Snowmelt Processes Research Articles

Research on Electric Heating Green Snow Melting and Anti-icing Technology for Tunnel, Bridge, and Culvert Entrances

This paper addresses the issue of electric heating snow melting at tunnel, bridge, and culvert entrances. It explores a novel carpet-style electric heating snow melting and anti-icing system for these areas. The study analyzes the shortcomings of existing electric heating snow melting methods and proposes effective measures for improvement. To accurately assess the characteristics of current snow melting techniques, the research considers the damage caused by traditional snow melting methods to road structures and subsequent maintenance issues, while emphasizing energy-saving and environmentally friendly technologies. The snow melting process was simulated using Star-CCM+. The results show that the new carpet-style electric heating elements significantly improve snow melting efficiency. Preheating the road surface before snow accumulation greatly reduces snow retention time, enabling timely snow and ice removal. Typically, electric heating elements in snow melting systems are installed beneath the road surface at tunnel entrances. After installation, to protect the elements and ensure smoothness, a layer of cement or another suitable material is poured over them. However, these heating elements require regular inspection or replacement, which necessitates breaking up the road surface, increasing maintenance costs, and disrupting normal traffic operations. This study overcomes these challenges by providing significant convenience for installation and maintenance work, reducing costs, and minimizing the impact on traffic operations.

Hourly estimation of water reaching the ground surface in snow-covered regions of Japan using mesoscale meteorological data with the heat balance method

In snow-covered regions, landslides, including debris flows, frequently result from snowmelt and cause significant hazards. Because these snowmelt-induced landslides are triggered by increasing groundwater level and pore-water pressure acting on the slip surface due to infiltration of snowmelt, sometimes mixed with rainwater, accurate estimation of water reaching the ground surface (abbreviated MR in this study) on an hourly basis is essential for developing countermeasures such as an early warning system. This study introduces a model for estimating hourly MR, as well as snow water equivalent and snow depth, using mesoscale meteorological data provided by the Japan Meteorological Agency. The model considers four processes: precipitation, snow accumulation and densification, snowmelt, and infiltration. In the snowmelt process, snowmelt is estimated using the heat balance method. We applied the model to a snow observation station in Niigata Prefecture, central Japan, where MR, snow water equivalent, and snow depth were observed during the winter seasons of 2017–18 through 2020–21. Comparisons of observed and calculated values demonstrated that the model successfully reproduced the observations, regardless of the amount of snowfall in individual winter seasons. We propose that this model can estimate hourly MR, snow water equivalent, and snow depth at any location in snow-covered regions where on-site meteorological and snow observations are unavailable.

Study on physical properties and snow-melting performance of multilayer composite conductive-pervious concrete for improving the snow-melting efficiency and energy consumption of ECON

One of the most significant challenges facing transport networks in cold regions is snow accumulation on road surfaces. This can lead to traffic congestion and safety issues. While electrically conductive concrete (ECON) heated pavement systems (HPS) offer several advantages, including stability, and low environmental impact, they also present certain limitations, such as low snow-melting efficiency and high snow-melting energy consumption. In this study, a SiO2 aerogel mortar thermal-insulation layer and a self-compacting concrete structural layer were set underneath the reduced graphene oxide (RGO) composite conductive concrete, and the straight pervious channels were prefabricated, to reduce the snow-melting efficiency and energy consumption through the preparation of multilayer composite conductive-pervious concrete integrated specimen. The initial objective was to investigate the influence of SiO2 aerogel substitution rate on the mechanical properties and thermal conductivity of thermal-insulation mortar. Secondly, the impact of the thickness of the conductive layer and thermal-insulation layer on the mechanical properties of the integrated specimen was analyzed. Finally, the impact of SiO2 aerogel thermal-insulation mortar and straight pervious channels on the integrated specimen's snow-melting performance was evaluated through a snow-melting test, which assessed the efficiency and energy consumption of the snow-melting process. The results indicate that when the SiO2 aerogel substitution rate was 40 %, the thermal-insulation mortar exhibited favorable mechanical and thermal-insulation properties. The 5 cm conductive layer and the 1 cm thermal-insulation layer achieved good mechanical properties of the integrated specimen. In comparison to single-layer composite conductive concrete, the snow-melting time and energy consumption of multilayer composite conductive-pervious concrete integrated specimen were reduced by 23.05 % and 21.30 %, respectively. The straight pervious channels permit the immediate discharge of water produced by snow melting, thereby reducing the heat transfer from the snow-melting board to the water film. The setting of SiO2 aerogel thermal-insulation mortar enables the retention of heat for the heating of the snow-melting board and snow melting, which improves the snow-melting performance.

