Calibration of hydrological models in the spectral domain: An opportunity for scarcely gauged basins?

Montanari, Alberto; Toth, Elena

doi:10.1029/2006wr005184

Cited by 96 publications

(104 citation statements)

References 35 publications

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“…To tackle this issue, recent studies (De Thomasis and Grimaldi, 2001;Chiang et al, 2002;Grimaldi, 2004;Corduas, 2011) proposed to analyze the streamflow temporal dynamics through the parameter sets of linear models estimated on the corresponding streamflow time series. A more parsimonious, but less refined and necessarily approximated, approach is applied here for representing the autocorrelation structure: in addition to the lag-1 autocorrelation coefficient (previously used in regionalisation studies, for example, by Montanari and Toth (2007);Castiglioni et al (2010); Lombardi et al (2012) for the parameterisation of a rainfall-runoff model), it is here proposed to use an index representing the shape of the ACF, i.e. the correlation scaling exponent.…”

Section: E Toth: Catchment Classificationmentioning

confidence: 99%

Catchment classification based on characterisation of streamflow and precipitation time series

Toth

2013

Hydrol. Earth Syst. Sci.

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Abstract. The formulation of objective procedures for the delineation of homogeneous groups of catchments is a fundamental issue in both operational and research hydrology. For assessing catchment similarity, a variety of hydrological information may be considered; in this paper, gauged sites are characterised by a set of streamflow signatures that include a representation, albeit simplified, of the properties of fine time-scale flow series and in particular of the dynamic components of the data, in order to keep into account the sequential order and the stochastic nature of the streamflow process.The streamflow signatures are provided in input to a clustering algorithm based on unsupervised SOM neural networks, obtaining groups of catchments with a clear hydrological distinctiveness, as highlighted by the identification of the main patterns of the input variables in the different classes and the interpretation of their interrelations. In addition, even if no geographical, morphological nor climatological information is provided in input to the SOM network, the clusters exhibit an overall consistency as far as location, altitude and precipitation regime are concerned.In order to assign ungauged sites to such groups, the catchments are represented through a parsimonious set of morphometric and pluviometric variables, including also indexes that attempt to synthesise the variability and correlation properties of the precipitation time series, thus providing information on the type of weather forcing that is specific to each basin. Following a principal components analysis, needed for synthesizing and better understanding the morpho-pluviometric catchment properties, a discriminant analysis finally assigns the ungauged catchments, through a leave-one-out cross validation, to one of the above identified hydrologic response classes. The approach delivers a quite satisfactory identification of the membership of ungauged catchments to the streamflow-based classes, since the comparison of the two cluster sets shows a misclassification rate of around 20 %.Overall results indicate that the inclusion of information on the properties of the fine time-scale streamflow and rainfall time series may be a promising way for better representing the hydrologic and climatic character of the study catchments.

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Section: E Toth: Catchment Classificationmentioning

confidence: 99%

Catchment classification based on characterisation of streamflow and precipitation time series

Toth

2013

Hydrol. Earth Syst. Sci.

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“…Calibration approaches that do not rely on direct time-series versus time-series comparison are useful in such situations. Prior approaches to model calibration without direct time series comparison include calibration to spectral properties (Montanari and Toth, 2007), recession curves (Winsemius et al, 2009), slope of the flow-duration curve (Yadav et al, 2007;Yilmaz et al, 2008), base-flow index (Bulygina et al, 2009) and the use of a performance measure based on specified exceedance percentages of a synthetic regional flow-duration curve (FDC) for calibration at un-gauged sites (Yu and Yang, 2000). However, in these studies uncertainties in observed discharge are not considered explicitly.…”

Section: Introductionmentioning

confidence: 99%

Calibration of hydrological models using flow-duration curves

Westerberg

Guerrero

Younger

et al. 2011

Hydrol. Earth Syst. Sci.

244

243

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Abstract. The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested -based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WAS-MOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time Correspondence to: I. K. Westerberg (ida.westerberg@hyd.uu.se) series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

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“…Whittle's likelihood (Whittle, 1953) (15) is a frequency-based approximation of the Gaussian likelihood and can be interpreted as minimum distance estimate of the distance between the parametric spectral density and the (nonparametric) periodogram. It also minimizes the asymptotic KullbackeLeibler divergence and, for autoregressive processes, provides asymptotically consistent estimates for Gaussian and non-Gaussian data, even in the presence of longrange dependence (Montanari and Toth, 2007). Likelihood function 16, also referred to as Laplace or double exponential distribution, differs from all other likelihood functions in that it assumes a [ 1 -norm of the error residuals.…”

Section: Input Argument 2: Dreamparmentioning

confidence: 99%

Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation

Vrugt

2016

Environmental Modelling & Software

619

532

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Calibration of hydrological models in the spectral domain: An opportunity for scarcely gauged basins?

Cited by 96 publications

References 35 publications

Catchment classification based on characterisation of streamflow and precipitation time series

Catchment classification based on characterisation of streamflow and precipitation time series

Calibration of hydrological models using flow-duration curves

Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation

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