Analysis of projected hydrological behavior of catchments based on signature indices

Casper, Markus; Grigoryan, G. V.; Gronz, Oliver; Gutjahr, Oliver; Heinemann, Günther; Ley, Rita; Rock, Andreas

doi:10.5194/hess-16-409-2012

Cited by 31 publications

(18 citation statements)

References 38 publications

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“…The time period used is important if signatures are used for catchment classification: an unusual event such as a large flood may shift the signature values (Casper et al, 2012). Additional uncertainty sources can be important in other catchments, such as catchment boundary uncertainty and flow bypassing the gauge (Graham et al, 2010a).…”

Section: Methods Limitations and Future Developmentsmentioning

confidence: 99%

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Uncertainty in hydrological signatures

Westerberg

McMillan

2015

Hydrol. Earth Syst. Sci.

153

141

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Abstract. Information about rainfall-runoff processes is essential for hydrological analyses, modelling and watermanagement applications. A hydrological, or diagnostic, signature quantifies such information from observed data as an index value. Signatures are widely used, e.g. for catchment classification, model calibration and change detection. Uncertainties in the observed data -including measurement inaccuracy and representativeness as well as errors relating to data management -propagate to the signature values and reduce their information content. Subjective choices in the calculation method are a further source of uncertainty.We review the uncertainties relevant to different signatures based on rainfall and flow data. We propose a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrate it in two catchments for common signatures including rainfall-runoff thresholds, recession analysis and basic descriptive signatures of flow distribution and dynamics. Our intention is to contribute to awareness and knowledge of signature uncertainty, including typical sources, magnitude and methods for its assessment.We found that the uncertainties were often large (i.e. typical intervals of ± 10-40 % relative uncertainty) and highly variable between signatures. There was greater uncertainty in signatures that use high-frequency responses, small data subsets, or subsets prone to measurement errors. There was lower uncertainty in signatures that use spatial or temporal averages. Some signatures were sensitive to particular uncertainty types such as rating-curve form. We found that signatures can be designed to be robust to some uncertainty sources. Signature uncertainties of the magnitudes we found have the potential to change the conclusions of hydrological and ecohydrological analyses, such as cross-catchment comparisons or inferences about dominant processes.

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Section: Methods Limitations and Future Developmentsmentioning

confidence: 99%

“…unrecognised blocked gauges). Point uncertainty includes random errors such as turbulent airflow around the gauge (Ciach, 2003) and is usually assessed using co-located gauges. Systematic point errors are also common (e.g.…”

Section: Identification Of Uncertainty Sourcesmentioning

confidence: 99%

Uncertainty in hydrological signatures

Westerberg

McMillan

2015

Hydrol. Earth Syst. Sci.

153

141

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“…In line with their findings, we also found characteristic regime shifts in dDMC between "steep" winter and "flat" summer conditions; however, our test catchments are 1 to 2 orders of magnitude larger than those used in their studies. Our results revealed also that neither seasonal FDC nor usual FDC, which are frequently used to characterize runoff regimes in catchment inter-comparison studies (Oudin et al, 2010;Sawicz et al, 2011;Casper et al, 2012;Viglione et al, 2013), yielded similar information. We hence conclude that season-specific dDMC are a well-suited fingerprint for characterizing seasonal runoff formation in mesoscale catchments of temperate environments and that dDMC are suited for inter-comparison studies.…”

Section: Potential and Limitations Of Dimensionless Double Mass Curvesmentioning

confidence: 59%

“…(Blöschl et al, 2013, p. 7). Today, runoff signatures are commonly accepted and usually defined as specific characteristics of the hydrograph such as autocorrelation, slope of or bias in the flow duration curve or different segments thereof, rising limb density, peak distribution, and/or as flow statistics such as mean, variance, skewness, or the coefficient of variation (Pokhrel and Yilmaz, 2012;Casper et al, 2012;Pfannerstill et al, 2014;Euser et al, 2013Euser et al, , 2015. Runoff signatures are widely used for similarity assessment but also for model evaluation.…”

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confidence: 99%

Unravelling abiotic and biotic controls on the seasonal water balance using data-driven dimensionless diagnostics

Seibert

Jackisch

Ehret

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The baffling diversity of runoff generation processes, alongside our sketchy understanding of how physiographic characteristics control fundamental hydrological functions of water collection, storage, and release, continue to pose major research challenges in catchment hydrology. Here, we propose innovative data-driven diagnostic signatures for overcoming the prevailing status quo in catchment inter-comparison. More specifically, we present dimensionless double mass curves (dDMC) which allow inference of information on runoff generation and the water balance at the seasonal and annual timescales. By separating the vegetation and winter periods, dDMC furthermore provide information on the role of biotic and abiotic controls in seasonal runoff formation.A key aspect we address in this paper is the derivation of dimensionless expressions of fluxes which ensure the comparability of the signatures in space and time. We achieve this by using the limiting factors of a hydrological process as a scaling reference. We show that different references result in different diagnostics. As such we define two kinds of dDMC which allow us to derive seasonal runoff coefficients and to characterize dimensionless streamflow release as a function of the potential renewal rate of the soil storage. We expect these signatures for storage controlled seasonal runoff formation to remain invariant, as long as the ratios of release over supply and supply over storage capacity develop similarly in different catchments.We test the proposed methods by applying them to an operational data set comprising 22 catchments (12-166 km 2 ) from different environments in southern Germany and hydrometeorological data from 4 hydrological years. The diagnostics are used to compare the sites and to reveal the dominant controls on runoff formation.The key findings are that dDMC are meaningful signatures for catchment runoff formation at the seasonal to annual scale and that the type of scaling strongly influences the diagnostic potential of the dDMC. Adding discrimination between growing season and winter period was of fundamental importance and easy to implement by means of a temperatureindex model. More specifically, temperature aggregates explain over 70 % of the variability of the seasonal summer runoff coefficients. The results also show that the soil topographic index, i.e. the product of topographic gradient and saturated hydraulic conductivity, is significantly correlated with winter runoff coefficients, whereas the topographic gradient and the hydraulic conductivity alone are not. We conclude that proxies for gradients and resistances should be interpreted as a pair. Lastly, the dDMC concept reveals memory effects between summer and winter runoff regimes that are not relevant in spring between the transition from winter to summer.

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“…7a) were used to define 21 different river discharge signatures that cover a range of temporal scales and flow magnitudes. The majority of these signatures were based on previous studies (Coxon et al, 2014;Yilmaz et al, 2008;Westerberg et al, 2016;Shafii and Tolson, 2015;Hrachowitz et al, 2014;Schaefli, 2016;Viglione et al, 2013;Euser et al, 2013;Garavaglia et al, 2017;Yadav et al, 2007;Casper et al, 2012; (99-66% flow exceedance), high flow section (15-5% flow exceedance) and highest flow section (5-0.5% flow exceedance).…”

Section: River Discharge 15mentioning

confidence: 99%

Glacio-hydrological melt and runoff modelling: a limits of acceptability framework for model selection

Mackay

Barrand

Hannah

et al. 2018

Preprint

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Abstract. Glacio-hydrological models (GHMs) underpin our understanding how future climate change will affect river flow regimes in glaciated watersheds. A variety of simplified GHM structures and parameterisations exist, yet the performance of these are rarely quantified at the process-level or with metrics beyond global summary statistics. A fuller understanding of the deficiencies in competing model structures and parameterisations and the ability of models to simulate physical processes require performance metrics utilising the full range of uncertainty information within input observations. Here, the glacio-5 hydrological characteristics of the Virkisá river basin in southern Iceland are characterised using 33 'signatures' derived from observations of ice melt, snow coverage and river discharge. The uncertainty of each set of observations are harnessed to define 'limits of acceptability' (LOA), a set of criteria used to objectively evaluate the acceptability of different GHM structures and parameterisations. This framework is used to compare and diagnose deficiencies in three melt and three runoff-routing model structures. Increased model complexity is shown to improve acceptability when evaluated against specific signatures, but does 10 not always result in better consistency across all signatures, emphasising the difficulty in appropriate model selection and the need for multi-model prediction approaches to account for model selection uncertainty. Melt and runoff-routing structures demonstrate a hierarchy of influence on river discharge signatures with melt model structure having the most influence on discharge hydrograph seasonality and runoff-routing structure on shorter-timescale discharge events. None of the tested GHM structural configurations returned acceptable simulations across the full population of signatures. The framework outlined here 15 provides a comprehensive and rigorous assessment tool for evaluating the acceptability of different GHM process hypotheses.Future melt and runoff model forecasts should seek to diagnose structural model deficiencies and evaluate diagnostic signatures of system behaviour using the LOA framework.

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Analysis of projected hydrological behavior of catchments based on signature indices

Cited by 31 publications

References 38 publications

Uncertainty in hydrological signatures

Uncertainty in hydrological signatures

Unravelling abiotic and biotic controls on the seasonal water balance using data-driven dimensionless diagnostics

Glacio-hydrological melt and runoff modelling: a limits of acceptability framework for model selection

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