Evaluation of a typical hydrological model in relation to environmental flows

Olsen, Martin; Troldborg, Lars; Henriksen, Hans Jørgen; Conallin, John; Refsgaard, Jens Christian; Boegh, Eva

doi:10.1016/j.jhydrol.2013.10.022

Cited by 29 publications

(13 citation statements)

References 48 publications

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“…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.…”

Section: On the Importance Of The Choice Of The Objective Functionsupporting

confidence: 91%

“…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.…”

Section: Model Performance Regarding Sfcssupporting

confidence: 82%

“…These disadvantages can be partially overcome by applying runoff models. Simulated streamflow time series from runoff models can be used to calculate any SFC and, by changing model input and parameters, different scenarios such as climate change, groundwater withdrawals, land use, and riverine change can be simulated Murphy et al, 2013;Olsen et al, 2013;Shrestha et al, 2014). While statistical models such as multiple linear regressions often provide greater accuracy (Murphy et al, 2013), runoff models provide opportunities for also evaluating climate or land-use change scenarios.…”

Section: S Pool Et Al: Streamflow Characteristics From Modeled Runomentioning

confidence: 99%

“…They found that including all SFCs of interest in the model calibration resulted in better SFC estimates than a calibration using the Kling-Gupta efficiency. Instead of aiming at a well-simulated, general hydrograph, Hingray et al (2010) and Olsen et al (2013) focused on certain aspects of the streamflow regime that were considered most important. Their results, which are echoed by Murphy et al (2013), suggest that the runoff model performs reasonably well for the aspects on which it is calibrated, whereas it only modestly represents other runoff characteristics.…”

Section: S Pool Et Al: Streamflow Characteristics From Modeled Runomentioning

confidence: 99%

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

Pool

Vis

Knight

et al. 2017

Hydrol. Earth Syst. Sci.

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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., 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.

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Section: On the Importance Of The Choice Of The Objective Functionsupporting

confidence: 91%

Section: Model Performance Regarding Sfcssupporting

confidence: 82%

Section: S Pool Et Al: Streamflow Characteristics From Modeled Runomentioning

confidence: 99%

Section: S Pool Et Al: Streamflow Characteristics From Modeled Runomentioning

confidence: 99%

See 2 more Smart Citations

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

Pool

Vis

Knight

et al. 2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Therefore identifying factors such as depth, cover and substrate as being significant variables (that can be manipulated by management actions), this knowledge can aid in decision making. Furthermore, the developed HSIs from this study are well suited for integrated hydrological-ecological assessments of (ground)water abstraction impacts and stream management activities (including restoration) on physical instream habitat conditions (Thorn and Conallin, 2006;Conallin et al, 2010b;Olsen et al, 2013).…”

Section: P11mentioning

confidence: 99%

Daytime habitat selection for juvenile parr brown trout (Salmo trutta) in small lowland streams

Conallin

Boegh

Olsen

et al. 2014

Knowl. Managt. Aquatic Ecosyst.

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Key-words:Daytime refugia, habitat selection, habitat suitability indices, predation, water management Physical habitat is important in determining the carrying capacity of juvenile brown trout, and within freshwater management. Summer daytime physical habitat selection for the parr lifestage (7-20 cm) juvenile brown trout (Salmo trutta) was assessed in 6 small lowland streams. Habitat preference was determined for the four variables; water velocity, water depth, substrate and cover, and the preferences for physical habitat selection were expressed in terms of habitat suitability indices (HSI's). The statistical confidence of HSI's was evaluated using power analysis. It was found that a minimum of 22 fish observations was needed to have statistical confidence in the HSIs for water depth, and a minimum of 92 fish observations for water velocity during daytime summer conditions. Generally parr were utilising the deeper habitats, indicating preference for deeper water. Cover was also being selected for at all sites, but selection was inconsistent among sites for the variables substrate and velocity. The results indicate that during daytime summer conditions water depth is a significant variable for parr habitat selection in these small lowland streams, with cover also being important. Therefore, daytime refugia may be a critical limiting factor for parr in small lowland streams, and important for stream management actions under the Water Framework Directive.

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Predictability of flow metrics calculated using a distributed hydrologic model across ecoregions and stream classes: Implications for developing flow–ecology relationships

et al. 2022

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Developing environmental flow standards requires an empirical understanding of the relationship between species' ecology and instream flow. However, when congruent biological and hydrologic data are lacking, the accurate simulation of hydrologic metrics (HMs) corresponding to the locations of biological data is needed. Methods to predict HMs vary in formulation (i.e., statistical vs. process‐based hydrologic models), ability to simulate HMs across the full range of the hydrologic regime (i.e., magnitude, duration, frequency, rate of change and timing) and ability to transfer HMs from gaged to ungaged locations. Yet, despite the breadth of modelling approaches, less attention has been paid to the variability in HMs associated with each approach. In this study, we apply a distributed hydrologic model to the diverse watersheds of South Carolina to examine the predictability of HMs from simulated daily time series of streamflow across ecoregions, stream classifications and level of human alteration. In doing so, we contextualize the predictability of HMs, giving managers and researchers in South Carolina the flexibility of choosing HMs that are best suited for quantifying flow–ecology relationships based on the location, flow regime components of interest and uncertainty of HMs. We found that at least one HM within each of the five flow regime components (out of a selected subset of 41 non‐redundant HMs) was consistently and accurately predicted across the diverse streams of the study area. We discuss the patterns of predictability related to site characterizations and individual HMs and their implications for developing environmental flow standards.

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Evaluation of a typical hydrological model in relation to environmental flows

Cited by 29 publications

References 48 publications

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

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

Daytime habitat selection for juvenile parr brown trout (Salmo trutta) in small lowland streams

Predictability of flow metrics calculated using a distributed hydrologic model across ecoregions and stream classes: Implications for developing flow–ecology relationships

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