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A hybrid stochastic-weather-generation method for temporal disaggregation of precipitation with consideration of seasonality and within-month variations

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Abstract

Linking atmospheric and hydrological models is challenging because of a mismatch of spatial and temporal resolutions in which the models operate: dynamic hydrological models need input at relatively fine temporal (daily) scale, but the outputs from general circulation models are usually not realistic at the same scale, even though fine scale outputs are available. Temporal dimension downscaling methods called disaggregation are designed to produce finer temporal-scale data from reliable larger temporal-scale data. Here, we investigate a hybrid stochastic weather-generation method to simulate a high-frequency (daily) precipitation sequence based on lower frequency (monthly) amounts. To deal with many small precipitation amounts and capture large amounts, we divide the precipitation amounts on rainy days (with non-zero precipitation amounts) into two states (named moist and wet states, respectively) by a pre-defined threshold and propose a multi-state Markov chain model for the occurrences of different states (also including non-rain days called dry state). The truncated Gamma and censored extended Burr XII distributions are then employed to model the precipitation amounts in the moist and wet states, respectively. This approach avoids the need to deal with discontinuity in the distribution, and ensures that the states (dry, moist and wet) and corresponding amounts in rainy days are well matched. The method also considers seasonality by constructing individual models for different months, and monthly variation by incorporating the low-frequency amounts as a model predictor. The proposed method is compared with existing models using typical catchment data in Australia with different climate conditions (non-seasonal rainfall, summer rainfall and winter rainfall patterns) and demonstrates better performances under several evaluation criteria which are important in hydrological studies.

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Acknowledgments

We gratefully acknowledge funding support from the Water Information Research and Development Alliance (WIRADA), which is a strategic investment between the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Australian Bureau of Meteorology (BoM). We wish to acknowledge useful discussions from BoM collaborators Narendra Kumar Tuteja, Daehyok Shin and David Kent, and help on data from our colleagues James Bennett, Hongxing Zheng and BoM collaborator Andrew Schepen. Louie Zhang is also supported by the CSIRO Graduate Fellow Program. We also thank Dr Hongxing Zheng for his great comments during CSIRO internal review. We would like also express our thanks to the editor, an associate editor and two anonymous referees for their careful and constructive reviews.

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Authors and Affiliations

  1. CSIRO Digital Productivity, Private Bag 5, Wembley, WA, 6913, Australia

    Quanxi Shao & Louie Zhang

  2. CSIRO Land and Water, PO Box 56, Highett, VIC, 3190, Australia

    Q. J. Wang

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  1. Quanxi Shao
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  2. Louie Zhang
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  3. Q. J. Wang
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Correspondence to Quanxi Shao.

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Shao, Q., Zhang, L. & Wang, Q.J. A hybrid stochastic-weather-generation method for temporal disaggregation of precipitation with consideration of seasonality and within-month variations. Stoch Environ Res Risk Assess 30, 1705–1724 (2016). https://doi.org/10.1007/s00477-015-1177-3

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  • DOI: https://doi.org/10.1007/s00477-015-1177-3

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