Search Results (321)

Climate-driven hydrological extremes and anthropogenic interventions are increasingly altering streamflow regimes worldwide. While prior studies have explored climate or regulation effects separately, few have integrated multiple teleconnection indices and reservoir chronologies within a cross-basin comparative framework. This study addresses this gap by assessing long-term streamflow nonstationarity and its drivers at two key stations—Khabarovsk on the Amur River and Datong on the Yangtze River—representing distinct hydroclimatic settings. We utilized monthly discharge records, meteorological data, and large-scale climate indices to apply trend analysis, wavelet transform, percentile-based extreme diagnostics, lagged random forest regression, and slope-based attribution. The results show that Khabarovsk experienced an increase in winter baseflow from 513 to 1335 m³/s and a notable reduction in seasonal discharge contrast, primarily driven by temperature and cold-region reservoir regulation. In contrast, Datong displayed increased discharge extremes, with flood discharges increasing by +71.9 m³/s/year, equivalent to approximately 0.12% of the mean flood discharge annually, and low discharges by +24.2 m³/s/year in recent decades, shaped by both climate variability and large-scale hydropower infrastructure. Random forest models identified temperature and precipitation as short-term drivers, with ENSO-related indices showing lagged impacts on streamflow variability. Attribution analysis indicated that Khabarovsk is primarily shaped by cold-region reservoir operations in conjunction with temperature-driven snowmelt dynamics, while Datong reflects a combined influence of both climate variability and regulation. These insights may provide guidance for climate-responsive reservoir scheduling and basin-specific regulation strategies, supporting the development of integrated frameworks for adaptive water management under climate change. Full article

This study presents a hybrid approach to hydrological forecasting by integrating the physically based Soil and Water Assessment Tool (SWAT) model with Prophet time-series modeling and machine learning–based multi-output regression. Applied to the Cahaba watershed, the objective is to predict key environmental variables (precipitation, evapotranspiration (ET), potential evapotranspiration (PET), and snowmelt) and their influence on hydrological responses (surface runoff, groundwater flow, soil water, sediment yield, and water yield) under present (2010–2022) and future (2030–2042) climate scenarios. Using SWAT outputs for calibration, the integrated SWAT-Prophet-ML model predicted ET and PET with RMSE values between 10 and 20 mm. Performance was lower for high-variability events such as precipitation (RMSE = 30–50 mm). Under current climate conditions, R² values of 0.75 (water yield) and 0.70 (surface runoff) were achieved. Groundwater and sediment yields were underpredicted, particularly during peak years. The model’s limitations relate to its dependence on historical trends and its limited representation of physical processes, which constrain its performance under future climate scenarios. Suggested improvements include scenario-based training and integration of physical constraints. The approach offers a scalable, data-driven method for enhancing monthly water balance prediction and supports applications in watershed planning. Full article

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans. Full article

Snow water equivalent (SWE), an essential parameter of snow, is largely studied to understand the impact of climate regime effects on snowmelt patterns. This study developed a Siamese Attention U-Net (Si-Att-UNet) model to detect daily change events in the winter season. The daily SWE change event detection task is treated as an image content comparison problem in which the Si-Att-UNet compares a pair of SWE maps sampled at two temporal windows. The model detected SWE similarity and dissimilarity with an F1 score of 99.3% at a 50% confidence threshold. The change events were derived from the model’s prediction of SWE similarity using the 50% threshold. Daily SWE change events increased between 1979 and 2018. However, the SWE change events were significant in March and April, with a positive Mann–Kendall test statistic (tau = 0.25 and 0.38, respectively). The highest frequency of zero-change events occurred in February. A comparison of the SWE change events and mean change segments with those of the northern hemisphere’s climate anomalies revealed that low temperature and low precipitation anomalies reduced the frequency of SWE change events. The findings highlight the influence of climate variables on daily changes in snow-related water storage in March and April. Full article

Snow-dominated watersheds experience a snowmelt-driven peak in streamflow that occurs in the spring or early summer. Some of the headwater basins in Colorado, USA have two or more peaks in streamflow, including the Uncompahgre River, a Colorado River tributary. The timing of peak streamflow is important for water management and recreational planning. As such, we examined the connection between the timing of each streamflow peak and readily available snow measurement information in the forest and alpine zones. These station data are the date of the initiation of snowmelt, 50% melt-out, and complete melt-out or the snow disappearance date (SDD). When it occurs before mid-June (14 of 20 years), the timing of the first peak is well correlated with the forested snow measurement station SDD. The second streamflow peak timing is well correlated with SDD from the alpine station except for very early (3 years) and very late (2 years) SDD. We also examine the spatial variability of snow disappearance and peak snow water equivalent (SWE) across the four seasonally snow-covered headwater sub-basins using a dataset from a coupled meteorological–snowpack model. Full article

