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    Comparison of different quantile regression methods to estimate predictive hydrological uncertainty in the Upper Chao Phraya River Basin, Thailand

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    Author
    Acharya, SC; Babel, MS; Madsen, H; Sisomphon, P; Shrestha, S
    Date
    2020-03
    Source Title
    Journal of Flood Risk Management
    Publisher
    Wiley
    University of Melbourne Author/s
    Acharya, Suwash Chandra
    Affiliation
    Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Acharya, S. C., Babel, M. S., Madsen, H., Sisomphon, P. & Shrestha, S. (2020). Comparison of different quantile regression methods to estimate predictive hydrological uncertainty in the Upper Chao Phraya River Basin, Thailand. Journal of Flood Risk Management, 13 (1), https://doi.org/10.1111/jfr3.12585.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/252457
    DOI
    10.1111/jfr3.12585
    Abstract
    The estimation of predictive uncertainty and its application as a post‐processor of hydrological model output, such as water level, can provide additional information useful for short‐term hydrological forecasting. In this study, We applied quantile regression models for estimating predictive hydrological uncertainty and used it to derive probabilistic hydrological forecasts. Forecast water levels and associated forecast errors were used as predictor and predictand, respectively, to develop three regression models: (a) linear quantile regression (LQR), (b) weighted LQR and (c) LQR in Gaussian space using Normal Quantile Transformation. These different models for hydrological forecasting were developed for, and applied to, the operational flood forecasting system in the Upper Chao Phraya River, Thailand. The quality of these forecasts in terms of reliability, sharpness and overall skill were assessed using various graphical and numerical verification metrics. Results show that the improvement of forecast in terms of either reliability or sharpness depends upon the configurations used. With comparable overall performance, weighted LQR provided a relatively simple configuration, which can be used for estimating uncertainty in hydrological forecasting.

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