Search Results (2,600)

The stability of slopes is influenced by seasonal variations in thermal, hydrological, and mechanical processes. This study investigates the role of snowmelt in triggering shallow landslides through controlled laboratory experiments simulating winter, spring, and summer conditions. Snowpack dynamics and water movement were analyzed to understand filtration, infiltration, and runoff mechanisms. The results show that during winter, snow acts as a protective layer, slowing infiltration through its insulating and loading effects. In spring, rising temperatures melt snow, increasing water infiltration and filtration, accelerating soil saturation, and triggering slope failures. Summer rainfall-induced landslides exhibit distinct mechanisms, driven by progressive saturation. The transition from winter to spring highlights a critical phase where snowmelt interacts with warmer soils, intensifying slope instability risks. Numerical simulations using HYDRUS 1D validated the experimental findings, demonstrating its utility in modeling infiltration under varying thermal gradients. This study underscores the importance of incorporating snowmelt dynamics into landslide risk assessments and early warning systems, particularly as climate change accelerates snowmelt cycles in mountainous regions. These findings provide essential insights into seasonal variations in collapse mechanisms, emphasizing the need for further research to address the increasing impact of snowmelt in shallow landslides. Full article

Urban green spaces, including green roofs (GRs), are vital infrastructure for climate resilience, retaining water in city landscapes and supporting ecohydrological processes. Quantifying the hydrologic performance of GRs in the urban environment for future climate scenarios is the original contribution of this research developed within the URCA! project. For this purpose, a continuous modelling approach is undertaken to evaluate the hydrological performance of GRs expressed by means of the runoff volume and peak flow reduction at the event scale for long data series (at least 20 years). To investigate the prediction of GRs performance in future climates, a simple methodological approach is proposed, using monthly projection factors for the definition of future rainfall and temperature time series, and transferring the system parametrization of the current model to the future one. The proposed approach is tested for experimental GR sites in Genoa and Rende, located in Northern and Southern Italy, respectively. Referring to both the Genoa and Rende experimental sites, simulation results are analysed to demonstrate how the GR performance varies with respect to rainfall event characteristics, including total depth, maximum rainfall intensity and ADWP for current and future scenarios. Full article

Canopy hydrology and forest water inputs are directly linked to the physical properties of tree crowns (e.g., foliar and woody surfaces), which determine a tree’s capacity to intercept and retain incident rainfall. The changing forest structure, notably the decline of oak’s (Quercus) dominance and encroachment of non-oak species in much of the upland hardwood forests of the eastern United States, challenges our understanding of how species-level traits scale up to control the forest hydrologic budget. The objective of this study was to determine how the leaf water storage capacity varies across species and canopy layers, and how these relationships change throughout the growing season. We measured the leaf water storage capacity of overstory and midstory trees of native deciduous oaks (Q. alba, Q. falcata, Q. stellata) and non-oak species (Carya tomentosa, Acer rubrum, Ulmus alata, Liquidambar styraciflua, Nyssa sylvatica) using two methods (water displacement and rainfall simulation). Overstory Q. alba leaves retained 0.5 times less water per unit leaf area than other overstory species (p < 0.001) in the early growing season, while in the late growing season, C. tomentosa leaves had the lowest storage capacity (p = 0.024). Quercus falcata leaves displayed a minimal change in storage between seasons, while Q. alba and Q. stellata leaves had higher water storage in the late growing season. Midstory U. alata leaves had 3.5 times higher water storage capacity in the early growing season compared to all the other species (p < 0.001), but this difference diminished in the late growing season. Furthermore, the water storage capacities from the simulated rainfall experiments were up to two times higher than those in the water displacement experiments, particularly during the early growing season. These results underscore the complexity of leaf water storage dynamics, the methodology, and the implications for forest hydrology and species interactions. Broader efforts to understand species-level controls on canopy water portioning through leaf and other crown characteristics are necessary. Full article

