Probabilistic streamflow forecasts based on hydrologic persistence in central Texas
Document Type
Conference Proceeding
Publication Date
10-26-2009
Abstract
In many cases, streamflow persistence (month-to-month or season-to-season correlation) can be used in place of climate forecasts to provide useful forecast information to water managers. In this study, an ordinal polytomous logistic regression model is proposed to generate tercile probability stream flow forecasts (i.e., probability of low, medium, and high categories) based on persistence for the Lower Colorado River system in central Texas. Forecast performance is evaluated by cross-validation using the Brier skill score (BSS) and the Ranked probability skill score (RPSS). The results show that stream flow persistence can provide significant forecast skill during the winter and spring seasons, when water allocation decisions are being made for the coming summer growing season. © 2009 ASCE.
Publication Title
Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers
Recommended Citation
Wei, W.,
&
Watkins, D.
(2009).
Probabilistic streamflow forecasts based on hydrologic persistence in central Texas.
Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers,
342, 5067-5076.
http://doi.org/10.1061/41036(342)512
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/8715