Experimental study and numerical simulation of concrete pavement electrical heating for snow melting

Road snow accumulation can lead to severe traffic delays, and commonly used deicing agents may pose potential environmental risks. Electrically heated concrete pavement systems utilizing heating pipe technology offer a safe and environmentally sustainable alternative. The snow-free ratio was frequently used as an evaluation index to evaluate the snow-melting performance in road infrastructure. However, the unique wavy temperature distribution during the snow-melting process of the heating system results in discontinuous melting, making the existing snow-free ratio inadequate for accurately assessing its performance and effectively guiding the operational strategy of the heating system. Therefore, this study analyzed the special temperature distribution and proposed a new calculation method for snow-free ratio based on the average temperature and the temperature non-uniformity coefficient of the pavement surface. First, the effects of factors such as heating pipe spacing, embedded depth, heating power, and wind velocity on average temperature and the temperature non-uniformity coefficient were analyzed using finite element simulation. Prediction models for average temperature and the temperature non-uniformity coefficient were then established and validated using the response surface method and experiments. Subsequently, using average temperature and the temperature non-uniformity coefficient as intermediate variables, a new functional relationship between the snow-free ratio and the four factors was established, allowing for satisfactory snow-melting performance to be achieved by adjusting the relevant factors of the heating system. The results show that the error rate between the proposed new calculation method and simulation results is only 4.44 %. Moreover, it was concluded that adjusting spacing and heating power is the most effective strategy for heating systems to achieve optimal snow-melting performance.

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.'

Estimating Stage-Frequency Curves for Engineering Design in Small Ungauged Arctic Watersheds

The design of hydraulic structures in the Arctic is complicated by shallow relief, which cause unique runoff processes that promote snow-damming and refreeze of runoff. We discuss the challenges encountered in modeling snowmelt runoff into two coastal freshwater lagoons in Utqiaġvik, Alaska. Stage-frequency curves with quantified uncertainty were required to design two new discharge gates that would allow snowmelt runoff flows through a proposed coastal revetment. To estimate runoff hydrographs arriving at the lagoons, we modeled snowpack accumulation and ablation using SnowModel which in turn was used to force a physically-based hydraulic runoff model (HEC-RAS). Our results demonstrate the successful development of stage-frequency curves by incorporating a Monte Carlo simulation approach that quantifies the variability in runoff timing and volume. Our process highlights the complexities of Arctic hydrology by incorporating significant delays in runoff onset due to localized snow accumulation and melting processes. This methodology not only addresses the uncertainty in snow-damming and refreeze processes which affect the arrival time of snowmelt inflow peaks, but is also adaptable for application in other challenging environments where secondary runoff processes are predominant.

Does snow storage affect the Palmer drought severity index? Revisiting PDSI drought indicator via conceptual model and large-scale information

Climate change and its impacts have significantly altered the hydro-climatological conditions worldwide. Accurate monitoring of drought is a crucial work for assessing water availability and environmental suitability. The Palmer Drought Severity Index (PDSI) is a key indicator used to measure hydrological drought. However, its application in mountainous and snow-covered regions is limited due to the indicator's inadequate representation of snow hydrology and its impact during spring and summer. In this research, the authors aimed to enhance the PDSI drought indicator by incorporating snow storage and snowmelt processes via conceptual approach and large-scale snow information. Four different scenarios were developed and tested in the Gheshlagh watershed, a mountainous basin in western Iran. To ensure a comprehensive analysis, the Standardized Precipitation Index (SPI) was also employed to evaluate the results. Additionally, an alternative water balance model, better suited for the watershed, was used in place of the Palmer two-soil-layer water balance model. While the simulated indicators showed good agreement with the benchmark PDSI model, the results revealed significant changes in drought indicators, particularly during summer and spring, when considering snow storage. Moreover, the impact of using a different water balance model was found to be more influential than incorporating snow storage in the PDSI framework. Based on the findings, the authors recommend revisiting the PDSI approach by incorporating region-specific water balance models and accounting for the effects of snow storage.