In arid inland river basins, the upstream runoff generation zones contribute the majority of the basin’s water resources. Global warming and land use changes will produce uncertain impacts on runoff variations in the headwaters of inland rivers in arid regions. Deeply understanding the response mechanisms of runoff to climate and land use changes is fundamental for scientifically developing watershed water resource utilization planning and achieving sustainable socio-economic and ecological development. By integrating meteorological data, hydrological data, and multi-source remote sensing data, this study systematically evaluates the factors influencing changes in watershed hydrological processes. The results show: (1) From 1976 to 2016, the Xiying River runoff exhibited a slight increasing trend, with an increment of 0.213 mm per decade. (2) At the interannual scale, runoff is primarily influenced by precipitation changes, with a trend of further weakening ice and snowmelt effects. (3) The land use types in the Xiying River Basin are predominantly forestland, grassland, and unused land. With increasing forestland and cultivated land and decreasing grassland and construction land area, the watershed’s water conservation capacity has significantly improved. Full article

Winter pavement maintenance faces challenges in balancing large-scale upkeep and driving safety, particularly regarding the application of active slow-release materials. This study proposes a gel-modified salt-storing ceramsite asphalt mixture to enhance ice-melting capabilities through controlled salt release. By replacing a conventional coarse aggregate with salt-storing ceramsite in SMA-10 graded mixtures (0–80% content), we systematically evaluate its mechanical performance and de-icing functionality. The experimental results demonstrate that 40% salt-storing ceramsite content optimizes high-temperature stability while maintaining acceptable low-temperature performance and water resistance. Microstructural analysis reveals that silicone–acrylic emulsion forms a hydrophobic film on ceramsite surfaces, enabling uniform salt distribution and sustained release. The optimal 10% gel modification achieves effective salt retention and controlled release through pore-structure regulation. These findings establish a 40–60% salt-storing ceramsite content range as the practical range for winter pavement applications, offering insights into the design of durable snow-melting asphalt surfaces. Full article

In recent years, both anthropogenic and climate changes have caused the depletion of surface water resources, shifts in rainfall and accelerations in temperature, which indicates the importance of their examination to flood forecasting analyses. This paper studies the importance of synchronised water management strategies, considering upstream and downstream dynamics using field data from 1986 to 2018. Seasonal and decadal variations show the need for adaptive management strategies to address potential climate change impacts on discharge, precipitation and temperature patterns in the Zarrinehrud River, Iran. The regression analysis was considered via R² values, and the statistical analysis was regarded by p-values. The regression analysis of monthly river peak discharge indicates strong correlations between the discharge of specific months (September–October upstream, December–January downstream). By the 2000s and 2020s, both stations showed a shift in peak precipitation to the spring months (April–May for upstream and May–June for downstream). This confirms a synchronisation of rainfall trends, which are influenced by climate changes or regional hydrological patterns. This temporal offset between stations confirms the spatial and seasonal variation in rainfall distribution across the basin. Higher temperatures during the dominant months, particularly late summer to early autumn, accelerate snowmelt from upstream catchments. This aligns with the river discharge peaks observed in the hydrograph. The statistical analysis of river peak discharge indicated that the Weibull (p-value = 0.0901) and the Lognormal (p-value = 0.1736) distributions are the best fitted distributions for the upstream and downstream stations, respectively. Full article

The assessment of the maximum snow-water equivalent in mountains is important for understanding the mechanism of their formation, as well as for hydrological calculations. The low density of the observation network and the high complexity of ground-based snow-measuring operations have led to the widespread use of remote methods to obtain such data. In this study, the maximum water reserve of the Uba River basin was calculated for the period of 2020–2023, based on data from the Sentinel-2 satellite regarding the position of the seasonal snow line, obtained using the temperature-based melt-index method. This study determined the snowmelt coefficients for the meteorological stations at Zmeinogorsk, Shemonaikha, and Ridder. Maps were constructed to show the distribution of the maximum snow-water equivalent in the Uba River basin. The spatial differentiation features of the snow cover were revealed, depending on the elevation, slope exposure, and distance from the watersheds. It was established that the altitudinal distribution of snow cover on the northern and southern macro-slopes of the ridges is asymmetric: in the western part of the basin, within the elevation range of 500–1200 m, the maximum water reserves of snow cover are greater on the southern slopes, but they become higher on the northern slopes above 1200 m. In the eastern part of the basin, they are always larger on the northern slopes. The greatest differences in the distribution of snow cover between the slopes occur near the watersheds. Full article

Climate change has intensified the melting of glaciers and permafrost in high-altitude cold regions, leading to more frequent extreme hydrological events. This has caused significant variations in the spatiotemporal distribution of meltwater runoff from the headwater cryosphere, posing a major challenge to regional water security. In this study, the HBV hydrological model was set up and driven by CMIP6 global climate model outputs to investigate the multi-scale temporal variations of runoff under different climate change scenarios in the Tuotuo River Basin (TRB) within the source region of the Yangtze River (SRYR). The results suggest that the TRB will undergo significant warming and wetting in the future, with increasing precipitation primarily occurring from May to October and a notable rise in annual temperature. Both temperature and precipitation trends intensify under more extreme climate scenarios. Under all climate scenarios, annual runoff generally exhibits an upward trend, except under the SSP1-2.6 scenario, where a slight decline in total runoff is projected for the late 21st century (2061–2090). The increase in total runoff is primarily concentrated between May and October, driven by enhanced rainfall and meltwater contributions, while snowmelt runoff also shows an increase, but accounts for a smaller percentage of the total runoff and has a smaller impact on the total runoff. Precipitation is the primary driver of annual runoff depth changes, with temperature effects varying by scenario and period. Under high emissions, intensified warming and glacier melt amplify runoff, while low emissions show stable warming with precipitation dominating runoff changes. Full article

Snow cover variations significantly affect the stability of regional water supply and terrestrial ecosystems in arid northwest China. This study comprehensively evaluates snow resource changes since 2000 by integrating multisource remote sensing datasets and analyzing four key indicators: snow cover area (SCA), snow phenology (SP), snow depth (SD), and snow water equivalent (SWE). The results reveal a slight downtrend in SCA over the past two decades, with an annual decline rate of 7.13 × 10³ km². The maximum SCA (1.28 × 10⁶ km²) occurred in 2010, while the minimum (7.25 × 10⁵ km²) was recorded in 2014. Spatially, SCA peaked in December in the north and January in the south, with high-altitude subregions (Ili River Basin (IRB), Tarim River Region (TRR), North Kunlun Mountains (NKM), and Qaidam Basin (QDB)) maintaining stable summer snow cover due to low temperatures and high precipitation. Analysis of snow phenology indicates a significant shortening of snow cover duration (SCD), with 62.40% of the study area showing a declining trend, primarily driven by earlier snowmelt. Both SD and SWE exhibited widespread declines, affecting 75.09% and 84.85% of the study area, respectively. The most pronounced SD reductions occurred in TRR (94.44%), while SWE losses were particularly severe in North Tianshan Mountains (NTM, 94.61%). The total snow mass in northwest China was estimated at 108.95 million tons, with northern Xinjiang accounting for 66.24 million tons (60.8%), followed by southern Xinjiang (37.44 million tons) and the Hexi Inland Region (5.27 million tons). Consistency analysis revealed coherent declines across all indicators in 55.56% of the study area. Significant SD and SCD reductions occurred in TRR and Tuha Basin (THB), while SWE declines were widespread in NTM and IRB, driven by rising temperatures and decreased snowfall. The findings underscore the urgent need for adaptive strategies to address emerging challenges for water security and ecological stability in the region. Full article

The following study presents the results obtained from a comparative analysis of dry (powder and hard snow) and wet snow based on satellite data and in situ data methods for monitoring in the high mountain belt of Bulgaria. The aim of the study is to analyze the effectiveness of different spectral indices based on satellite data from Synthetic Aperture Radar (SAR), high-resolution (HR) imagery, and spectrometer data for assessing the state and dynamics of the snow cover. The methods studied and the results obtained were validated by instrument-based field observations, with instruments using thermal imaging cameras, spectrometer measurements, ground control points, and HR imagery. Satellite data offer an ever-widening view of trends in snow distribution over time. All these data combined provide a detailed picture of surface temperature and snow properties, which are crucial for understanding snowmelt processes and the energy balance in the high-altitude belt. The findings suggest that a multi-method approach, utilizing the combined advantages of SAR satellite data, offers the most comprehensive and accurate framework for satellite-based snow cover monitoring in the high mountain regions of Bulgaria, such as Rila Mountain. This integrative strategy not only improves the precision of snow cover estimates but can also support many water resource-related studies, such as snowmelt runoff studies, snow avalanche modeling, and better-informed decisions in the management and maintenance of winter tourism resorts. Full article