This study critically examines the applications of the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) in hydrological research from 2000 to 2023, with a focus on its use in event-based and continuous simulations. A bibliometric analysis reveals a steady growth in research productivity and identifies key thematic areas, including hydrologic modeling, climate change impact assessment, and land use analysis. Event-based modeling, employing methods such as the SCS curve number (CN) and SCS unit hydrograph, demonstrates exceptional performance in simulating short-term hydrological responses, particularly in flood risk management and stormwater applications. In contrast, continuous modeling excels in capturing long-term processes, such as soil moisture dynamics and groundwater contributions, using methodologies like soil moisture accounting and linear reservoir baseflow approaches, which are critical for water resource planning and climate resilience studies. This review highlights the adaptability of HEC-HMS, showcasing its successful integration of event-based precision and continuous process modeling through hybrid approaches, enabling robust analyses across temporal scales. By synthesizing methodologies, performance metrics, and case studies, this study offers practical insights for selecting appropriate modeling techniques tailored to specific hydrological objectives. Moreover, it identifies critical research gaps, including the need for advanced calibration methods, enhanced parameter sensitivity analyses, and improved integration with hydraulic models. These findings highlight HEC-HMS’s critical role in improving hydrological research and give a thorough foundation for its use in addressing current water resource concerns. Full article

Accurately predicting the impacts of climate change on hydrological fluxes in ungauged basins continues to be a complex task. In this study, we investigated the transferability of the model parameters SCS-SMA-LR, available in the ATHYS platform, to simulate hydrological behavior within catchments of a large South Mediterranean transboundary basin, i.e., the Medjerda bordering Tunisia and Algeria, characterized by contrasting climatic and physiographic conditions. A robustness analysis was set up for donor and receptor catchments situated in the Medjerda catchment in Tunisia. The model was initially calibrated for two donor catchments, for the 127 km² catchment of the Lakhmess watershed situated on the right bank and for the 362 km² catchment of the Raghay watershed situated on the left bank of the Medjerda basin in Tunisia, using input data from 1990 to 1994. The model performance was evaluated through multiple accuracy criteria based on the Best Linear Unbiased Estimator (BLUE) for the automatic calibration to quantify the model simulation, proving its good performance. The temporal transferability was assessed by evaluating model performance, transferring the calibrated parameters for the two catchments as validation on data for 3-year periods outside the calibration domain to test the robustness of the model through a diachronic analysis from different decades, i.e., for the periods 1994–1997, 2001–2004, and 2014–2017, respectively. The spatial transferability was assessed by transferring the parameters calibrated on the donor catchments to be applied to the receptor catchments based on similarity and data availability. The model was upgraded to a greater catchment for data from 1994 to 2016 for the right bank, the Siliana Upstream catchment, and to the nearest catchment with a similar area for the data from 2008 to 2017 for the left bank of the Medjerda basin, the Bouheurtma catchment. The capacity of the soil reservoir and the flow velocity parameters proved to have an important impact on the modeling implementations at, respectively, 123.03 mm and 1 m/s for Raghay, and 95.05 mm and 2.5 m/s for Lakhmes. The results show that the space–time transfer process of model parameters produces an acceptable simulation of flow volumes and timing. The proposed methodology proved to be a successful way to monitor ungauged catchments and strengthens the robustness of the SCS-SMA-LR model for hydrological modeling and impact studies in ungauged basins of the Southern Mediterranean region. Full article

