Macroinvertebrate and diatom community responses to thermal alterations below water supply reservoirs

Abstract

River impoundments have transformed river ecosystems globally due to the modification of various abiotic and biotic factors. This study provides rare evidence quantifying how water supply reservoirs alter water temperature regimes and its effects on macroinvertebrate and diatom communities over a 4‐year period. We obtained near‐continuous water temperature measurements upstream and downstream of three reservoirs and analysed thermal variables in association with macroinvertebrate and diatom community indices (including taxonomic richness, proportion of Ephemeroptera, Plecoptera and Trichoptera taxa [%EPT] and diatom ecological guilds). Reservoirs typically decreased downstream thermal variability, with reduced summer temperatures and increased winter temperatures, and a delayed timing of annual temperature extremes. Marked differences in thermal regime modifications between reservoirs were observed, including evidence of inter‐annual variation associated with inter‐basin water transfers downstream of one reservoir. Biomonitoring indices showed associations with thermal indices that differed between site types (regulated versus non‐regulated) and seasons (spring vs. autumn). Various macroinvertebrate and diatom indices capturing community diversity elements and sensitivities to different environmental pressures were associated with higher maximum summer temperatures and lower minimum winter temperatures, suggesting ecological effects of reduced thermal variation downstream of reservoirs. Different ecological responses to thermal indices were observed between seasons, likely due to organism life‐cycle effects and intra‐annual thermal variations. Contrasting macroinvertebrate and diatom communities were observed between regulated and non‐regulated sites, which may be driven by differences in the thermal regime and other abiotic factors at regulated sites, including nutrient, sediment and flow regimes. Long‐term continuous water temperature monitoring of both multiple regulated and non‐regulated river systems is necessary to better understand the environmental and ecological effects of river impoundments. Given the extent to which river impoundment has modified stream temperatures globally, the inclusion of thermal regime data in environmental flow studies alongside hydrological information may guide the implementation of mitigation measures on impounded waterbodies.

Effects of water temperature on freshwater macroinvertebrates: a systematic review

“…Accordingly, benthic invertebrates have been increasingly used as bioindicators since the 1950s (Beck, 1955 ), and many ecological indices based on macroinvertebrate assemblages have been developed. Such indices have been used to evaluate the effects of temperature on features of macroinvertebrate communities, for example, total taxa richness, relative proportion and/or richness of Ephemeroptera, Plecoptera and Trichoptera (EPT) (Jourdan et al ., 2018 ; Fornaroli et al ., 2020 ; Krajenbrink et al ., 2021 ), Simpson and Shannon diversity indices (Arai et al ., 2015 ) and Jaccard and Bray–Curtis indices of similarity (Burgmer, Hillebrand & Pfenninger, 2007 ).…”

Drought impacts on river water temperature: A process‐based understanding from temperate climates

“…Finally, inter‐basin transfer schemes between impounded systems can alter the thermal dynamics of rivers depending on the size and T w regimes of the donor and receiving waterbodies. For instance, Krajenbrink et al (2022) found that a water transfer scheme reversed the summer cooling effect of a reservoir as the donor basin yielded warmer water temperatures, thus elevating daily mean T w values by 5°C during a non‐drought summer; although evidence on the implications of such inter‐basin water transfer schemes on T w is limited globally.…”

“…There remains a fundamental lack of understanding on how different reservoir properties collectively govern T w , including during drought conditions where flow releases could exacerbate of mitigate thermal peaks (Cowx et al, 1987). Various studies from Cfb climates have reported a thermal 'compressing' effect of water supply reservoirs on annual T w ranges, whereby summer discharges specifically are cooled by continuous compensation flow releases that restrict the occurrence of extreme low-flows and thus thermal peaks (Krajenbrink et al, 2022;Webb & Walling, 1993, 1997, although such trends are inconsistent.…”

“…There remains a fundamental lack of understanding on how different reservoir properties collectively govern T w , including during drought conditions where flow releases could exacerbate of mitigate thermal peaks (Cowx et al, 1987). Various studies from Cfb climates have reported a thermal ‘compressing’ effect of water supply reservoirs on annual T w ranges, whereby summer discharges specifically are cooled by continuous compensation flow releases that restrict the occurrence of extreme low‐flows and thus thermal peaks (Krajenbrink et al, 2022; Webb & Walling, 1993, 1997), although such trends are inconsistent. For instance, Cowx et al (1987) reported that one regulated system possessed T w values ~2°C cooler than a nearby free flowing river during the summer 1976 drought, but another impounded river was ~0.5°C warmer during the same event; the author also reported that the thermal effects of each reservoir were broadly comparable between drought and non‐drought years.…”

From a continuous thermal profile to a stepped one: The effect of run of river hydropower plants on the river thermal regime

“…Water temperature data were downloaded and a linear interpolation between consecutive measurements was performed to obtain a continuous series of the water temperature (one value per minute) and lastly the daily mean, maximum and minimum values. When water temperature data were lacking due to sensor failure or loss ($30% of the data) the values were estimated separately for each site from air temperature by Generalized Additive Models (GAM) using daily water temperature (mean, maximum, and minimum) and air temperature (mean) time series plus the week number as an extra smoothing parameter (see Krajenbrink et al (2021) for details) to have a continuous data set. GAMs were developed by "mgvc" (Wood, 2022) R package using a Gaussian distribution and provided an excellent performance (Root Mean Square Error [RMSE] $0.82 C, see Table 1A).…”