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Spring snowmelt occurs for a short duration on an annual time scale, but their timings considerably affect the carbon and hydrological cycle in high‐latitude ecosystems. Here, we developed a simple snowmelt model, treating the ecosystem surface as a bulk‐surface layer. Energy fluxes across this bulk surface and the snow‐soil boundary determine snow temperature and the energy utilized for snowmelt. Parameterizing the bulk surface using decade‐long eddy covariance site data from two Alaskan open black spruce forests offered an opportunity to quantitatively evaluate meteorological drivers affecting snowmelt timings without the needs for detailed canopy information. The sensitivity analysis suggested that the total snowfall on the forest floor, ranging from 0.35 m in 2016 to about 1 m in 2018 and 2020, was the most crucial driver for snowmelt timing. This factor accounted for a 10‐day difference in the interannual variations in snow disappearance dates. The importance of the snowfall varied from year to year, and in 2013, the late snowmelt was characterized by low air temperatures, which increased sensible heat loss from the snowpack. The importance of atmospheric radiation was revealed in relatively warm years, such as 2016 and 2019. Our modeling approach necessitates adjusting one empirical parameter that reflects the heat conductivity from the bulk surface to the snow, based on observations. Nevertheless, despite this need for adjustment, the bulk‐surface approach helps identify important meteorological drivers underlying observed snowmelt within a simple theoretical framework. Plain Language Summary It is essential to understand why and how spring snowmelt occurs in boreal forests because of its impact on both the biological and hydrological processes in the ecosystem. However, meteorological drivers related to snowmelt often change simultaneously, which makes it challenging to determine which single driver is vital for snowmelt. To address this challenge, we constructed a simple snowmelt model that treats the ecosystem surface as a single bulk layer. The simple model bypasses the needs for detailed canopy information and is easily constrained by observed data. Applying the model to decadal observation data from Alaskan boreal forests, we found that snowfall, air temperature, and atmospheric radiation were important meteorological drivers for explaining interannual variations in snowmelt timing. Each of them explained about 1–2 weeks of snowmelt disappearance dates. We also demonstrated that the model helped explain why the snowmelt was late in 2013 and early in 2016 and 2019 and why snow disappearance dates differed between study sites. While a simple model has its limitations, it can be beneficial for understanding snowmelt characteristics within a simple theoretical framework. Key Points A model‐based approach was introduced to delineate the roles of meteorological drivers on interannual variations in snowmelt timing Snowfall was the most important driver of the interannual variation in snowmelt timing followed by air temperature and atmospheric radiation Late snowmelt in 2013 was attributed to low air temperature, increasing sensible heat loss from the snowpack

Spring snowmelt has a significant impact on the hydrological cycle in seasonally frozen soil areas. However, scholars hold differing, and even opposing, views on the role of snowmelt during the thawing period in groundwater recharge. To explore the potential recharge effects of spring snowmelt on groundwater in seasonal frozen soil areas, this study investigated the vadose zone dynamics controlled by soil freeze–thaw processes and snowmelt infiltration in the Northeast of China for 194 days from 31 October 2020 to 12 May 2021. Responses of groundwater level and soil moisture to snowmelt infiltration show that most snowmelt was infiltrated under the site despite the ground being frozen. During the unstable thawing period, surface snow had already melted, and preferential flow in frozen soil enabled the recharge groundwater by snowmelt (rainfall), resulting in a significant rise in groundwater levels within a short time. The calculated and simulated snowmelt (rainfall) infiltration coefficient revealed that during the spring snowmelt period, the recharge capacity of snowmelt or rainfall to groundwater at the site is 3.2 times during the stable thawing period and 4.5 times during the non-freezing period.

The Rio de Flag is an ephemeral stream that flows through Flagstaff, Arizona, flowing less than 10% of the year due to geology and climate. Seasonal flows can occur during snowmelt periods and occasionally during short duration intense summer monsoon storms. With multiple regional, and local, flood control plans for the channel, it is important to understand flow characteristics including annual water volume, flow timing, and spatial and temporal trends in surface water availability. This study examines the springtime (February to May) stream gauge data from six stream gauges within the Rio de Flag, and three tributaries (Clay Avenue, Spruce, and Sinclair Washes) between 2019 and 2023. Analyses included existing gauge data to determine seasonal flow, runoff per area, and understand the relationship of snowmelt and discharge for these selected stream reaches. This study aims to gain a better understanding of ephemeral flow variability in northern Arizona and the surrounding southern Colorado Plateau to inform stream management as well as provide baseline data for understanding the impacts flow variability on riparian ecosystems.

