Abstract
Abstract. The ecological integrity of freshwater ecosystems is intimately linked to natural fluctuations in the river flow regime. In catchments with little human-induced alterations of the flow regime (e.g. abstractions and regulations), existing hydrological models can be used to predict changes in the local flow regime to assess any changes in its rivers' living environment for endemic species. However, hydrological models are traditionally calibrated to give a good general fit to observed hydrographs, e.g. using criteria such as the Nash–Sutcliffe efficiency (NSE) or the Kling–Gupta efficiency (KGE). Much ecological research has shown that aquatic species respond to a range of specific characteristics of the hydrograph, including magnitude, frequency, duration, timing, and the rate of change of flow events. This study investigates the performance of specially developed and tailored criteria formed from combinations of those specific streamflow characteristics (SFCs) found to be ecologically relevant in previous ecohydrological studies. These are compared with the more traditional Kling–Gupta criterion for 33 Irish catchments. A split-sample test with a rolling window is applied to reduce the influence on the conclusions of differences between the calibration and evaluation periods. These tailored criteria are shown to be marginally better suited to predicting the targeted streamflow characteristics; however, traditional criteria are more robust and produce more consistent behavioural parameter sets, suggesting a trade-off between model performance and model parameter consistency when predicting specific streamflow characteristics. Analysis of the fitting to each of 165 streamflow characteristics revealed a general lack of versatility for criteria with a strong focus on low-flow conditions, especially in predicting high-flow conditions. On the other hand, the Kling–Gupta efficiency applied to the square root of flow values performs as well as two sets of tailored criteria across the 165 streamflow characteristics. These findings suggest that traditional composite criteria such as the Kling–Gupta efficiency may still be preferable over tailored criteria for the prediction of streamflow characteristics, when robustness and consistency are important.
Highlights
River flow is the cornerstone of freshwater ecosystems, the ecological integrity of which relies on natural fluctuations in the river flow regime (Poff et al, 1997)
This is because ESKFPC and ESPFC contain a majority of streamflow characteristics (SFCs) for high-flow conditions (Table 1), while ESKFC contains a majority of SFCs for low-flow conditions
This study explored these aspects for six different objective functions intended to predict three combinations of streamflow characteristics that are assumed to be relevant for stream ecology
Summary
River flow is the cornerstone of freshwater ecosystems, the ecological integrity of which relies on natural fluctuations in the river flow regime (Poff et al, 1997). Hallouin et al.: Calibration of hydrological models for ecologically relevant streamflow predictions referred to as streamflow characteristics (SFCs) (Vis et al, 2015; Pool et al, 2017), ecologically relevant flow statistics (ERFSs) (Caldwell et al, 2015), or indicators of hydrological alteration (IHAs) (Richter et al, 1996) These SFCs describe specific aspects of the river flow regime that can be extracted from the streamflow hydrograph and can be categorised on the basis of magnitude, frequency, rate of change, timing, and duration of high-, average-, and low-flow events (Poff et al, 1997). Olden and Poff (2003) listed a range of such SFCs used to characterise river flow regime in relation to ecological species’ preferences The prediction of these SFCs at ungauged locations has historically been done using statistical analyses such as regional regression models that relate them to some climatic and physiographic descriptors (e.g. Carlisle et al, 2011; Knight et al, 2014). Hydrological models can allow for such scenario analyses, and they produce simulated streamflow hydrographs from which the streamflow characteristics can be computed (e.g. Shrestha et al, 2014; Caldwell et al, 2015)
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