Search Results (204)

Erosion monitoring was carried out between 2003 and 2022 using a hydrological station with a Thomson overflow, a water gauge, and a limnigraph installed at the outlet of the Kolonia Celejów gully system. The study area is located in the north-western part of the Lublin Upland in the Nałęczów Plateau mesoregion (SE Poland). The total amount and intensity of precipitation were measured using an automatic station and water runoff and suspended sediment yield (SST) were also continuously measured. High variability in water runoff was observed during this period (max. of about 76,000 m³ and mean > 26,000 m³), and as a result of numerous heavy rains, a significant increase in SST (max. of about 95 Mg to about 1200 Mg and mean of 24 Mg to about 215 Mg) was noted in the second half of the measurement period. Most of the material removed at that time came from the cutting of the gully bottom and from the redeposition of material transported from the catchment used for agricultural purposes. In order to determine the volume of material delivered to the slope–gully cascade system in November 2012, a second station was installed at the gully head, which only operated until June 2013. However, the measurements covered all snowmelts and summer runoffs, as well as the June downpours. At the same time, these measurements represent the first unique attempt to quantify the delivery of material from the slope subcatchment to the gully system. The year 2013 was also important in terms of water runoff from the loess gully catchment area (about 40,000 m³) and was a record year (SST > 1197 Mg) for the total amount of suspended material runoff (7.6% and 33.5% of the 20-year total, respectively). During the cool half of the year, 16,490 m³ of water (i.e., 42% of the annual total) flowed out of the gully catchment area, and during the warm half of the year, 23,742 m³ of water (59% of the annual total) flowed out. In contrast, 24,076.7 m³ of water flowed out of the slope subcatchment area during the year, with slightly more flowing out in the cool half of the year (12,395.9 m³ or 51.5% of the annual total). In the slope and gully subcatchment areas, the suspended sediment discharge clearly dominated in the warm half of the year (98% and 97%). The record-breaking SST amount in June was over 1100 Mg of suspended sediment, which accounted for 93% of the annual SST from the gully catchment area and over 94% in the case of the slope subcatchment area. The relationships in the slope–gully cascade system in 2013 were considered representative of the entire measurement series, which were used to determine the degree of connectivity between the slope and gully subsystems. During summer downpours, the delivery of slope material from agricultural fields accounted for approx. 15% of the material removed from the catchment area, which confirms the predominance of transverse transport in the slope catchment area and longitudinal transport in the gully. The opposite situation occurs during thaws, with as much as 90% of the material removed coming from the slope catchment area. At that time, longitudinal transport dominates on the slope and transverse transport dominates in the gully. Full article

Winter road safety is threatened by black ice, while traditional de-icing methods, such as chemical spreading and electrically heated pavement systems, raise concerns about environmental impact and economic costs. This study proposed a hydronic heated pavement system utilizing geothermal energy (HHPS-G)-integrated concrete pavement that ensures environmental sustainability and structural stability. The design utilizes hollow steel pipes as both reinforcement and heat exchange conduits, thereby eliminating the need for separate high-density polyethylene (HDPE) pipes. To enhance upward heat transfer, bottom-ash concrete was introduced as an alternative to conventional insulation, providing thermal insulation and structural strength. A validated numerical model was developed to compare the de-icing and snow-melting performance of different pipe types. The results show that hollow steel pipes reduced the time to reach 0 °C on the concrete pavement surface by 30.86% and improved heat flux by 10.19% compared to HDPE. The depth of pipe installation significantly influenced performance: positioning the pipes near the surface achieved the fastest heating (up to 70.11% faster), while mid-depth placement, recommended for structural integrity, still provided substantial thermal benefits. Variations in insulation thermal conductivity below 1 W/m·K had little effect, whereas replacing the base layer with bottom-ash concrete provided both insulation and strength without the need for separate insulation layers. Full article

Identifying the impact of climate change and changes in underlying surface conditions on river runoff changes is critical for sustainable water resource use and watershed management in arid regions. The Kriya River is not only a key support for water resources in the arid environment of the Tarim Basin, but also a solid foundation for the survival and development of agricultural oases. In this study, the Kriya River Basin in Xinjiang, China, was taken as the research object, and the Mann–Kendall, Sen’s Slope, Cumulative Sum, and other methods were used to systematically analyze the temporal evolution law and multi-modal characteristics of runoff in the basin. Based on the Budyko hydrothermal coupling equilibrium equation, the contribution of temperature, evaporation, and the underlying surface to runoff variation was quantitatively interpreted. The study found that the annual runoff depth of the Kriya River Basin has shown a significant positive evolution trend in the past 60 years, with an increase rate of 0.5189 mm/a (p ≤ 0.01). Through the identification of mutation points, the runoff time series of the Kriya River was divided into the base period 1957–1999 and the change period 2000–2015. Without considering the supply of snowmelt runoff, the contribution rate of precipitation to runoff change was 75.23%, followed by the change in underlying surface (23.08%), and the potential evapotranspiration was only 1.69%. The results of this study provide a good scientific reference for water resources management and environmental governance in the Kriya River Basin. Full article

