Spatial variability of precipitation extremes over Italy using a fine-resolution gridded product

Moccia, Benedetta; Papalexiou, S.; Russo, Fabio; Napolitano, Francesco

doi:10.1016/j.ejrh.2021.100906

Cited by 15 publications

(11 citation statements)

References 46 publications

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“…For example, precipitation amounts do not follow the Gaussian distribution. Typical models used include the exponential [28], gamma [29][30][31], lognormal [32][33][34], Weibull [28], generalized extreme value (GEV) [35][36][37][38][39][40], and Pareto distributions [41]. The optimal model depends on the geographical location, the climate zone, as well as the analyzed temporal and spatial scale [3,21,27].…”

Section: Methodsmentioning

confidence: 99%

Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes

Agou

Pavlides

Hristopulos

2022

Entropy

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Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Herein we propose a data-driven model of precipitation amount which employs a novel, data-driven (non-parametric) implementation of warped Gaussian processes. We investigate the proposed warped Gaussian process regression (wGPR) using (i) a synthetic test function contaminated with non-Gaussian noise and (ii) a reanalysis dataset of monthly precipitation from the Mediterranean island of Crete. Cross-validation analysis is used to establish the advantages of non-parametric warping for the interpolation of incomplete data. We conclude that wGPR equipped with the proposed data-driven warping provides enhanced flexibility and—at least for the cases studied– improved predictive accuracy for non-Gaussian data.

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Section: Methodsmentioning

confidence: 99%

Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes

Agou

Pavlides

Hristopulos

2022

Entropy

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“…Finally, the block and POT maxima methods are not free of limitations. For example, convergence to these liming laws is not guaranteed and in many cases, the estimated parameters indicate that extremes have an upper bound which might lead in underestimating risk (e.g., Moccia et al., 2021).…”

Section: New System Of Probability Distributionsmentioning

confidence: 99%

Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling

Papalexiou

2022

Water Resources Research

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What elements should a parsimonious model reproduce at a single scale to precisely simulate rainfall at many scales? We posit these elements are: (a) the probability of dry and linear correlation structure of the wet/dry sequence as a proxy reproducing the distribution of wet/dry spells, and (b) the marginal distribution of nonzero rainfall and its correlation structure. We build a two‐state rainfall model, the CoSMoS‐2s, that explicitly reproduces these elements and is easily applicable at any timescale. Additionally, the paper: (a) introduces the Generalized Exponential (scriptGscriptE $\mathcal{G}\mathcal{E}$) distribution system comprising six flexible distributions with desired properties to describe nonzero rainfall and facilitate time series generation; (b) extends the CoSMoS framework to allow simulations with negative correlations; (c) simplifies the generation of binary sequences with any correlation structure by analytical approximations; (d) introduces the rank‐based CoSMoS‐2s that preserves Spearman's correlations, has an analytical formulation, and is also applicable for infinite variance time series, (e) introduces the copula‐based CoSMoS‐2s enabling intermittent times series generation with nonzero values having the dependence structure of any desired copula, and (f) offers conceptual generalizations for rainfall modeling and beyond, with specific ideas for future improvements and extensions. The CoSMoS‐2s is tested using four long hourly rainfall records; the simulations reproduce rainfall properties at multiple scales including the wet/dry spells, probability of dry, characteristics of nonzero rainfall, and the behavior of extremes.

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“…In recent years, many authors assessed the potentiality and reliability of satellite data to estimate IDF curves. Satellite products are increasingly used by the scientific community in hydrological applications and extreme events characterization (e.g., Moccia et al [38]). Gridded products are now available at the global scale with various temporal resolutions based on different data sources (e.g., ground observations, satellites, radar, and reanalysis) and data-merging methods.…”

Section: Challenges In Idf Curve Definitionmentioning

confidence: 99%

Intensity–Duration–Frequency Curves in a Data-Rich Era: A Review

Lanciotti

Ridolfi

Russo

et al. 2022

Water

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Intensity–duration–frequency (IDF) curves are widely used in the hydrological design of hydraulic structures. This paper presents a wide review of methodologies for constructing IDF curves with a specific focus on the choice of the dataset type, highlighting the main characteristics, possible uncertainties, and benefits that can be derived from their application. A number of studies based on updating IDFs in relation to climate change are analyzed. The research was based on a comprehensive analysis of more than 100 scientific papers and reports, of which 80 were found to be suitable for the aim of this study. To classify the articles, the key was mainly intensity–duration–frequency curves in relation to the types of datasets most used for their construction, specific attention was paid to the case study area. The paper aims to answer the following research questions. (i) What is the contribution of a data-rich era? (ii) Are remotely sensed data reliable to build IDFs in ungauged or partially gauged watersheds? (ii) How is uncertainty dealt with when developing IDFs? Remotely sensed data appear to be an alternative to rain-gauge data in scarcely gauged or ungauged areas; however, rain-gauge data are still a preferred dataset in the development of IDFs. The main aim of the present work is to provide an overview of the state of the art on the use of different types of data to build IDFs. The paper is intended to support the inclusion of different data types in hydrological applications.

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Spatial variability of precipitation extremes over Italy using a fine-resolution gridded product

Cited by 15 publications

References 46 publications

Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes

Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes

Rainfall Generation Revisited: Introducing CoSMoS‐2s and Advancing Copula‐Based Intermittent Time Series Modeling

Intensity–Duration–Frequency Curves in a Data-Rich Era: A Review

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