Regional sub-daily stochastic weather generator based on reanalyses for surface water stress estimation in central Tunisia. (September 2022)

• Record Type: Journal Article
• Authors: Farhani, Nesrine; Carreau, Julie; Kassouk, Zeineb; Mougenot, Bernard; Le Page, Michel; Lili-Chabaane, Zohra; Zitouna-Chebbi, Rim; Boulet, Gilles
• Is Part Of: Environmental modelling & software. Volume 155(2022)
• Publication Date: 2022-09
• Subjects: stochastic weather generator -- bias correction -- surface water stress estimation -- sub-daily resolution -- ERA5 reanalyses; 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.2022.105448
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We present MetGen : a sub-daily multi-variable stochastic weather generator implemented as an R library that can be used to perform gap-filling and to extend in time meteorological observation series. MetGen is tailored to provide surrogate series of air temperature, relative air humidity, global radiation and wind speed needed for surface water stress estimation that requires sub-daily resolution. Multiple gauged stations can be used to increase the calibration data although spatial dependence is not modeled. The approach relies on Generalized Linear Models that use, among their covariates, large-scale variables derived from ERA5 reanalyses. MetGen aims at preserving key features of the meteorological variables along with inter-variable dependencies. We illustrate the abilities of MetGen using a case study with three stations in central Tunisia. We consider as alternatives a univariate and a multivariate bias correction techniques along with the un-processed large-scale variables. Highlights: Stochastic weather generator at sub-daily resolution available as an R library. Gap-filling and temporal extension of multivariate meteorological observation series. Tailored for plant water stress estimation in semi-arid areas. Based on generalized linear models with covariates derived from ERA5 reanalyses.