Catchment classification based on characterisation of streamflow and precipitation time series

Toth, Elena

doi:10.5194/hess-17-1149-2013

Cited by 94 publications

(66 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, exploring the performance of different methods on similar catchments (Sawicz et al, 2011;Toth, 2013;Patil and Stieglitz, 2011;Sivakumar et al, 2014) and finding bases for generalized guidelines on the selection of the most appropriate predictive uncertainty method in operational flood forecasting practices is also important and could be considered in the further studies as well.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dogulu

López

Solomatine

et al. 2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.

show abstract

Section: Conclusion and Recommendationsmentioning

confidence: 99%

“…In this respect comparing different methods, which are often developed and tested in isolation, receives the attention of researchers, e.g. as suggested within the HEPEX framework (see van Andel et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dogulu

López

Solomatine

et al. 2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…In the recent years, SOMs were also successfully applied for catchments classification either based on geo-morphoclimatic descriptors (Hall and Minns, 1999;Hall et al, 2002;Srinivas et al, 2008;Di Prinzio et al, 2011) or based on hydrological signatures (Chang et al, 2008;Ley et al, 2011;Toth, 2013); however, it is important to underline that the clustering is not carried out here in order to identify a pooling group of similar catchments for developing a region- specific model, but for the optimal division of the available data for the parameterisation and independent testing of a single model to be applied over the entire study area.…”

Section: Identification Of Balanced Cross-validation Subsets With Sommentioning

confidence: 99%

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Toth

2016

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. In many real-world flood forecasting systems, the runoff thresholds for activating warnings or mitigation measures correspond to the flow peaks with a given return period (often 2 years, which may be associated with the bankfull discharge). At locations where the historical streamflow records are absent or very limited, the threshold can be estimated with regionally derived empirical relationships between catchment descriptors and the desired flood quantile. Whatever the function form, such models are generally parameterised by minimising the mean square error, which assigns equal importance to overprediction or underprediction errors.Considering that the consequences of an overestimated warning threshold (leading to the risk of missing alarms) generally have a much lower level of acceptance than those of an underestimated threshold (leading to the issuance of false alarms), the present work proposes to parameterise the regression model through an asymmetric error function, which penalises the overpredictions more.The estimates by models (feedforward neural networks) with increasing degree of asymmetry are compared with those of a traditional, symmetrically trained network, in a rigorous cross-validation experiment referred to a database of catchments covering the country of Italy. The analysis shows that the use of the asymmetric error function can substantially reduce the number and extent of overestimation errors, if compared to the use of the traditional square errors. Of course such reduction is at the expense of increasing underestimation errors, but the overall accurateness is still acceptable and the results illustrate the potential value of choosing an asymmetric error function when the consequences of missed alarms are more severe than those of false alarms.

show abstract

“…The K-means algorithm is described in more detail by Hartigan [31] and Hartigan and Wong [32]. In hydrology, the K-means algorithm and its variants have been used primarily in the regionalization of watersheds [33][34][35][36][37]. In this study, K-means analysis is performed for predefined cluster numbers varying from 5 to 15, where 15 is the maximum number of groups that maintains sufficient sample sizes in each group.…”

Section: K-means Clusteringmentioning

confidence: 99%

Water Level Fluctuations in the Congo Basin Derived from ENVISAT Satellite Altimetry

Becker

Silva²,

Calmant

et al. 2014

Remote Sensing

View full text Add to dashboard Cite

Abstract:In the Congo Basin, the elevated vulnerability of food security and the water supply implies that sustainable development strategies must incorporate the effects of climate change on hydrological regimes. However, the lack of observational hydro-climatic data over the past decades strongly limits the number of studies investigating the effects of climate change in the Congo Basin. We present the largest altimetry-based dataset of water levels ever constituted over the entire Congo Basin. This dataset of water levels illuminates the hydrological regimes of various tributaries of the Congo River. A total of 140 water level time series are extracted using ENVISAT altimetry over the period of 2003 to 2009. To improve the understanding of the physical phenomena dominating the region, we perform a K-means cluster analysis of the altimeter-derived river level height variations to identify groups of hydrologically similar catchments. This analysis reveals nine distinct hydrological regions. The proposed regionalization scheme is validated and therefore considered reliable for estimating monthly water level variations in the Congo Basin. This result confirms the potential of satellite altimetry in monitoring spatio-temporal water OPEN ACCESSRemote Sens. 2014, 6 9341 level variations as a promising and unprecedented means for improved representation of the hydrologic characteristics in large ungauged river basins.

show abstract

Catchment classification based on characterisation of streamflow and precipitation time series

Cited by 94 publications

References 49 publications

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Water Level Fluctuations in the Congo Basin Derived from ENVISAT Satellite Altimetry

Contact Info

Product

Resources

About