Urbanization and economic growth have substantially modified the land utilization structure, affecting ecosystem services and their spatial distribution. As a crucial component of Beijing’s urban framework, the city’s green belts, located at the periphery of its core metropolitan area, play a vital role in supplying urban ecosystem services. They also represent a focal point for land use transformation conflicts, making them an important study area. This research utilizes land utilization data from 2000, 2005, 2010, 2015, and 2020 as the primary dataset. It adopts a modified standard equivalent factor and integrates it with the Patch-Generaling Land Use Simulation (PLUS) model to model land utilization in Beijing’s green belts for 2035 under three scenarios: the natural development scenario (NDS), ecological protection scenario (EPS) and cultivated protection scenario (CPS). The study aims to analyze and project the spatial and temporal evolution of ecosystem service values (ESVs) in 2035 under different scenarios in the green belts of Beijing. The results indicate that (1) land use in Beijing’s green belts is dominated by cropland and construction land. Construction land has expanded significantly since 2000, increasing by 500.78 km², while cropland has decreased by 488.47 km². Woodland, grassland, and water have also seen a reduction. Overall, there is a trend of woodland and water being converted into cropland, with cropland subsequently transitioning into construction land. (2) In the NDS, construction land increases by 91.76 km², while cropland, grassland, and water decrease. In EDS, the growth of construction land decelerates to 22.09 km², the reduction in cropland decelerates, and the conversion of cropland to construction land is limited. Grassland and water remain largely unchanged, and woodland experiences a slight increase. In CPS, the conversion of cropland to construction land is notably reduced, with construction land increasing by 11.97 km², woodland increasing slightly, and grassland and water decreasing slightly. (3) The ESV ranking across scenarios is as follows: EPS 1830.72 mln yuan > CPS 1816.23 mln yuan > NDS 1723.28 mln yuan. Hydrological regulation and climate regulation are the dominant services in all scenarios. ESV in EPS attains the greatest economic gains. This study contributes to understanding the effects of land utilization changes on ESV, offering valuable empirical evidence for sustainable development decision-making in swiftly urbanizing areas. Full article

Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. Traditional flood forecasting in Central Asia still relies on statistical models developed during the Soviet era, which are limited in their ability to incorporate non-stationary climate and anthropogenic influences. This study addresses this gap by applying the Soil and Water Integrated Model (SWIM) to project climate-driven changes in the hydrological regime of the Zhabay River. The study employs a process-based, high-resolution hydrological model to simulate flood dynamics under future climate conditions. Historical hydrometeorological data were used to calibrate and validate the model at the Atbasar gauge station. Future flood scenarios were simulated using bias-corrected outputs from an ensemble of General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 for the periods 2011–2040, 2041–2070, and 2071–2099. This approach enables the assessment of seasonal and interannual variability in flood magnitudes, peak discharges, and their potential recurrence intervals. Findings indicate a substantial increase in peak spring floods, with projected discharge nearly doubling by mid-century under both climate scenarios. The study reveals a 1.8-fold increase in peak discharge between 2010 and 2040, and a twofold increase from 2041 to 2070. Under the RCP 4.5 scenario, extreme flood events exceeding a 100-year return period (2000 m³/s) are expected to become more frequent, whereas the RCP 8.5 scenario suggests a stabilization of extreme event occurrences beyond 2071. These findings underscore the growing flood risk in the region and highlight the necessity for adaptive water resource management strategies. This research contributes to the advancement of climate-resilient flood forecasting in Central Asian river basins. The integration of process-based hydrological modelling with climate projections provides a more robust framework for flood risk assessment and early warning system development. The outcomes of this study offer crucial insights for policymakers, hydrologists, and disaster management agencies in mitigating the adverse effects of climate-induced hydrological extremes in Kazakhstan. Full article

Rainfall–runoff models are crucial tools for managing water resources. The absence of reliable rainfall data in many regions of the world is a major limitation for these models, notably in many African countries, although some recent global rainfall products can effectively monitor rainfall from space. In Algeria, to identify a relevant modeling approach using this new source of rainfall information, the present research aims to (i) compare a conceptual model (GR4J) and seven machine learning algorithms (FFNN, ELM, LSTM, LSTM2, GRU, SVM, and GPR) and (ii) compare different types of precipitation inputs, including four satellite products (CHIRPS, SM2RAIN, GPM, and PERSIANN), one reanalysis product (ERA5), and observed precipitation, to assess which combination of models and precipitation data provides the optimal performance for river discharge simulation. The results show that the ELM, FFNN, and LSTM algorithms give the best performance (NSE > 0.6) for river runoff simulation and provide reliable alternatives compared to a conceptual hydrological model. The SM2RAIN-ASCAT and ERA5 rainfall products are as efficient as observed precipitation in this data-scarce context. Consequently, this work is the first step towards the implementation of these tools for the operational monitoring of surface water resources in Algeria. Full article

