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Understanding the hydrological impacts of climate change is essential for robust and sustainable water management. This study assessed the hydrologic conditions under changing climate in the Jinshajiang River basin, the source region of the Yangtze River, using the hydrological model SWAT with the historical observations and the future climate simulations under two Shared Socioeconomic Pathways (SSP2‐4.5 and SSP5‐8.5). For the historical period, with an increasing trend of precipitation, evapotranspiration, and snowmelt, streamflow increases in upstream region but keeps decreasing in the downstream catchment. For future scenarios, a warmer and wetter climate is projected for the basin throughout the 21st century, leading to an overall increase in mean and extreme streamflow. The streamflow magnitude increases more significantly in the far future than in the near future, and more significant under SSP5‐8.5 than SSP2‐4.5. The projected remarkable increase in precipitation causes the transition in changing trend of streamflow compared with the historical period. The projected warming leads to a continuing decline in snowfall and snow water equivalent, followed by an earlier snowmelt and higher peak streamflow, especially at the upstream catchment. Ultimately, reservoirs in the basin are expected to gain more inflows, however, with greater variability including higher likelihoods of flood and drought events, which impose potential challenges on reservoir operations. These outcomes indicate the importance of adaptive water resources management in the melting water contributed basin to sustain and enhance its services under global warming. [ABSTRACT FROM AUTHOR]

This article researches a superhydrophobic anti‐fouling material that can be used for agricultural medicine boxes, aiming to solve the problems of residual medicine, difficult recovery, and low reuse rate in medicine boxes. Double modification (volume and surface modification) is carried out on the ultra‐high molecular weight polyethylene (UHMWPE) matrix, and the high fluidity of melted snow wax is connected to UHMWPE and gas‐phase silica (SiO2) to prepare superhydrophobic composite materials U‐C‐WSiO2, achieving multiple combinations of superhydrophobicity, high mechanical properties, and high stability. The high‐strength bonding between snow wax and UHMWPE matrix acts as an adhesive, strengthening the bonding degree between silica particles and the matrix, achieving excellent superhydrophobicity, static contact angle reaches 157.0° on its surface while maintaining a relatively stable hydrophobic surface after multiple droplet impacts. In addition, unique surface modification methods endow U‐C‐WSiO2 composite materials with high stability, which can resist pesticide erosion and maintain surface corrosion resistance. This superhydrophobic composite material can solve the problem of pesticide residue in medicine boxes and has broad prospects for agricultural applications. Its surface water repellent and drug repellent properties further expand the application of thermoplastic engineering plastics in agricultural production. [ABSTRACT FROM AUTHOR]

Growing streamflow uncertainty, which is especially evident in high and cold regions, is one indication of climate change impacts on the hydrological cycle. In this work, an interval forecasting model that couples a snowmelt module and an uncertainty module is established based on the Xinanjiang model. The proposed model can consider climate change impacts by quantifying streamflow variations in the form of interval forecasts. Its performance was assessed by applying it in the headwater region of the Yellow River Basin. Interval forecasts and uncertainty analyses were conducted, and results show that the model can accurately describe the daily streamflow process in the study area. Unlike the deterministic forecasting model, the interval forecasting model effectively addresses shortcomings in forecasting high-flow scenarios. Furthermore, uncertainty analyses indicate that the model parameter K (the ratio of potential evapotranspiration to pan evaporation) plays a crucial role in water balance computations; the model parameter B (exponent of the distribution of the soil tension water capacity curve) exhibits sensitivity, suggesting challenges in attaining complete soil saturation across the entire basin. The insensitivity of the snowmelt module parameters implies that the proportion of snowmelt streamflow is relatively low in the annual streamflow and remains stable. The results can provide theoretical references for assessing the uncertainty of streamflow variations and for reservoir regulation in the Yellow River Basin. Highlights: An interval forecasting model is established to consider the climate change impacts based on the Xinanjiang model. The interval forecasting model effectively addresses shortcomings in forecasting high-flow scenarios compared with the deterministic forecasting model. The evapotranspiration plays a crucial role on the water cycle, influencing the calculation of water balance in the study area. The proportion of snowmelt streamflow is relatively low in the annual streamflow and remains stable in the study area. [ABSTRACT FROM AUTHOR]

ABSTRACT The reliability of seasonal snow cover information is constrained by limitation of in situ observations and uncertainties in remote sensing data and model simulations in alpine region, thus posing important challenges to understanding the climate system and water resource management in alpine region. Here, the assimilation of daily cloud‐free Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference snow index (NDSI) product into an intermediate complexity snow mass and energy balance model—Flexible Snow Model (version FSM2_MO)—was implemented. The aim is to improve the model simulations of seasonal snow cover (snow‐covered extent; SCE, snow depth; SD, snow water equivalent; SWE, and snowmelt runoff; SMR) in the alpine region (a case of the upper‐middle reaches of the Heihe River basin, Northwest China). The results indicate comprehensive improvement in the simulation of SCE, SD, and SMR in the study area through data assimilation, with the ability to significantly reduce prior biases of the FSM2_MO. Based on the independent daily cloud‐free Advanced Very High Resolution Radiometer (AVHRR) SCE product, the updated SCE simulation (i.e., data assimilation) showed a reduction in mean absolute error (MAE) from 10.46% to 7.16%, root mean square error (RMSE) from 16.14% to 12.26%, and an increase in Pearson's correlation coefficient (CC) from 0.18 to 0.67 compared with the open loop simulation (i.e., without assimilation). The evaluation results of SD observation data showed that data assimilation improved SD simulation compared with the open loop run (OL). And utilising the monthly discharge observations at the Yingluoxia hydrological station, data assimilation slightly improved the SMR simulation. The updated SMR simulation achieved a CC of 0.91, Nash‐Sutcliffe efficiency coefficient (NSE) of 0.73, and Kling‐Gupta efficiency coefficient (KGE) of 0.76. Moreover, the Landsat 8‐derived snow cover map and Sentinel‐1‐derived SD also indicated that the updated simulation effectively filled in the missing snow cover and removed the superfluous snow cover predicted by the OL simulation in terms of spatial distribution. [ABSTRACT FROM AUTHOR]

Snowfall/cover and snowmelt are essential determinants of winter soil processes/events that may force the biogeochemical dynamics of soils in temperate regions. Increasing variability of the European hydroclimate is expected to lead to more frequent intermittent warm periods, which cause snowmelt during winter and rapidly mobilize large amounts of dissolved organic matter (DOM). This study, conducted at the Hainich Critical Zone Exploratory in Germany, seeks to address a significant gap in understanding the molecular impacts of snowmelt-induced DOM flows and their ability to alter soil ecosystems rapidly. During two snowmelt events between January and March 2021, we observed that DOM concentration and composition varied more in forest soil seepage than in grassland soil seepage. Forest seepage showed a pronounced DOM flux peak and synchronous increases in the relative abundances of aromatic DOM components, indicating surface-derived transport of plant litter carbon. In the grassland, however, peak DOM fluxes were characterized by a marked increase in nitrogen-containing (N-containing) DOM components, indicating a predominance of microbial carbon. Notably, the unique DOM components specific to each ecosystem increased during peak fluxes in the forest but decreased in the grassland. We suggest that an overall higher molecular richness and the broader functional metabolic potentials in grassland may account for its relatively greater DOM stability compared to the forest during peak snowmelt events. [ABSTRACT FROM AUTHOR]

