Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

Albert I.J.M. Van Dijk, Jorge L. Peña-Arancibia, Eric F. Wood, Justin Sheffield, Hylke E. Beck

Research output: Contribution to journalArticlepeer-review

102 Scopus citations

Abstract

Key Points Global bimonthly streamflow forecasts show potentially valuable skill Initial catchment conditions are responsible for most skill Skill can be estimated from model performance and theoretical skill Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1° resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (
Original languageEnglish (US)
Pages (from-to)2729-2746
Number of pages18
JournalWater Resources Research
Volume49
Issue number5
DOIs
StatePublished - May 1 2013
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-14

Fingerprint

Dive into the research topics of 'Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide'. Together they form a unique fingerprint.

Cite this