Estimating flood quantiles at ungauged sites using nonparametric regression methods with spatial components
| Autor: | Shabnam Mostofi Zadeh, Fahim Ashkar, Martin Durocher, Donald H. Burn |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
bepress|Engineering|Civil and Environmental Engineering|Other Civil and Environmental Engineering
010504 meteorology & atmospheric sciences Flood myth Flood frequency analysis bepress|Engineering EarthArXiv|Engineering|Civil and Environmental Engineering Generalized additive model EarthArXiv|Engineering 0207 environmental engineering Local regression 02 engineering and technology 01 natural sciences Nonparametric regression bepress|Engineering|Civil and Environmental Engineering EarthArXiv|Engineering|Civil and Environmental Engineering|Other Civil and Environmental Engineering 13. Climate action Kriging Statistics 020701 environmental engineering 0105 earth and related environmental sciences Water Science and Technology Mathematics Quantile |
| Zdroj: | Hydrological Sciences Journal. 64:1056-1070 |
| ISSN: | 2150-3435 0262-6667 |
| DOI: | 10.1080/02626667.2019.1620952 |
| Popis: | Prediction of flood quantiles at ungauged sites has been investigated using several nonparametric regression methods including: local regression based on regions of influence, neural networks and generalized additive models (GAM). These methods were used to describe the relationship between run-off variables and catchment descriptors to predict flood quantiles. Previous work reported the presence of spatial correlation in the residuals for these models. To this end, this study proposes and investigates ways of incorporating spatial components. An L-moments regression technique (LRT) is developed to predict L-moments of target sites and flood quantiles are derived by aggregating quantiles from multiple candidate distributions. The predictive power of the proposed methods is evaluated on a large database of Canadian rivers using cross-validation. The results are examined inside different provinces and hydrological regions to assess the behaviour of the methods. The results show that GAM and local regression using respectively thin plate spline and kriging provide the best predictive powers among the considered methods. Additionally, the LRT method is found to improve prediction power over the well-known index-flood model and has similar results to quantile regression techniques (QRT) when using the same nonparametric regression approaches. |
| Databáze: | OpenAIRE |
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