The Bagmati River Basin is experiencing significant water stress due to a reduction of surface and groundwater resources, especially during the dry season. The basin's heavy reliance on monsoon-dominated precipitation, without the buffer of snow or glacier melt, exacerbates these issues. Dam construction is seen as a viable solution for maintaining river flow and regulating river ecosystems. Thus, this study leveraged multi-criteria decision-making tools, particularly the analytical hierarchy process (AHP) and fuzzy AHP (FAHP) in conjunction with the Geographic Information System(GIS), to identify suitable dam construction sites in the Bagmati River Basin. Through an extensive literature review, nine criteria were identified: stream density, rainfall, slope, land use, elevation, soil type, distance from faults, distance from settlements, and distance from roads. Pairwise comparison matrices, based on expert surveys, were used to assign weights to each criterion, with validation against existing and proposed dams. Results show that approximately 31% of the basin area is suitable for dam construction, with about 4.45% area being highly suitable. FAHP only slightly outperforms AHP in assessing existing dam locations, demonstrating the robustness of both methodologies. For the validation of suitability analysis, location of existing dams are compared. While Nepal is not generally water-stressed, inter-seasonal water availability is high. Dam construction for multiple uses is nascent in Nepal, and location analysis studies are rare. The methodology used here can be replicated in other regions, offering valuable insights for decision-makers. [ABSTRACT FROM AUTHOR]

This study focuses on how multisite downscaling procedures can provide a significant improvement in the accuracy of impact assessment results as compared to single-site downscaling techniques. The study compared the accuracy of snowmelt computations given by the multisite multivariate statistical downscaling (MMSD) method and the single-site downscaling (SDSM) procedure. Results of an illustrative application using daily maximum (Tmax) and minimum (Tmin) temperature series at seven stations in southern Quebec–Ontario region in Canada indicated that MMSD method was able to reproduce accurately the observed spatial and temporal variabilities of daily extreme temperature processes over different locations. MMSD approach preserved more accurately the interstation correlations of daily Tmax (0.78 and 0.7) and Tmin (0.94 and 0.84), respectively, for calibration and validation period than SDSM (0.08 and 0.11 for Tmax; and 0.02 for Tmin). MMSD was found capable to produce more accurate snowmelt estimations than those given by the SDSM. [ABSTRACT FROM AUTHOR]

Sodium alkylbenzene sulfonates (ABS), or linear alkylbenzene sulfonates (LAS), are among the most common synthetic anionic surfactants and water pollutants, capable of causing both acute and chronic toxic effects on aquatic organisms. The selective determination of sodium alkylbenzene sulfonates as a distinct class of anionic surfactants in complex natural matrices is feasible by gas chromatography with mass spectrometric detection (GC–MS) in the form of linear alkylbenzenesulfonic acid methyl esters (LABSA ME). The behavior of LAS and LABSA ME was studied under ascending high-performance thin-layer chromatography (HPTLC) using Kieselgel 60 F254 and Sorbfil plates to purify extracts and preconcentrate analytes. A mixture of n-hexane with methanol (23 : 1, v/v) is proposed as a mobile phase. Under these conditions, sodium alkylbenzene sulfonates remain at the start line, while their derivatives (LABSA ME), obtained by methylation with trimethyl orthoformate in the presence of trifluoroacetic acid (yield η = 98%), form zones characterized by retention factors Rf of 0.62–0.71 on Kieselgel 60 F254 and Sorbfil plates, respectively. The repeatability of Rf values is characterized by a relative standard deviation of 6.1 and 5.9%, respectively (n = 16). The completeness of the extraction (95.0–100.0%) of analytes from the plates is noted using descending HPTLC with acetonitrile. The applicability of the HPTLC method for preconcentrating analytes and purifying extracts is demonstrated using real water samples. Using GC–MS with electron impact ionization, concentrations of sodium alkylbenzene sulfonates were found in water sampled from the southern basin of Lake Baikal at a depth of 400 m (0.24 ± 0.02 µg/L) and in water obtained from melting snow collected from the ice of the Krestovka River at its mouth near the settlement of Listvyanka (31.1 ± 1.0 µg/L). [ABSTRACT FROM AUTHOR]

This study presents an implementation of a new melting layer model in the ZJU‐AERO radar observation operator (Accurate and Efficient Radar Operator designed by ZheJiang University). The proposed model utilizes a coated spheroid to represent melting snow and graupel. It consists of three stages–coating, soaking, and melting–to account for the dielectric and density effects of melting particles. The scattering properties of the melting particles are computed with the Invariant‐Imbedding T‐Matrix (IITM) method, and the results are tabulated as look‐up tables for the radar operator. Regarding the parameterization of bulk optical properties, a flux‐conservation scheme is employed to estimate the size distribution of melting particles. To demonstrate its flexibility and superiority, the single and bulk scattering properties of our multi‐stage melting model are compared against the traditional homogeneous model, which uses the effective medium approximation (EMA). The effectiveness of the multi‐stage melting model has also been assessed by mapping model states in the regional mesoscale model of the China Meteorology Administration (CMA‐MESO) to radar observations. In the microphysics package of CMA‐MESO, the melting process is not explicitly represented, and we assume that melting hydrometeors occur where solid and liquid phases overlap. When compared with observations, the present multi‐stage melting model successfully reproduces melting layer signatures, highlighting its potential for microphysic validation, quantitative precipitation estimations, and data assimilation studies. Plain Language Summary: In this study, we present a new melting particle modeling scheme for a radar observation simulator. This scheme features the representation of the melting procedure of solid precipitation particles through three stages: an initial coating stage, an intermediate soaking stage, and a final melting stage. By incorporating these stages into the simulation, this new melting model surpasses the limitation of the commonly used model that treats melting particles as homogeneous mixtures of water, air, and ice. Additionally, we calculate the probability distributions of melting particle sizes using a rigorous falling‐flux‐conservation rule that considers the acceleration of particles during melting. The scattering properties of these coated‐spheroid melting particles are computed using the Invariant‐Imbedding T‐Matrix (IITM) method. Finally, the results are compared against those obtained using the traditional melting particle model. These comparisons demonstrate the potential of this newly proposed approach for improving the accuracy and reliability of radar simulations. Key Points: A three‐stage nonspherical particle model is proposed to account for the changes of dielectric constant and density of melting hydrometeorsA rigorous flux‐conservation bulk parameterization scheme is implemented to incorporate the terminal velocity variation as particles meltThe scattering properties of melting particles are rigorously computed using the state‐of‐the‐art invariant‐imbedding T‐matrix method [ABSTRACT FROM AUTHOR]

The phenomenon of global climate change has led to an increase in the frequency of extreme precipitation events, an acceleration in the melting of glaciers and snow cover, and an elevation of the risk of flooding. In this study, the DB-IWHR model was employed in conjunction with the MIKE 21 hydrodynamic model to develop a simulation system for the dam failure flow process of an earth and rock dam. The study concentrated on the KET reservoir, and 12 dam failure scenarios were devised based on varying design flood criteria. The impact of reservoir failures on flood-risk areas was subjected to detailed analysis, with consideration given to a range of potential failure scenarios and flood sizes. It was determined that under identical inflow frequency conditions, the higher the water level, the more rapid the breakout process and the corresponding increase in flood peak discharge. Conversely, for a given frequency of incoming water, an elevated water level results in a transient breach process, accompanied by a reduction in flood peak flow. Moreover, for a given water level, an increase in water frequency results in a reduction in breaching time, an extension of flood duration, and an increase in flood peak flow. The observed trend of flood spreading is generally north-south, and this process is highly compatible with the topographic and geomorphological features, demonstrating good adaptability. [ABSTRACT FROM AUTHOR]

