A Procedure for Combining Improved Correlated Sampling Methods and a Resampling Strategy to Generate a Multi-Site Conditioned Streamflow Process

Wang, Quansen; Zhou, Jianzhong; Huang, Kangdi; Dai, Ling; Jia, Benjun; Chen, Lu; Hui, Qing

doi:10.1007/s11269-021-02769-8

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Wang, Zhou, et al. (2021) combined correlated sampling methods and a resampling strategy to generate multisite conditioned streamflow series.…”

Section: Introductionmentioning

confidence: 99%

“…Porto et al (2021) presented a multisite annual streamflow generation model that combines the Generalized Linear Model (GLM), for determining the temporal structure, with copulas, for modeling the spatial dependence joint distributions. Wang, Zhou, et al (2021) combined correlated sampling methods and a resampling strategy to generate multisite conditioned streamflow series.…”

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confidence: 99%

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Development of a Stepwise‐Clustered Multi‐Catchment Hydrological Model for Quantifying Interactions in Regional Climate‐Runoff Relationships

Wang

Huang

et al. 2022

Water Resources Research

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The concurrent variations of multi‐catchment runoffs exist widely in natural hydrological systems. Approaching such variations requires integrated analyses of not only the climate‐runoff relationships within individual catchments but also the distributive interactions among multiple catchments. In this study, a stepwise‐clustered multi‐catchment hydrological model (SCMW) is proposed to tackle the interactive relationships among multi‐catchment runoffs and their concurrent variations within a watershed system. Through multivariate inference based on Wilks likelihood ratio criterions and F tests, the proposed model can deal with both continuous and discrete variables as well as nonlinear relations among multiple variables without the assumptions of functional relationships. The proposed SCMW is applied to the Iskut‐Stikine Watershed, Canada. The effects of multiple uncertain factors are traced through multilevel factorial analysis. Overall, the accuracies of SCMW‐simulated mean and interval flows demonstrate that the developed method can well reproduce the distributive and interactive relationships between climatic variables and multi‐catchment runoffs. At the same time, the contributions of climate variables can be quantified; for example, it is found that near‐surface minimum temperature (at Below Johnson Station) can explain 25.6% concurrent variations of multi‐catchment runoffs, and vapor pressure (at Below Johnson Station) and precipitation (at Telegraph Creek Station) can explain 17.5% and 4.6% of such variations, respectively. The results of the multilevel factorial analysis indicate that the uncertainties in the simulated runoff levels are mainly from the modeling approach (43.3%); also, significant effects exist from interactions among multiple impact factors. The molding of concurrent variations of multi‐catchment runoffs through SCMW is helpful for improving simulation accuracy for watersheds with spatially heterogeneous climate‐runoff relationships.

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“…Wang, Zhou, et al. (2021) combined correlated sampling methods and a resampling strategy to generate multisite conditioned streamflow series.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%