Snowmelt Research Articles

Mechanisms of spring freshet generation in southern Quebec, Canada

Seasonal forecasting of spring floods in snow-covered basins is challenging due to the ambiguity in the driving processes, uncertain estimations of antecedent catchment conditions, and the choice of predictor variables. In this study, we attempt to improve the prediction of spring flow peaks in southern Quebec, Canada, by studying the preconditioning mechanisms of runoff generation and their impact on inter-annual variations in the timing and magnitude of spring peak flow. Historical observations and simulated data from a hydrological and snowmelt model were used to study the antecedent conditions that control flood characteristics in 12 unregulated snow-dominated catchments. Maximum snow accumulation (peak SWE), snowmelt and rainfall volume and intensity, soil moisture, river baseflow, and the air freezing index, a proxy for soil freezing, were estimated during the pre-flood period. Stepwise multiple linear regression was used to identify the most relevant predictors and assess their relative contribution to interannual variability flood variability. Peak SWE was not by itself a strong predictor of spring flood magnitude and timing. The snowmelt rate during the pre-flood period was the most ubiquitous and skillful predictor of spring flood magnitude, followed by the rainfall intensity. The ‘soil memory’ effect, represented here by the simulated soil moisture content and freezing depth, was generally poorly related to flood characteristics. SWE and snowmelt dynamics dominated the interannual variability of flood magnitude in the southern and agricultural basins while rainfall intensity had a stronger influence in the northern, snowier forested basins. More complex machine learning models (random forest and support vector regression) did not perform better than the simple linear models to predict peakflow from antecedent hydroclimatic factors. These results highlight the impact of climate and land cover and use on spring flood generation mechanism and the moderate predictability potential of spring floods based on antecedent hydrological factors.

Influence of habitat on fine-scale space use by brown lemmings (Lemmus trimucronatus) in the High Arctic.

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.

Modelling of snow and glacier melt dynamics in a mountainous river basin using integrated SWAT and machine learning approaches

Modelling of snow and glacier melt dynamics in a mountainous river basin using integrated SWAT and machine learning approaches

Upper‐flow‐regime deposits related to glacio‐volcanic interactions in Patagonia: Insights from the Pleistocene record in Southern Andes

AbstractSupercritical flows and their associated upper‐flow‐regime bedforms have been increasingly recognized in several contrasting depositional environments around the world, including volcano‐sedimentary settings. In recent years, there have been significant advances in flow modelling, architecture and depositional dynamics in upper‐flow‐regime bedforms related to glacio‐volcanic interaction, however most of these works come from specific volcanic landscapes. Examples of other regions with different tectonic and volcanic settings are needed in order to gain a more global perspective regarding the generation and preservation of upper‐flow‐regime glacio‐volcanic bedforms. This study presents a detailed analysis of Pleistocene volcaniclastic deposits accumulated in alluvial to lacustrine deltaic environments in the Southern Andes within the Southern Volcanic Zone. Six facies were defined and related to distinctive bedforms by examining sedimentary features, including textural characteristics, sedimentary structures, architecture and composition. Among these facies, five are distinguished by deposition from supercritical flows and upper‐flow‐regime bedforms, such as cyclic steps, chutes and pools, and antidunes. The stratigraphic succession reveals a predominance of repeated supercritical flows, which appear to be influenced by the availability of detritus for removal along with sudden water discharges. Compositional analyses indicate that explosive volcanism was the primary source of detritus, occurring synchronously with sedimentation. Additionally, the widespread occurrence of Pleistocene glacial conditions in this region suggests that volcanic‐induced melting ice or snow may have been an important source of water supply. Regarding the Southern Andes, the presence of local depressions, such as volcano‐tectonic calderas plays an essential role in the generation and preservation of the upper‐flow‐regime bedforms related to glacio‐volcanism. Furthermore, the importance of carrying out detailed and multidisciplinary studies seems to be critical for the recognition of these deposits in the record of the Andes.

Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better

AbstractEstimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management‐relevant aspects of the hydrograph using non‐parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer‐resolution simulations are useful.

Comparing standardised precipitation and barley yield across Quebec.

Climate change and variability continue to affect crop production across the world in general and in Quebec in particular. Therefore, it is important to better understand this climate-yield nexus. Unfortunately, in Quebec, there are currently no studies that analyse both precipitation and barley yield. This study aims at filling this research gap by comparing standardised precipitation (SPI) against barley yield gaps across Quebec for three peripheral and three southern/central regions of Quebec. The study uses growing season precipitation data from Ouranos and barley yield data from Institut de la Statistique du Québec. This work deploys the use of standardised index (SPI)index and a machine learningyield gapalgorithm (actual barley yield minus projected barley yield) to provide a provincial portrait of the relationships between SPI and barley yield. The results show that the peripheral regions record below zero SPIs (Abitibi-Temiscamingue - 0.48, Saguenay-Lac-Saint-Jean - 0.14 and Outaouais - 0.10) and more yield gap years, while the southern/central regions record positive SPIs (Estrie 1.17, Centre du Quebec 0.86 and Monteregie: 0.33) and fewer years with yield gaps. This shows that there is a south-north gradient in the variations of SPI and yield gaps. The SPI and yield patterns can be explained by prolonged winters further north of Quebec and recently winters with insufficient snow cover which triggers rapid snow melt and thus shortening the growing season for barley. Policy actions around drought-resistant varieties within a co-creation context and more research that explores daily and monthly liquid precipitation totals during the growing season of the crop need to be explored. There is also a need to better understand the economic costs and benefits of the associated yield gapsas well as the impacts of temperature.

Snowmelt Onset and Caribou (Rangifer tarandus) Spring Migration

Caribou (Rangifer tarandus) undergo exceptionally large, annual synchronized migrations of thousands of kilometers, triggered by their shared environmental stimuli. The proximate triggers of those migrations remain mysterious, though snow characteristics play an important role due to their influence on the mechanics of locomotion. We investigate whether the snow melt–refreeze status relates to caribou movement, using previously collected Global Positioning System (GPS) caribou collar data. We analyzed 117 individual female caribou with >30,000 observations between 2007 and 2016 from the Bathurst herd in Northern Canada. We used a hierarchical model to estimate the beginning, duration, and end of spring migration and compared these statistics against snow pack melt characteristics derived from 37 GHz vertically polarized (37V GHz) Calibrated Enhanced-Resolution Brightness Temperatures (CETB) at 3.125 km resolution. The timing of migration for Bathurst caribou generally tracked the snowmelt onset. The start of migration was closely linked to the main melt onset in the wintering areas, occurring on average 2.6 days later (range −1.9 to 8.4, se 0.28, n = 10). The weighted linear regression was also highly significant (p-value = 0.002, R2=0.717). The relationship between migration arrival times and the main melt onset on the calving grounds (R2 = 0.688, p-value = 0.003), however, had a considerably more variable lag (mean 13.3 d, se 0.67, range 3.1–20.4). No migrations ended before the main melt onset at the calving grounds. Thawing conditions may provide a trigger for migration or favorable conditions that increase animal mobility, and suggest that the snow properties are more important than snow presence. Further work is needed to understand how widespread this is and why there is such a relationship.

