Next Article in Journal
Determinants of Farmers’ Decisions on Risk Coping Strategies in Rural West Java
Previous Article in Journal
Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
Open AccessArticle

Decadal Oscillation in the Predictability of Palmer Drought Severity Index in California

Met European Research Observatory, 82100 Benevento, Italy
Division of Statistics, Northern Illinois University, DeKalb, IL 60115, USA
Computational Science Research Center, San Diego State University, San Diego, CA 92182-7720, USA
UCA, INRA, VetAgro Sup, Unité Mixte de Recherche sur Écosystème Prairial (UREP), 63000 Clermont-Ferrand, France
Author to whom correspondence should be addressed.
Climate 2019, 7(1), 6;
Received: 23 November 2018 / Revised: 29 December 2018 / Accepted: 1 January 2019 / Published: 3 January 2019
Severity of drought in California (U.S.) varies from year-to-year and is highly influenced by precipitation in winter months, causing billion-dollar events in single drought years. Improved understanding of the variability of drought on decadal and longer timescales is essential to support regional water resources planning and management. This paper presents a soft-computing approach to forecast the Palmer Drought Severity Index (PDSI) in California. A time-series of yearly data covering more than two centuries (1801–2014) was used for the design of ensemble projections to understand and quantify the uncertainty associated with interannual-to-interdecadal predictability. With a predictable structure elaborated by exponential smoothing, the projections indicate for the horizon 2015–2054 a weak increase of drought, followed by almost the same pace as in previous decades, presenting remarkable wavelike variations with durations of more than one year. Results were compared with a linear transfer function model approach where Pacific Decadal Oscillation and El Niño Southern Oscillation indices were both used as input time series. The forecasted pattern shows that variations attributed to such internal climate modes may not provide more reliable predictions than the one provided by purely internal variability of drought persistence cycles, as present in the PDSI time series. View Full-Text
Keywords: drought; ensemble forecast; exponential smoothing; transfer function modelling drought; ensemble forecast; exponential smoothing; transfer function modelling
Show Figures

Figure 1

MDPI and ACS Style

Diodato, N.; De Guenni, L.B.; Garcia, M.; Bellocchi, G. Decadal Oscillation in the Predictability of Palmer Drought Severity Index in California. Climate 2019, 7, 6.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop