Search Results (5,485)

Surface runoff is a key component of the hydrological cycle and is essential for water resource management and water ecological balance in river basins. It is important to accurately reveal the spatial and temporal dynamics of regional surface runoff over long time scales and to quantify the impacts of climate change and human activities on surface runoff changes for sustainable water resources management and utilization. In this study, the Minjiang River Basin (Chengdu section) was selected, which has significant natural and anthropogenic variations, and a comprehensive analysis of runoff and its drivers will help to formulate an effective regional water resource management strategy. We mainly used SWAT to simulate the monthly-scale runoff in the Chengdu section of the Minjiang River Basin from 1990 to 2019 and combined SWAT-CUP to perform sensitivity analysis on the model parameters and Partial Least Squares Structural Equation Modeling (PLS-SEM) to quantitatively analyze the main drivers of the changes in surface runoff. The results show that the average multi-year runoff in the Minjiang River Basin (Chengdu section) ranges from 628.96 to 1088.46 mm, with an average value of 834.13 mm, and that the overall annual runoff in the past 30 years shows a fluctuating tendency. The goodness-of-fit of the PLS-SEM model is 0.507; the validity and reliability assessment indicated that the model was reasonable, and its results showed that economic and landscape factors had significant negative impacts on runoff changes, while natural factors had positive impacts on runoff changes, with path coefficients of −0.210, −0.131, and 0.367, respectively. Meanwhile, this study also identified two potential indirect impact pathways, i.e., the economic factors had an indirect negative impact on runoff by changing the distribution of landscapes, and the natural factors had indirect negative impacts on runoff by influencing economic activities, reflecting the complex interactions among economic activities, landscape distribution, and natural factors in influencing surface runoff. This study provides a research framework and methodology for quantitatively modeling surface runoff and the analysis of influencing factors in watersheds, contributing to a deeper scientific understanding of long-term runoff changes and the contribution of their drivers. Full article

Water yield is a critical component of hydrological ecosystem services, influenced by both natural environments and human activities. Changes in land use and land cover (LULC) are particularly pivotal in causing water yield variations at the basin level, particularly for the ecologically fragile Ganjiang River Basin (GRB) in southern Jiangxi province, China. Over the last 33 years, the GRB has undergone substantial LULC changes that have significantly affected its water yield. Initially, this study assessed water yield from 1990 to 2022 using the InVEST model, then predicted future LULC scenarios using the PLUS model, including natural development (ND), cropland protection (CP), ecological protection (EP), and urban development (UD). The Geodetector model was then employed to analyze the influence of various factors on water yield changes. Key findings include the following: (1) Significant landscape changes were observed, including increases in impervious surfaces, cropland, and water areas, accompanied by substantial reductions in forest and other natural lands. The most pronounced decline occurred in forested regions. (2) The total water yield decreased by 0.44 × 10¹⁰ m³ over the study period, exhibiting fluctuations until 2016 and stabilizing afterward. Water yield was generally higher in the northeast and lower in the southwest, primarily influenced by actual evapotranspiration, LULC, and precipitation. (3) The impact of LULC changes on water yield varied by scenario, with the scenarios ranked from most to least impactful as follows: UD, ND, CP, EP. This variation is mainly due to the different rates of evapotranspiration and infiltration associated with land cover. These insights are crucial for guiding policymakers in developing effective LULC strategies that promote ecological restoration and sustainable water management in the basin. Full article

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. Full article

This paper investigates the impact of flood-induced hydrodynamic forces and high discharge on the masonry arch “Hannibal Bridge” (called “Ponte di Annibale” in Italy) using the Hydraulic Engineering Center’s River Analysis Simulation (HEC-RAS) v6.5.0. hydraulic numerical method, incorporating Unmanned Aerial Vehicle (UAV) photogrammetry and aerial Light Detection and Ranging (LIDAR) data for visual analysis. The research highlights the highly transient behavior of fast flood flows, particularly when carrying debris, and their effect on bridge superstructures. Utilizing a Digital Elevation Model to extract cross-sectional and elevation data, the research examined 23 profiles over 800 m of the river. The results indicate that the maximum allowable water depth in front of the bridge is 4.73 m, with a Manning’s coefficient of 0.03 and a longitudinal slope of 9 m per kilometer. Therefore, a novel method to identify the risks through HEC-RAS modeling significantly improves the conservation of masonry bridges by providing precise topographical and hydrological data for accurate simulations. Moreover, the detailed information obtained from LIDAR and UAV photogrammetry about the bridge’s materials and structures can be incorporated into the conservation models. This comprehensive approach ensures that preservation efforts are not only addressing the immediate hydrodynamic threats but are also informed by a thorough understanding of the bridge’s structural and material conditions. Understanding rating curves is essential for water management and flood forecasting, with the study confirming a Manning roughness coefficient of 0.03 as suitable for smooth open-channel flows and emphasizing the importance of geomorphological conditions in hydraulic simulation. Full article