Mountain regions are particularly sensitive and vulnerable to the impacts of climate change. Over the past three decades, mountain temperatures have risen significantly faster than those in lowland areas. The Hindu Kush–Karakoram–Himalaya region, often referred to as the “water tower of Asia”, is the largest freshwater source outside the polar regions. However, it is currently undergoing cryospheric degradation as a result of climatic change. In this study, the Normalized Difference Glacier Index (NDGI) was calculated using Landsat and Sentinel satellite images. The results revealed that glaciers in Chitral, located in the Eastern Hindu Kush Mountains of Pakistan, lost 816 km² (31%) of their total area between 1992 and 2022. On average, 27 km² of glacier area was lost annually, with recession accelerating between 1997 and 2002 and again after 2007. Satellite analyses also indicated a significant increase in both maximum (+7.3 °C) and minimum (+3.6 °C) land surface temperatures between 1992 and 2022. Climate data analyses using the Mann–Kendall test, Theil–Sen Slope method, and the Autoregressive Integrated Moving Average (ARIMA) model showed a clear increase in air temperatures from 1967 to 2022, particularly during the summer months (June, July, and August). This warming trend is expected to continue until at least 2042. Over the same period, annual precipitation decreased, primarily due to reduced snowfall in winter. However, rainfall may have slightly increased during the summer months, further accelerating glacial melting. Additionally, the snowmelt season began consistently earlier. While initial glacier melting may temporarily boost water resources, it also poses risks to communities and economies, particularly through more frequent and larger floods. Over time, the remaining smaller glaciers will contribute only a fraction of the former runoff, leading to potential water stress. As such, monitoring glaciers, climate change, and runoff patterns is critical for sustainable water management and strengthening resilience in the region. Full article

Being an agricultural country, Pakistan requires lots of water for irrigation. A major portion of its water resources is located in the upper indus basin (UIB). The snowmelt runoff generated from high-altitude areas of the UIB provides inflow into the Indus river system that boosts the water supply. Snow accumulation during the winter period in the highlands in the watershed(s) becomes a source of water inflow during the snow-melting period, which is described according to characteristics like snow depth, snow density, and snow water equivalent. Snowmelt water release (SWE) and snowmelt water depth (SD) maps are generated by tracing snow occurrence from MODIS-based images of the snow-cover area, evaluating the heating degree days (HDDs) from MODIS-derived images of the land surface temperature, computing the solar radiation, and then assimilating all the previous data in the form of the snowmelt model and ground measurements of the snowmelt water release (SWE). The results show that the average snow-cover area in the Astore river basin, in the upper indus basin, ranges from 94% in winter to 20% in summer. The maps reveal that the annual average values of the SWE range from 150 mm to 535 mm, and the SD values range from 600 mm to 2135 mm, for the snowmelt period (April–September) over the years 2010–2020. The areas linked with vegetation experience low SWE accumulation because of the low slopes in the elevated regions. The meteorological parameters and basin characteristics affect the SWE and can determine the SD values. Full article

The snow water equivalent (SWE) in high-altitude regions is crucial for water resource management and disaster risk reduction, yet accurate predictions remain challenging due to complex snowmelt processes, nonlinear meteorological factors, and time-lag effects. This study used snow remote sensing products from the Advanced Microwave Scanning Radiometer (AMSR) as the predictand for evaluating SWE predictions. It applied nine machine learning models—linear regression (LR), decision trees (DT), support vector regression (SVR), random forest (RF), artificial neural networks (ANNs), AdaBoost, XGBoost, gradient boosting decision trees (GBDT), and CatBoost. For each machine learning model, submodels were constructed to predict the SWE for the next 1 to 30 days. The 30 submodels of each machine learning model formed the prediction model for the snow water equivalent over the next 30 days. Through an accuracy evaluation and ensemble forecasting, the snow water equivalent prediction for the next 30 days in the Yalong River above the Ganzi Basin was finally achieved. The results showed that for all models, the average Nash–Sutcliffe Efficiency (NSE) rate was greater than 0.8, the average root mean square error (RMSE) was under 8 mm, and the average relative error (RE) was below 7% across three lead time periods (1–10, 11–20, and 21–30 days). The ensemble average model, combining ANNs, GBDT, and CatBoost, demonstrated superior accuracy, with NSE values exceeding 0.85 and RMSE values under 6 mm. A sensitivity analysis using the Shapley Additive Explanations (SHAP) model revealed that temperature variables (average, minimum, and maximum temperatures) were the most influential factors, while relative humidity (Rhu) significantly affected the SWE by reducing evaporation. These findings provide insights for improving SWE prediction accuracy and support water resource management in high-altitude regions. Full article