IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i20p11485-d658711.html
   My bibliography  Save this article

A Review on Snowmelt Models: Progress and Prospect

Author

Listed:
  • Gang Zhou

    (College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China)

  • Manyi Cui

    (College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China)

  • Junhong Wan

    (College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China)

  • Shiqiang Zhang

    (College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China)

Abstract

The frequency and intensity of flood events have been increasing recently under the warming climate, with snowmelt floods being a significant part. As an effective manner of simulating snowmelt flood, snowmelt models have attracted more and more attention. Through comprehensive analysis of the literature, this paper reviewed the characteristics and current status of different types of snowmelt models, as well as the different coupling methods of models for runoff generation and confluence. We then discussed key issues in snowmelt modelling, including blowing snow model, frozen ground model, and rain-on-snow model. Finally, we give some perspectives from four aspects: data, model structure, forecast and early warning, and forecast and estimation. At present, most of the snowmelt models do not have blowing snow or frozen ground modules. Explicit consideration of blowing snow and soil freezing/thawing processes can improve the accuracy of snowmelt runoff simulations. With climate warming, rain-on-snow events have increased, but the mechanism of enhanced rain and snow mixed flooding is still unclear, particularly for the mechanism of rain-snow-ice mixed runoff generation. The observation and simulation of rain and snow processes urgently need further study. A distributed physical snowmelt model based on energy balance is an advanced tool for snowmelt simulation, but the model structure and parameter schemes still need further improvements. Moreover, the integration of satellite-based snow products, isotopes, and terrestrial water storage change, monitored by gravity satellites, can help improve the calibration and validation of snowmelt models.

Suggested Citation

  • Gang Zhou & Manyi Cui & Junhong Wan & Shiqiang Zhang, 2021. "A Review on Snowmelt Models: Progress and Prospect," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11485-:d:658711
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/20/11485/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/20/11485/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    2. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
    3. Keith N. Musselman & Flavio Lehner & Kyoko Ikeda & Martyn P. Clark & Andreas F. Prein & Changhai Liu & Mike Barlage & Roy Rasmussen, 2018. "Projected increases and shifts in rain-on-snow flood risk over western North America," Nature Climate Change, Nature, vol. 8(9), pages 808-812, September.
    4. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miao Yu & Nadezhda Pavlova & Changlei Dai & Xianfeng Guo & Xiaohong Zhang & Shuai Gao & Yiru Wei, 2023. "Simulation and Analysis of the Dynamic Characteristics of Groundwater in Taliks in the Eruu Area, Central Yakutia," Sustainability, MDPI, vol. 15(12), pages 1-17, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    2. Alberti, Federica & Mantilla, César, 2020. "Provision of noxious facilities using a market-like mechanism: A simple implementation in the lab," Working papers 35, Red Investigadores de Economía.
    3. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Post-Print halshs-00917797, HAL.
    4. Sandro Radovanovic & Boris Delibasic & Milija Suknovic & Dajana Matovic, 2019. "Where will the next ski injury occur? A system for visual and predictive analytics of ski injuries," Operational Research, Springer, vol. 19(4), pages 973-992, December.
    5. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    6. Yang Zhang & Bora Cetin & Tuncer B. Edil, 2021. "Seasonal Performance Evaluation of Pavement Base Using Recycled Materials," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    7. Paolo Lazzeroni & Brunella Caroleo & Maurizio Arnone & Cristiana Botta, 2021. "A Simplified Approach to Estimate EV Charging Demand in Urban Area: An Italian Case Study," Energies, MDPI, vol. 14(20), pages 1-18, October.
    8. Zhang, Guike & Gao, Zengan & Dong, June & Mei, Dexiang, 2023. "Machine learning approaches for constructing the national anti-money laundering index," Finance Research Letters, Elsevier, vol. 52(C).
    9. Lee Anthony & Caron Francois & Doucet Arnaud & Holmes Chris, 2012. "Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-31, January.
    10. Dexin Chen & Meiting Fu & Liangjie Chi & Liyan Lin & Jiaxin Cheng & Weisong Xue & Chenyan Long & Wei Jiang & Xiaoyu Dong & Jian Sui & Dajia Lin & Jianping Lu & Shuangmu Zhuo & Side Liu & Guoxin Li & G, 2022. "Prognostic and predictive value of a pathomics signature in gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    11. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2016. "The lasso for high dimensional regression with a possible change point," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 193-210, January.
    12. Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2014. "Efficient iterative maximum likelihood estimation of high-parameterized time series models," SFB 649 Discussion Papers 2014-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. Eldar Yeskuatov & Sook-Ling Chua & Lee Kien Foo, 2022. "Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
    14. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
    15. repec:hum:wpaper:sfb649dp2014-010 is not listed on IDEAS
    16. Hettihewa, Samanthala & Saha, Shrabani & Zhang, Hanxiong, 2018. "Does an aging population influence stock markets? Evidence from New Zealand," Economic Modelling, Elsevier, vol. 75(C), pages 142-158.
    17. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    18. Andrés Gómez & Oleg A. Prokopyev, 2021. "A Mixed-Integer Fractional Optimization Approach to Best Subset Selection," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 551-565, May.
    19. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    20. Qi Chu & Guang Bao & Jiayu Sun, 2022. "Progress and Prospects of Destination Image Research in the Last Decade," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    21. Israr Ullah & Bilal Aslam & Syed Hassan Iqbal Ahmad Shah & Aqil Tariq & Shujing Qin & Muhammad Majeed & Hans-Balder Havenith, 2022. "An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping," Land, MDPI, vol. 11(8), pages 1-20, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11485-:d:658711. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.