Estimation of Continuous Streamflow in Ontario Ungauged Basins: Comparison of Regionalization Methods

Samuel, Jos; Coulibaly, Paulin; Metcalfe, Robert A.

doi:10.1061/(asce)he.1943-5584.0000338

Cited by 202 publications

(160 citation statements)

References 37 publications

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“…Hence, the density of hydrometric gauging stations may affect the performance of the method as the heterogeneity in runoff response may increase as the distance between the donor and recipient catchments increases. Among others, Oudin et al (2008), Zhang et al (2009) andSamuel et al (2011) used distances between catchment centroids; other distances (see Gottschalk et al, 2011) can also be used. This method is based on either a single-donor nearest neighbor catchment (e.g.…”

Section: Regionalization Methodsmentioning

confidence: 99%

“…Merz and Blöschl, 2004;Parajka et al, 2005) or multi-donor nearby catchments (e.g. Parajka et al, 2005;Oudin et al, 2008;Samuel et al, 2011;Arsenault and Brissette, 2014).…”

Section: Regionalization Methodsmentioning

confidence: 99%

“…Kokkonen et al, 2003;McIntyre et al, 2005;Parajka et al, 2005;Oudin et al, 2008;Reichl et al, 2009;Zhang and Chiew, 2009;Samuel et al, 2011;Arsenault and Brissette, 2014), which is based on the assumption that similarity in some physical attributes that govern the runoff response could explain the hydrological similarity. This method is also based on either a single-donor catchment or multi-donor catchments.…”

Section: Regionalization Methodsmentioning

confidence: 99%

“…Several previous studies (e.g. Croke and McIntyre, 2013;Yadav et al, 2007;Oudin et al, 2008;Samuel et al, 2011;Kim and Kaluarachchi, 2008) noted the importance of considering the representativeness or quality of input climate data for PUB.…”

Section: Factors That Influence Regionalization Performancementioning

confidence: 99%

“…the HBV model (e.g. Siebert, 1999;Merz and Blöschl, 2004;Parajka et al, 2005;Götzinger and Bárdossy, 2007;Samuel et al, 2011), the Probability Distributed Model (PDM; Moore, 1985) and its variants (e.g. McIntyre et al, 2005;Zhang and Chiew, 2009;Pechlivanidis et al, 2010).…”

Section: Factors That Influence Regionalization Performancementioning

confidence: 99%

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Evaluation of Regionalization Methods for Hourly Continuous Streamflow Simulation Using Distributed Models in Boreal Catchments

et al. 2015

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Regionalization for prediction in ungauged basins at hourly resolution is important for water resources management (e.g. floods and hydropeaking). In this paper, calibration of 26 catchments (39-3090 km 2 ) in mid-Norway was performed using hourly records and three spatially distributed (1x1 km 2 ) precipitation-runoff models: a first-order nonlinear system model (hereafter Kirchmod), the HBV model and the Basic-Grid-Model (BGM). Four regionalization methods for each model namely parameter set yielding maximum regional weighted average (MRWA) performance measures (PM), regional median of optimal parameters (RMedP), nearest neighbor (NN) and physical similarity (PS) were evaluated and compared with three benchmarks. Parameter transfer from best regional donor (BRD) and from an ideal best arbitrary single-donor (BASD), and local calibration (LC) were as benchmarks. The physical similarity attributes include hypsometric curves (PSH), land use (PSL), drainage density (PSD), catchment area (PSA), terrain slope (PSS), bedrock geology (PSR), soil type (PSSO) and combination of all (PSC).Comprehensive evaluation of single-and multi-donors, simple benchmarks and more advanced regionalization methods using multi-models, two PM and their statistical evaluation indicate that the identification of regionalization methods is dependent on the models, the PM and their statistical evaluation. In general, the PSH, PSC and BRD methods performed better for the Nash-Sutcliffe efficiency (NSE) based on boxplots and regional median values of both the NSE and relative deterioration or improvement of the NSE from the local calibration due to the regionalization. The methods also performed better for the individual catchments. The PSS, RMedP, MRWA and BRD methods performed better for the log-transformed streamflow 2 (NSEln) based on the same evaluation criteria. Similar performance to the more advanced regionalization methods of transfer of homogeneous parameter sets across the whole region from the BRD for both NSE and NSEln indicate the potential of the simple regionalization approach for predicting runoff response in the region.

