISDc-2L. The Bayesian model averaging method was utilized to compare the performance of each individual model and the multi-model ensemble in runoff simulation. Results: (1) For the calibration and validation periods of four hydrological stations, the mean values of KGE, NS, and R² for the models GR4J, IHACRES, TVGM, and MISDc-2L were all above 0.7, and the mean values of |RE| were below 8.3%, without significant simulation accuracy variations when basin size changes, demonstrating strong regional applicability for runoff simulation; (2) The multi-model ensemble simulations using Bayesian model averaging of GR4J, TVGM, and MISDc-2L exhibited higher accuracy than individual models; (3) The MISDc-2L model demonstrated strong applicability in daily runoff simulations for both small and medium-sized river basins in Fujian Province and the large-sized Dongting Lake basin, showing that it is worthy of further application in other river basins across China. The findings of this study provide a reference for the selection and application of hydrological models for runoff simulation in small and medium-sized river basins of southeastern coastal China. Full article