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

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    109 Citations (Scopus)

    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 (<10,000 km2) catchments worldwide. The results show that initial catchment conditions provide the main source of skill. Post-ESP sampling enhanced skill in equatorial South America and Southeast Asia, particularly in terms of tercile probability skill, due to the persistence and influence of the El Niño Southern Oscillation. Actual skill was on average 54% of theoretical skill but considerably more for selected regions and times of year. The realized fraction of the theoretical skill probably depended primarily on the quality of precipitation estimates. Forecast skill could be predicted as the product of theoretical skill and historic model performance. Increases in seasonal forecast skill are likely to require improvement in the observation of precipitation and initial hydrological conditions.

    Original languageEnglish
    Pages (from-to)2729-2746
    Number of pages18
    JournalWater Resources Research
    Volume49
    Issue number5
    DOIs
    Publication statusPublished - May 2013

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