The Yangtze River Basin serves as a vital ecological barrier in China, with its water conservation function playing a critical role in maintaining regional ecological balance and water resource security. This study takes the Minjiang River Basin (MRB) as a case study, employing fractal theory in combination with the InVEST model and the SWAT-BiLSTM model to conduct an in-depth analysis of the spatiotemporal patterns of regional water conservation. The research aims to uncover the relationship between the spatiotemporal dynamics of watershed water conservation capacity and its ecosystem service functions, providing a scientific basis for watershed ecological protection and management. Firstly, fractal theory is introduced to quantify the complexity and spatial heterogeneity of natural factors such as terrain, vegetation, and precipitation in the Minjiang River Basin. Using the InVEST model, the study evaluates the water conservation service functions of the research area, identifying key water conservation zones and their spatiotemporal variations. Additionally, the SWAT-BiLSTM model is employed to simulate the hydrological processes of the basin, particularly the impact of nonlinear meteorological variables on hydrological responses, aiming to enhance the accuracy and reliability of model predictions. At the annual scale, it achieved NSE and R² values of 0.85 during calibration and 0.90 during validation. At the seasonal scale, these values increased to 0.91 and 0.93, and at the monthly scale, reached 0.94 and 0.93. The model showed low errors (RMSE, RSR, RB). The findings indicate significant spatial differences in the water conservation capacity of the Minjiang River Basin, with the upper and middle mountainous regions serving as the primary water conservation areas, whereas the downstream plains exhibit relatively lower capacity. Precipitation, terrain slope, and vegetation cover are identified as the main natural factors affecting water conservation functions, with changes in vegetation cover having a notable regulatory effect on water conservation capacity. Fractal dimension analysis reveals a distinct spatial complexity in the ecosystem structure of the study area, which partially explains the geographical distribution characteristics of water conservation functions. Furthermore, simulation results based on the SWAT-BiLSTM model show an increasingly significant impact of climate change and human activities on the water conservation functions of the Minjiang River Basin. The frequent occurrence of extreme climate events, in particular, disrupts the hydrological processes of the basin, posing greater challenges for water resource management. Model validation demonstrates that the SWAT model integrated with BiLSTM achieves high accuracy in capturing complex hydrological processes, thereby better supporting decision-makers in formulating scientific water resource management strategies. Full article

Agricultural runoff ranks second only to atmospheric deposition as a source of nitrogen pollution to streams in the southeastern United States. Climate-smart practices such as irrigation have the potential to reduce these impacts and provide resilience in the face of climate change. The purpose of this study is to evaluate the impact of irrigation amounts and fertilizer application strategies on surface nitrate export to surrounding steams. Data from an existing experiment on corn nitrogen fertilization in the Southeastern US was utilized and a crop simulation model was employed to simulate the water and nitrogen dynamics within the soil with particular emphases on nutrient uptake and residual nutrients. left in the soil after harvest under varying fertilization scenarios. A hydrologic and nutrient export model was developed to run in conjunction with the crop model to simulate lateral export from the fields. The results of this study indicate that climate and nutrient management are the dominant factors in determining surface nutrient transport under both rain fed and irrigated conditions, confirming previous studies. The overall results show that irrigation, on average, reduced nutrient export from the surface, especially in dry years. The effect is even greater if the nutrients are applied later in the year while irrigation is on-going. While this present study provides an initial look at the potential impacts of irrigation on nutrient export in humid areas, the available on-farm observational data is limited in its content. However, the results obtained support existing literature and provide further evidence on the impact of irrigation as a climate resilient practice and will help direct future studies in the region. Full article