The environmental effects of airborne micro‐ and nano‐size plastic particles are poorly understood. Microscopy and chemical analyses of atmospherically deposited particles on snow surfaces at high elevation (2,865–3,690 m) in the Upper Colorado River basin (UCRB; Colorado Rocky Mountains) revealed the presence of black substances intimately associated with microplastic fibers, particles interpreted to have originated as tire matter. Identical and similar particles occur in shredded tires and road‐surface samples. The substance responsible for the black color of all tires is carbon black, a graphitic light‐absorbing tire additive produced by hydrocarbon combustion that homogeneously permeates the mixture of tire polymers and other additives. Such black tire matter may thus exert radiative effects closely similar to those of black carbon. The presence in snow of many organic compound types common to tires, measured by two‐dimensional gas chromatography, suggests that atmospherically deposited black road‐tire‐wear matter is among the light‐absorbing particulates that advance the onset and rate of snow melt in the UCRB. The mass of road‐tire‐wear particles shed from vehicles may be estimated by multiplying measured amounts of eroded tire‐per‐distance traveled by vehicular distances. Under a combination of measurements and assumptions about the amounts and radiative properties of atmospheric tire‐wear particles, the radiative effects of these particles might add about 10%–30% to those effects from black carbon, an estimate ripe for revision. On regional and global scales, the amounts and effects of emitted and deposited tire‐wear matter likely vary by factors of geographic source, transport pathway, and depositional setting. Plain Language Summary: A major source for airborne microplastic particles is road‐tire wear. Microscopy and chemical analyses of wind‐blown particles on dirty, high‐elevation (2,865–3,690 m) snow surfaces in the Colorado Rocky Mountains revealed the presence of black substances intimately mixed with microplastics, particles interpreted as tire matter. Identical and similar particles occur in shredded tires and in samples collected from road surfaces. The black substance responsible for the black color of all tire particles is carbon black that homogeneously permeates the mixture of tire polymers and other additives. The mass of particles produced by road‐tire wear may be estimated by multiplying measured amounts of eroded tire‐per‐distance traveled by vehicular travel distances. Under assumptions of amounts of tire‐wear matter emitted to the atmosphere, their presence as light‐absorbing particles likely contribute to melting of snow and ice as well as to warming the atmosphere. Key Points: Light absorbing particles, as blackened microplastic matter from road‐tire wear, occur in snow of the Colorado Rocky Mountains (2013–2021)The mass of road‐tire‐wear particles annually generated is estimated to be 6,550 kt, of which about 655–1,965 kt might become airborneThe results are relevant to radiation modeling of snow and ice surfaces as well as the atmosphere [ABSTRACT FROM AUTHOR]

Algae populate multiple habitats, including snow and ice, where they can form red blooms. These decrease snow albedo, accelerating snowmelt and potentially feeding back on snow and glacier decline caused by climate change. Quantifying this feedback requires the understanding of bloom evolution with climate change. Little, however, is known about the drivers of red snow blooms. Here, we develop an algorithm to analyze 5 y of satellite data from the European Alps and separate bloom occurrences from similarly colored Saharan dust depositions. In a second step, we combine the occurrences of blooms with meteorological data and snow simulations to identify the drivers of blooms. Results show that the upward migration of algae from the ground and blooming requires the presence of liquid water throughout the whole snow column for at least 46 d. Our limited data suggest that moderate dust amounts provide nutrients favorable to bloom, whereas large dust amounts hasten snowmelt and reduce its duration below the threshold required for blooming. Over the period studied, blooms cover 1.3% of the area above 1,800m elevation, advancing the snow melt-out date by 4 to 21 d in these areas. Under warmer climates, maximum snow mass will decrease whereas snowmelt duration, that controls algal blooms' occurrences, is less sensitive to global temperature increase. In this respect, the impact of bloom on snowmelt will either remain stable (RCP4.5) or decrease (RCP8.5). Algal blooms in the Alps therefore do not constitute a positive climate feedback. [ABSTRACT FROM AUTHOR]

Modelling streamflow in snow-covered mountainous regions with complex hydrology and topography poses a significant challenge, particularly given the pronounced influence of temperature lapse rate (TLAPS) and precipitation lapse rate (PLAPS). The Present study area covers 54,990 km2 in the western Himalayas, including the Tibetan Plateau and the Indian portion of the USRB up to Bhakra Dam in Himachal Pradesh. In order to estimate the snowmelt and rainfall runoff contributions to the catchment, an integrated Soil and Water Assessment Tool (SWAT) model incorporates a Temperature Index with an Elevation Band approach. The uncertainty analysis of the SWAT model has been conducted using the Sequential Uncertainty Fitting algorithm (SUFI-2). Furthermore, machine-learning models such as Long Short-Term Memory (LSTM) neural networks and Random Forest (RF) are integrated with the SWAT model to enhance the accuracy of streamflow predictions resulting from snowmelt. The performance indices of a model for the monthly calibration period are R2 = 0.83, NSE = 0.82, P-BIAS = 2.3, P-factor = 0.82, and R-factor = 0.81. The corresponding values for the validation period are R^2 = 0.78, NSE = 0.77, P-BIAS = 5.7, P-factor = 0.72 and R-factor = 0.66. The results show that 63.08% of the Bhakra gauging station's annual streamflow has attributed to snow and glacier melt. The highest snow and glacier melt occur from May to August, while the minimum is observed from November to February. Regarding snowmelt forecasting, the LSTM model outperforms the RF model with an R2 value of 0.86 and 0.85 during training and testing, respectively. Additionally, sensitivity analysis highlights that soil and groundwater flow parameters, specifically SOL_K, SOL_AWC, and GWQMN, are the most sensitive parameters for streamflow modelling. The study confirms the effectiveness of SWAT for water resource planning and management in the mountainous USRB. [ABSTRACT FROM AUTHOR]

The snowmelt process plays a crucial role in hydrological forecasting, climate change, disaster management, and other related fields. Accurate detection of wet snow distribution and its changes is essential for understanding and modeling the snow melting process. To address the limitations of conventional fixed-threshold methods, which suffer from poor adaptability and significant interference from scattering noise, we propose a weakly supervised deep learning change detection algorithm with Sentinel-1 multi-temporal data. This algorithm incorporates the Multi-Region Convolution Module (MRC) to enhance the central region while effectively suppressing edge noise. Furthermore, it integrates the ResNet residual network to capture deeper image features, facilitating wet snow identification through feature fusion. Various combinations of differential images, polarization data, elevation, and slope information during and after snowmelt were input into the model and tested. The results suggest that the combination of differential images, VV polarization data, and slope information has greater advantages in wet snow extraction. Comparisons between our method, the fixed-threshold method, OTSU algorithm, and FCM algorithm against the results of Landsat images indicates that the overall accuracy of our method improves significantly when the proportion of wet snow cover is large, and the average overall accuracy of wet snow extraction is 85.2%. This study provides clues for the accurate identification of wet snow during the mid-snowmelt phase. [ABSTRACT FROM AUTHOR]

Space use by small mammals should mirror their immediate needs for food and predator shelters but can also be influenced by seasonal changes in biotic and abiotic factors. Lemmings are keystone species of the tundra food web, but information on their spatial distribution in relation to habitat heterogeneity is still scant, especially at a fine scale. In this study, we used spatially explicit capture–recapture methods to determine how topography, hydrology, vegetation, and soil characteristics influence the fine-scale spatial variations in summer density of brown lemmings (Lemmus trimucronatus). Lemmings were monitored throughout the summer in wet and mesic tundra habitats and in a predator exclusion grid, which was also located in mesic tundra. We found that in wet tundra, lemming densities were higher at sites with a rugged topography dominated by hummocks, but only during snow melt. In both mesic tundra sites, lemming densities were higher in sites with poor drainage and low aspect throughout the summer. We found no clear association between lemming densities and any tested vegetation or soil variables. Overall, hydrology and topography appear to play a dominant role in small-scale space use of brown lemmings with a secondary role for predator avoidance and food plant abundance. [ABSTRACT FROM AUTHOR]

Communities around the world rely on snowmelt to meet water demands, and periods of lower than normal snow accumulation, snow droughts, can decrease water supplies. Leveraging 172 minimally disturbed and seasonally snow-covered watersheds, we developed an approach to examine the effects of cool & dry, warm & dry, and warm & wet snow droughts on streamflow timing and magnitude by hydrologic region. Our results showed all types of snow droughts in all regions correlate with lower annual streamflow, lower maximum and minimum flows, and lower runoff ratios, with more numerous low flow days and earlier streamflow timing. However, departures from non-snow drought conditions differed substantially between drought types and regions. Consecutive snow droughts further reduced runoff ratios and increased low flow days, likely due to additional subsurface storage depletion. With warm snow drought occurrence expected to increase, we discuss impacts for water management systems whose design specifications may not reflect the changing hydroclimate. [ABSTRACT FROM AUTHOR]