Dynamic Snow Melting Process and Its Driving Factors in Northern Grasslands

Hourly automatic snow depth stations have enhanced insights into the dynamics and spatial variability of daily snowmelt. From 2021 to 2022, we gathered hourly snow depth measurements from six Hulun Buir grassland stations. Our analysis shed light on the dynamics of snowmelt and the key drivers in this northern region. We found that in northern China’s mid-high latitude grasslands, winter snow cover persists for about 80 to 134 days. The transition to the melting phase in early March spans 5 to 12 days, with continuous and rapid phases. Snow under 3 cm quickly collapses. If the average temperature from 10:00 to 18:00 exceeds 0 °C, complete melting occurs within 36 h. Daily snow melting sees initial stability, swift decline, and gradual reduction, peaking between 11:00 and 14:00. Finally, thermal conditions primarily drive snow melt dynamics, with 14:00 ground temperature being pivotal. These findings shed light on snow dynamics and key factors in the mid-high latitude grasslands of northern China.

Study of the active anti-icing properties of modified biological antifreeze protein micro-surfacing

The accumulation of snow and ice on the surface of roadways can threaten driving safety, which is a major pain point in winter. To address the issue of pavement icing, this paper researched the biological antifreeze protein (AFP) filler, which was then mixed with a portion of mineral powder to create a blend. This blend was further combined with emulsified asphalt modified by a combination of styrene-butadiene rubber (SBR) and waterborne epoxy resin (WER) to produce a micro-surfacing mixture with active anti-icing capabilities. The road performance tests were conducted to assess whether the anti-icing micro-surfacing mixture’s road performance was affected. Subsequently, improved and innovative anti-icing tests were conducted to evaluate the anti-icing performance of the anti-icing micro-surfacing. The anti-icing tests encompassed freezing tests, ice-melting and snow-melting tests, active de-icing simulation tests, and ice adhesion force tests. The road performance tests indicate that the incorporation of antifreeze filler enhances the resistance of the micro-surfacing mixture against water erosion during freeze-thaw cycles, with a minimal impact on rutting resistance and abrasion resistance. The freezing test demonstrates the excellent active antifreeze function of the anti-icing micro-surfacing. Furthermore, the ice-melting and snow-melting test reveals that the anti-icing micro-surfacing effectively speeds up the melting process of snow and ice on the surface of roadways. The active de-icing simulation test and adhesion test confirm that the anti-icing micro-surfacing significantly accelerates the rate of active de-icing. Overall, the anti-icing micro-surfacing demonstrates a significant active anti-icing effect while maintaining its road performance capabilities.

Using field-based, photogrammetric point cloud, orthophoto and LiDAR-derived metrics to assess forest structure–snowpack relationships in the Great Lakes–St. Lawrence Forest region

The efficacy of field-based, photogrammetric point cloud, orthophoto and light detection and ranging datasets to describe forest structure and resolve forest–snowpack relationships in a mixed forest region was evaluated over two years at the point and transect scales. Hemispheric photo-derived canopy metrics correlated well with remotely sensed metrics, but tree bole metrics were not effectively derived from remotely sensed data. Significant differences in melt rate and snow-free date were found across forest type at the transect scale. Field and remotely sensed estimates of canopy cover were highly correlated with melt rate and snow-free date at the point scale, which aligns with previous literature and understanding of snowmelt processes. However, significant correlations were only present during the 2016 study year, which was attributed to canopy-controlled solar radiation-driven melt in 2016 versus more spatially uniform turbulent flux-driven melt in 2017. Peak snow water equivalent metrics were not correlated well with canopy or tree height metrics, contrary to previous research. This was likely due to mid-winter melt events throughout both study years, where a mix of accumulation and melt processes confounded forest–snowpack relationships. This study demonstrates that widely available remotely sensed data with a broad coverage can be used to: (i) describe forest–snowpack relationships in mixed hardwood, coniferous forests and (ii) elucidate the variability of forest–snowpack relationships under different climate conditions in this environment.

Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

AbstractAccuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km2 mixed‐forest basin in the Great Lakes region of central Ontario, Canada. Sub‐basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi‐objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi‐objective calibration or data assimilation, as demonstrated in this study.

