Quantifying flow–ecology relationships across flow regime class and ecoregions in South Carolina

Bower, Luke M.; Peoples, Brandon K.; Eddy, Michele; Scott, Mark C.

doi:10.1016/j.scitotenv.2021.149721

Cited by 24 publications

(20 citation statements)

References 85 publications

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“…This is not surprising, as peak flows support many physical (e.g., maintenance and rejuvenation of habitat), biogeochemical (e.g., increase nutrient cycling and exchange) and biological (e.g., limit nonnative species through natural disturbance) functions (Supplementary Table S1). This relationship agrees with several studies that have determined peak flow metrics as important influences on macroinvertebrates (Buchanan et al, 2013;Stein et al, 2017;Mazor et al, 2018;Yarnell et al, 2020;Bower et al, 2022) and algae (Lake 2000;Tsai et al, 2013;Townsend et al, 2017).…”

Section: Identifying the Most Important Flow Componentssupporting

confidence: 91%

Application of Flow-Ecology Analysis to Inform Prioritization for Stream Restoration and Management Actions

Irving

Taniguchi-Quan

Aprahamian

et al. 2022

Front. Environ. Sci.

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A key challenge in managing flow alteration is determining the severity and pattern of alteration associated with the degradation of biological communities. Understanding these patterns helps managers prioritize locations for restoration and flow management actions. However, the choices made about how to use these flow-ecology relationships can have profound implications on management decisions (e.g., which biological endpoints, which thresholds, which seasonal flow components to use). We describe a process for using flow-ecology relationships to prioritize management actions that 1) Represents the most relevant components of the annual hydrograph, 2) Demonstrates an appropriate level of sensitivity in order to discriminate locations to inform decision making, 3) Aims to protect multiple biological assemblages, 4) Reduces misclassification of priority areas (i.e., error of omission). Our approach is based on the functional flows approach which uses multiple flow metrics that describe the frequency, timing, magnitude, duration, and rate of change of seasonal process-based components of the annual hydrograph. Using this approach, we performed a flow-ecology analysis of regional bioassessment data, through which we determined where flow alteration impacts biology and prioritized reaches for changes in flow management to protect aquatic resources in a highly urbanized region of southern California, where managing scarce water resources leads to difficult decisions about tradeoffs that require technical information. We identified three important functional flow metrics for each of two bioassessment indices, one based on benthic macroinvertebrates, and another based on benthic algae. Based on thresholds that describe levels of alteration as well as thresholds describing the probability of achieving a healthy biological condition, we compared nine biological threshold combinations for each index. We found instances of flow alteration that impact biological condition highly variable (0–100% of subbasins) between combinations and we present a method for finding the most appropriate combination for prioritizing locations for flow management. We apply the final thresholds to the study region and propose 16 subbasins of high priority for implementing flow management and restoration. Importantly, we show that focusing on a single biological group would result in biologically altered locations being effectively ignored.

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Section: Identifying the Most Important Flow Componentssupporting

confidence: 91%

Application of Flow-Ecology Analysis to Inform Prioritization for Stream Restoration and Management Actions

Irving

Taniguchi-Quan

Aprahamian

et al. 2022

Front. Environ. Sci.

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“…It is within these ungaged stream reaches where biological measures are often made. The study conducted by Bower et al (2022) gathered fish and macroinvertebrate data from roughly 500 sites each across the state of South Carolina. Comparing that density of sites to the 36 USGS gages within South Carolina identified for this study makes the need for more site‐specific hydrologic representation clear.…”

Section: Discussionmentioning

confidence: 99%

“…In doing so, we sought to identify metrics that may be of particular use in flow-ecology analyses based on their performance. By accounting for anthropogenic effects such as land cover and direct flow manipulation, we sought to simulate streamflows from both natural and altered drainage areas with relatively equal levels of performance, which is necessary to fully account for the varying biological conditions expected in the observed data that will be paired with these hydrologic data in any subsequent flow-ecology analyses (i.e., Bower et al, 2022). Given these different categories of assessment, we examine potential sources and levels of uncertainty within the modelling.…”

Section: Overviewmentioning

confidence: 99%

“…To provide metric stability, all EflowStats metrics are computed for the 30-year modelled period (1 October 1988 through 30 September 2018) (Kennard et al, 2010). To provide a detailed assessment on the predictability of HMs across all stream classifications found within the study area (i.e., stable baseflow, perennial run-off, peren- Poff based on guidance from the TAC and the analyses conducted in the subsequent flow-ecology assessment (Bower et al, 2022).…”

Section: Hydrologic Metricsmentioning

confidence: 99%

“…developing flow-ecology relationships and water management policy at large spatial extents but fine spatial resolution. In this study, we simulated HMs for every NHDPlus stream segment in South Carolina to facilitate the development of statewide flow-ecology relationships(Bower et al, 2022). This work serves to contextualize HMs by assessing their predictability, giving managers and researchers in South Carolina the flexibility of choosing HMs that are best suited for the project based on the location, flow regime components of interest and uncertainty of HMs while also providing the location specific HMs necessary to pair with biological data.Because uncertainty was heterogeneous among HMs, the metric selection process should consider ecological relevance as well as predictability of HMs.…”

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confidence: 99%

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

et al. 2022

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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.

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Quantifying streamflow properties using a calculus‐based differential approach

Koehler

2023

Ecohydrology

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This research quantifies streamflow temporal configuration and demonstrates the advantages of examining the hydrologic record on a day‐to‐day basis, heretofore unavailable with statistical‐based index parameters. The streamflow chronological structure represents a previously untapped wealth of information that would benefit ecohydrology allowing alternate lines of investigation, re‐evaluating past research in a new light and expanding analysis options for ecohydrologists. Innovative approaches are introduced to quantify this important but overlooked hydrologic property. To accomplish this, a non‐index calculus‐based differential approach has been developed using the lag(1) temporal autocorrelation signature of streamflow. The techniques for quantifying discharge (Q), day‐to‐day discharge change (dQ/dt), rate of day‐to‐day discharge change (d2Q/dt2) and sequential discharge summations are explained and presented using updated data visualization methods. A dam release river impact case study for the Colorado River at Lees Ferry, Arizona, demonstrates this novel way of analyzing and comparing discharge datasets. A set of highly customizable tools for this new approach can be used as a stand‐alone analysis or to complement other existing techniques. The result is a better understanding of the hydrologic regime, permitting more focused research and more effective management planning.

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Quantifying flow–ecology relationships across flow regime class and ecoregions in South Carolina

Cited by 24 publications

References 85 publications

Application of Flow-Ecology Analysis to Inform Prioritization for Stream Restoration and Management Actions

Application of Flow-Ecology Analysis to Inform Prioritization for Stream Restoration and Management Actions

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

Quantifying streamflow properties using a calculus‐based differential approach

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