PREDICTIVE MODEL TO MINIMIZE THE EFFECT OF EXTREME TEMPERATURE IN SKARDU AND ASTORE, GILGIT BALTISTAN

Climate is a fundamental factor of the natural environment that has a role in both natural and human existence. Temperature is an important climatic element that influences snow melting, evaporation, and frost directly. Current study has used Mean Monthly Minimum Temperature (MMMT) of Skardu from 1972 to 2021 and of Astore from 1993 to 2021 based on the availability of data. In this work; we have used SARIMA (Seasonal Auto Regressive Integrated Moving Average Model) to forecast mean monthly minimum temperature. Skardu data is stationary at level form, which suggests SARMA model for Skardu station and Astore data is stationary at first difference so SARIMA time series is appropriate for mean monthly minimum temperature of Astore. Using Box Jenkins’s approach it is found that the most appropriate model for Skardu is SARMA(1,0)(1,0)12 and Astore is SARIMA (0,1,1)(4,1,4)12 respectively. These models have been utilized to forecast MMMT from 2022 to 2036. Yearly mean minimum temperature forecasts show that the mean minimum temperature at Skardu and Astore stations is slightly decreasing. The yearly mean minimum temperature at Skardu station is 4.0 °C in 2022 and will decrease to 2.3 °C till 2036, while at Astore station it is 4.0 °C in 2022 and will fall to 2.6 °C in 2036. Our results will be useful for decision makers and insurance companies for better future planning to minimize the effect of lowest temperature.

Evaluation of the impact of anthropogenic storage on the hydrological drought propagation in two contrasting semi-arid river basins

ABSTRACT This study quantitatively investigated reservoir-effects on drought propagation in two semi-arid river basins: India’s Tapi basin with the Ukai reservoir and Uzbekistan’s Chirchik basin with the Charvak reservoir. Meteorological drought (MD) is analyzed using the Standardized Precipitation Index (SPI) and hydrological drought (HD) using the Standardized Streamflow Index (SSI) for the duration from 1980 to 2004. Both the river basins, especially in upstream reservoir areas, exhibited a notable correlation between HD and MD. Reservoir operation was observed to reduce the downstream MD–HD correlation at shorter SPI timescales. Hit-score-based evaluations indicated that reservoir operation has induced changes in the drought propagation patterns for both river basins. Due to the contrasting characteristics, the river basins showed a significant variation in the drought propagation time (DPT), with distinct influences from monsoon (Tapi) and snow-melting (Chirchik). The average DPT (average DPT over 12 months) for the reservoir-influenced part of the Tapi (∼ six months) and Chirchik (∼ nine months) basins is higher than that of the natural parts of both basins (Tapi: ∼ four months; Chirchik: ∼ six months) as a result of the natural and anthropogenic storage influence.

Surface primary producer phenology in Dease Strait, NU, Canada, examined using submersed oceanographic sensors and satellite remote sensing

Thinning sea ice cover and earlier melt in the Arctic impact primary producer (PP) phenology, causing earlier ice algal bloom termination and phytoplankton bloom commencement. However, logistic constraints limit capturing the complete seasonal evolution of PPs and their physical drivers. Here, we combine spectral irradiance data from subsurface oceanographic moorings with synthetic aperture radar backscatter and meteorological variables to study light in Dease Strait, investigating its relation to timing and magnitude of surface PPs for 2017 and 2019. Ice algal blooms in 2017 and 2019 lasted 66 and 84 days, respectively, peaking within 2 days of snow melt onset. In 2019, lower temperatures and a deeper snowpack before snow melt extended the ice algal bloom. Melt pond formation increased light transmission, enabling a short, 6–7-day under-ice phytoplankton bloom in both years that was likely nutrient-limited. The 2019 phytoplankton bloom was less productive, possibly due to the longer ice algal bloom depleting surface nutrients. After ice break-up in 2019, a 31-day late-summer bloom occurred via wind-driven mixing. Our findings suggest that the combined remote sensing technique has novel applicability in other settings, providing insights into the changing state of PP phenology, and the need for long-term Arctic observations to discern regional climate change effects.