Impact of land surface model schemes in snow-dominated arid and semiarid watersheds using the WRF-hydro modeling systems

<abstract> <p>In the past century, water demand increased extensively due to the rapid growth of the human population. Ground observations can reveal hydrological dynamics but are expensive in the long term. Alternatively, hydrological models could be utilized for assessing streamflow with historical observations as the control point. Despite the advancements in hydrological modeling systems, watershed modeling over mountainous regions with complex terrain remains challenging. This study utilized the multi-physical Weather Research and Forecasting Hydrological enhancement model (WRF-Hydro), fully distributed over the Amu River Basin (ARB) in Afghanistan. The calibration process focused on land surface model (LSM) physics options and hydrological parameters within the model. The findings emphasize the importance of LSM for accurate simulation of snowmelt–runoff processes over mountainous regions. Correlation coefficient (R), coefficient of determination (R<sup>2</sup>), Nash-Sutcliff efficiency (NSE), and Kling-Gupta efficiency (KGE) were adopted for accuracy assessment over five discharge observation stations at a daily time scale; overall performance results were as follows: R was 0.85–0.42, R<sup>2</sup> was 0.73–0.17, NSE was 0.52 to −8.64, and KGE was 0.74 to −0.56. The findings of the current study can support snowmelt process simulation within the WRF-Hydro model.</p> </abstract>

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

It is essential to investigate the influences of environmental elements on snow cover to understand the mechanism of the snowmelt process. These elements, as influencing factors, have spatial heterogeneity, which results in significant differences and uncertainties in the extent and range of their effects at different scales. However, little research has been conducted on the spatial interaction and mechanisms of these factors at multiple scales. This study selected the Manas River basin in the Tianshan Mountains as the study area. The study period is 2015–2020. The snow cover status index is calculated based on available Landsat8-OLS/TIRS data; influencing factors are collected from multiple datasets. Their relationships are explored using a novel zonal spatial panel model, fully considering the spatial, proximity, and scale effects. The findings are as follows: (1) There is a robust spatial interaction and proximity effect between snowmelt and various factors, and such effects display distinct spatial heterogeneity. The elevation (ELE), slope (SLP), land surface temperature (LST), and normalized digital vegetation index (NDVI) showed significant overall dominant effects on the snow melting process. The influencing factors with apparent proximity effects are LST, ELE, SLP, NDVI, and air temperature (TEMP), and their influence ranges are different. (2) The relative importance and significance rank of dominant influencing factors vary under different partition schemes and scales. As the scale decreases, the significance of terrain- and vegetation-related factors increases, whereas the significance of temperature- and elevation-related factors decreases, and the number of dominant factors also decreases. (3) The influencing factors represent distinct characteristics among each zone at the optimally partitioned scale we defined. The overall influencing pattern demonstrates a characteristic of being globally dictated by elevation and temperature, with local terrain factors, vegetation, and wind speed modifying this pattern. Our study provides practical data support and a theoretical basis for deepening our understanding of the influence mechanism of the snow melting process.

Simulated hydrological effects of grooming and snowmaking in a ski resort on the local water balance

Abstract. The presence of a ski resort modifies the snow cover at the local scale, due to snow management practices on ski pistes, especially grooming and snowmaking. Snow management exerts 2-fold effects on the local hydrological cycle, through (i) abstraction and transfer of water used for snowmaking, and (ii) changes in water runoff due to added snow mass through snowmaking and/or delayed melting of the snowpack due to snow grooming. This induces a local pressure on water resources, which has seldom been addressed in scientific studies hitherto. Here we introduce a method to compute the hydrological effects of snow management on ski pistes and we apply and illustrate its results for the case study of the La Plagne ski resort in the Northern French Alps. The approach mainly relies on snow cover modelling using the Crocus snow cover driven by SAFRAN reanalysis and climate projections. Model results are evaluated against in-situ hydrological observations and show that the modelling approach, although very simplified for many hydrological processes, provides relevant information and insights in terms of the influence of snow-related processes on water resources. Our study shows a visible impact of grooming, virtually eliminating snowmelt in winter, thus delaying the onset of snowmelt. This results is a lower snowmelt flux during the wintertime, low flow period, on the order of −10 % to −20 %, compensated by higher amounts when snow melts. While about 10 % of the water used for snowmaking is estimated to be lost by evaporation through the ice formation process from the liquid water droplets, we find that, in the case studied, the annual scale alteration of water resources is limited and estimated to be on the order of 1 % to 2 %. This is due to the fact that the amount of water used for snowmaking on ski pistes represents a fraction of 10 % to 20 % of total annual precipitation, that ski pistes cover typically 10 % of the surface area of catchments within which ski resorts are located, and that snowmaking equipment covers, in the case of La Plagne, 40 % of the surface area of ski pistes. Therefore, in this case, snowmaking mainly leads to a moderate shift in snow cover formation and snowmelt processes and plays, for example, a smaller role than the influence of future climate change on mountain hydrology. This study provides an initial overview of the influence of grooming and snowmaking on river flows in a mountain catchment, which can inform future studies on water management and climate change adaptation in areas with ski tourism facilities. This study does not discuss long-term sustainability challenges of ski tourism and other aspects of the local environmental impacts (landscape, biodiversity) of snow management, such as the construction and use of mountain water reservoirs and other earthworks in ski resorts.

