Statistical downscaling of river flows

Tisseuil, Clément; Vrac, Mathieu; Lek, Sovan; Wade, Andrew J.

doi:10.1016/j.jhydrol.2010.02.030

Cited by 109 publications

(74 citation statements)

References 64 publications

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“…For instance, standard global and regional climate models do not simulate realistic runoff (Flato et al, 2013;Materia et al, 2010;Tisseuil et al, 2010), and do not simulate sea surges. Here, our model can be used to downscale these contributing variables, e.g.…”

Section: Datasetmentioning

confidence: 99%

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Bevacqua

Maraun

Haff

et al. 2017

Hydrol. Earth Syst. Sci.

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266

304

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Abstract. Compound events (CEs) are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint -dependent -occurrence causes an extreme impact. Conventional univariate statistical analysis cannot give accurate information regarding the multivariate nature of these events. We develop a conceptual model, implemented via pair-copula constructions, which allows for the quantification of the risk associated with compound events in present-day and future climate, as well as the uncertainty estimates around such risk. The model includes predictors, which could represent for instance meteorological processes that provide insight into both the involved physical mechanisms and the temporal variability of compound events. Moreover, this model enables multivariate statistical downscaling of compound events. Downscaling is required to extend the compound events' risk assessment to the past or future climate, where climate models either do not simulate realistic values of the local variables driving the events or do not simulate them at all. Based on the developed model, we study compound floods, i.e. joint storm surge and high river runoff, in Ravenna (Italy). To explicitly quantify the risk, we define the impact of compound floods as a function of sea and river levels. We use meteorological predictors to extend the analysis to the past, and get a more robust risk analysis. We quantify the uncertainties of the risk analysis, observing that they are very large due to the shortness of the available data, though this may also be the case in other studies where they have not been estimated. Ignoring the dependence between sea and river levels would result in an underestimation of risk; in particular, the expected return period of the highest compound flood observed increases from about 20 to 32 years when switching from the dependent to the independent case.

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Section: Datasetmentioning

confidence: 99%

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Bevacqua

Maraun

Haff

et al. 2017

Hydrol. Earth Syst. Sci.

Self Cite

266

304

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“…These NCEP/NCAR reanalysis data (available from 1948 till today) are outputs of a GCM, corrected and quality controlled at several stages (Kalnay et al, 1996). Many authors have used the NCEP/NCAR reanalysis data set for the calibration and validation of their downscaling models in the past (Tripathi et al, 2006;Anandhi et al, 2008Anandhi et al, , 2009Ghosh and Mujumdar, 2008;Tisseuil et al, 2010). The NCEP/NCAR reanalysis data are treated as an output of an ideal GCM (Cannon and Whitfield, 2002).…”

Section: Study Area and Datamentioning

confidence: 99%

“…Cheng et al (2008) applied MLR to downscale daily mean surface wind speed, mean sea-level pressure and minimum/maximum/mean temperatures. GAMs, generalized linear models, aggregated boosted trees and ANN were used for predicting daily streamflows by Tisseuil et al (2010). Ghosh and Mujumdar (2008) implemented SVM and relevance vector machine to predict monthly streamflows.…”

Section: Introductionmentioning

confidence: 99%

“…Direct downscaling of GCM predictors to streamflows using statistical downscaling methods allows for the forecasting of flows over larger regions under changing climate (Tisseuil et al, 2010). In direct downscaling of GCM predictors to streamflows, influences of land use, soil cover and groundwater storage on streamflow are not considered (Xu, 1999), and it permits to make a quick estimate of streamflow.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows

Sachindra

Huang

Barton

et al. 2012

Intl Journal of Climatology

111

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This study employed least square support vector machine regression (LS-SVM-R) and multi-linear regression (MLR) for statistically downscaling monthly general circulation model (GCM) outputs directly to monthly catchment streamflows. The scope of the study was limited to calibration and validation of the downscaling models. The methodology was demonstrated by its application to a streamflow site in the Grampian water supply system in northwestern Victoria, Australia. Probable predictors for the study were selected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set based on the past literature and hydrology. Probable variables that displayed the best significant correlations, consistently with the streamflows over the entire period of the study and under three 20-year time slices (1950-1969, 1970-1989 and 1990-2010) were selected as potential predictors. To better capture seasonal variations of streamflows, downscaling models were developed for each calendar month. The standardized potential predictors were introduced to the LS-SVM-R and MLR models, starting from the best correlated three and then, others one by one, based on their correlations with the streamflows, until the model performance in validation was maximized. This stepwise model development enabled the identification of the optimum number of potential variables for each month. The model calibration was performed over the period 1950-1989 and validation was done for 1990-2010. LS-SVM-R model parameter optimization was achieved using simplex algorithm and leave-one-out cross-validation. The MLR models were optimized by minimizing the sum of squared errors. In both modelling techniques, validation was performed as an independent simulation. In calibration, LS-SVM-R and MLR models displayed equally good performances with a trend of under-predicting high flows. During validation, LS-SVM-R outperformed MLR, though both techniques over-predicted most of the streamflows. It was concluded that LS-SVM-R is a better technique for statistically downscaling GCM outputs to streamflows than MLR, but still MLR is a potential technique for the same task.

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“…The main approach generally consists of linking a large-scale predictor from the GCM to a fine-scale variable (von Storch, 1995(von Storch, , 1999. It is even possible to directly develop a statistical relationship between large-scale variables from a GCM, and river flow (Tisseuil et al, 2010). Weather generators (Dubrovský et al, 2004) are also often used.…”

Section: Introductionmentioning

confidence: 99%

Comparison of past and future Mediterranean high and low extremes of precipitation and river flow projected using different statistical downscaling methods

Quintana-Seguí

Habets

Martin

2011

Nat. Hazards Earth Syst. Sci.

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Abstract. The extremes of precipitation and river flow obtained using three different statistical downscaling methods applied to the same regional climate simulation have been compared. The methods compared are the anomaly method, quantile mapping and a weather typing. The hydrological model used in the study is distributed and it is applied to the Mediterranean basins of France. The study shows that both quantile mapping and weather typing methods are able to reproduce the high and low precipitation extremes in the region of interest. The study also shows that when the hydrological model is forced with these downscaled data, there are important differences in the outputs. This shows that the model amplifies the differences and that the downscaling of other atmospheric variables might be very relevant when simulating river discharges. In terms of river flow, the method of the anomalies, which is very simple, performs better than expected. The methods produce qualitatively similar future scenarios of the extremes of river flow. However, quantitatively, there are still significant differences between them for each individual gauging station. According to these scenarios, it is expected that in the middle of the 21st century (2035-2064), the monthly low flows will have diminished almost everywhere in the region of our study by as much as 20 %. Regarding high flows, there will be important increases in the area of the Cévennes, which is already seriously affected by flash-floods. For some gauging stations in this area, the frequency of what was a 10-yr return flood at the end of the 20th century is expected to increase, with such return floods then occurring every two years in the middle Correspondence to: P. Quintana-Seguí (pquintana@obsebre.es) of the 21st century. Similarly, the 10-yr return floods at that time are expected to carry 100 % more water than the 10-yr return floods experienced at the end of the 20th century. In the northern part of the Rhône basin, these extremes will be reduced.

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Statistical downscaling of river flows

Cited by 109 publications

References 64 publications

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy)

Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows

Comparison of past and future Mediterranean high and low extremes of precipitation and river flow projected using different statistical downscaling methods

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