Surface soil moisture (SSM) is a key parameter for land surface hydrological processes. In recent years, satellite remote sensing images have been widely used for SSM estimation, and many methods based on satellite-derived spectral indices have also been used to estimate the SSM content in various climatic conditions and geographic locations. However, achieving an accurate estimation of SSM content at a high spatial resolution remains a challenge. Therefore, improving the precision of SSM estimation through the synergies of multi-source remote sensing data has become imperative, particularly for informing forest management practices. In this study, the integration of multi-source remote sensing data with random forest and support vector machine models was conducted using Google Earth Engine in order to estimate the SSM content and develop SSM maps for temperate forests in central Japan. The synergy of Sentinel-2 and terrain factors, such as elevation, slope, aspect, slope steepness, and valley depth, with the random forest model provided the most suitable approach for SSM estimation, yielding the highest accuracy values (overall accuracy for testing = 91.80%, Kappa = 87.18%, r = 0.98) for the temperate forests of central Japan. This finding provides more valuable information for SSM mapping, which shows promise for precision forestry applications. Full article

Water depth affects many aspects of wetland ecology, hydrology, and biogeochemistry. However, acquiring water depth data is often difficult due to inadequate monitoring or insufficient funds. Satellite-derived bathymetry (SBD) data provides cost-effective and rapid estimates of the water depth across large areas. However, the applicability and performance of these techniques for inland wetlands have not been thoroughly evaluated. Here, a time series of bathymetry data for inland wetlands in West Kentucky and Tennessee were derived from Landsat 8 images using two widely used empirical models, Stumpf and a modified Lyzenga model and three machine learning models, Random Forest, Support Vector regression, and k-Nearest Neighbor. We processed satellite images using Google Earth Engine and compared the performance of water depth estimation among the different models. The performance assessment at validation sites resulted in an RMSE in the range of 0.18–0.47 m and R² in the range of 0.71–0.83 across all models for depths < 3.5 m, while in depths > 3.5 m, an RMSE = 1.43–1.78 m and R² = 0.57–0.65 was obtained. Overall, the empirical models marginally outperformed the machine learning models, although statistical tests indicated the results from all the models were not significantly different. Testing of the models beyond the domain of the training and validation data suggested the potential for model transferability to other regions with similar hydrologic and environmental characteristics. Full article

Due to the joint influence of climate change and human activities, the hydrological rhythm of Poyang Lake has changed in recent years, leading to an increasingly severe drought problem during autumn and winter in this region. Notably, the extreme drought that occurred in 2022 had profound impacts on shipping, water supply and the ecological environment of the wetlands in the Poyang Lake Delta, sparking widespread concern. Based on the historical hydrometeorological data of Poyang Lake, we used statistical models (such as Chow test, correlation analysis, etc.) to analyze the cause of the extreme drought in the Poyang Lake Delta from the perspectives of natural factors and human activity. Through correlation analysis, we found that the water level, discharge, and drought duration of the Poyang Lake Delta were all significantly affected by climate change, particularly rainfall in the Poyang Lake basin. Furthermore, combining the results of Chow test and correlation analysis, we also found that the operation of the Three Gorges Reservoir had a notable impact on the water level of the Poyang Lake Delta. Based on remote sensing images, ecological and environmental sampling monitoring, the damage inflicted by the extreme drought event on the Poyang Lake Delta was analyzed. The results show that the inundated area of the delta wetlands in the extreme-drought year (2022) decreased by 45.75% compared with that in a normal year (2017). In addition, the ecological environment of the wetlands deteriorated significantly. The water quality parameters (TN, TP, NH₄⁺-N) increased by 50.2%, 240% and 64.7%, respectively. The concentrations of TN and TP were 3.8 mg/L and 0.17 mg/L, respectively, while the context values in the delta were 1.2 mg/L and 0.075 mg/L. The density and biomass of algae increased by 87.2% and 557.9%, respectively. In contrast, the density and biomass of benthos decreased by 59.9% and 78.5%, respectively. The control strategy for the Poyang Lake Delta under extreme drought was studied through an experiment on the operation of hydraulic controllers. The results show that under extreme drought conditions, the newly built hydraulic controllers could raise the water level of the delta from 9.1 ± 0.7 m to 14.2 ± 1.8 m, thus effectively solving the water cut-off problem in the four branches of the delta. Furthermore, by adjusting the distributive ratio of the main, north, middle and south branches of the delta to 50%, 4%, 24% and 22% through newly built hydraulic controllers, the water area can be increased by 56%. Full article