Deep saline aquifers provide significant potential for CO₂ storage and are crucial in carbon capture, utilization, and storage (CCUS). However, ensuring the long-term safe storage of CO₂ remains challenging due to the complexity of coupled thermal, hydrological, mechanical, and chemical (THMC) processes. This study is one of a few to incorporate fault-controlled reservoir structures in the Enping 15-1 oilfield to simulate the performance of CO₂ geological storage. A systematic analysis of factors influencing CO₂ storage safety, such as the trap area, aquifer layer thickness, caprock thickness, reservoir permeability, and reservoir porosity, was conducted. We identified the parameters with the most significant impact on storage performance and provided suitable values to enhance storage safety. The results show that a large trap area and aquifer thickness are critical for site selection. Low permeability and large caprock thickness prevent CO₂ from escaping, which is important for long-term and stable storage. These findings contribute to developing site-specific guidelines for CO₂ storage in faulted reservoirs. Full article

While fluvial features are plentiful on Mars and offer valuable insights into past surface conditions, the climatic conditions inferred from these valleys, like precipitation and surface runoff discharges, remain the subject of debate. Model-based estimations have already been applied to several Martian valleys, but exploration of the related numerical estimations has been limited. This work applies an improved precipitation-based, steady-state erosion/accumulation model to a Martian valley and compares it to a terrestrial Mars analogue dessert catchment area. The simulations are based on a previously observed precipitation event and estimate the fluvial-related hydrological parameters, like flow depth, velocity, and erosion/accumulation processes in two different but morphologically similar watersheds. Moderate differences were observed in the erosion/accumulation results (0.13/−0.06 kg/m²/s for Zafit (Earth) and 0.01/−0.007 for Tinto B (Mars)). The difference is probably related to the lower areal ratio of surface on Mars where the shield factor is enough to trigger sediment movement, while in the Zafit basin, there is a larger area of undulating surface. The model could be applied to the whole surface of Mars. Using grain size estimation from the global THEMIS dataset, the grain size value artificially increased above that observed, and decreased hypothetic target rock density tests demonstrated that the model works according to theoretical expectations and is useful for further development. The findings of this work indicate the necessity of further testing of similar models on Mars and a better general analysis of the background geomorphological understanding of surface evolution regarding slope angles. Full article

Flood disasters pose one of the greatest threats to humanity. Effectively addressing this challenge requires improving the accuracy of flood simulation. Taking Xunhe watershed in Shandong Province as the study area, the Random Forest model was utilized to classify historical flood events within the watershed based on rainfall conditions, such as varying rainfall durations, intensities, and total precipitations. Multiple sets of hydrological model parameters were established to conduct flood classification simulation, reducing the error caused by using a single parameter set for the entire watershed. The results indicate that the Random Forest model can be applied to flood classification simulation in Xunhe watershed. Compared to unclassified simulations, the method proposed in this study leads to an improvement in the Nash coefficient by 0.06 to 0.14, a reduction in the relative error of peak discharge by 3% to 11.24% and a reduction in the relative error of flood volume by 1.46% to 9.44%. The flood classification simulation method proposed in this study has certain applicability in reducing flood simulation errors under different rainfall scenarios and improving accuracy in the watershed, providing new insights for flood control and disaster reduction efforts. Full article

Climate change is impacting hydrological conditions in the Dulongjiang-Irrawaddy River basin. This study employs a CV-LSTM model to evaluate the hydrological responses of precipitation, temperature, and runoff under various climate change scenarios. The findings indicate the following: (1) The CV-LSTM model performed excellently in simulating hydrological processes at the Pyay station. (2) From 2025 to 2100, precipitation in the Dulongjiang-Irrawaddy River basin is projected to increase, becoming more concentrated during the rainy season, with a more uneven annual distribution compared to the baseline period (1996–2010). The average temperature is also expected to rise, with an increase of 1.57 °C under the SSP245 scenario and 2.26 °C under the SSP585 scenario compared to the baseline period (1996–2010). (3) Multi-year average flow projections from three GCM models indicate changes of −1.1% to 20.6% under SSP245 and 7.8% to 31.5% under SSP585, relative to the baseline period (1996–2010). (4) Runoff will become more concentrated during the flood season, with greater annual variability, increasing the risks of flooding and drought. 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