Modeling dependencies between climate extremes is important for climate risk assessment,
for instance when allocating emergency management funds. In statistics, multivariate …

Using potentially best available rainfall data sets for the entire country of Japan (spatial
scales of 1‐and 20‐km), we analyze the 1–24 hr and city‐scale (1–400 km2) extreme …

High-resolution gridded rainfall products at sub-daily and 10 0 km scales are required for
hydrological applications in mountainous and urban catchments. As most catchments are …

Space–Time Characteristics of Areal Reduction Factors and Rainfall Processes in: Journal
of Hydrometeorology Volume 21 Issue 4 (2020) Jump to Content Jump to Main Navigation …

Classical models for multivariate or spatial extremes are mainly based upon the
asymptotically justified max-stable or generalized Pareto processes. These models are …

The areal reduction factor (ARF) is a concept widely used in many hydrological applications.
In this paper, assuming a “storm-centered” approach, new empirical laws for ARF estimation …

Areal reduction factors (ARFs) convert a point estimate of extreme precipitation to an
estimate of extreme precipitation over a spatial domain, and are commonly used in flood risk …

We propose a new class of models for variable clustering called Asymptotic Independent
block (AI-block) models, which defines population-level clusters based on the independence …

Aiming to deliver improved precipitation simulations for hydrological impact assessment
studies, we develop a methodology for modelling and simulating high-dimensional spatial …

The task of simplifying the complex spatio-temporal variables associated with climate
modeling is of utmost importance and comes with significant challenges. In this research …