In recent years, climate events such as Drought, El Niño, and La Niña have become increasingly frequent and more intense. Oceanographic monitoring was used to collect hydrological data in the middle and lower sectors of the Caeté estuary in different years. Negative rainfall anomalies of up to 45% were recorded during periods marked by drought and El Niño events, which make the water in the Caeté estuary more saline and alkaline. During these events, the retention of dissolved inorganic nutrients in the middle sector appears to support increased eutrophication and more productive waters, whereas moderate eutrophication and lower productivity were observed in the lower sector. During La Niña events, by contrast, positive rainfall anomalies may reach 60%, resulting in more oxygenated water in the estuary. In addition, the lower sector tends to be more eutrophic during periods of high rainfall and freshwater discharge, as observed in this study during a La Niña event. The paucity of data on the effects of extreme climate events in Amazonian environments means that the findings of the present study may provide a useful model for the assessment of the effects of these events on other natural environments in the Amazon region. Full article

This study compares the calculation of snow water equivalent (SWE) using machine learning algorithms with the conventional degree-day method. The study uses machine learning techniques such as LASSO, Random Forest, Support Vector Machines, and CatBoost. It proposes an innovative use of feature engineering (FE) to improve the accuracy and robustness of SWE predictions by machine learning intended for interpolation, extrapolation, or imputation of missing data. The performance of machine learning approaches is evaluated against the traditional degree-day method for predicting SWE. The study emphasizes and demonstrates gains when modeling is enhanced by transforming basic, raw data through feature engineering. The results, verified in a case study from the mountainous region of Slovakia, suggest that machine learning, particularly CatBoost with feature engineering, shows better results in SWE estimation in comparison with the degree-day method, although the authors present a refined application of the degree-day method by utilizing genetic algorithms. Nevertheless, the study finds that the degree-day method achieved accuracy with a Nash–Sutcliffe coefficient of efficiency NSE = 0.59, while the CatBoost technique enhanced with the proposed FE achieved an accuracy NSE = 0.86. The results of this research contribute to refining snow hydrology modeling and optimizing SWE prediction for improved decision-making in snow-dominated regions. Full article

Floods are a widespread natural disaster that occur in most areas of the world, except for the polar regions. To minimize the damage caused by floods, effective management strategies and policies must be implemented. Petra and Wadi Musa areas are prone to floods, which happen every 2–3 years and result in significant harm to both lives and properties. To address this issue, a composite hazard and vulnerability index is commonly utilized to evaluate flood risk and guide policy formation for flood risk reduction. These tools are efficient and cost-effective in generating accurate results. Accordingly, the present study aims to determine the morphological and hydrometeorological parameters that affect flash floods in Petra catchment area and to identify high-risk zones using GIS, hydrological, and analytical hierarchy (AHP) modeling. Nine factors, including Elevation (E), Landuse/Landcover LULC, Slope (S), Drainage density (DD), Flood Control Points (FCP) and Rainfall intensity (RI), which make up the six risk indices, and Population Density (PD), Cropland (C), and Transportation (Tr), which make up the three vulnerability indices, were evaluated both individually and in combination using AHP in ArcGIS 10.8.2 software. These parameters were classified as hazard and vulnerability indicators, and a final flood map was generated. The map indicated that approximately 37% of the total area in Petra catchment is at high or very high risk of flooding, necessitating significant attention from governmental agencies and decision-makers for flood risk mitigation. The AHP method proposed in this study is an accurate tool for flood mapping that can be easily applied to other regions in Jordan to manage and prevent flood hazards. Full article

