Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter
Non-stationary flood frequency analysis (NFFA) plays an important role in addressing the issue of the stationary assumption (independent and identically distributed flood series) that is no longer valid in infrastructure-designed methods. This confirms the necessity of developing new statistical mod...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
IWA Publishing
2020
|
| Subjects: |
| _version_ | 1796865293050445824 |
|---|---|
| author | Mat Jan, Nur Amalina Shabri, Ani Samsudin, Ruhaidah |
| author_facet | Mat Jan, Nur Amalina Shabri, Ani Samsudin, Ruhaidah |
| author_sort | Mat Jan, Nur Amalina |
| collection | ePrints |
| description | Non-stationary flood frequency analysis (NFFA) plays an important role in addressing the issue of the stationary assumption (independent and identically distributed flood series) that is no longer valid in infrastructure-designed methods. This confirms the necessity of developing new statistical models in order to identify the change of probability functions over time and obtain a consistent flood estimation method in NFFA. The method of Trimmed L-moments (TL-moments) with time covariate is confronted with the L-moment method for the stationary and non-stationary generalized extreme value (GEV) models. The aims of the study are to investigate the behavior of the proposed TL-moments method in the presence of NFFA and applying the method along with GEV distribution. Comparisons of the methods are made by Monte Carlo simulations and bootstrap-based method. The simulation study showed the better performance of most levels of TL-moments method, which is TL(η,0), (η = 2, 3, 4) than the L-moment method for all models (GEV1, GEV2, and GEV3). The TL-moment method provides more efficient quantile estimates than other methods in flood quantiles estimated at higher return periods. Thus, the TL-moments method can produce better estimation results since the L-moment eliminates lowest value and gives more weight to the largest value which provides important information. |
| first_indexed | 2024-03-05T20:54:47Z |
| format | Article |
| id | utm.eprints-91769 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:54:47Z |
| publishDate | 2020 |
| publisher | IWA Publishing |
| record_format | dspace |
| spelling | utm.eprints-917692021-07-28T08:42:43Z http://eprints.utm.my/91769/ Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter Mat Jan, Nur Amalina Shabri, Ani Samsudin, Ruhaidah QA Mathematics Non-stationary flood frequency analysis (NFFA) plays an important role in addressing the issue of the stationary assumption (independent and identically distributed flood series) that is no longer valid in infrastructure-designed methods. This confirms the necessity of developing new statistical models in order to identify the change of probability functions over time and obtain a consistent flood estimation method in NFFA. The method of Trimmed L-moments (TL-moments) with time covariate is confronted with the L-moment method for the stationary and non-stationary generalized extreme value (GEV) models. The aims of the study are to investigate the behavior of the proposed TL-moments method in the presence of NFFA and applying the method along with GEV distribution. Comparisons of the methods are made by Monte Carlo simulations and bootstrap-based method. The simulation study showed the better performance of most levels of TL-moments method, which is TL(η,0), (η = 2, 3, 4) than the L-moment method for all models (GEV1, GEV2, and GEV3). The TL-moment method provides more efficient quantile estimates than other methods in flood quantiles estimated at higher return periods. Thus, the TL-moments method can produce better estimation results since the L-moment eliminates lowest value and gives more weight to the largest value which provides important information. IWA Publishing 2020 Article PeerReviewed Mat Jan, Nur Amalina and Shabri, Ani and Samsudin, Ruhaidah (2020) Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter. Journal of Water and Climate Change, 11 (4). pp. 966-979. ISSN 2040-2244 http://dx.doi.org/10.2166/wcc.2019.055 |
| spellingShingle | QA Mathematics Mat Jan, Nur Amalina Shabri, Ani Samsudin, Ruhaidah Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter |
| title | Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter |
| title_full | Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter |
| title_fullStr | Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter |
| title_full_unstemmed | Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter |
| title_short | Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter |
| title_sort | handling non stationary flood frequency analysis using tl moments approach for estimation parameter |
| topic | QA Mathematics |
| work_keys_str_mv | AT matjannuramalina handlingnonstationaryfloodfrequencyanalysisusingtlmomentsapproachforestimationparameter AT shabriani handlingnonstationaryfloodfrequencyanalysisusingtlmomentsapproachforestimationparameter AT samsudinruhaidah handlingnonstationaryfloodfrequencyanalysisusingtlmomentsapproachforestimationparameter |