Predictability of flow metrics calculated using a distributed hydrologic model across ecoregions and stream classes: Implications for developing flow-ecology relationships

Developing environmental flow standards requires an empirical understanding of the relationship between species' ecology and instream flow. However, when congruent biological and hydrologic data are lacking, the accurate simulation of hydrologic metrics (HMs) corresponding to the locations of biological data is needed. Methods to predict HMs vary in formulation (i.e., statistical vs. process-based hydrologic models), ability to simulate HMs across the full range of the hydrologic regime (i.e., magnitude, duration, frequency, rate of change and timing) and ability to transfer HMs from gaged to ungaged locations. Yet, despite the breadth of modelling approaches, less attention has been paid to the variability in HMs associated with each approach. In this study, we apply a distributed hydrologic model to the diverse watersheds of South Carolina to examine the predictability of HMs from simulated daily time series of streamflow across ecoregions, stream classifications and level of human alteration. In doing so, we contextualize the predictability of HMs, giving managers and researchers in South Carolina the flexibility of choosing HMs that are best suited for quantifying flow-ecology relationships based on the location, flow regime components of interest and uncertainty of HMs. We found that at least one HM within each of the five flow regime components (out of a selected subset of 41 non-redundant HMs) was consistently and accurately predicted across the diverse streams of the study area. We discuss the patterns of predictability related to site characterizations and individual HMs and their implications for developing environmental flow standards.

Automated summary

This study applies a distributed hydrologic model to examine the predictability of hydrologic metrics (HMs) from simulated daily streamflow across different watersheds in South Carolina. The findings provide insights into the variability and predictability of HMs and can inform the development of environmental flow standards.