Archived Projects:
Hydro-Climate Research and Decision Making

Output Post-processing

The research goal here is to study if it is possible to combine information from multiple ensembles to provide probabilistic simulations of runoff that have greater accuracy than the raw ensembles.

We have done an initial study on the Colorado River to post-process ESP traces using a suite of weighting schemes, and found that more skillful probabilistic forecasts can be generated through such an approach. The abstract of the paper written from this study is given below.


Paper Title: An analysis of weighting schemes using climate indices for seasonal volume forecasts produced from the ensemble streamflow prediction system of the National Weather Service

Abstract: This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small to medium river sub-basins roughly following a longitudinal in the Colorado River basin with different El Nino – Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for introducing known relations to the Nino3.4 index are presented. The first method, post-adjustment, directly modifies the probability distribution of the spring runoff produced from ESP. Four different post-adjustment weighting schemes are presented. The second method, pre-adjustment, modifies the probability distribution of the temperature and precipitation observations used to drive ESP. Two pre-adjustment methods are presented. This study shows the distance sensitive nearest neighbor post adjustment with both adjustable parameters optimized to be superior to the other post adjustment weighting schemes. Further, for the basins used in this study forecasts based on post-adjustment techniques out performed those based on pre-adjustment techniques.

The paper is currently under review in the Journal of Hydrometeorology. In the future, we plan to test other ensemble post-processing techniques such as Bayesian Model Averaging.