An R package for daily precipitation climate series reconstruction. (March 2017)

• Record Type: Journal Article
• Authors: Serrano-Notivoli, Roberto; de Luis, Martín; Beguería, Santiago
• Is Part Of: Environmental modelling & software. Volume 89(2017)
• Page Start: 190
• Page End: 195
• Publication Date: 2017-03
• Subjects: reddPrec -- Daily precipitation -- Quality control -- Missing values -- Grid; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.11.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Daily precipitation datasets are usually large, bulky and hard to handle, but they are of key importance in many environmental studies. We developed a tool to create custom datasets from observed daily precipitation records. Reference values (RV) are computed for each day and location using multivariate logistic regression with altitude, latitude and longitude as covariates. The operations were compiled in an Open Source R package calledreddPrec . ThereddPrec package consists of a set of functions used to: i) apply a comprehensive quality control over original daily precipitation datasets, flagging suspect data based on five predefined criteria; ii) fill missing values in original data series by estimating precipitation values using the 10 nearest observations for each day; and iii) create new series and gridded datasets in locations where no data were recorded. Highlights: A daily precipitation reconstruction method is compiled in an Open Source R package. Three functions are provided to apply quality control, fill gaps and create grids. Users are able to obtain complete datasets and estimate new data at ungauged locations.