Databased comparison of Sparse Bayesian Learning and Multiple Linear Regression for statistical downscaling of low flow indices

Joshi, Deepti; St‐Hilaire, André; Daigle, Anik; Ouarda, Taha B. M. J.

doi:10.1016/j.jhydrol.2013.02.040

Cited by 22 publications

(9 citation statements)

References 53 publications

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“…The SVM regression model is calibrated with monthly streamflow and re-analysis data for two locations in Australia. An SVM has been proven to be effective in downscaling streamflow (Ghosh and Mujumdar 2008, Joshi et al 2013, Sachindra et al 2013. Following Eghdamirad et al (2017), the SVM is used via the package "e1071" in the R computing platform using a radial basis function (RBF) as the kernel function.…”

Section: Svm Model Set-upmentioning

confidence: 99%

The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

Eghdamirad

Johnson

Kim

2019

Hydrological Sciences Journal

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Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the interdependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure.

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Section: Svm Model Set-upmentioning

confidence: 99%

The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

Eghdamirad

Johnson

Kim

2019

Hydrological Sciences Journal

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“…The variables selected as predictors for direct downscaling and the comparison of the downscaling efficiencies of DD1 and DD2 are presented in detail in Joshi et al (2013). With respect to the correlation between climate variables and selected low flow indices (Table 3), it was observed that the indices were primarily influenced by wind components (Vertical, Zonal and Meridonal) and humidity variables (Specific and Relative humidity).…”

Section: Intercomparison Of Direct Downscaling Approaches (Dd1 and Dd2)mentioning

confidence: 99%

“…The details of the results of DD have been shown in Joshi et al (2013) and are therefore briefly presented in the results section. The current study discusses the results of ID methods and their comparison with DD methods.…”

Section: Introductionmentioning

confidence: 99%

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices

Joshi

St‐Hilaire

Ouarda

et al. 2016

Hydrological Sciences Journal

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This work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability.

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“…There were no clear guidelines for selecting calibration and validation periods. In general, the training data accounted for 70–80% of the time series and the rest was used as the testing subset (Tisseuil et al ., 2010; Joshi et al ., 2013; Tofiq and Guven, 2014; Okkan and Inan, 2015). Das and Nanduri (2018), however, used 90% of the data as the training subset in order to maximize the number of data available for training the nonlinear models.…”

Section: Introductionmentioning

confidence: 99%

Direct statistical downscaling of monthly streamflow from atmospheric variables in catchments with differing contributions from snowmelt

Huang

Lawrence

Nilsen

et al. 2020

Intl Journal of Climatology

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The climate-hydrological modelling chain is the most widely used approach for assessing likely future changes in streamflow but is also associated with large uncertainties. We present an alternative approach for modelling monthly streamflow directly from large-scale atmospheric variables using statistical downscaling to streamflow for 42 catchments with flow generation regimes ranging from largely snowmelt-driven to entirely rainfall-driven. It aims to find the most appropriate combination of predictors and methods, which can best include information pertaining to snow storage and provide robust streamflow simulations under changing climate conditions. The effects of selection of probable predictors (five groups of potential variables from reanalysis data, three moving windows, calibration periods of two different lengths) and methods (eight combinations of pre-processing methods and statistical models) were considered. Results indicate that the following selection of probable predictors gave a good and robust model performance: a combination of 13 independent climate variables and their lagged information, a 3-month moving window and a calibration period of 31 years. Three combinations of methods performed best: (a) principal component analysis (PCA) and the relevance vector machine (RVM) statistical model using gridded data, (b) PCA and RVM using interpolated data and (c) the Pearson correlation analysis and RVM using interpolated data. Monthly streamflow was well reproduced for most catchments, with a median NSE (Nash and Sutcliffe efficiency) of 0.82 and PBIAS (percentage bias) of 0 in the calibration period and a median NSE over 0.60 and PBIAS less than −6 in the validation period. The best performance of the methods was associated with snow-dominated catchments in inland regions. We also established that lagged information on atmospheric variables gave better results than snow variables from reanalysis data. The poorest results were associated with rainfall-dominated catchments, and this was

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Databased comparison of Sparse Bayesian Learning and Multiple Linear Regression for statistical downscaling of low flow indices

Cited by 22 publications

References 53 publications

The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices

Direct statistical downscaling of monthly streamflow from atmospheric variables in catchments with differing contributions from snowmelt

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