Spatial variability of the parameters of a semi-distributed hydrological model

Lavenne, Alban de; Thirel, Guillaume; Andréassian, Vazken; Perrin, Charles; Ramos, Maria‐Helena

doi:10.5194/piahs-373-87-2016

Cited by 30 publications

(17 citation statements)

References 28 publications

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“…Split-sample tests are commonly used to analyse the performance of hydrological models (Klemeš, 1986). Coron et al (2012) proposed a generalised split-sample test using a slid-ing window of a given duration across the study period: calibration is carried out on the given window, and the model performance is evaluated for all other independent windows in the study period, thus evaluating on more than one period. This approach was simplified by de Lavenne et al 2016to evaluate on all data not included in the window (i.e.…”

Section: Split-sample Testsmentioning

confidence: 99%

Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets?

Hallouin¹,

Bruen²,

O’Loughlin³

2020

Hydrol. Earth Syst. Sci.

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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.

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Section: Split-sample Testsmentioning

confidence: 99%

Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets?

Hallouin¹,

Bruen²,

O’Loughlin³

2020

Hydrol. Earth Syst. Sci.

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“…A common approach for the calibration of semi-distributed models is the so-called "sequential" approach, where subcatchments are calibrated sequentially from upstream to downstream (e.g. Verbunt et al, 2006;Feyen et al, 2008;Lerat et al, 2012;De Lavenne et al, 2016). Although this approach may provide good fits and therefore has its practical utility where data are available, it does not provide understanding of the causes of streamflow spatial variability and results in models that are not spatially transferable.…”

Section: Introductionmentioning

confidence: 99%

Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment

Molin

Schirmer

Zappa

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km2), an alpine and pre-alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability and build a model that reflects them, we follow a two-stage approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgement to identify the most plausible cause–effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow-related processes, and landscape features such as geology produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space–time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology, and geology maps) are available in numerous regions around the globe.

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“…Coron et al (2012) proposed a generalised split-sample test using a sliding window for calibration, and evaluating the model performance on all other independent windows in the period. de Lavenne et al (2016) adapted this strategy to calibrate a catchment model with a sliding window, and to evaluate the simulations on all other years (before and/or after the sliding window) in the study period.…”

Section: Split-sample Testsmentioning

confidence: 99%

Calibration of hydrological models for ecologically-relevant streamflow predictions: a trade-off between performance and consistency

Hallouin

Bruen

O’Loughlin

2019

Preprint

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The ecological integrity of freshwater ecosystems is intimately linked to natural fluctuations in the river flow regime.Anthropogenic alterations in flow regimes threaten water security and freshwater biodiversity in many regions of the world.The impacts of climate change on the hydrological cycle change local flow regimes and thus impact on the ecological systems. In catchments with little human-induced hydro-morphological changes, existing hydrological models can be used to predict changes in local flow regime in order to assess whether its rivers remain a suitable living environment for endemic 5 species. However, hydrological models are traditionally calibrated to give a good general fit between observed and simulated hydrographs, e.g., using an optimisation with an objective function such as the Nash-Sutcliffe, or the Kling-Gupta efficiencies.Much ecological research has shown that aquatic species respond to very specific characteristics of the hydrograph, whether magnitude, frequency, duration, timing, and rate of change of flow events. Since each community in a river may be particularly sensitive to a few very specific streamflow characteristics, alternative hydrological model calibration strategies are needed, 10 focussing on good performance for those specific characteristics. This study investigates the performance of a set of specially developed, bespoke, objective functions made of combinations of specific streamflow characteristics relevant for fish and invertebrate communities. These are compared with the more traditional objective functions on a set of 33 Irish catchments with little human regulation. A split-sample test with a rolling-window procedure is applied to reduce the influence of variations between the calibration/evaluation periods on the conclusions. These bespoke objective functions are shown to be better suited to 15 predict the targetted streamflow characteristics in terms of performance in evaluation; however, traditional objective functions yield more consistent behavioural parameter sets, indicating a trade-off between model performance and model consistency when predicting streamflow characteristics, especially when the number of target streamflow characteristics are low.

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Spatial variability of the parameters of a semi-distributed hydrological model

Cited by 30 publications

References 28 publications

Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets?

Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets?

Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment

Calibration of hydrological models for ecologically-relevant streamflow predictions: a trade-off between performance and consistency

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