High-resolution and accurate precipitation data are essential for hydrological, meteorological, and ecological research at the watershed scale. However, in regions with complex terrain and significant rainfall variability, the limited number of rain gauge stations (RGS) is insufficient, and the spatial resolution of existing satellite precipitation data is too low to capture detailed precipitation patterns at the watershed scale. To address this issue, the downscaling of satellite precipitation products has become an effective method to obtain high-resolution precipitation data. This study proposes a monthly downscaling method based on a random forest model, aiming to improve the resolution of precipitation data in cloudy and rainy regions at mid-to-low latitudes. We combined the Google Earth Engine (GEE) platform with a local Python environment, introducing cloud cover characteristics into traditional downscaling variables (latitude, longitude, topography, and vegetation index). The TRMM data were downscaled from 25 km to 1 km, generating high-resolution monthly precipitation data for the Dongting Lake Basin from 2001 to 2019. Furthermore, we analyzed the spatiotemporal variation characteristics of precipitation in the study area. The results show the following: (1) In cloudy and rainy regions, our method improves resolution and detail while maintaining the accuracy of precipitation data; (2) The response of monthly precipitation to environmental variables varies, with cloud cover characteristics contributing more to the downscaling model than vegetation characteristics, helping to overcome the lag effect of vegetation characteristics; and (3) Over the past 20 years, there have been significant seasonal trends in precipitation changes in the study area, with a decreasing trend in winter and spring (January–May) and an increasing trend in summer and autumn (June–December). These results indicate that the proposed method is suitable for downscaling monthly precipitation data in cloudy and rainy regions of the Dongting Lake Basin. Full article

Assessing glaciers using recent and historical data and predicting the future impacts on them due to climate change are crucial for understanding global glacier mass balance, regional water resources, and downstream hydrology. Computational methods are crucial for analyzing current conditions and forecasting glacier changes using remote sensing and other data sources. Due to the complexity and large data volumes, there is a strong demand for accelerated computing. AI-based approaches are increasingly being adopted for their efficiency and accuracy in these tasks. Thus, in the current state-of-the-art review work, available research results on the application of AI methods for glacier studies are addressed. Using selected search terms, AI-based publications are collected from research databases. They are further classified in terms of their geographical locations and glacier-related research purposes. It was found that the majority of AI-based glacier studies focused on inventorying and mapping glaciers worldwide. AI techniques like U-Net, Random forest, CNN, and DeepLab are mostly utilized in glacier mapping, demonstrating their adaptability and scalability. Other AI-based glacier studies such as glacier evolution, snow/ice differentiation, and ice dynamic modeling are reviewed and classified, Overall, AI methods are predominantly based on supervised learning and deep learning approaches, and these methods have been used almost evenly in glacier publications over the years since the beginning of this research area. Thus, the integration of AI in glacier research is advancing, promising to enhance our comprehension of glaciers amid climate change and aiding environmental conservation and resource management. Full article

Investigating the changes in snow cover caused by climate change is extremely important and has attracted increasing attention in cryosphere and climate research. Optimal remote sensing-based snow datasets can provide long-term daily and global spatial-temporal snow-cover distribution at regional and global scales. However, the application of these snow-cover products is inevitably limited because of the space–time discontinuities caused by cloud obscuration, which poses a significant challenge in snowpack-related studies, especially in High Mountain Asia (HMA), an area that has high-elevation mountains, complex terrain, and harsh environments and has fewer observation stations. To address this issue, we developed an improved five-step hybrid cloud removal strategy by integrating the daily merged snow-cover probability (SCP) algorithm, eight-day merged SCP algorithm, decision tree algorithm, temporal downscaling algorithm, and optimal threshold segmentation algorithm to produce a 21-year, daily cloud-free snow-cover dataset using two daily MODIS snow-cover products over the HMA. The accuracy assessment demonstrated that the newly developed cloud-free snow-cover product achieved a mean overall accuracy of 93.80%, based on daily classified snow depth observations from 86 meteorological stations over 10 years. The time series of the daily percentage of binary snow-cover over HMA was analyzed during this period, indicating that the maximum snow cover tended to change more dramatically than the minimum snow cover. The annual snow-cover duration (SCD) experienced an insignificantly increasing trend over most of the northeastern and southwestern HMA (e.g., Qilian, eastern Kun Lun, the east of Inner Tibet, the western Himalayas, the central Himalayas, and the Hindu Kush) and an insignificant declining trend over most of the northwestern and southeastern HMA (e.g., the eastern Himalayas, Hengduan, the west of Inner Tibet, Pamir, Hissar Alay, and Tien). This new high-quality snow-cover dataset will promote studies on climate systems, hydrological modeling, and water resource management in this remote and cold region. Full article

