Explore the vast range of titles available.

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R2, and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas.

The timing of snowmelt runoff is critical for water resource applications, but its spatiotemporal evolution remains poorly understood. We present a scalable approach to map snowmelt runoff onset using Sentinel‐1 synthetic aperture radar data for the past 8 years with 10 m spatial resolution and a median temporal resolution of 3.9 days. A systematic analysis of stratovolcanoes in the Western United States showed that snowmelt runoff onset is strongly dependent on elevation (r = 0.81), with a median runoff onset lapse rate of 4.9 days per 100 m of elevation gain. During the 2015 snow drought, we observed snowmelt runoff onset 25 days early relative to the 2015–2022 median. We document a median shift in snowmelt runoff onset of +2.0 days later in the year per year between 2016 and 2022. Our open‐source tools can be used to create snowmelt runoff onset maps anywhere on Earth. Plain Language Summary Snowmelt timing is important–knowing when water leaves mountain snowpack is critical for downstream water resource applications like irrigation and hydropower. Snowmelt timing is also affected by regional climate change. However, it is hard to make detailed measurements of when and where snow melts across mountainous regions. We developed an improved method to map snowmelt using satellite based radar data, and we applied this method to study snow on mountains in the Western United States. We documented the detailed relationship between elevation and snowmelt, and how this relationship changed over the past 8 years. In general, at higher elevations and latitudes, snow melts later in the year. We also observed snow melting much earlier than it usually does during the 2015 snow drought, which helps us prepare for future years with low snow accumulation. Finally, from 2016 to 2022, we documented a shift toward snowmelt happening earlier in the year, which means earlier spring flow in rivers. We publicly released interactive, user‐friendly software, so anyone can use our method to study snowmelt timing anywhere on Earth. Collectively, our work will help scientists better understand regional climate change and allow water managers to better manage water resources today and in the future. Key Points We present a scalable method and open toolbox to map snowmelt runoff onset timing with high spatial and temporal resolution We quantify the topographic and geographic controls on snowmelt runoff onset for stratovolcanoes in the Western United States We document early snowmelt runoff onset during the 2015 snow drought and intra/interannual variability from 2016 to 2022

Snowpack stores cold-season precipitation to meet warm-season water demand. Climate change threatens to disturb this balance by altering the fraction of precipitation falling as snow and the timing of snowmelt, which may have profound effects on food production in basins where irrigated agriculture relies heavily on snowmelt runoff. Here, we analyse global patterns of snowmelt and agricultural water uses to identify regions and crops that are most dependent on snowmelt water resources. We find hotspots primarily in high-mountain Asia (the Tibetan Plateau), Central Asia, western Russia, western US and the southern Andes. Using projections of sub-annual runoff under warming scenarios, we identify the basins most at risk from changing snowmelt patterns, where up to 40% of irrigation demand must be met by new alternative water supplies under a 4 °C warming scenario. Our results highlight basins and crops where adaptation of water management and agricultural systems may be especially critical in a changing climate.Snowmelt runoff is an important source of water for irrigating agricultural crops in high-mountain Asia, Central Asia, western Russia, western US and the southern Andes. Climate change places water resources in these basins at risk, indicating the need to adapt water management.