Streamflow generated from snowmelt is important, and changing, in snow dominated regions of the world. We used a recently developed technique to estimate the start and end of snowmelt streamflow for 39 gauging stations across Colorado and determined the 40-year trends from 1981 to 2020. Most watersheds showed a trend towards an earlier start (34 watersheds) or end (29 watersheds) of snowmelt streamflow, but the mean of the start and end dates showed mixed trends (earlier in 12 watersheds and later in 20). We determined the correlation between these streamflow snowmelt trends and terrain parameters plus trends in canopy cover, winter precipitation, peak snow water equivalent, and melt-period temperature. There were some significant correlations, primarily for total annual streamflow and the timing and volume of the end of snowmelt streamflow contribution to winter precipitation (decreasing), minimum temperature (warming), and slope (negatively). Higher elevation watersheds tend to be steeper, less snow has been observed at higher elevations, and the snowpack is melting sooner. Snowmelt streamflow trends are partially explained by climate trends and watershed characteristics. Full article

Accurate mapping of deforestation in regions characterized by complex, heterogeneous landscapes and frequent cloud cover remains a major challenge in remote sensing. This study presents a phenology-informed, spatiotemporal data fusion framework for robust deforestation mapping in North Korea, focusing particularly on hillside fields and unstocked forests—two dominant deforested land cover types in the region. By integrating multi-temporal satellite observations with variables derived from phenological dynamics, our approach effectively distinguishes spectrally similar classes that are otherwise challenging to separate. The Flexible Spatiotemporal Data Fusion Algorithm (FSDAF) was employed to generate high-frequency, Landsat-like time-series from MODIS data, thereby ensuring fine spatial detail alongside temporal consistency. Key classification features—including NDVI, NDSI, NDWI, and snowmelt timing—were identified and ranked using the Random Forest (RF) algorithm. The classification results were validated against reference Landsat imagery, achieving high correlation coefficients (R > 0.8) and structural similarity index values (SSIM > 0.85). The RF-based land cover classification reached an overall accuracy of 86.1% and a Kappa coefficient of 0.837, reflecting strong agreement with ground reference data. Comparative analyses demonstrated that this method outperformed global land cover products, such as MCD12Q1, in capturing the spatial variability and fragmented patterns of deforestation at the regional scale. This research underscores the value of combining spatiotemporal fusion with phenological indicators for accurate, high-resolution deforestation monitoring in data-limited environments, providing practical insights for sustainable forest management and ecological restoration planning. 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

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

Snow cover is an important freshwater source in many mountain ranges around the world and is heavily affected by climate change, often leading to reduced overall snow cover availability and duration as well as shifts in seasonality. To monitor these changes and long-term trends, the analysis of remote sensing is a commonly used tool, as data are available consistently and for long time series. In this study we acquired and processed the whole archive of available Landsat data between 1985 and 2024 for two catchments in the Chilean Andes, Aconcagua and Río Maipo, located in the Valparaíso and Santiago de Chile metropolitan regions, respectively. We generated monthly Snow Line Elevation (SLE) time series from the entire archive for both catchments and performed trend analyses on these time series. Strong positive long-term SLE change rates of 11.25 m per year for the Aconcagua catchment and 9.85 m to 15.65 m per year for the Río Maipo catchment were detected, indicating a decrease in snow cover as well as available freshwater from snowmelt. The projection to the year 2050 revealed a potential loss of snow covered area of up to 42% during summer months, with the SLE receding up to 231 m. 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

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

Baseflow is a crucial component of river flow in alpine inland basins, playing an essential role in watershed ecological health and water resource management. In high-latitude cold regions, seasonal freeze-thaw processes make baseflow formation mechanisms particularly complex. However, the dominant factors affecting baseflow and their relative contributions remain unclear, limiting the accuracy of flow estimation and effective water resource management. This study employed baseflow separation techniques and statistical methods, including the Mann-Kendall test, to investigate temporal trends and abrupt changes in baseflow and the baseflow index (BFI) at multiple time scales (annual, seasonal, and monthly) from 2005 to 2012. Additionally, the timing of snowmelt and its impact on baseflow were examined. Key findings include the following: (1) Baseflow and BFI showed distinct temporal variability with non-significant upward trends across all time scales. Annual BFI ranged from 0.48 to 0.61, contributing approximately 50% of total runoff. (2) At the seasonal scale, baseflow remained relatively stable in spring, increased in autumn, and showed non-significant decreases in summer and winter. Monthly baseflow exhibited an increasing trend. (3) The snowmelt period occurred between April and May, with baseflow during this period strongly correlated with climatic factors in the following order: winter precipitation > positive accumulated temperature > winter air temperature > negative accumulated temperature. The strongest positive correlation was observed between baseflow and winter precipitation (R = 0.724), while negative correlations were found with accumulated temperatures and winter air temperature. These findings offer valuable insights for predicting water resource availability and managing flood and ice-jam risks in cold regions. Full article