Understanding the hydrological performance of green and grey roofs during winter in cold climate regions

Green and grey roofs have emerged as promising and sustainable measures for effectively managing stormwater in urban catchments. However, there is a gap in the literature in understanding and modelling the hydrological performance of these roofs during winter and snow-covered periods in cold climate regions. The present study attempted to address this gap by validating the use of a snow module in simulating the dynamics of snow accumulation and melting of green and grey roofs. Then, the validated model was used to identify and separate the different events that occur in winter (melt only, rainfall only, rain-on-snow) to assess the hydrological performance of six different configurations of green and grey roofs in Trondheim, Norway. The snow module accurately simulated snow accumulation and melting of green and grey roofs. The results showed that rain-on-snow events in winter have longer duration compared to other events including rainfall events in summer. Consequently, rain-on-snow events yield a higher amount of inflow to the roofs compared to rainfall events in summer, despite summer events having higher intensities. The retention and detention performances of green and grey roofs were found to be lowest for rain-on-snow events compared to other types of events, but still yielding significantly lower peak runoffs when compared to standard black roofs. The decrease in retention and detention performances in winter were attributed to the long duration of events, accumulation effect of snow, freezing of roof surface layers, and reduction of evapotranspiration. The study highlights the importance of considering winter conditions in the design of green and grey roofs in cold climates to enhance stormwater management.

Seasonal crustal movements in Northeast Japan revisited

Seasonal movements of global navigation satellite system (GNSS) stations in Northeast (NE) Japan are mainly driven by elastic loading of snow, reaching a few meters deep along the western flank of the backbone range. Here we study them in a comprehensive manner to solve remaining problems. GNSS stations in the inland area show sharp and strong subsidence peaks in winter and remain flat in other seasons, a response consistent with the snow loading. On the other hand, those close to coasts show broad and weak winter subsidence peaks. This needs additional loads in spring to retard the rapid decay of snow loading. We propose that rice fields work as natural reservoirs until early summer and evaluate quantitative consistency of the hypothesis. We also found that some stations show abnormally large winter subsidence that cannot be explained by snow loading alone. Here we examine two factors, i.e., groundwater extraction in winter for melting snow on roads, and snow accretion onto GNSS antenna radomes. We also evaluate seasonal ocean water mass changes in the Japan Sea using GRACE satellite gravimetry data. We found its role in seasonal crustal movements in NE Japan quite small.

Effect of phase change materials on thermophysical properties of asphalt mixtures and snow melting performance

The increased Thermal Conductivity and Thermal Diffusion Coefficient of asphalt mixtures means that the material is able to conduct and disperse heat more efficiently. In winter, this property can be used to accelerate the melting process of snow and ice, reducing the risk of road sliding due to snow and ice build-up. In this study, the thermal conductivity, thermal diffusion coefficient, and specific heat capacity of phase change asphalt (PCA) mixtures were investigated using a transient flat-plate heat source method. Analysis was performed with a thermal constant analyzer, a custom-designed thermal conductivity experiment, and a differential scanning calorimeter. Additionally, a phase change asphalt mixture thermoregulation rutting plate was prepared to evaluate its thermoregulation and snow and ice resistance under actual winter and indoor conditions. Results indicated that the thermal conductivity and thermal diffusion coefficient of PCA mixtures were superior to those of conventional asphalt mixtures. Notably, the specific heat capacity of these mixtures increased significantly within the phase change temperature range. The integration of phase change materials enhanced heat transfer within the asphalt mixtures, allowing for a delayed cooling process when temperatures decreased, with the maximum cooling range observed to be 2.8°C. Snow melting tests confirmed the early snowfall efficacy of the phase change asphalt mixture rutting plate, effectively achieving minimal snow accumulation and demonstrating the capability of 'melting light snow.'