Snow avalanche run-out is controlled by flow regime, which is heavily dependent on snow temperature and water content. Existing avalanche models calculate temperature change and snow melting arising from frictional work, snowcover entrainment and particle collisions, but ignore the influence of the ambient air temperature. Here, we propose an experimentally-based relationship to describe the sensible heat flux between the flowing avalanche and the ambient air. A snow avalanche case study and a sensitivity analysis are performed using the proposed model. Modeling results suggest a marginal contribution of heat to the avalanche core, but one, that nonetheless can have significant effect on avalanche runout. For wet (warm) avalanches, higher air temperature will amplify water lubrication effects, leading to longer avalanche run-out. Warmer air temperatures also influence the behaviour of dry, cold avalanches, as the presence of meltwater increases snow cohesion, restricting the formation of strong powder clouds and reducing the avalanche run-out. Colder air temperatures facilitate the formation of more fluidized powder snow avalanches with long run-out and strong air-blasts. The proposed model quantifies the temperature change in the avalanche and can be included in avalanche hazard assessments in which temperature and flow regime are important elements, including the understanding of air temperature and avalanche flow in a changing climate. [ABSTRACT FROM AUTHOR]

The use of machine learning models for landslide susceptibility mapping is widespread but limited to spatial prediction. The potential of employing these techniques in spatiotemporal landslide forecasting remains largely unexplored. To address this gap, this study introduces an innovative dynamic (i.e., space–time-dependent) application of the random forest algorithm for evaluating landslide hazard (i.e., spatiotemporal probability of landslide occurrence). An area in Norway has been chosen as the case study because of the availability of a comprehensive, spatially, and temporally explicit rainfall-induced landslide inventory. The applied methodology is based on the inclusion of dynamic variables, such as cumulative rainfall, snowmelt, and their seasonal variability, as model inputs, together with traditional static parameters such as lithology and morphologic attributes. In this study, the variables' importance was assessed and used to interpret the model decisions and to verify that they align with the physical mechanism responsible for landslide triggering. The algorithm, once trained and tested against landslide and non-landslide data sampled over space and time, produced a model predictor that was subsequently applied to the entire study area at different times: before, during, and after specific landslide events. For each selected day, a specific and space–time-dependent landslide hazard map was generated, then validated against field data. This study overcomes the traditional static applications of machine learning and demonstrates the applicability of a novel model aimed at spatiotemporal landslide probability assessment, with perspectives of applications to early warning systems. [ABSTRACT FROM AUTHOR]

[Objective] Soil erosion caused by compound forces is obvious in Mollisol region, studying the annual alternating superposition of natural driving factors for soil composite erosion in different typical intervals in Northeast Mollisol Region has practical significance for soil erosion control in the region. [Methods] We analyzed and compared the spatiotemporal distribution of natural driving factors of water, wind, freeze-thaw, and snowmelt in 11 typical regions of the region, and clarified the alterative and superposition characteristics of each factor. [Results] In terms of time, the driving factors of hydraulic power, wind power, freeze-thaw and snowmelt in northeast Mollisol region occurred in superposition and alternation within the year. In spring, the driving factors of wind power, freeze-thaw and snowmelt were the most complex. In Summer, the driving factors of hydraulic power, wind power and freeze-thaw were the main driving factors. In autumn, the combined effects of hydraulic power, wind power and freezing and thawing were limited, while in winter, the effects of wind power and freezing were obvious. In terms of space, the hydraulic driving effect gradually decreased from southeast to northwest. Wind effect gradually decreased from southwest to northeast. The freeze-thaw effect gradually decreased from southwest to northeast. Snowmelt gradually decreased from northwest to southeast. According to the different characteristics of erosion driving factors, the whole region was divided into hydraulic, water-wind-freeze- thaw, water-wind-snow melt, water-freeze-thaw-snow melt erosion zones, and targeted soil erosion control could be carried out in different regions. [Conclusion] Each camp showed obvious temporal and spatial differences in their erosion performance. The characteristics of the composite erosion driving factors in a year were obtained based on the alternating superposition of natural driving factors within a year, providing reference for targeted soil erosion control and in-depth research on the composite erosion prevention and control in different regions. [ABSTRACT FROM AUTHOR]

Mountain springs are the only water source for most of the communities in the Kashmir Himalayas. However, over the past two decades, the discharge of these springs has decreased, and some springs have dried up. The current study mapped springs from the Sindh basin of the Kashmir Himalaya via a GPS-based Field survey. The dynamics of spring discharge were evaluated vis-à-vis climatic variability, land use changes, snow cover area, and any major developmental projects in the catchment. The observed temperature and precipitation data were subjected to trend analysis using the Mann Kendall's tau test and Sen's slope estimator. MODIS data were used to understand the dynamics of the Snow Cover Area (SCA) from 2010 to 2021. Landsat satellite imagery was processed to quantify the changes in Land use/cover (LULC). The study found that 13% of the springs below 1600 masl, 22% from 1600 to 2500 masl, and 28.9% above 2500 masl have witnessed a high decrease in discharge. The seasonality of the spring discharge implies snowmelt is the predominant source. The time series analysis shows a weak decreasing trend in the precipitation during the growing season (Spring and Summer). However, both minimum and maximum temperatures show a statistically significant upward trend and result in the early melting of the snow, also confirmed by the analysis of SCA. The study reveals that climate change and LULC change are responsible for the drying of the springs. [ABSTRACT FROM AUTHOR]

Persistent warm temperature anomalies can drive streamflow in regions where snow and glacier melt are important constituents of streamflow. However, the spatiotemporal variability of the streamflow response depends on both the magnitude of the forcing temperature anomalies and the nature of the underlying hydrological system. Here we ask: when, where, and for what magnitude of temperature anomalies will the streamflow response change most rapidly under warming? We use observed streamflow and temperature for 868 basins across Canada to quantify the streamflow response during warm temperature anomalies and how such responses vary in space, time, and by anomaly magnitude. We first identify two temporal modes of the streamflow response, one in autumn and one in spring, the relative strength of which varies by climate. We then use sinusoidal approximations of seasonal temperature cycles to characterize the sensitivity of such modes to changes in annual temperature. At individual basins, we find that relative to moderate warm events, the streamflow response to more extreme warm events is more sensitive to changes in mean annual temperatures, and this sensitivity is greatest in the coastal, southern, and central regions of Canada. Our results have implications for how the hydrological impacts of extreme events, such as heatwaves, will change in space and time under future climate change. Plain Language Summary: Warm temperature anomalies can drive streamflow in places that depend on snow and glacier melt. However, it is not well understood the rate at which the characteristics of the streamflow responses to warm temperature anomalies will change under warming. We present a straightforward method to characterize the observed streamflow responses to multi‐day warm events, and to understand how their sensitivity to warming varies in space, through time, and by magnitude of warm event. We find that relative to moderate warm events, the streamflow response to more extreme warm events is more sensitive to changes in mean annual temperatures. This implies that under warming, there will be an outsized change in the streamflow responses to extreme warm events relative to moderate warm events, with implications for water management under extreme events such as heatwaves. Key Points: We investigate the variability of streamflow responses to multiday warm temperature anomalies and their sensitivity to warming across CanadaUnder warming, the streamflow response to extreme temperature anomalies will change more than the streamflow response to moderate anomaliesOur findings have implications for downstream ecosystems and water users as warm events become more frequent and intense [ABSTRACT FROM AUTHOR]

