An ensemble approach for attribution of hydrologic prediction uncertainty

Details An ensemble approach for attribution of hydrologic prediction uncertainty An ensemble approach for attribution of hydrologic prediction uncertainty

Jul 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008georl..3514401w&link_type=abstract

Geophysical Research Letters, Volume 35, Issue 14, CiteID L14401

Statistics

Computation

7

Hydrology: Computational Hydrology, Hydrology: Estimation And Forecasting, Hydrology: Hydrogeophysics, Hydrology: Instruments And Techniques: Modeling

Scientific paper

Hydrologic prediction errors arise from uncertainty in initial moisture states (mainly snowpack and soil moisture), in boundary forcings (primarily future precipitation and temperature), and from model structure and parameter uncertainty. We evaluate the relative importance of initial condition and boundary forcing uncertainties using a hindcast-based framework that contrasts Ensemble Streamflow Prediction (ESP) with an approach that we term ``reverse-ESP''. In ESP, a hydrologic model with assumed perfect initial conditions (ICs) is forced by a forecast ensemble resampled from observed meteorological sequences; whereas reverse-ESP combines an ensemble of resampled ICs with a perfect meteorological forecast. The framework shows that in northern California, US, ICs yield streamflow prediction skill for up to 5 months during the transition between the wet and dry seasons, whereas during the reverse transition, climate forecast information is critical. In southern Colorado, IC knowledge outweighs climate prediction skill for shorter periods due to a more uniform precipitation regime.

Affiliated with

Also associated with

No associations

Rating

An ensemble approach for attribution of hydrologic prediction uncertainty does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with An ensemble approach for attribution of hydrologic prediction uncertainty, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and An ensemble approach for attribution of hydrologic prediction uncertainty will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-1318966