Characteristics of runoff changes and their climatic factors in two different glacier-fed basins

ABSTRACT The hydrological regulation function of glaciers in different watersheds is different. This study took the Yanggong River Basin (YRB) and Urumqi River Basin (URB) as two typical cases, to explore the runoff change differences and their responses to climate factors from 1979 to 2017. In the past 39 years, the YRB’s annual runoff showed an insignificant trend of increasing first and then decreasing, while the URB’s increasing trend was significant. From the 1980s to the 2010s, the YRB’s monthly runoff extremum occurred earlier than before, and the peak value decreased. The time of monthly runoff extremum in the URB has not changed, but the peak value is increasing. Both basins experienced an increase in annual temperatures from 1979 to 2017, with the rate of warming being more pronounced in the URB. The precipitation in the YRB had no significant trend from 1979 to 2017, and the significant increasing trend in annual precipitation extended in the URB from 1998 to 2010. The YRB’s runoff change was mainly influenced by the flood season precipitation, while the URB’s runoff increase was due to the temperature rise causing faster glacier and snow melt, and the summer precipitation change. In continental glacier basins with many glaciers, the regulation function of glaciers on total runoff is more significant.

High altitude horse use and early horse transport in eastern Eurasia: New evidence from melting ice

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.

The dual role of meltwater in buffering river runoff in the Yarlung Zangbo Basin, Tibetan Plateau

Study regionThe Yarlung Zangbo Basin (YZB) on the Tibetan Plateau, the world's highest river basin, features a significant cryosphere with glaciers and seasonal snow cover crucial to its hydrology. The study focuses on the region between the Nuxia and Dexing river gauging stations, where glaciers cover 15.4 % of the area. Study focusThe research quantifies the contributions of snow melt (SM) runoff, glacier melt (GM) runoff, rainfall runoff, and baseflow to the total runoff in the YZB. The Spatial Processes in Hydrology (SPHY) model, enhanced with a cryosphere module, was utilized, calibrated with runoff data from the Nuxia station and evapotranspiration data from 2003 to 2014. New hydrological insightsThe study found rainfall runoff to be the primary contributor to annual runoff (66.3 %), followed by snow melt runoff (19.7 %), glacier melt runoff (6.2 %), and baseflow (7.8 %). Snow melt runoff is dominant in early spring, while baseflow prevails in winter. Glacier melt runoff contributes directly to river flow (90.1 %) and replenishes groundwater (9.9 %), which then drains as baseflow. In glacier-rich areas, percolated glacier meltwater significantly recharges groundwater, underscoring its vital role in sustaining river flow in the YZB. This research enhances the understanding of hydrological processes in large alpine river basins and highlights the crucial role of glacier and snow melt in maintaining the Tibetan Plateau's water resources.

Intermediate complexity atmospheric modeling in complex terrain: is it right?

Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, developed for hectometer scale applications. HICAR can run more than 500x faster than conventional atmospheric models, while containing many of the same physics parameterizations. Station measurements of air temperature, wind speed, and radiation, in combination with data from a scanning Doppler wind LiDAR, are compared to 50 m resolution HICAR output during late spring. We examine the model’s performance over bare ground and melting snow. The model shows a smaller root mean squared error in 2 m air temperature than the driving model, and approximates the 3D flow features present around ridges and along slopes. Timing and magnitude of changes in shortwave and longwave radiation also show agreement with measurements. Nocturnal cooling during clear nights is overestimated at the snow covered site. Additionally, the thermal wind parameterization employed by the model typically produces excessively strong surface winds, driven in part by this excessive nocturnal cooling over snow. These findings highlight the utility of HICAR as a tool for dynamically downscaling forcing datasets, and expose the need for improvements to the snow model used in HICAR.

Quantifying Volumetric Scattering Bias in ICESat‐2 and Operation IceBridge Altimetry Over Greenland Firn and Aged Snow

AbstractThe Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission has collected surface elevation measurements for over 5 years. ICESat‐2 carries an instrument that emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat‐2 acquisitions during the melting season. We performed two case studies over the Greenland Ice Sheet to quantify bias in ICESat‐2 signals over snow: one to validate Airborne Topographic Mapper (ATM) data against Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS‐NG) grain sizes, and a second to estimate ICESat‐2 bias relative to ATM. We used snow optical grain sizes derived from ATM and AVIRIS‐NG to attribute altimetry bias to snowpack properties. For the first case study, the mean and standard deviation of optical grain sizes were 340 ± 65 µm (AVIRIS‐NG) and 670 ± 420 µm (ATM). A mean altimetry bias of 4.81 ± 1.76 cm was found for ATM, with larger biases linked to increases in grain size. In the second case study, we found a mean grain size of 910 ± 381 µm and biases of 6.42 ± 1.77 cm (ICESat‐2) and 9.82 ± 0.97 cm (ATM). The grain sizes and densities needed to recreate biases with a model are uncommon in nature, so we propose that additional surface attributes must be considered to characterize ICESat‐2 bias over snow. The altimetry biases are within the accuracy requirements of the ICESat‐2 mission, but we cannot rule out more significant errors over coarse‐grained snow.

