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Large-domain multisite precipitation generation: Operational blueprint and demonstration for 1,000 sites

Lookup NU author(s): Dr Francesco Serinaldi



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Stochastic simulations of spatiotemporal patterns of hydroclimatic processes, such as precipitation, are needed to build alternative but equally plausible inputs for water-related design and management, and to estimate uncertainty and assess risks. However, while existing stochastic simulation methods are mature enough to deal with relatively small domains and coarse spatiotemporal scales, additional work is required to develop simulation tools for large-domain analyses, which are more and more common in an increasingly interconnected world. This study proposes a methodological advancement in the so-called CoSMoS framework, which is a flexible simulation framework preserving arbitrary marginal distributions and correlations, to dramatically decrease the computational burden and make the algorithm fast enough to perform large-domain simulations in relatively short time. The proposed approach focuses on correlated processes with mixed (zero-inflated) Uniform marginal distributions. These correlated processes act as intermediates between the target process to simulate (precipitation) and parent Gaussian processes that are the core of the simulation algorithm. Working in the mixed-Uniform space enables us to make a substantial simplification of the so-called correlation transformation functions, which represent a computational bottle neck in the original CoSMoS formulation. As a proof of concept, we simulate 40 years of daily precipitation records from 1000 gauging stations in the Mississippi River basin. Moreover, we extend CoSMoS incorporating parent non-Gaussian processes with different degrees of tail dependence and suggest potential improvements including the separate simulation of occurrence and intensity processes, advection, anisotropy, and nonstationary spatiotemporal correlation functions.

Publication metadata

Author(s): Papalexiou SM, Serinaldi F, Clark MP

Publication type: Article

Publication status: Published

Journal: Water Resources Research

Year: 2023

Volume: 59

Issue: 3

Online publication date: 07/03/2023

Acceptance date: 02/03/2023

Date deposited: 03/04/2023

ISSN (print): 0043-1397

ISSN (electronic): 1944-7973

Publisher: Wiley-Blackwell Publishing, Inc.


DOI: 10.1029/2022WR034094


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Funder referenceFunder name
Natural Sciences and Engineering Research Council of Canada