• Wind and Structures
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• v.32 no.5
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• pp.453-469
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• 2021

Snow accumulation on the road frequently induces a big traffic problem in the cold snowy region. Accurate prediction on snow distribution is fundamental for solving drifting snow disasters on roads. The present study adopts the transient method to simulate the wind-induced snow distribution on embankment based on the mixture multiphase model and dynamic mesh technique. The simulation and field measurement are compared to confirm the applicability of the simulation. Furthermore, the process of snow accumulation is revealed. The effects of friction velocity and snow concentration on snow accumulation are analyzed to clarify its mechanism. The results show that the simulation agrees well with the field measurement in trends. Moreover, the snow accumulation on the embankment can be approximately divided into three stages with time, the snow firstly deposited on the windward side, then, accumulation occurs on the leeward side which induced by the wake vortex, finally, the snow distribution reaches an equilibrium state with the slope of approximately 7°. The friction velocity and duration have a significant influence on the snow accumulation, and the vortex scale directly affected the snow deposition range on the embankment leeward side.

• Wind and Structures
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• v.37 no.1
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• pp.25-38
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• 2023

Due to the complexity and difficulty in meeting the multiphase flow complexity, similarity, and multiscale characteristics, the mechanism of snow drift is so complicated that the snow deposition prediction is still inaccurate and needs to be far improved. Meanwhile, the validation of prediction methods is also limited due to a lack of field-measured data about snow deposition. To this end, a field measurement activity about snow deposition around a cube with time was carried out, and the snow accumulation process was measured under blowing snow conditions in northwest China. The maximum snow depth, snow profile, and variation in snow depth around the cube were discussed and analyzed. The measured results indicated three stages of snow accumulation around the cube. First, snow is deposited in windward, lateral and leeward regions, and then the snow depth in windward and lateral regions increases. Secondly, when the snow in the windward region reaches its maximum, the downwash flow erodes the snow against the front wall. Meanwhile, snow range and depth in lateral regions have a significant increase. Thirdly, a narrow road in the leeward region is formed with the increase in snow range and depth, which results in higher wind speed and reforming snow deposition there. The field measurement study in this paper not only furthers understanding of the snow accumulation process instead of final deposition under complex conditions but also provides an important benchmark for validating prediction methods.

• Wind and Structures
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• v.27 no.4
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• pp.255-267
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• 2018

This paper investigated the wind-snow flow around the bogie region of a high-speed train under crosswinds using a coupled numerical method of the unsteady Realizable $k-{\varepsilon}$ turbulence model and discrete phase model (DPM). The flow features around the bogie region were discussed and the influence of bogie fairing height on the snow accumulation on the bogie was also analyzed. Here the high-speed train was running at a speed of 200 km/h in a natural environment with the crosswind speed of 15 m/s. The mesh resolution and methodology for CFD analysis were validated against wind tunnel experiments. The results show that large negative pressure occurs locally on the bottom of wheels, electric motors, gear covers, while the positive pressure occurs locally on those windward surfaces. The airflow travels through the complex bogie and flows towards the rear bogie plate, causing a backflow in the upper space of the bogie region. The snow particles mainly accumulate on the wheels, electric motors, windward sides of gear covers, side fairings and back plate of the bogie. Longer side fairings increase the snow accumulation on the bogie, especially on the back plate, side fairings and brake clamps. However, the fairing height shows little impact on snow accumulation on the upper region of the bogie. Compared to short side fairings, a full length side fairing model contributes to more than two times of snow accumulation on the brake clamps, and more than 20% on the whole bogie.

• Wind and Structures
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• v.39 no.3
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• pp.207-222
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• 2024

This paper presents a numerical simulation method for snow drift that takes into account the cohesion effect of snow particles. The critical state of free collapse accumulation of idealized snow particles is used to indirectly infer the effect of interparticle interactions on snow transport and re-accumulation. With the help of the Hertz-Mindlin with JKR cohesion contact model, the particle angle of repose is calibrated with a number of contact parameters through numerical experiment. The surface energy for a given property of snow particles is determined using the observed snow angle of repose, and a continuous-discrete snow drift two-way coupled numerical model incorporating these optimized contact parameters is developed. The snow redistribution pattern on a stepped flat roof structure is simulated, and the results are found to be consistent with those of the field measured in terms of phenomena and general laws, verifying the achievability and effectiveness of the presented method. To eliminate the influence of environmental conditions, wind tunnel tests are also conducted, and it is found that the reconstructed depth and reaccumulated angle of snowdrift resulting from the numerical simulation are in closer agreement with the experimental results, further confirming the enhancement achieved by introducing the contact effect.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.1-9
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• 2008

The impact of snow accumulation and snowmelt in rainfall-runoff modelling was analyzed for the Soyanggang dam basin by comparing the measured and simulated discharges simulated by the NWS-PC model. Sugawara's conceptual model was used to simulate the snow accumulation and snowmelt phenomena and NWS-PC model was employed to simulate rainfall-runoff. Parameters in model calibration were estimated by the Multi-step Automated Calibration Scheme and optimized using SCE-UA algorithm in each step. The results of the model calibration and verification show that the model considering snowmelt process is better than the one without consideration of snowmelt under the performance criteria such as RMSE, PBIAS, NSE, and PME. The measured discharge time series has over 60 days of persistence. Correlograms for each simulation showed that the simulated discharge with snowmelt model reproduce the persistence closely to the measured discharge's while the one without snow accumulation and snowmelt model reproduce only 20 days of persistence. The study result indicates that the inclusion of snow accumulation and snowmelt model is important for the accurate simulation of rainfall-runoff phenomena in the Soyanggang dam basin.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of Korea Water Resources Association
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• v.36 no.5
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• pp.851-861
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• 2003

