A comparison of hydrologic models for ecological flows and water availability

Abstract

Robust hydrologic models are needed to help manage water resources for healthy aquatic ecosystems and reliable water supplies for people, but there is a lack of comprehensive model comparison studies that quantify differences in streamflow predictions among model applications developed to answer management questions. We assessed differences in daily streamflow predictions by four fine‐scale models and two regional‐scale monthly time step models by comparing model fit statistics and bias in ecologically relevant flow statistics (ERFSs) at five sites in the Southeastern USA. Models were calibrated to different extents, including uncalibrated (level A), calibrated to a downstream site (level B), calibrated specifically for the site (level C) and calibrated for the site with adjusted precipitation and temperature inputs (level D). All models generally captured the magnitude and variability of observed streamflows at the five study sites, and increasing level of model calibration generally improved performance. All models had at least 1 of 14 ERFSs falling outside a +/−30% range of hydrologic uncertainty at every site, and ERFSs related to low flows were frequently over‐predicted. Our results do not indicate that any specific hydrologic model is superior to the others evaluated at all sites and for all measures of model performance. Instead, we provide evidence that (1) model performance is as likely to be related to calibration strategy as it is to model structure and (2) simple, regional‐scale models have comparable performance to the more complex, fine‐scale models at a monthly time step. Copyright © 2015 John Wiley & Sons, Ltd.

Streamflow characteristics from modeled runoff time series – importance of calibration criteria selection

“…This finding confirms the findings of previous studies (e.g., Hingray et al, 2010;Westerberg et al, 2011;Murphy et al, 2013;Olsen et al, 2013;Pfannerstill et al, 2014;Shrestha et al, 2014;Caldwell et al, 2015;Vis et al, 2015) and points out the importance of making a careful choice of the objective function for model calibration. I Multi showed little change compared to calibrations with R eff brings into question the benefit of including SFCs in model calibration instead of applying a traditional calibration approach when aiming at estimating a suite of SFCs.…”

“…These results are consistent with those of Olsen et al (2013), Caldwell et al (2015), Vis et al (2015), and Kiesel et al (2017) and can partly be explained by the model behavior characterized by a less pronounced runoff response to precipitation events but increased groundwater discharge to the stream during drier periods compared to the observed data (Vis et al, 2015). The observations that average flow conditions are better simulated than extremes (Caldwell et al, 2015;Vis et al, 2015) or that high-flow-related SFCs are more accurately estimated than those related to low flow (Shrestha et al, 2014;Ryo et al, 2015) cannot be confirmed with our results. None of these earlier studies explicitly included SFCs in model calibration and the deviating results could be attributed to the differing approaches to defining the objective function(s).…”

“…Their multiobjective calibration approach resulted in runoff simulations favoring high flows at the expense of the estimation accuracy of low flows. The large variability in estimated SFC accuracy as well as the bias in the estimates can generally be observed independently of the model used to simulate the runoff time series (Caldwell et al, 2015). A remedy to this large variability and bias is to incorporate SFCs into model calibration schemes.…”

“…This set of 13 SFCs represents each of the major flow regime components commonly used in ecological studies (e.g., Olden and Poff, 2003;Arthington et al, 2006;Caldwell et al, 2015): magnitude, ratio, frequency, variability, and date (Table 1). For this study the SFCs were additionally grouped according to flow conditions (mean, low, and high flow), because different aspects of the hydrograph have been shown to be sensitive to the objective function used for model calibration (for an overview, see Pfannerstill et al, 2014).…”

“…This question is especially important for resource managers who need to make decisions based on model results from different studies (Caldwell et al, 2015). A comparison of runoff models with different spatial scales that rely on different data inputs was conducted by Caldwell et al (2015).…”