streamflow characteristics from modeled runoff time series – importance of calibration criteria selection
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2017
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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.,
Nash–Sutcliffe 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.
| Reference Key |
pool2017hydrologystreamflow
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|---|---|
| Authors | ;S. Pool;M. J. P. Vis;R. R. Knight;J. Seibert;J. Seibert;J. Seibert |
| Journal | materials research bulletin |
| Year | 2017 |
| DOI |
10.5194/hess-21-5443-2017
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| URL | |
| Keywords |
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