This study applied the fully coupled model WRF/WRF-Hydro to simulate land, air, and water cycles in the Changbai Mountain area (CMA) in Northeast China. This study evaluated the applicability of the coupled model in the region and analyzed the impact of regional climate change on the water cycle in the study area over the past half-century. The temperature in the Changbai Mountains increased significantly from 1975 to 2020. Precipitation, canopy water, and all types of evapotranspiration showed different increasing trends, whereas surface runoff showed a decreasing trend. The comparison revealed that precipitation, canopy water, canopy evaporation, and total evapotranspiration increased gradually in the low-latitude subbasins, whereas runoff decreased more rapidly. Runoff in the study area showed an annual double peak, which was due to snowmelt in spring and abundant precipitation in summer. Under the influence of climate change, the thawing time of frozen soil and snow cover in the study area will advance, leading to an increase in the spring runoff peak and earlier occurrence time. Our results provide a reference for the study of the water cycle process of the coupled model in cold mountainous areas and a scientific reference for the scientific response to climate change and the protection of regional water resource security. Full article

This paper presents a flood season staging study for the Altash Water Control Project in Xinjiang, aiming to enhance water resource utilization efficiency. A combined qualitative and quantitative analysis approach is adopted to address the challenges posed by complex flood mechanisms in snow-melt-dominated arid basins. This methodology responds to the limitations of traditional fixed flood limit water level operations, which often fail to satisfy optimized management requirements. The study systematically evaluates flood occurrence timing distributions, seasonal runoff variations, and watershed precipitation patterns through both meteorological causation and mathematical statistical methodologies. Qualitative analysis determines the flood stage boundary points, complemented by quantitative calculations utilizing an ordered clustering methodology. The integration of these analytical and computational outcomes facilitates the definitive identification of flood stage boundary points. The findings indicate unique phased characteristics in the project. The proposed phasing scheme corresponds with seasonal weather pattern variations, thereby offering guidance for dynamic reservoir flood limit water level control. This research addresses conventional fixed flood limit water level operational constraints in arid zones while exploring appropriate flood season staging methods for basins primarily influenced by snowmelt. Multiple methods and indicators inform the staging results through a methodology that combines meteorological causation analysis, mathematical statistics, and ordered clustering methods. The research establishes a scientifically justified flood season division for the Altash Basin and proposes a rational staging scheme. These findings offer a scientific foundation for optimized reservoir management and enhanced water resource efficiency in arid environments. In addition, they represent a valuable reference for flood season staging analyses in similar basin systems. Full article

Climate warming has led to changes in floods in snow-packed mountain areas, but how snowmelt contributes to floods in the high-altitude Tibetan Plateau remains to be studied. To solve this problem, we propose a more reasonable method for evaluating snowmelt’s contributions to floods. We use a distributed hydrological model with the capability to track snowmelt paths in different media, such as snowpack, soil, and groundwater, to assess snowmelt’s contribution to peak discharge. The study area, the Xiying River basin, is located northeast of the Tibetan Plateau. Our results show that in the past 40 years, the average annual air temperature in the basin has increased significantly at a rate of 0.76 °C/10a. The annual precipitation (precipitation is the sum of rainfall and snowfall) decreased at a rate of 5.59 mm/10a, while the annual rainfall increased at a rate of 11.01 mm/10a. These trends were not obvious. The annual snowfall showed a significant decrease, at a rate of 14.41 mm/10a. The contribution of snowmelt to snowmelt-driven floods is 85.78%, and that of snowmelt to rainfall-driven floods is 10.70%. Under the influence of climate change, the frequency of snowmelt-driven floods decreased significantly, and flood time advanced notably, while the intensity and frequency of rainfall-driven floods slowly decreased in the basin. The causes of the change in snowmelt-driven floods are the significant increase in air temperature and the noticeable decrease in snowfall and snowmelt runoff depth. The contribution of snowmelt to rainfall-driven floods slowly weakened, resulting in a slight decrease in the intensity and frequency of rainfall-driven floods. The results also indicate that rising air temperature could decrease snowmelt-driven floods. In snow-packed mountain areas, rainfall and snowmelt together promote the formation of and change in floods. While rainfall dominates peak discharge, snowpack and snowmelt play a significant role in the formation and variability of rainfall-driven floods. The contributions of snowmelt and rainfall to floods have changed under the influence of climate change, which is the main cause of flood variability. The changed snowmelt adds to the uncertainties and could even decrease the size and frequency of floods in snow-packed high mountain areas. This study can help us understand the contributions of snowmelt to floods and assess the flood risk in the Tibetan Plateau under the influence of climate change. Full article