Toward a robust method for subdaily rainfall downscaling from daily data

Compared to daily rainfall data, observed subdaily rainfall times are rare and often very short. For hydrologic modeling, this problem is often addressed by generating synthetic hourly rainfall series, with rainfall generators calibrated on relevant rainfall statistics. The required subdaily rainfal...

Full description

Saved in:
Bibliographic Details
Published in:Water resources research 2011-09, Vol.47 (9), p.n/a
Main Authors: Beuchat, X., Schaefli, B., Soutter, M., Mermoud, A.
Format: Article
Language:English
Subjects:
Climate
Climate change
climate dynamics
Datasets
downscaling
Gauges
hourly rainfall generation
Hydrologic data
Hydrology
multivariate adaptive regression splines (MARS)
Parameter estimation
Precipitation
Rain
Rainfall
regionalization
Statistics
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Compared to daily rainfall data, observed subdaily rainfall times are rare and often very short. For hydrologic modeling, this problem is often addressed by generating synthetic hourly rainfall series, with rainfall generators calibrated on relevant rainfall statistics. The required subdaily rainfall statistics are traditionally derived from daily rainfall records by assuming some temporal scaling behavior of these statistics. However, as our analyzes of a large data set suggest, the mathematical form of this scaling behavior might be specific to individual gauges. This paper presents, therefore, a novel approach that bypasses the temporal scaling behavior assumption. The method uses multivariate adaptive regression splines; it is learning‐based and seeks directly relationships between target subdaily statistics and available predictors (including (supra‐) daily rainfall statistics and external information such as large‐scale atmospheric variables). A large data set is used to investigate these relationships, including almost 340 hourly rainfall series coming from gauges spread over Switzerland, the USA and the UK. The predictive power of the new approach is assessed for several subdaily rainfall statistics and is shown to be superior to the one of temporal scaling laws. The study is completed with a detailed discussion of how such reconstructed statistics improve the accuracy of an hourly rainfall generator based on Poisson cluster models. Key Points Existing temporal scaling laws regionalization might be challenging A new learning‐based scaling methdology based on MARS is introduced The fitted models hold for a large variety of climates
ISSN:0043-1397
1944-7973
DOI:10.1029/2010WR010342