Assessing the added value of antecedent streamflow alteration information in modeling stream biological condition

Science of the Total Environment
By: , and 



In stream systems, disentangling relationships between biology and flow and subsequent prediction of these relationships to unsampled streams is a common objective of large-scale ecological modeling. Often, streamflow metrics are derived from aggregating continuous streamflow records available at a subset of stream gages into long-term flow regime descriptors. Despite demonstrated value, shortcomings of these long-term approaches include spatial restriction to locations with long-term continuous flow records (commonly, biased toward larger systems) and omission of potentially ecologically important short-term (i.e., ≤1 year) antecedent streamflow information. We used long-term flow regime and short-term antecedent streamflow alteration information to evaluate relative performance in modeling stream fish biological condition. We compared results to understand whether short-term antecedent streamflow information improved models of fish biological condition. Results indicated that models incorporating short-term antecedent data performed better than those relying solely on long-term flow regime data (kappa statistic = 0.29 and 0.23, respectively) and improved prediction accuracy among stream sizes and in six of nine ecoregions. Additionally, models relying solely on short-term streamflow information performed similarly to those with only long-term streamflow information (kappa = 0.23). Incorporating short-term antecedent streamflow metrics may provide added ecological information not fully captured by long-term flow regime summaries in macroscale modeling efforts or perform similarly to long-term streamflow data when long-term data are not available.

Publication type Article
Publication Subtype Journal Article
Title Assessing the added value of antecedent streamflow alteration information in modeling stream biological condition
Series title Science of the Total Environment
DOI 10.1016/j.scitotenv.2023.168258
Volume 908
Year Published 2024
Language English
Publisher Elsevier
Contributing office(s) Leetown Science Center, Dakota Water Science Center, Eastern Ecological Science Center
Description 168258, 9 p.
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