Runoff in high-latitude water towers is crucial for ecological and human water demands during freeze–thaw periods but is highly sensitive to climate change and human activities. This study focuses on Changbai Mountain, the source of the Songhua, Tumen, and Yalu rivers, analyzing runoff variation and its environmental responses using the modified Mann–Kendall method and the water–energy balance equation. The results show significant non-stationarity in runoff trends, with an increasing trend in the Yalu River basin (p < 0.05), a decreasing trend in the Tumen River basin (p < 0.05), and complex trends in the Songhua River basin. Additionally, the relationship between runoff and driving factors during freeze–thaw periods was quantized. When the snowfall, potential evapotranspiration (E0), and subsurface changes increased by 1%, the snowmelt runoff changes were 1.58~1.96%, −0.58~−1.96%, and −0.86~−1.11% in the Yalu River basin; 2.16~2.35%, −1.04~−1.35%, and −1.56~−1.95% in the Tumen River basin; and 1.44~2.41%, −0.44~−1.41%, and −0.72~−1.62% in the Songhua River basin. The increased snowfall was the most prominent reason for the increase in snowmelt runoff during spring. The results of this study will benefit ecosystem conservation and the stability of downstream water supply in this high-latitude water tower. [ABSTRACT FROM AUTHOR]

Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making. [ABSTRACT FROM AUTHOR]

Shorter duration of ice‐free conditions at successively northern latitudes increasingly constrains large‐bodied waterbirds from completing their reproductive cycle before freeze‐up, affecting their migration and breeding schedules. We compared migration timing and duration of stay in summering areas between 127 tracked East Asian Whooper Swans Cygnus cygnus breeding at high latitude (HLP, 67°N, Japan wintering) and mid‐latitude (MLP, 48°N, China wintering) based on tracking and remote‐sensing data. Adult breeding site arrival dates were positively correlated with latitude. HLP adults arrived just before 50% snow melt, and summered for 142 days (the duration of the ice‐free period there), just over the c.130 days required to hatch and raise cygnets to fledging. MLP adults arrived 3 weeks after 50% snow melt, and summered for c.209 days (c.80% of the total ice‐free period of 267 days), laying eggs c.33 days after arrival. These results suggest that HLP breeders were more likely to invest stored nutrients and energy brought from winter quarters and/or on migration to enable rapid egg‐laying post‐arrival at the breeding grounds (a more 'capital' breeding strategy). By contrast, MLP females appear able to obtain most, if not all, of their energetic and nutritional reproductive needs exogenously post‐arrival at breeding areas (a more 'income' breeding strategy). HLP individuals left the breeding grounds before MLP, but all departed before freeze‐up. Migration duration did not differ between the two groups, so differential summer residency times affected duration on wintering areas. We recommend follow‐up investigations of the relative degree of capital/income investment among birds from both groups and their effects on their respective demographics, population dynamics and growth rates. [ABSTRACT FROM AUTHOR]

Runoff from glaciated catchments is an integrated process that includes glacier melt, snowmelt, rainfall and surface and subsurface runoff of meltwater from glacierized and non-glacierized areas. Monitoring and quantifying the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Himalayan basins is essential for decision-making in the water sector, particularly in water resources management and flood risk reduction in the region. An attempt has been made to characterize and hydrologically model streamflow (Bhagirathi River) for the Gangotri Glacier (Central Himalaya, India). A semi-distributed conceptual hydrological model is used for the streamflow modelling and assessing the major streamflow components (snow melt, glacier melt and rainfall runoff). Initially, the model was calibrated using the available in situ hydro-meteorological records for the ablation seasons of 2013–14 to 2015–16 (3 years), and further validated for the ablation seasons of 2016–17 to 2018–19 (3 years). The model performed well for all the studied years except for some months, where abrupt changes in the contrasting weather parameters (precipitation and temperature) were recorded. In the Gangotri Glacier Valley (upper Bhagirathi River catchment), snowmelt contributed the largest portion (55.5%) to total streamflow followed by glacier melt (29.7%) and rainfall runoff components (14.7%). [ABSTRACT FROM AUTHOR]

In 2020, Colorado experienced the most severe wildfire season in recorded history, with wildfires burning 625 357 acres across the state. Two of the largest fires burned parts of Rocky Mountain National Park (RMNP), and a study was initiated to address concerns about potential effects on drinking water quality from mobilization of ash and sediment. The study took advantage of a wealth of pre‐fire data from adjacent burned and unburned basins in western RMNP. Pre‐ and post‐fire data collection included discrete sample collection and high‐frequency water‐quality measurements using in‐stream sensors. Kruskal–Wallis tests on discrete data indicated that specific conductance, base cations, sulphate, chloride, nitrate, and total dissolved nitrogen concentrations increased post‐fire, whereas silica and dissolved organic carbon (DOC) did not (p ≤ 0.05). In‐stream sensors captured large spikes in concentrations of nutrients, turbidity, and DOC in the burned basin that were missed by discrete sampling. Sensor data indicated nitrate and turbidity increased by up to one and two orders of magnitude, respectively, from pre‐event concentrations during storms, and DOC increased up to 3.5×. Empirical regression equations were developed using pre‐fire data and applied to the post‐fire period to estimate expected stream chemistry in the absence of fire (a 'no‐fire' scenario). Overlays of actual post‐fire chemistry showed the timing and magnitude of differences between observed and 'estimated' chemistry. For most solutes, observed post‐fire concentrations were notably greater than expected under the 'no‐fire' scenario, and differences were greatest during storm events. Comparison of data from the burned and unburned basins indicated DOC concentrations were affected by climate as well as fire. Results from this study demonstrate the importance of both pre‐fire data and high‐frequency data for characterizing dynamic hydrochemical responses in wildfire‐affected areas. [ABSTRACT FROM AUTHOR]

The article discusses the importance of setting expectations for hydrologic model performance using an ensemble of simple benchmarks. It highlights the limitations of traditional metrics and the need for objective benchmarks to assess model performance. The study demonstrates that relying on the mean annual flow as a benchmark is insufficient, and an ensemble of simple benchmarks provides a more accurate context for model performance evaluation. The use of benchmark models helps in understanding regional differences in model predictability and provides a basis for comparing model performance against simpler models. [Extracted from the article]

Changes in the volume, rate, and timing of the snowmelt water pulse have profound implications for seasonal soil moisture, evapotranspiration (ET), groundwater recharge, and downstream water availability, especially in the context of climate change. Here, we present an empirical analysis of water available for runoff using five eddy covariance towers located in continental montane forests across a regional gradient of snow depth, precipitation seasonality, and aridity. We specifically investigated how energy‐water asynchrony (i.e., snowmelt timing relative to atmospheric demand), surface water input intensity (rain and snowmelt), and observed winter ET (winter AET) impact multiple water balance metrics that determine water available for runoff (WAfR). Overall, we found that WAfR had the strongest relationship with energy‐water asynchrony (adjusted r2 = 0.52) and that winter AET was correlated to total water year evapotranspiration but not to other water balance metrics. Stepwise regression analysis demonstrated that none of the tested mechanisms were strongly related to the Budyko‐type runoff anomaly (highest adjusted r2 = 0.21). We, therefore, conclude that WAfR from continental montane forests is most sensitive to the degree of energy‐water asynchrony that occurs. The results of this empirical study identify the physical mechanisms driving variability of WAfR in continental montane forests and are thus broadly relevant to the hydrologic management and modelling communities. [ABSTRACT FROM AUTHOR]

Summary Climate change is disrupting floral traits that mediate mutualistic and antagonistic species interactions. Plastic responses of these traits to multiple shifting conditions may be adaptive, depending on natural selection in new environments. We manipulated snowmelt date over three seasons (3–11 d earlier) in factorial combination with growing‐season precipitation (normal, halved, or doubled) to measure plastic responses of volatile emissions and other floral traits in Ipomopsis aggregata. We quantified how precipitation and early snowmelt affected selection on traits by seed predators and pollinators. Within years, floral emissions did not respond to precipitation treatments but shifted with snowmelt treatment depending on the year. Across 3 yr, emissions correlated with both precipitation and snowmelt date. These effects were driven by changes in soil moisture. Selection on several traits changed with earlier snowmelt or reduced precipitation, in some cases driven by predispersal seed predation. Floral trait plasticity was not generally adaptive. Floral volatile emissions shifted in the face of two effects of climate change, and the new environments modulated selection imposed by interacting species. The complexity of the responses underscores the need for more studies of how climate change will affect floral volatiles and other floral traits. [ABSTRACT FROM AUTHOR]