The impact of rain‐on‐snow events on the snowmelt process: A field study

AbstractPrevious studies have primarily focused on the hydrological response of snowpack during rain‐on‐snow (ROS) events, with limited attention given to their subsequent stages, despite the significance of these stages. Therefore, this study selected two snow plots with similar initial parameters in the Qilian Mountains at an altitude of 4151 m. One of the snow plots underwent artificial rainfall simulation, and the changes in snow albedo, liquid water content, and snow depth of two snow plots were observed and analysed during the rainfall experiment and for a period of 7 days thereafter. The results indicate that ROS events significantly accelerate the rate of decrease in snow albedo and snow depth, increase the liquid water content within the snowpack, and these effects persist for several days after the rainfall event. Furthermore, the input of liquid water leads to rapid saturation of the snowpack, altering the transport mechanism of water within the snowpack, and transforming a process that would otherwise take a long time to complete into a short time. The decrease in snow albedo enhances the absorption of more energy by the snow, thereby accelerating snowmelt. Compared to natural snowmelt, ROS events cause the snowpack with high liquid water content to rapidly melt over a short period, resulting in a rapid increase in river flow, which may be one of the causes of ROS‐induced flooding. These research findings provide scientific insights for a better understanding of the disaster mechanisms associated with ROS events.

Evaluating Climate Change Effects on a Snow-Dominant Watershed: A Multi-Model Hydrological Investigation

Assessing the impact of climate change on water systems often requires employing a hydrological model to estimate streamflow. However, the choice of hydrological model, process representation, input data resolution, and catchment discretization can potentially influence such analyses. This study aims to evaluate the sensitivity of climate change impact assessments to various hydrological modeling configurations in a snow-dominated headwater system in Alberta, Canada. The HBV-MTL and GR4J models, coupled with the Degree-Day and CemaNeige snowmelt modules, were utilized and calibrated using point- and grid-based climate data on lumped and semi-distributed catchment discretization. The hydrological models, in conjunction with a water allocation model, were supplied with climate model outputs to project changes in the basin. While all models revealed a unanimous increase in peak flow, the difference between their estimations could be as substantial as 42%. In contrast, their divergence was minimal in projecting median flow. Furthermore, most models projected an aggravated water supply deficit between 16% and 40%. Overall, the quantified climate change impacts were the most sensitive to the choice of snow routine module, followed by the model type, catchment discretization, and data resolution in this snow-dominant basin. Therefore, particular attention should be given to the proper representation of snowmelt processes.

Analysis of nationwide groundwater monitoring networks using lumped-parameter models

Many countries maintain nationwide groundwater networks to monitor the status of their groundwater resources. For effective groundwater resource management, it is fundamental to understand the groundwater dynamics measured in the individual monitoring wells. Nationwide monitoring networks typically cover multiple aquifer systems with different degrees of environmental complexity. The analysis of the data of such networks thus requires flexible modeling approaches. In this study, we assessed the applicability and performance of lumped-parameter models using impulse response functions, as implemented in the Pastas software, to simulate hydraulic head data from the nationwide groundwater monitoring network in Switzerland. The selected 28 monitoring wells in the network are situated in unconsolidated, relatively shallow aquifers across Switzerland. Given the very diverse topography in the study area, snowmelt processes affect some aquifers, while groundwater–surface water interactions are important in the valleys. Different linear and nonlinear models were tested to take precipitation and potential evaporation into account, with a new model developed as part of this study to account for the effect of snow processes on recharge generation. After generally good fits in both the calibration and evaluation were achieved, the models were used to identify and quantify which stresses (e.g., precipitation, river stages) control the groundwater dynamics. The results show that precipitation and evaporation explain large parts of the measured dynamics, and about half of the monitoring wells in the network appear to be influenced by river stages. Explicitly accounting for snow processes in the recharge generating process is found to improve the simulation of the head dynamics across Switzerland, particularly for wells in high-altitude aquifers. This study demonstrates for the first time the applicability of lumped-parameter models using impulse response functions to model heads in Switzerland, and more generally, in climatic settings where snow processes are impacting the head dynamics.

