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Precipitation Bias Correction: A Novel Semi-parametric Quantile Mapping Method

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F23%3A97245" target="_blank" >RIV/60460709:41330/23:97245 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1029/2023EA002823" target="_blank" >http://dx.doi.org/10.1029/2023EA002823</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1029/2023EA002823" target="_blank" >10.1029/2023EA002823</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Precipitation Bias Correction: A Novel Semi-parametric Quantile Mapping Method

  • Original language description

    Bias correction methods are used to adjust simulations from global and regional climate models to use them in informed decision-making. Here we introduce a semi-parametric quantile mapping (SPQM) method to bias-correct daily precipitation. This method uses a parametric probability distribution to describe observations and an empirical distribution for simulations. Bias-correction techniques typically adjust the bias between observation and historical simulations to correct projections. The SPQM however corrects simulations based only on observations assuming the detrended simulations have the same distribution as the observations. Thus, the bias-corrected simulations preserve the climate change signal, including changes in the magnitude and probability dry, and guarantee a smooth transition from observations to future simulations. The results are compared with popular quantile mapping techniques, that is, the quantile delta mapping (QDM) and the statistical transformation of the CDF using splines (SSPLINE). The SPQM performed well in reproducing the observed statistics, marginal distribution, and wet and dry spells. Comparatively, it performed at least equally well as the QDM and SSPLINE, specifically in reproducing observed wet spells and extreme quantiles. The method is further tested in a basin-scale region. The spatial variability and statistics of the observed precipitation are reproduced well in the bias-corrected simulations. Overall, the SPQM is easy to apply, yet robust in bias-correcting daily precipitation simulations.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10511 - Environmental sciences (social aspects to be 5.7)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Earth and Space Science

  • ISSN

    2333-5084

  • e-ISSN

    2333-5084

  • Volume of the periodical

    10

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    17

  • Pages from-to

    1-17

  • UT code for WoS article

    000978224200001

  • EID of the result in the Scopus database

    2-s2.0-85153791014