Snow on the Tibetan Plateau (TP) is an important signal for the prediction of East Asian climate. In this study, the relationship between the TP spring snowmelt and spring and summer precipitation in Northwest China (NWC) was investigated, along with the possible mechanisms linked to the impacts of snowmelt on precipitation. The results showed that the TP spring snowmelt had significant impacts on spring and summer precipitation in NWC. For example, when there was a large spring snowmelt in the central- eastern TP, the spring and summer precipitation in the Hexi Corridor and southeast NWC was excessive, especially in summer; when there was a large spring snowmelt in the northern TP, the spring and summer precipitation was deficient across the whole of NWC, while a large spring snowmelt in the western TP led to deficient spring and summer precipitation in eastern NWC but excessive precipitation in western NWC. The possible mechanisms for this included the fact that more spring snowmelt over the TP led to higher soil moisture contents, which further resulted in weakened subtropical westerly and enhanced ridge over Xinjiang. By changing the TP thermal forcing, these anomalous atmospheric circulation conditions transported water vapor into NWC, thus creating excessive summer precipitation in that region.

In this study, the future snowmelt runoff in the chilly northeast region’s Tangwang River Basin was simulated and predicted using the SWAT model, which was built and used based on the NEX-GDDP-CMIP6 climate model. This study conducted a detailed analysis of the spatial and temporal distribution characteristics of snowmelt runoff using high-resolution DEM, land use, and soil data, along with data from historical and future climatic scenarios. Using box plots and the Bflow digital filtering approach, this study first determined the snowmelt runoff period before precisely defining the snowmelt periods. Sensitivity analysis and parameter rate determination ensured the simulation accuracy of the SWAT model, and the correlation coefficients of the total runoff validation period and rate period were 0.75 and 0.76, with Nashiness coefficients of 0.75 for both. The correlation coefficients of the snowmelt runoff were 0.73 and 0.74, with Nashiness coefficients of 0.7 and 0.68 for both, and the model was in good agreement with the measured data. It was discovered that while temperatures indicate an increasing tendency across all future climate scenarios, precipitation is predicted to increase under the SSP2-4.5 scenario. The SSP2-4.5 scenario predicted a decreasing trend regarding runoff, while the SSP1-2.6 and SSP5-8.5 scenarios showed an increasing trend with little overall change and the SSP5-8.5 scenario even showed a decrease of 6.35%. These differences were evident in the monthly runoff simulation projections. Overall, the findings point to the possibility that, despite future climate change having a negligible effect on the hydrological cycle of the Tangwang River Basin, it may intensify and increase the frequency of extreme weather events, creating difficulties for the management of water resources and the issuing of flood warnings. For the purpose of planning water resources and studying hydrological change in this basin and other basins in cold regions, this study offers a crucial scientific foundation. An in-depth study of snowmelt runoff is of great practical significance for optimizing water resource management, rational planning of water use, spring flood prevention, and disaster mitigation and prevention, and provides valuable data support for future research on snowmelt runoff.

Snowmelt is the main source of runoff in the alpine regions of northern China. When using the soil and water assessment tool (SWAT) to simulate snowmelt runoff, the snowmelt date and snowmelt factor parameters are set according to the North American values. To improve the accuracy of the runoff simulation in northern China, we innovatively used a baseflow segmentation method to determine the snowmelt time, taking temperature as a reference. The snowmelt period was extracted from statistical data, and the corresponding parameters in the source code of SWAT were optimized for the study area. After the calibration was completed, the modified simulation value was compared with the original code simulation value. The simulation accuracy of the daily runoff was improved, and we found that the greater the difference between the source code simulation value and the observed value was, the better the simulation accuracy. Therefore, modifying the source code in SWAT is an effective way to improve the accuracy of simulations of Alpine regions in Northern China. The results show that adjustments to the snowmelt modules of SWAT to reflect local conditions can be an effective way to improve the predictions.

For many of the Siberian rivers, and the Upper Ob in particular, 70–80% of the volume of the annual water runoff is formed during the spring flood. Thus, factors influencing the formation of water runoff during the spring flood are paramount. We explain changes in the isotopic composition of the Upper Ob surface waters by changing different components’ contribution to the runoff water discharge over the spring flood period. We suggest estimating the time of meltwater flow from the Upper Ob watershed to the outlet section using the difference between the date of the complete melting of the snow cover in the catchment area and the date of the maximum light isotope composition of water in the outlet section. We show that a sharp short-term weighting of the isotopic composition of water in the river at the end of the first phase of the flood may be associated with the influx of autumn soil moisture, displaced from the soils by snowmelt waters.

