Ken Eng
Daren M. Carlisle
Matthew J. Cashman
Michael R. Meador
Karen R. Ryberg
Kelly O. Maloney
Taylor Woods
2024
<div id="ab0005" class="abstract author" lang="en"><div id="as0005"><p id="sp0030">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,<span> </span><a class="topic-link" title="Learn more about streamflow from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/streamflow" data-mce-href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/streamflow">streamflow</a><span> </span>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.</p></div></div>
application/pdf
10.1016/j.scitotenv.2023.168258
en
Elsevier
Assessing the added value of antecedent streamflow alteration information in modeling stream biological condition
article