Options
Unified theory for stochastic modelling of hydroclimatic processes: preserving marginal distributions, correlation structures, and intermittency
Publikationstyp
Journal Article
Date Issued
2018-05-01
Sprache
English
Author(s)
Journal
Volume
115
Start Page
234
End Page
252
Citation
Advances in water resources 115: 234-252 (2018)
Publisher DOI
Scopus ID
Publisher
Elsevier
Hydroclimatic processes come in all “shapes and sizes”. They are characterized by different spatiotemporal correlation structures and probability distributions that can be continuous, mixed-type, discrete or even binary. Simulating such processes by reproducing precisely their marginal distribution and linear correlation structure, including features like intermittency, can greatly improve hydrological analysis and design. Traditionally, modelling schemes are case specific and typically attempt to preserve few statistical moments providing inadequate and potentially risky distribution approximations. Here, a single framework is proposed that unifies, extends, and improves a general-purpose modelling strategy, based on the assumption that any process can emerge by transforming a specific “parent” Gaussian process. A novel mathematical representation of this scheme, introducing parametric correlation transformation functions, enables straightforward estimation of the parent-Gaussian process yielding the target process after the marginal back transformation, while it provides a general description that supersedes previous specific parameterizations, offering a simple, fast and efficient simulation procedure for every stationary process at any spatiotemporal scale. This framework, also applicable for cyclostationary and multivariate modelling, is augmented with flexible parametric correlation structures that parsimoniously describe observed correlations. Real-world simulations of various hydroclimatic processes with different correlation structures and marginals, such as precipitation, river discharge, wind speed, humidity, extreme events per year, etc., as well as a multivariate example, highlight the flexibility, advantages, and complete generality of the method.
Subjects
Humidity
Hydroclimatic processes
Parent-Gaussian framework
Precipitation
River discharge
Stochastic modelling
Temperature
Transformations
Weather generator
Wind speed
DDC Class
551: Geology, Hydrology Meteorology