Snow accumulation and snow melt was simulated and included in the computation of the watershed runoff for Soyanggang Dam and Chungju Dam. A modified Tank Model was used for the simulation, which has three serial tanks and a pulse response function. The model parameters were estimated through the global optimization method of Shuffled Complex Evolution-University of Arizona (SCE-UA). A watershed was divided into four zones of elevation. The temperature decrease of the zones was a rate of -0.6$^{\circ}C$/100m. Almost all precipitation from December to February become accumulated as snow, and then the snow melts and runs off from March to April. The average runoff with snow melt was greater than the average runoff without snow melt during the period from March to April. The improved amount from snow melt simulation was about one fifth of the observed one for Soyanggang Dam. The increased amount for Chungju Dam was about one fourth of the observed average runoff during the same period. Although the watershed runoff was simulated including snow melt, it was less than the observed one for both of the dams.

• International Journal of Highway Engineering
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• v.16 no.3
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• pp.59-66
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• 2014

PURPOSES : This study is to suggest heavy snow risk analysis in Seoul. METHODS : Recently, the increase of extreme weather caused by global warming raises the occurrences of unpredictable natural disasters and the loss potential of human disasters by land use facilities accumulation. It is necessary to develop the risk analysis for the natural and human disasters. RESULTS : In this study, heavy snow risk analysis among natural disasters in Seoul was suggested. The spatial unit of risk analysis level was established for the lines and administrative districts. CONCLUSIONS : The risk analysis was performed using risk matrix of disaster occurrence score and disaster damage score. The components affecting the risk disaster analysis by types were analyzed and the application of heavy snow risk analysis was suggested.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.56-60
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• 2008

Increasing urban sprawl and climate changes have been causing unexpected high-intensity rainfall events. Thus there are needs to enhance conventional disaster management system for comprehensive actions to secure safety. Therefore long-term and comprehensive flood management plans need to be well established. Recently torrential snowfall are occurring frequently, causing have snow traffic jams on the road. To secure safety and on-time operation of the Bi-modal tram system, well-structured disaster management system capable of analyzing the urban flash flooding and snow pack melt/freezing due to unexpected rainfall event and snowfall are needed. To secure safety of the Bi-modal tram system due to torrential snowfall, the snow melt simulation capability was investigated. The snow accumulation and snow melt were measured to validate the SWMM snow melt component. It showed that there was a good agreement between measured snow melt data and the simulated ones. Therefore, the Bi-modal tram disaster management system will be able to predict snow melt reasonably well to secure safety of the Bi-modal tram system during the winter. The Bi-modal tram disaster management system can be used to identify top priority area for snow removal within the tram route in case of torrential snowfall to secure on-time operation of the tram. Also it can be used for detour route in the tram networks based on the disaster management system predicted data.

• Journal of Wetlands Research
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• v.7 no.4
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• pp.33-42
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• 2005

Wetlands often function as a nutrient sink. It is well known that increased input of nutrient increases the primary productivity but it is not well understood what is the fate of produced biomass in wetland ecosystem. Water and sediment quality, decomposition rate of cellulose, and sediment accumulation rate in 11 montane marshes in northern Sierra Nevada, California were analyzed to trace the effect of nitrogen and phosphorus content in water on nutrient dynamics. Concentrations of ammonium, nitrate, soluble reactive phosphorus (SRP) in water were in the range of 27 to 607, 8 to 73, and 6 to 109 ppb, respectively. Concentrations of ammonium, calcium, magnesium, sodium, and potassium in water were the highest in Markleeville, which has been impacted by animal farming. Nitrate and SRP concentrations in water were the highest in Snow Creek, which has been impacted by human residence and a golf course. Cellulose decomposition rates ranged from 4 to 75 % per 90 days and the highest values were measured in Snow Creek. Concentrations of total carbon, nitrogen, and phosphorus in sediment ranged from 8.0 to 42.8, 0.5 to 3.0, and 0.076 to 0.162 %, respectively. Accumulation rates of carbon, nitrogen, and phosphorus fluctuated between 32.7 to 97.1, 2.4 to 9.0, and 0.08 to $1.14gm^{-2}yr{-1}$, respectively. Accumulation rates of carbon and nitrogen were highest in Markleeville and that of phosphorus was highest in Lake Van Norden. Correlation analysis showed that decay rate is correlated with ammonium, nitrate, and SRP in water. There was no correlation between element content in sediment and water quality. Nitrogen accumulation rate was correlated with ammonium in water. These results showed that element accumulation rates in montane wetland ecosystems are determined by decomposition rate rather than nutrient input. This study stresses a need for eco-physiological researches on the response of microbial community to increased nutrient input and environmental change because the microbial community is responsible for the decomposition process.