The use of GIS technologies for modeling the spatial distribution of snow cover in spring is considered. The authors developed a method for the snowmelt calculation by the example of the Votkinsk Reservoir catchment area. It is proposed to take into account the uneven distribution of snow cover across the catchment depending on the terrain features. The paper estimates how the raster cell size variation affects the displaying the modeling results of the spatial distribution of snow cover.

River water quality was evaluated with respect to eutrophication and land use during spring snowmelt and summer base flow periods in Abashiri (mixed cropland-livestock farming) and Okoppe (grassland-based dairy cattle farming), eastern Hokkaido, Japan. Water from rivers and tributaries was sampled during snowmelt and summer base flow periods in 2005, and river flow was measured. Total N (TN), NO 3 -N, and Si concentrations were determined using standard methods. Total catchment and upland areas for each sampling site were determined with ArcGIS hydrology modeling software and 1:25,000-scale digital topographic maps. Specific discharge was significantly higher during snowmelt than during base flow. In both areas, TN concentrations increased, whereas Si concentrations decreased, with increased specific discharge, and were significantly higher during snowmelt. The Si:TN mole ratio decreased to below or close to the threshold value for eutrophication (2.7) in one-third of sites during snowmelt. River NO 3 -N concentrations during base flow were significantly and positively correlated with the proportion of upland fields in the catchment in both the Abashiri ( r =0.88, P <0.001) and Okoppe ( r =0.43, P <0.01) areas. However, the regression slope, defined as the impact factor (IF) of water quality, was much higher in Abashiri (0.025) than in Okoppe (0.0094). The correlations were also significantly positive during snowmelt in both areas, but IF was four to eight times higher during snowmelt than during base flow. Higher discharge of N from upland fields and grasslands during snowmelt and the resulting eutrophication in estuaries suggest that nutrient discharge during snowmelt should be taken into account when assessing and monitoring the annual loss of nutrients from agricultural fields.

In order to help develop a better understanding of relevant catchment processes, this paper presents the changes in physico-chemical features of the Wieprz River water during the spring snowmelt flood of 2006. The obtained results showed that the groundwater sampled from the springs and the water sampled from the river had a similar and quite stable composition of the basic physicochemical features in the period of solely groundwater feeding (the river is fed only with the water coming from underground sources). The physico-chemical composition of river water during snowmelt depended on the contribution of surface runoff in total outflow and the flood phase. The correlation coefficients between the discharge in the Wieprz River and the concentrations in the studied indices were significantly negative: pH, SEC, HCO3, Ca, Mg, Na, Sr, SiO2, Cl, SO4, F. Significantly positive correlations associated with an increase in discharge were observed in the case of: K, NO3, NO2, total organic carbon, chemical oxygen demand and biochemical oxygen demand. Step and bidirectional responses were noted during the snowmelt flood in the case of the content of NH4 and PO4. W pracy przedstawiono zmiany cech fizyczno-chemicznych wody rzeki Wieprz w Guciowie (SE Polska) w czasie wiosennego wezbrania roztopowego 2006 roku. Wyniki badań wykazały, że wody gruntowe pobrane ze źródeł oraz wody pobrane z koryta rzeki Wieprz w okresie wyłącznego zasilania podziemnego, miały zbliżony i stabilny skład podstawowych wskaźników fizyczno-chemicznych. Podczas roztopów wartości parametrów fizyczno-chemicznych wody w rzece były uzależnione od stopnia przemarznięcia pokrywy glebowej, udziału spływu powierzchniowego w odpływie całkowitym oraz fazy wezbrania. Współczynniki korelacji między przepływem wody w rzece Wieprz a stężeniem badanego wskaźnika były istotne ujemne w przypadku: pH -0,78, SEC -0,92, TH -0,92, HCO3 -0,93, Ca -0,89, Mg -0,88, Na -0,81, Sr -0,87, SiO2 -0,81, Cl -0,87, SO4 -0,83, F -0,59. Istotnie dodatnie zależności związane ze zwiększeniem przepływu zanotowano w przypadku: K 0,73, NO3 0,71, NO2 0,58, TOC 0,62, COD 0,63 i BOD 0,62. Skokową i różnokierunkową reakcję podczas wezbrania roztopowego notowano w przypadku: NH4 0,34 i PO4 0,13.