IDEAS home Printed from https://ideas.repec.org/a/spr/climat/v165y2021i3d10.1007_s10584-021-03083-3.html
   My bibliography  Save this article

Challenges and potential solutions in statistical downscaling of precipitation

Author

Listed:
  • Jie Chen

    (Wuhan University)

  • Xunchang John Zhang

    (USDA-ARS, Grazinglands Research Laboratory)

Abstract

Downscaling is an effective technique to bridge the gap between climate model outputs and data requirements of most crop and hydrologic models for assessing local and site-specific climate change impacts, especially on future food security. However, downscaling of temporal sequences, extremes in daily precipitation, and handling of nonstationary precipitation in future conditions are considered common challenges for most statistical downscaling methods. This study reviewed the above key challenges in statistical downscaling and proposed potential solutions. Ten weather stations located across the globe were used as proof of concept. The use of a stochastic Markov chain to generate daily precipitation occurrences is an effective approach to simulate the temporal sequence of precipitation. Also, the downscaling of precipitation extremes can be achieved by adjusting the skewness coefficient of a probability distribution, as they are highly correlated. Nonstationarity in precipitation downscaling can be handled by adjusting parameters of a probability distribution according to future precipitation change signals projected by climate models. The perspectives proposed in this study are of great significance in using climate model outputs for assessing local and site-specific climate change impacts, especially on future food security.

Suggested Citation

  • Jie Chen & Xunchang John Zhang, 2021. "Challenges and potential solutions in statistical downscaling of precipitation," Climatic Change, Springer, vol. 165(3), pages 1-19, April.
    • Handle: RePEc:spr:climat:v:165:y:2021:i:3:d:10.1007_s10584-021-03083-3
    DOI: 10.1007/s10584-021-03083-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10584-021-03083-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10584-021-03083-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jie Chen & François P. Brissette & Daniel Caya, 2020. "Remaining error sources in bias-corrected climate model outputs," Climatic Change, Springer, vol. 162(2), pages 563-582, September.
    2. R. Manzanas & L. Fiwa & C. Vanya & H. Kanamaru & J. M. Gutiérrez, 2020. "Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi," Climatic Change, Springer, vol. 162(3), pages 1437-1453, October.
    3. Jie Chen & François Brissette & Robert Leconte, 2012. "Coupling statistical and dynamical methods for spatial downscaling of precipitation," Climatic Change, Springer, vol. 114(3), pages 509-526, October.
    4. Matthias Themeßl & Andreas Gobiet & Georg Heinrich, 2012. "Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal," Climatic Change, Springer, vol. 112(2), pages 449-468, May.
    5. Hsin-Fu Yeh & Hsin-Li Hsu, 2019. "Using the Markov Chain to Analyze Precipitation and Groundwater Drought Characteristics and Linkage with Atmospheric Circulation," Sustainability, MDPI, vol. 11(6), pages 1-18, March.
    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. Espoir M. Bagula & Jackson Gilbert M. Majaliwa & Gustave N. Mushagalusa & Twaha A. Basamba & John-Baptist Tumuhairwe & Jean-Gomez M. Mondo & Patrick Musinguzi & Cephas B. Mwimangire & Géant B. Chuma &, 2022. "Climate Change Effect on Water Use Efficiency under Selected Soil and Water Conservation Practices in the Ruzizi Catchment, Eastern D.R. Congo," Land, MDPI, vol. 11(9), pages 1-22, August.
    2. Mark D. Risser & Daniel R. Feldman & Michael F. Wehner & David W. Pierce & Jeffrey R. Arnold, 2021. "Identifying and correcting biases in localized downscaling estimates of daily precipitation return values," Climatic Change, Springer, vol. 169(3), pages 1-20, December.

