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Subdaily Rainfall Estimation through Daily Rainfall Downscaling Using Random Forests in Spain

Environmental Hydraulics Institute, Universidad de Cantabria—Avda. Isabel Torres, 15, Parque Científico y Tecnológico de Cantabria, 39011 Santander, Spain
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These authors contributed equally to this work.
Water 2019, 11(1), 125; https://doi.org/10.3390/w11010125
Received: 5 December 2018 / Revised: 30 December 2018 / Accepted: 7 January 2019 / Published: 11 January 2019
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
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Abstract

Subdaily rainfall data, though essential for applications in many fields, is not as readily available as daily rainfall data. In this work, regression approaches that use atmospheric data and daily rainfall statistics as predictors are evaluated to downscale daily-to-subdaily rainfall statistics on more than 700 hourly rain gauges in Spain. We propose a new approach based on machine learning techniques that improves the downscaling skill of previous methodologies. Results are grouped by climate types (following the Köppen–Geiger classification) to investigate possible missing explanatory variables in the analysis. The methodology is then used to improve the ability of Poisson cluster models to simulate hourly rainfall series that mimic the statistical behavior of the observed ones. This approach can be applied for the study of extreme events and for daily-to-subdaily precipitation disaggregation in any location of Spain where daily rainfall data are available. View Full-Text
Keywords: rainfall modeling; temporal downscaling; machine learning; synthetic simulation; rainfall extremes rainfall modeling; temporal downscaling; machine learning; synthetic simulation; rainfall extremes
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Diez-Sierra, J.; del Jesus, M. Subdaily Rainfall Estimation through Daily Rainfall Downscaling Using Random Forests in Spain. Water 2019, 11, 125.

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