Compounding changes in climate and human activities stand to increase sediment input to rivers in many landscapes, including via discrete perturbations such as post‐fire debris flows. Because sediment supply is a dominant control on river morphology, understanding mountain river responses to sediment regime perturbations is critical to predicting and addressing downstream effects to infrastructure, water security and aquatic habitat. A growing body of literature explores the causes, likelihood, size and composition of post‐fire debris flows, but the channel response to these disturbances remains poorly studied. This study used repeat field surveys, time‐lapse photographs and pre‐ and post‐disturbance remote sensing datasets to document and analyse space‐ and time‐varying channel response to post‐fire debris flows along a steep mountain stream in the Upper Colorado River Basin, USA. Specifically, we evaluated channel morphology and bed composition changes, correlations between channel changes and valley and channel attributes, and the relative importance of spring snowmelt versus summer monsoon events. Several cross‐sectional channel change types were observed from lidar a month after post‐fire debris‐flow events, including channelized and braided incision into deposits, incision into the pre‐fire channel bed, bank erosion and no change. Channel changes were most correlated with pre‐fire channel width, valley width and unit stream power, and these relationships could be tested in other burned locations to evaluate their transferability. Repeat channel surveys before and after snowmelt indicate rapid recovery and channel narrowing following major sediment disturbances, although sediment deposits remained in the channel margins. Together, these results highlight the importance of field and remote sensing‐based channel surveys to improve understanding of, and potential to predict, mountain channel response to compounding climate disturbances. [ABSTRACT FROM AUTHOR]

In this study, an ensemble inflow-prediction system was developed for a hydropower-generation dam in the upper Sai River basin, and the accuracy of ensemble inflow prediction, which is important for efficient dam operation, was investigated. First, the Water and Energy Based Distributed Hydrological Model for Snow (WEB-DHM-S), a hydrological model developed for the Sai River basin, can represent the hydrological process from warm to cold seasons. Next, a system was developed on the Data Integration and Analysis System (DIAS) to predict inflows into the dam by inputting real-time meteorological data and ensemble rainfall forecast data into WEB-DHM-S. The WEB-DHM-S was calibrated and validated over a 3-year period from August 2015 to July 2018, and showed good agreement with observed inflows from base flow to peak flow and snowmelt runoff in each year. The results of inflow forecasting during frontal rainfall in August 2021 by inputting ensemble rainfall forecasts up to 39 h ahead showed that at the Inekoki Dam site, the total inflow (volume) to the peak was predicted with an accuracy of within 20% at 30 h, 24 h, 18 h, 12 h, and 6 h before the peak. These ensemble inflow forecasts can help optimize dam operations. [ABSTRACT FROM AUTHOR]

The occurrence of natural disasters, accelerated by climate change, has become a continuous menace to the environment and consequently impacts the socioeconomic well-being of people. Flood events are natural disasters resulting from excessive rainfall duration, intensity, and snow melt. Flood disaster management systems that are machine learning-based have been increasingly suggested and applied to forestall the impacts of floods on the environment in terms of monitoring and warning. This study aims to critically review various studies conducted on flood management systems to identify applicable data sources and machine learning models. The study applied Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to source data from an academic database using some selected keywords, which were identified for the review process after filtering a total number of forty-two pertinent research papers was used. The review identified different combinations of flood data, flood management techniques, flood models, application of machine learning in flood predictions, optimization techniques, data processing techniques, and evaluation techniques. The study concluded that a standard approach should be applied in building robust and efficient flood disaster management systems. Lastly, informed future research directions on using machine learning for flood prediction and susceptibility mapping are provided. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of spatiotemporal runoff changes in the Yarlung Zangbo (YZ) basin in the southern Tibetan Plateau (TP) at a sub-basin scale, amidst varying climatic and cryospheric conditions, is imperative for effective water resources management. However, spatiotemporal differences of runoff composition and change and their attribution within the YZ basin have not been extensively explored, primarily due to the lack of hydrometeorological observations, especially in the downstream region. In this study, we investigated historical and future evolution of annual and seasonal total water availability, as well as glacier runoff and snowmelt contributions across six sub-basins of the YZ, with a particular focus on the comparison between the upstream Nuxia (NX) basin and the downstream Nuxia–Pasighat (NX-BXK) basin, based on a newly generated precipitation dataset and a well-validated model with streamflow, glacier mass, and snow cover observations. Our findings revealed that large spatiotemporal differences in changes exist within the YZ basin for 1971–2020. Firstly, runoff generation was dominated by rainfall runoff throughout the YZ basin, with glacier runoff playing a more important role in the annual total runoff (19 %) in the NX-BXK sub-basin compared to other sub-basins. Notably, glacier runoff contributed 52 % of the total runoff at the Pasighat outlet of the YZ basin. Secondly, annual runoff exhibited an increasing trend in the NX basin but a decreasing trend in the NX-BXK, primarily attributed to rainfall runoff changes influenced by atmospheric moisture. Glacier runoff enhanced water supply by offsetting the decreasing contribution from rainfall. Total runoff will consistently increase (27–100 mm (10 yr) -1) across the sub-basins through the 21st century, resulting from increased rainfall runoff and a minor effect of increased snowmelt and glacier runoff. [ABSTRACT FROM AUTHOR]

East Asian herbivorous waterfowl intensively use farmland in spring, next to their natural habitat. Accordingly, they might have expanded their migration strategy from merely tracking the green wave of newly emerging vegetation to also incorporating the availability of post-harvest agricultural seeds (here dubbed the seed wave). However, if and how waterfowl use multiple food resources to time their seasonal migration is still unknown. We test this migration strategy using 167 spring migration tracks of five East Asian herbivorous waterfowl species and mixed-effect resource selection function models. We found that all study species arrived at their core stopover sites in the Northeast China Plain after agricultural seeds became available, extended their stay after spring vegetation emerged and arrived at their breeding sites around the emergence of vegetation. At the core stopover sites, all study species used snowmelt as a cue to track seed availability, although smaller-bodied species tended to arrive later. At the breeding sites, swans tracked the onset of vegetation emergence and geese tracked the mid- or end phases of snowmelt. Our findings suggest that waterfowl track multiple resource waves to fine-tune their migration, highlighting new opportunities for conservation. [ABSTRACT FROM AUTHOR]

Salting of roadways contaminates local waterways via snowmelt and precipitation runoff, eliciting various toxicological impacts on aquatic ecosystems. Recently, “eco‐friendly” deicing alternatives have been introduced in hopes of mitigating environmental impacts of deicing agents, while maintaining human safety. These “eco‐friendly” alternatives may pose their own set of environmental concerns that require further study. While the potential toxicity of road salts has been evaluated for various aquatic species, the environmental factors that may influence this toxicity are less understood; and for emerging deicing alternatives, there is a lack of literature documenting these potential implications. For aquatic organisms, the highest exposure to road salts may coincide with reduced food availability, namely during the winter months. The present study evaluates the effect of a conditioning diet on the sensitivity of adult Hyalella azteca to an “eco‐friendly”‐labeled beet deicer (Snow Joe MELT Beet‐IT). Various conditioning diets were examined, including TetraMinTM, TetraMin and diatom (Thalassiosira weissflogii) combinations, and TetraMin and conditioned Acer sacharum leaves. For each diet type, 48‐ and 96‐h water‐only toxicity bioassays were conducted with adult H. azteca. These results were compared to organisms which experienced a 96‐h starvation period prior to exposure and culture organisms. Diet types representing excess quality and quantity of food significantly decreased the toxicity of beet deicer to the organisms. However, starvation likely increases the toxicity of road salts to H. azteca. Therefore, the quantity and quality of food available to H. azteca may influence their sensitivity to deicing agents. Environ Toxicol Chem 2024;00:1–8. © 2024 SETAC [ABSTRACT FROM AUTHOR]

The impact of a slightly modified broadband snow surface albedo parameterization, which explicitly considers the cloud dependence of the snow albedo, is evaluated in simulations of a coupled regional climate model of the Arctic. The cloud dependence of the snow albedo leads to a more realistic simulation of the variability of the surface albedo during the snowmelt period in late May and June. In particular, the reproduction of lower albedo values under cloud-free/broken-cloud conditions during the snowmelt period represents an improvement and results in an earlier disappearance of the snow cover and an earlier onset of sea-ice melt. In this way, the consideration of the cloud dependence of the snow albedo results in an amplification of the two-stage snow/ice albedo feedback in the model. This finds expression in considerably increased sea-ice melt during the summer months and ends up in a new quasi-stationary equilibrium in sea ice with statistically significant lower sea-ice volume and statistically significant lower summer sea-ice area. [ABSTRACT FROM AUTHOR]

Climate change is expected to significantly impact temperature and precipitation, as well as snow accumulations and melt in mid-latitudes, including in the Baltic region, ultimately affecting the quantity and seasonal distribution of streamflow. This study aims to investigate the changes in the magnitude and timing of annual maximum discharge for 30 hydrological monitoring stations across Latvia from 1950/51 to 2021/22. Circular statistics and linear mixed effects models were applied to identify the strength of seasonality and timing. Trend analysis of the magnitude and timing of flood peaks were performed by using the Theil–Sen method and Mann–Kendall test. We analyzed regional significance of trends across different hydrological regions and country using the Walker test. Results indicate strong seasonality in annual flood peaks in catchments, with a single peak occurring in spring in the study sub-period of 1950/51–1986/87. Flood seasonality has changed over recent decades (i.e., 1987/88–2021/22) and is seen as a decrease in spring maximum discharge and increase in winter flood peaks. Alterations in annual flood occurrence also point towards a shift in flow regime from snowmelt dominated to mixed snow–rainfall dominated, with consistent changes towards the earlier timing of the flood peak, with a more or less pronounced gradation from west to east. Analysis shows that a significant trend of decrease in the magnitude and timing of annual maximum discharge was detected. [ABSTRACT FROM AUTHOR]

While few places on earth have been as deeply impacted by the human-horse relationship as the steppes of Mongolia and eastern Eurasia, gaps in the archaeological record have made it strikingly difficult to trace when and how the first domestic horses were integrated into ancient societies in this key region of the world. Recently, organic materials preserved in melting mountain ice have emerged as a key source of archaeological insight into the region's deep past. Newly-identified artefacts recovered from melting snow and ice in the Altai Mountain range of western Mongolia (including metal artefacts, skeletal remains, and hoof fragments) provide archaeological evidence for the use of horses at high altitudes from the Bronze Age through the 20th century. Direct radiocarbon dating and genomic sequencing demonstrate the presence of Przewalski's horse in the region during the early second millennium BCE, suggesting that this taxon may have once foraged at high altitudes frequented by human hunters. Importantly, directly-dated remains of horse hoof trimmings provide some of the oldest direct evidence of horse transport in the Eastern Steppe as early as the 14th century BCE, and suggest a role for high-mountain hunting in the innovation of reliable mounted riding. [ABSTRACT FROM AUTHOR]

Against the backdrop of the Snowflake Castle collapse and through the dialogue between Little Snowflake and the Doctor, this paper introduces the main components, principles of action, and applications of snow melting agents. Using snow melting agents as an example, it further explores the concept of colligative properties of dilute solutions, their related applications, and the methods of using snow melting agents. The aim is to familiarize readers with the concept of colligative properties in physical chemistry and to promote the concept of environmental protection. [ABSTRACT FROM AUTHOR]

openAMUNDSEN (the open source version of the Alpine MUltiscale Numerical Distributed Simulation ENgine) is a fully distributed snow-hydrological model, designed primarily for calculating the seasonal evolution of snow cover and melt rates in mountain regions. It resolves the mass and energy balance of snow-covered surfaces and layers of the snowpack, thereby including the most important processes that are relevant in complex mountain topography. The potential model applications are very versatile; typically, it is applied in areas ranging from the point scale to the regional scale (i.e., up to some thousands of square kilometers) using a spatial resolution of 10–1000 m and a temporal resolution of 1–3 h or daily. Temporal horizons may vary between single events and climate change scenarios. The openAMUNDSEN model has already been used for many applications, which are referenced herein. It features a spatial interpolation of meteorological observations, several layers of snow with different density and liquid-water contents, wind-induced lateral redistributions, snow–canopy interactions, glacier ice responses to climate, and more. The model can be configured according to each specific application case. A basic consideration for its development was to include a variety of process descriptions of different complexity to set up individual model runs which best match a compromise between physical detail, transferability, simplicity, and computational performance for a certain region in the European Alps, typically a (preferably gauged) hydrological catchment. The Python model code and example data are available as an open-source project on GitHub (https://github.com/openamundsen/openamundsen , last access: 1 June 2024). [ABSTRACT FROM AUTHOR]

Snowmelt erosion and rainfall erosion are important components of soil erosion in Northeast China. Studying how snowmelt and rainfall affect runoff and sediment yield is essential to control soil erosion in this region. Based on the soil and water loss observation data in small watershed during snowmelt and rainfall periods, snowmelt and rainfall erosion processes were studied. The runoff–sediment relationship was analyzed. The results indicate that 13 snowmelt runoff events and 14 rainfall runoff events were recorded. During the snowmelt period, runoff depth (RD) and sediment yield (SY) exhibited a slow increase (early stage), rapid increase (middle stage), and then rapid decrease (late stage) trend. RD and SY in the middle stage were higher than those in the early and late stages. Moreover, during the rainfall period, soil erosion mainly occurred from July to August; during these two months, the rainfall and rainfall erosivity accounted for 66.0 and 91.1% of the total rainfall and rainfall erosivity, respectively; and the RD and SY in this period accounted for 88.4 and 89.8% of total amounts in the whole rainfall period, respectively. In addition, the contributions of snowmelt and rainfall to RD and SY were 18.6 and 81.4%, and 3.8 and 96.2%, respectively. Hysteresis analysis suggested that clockwise hysteresis loop was the dominant pattern, followed by eight-shaped anticlockwise loop, and complex anticlockwise loop. [ABSTRACT FROM AUTHOR]

Understanding the triggering factors leading to rockfall is essential in managing their risk to transportation infrastructure. Precipitation and freeze-thaw (FT) are widely studied rockfall triggers, but developing reliable, quantitative methods to forecast rockfall in response to weather events remains challenging. Terrestrial laser scanning (TLS) is a powerful tool for high-accuracy modeling of rock slopes, but the frequency of scanning is often too low to correlate rockfall behavior with weather events or seasonal trends. We conducted a TLS campaign between 2017 and 2022 in Glenwood Canyon, Colorado, to investigate the seasonal variability in rockfall triggering and conditioning mechanisms. A total of 44 scans were collected over 5 years and were processed to allow for consistent detection of rockfalls larger than 0.0036 m3 in volume. Meteorological variables relating to precipitation, snowpack, and temperature were modeled using the National Weather Service SNODAS product and were used to complete an exploratory analysis of the correlation of various weather indices with rockfall rate over time. It was found that the short-term sum of liquid precipitation and snowmelt (averaged over the scanning interval or the max single-day total) was a strong predictor of rockfall volume rate between 2018 and 2020, especially in the spring and summer months; a linear model of max daily liquid was able to explain 65% of the variance (R2adj) in rockfall volume rate in March through August. This implicates springtime snowmelt and rain-on-snow events as strong predictors of rockfall at the study site. We interpret these observations to indicate that snowmelt and rainfall are acting to trigger blocks that have been conditioned (destabilized) over the preceding winter. [ABSTRACT FROM AUTHOR]

Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth's energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor snow cover dynamics effectively. To address these formidable challenges, this study introduces a novel data fusion framework specifically designed to generate high-resolution (1 km) daily FSC estimation across vast regions like North America, regardless of weather conditions. It achieved this by effectively integrating the complementary spatiotemporal characteristics of both coarse- and fine-resolution FSC data through a multi-stage processing pipeline. This pipeline incorporates innovative strategies for bias correction, gap filling, and consideration of dynamic characteristics of snow cover, ultimately leading to high accuracy and high spatiotemporal completeness in the fused FSC data. The accuracy of the fused FSC data was thoroughly evaluated over the study period (September 2015 to May 2016), demonstrating excellent consistency with independent datasets, including Landsat-derived FSC (total 24 scenes; RMSE=6.8–18.9 %) and ground-based snow observations (14,350 stations). Notably, the fused data outperforms the widely used Interactive Multi-sensor Snow and Ice Mapping System (IMS) daily snow cover extent data in overall accuracy (0.92 vs. 0.91), F1_score (0.86 vs. 0.83), and Kappa coefficient (0.80 vs. 0.77). Furthermore, the fused FSC data exhibits superior performance in accurately capturing the intricate daily snow cover dynamics compared to IMS data, as confirmed by superior agreement with ground-based observations in four snow-cover phenology metrics. In conclusion, the proposed data fusion framework offers a significant advancement in snow cover monitoring by generating high-accuracy, spatiotemporally complete daily FSC maps that effectively capture the spatial and temporal variability of snow cover. These FSC datasets hold substantial value for climate projections, hydrological studies, and water management at both global and regional scales. [ABSTRACT FROM AUTHOR]

Climate change is a global threat to the environment and human health. Two of the main greenhouse gases that cause the greenhouse effect and raise global temperatures are carbon dioxide and nitrogen oxides. In this review paper, we investigated the effects of carbon dioxide and nitrogen oxides on climate change and the effects of climate change on Afghanistan. We found that high concentrations of carbon dioxide, which is now CO2 levels, have increased by 50% than before the Industrial Revolution, contributing to a rise in global temperature and precipitation. At the same time, Nitrous oxide is an important greenhouse gas, with 310-fold higher potential for global warming than CO2 and leads to the depletion of stratospheric Ozone and other Nitrogen oxides, has a significant impact on plant health, including effects on chlorophyll levels, oxidative stress, and antioxidant responses. Afghanistan's climate change is predicted to increase the country's prevalence of illnesses linked to dust storms and poor air quality, especially in Kabul, the nation's capital. In addition, air pollution in Kabul is also likely to increase as a result of climate change. The alarming impacts of air pollution, with more than 3,000 deaths attributed to air pollution annually. Additionally, at least 700,000 individuals in Kabul have experienced various respiratory diseases. Due to climate change, Afghanistan's total glacier area has shrunk by 13.8%. In 2023, Afghanistan experienced early snow melt and below-average precipitation, causing second-season and irrigated crops to have less access to water. Reducing emissions and coping with the changing climate are essential steps towards tackling the complex issues these gases present and their wider effects on the environment and human health. [ABSTRACT FROM AUTHOR]

Climate change has significant impacts on natural systems, especially in terms of altering hydrological patterns due to changing precipitation and melting snow and ice. This study examines projected changes in precipitation for central and southern Iraq under the RCP4.5 and RCP8.5 scenarios for the periods 2046-2065 and 2081-2100 using CMIP5 climate model HadGEM2-AO output. The results revealed that under RCP4.5, there was large spatial variation in the projected precipitation over the region during 2064-2065, ranging from as low as 27 mm in western part to as high as 1541 mm in the eastern part. The spatial variability as well as precipitation amount decreased considerably during 2081-2100 periods. Under RCP8.5 the projected precipitation was only 14-164 mm in 2046-2065 and 36-532 mm in 2081-2100 periods. Thus, under RCP8.5 scenario anticipated precipitation will be quite low. [ABSTRACT FROM AUTHOR]

Snow cover is an essential component of the water cycle, and the accumulation and melting of snow have significant impacts on socio-economic development downstream. This study employs trend analysis, correlation analysis, and other methods to analyze the spatiotemporal distribution characteristics and trends of snow cover area and snow cover days (SCD) on the northern slope of Kunlun Mountain of Xinjiang, China from 2000 to 2020. It also examines the elevation effect and the impact of climate on snow cover. The results indicate: (1) From 2000 to 2020, the snow cover area showed a decreasing trend (-152.4 km²•a-1), with a positive anomaly before 2010 and a negative one after. (2) On a monthly scale, the snow cover percentage (SCF) showed a trend of initially increasing and then decreasing from August to the following July, being higher in winter and spring than in summer and autumn. (3) The distribution of SCD exhibits obvious spatial heterogeneity, with changes in the middle region affecting the annual total number of SCD in the area. (4) The average number of SCD per year from 2000 to 2020 increased with elevation, reaching 331.6 d at elevations above 6.0 km, suggesting perennial snow at high altitudes. (5) Temperatures are significantly negatively correlated with the snow cover area (correlation coefficient -0.68, P<0.01), while precipitation is not significantly positively correlated with the snow cover area (correlation coefficient 0.14, P>0.05), and temperature has a stronger impact on snow cover than precipitation. [ABSTRACT FROM AUTHOR]

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The formation and concentration of liquid water (LW) in the snowpack constitute key processes linking snow and runoff. Hence, the LW content of the snowpack represents a crucial target variable to investigate for snowmelt‐induced runoff predictions. In this study, we capture the wet snow dynamics at higher than hectometre resolution in the alpine headwater catchment Rofental, Tyrol, Austria (98.1 km2) by means of distributed model simulations and remote sensing data for the 5 year period 10/2017–09/2022. The model simulations are conducted using the intermediate complexity open‐source snow‐hydrological model openAMUNDSEN. Simulation results are compared to wet snow maps (WSM) derived from Sentinel‐1 data. Our investigations indicate that distributed snow models of intermediate complexity, such as openAMUNDSEN and satellite‐based wet snow data are well capable of capturing the wet snow dynamics in high spatial and temporal resolutions. The areal extents of wet snow as well as the upward movement of the wet snow line to higher elevation with progressing snowmelt are captured well by both approaches. In order to evaluate the snow simulations, we use fractional snow cover (FSC) data based on Sentinel‐2, which proved to provide valuable small‐scale snow and snow redistribution patterns in alpine catchments. The comparison of model simulations with FSC maps with more than 50% of the non‐glaciated area being cloud‐free (i.e. 364 images) results in an accuracy of 0.91. This study represents a further step towards a serviceable operational snow‐hydrological monitoring and modelling framework for mountain regions including wet snow dynamics in high spatial and temporal resolutions. [ABSTRACT FROM AUTHOR]

Evaluation metrics play a pivotal role in the calibration process of hydrological models, serving as objective functions that directly influence the final values of model parameters and significantly affect users' perceptions of model performance. However, the choice and interpretation of evaluation metrics are subjective; therefore, this study provides a more objective framework for assessing model performance. This paper initially explored the applicability of various commonly used evaluation metrics, providing an overview of their limitations. Following this, we decomposed errors by analysing their physical significance and geometric representation in scatter plots, categorizing them into systematic and unsystematic errors. Through the decomposition and derivation of the Nash–Sutcliffe efficiency (NSE) formula, we established the quantitative relationship among various evaluation metrics. The soil and water assessment tool (SWAT) model was utilized to simulate monthly runoff in the Baishan basin (China), for the period 1994–2017, with NSE serving as the objective function for calibration. Our findings are consistent with previous studies, indicating that the model tends to slightly underestimate high flows while significantly overestimating low flows. Further analysis through error decomposition and the examination of relationships among various evaluation metrics revealed that unsystematic errors were dominant during the spring snowmelt runoff period, while systematic errors prevailed in the dry season. By evaluating the runoff series based on the magnitude of runoff or by categorizing it according to seasons and months, a more stringent assessment of the model's performance was achieved. These findings not only highlight the necessity for careful selection of evaluation metrics but also underscore the significance of our methodological advancements in enhancing hydrological model precision and reliability. [ABSTRACT FROM AUTHOR]