期刊
ECOLOGICAL MODELLING
卷 434, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ecolmodel.2020.109263
关键词
Rivers; Benthicalgae; Effectiveflow; Sedimentmobility; Streampower
类别
资金
- NIWA's eFlows Strategic Science Investment Fund Programme [FWWA1904]
Periphyton (benthic algae) forms the base of the lotic food web but, under particular conditions, can grow to nuisance levels. River managers need to understand under what conditions the rivers might develop nuisance periphyton biomass in order to set water quality and quantity limits and support healthy ecosystems. Factors that promote periphyton growth (e.g., light, temperature, nutrients) and the frequency of flows that remove periphyton (the periphyton removal flow) have long been recognized as primary controls on periphyton abundance. Predicting differences in periphyton removal flows (PRFs) between rivers has remained a challenge. The main objective of this study is to investigate how bed sediment mobility affects periphyton removal and whether it can be used to better predict PRFs at unmonitored sites with acceptable levels of certainty. We calculated the sediment based PRFs at 113 periphyton monitoring sites in rivers of different sizes and different environmental conditions by finding, for a range of particle sizes, the entrainment flow that produced the strongest correlation between periphyton biomass (measured by Chlorophyll a, chl a) and days since the last exceedance of that flow. We then grouped the monitoring sites based on the size of sediment mobile at the PRF and identified a suite of environmental variables that best distinguished these groups using a discriminatory function analysis. We then predicted PRFs at unvisited sites using the same river groupings and associated sediment-based flow metrics. Sediment-based PRFs were able to be identified at 93 of the 113 monitoring sites. Across these sites, the variance in chl a explained by the sediment-based PRF (R-2) ranged from 0.37 to 0.88 and averaged 0.58. 94% of these rivers with periphyton data were successfully discriminated into three periphyton removal flow groups, together with a non-identified group, by including 33 out of 44 tested environmental variables (e.g., temperature, nutrient concentrations, rainfall, width). The groups were then used to predict sediment-based periphyton removal flows for unvisited sites across a broad range of conditions. The value of our analysis lies in the ability to predict a range of sediment-based periphyton removal flows using only channel slope, substrate composition and wetted width. While acknowledging that sediment mobility is only one of several mechanistic controls on periphyton, this finding nonetheless offers a simple means to identify the range of sediment-based periphyton removal flows in a river reach, which can help inform river managers about the potential effects of changes in river flows on periphyton chl a.
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