Ongoing runoff declines in the Colorado River Basin have been shown to be predominately driven by decreasing albedo from warming‐driven snow‐cover loss, especially in late‐spring (hereafter snowmelt‐radiation feedback). Here, we explore the feedback's impact on annual runoff sensitivity to warming across the western U.S. (WUS) using hydrologic model simulations. For 1°C uniform warming, we show that runoff is most sensitive to warming in modestly snow‐covered, interior mountain headwaters, especially the Rocky Mountains. Runoff sensitivities are most associated with the snowmelt‐radiation feedback in basins with runoff coefficients between 0.2 and 0.6, where runoff sensitivity increases with more snow and lower winter temperature. In aggregate, ∼48% of WUS runoff sensitivity is attributable to the snowmelt‐radiation feedback and is especially pronounced in the warming‐sensitive river basins (annual runoff decreases >5%/°C). We also show that the feedback's impact decreases with increasing temperature, which has unresolved implications for streamflow declines in a less‐snow future. Plain Language Summary Regional climate warming is driving strong runoff changes in the western U.S. (WUS), especially the Upper Colorado River Basin (UCRB). Previous work showed that warming‐related snow cover reductions lead to more solar radiation absorption and evapotranspiration, which largely explain ongoing runoff declines in UCRB. Here, we assess the impact of this snowmelt‐radiation feedback on warming‐induced runoff changes across WUS. In a warmer world, we find that the largest annual runoff sensitivities are in the interior mountainous WUS with modest snow cover. The snowmelt‐radiation feedback explains over half of the warming‐induced runoff changes in warming‐sensitive WUS basins and about half of WUS' overall runoff sensitivity. In areas influenced by the snowmelt‐radiation feedback, both runoff sensitivity and the feedback's contribution become smaller with higher temperatures, suggesting a potentially slower rate of streamflow decline as temperatures rise in a warmer future. Key Points Snowmelt‐radiation feedback accounts for ∼1/2 of warming‐driven runoff decline across the Western U.S. (WUS) Runoff sensitivities are most linked to snowmelt‐radiation feedback in river basins with runoff coefficients in the range 0.2–0.6 Runoff sensitivities to warming are largest in modestly snow‐covered, interior mountainous parts of WUS, especially the Rocky Mountains

Understanding how snowmelt is partitioned into different hydrologic flowpaths/storages—and how this partitioning varies over time—is essential for predicting water availability and quality under climate variability. In this study, we examine the time‐variance of snowmelt partitioning patterns (SPP) in response to interannual variations in antecedent (Fall) rainfall before snowmelt seasons, across two snow‐dominated catchments in Canada and Sweden with contrasting geologic and topographic features. Using integrated subsurface–surface flow and transport modeling, combined with observational data, we simulate the partitioning of snowmelt into shallow flowpath, deep flowpath, evapotranspiration, and long‐term storage. To generalize our findings beyond the two case studies, we design a suite of virtual experiments that systematically vary catchment slope and the extent of the hydraulic conductivity's vertical and lateral heterogeneity. Results show that lateral heterogeneity in conductivity mediates the sensitivity of snowmelt partitioning to interannual variations in antecedent rainfall. While laterally homogeneous catchments display minimal sensitivity of snowmelt partitioning pattern to wet or dry Fall rainfall conditions, catchments with heterogeneous lateral structure store a significantly larger portion of snowmelt and reduce snow‐sourced shallow flow contributions in years with high pre‐snow rainfall than years with low pre‐snow rainfall. In contrast, while slope and vertical conductivity architecture govern SPP, they play a limited role in mediating SPP's temporal sensitivity to antecedent rainfall variability. These findings reveal that subsurface structure—including the extent of lateral subsurface heterogeneity—modulates the influence of climate variability on snowmelt partitioning and catchment hydrologic function. This has implications for predicting streamflow responses, groundwater recharge, and solute transport under changing climate regimes, and highlights the importance of representing time‐variable hydrologic behavior in hydrologic models.

Climate change is altering the seasonal accumulation and ablation of snow across mid-latitude mountainous regions in the Northern Hemisphere with profound implications for the water resources available to downstream communities and environments. Despite decades of empirical and model-based research on snowmelt-driven streamflow, our ability to predict whether streamflow will increase or decrease in a changing climate remains limited by two factors. First, predictions are fundamentally hampered by high spatial and temporal variability in the processes that control net snow accumulation and ablation across mountainous environments. Second, we lack a consistent and testable framework to coordinate research to determine which dominant mechanisms influencing seasonal snow dynamics are most and least important for streamflow generation in different basins. Our data-driven review marks a step towards the development of such a framework. We first conduct a systematic literature review that synthesizes knowledge about seasonal snowmelt-driven streamflow and how it is altered by climate change, highlighting unsettled questions about how annual streamflow volume is shaped by changing snow dynamics. Drawing from literature, we then propose a framework comprised of three testable, inter-related mechanisms—snow season mass and energy exchanges, the intensity of snow season liquid water inputs, and the synchrony of energy and water availability. Using data for 537 catchments in the United States, we demonstrate the utility of each mechanism and suggest that streamflow prediction will be more challenging in regions with multiple interacting mechanisms. This framework is intended to inform the research community and improve management predictions as it is tested and refined.

Hydrological precipitation and snowmelt events trigger large “pulse” releases of terrestrial dissolved organic matter (DOM) into drainage networks due to an increase in DOM concentration with discharge. Thus, low‐frequency large events, which are predicted to increase with climate change, are responsible for a significant percentage of annual terrestrial DOM input to drainage networks. These same events are accompanied by marked and rapid increases in headwater stream velocity; thus they also “shunt” a large proportion of the pulsed DOM to downstream, higher‐order rivers and aquatic ecosystems geographically removed from the DOM source of origin. Here we merge these ideas into the “pulse‐shunt concept” (PSC) to explain and quantify how infrequent, yet major hydrologic events may drive the timing, flux, geographical dispersion, and regional metabolism of terrestrial DOM. The PSC also helps reconcile long‐standing discrepancies in C cycling theory and provides a robust framework for better quantifying its highly dynamic role in the global C cycle. The PSC adds a critical temporal dimension to linear organic matter removal dynamics postulated by the river continuum concept. It also can be represented mathematically through a model that is based on stream scaling approaches suitable for quantifying the important role of streams and rivers in the global C cycle. Initial hypotheses generated by the PSC include: (1) Infrequent large storms and snowmelt events account for a large and underappreciated percentage of the terrestrial DOM flux to drainage networks at annual and decadal time scales and therefore event statistics are equally important to total discharge when determining terrestrial fluxes. (2) Episodic hydrologic events result in DOM bypassing headwater streams and being metabolized in large rivers and exported to coastal systems. We propose that the PSC provides a framework for watershed biogeochemical modeling and predictions and discuss implications to ecological processes.

In this study, a semi-distributed hydrologic model Soil and Water Assessment Tool (SWAT) has been employed for the Karnali River basin, Nepal to test its applicability for hydrological simulation. Further, model was evaluated to carry out the water balance study of the basin and to determine the snowmelt contribution in the river flow. Snowmelt Runoff Model (SRM) was also used to compare the snowmelt runoff simulated from the SWAT model. The statistical results show that performance of the SWAT model in the Karnali River basin is quite good (p-factor = 0.88 and 0.88, for daily calibration and validation, respectively; r-factor = 0.76 and 0.71, for daily calibration and validation, respectively). Baseflow alpha factor (ALPHA_BF) was found most sensitive parameter for the flow simulation. The study revealed that the average annual runoff volume available at the basin outlet is about 47.16 billion cubic metre out of which about 12% of runoff volume is contributed by the snowmelt runoff. About 25% of annual precipitation seems to be lost as evapotranspiration. The results revealed that both the models, SWAT and SRM, can be efficiently applied in the mountainous river basins of Nepal for planning and management of water resources.

Climate change has induced pronounced shifts in the reproductive phenology of plants, yet we know little about which environmental factors contribute to interspecific variation in responses and their effects on fitness. We integrate data from a 43 yr record of first flowering for six species in subalpine Colorado meadows with a 3 yr snow manipulation experiment on the perennial forb Boechera stricta (Brassicaceae) from the same site. We analyze shifts in the onset of flowering in relation to environmental drivers known to influence phenology: the timing of snowmelt, the accumulation of growing degree days, and photoperiod. Variation in responses to climate change depended on the sequence in which species flowered, with early-flowering species reproducing faster, at a lower heat sum, and under increasingly disparate photoperiods relative to later-flowering species. Early snow-removal treatments confirm that the timing of snowmelt governs observed trends in flowering phenology of B. stricta and that climate change can reduce the probability of flowering, thereby depressing fitness. Our findings suggest that climate change is decoupling historical combinations of photoperiod and temperature and outpacing phenological changes for our focal species. Accurate predictions of biological responses to climate change require a thorough understanding of the factors driving shifts in phenology.