Compound erosion effect of snowmelt, wind, and rainfall on sloping farmlands of Chinese typical Mollisol region.

Research on the processes and mechanisms of compound soil erosion by multiple forces can provide scientific guidance for precisely controlling cropland soil erosion. Based on the seasonal alternation of freezing-thawing, snowmelt, wind, and rainfall erosion forces on sloping farmlands under natural conditions from November to next October of each year, we used a set of indoor simulation experiments of multi-force superimpositions to analyze the compound soil erosion processes of snowmelt (1 and 2 L·min-1), wind (12 m·s-1), and rainfall (100 mm·h-1). We further discussed the erosion effects of multi-force superimpositions. The results showed that, under single snowmelt erosion, an increase in snowmelt flow had a greater effect on sloping snowmelt erosion intensity than that of sloping runoff rate. When sloping snowmelt flow increased from 1 L·min-1 to 2 L·min-1, sloping runoff rate and erosion intensity increased by 2.7 and 4.0 times, respectively. Under snowmelt-wind superimposition erosion, previous sloping snowmelt erosion inhibited late wind erosion occurrence. As sloping snowmelt flow increased from 1 L·min-1 to 2 L·min-1, the inhibiting action subsequently increased and wind erosion intensity caused by previous snowmelt reduced by more than 50%. Both wind erosion and snowmelt-wind superimposed erosion intensified late rainfall erosion. The early wind erosion increased rainfall erosion by 24.5%. The snowmelt-wind superimposed effect increased the later slope rainfall erosion by 132.8% and 465.4% under 1 and 2 L·min-1 snowmelt runoff rates, respectively. The compound soil erosion amount driven by multiple force superimposition was not the sum of the corresponding erosion amount caused by single erosion force, with promoting or inhibiting effects of erosion force superimposition. The erosion effect of snowmelt-wind superposition was negative, but that of wind-rainfall superposition and snowmelt-wind-rainfall superpositions were positive.

Composite Factors during Snowmelt Erosion of Farmland in Black Soil Region of Northeast China: Temperature, Snowmelt Runoff, Thaw Depths and Contour Ridge Culture

Snowmelt erosion could cause serious damage to soil quality and agricultural production conditions of slope farmland in the black soil region of northeast China. Contour ridge tillage is a traditional and effective measure to mitigate soil loss on slope farmland. However, the characteristics and influence factors of snowmelt erosion of slope farmland with contour ridge culture and the effect of this measure on the snowmelt process have not been comprehensively investigated, especially at the field scale. To bridge the gap, in situ observation was conducted on the snowmelt erosion process of a typical farmland in Baiquan County, Heilongjiang Province, China. The results revealed that during the snowmelt erosion period, the average daily snowmelt runoff volume and sediment concentration exhibited a trend of first increase and then a subsequent decrease. In the early stage, although the sediment concentration was large, limited discharge and soil thaw depths led to minimal soil loss. In the following stage, due to increased runoff and thaw depths, 94% of the total soil loss amount was obtained with an obvious erosion path formed. For each event, when soil thaw depths were shallow, sediment concentration had a high and early peak, whereas a reverse trend was observed when thaw depths increased. The hysteresis relationship of discharge–sediment indicated that the location where snowmelt erosion primarily occurred would change, under the influence of variations in runoff, freeze and thaw action, thaw depths, and micro-topography. The results could provide a guide in the control of soil erosion in seasonal snowmelt-erosion-prone areas.

Improvements and Evaluation of the Agro-Hydrologic VegET Model for Large-Area Water Budget Analysis and Drought Monitoring

We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of an evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources.