Modeling zero-inflated precipitation extremes
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909814967238656 |
|---|---|
| author | Abbas, Aamar Ahmad, Touqeer Ahmad, Ishfaq |
| author_facet | Abbas, Aamar Ahmad, Touqeer Ahmad, Ishfaq |
| contents | Accurate modeling of daily rainfall, encompassing both dry and wet days as well as extreme precipitation events, is critical for robust hydrological and climatological analyses. This study proposes a zero-inflated extended generalized Pareto distribution model that unifies the modeling of dry days, low, moderate, and extreme rainfall within a single framework. Unlike traditional approaches that rely on prespecified threshold selection to identify extremes, our proposed model captures tail behavior intrinsically through a tail index that aligns with the generalized Pareto distribution. The model also accommodates covariate effects via generalized additive modeling, allowing for the representation of complex climatic variability. The current implementation is limited to a univariate setting, modeling daily rainfall independently of covariates. Model estimation is carried out using both maximum likelihood and Bayesian approaches. Simulation studies and empirical applications demonstrate the models flexibility in capturing zero inflation and heavy-tailed behavior characteristics of daily rainfall distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11058 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling zero-inflated precipitation extremes Abbas, Aamar Ahmad, Touqeer Ahmad, Ishfaq Applications Accurate modeling of daily rainfall, encompassing both dry and wet days as well as extreme precipitation events, is critical for robust hydrological and climatological analyses. This study proposes a zero-inflated extended generalized Pareto distribution model that unifies the modeling of dry days, low, moderate, and extreme rainfall within a single framework. Unlike traditional approaches that rely on prespecified threshold selection to identify extremes, our proposed model captures tail behavior intrinsically through a tail index that aligns with the generalized Pareto distribution. The model also accommodates covariate effects via generalized additive modeling, allowing for the representation of complex climatic variability. The current implementation is limited to a univariate setting, modeling daily rainfall independently of covariates. Model estimation is carried out using both maximum likelihood and Bayesian approaches. Simulation studies and empirical applications demonstrate the models flexibility in capturing zero inflation and heavy-tailed behavior characteristics of daily rainfall distributions. |
| title | Modeling zero-inflated precipitation extremes |
| topic | Applications |
| url | https://arxiv.org/abs/2504.11058 |