Return to CIG


View All Publications

Go To Publication by Year:

View Publications by Topic:



Air Quality

Aquatic Ecosystems and Fisheries

Background Papers

Climate: Atmospheric Modeling

Climate: Coupled Atmosphere-Ocean Modeling

Climate: Diagnostics

Climate: Global Climate

Climate: Ocean Modeling

Climate: PNW Climate

Climate: Regional Climate Modeling

Coastal Ecosystems

Coastal Environments

Conservation Biology

Data Analysis and Sharing


Fact Sheets

Forecasts and Applications

Forest Ecosystems

Human Health

Hydrology and Water Resources


Integrated Assessment

Ocean Acidification


Program Documents

Science Advisory Reports

Societal Dimensions

Special Reports

Theses and Dissertations

View Publications by Author:

Search the Publication Abstracts:

Other CSES Links:

About CSES

CSES Personnel

Data / Links


Welcome to the publications directory for the Climate Impacts Group and the Climate Dynamics Group. Please contact the web administrator for assistance with any of these publications.

View: Abstract

Correcting errors in streamflow forecast ensemble mean and spread

Wood, A.W., and J.C. Schaake. 2008. Correcting errors in streamflow forecast ensemble mean and spread. Journal of Hydrometeorology 9(1): 132-148.


When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performance of traditional ESP forecasts by reducing errors in forecast mean and improving spread estimates, thereby increasing forecast reliability and skill.