Under varying flow rate conditions, the transport and retention of polydisperse microplastics (MPls), with an average particle size of 16 ± 6 µm, were investigated in saturated porous media. First-order reversible and irreversible kinetic sorption models were used to describe the sorption kinetics. Sensitivity analyses provided insight into the effects of each sorption parameter. Both numerical modeling and experimental measurements were utilized to evaluate the retention rates of sand filters. The influence of flow rate on sorption was reflected in variations in the distribution coefficient (K_d), the mass transfer coefficient (β), and the irreversible sorption rate (K₁). Lower flow rates were associated with higher K_d and β values, indicating increased sorption and reduced mass transfer rates. An increase in K_d resulted in a more gradual sorption process, with a decrease in peak concentration, whereas changes in β had a comparatively smaller impact on sorption rate and peak concentration. Lower K₁ values were linked to higher peak concentrations and decreased retention efficiency. Numerical modeling revealed retention rates of 28 ± 1% at a flow rate of 31 mL min⁻¹ and 17 ± 1% at 65 mL min⁻¹. The introduction of MPls into saturated sand environments modifies the transport dynamics within the medium. Consequently, these alterations affect the hydrological characteristics of porous media, impacting groundwater quality and agricultural output. The mean absolute error (MAE) of 6% between the modeled and observed retention rates indicated a high level of accuracy. This study underscores the importance of examining retention efficiency and the accuracy of numerical models in understanding MPl transport in porous media. Full article

Runoff is greatly influenced by changes in climate conditions. Predicting runoff and analyzing its variations under future climates are crucial for ensuring water security, managing water resources effectively, and promoting sustainable development within the catchment area. As the key step in runoff modeling, the calibration of hydrological model parameters plays an important role in models’ performance. Identifying an efficient and reliable optimization algorithm and objective function continues to be a significant challenge in applying hydrological models. This study selected new algorithms, including the strategic random search (SRS) and sparrow search algorithm (SSA) used in hydrology, gold rush optimizer (GRO) and snow ablation optimizer (SAO) not used in hydrology, and classical algorithms, i.e., shuffling complex evolution (SCE-UA) and particle swarm optimization (PSO), to calibrate the two-parameter monthly water balance model (TWBM), abcd, and HYMOD model under the four objective functions of the Kling–Gupta efficiency (KGE) variant based on knowable moments (KMoments) and considering the high and low flows (HiLo) for monthly runoff simulation and future runoff prediction in Tunxi basin, China. Furthermore, the identified algorithm and objective function scenario with the best performance were applied for runoff prediction under climate change projections. The results show that the abcd model has the best performance, followed by the HYMOD and TWBM models, and the rank of model stability is abcd > TWBM > HYMOD with the change of algorithms, objective functions, and contributing calibration years in the history period. The KMoments based on KGE can play a positive role in the model calibration, while the effect of adding the HiLo is unstable. The SRS algorithm exhibits a faster, more stable, and more efficient search than the others in hydrological model calibration. The runoff obtained from the optimal model showed a decrease in the future monthly runoff compared to the reference period under all SSP scenarios. In addition, the distribution of monthly runoff changed, with the monthly maximum runoff changing from June to May. Decreases in the monthly simulated runoff mainly occurred from February to July (10.9–56.1%). These findings may be helpful for the determination of model parameter calibration strategies, thus improving the accuracy and efficiency of hydrological modeling for runoff prediction. Full article