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

Abstract

Abstract. Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., NashSutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash-Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV -Hydrologiska Byråns Vattenavdelning -model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.

Mapping (dis)agreement in hydrologic projections

“…Not including the evaluation metrics in the optimization can lead to an inadequate depiction of those metrics (e.g. Pool et al, 2017). Including mean runoff and discharge timing as met- The models agree on the sign of the change Two models agree, one non−behavioural Two models disagree, one non−behavioural The models disagree on the sign of the change Two models non−behavioural All models non−behavioural…”

When is a hydrological model sufficiently calibrated to depict flow preferences of riverine species?

“…The IHAs were selected as optimization criteria to ensure that the hydrological model depicts the individual IHAs and, therefore, the species preferences as well as possible. This is necessary because hydrological models perform weakly in predicting IHAs if they are not included in the optimization process (Kiesel et al, ; Pool et al, ; Vigiak et al, ). The IHAs were selected based on Kakouei et al (), who investigated the most important and not cross‐correlated IHAs for the occurring species in the Treene and Kinzig.…”

“…Kakouei et al (2017) developed these linkages using the indicators of hydrological alteration (IHAs; Olden & Poff, 2003). Multiple studies showed that a successful representation of IHAs in hydrological models requires a targeted optimization process towards these IHAs (Pool, Vis, Knight, & Seibert, 2017). Kiesel et al (2017) developed a methodology for a tailored optimization of hydrological models for these IHAs.…”

The accuracy of ecological flow metrics derived using a physics‐based distributed rainfall–runoff model in the Great Plains, USA

“…Further, because subbasin‐lumped models rely on semiphysical based empirical parameters, calibration and subsequent simulations are constrained to streamflow gauging locations with sufficient record length to obtain reliable prediction of ecological response to forcing factors such as land‐use conversion or climate change. Gridded distributed models, when compared with lumped empirical models, offer an advantage in predicting high‐flow events unless precipitation errors cause more error in streamflow than for the lumped empirical models (Cartwright, Caldwell, Nebiker, & Knight, ; Pool, Vis, Knight, & Seibert, ; Smith, Ellis, Wagner, & Peterson, ; Teutschbein, Grabs, Laudon, Karlsen, & Bishop, ).…”

“…Gridded distributed models, when compared with lumped empirical models, offer an advantage in predicting high-flow events unless precipitation errors cause more error in streamflow than for the lumped empirical models (Cartwright, Caldwell, Nebiker, & Knight, 2017;Pool, Vis, Knight, & Seibert, 2017;Smith, Ellis, Wagner, & Peterson, 2012;Teutschbein, Grabs, Laudon, Karlsen, & Bishop, 2018).…”