    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. Salem, Golam Saleh Ahmed & Kazama, So & Shahid, Shamsuddin & Dey, Nepal C., 2018. "Impacts of climate change on groundwater level and irrigation cost in a groundwater dependent irrigated region," Agricultural Water Management, Elsevier, vol. 208(C), pages 33-42.
    2. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    3. Alvar-Beltrán, J. & Heureux, A. & Soldan, R. & Manzanas, R. & Khan, B. & Dalla Marta, A., 2021. "Assessing the impact of climate change on wheat and sugarcane with the AquaCrop model along the Indus River Basin, Pakistan," Agricultural Water Management, Elsevier, vol. 253(C).
    4. Omid Alizadeh, 2022. "Advances and challenges in climate modeling," Climatic Change, Springer, vol. 170(1), pages 1-26, January.
    5. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    6. Alessandro Dosio & Christopher Lennard & Jonathan Spinoni, 2022. "Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations," Climatic Change, Springer, vol. 170(1), pages 1-24, January.
    7. Markus Stoffel & Thomas Mendlik & Michelle Schneuwly-Bollschweiler & Andreas Gobiet, 2014. "Possible impacts of climate change on debris-flow activity in the Swiss Alps," Climatic Change, Springer, vol. 122(1), pages 141-155, January.
    8. Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
    9. A. Casanueva & J. Bedia & S. Herrera & J. Fernández & J. M. Gutiérrez, 2018. "Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool," Climatic Change, Springer, vol. 147(3), pages 411-425, April.
    10. Elvina Faustina Dhata & Chang Ki Kim & Hyun-Goo Kim & Boyoung Kim & Myeongchan Oh, 2022. "Site-Adaptation for Correcting Satellite-Derived Solar Irradiance: Performance Comparison between Various Regressive and Distribution Mapping Techniques for Application in Daejeon, South Korea," Energies, MDPI, vol. 15(23), pages 1-20, November.
    11. Philippe Roudier & Jafet Andersson & Chantal Donnelly & Luc Feyen & Wouter Greuell & Fulco Ludwig, 2016. "Projections of future floods and hydrological droughts in Europe under a +2°C global warming," Climatic Change, Springer, vol. 135(2), pages 341-355, March.
    12. Gumindoga, W. & Rientjes, T. H. M. & Haile, Alemseged Tamiru & Makurira, H. & Reggiani, P., 2019. "Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River Basin," Papers published in Journals (Open Access), International Water Management Institute, pages 23(7):2915-.
    13. Renate Wilcke & Thomas Mendlik & Andreas Gobiet, 2013. "Multi-variable error correction of regional climate models," Climatic Change, Springer, vol. 120(4), pages 871-887, October.
    14. Zhenya Li & Zulfiqar Ali & Tong Cui & Sadia Qamar & Muhammad Ismail & Amna Nazeer & Muhammad Faisal, 2022. "A comparative analysis of pre- and post-industrial spatiotemporal drought trends and patterns of Tibet Plateau using Sen slope estimator and steady-state probabilities of Markov Chain," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 547-576, August.
    15. Jie Chen & François P. Brissette & Daniel Caya, 2020. "Remaining error sources in bias-corrected climate model outputs," Climatic Change, Springer, vol. 162(2), pages 563-582, September.
    16. Mohammad Ahsan Uddin & ASM Maksud Kamal & Shamsuddin Shahid & Eun-Sung Chung, 2020. "Volatility in Rainfall and Predictability of Droughts in Northwest Bangladesh," Sustainability, MDPI, vol. 12(23), pages 1-20, November.
    17. Schönhart, Martin & Mitter, Hermine & Schmid, Erwin & Heinrich, Georg & Gobiet, Andreas, 2014. "Integrated Analysis of Climate Change Impacts and Adaptation Measures in Austrian Agriculture," Journal of International Agricultural Trade and Development, Journal of International Agricultural Trade and Development, vol. 63(3).
    18. Pablo Méndez-Lázaro & Frank E. Muller-Karger & Daniel Otis & Matthew J. McCarthy & Marisol Peña-Orellana, 2014. "Assessing Climate Variability Effects on Dengue Incidence in San Juan, Puerto Rico," IJERPH, MDPI, vol. 11(9), pages 1-20, September.
    19. Polo, Jesús & Ballestrín, Jesús & Carra, Elena, 2020. "Assessment and improvement of modeling the atmospheric attenuation based on aerosol optical depth information with applicability to solar tower plants," Energy, Elsevier, vol. 208(C).
    20. Philippe Roudier & Jafet C. M. Andersson & Chantal Donnelly & Luc Feyen & Wouter Greuell & Fulco Ludwig, 2016. "Projections of future floods and hydrological droughts in Europe under a +2°C global warming," Climatic Change, Springer, vol. 135(2), pages 341-355, March.

    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:spr:climat:v:165:y:2021:i:3:d:10.1007_s10584-021-03083-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.