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Section: Regionalization Methodsmentioning

confidence: 99%

“…Merz and Blöschl, 2004;Parajka et al, 2005) or multi-donor nearby catchments (e.g. Parajka et al, 2005;Oudin et al, 2008;Samuel et al, 2011;Arsenault and Brissette, 2014).…”

Section: Regionalization Methodsmentioning

confidence: 99%

Section: Regionalization Methodsmentioning

confidence: 99%

Section: Factors That Influence Regionalization Performancementioning

confidence: 99%

Section: Factors That Influence Regionalization Performancementioning

confidence: 99%

See 3 more Smart Citations

Evaluation of Regionalization Methods for Hourly Continuous Streamflow Simulation Using Distributed Models in Boreal Catchments

et al. 2015

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CRDEMO: Combined regionalization and dual entropy-multiobjective optimization for hydrometric network design

2013

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[1] Establishing an adequate hydrometric network to provide accurate and reliable continuous flow information for various users remains a major challenge. This includes the design of new networks or the evaluation of existing networks. This study proposes a combined regionalization and dual entropy-multiobjective optimization (CRDEMO) method for determining minimum network that meets the World Meteorology Organization (WMO) standards, which is considered herein an optimal minimum network. A regionalization approach is used to estimate flows in potential locations for new additional stations, and a dual entropy-multiobjective optimization approach is used to identify optimal trade-offs between the maximum possible information content and the minimum shared information among the stations. The method was examined in two Canadian River basins, namely, the St. John River and St. Lawrence River basins. The St. John River basin has a higher network density compared to the St. Lawrence River basin. Results of the analysis indicate that the St. Lawrence River basin requires a high number of new additional stations to meet the WMO minimum network density. It was also determined that existing stations do not have a significant influence in determining the locations of new stations in the St. Lawrence River basin. Conversely, however, in the St. John River basin, existing stations have significant influence on the determination of locations of new stations due to the higher number of existing stations. Overall, the CRDEMO technique is shown to be robust for designing optimal minimum hydrometric networks and can be a useful tool for evaluating current and proposed networks.Citation: Samuel, J., P. Coulibaly, and J. Kollat (2013), CRDEMO: Combined regionalization and dual entropy-multiobjective optimization for hydrometric network design, Water Resour. Res., 49,[8070][8071][8072][8073][8074][8075][8076][8077][8078][8079][8080][8081][8082][8083][8084][8085][8086][8087][8088][8089]

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Continuous streamflow prediction in ungauged basins: The effects of equifinality and parameter set selection on uncertainty in regionalization approaches

Arsenault

Brissette

2014

Water Resources Research

126

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This paper focuses on evaluating the uncertainty of three common regionalization methods for predicting continuous streamflow in ungauged basins. A set of 268 basins covering 1.6 million km 2 in the province of Quebec was used to test the regionalization strategies. The multiple linear regression, spatial proximity, and physical similarity approaches were evaluated on the catchments using a leave-one-out cross-validation scheme. The lumped conceptual HSAMI hydrological model was used throughout the study. A bootstrapping method was chosen to further estimate uncertainty due to parameter set selection for each of the parameter set/regionalization method pairs. Results show that parameter set selection can play an important role in regionalization method performance depending on the regionalization methods (and their variants) used and that equifinality does not contribute significantly to the overall uncertainty witnessed throughout the regionalization methods applications. Regression methods fail to consistently assign behavioral parameter sets to the pseudoungauged basins (i.e., the ones left out). Spatial proximity and physical similarity score better, the latter being the best. It is also shown that combining either physical similarity or spatial proximity with the multiple linear regression method can lead to an even more successful prediction rate. However, even the best methods were shown to be unreliable to an extent, as successful prediction rates never surpass 75%. Finally, this paper shows that the selection of catchment descriptors is crucial to the regionalization strategies' performance and that for the HSAMI model, the optimal number of donor catchments for transferred parameter sets lies between four and seven.

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Estimation of Continuous Streamflow in Ontario Ungauged Basins: Comparison of Regionalization Methods

Cited by 202 publications

References 37 publications

Evaluation of Regionalization Methods for Hourly Continuous Streamflow Simulation Using Distributed Models in Boreal Catchments

Evaluation of Regionalization Methods for Hourly Continuous Streamflow Simulation Using Distributed Models in Boreal Catchments

CRDEMO: Combined regionalization and dual entropy-multiobjective optimization for hydrometric network design

Continuous streamflow prediction in ungauged basins: The effects of equifinality and parameter set selection on uncertainty in regionalization approaches

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