Response of runoff and its components to climate change and its future trend in the source region of the Yangtze River

AbstractUnder global climate changes, the temperature and precipitation on the Tibetan Plateau changed significantly, causing accelerated melting of glaciers and snow and changes in runoff. The response of runoff and its components to climate change is important for water resource management and ecology conservation in the region. Therefore, the Spatial Processes in Hydrology model, a distributed cold‐zone hydrological model that contains a glacial ablation module, was used to simulate runoff in the Yangtze River source located in the middle of the Tibetan Plateau during 2000–2050, and the Nash‐Sutcliffe efficiency coefficient, relative error and coefficient of determination (R2) for the calibration period and validation period revealed that the model performed well in most years. The results showed that rainfall runoff contributed the most to the total runoff (61%), followed by baseflow (23%), snowmelt runoff (12%) and glacier runoff (4%) in the Yangtze River source during 2000–2020. The runoff amounts of the three source rivers, Dangqu River, Tuotuo River and Chumar River, accounted for approximately 53% of the total runoff in the Yangtze River source basin, with rainfall runoff (52%) contributing the most and glacier runoff (6%) contributing the least. Compared to the contributions of glacial runoff in the Dangqu River (9%) and Tuotuo River (7%), the proportion of glacial runoff in the Chumar River is small (1%). Under the CMIP6 climate model, the mean runoff depth is predicted to increase by approximately 13.5 mm from 2020 to 2050 compared with that from 2000 to 2020.

Gully regulates snowmelt runoff, sediment and nutrient loss processes in Mollisols region of Northeast China

Gully is a prominent indicator of land degradation in agroecosystems, functioning as a crucial pathway connecting upslopes to downstream channels. However, little is known about how gully regulates runoff, sediment, and nutrient loss processes in the catchment during snowmelt. In this study, we monitored these processes in situ at both the gully head (the upslope accumulated catchment of the gully head, CGH) and outlet of two representative and typical gully-dominated catchments (F1 and F2) during snowmelt in Mollisols region of Northeast China. Our results showed that runoff discharge of CGH and outlet exhibited a multi-peak trend during snowmelt, driven by the transition from snow melting to soil thawing. This transition resulted in distinct runoff patterns in both CGH and outlet, with significant differences in their response to air temperature. The total runoff yield of CGH accounted for 57.8 % in F1 and 40.6 % in F2 of the total runoff yield of the outlet. Notably, the peak sediment concentration displayed a marked lag compared to the peak runoff discharge, primarily dominated by the increased sensitivity of gully erosion after the thawing of gully slopes. Gully erosion was the main source of sediment yield in the catchment, contributing 98.2 % in F1 and 96.6 % in F2. Furthermore, nutrient concentrations exhibited a decreasing trend during snowmelt. The comparison of high nutrient concentrations in CGH and relatively low nutrient concentrations in outlet highlighted the gully's role in intercepting and diluting runoff nutrients. Hysteresis analysis confirmed the differential contribution of CGH and gully to nutrient sources. CGH accounting for 50.9 % and 93.3 % of runoff TN and runoff TP loss, while contributing only 8.3 % and 5.8 % to sediment TN and sediment TP loss, respectively. These findings offer valuable insights for effective erosion control and nonpoint source pollution management in gully-dominated agroecosystems during snowmelt.