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On temporal stochastic modeling of precipitation, nesting models across scales

On temporal stochastic modeling of precipitation, nesting models across scales
On temporal stochastic modeling of precipitation, nesting models across scales
We analyze the performance of composite stochastic models of temporal precipitation which can satisfactorily reproduce precipitation properties across a wide range of temporal scales. The rationale is that a combination of stochastic precipitation models which are most appropriate for specific limited temporal scales leads to better overall performance across a wider range of scales than single models alone. We investigate different model combinations. For the coarse (daily) scale these are models based on Alternating renewal processes, Markov chains, and Poisson cluster models, which are then combined with a microcanonical Multiplicative Random Cascade model to disaggregate precipitation to finer (minute) scales. The composite models were tested on data at four sites in different climates. The results show that model combinations improve the performance in key statistics such as probability distributions of precipitation depth, autocorrelation structure, intermittency, reproduction of extremes, compared to single models. At the same time they remain reasonably parsimonious. No model combination was found to outperform the others at all sites and for all statistics, however we provide insight on the capabilities of specific model combinations. The results for the four different climates are similar, which suggests a degree of generality and wider applicability of the approach.
multiplicative random cascade, poisson cluster model, stochastic rainfall models
0309-1708
152-166
Paschalis, Athanasios
e7626e9f-172b-4da2-882c-bddb219f3fb6
Molnar, Peter
99f2d15c-5348-4c80-bb35-fb54c133862d
Fatichi, Simone
2a12468d-8094-495b-922d-4d00aa0afb11
Burlando, Paolo
5484fcec-b4d3-45e9-a72c-206ccbb5265f
Paschalis, Athanasios
e7626e9f-172b-4da2-882c-bddb219f3fb6
Molnar, Peter
99f2d15c-5348-4c80-bb35-fb54c133862d
Fatichi, Simone
2a12468d-8094-495b-922d-4d00aa0afb11
Burlando, Paolo
5484fcec-b4d3-45e9-a72c-206ccbb5265f

Paschalis, Athanasios, Molnar, Peter, Fatichi, Simone and Burlando, Paolo (2014) On temporal stochastic modeling of precipitation, nesting models across scales. Advances in Water Resources, 63, 152-166. (doi:10.1016/j.advwatres.2013.11.006).

Record type: Article

Abstract

We analyze the performance of composite stochastic models of temporal precipitation which can satisfactorily reproduce precipitation properties across a wide range of temporal scales. The rationale is that a combination of stochastic precipitation models which are most appropriate for specific limited temporal scales leads to better overall performance across a wider range of scales than single models alone. We investigate different model combinations. For the coarse (daily) scale these are models based on Alternating renewal processes, Markov chains, and Poisson cluster models, which are then combined with a microcanonical Multiplicative Random Cascade model to disaggregate precipitation to finer (minute) scales. The composite models were tested on data at four sites in different climates. The results show that model combinations improve the performance in key statistics such as probability distributions of precipitation depth, autocorrelation structure, intermittency, reproduction of extremes, compared to single models. At the same time they remain reasonably parsimonious. No model combination was found to outperform the others at all sites and for all statistics, however we provide insight on the capabilities of specific model combinations. The results for the four different climates are similar, which suggests a degree of generality and wider applicability of the approach.

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More information

Accepted/In Press date: 20 November 2013
e-pub ahead of print date: 28 November 2013
Published date: January 2014
Keywords: multiplicative random cascade, poisson cluster model, stochastic rainfall models
Organisations: Water & Environmental Engineering Group

Identifiers

Local EPrints ID: 385306
URI: http://eprints.soton.ac.uk/id/eprint/385306
ISSN: 0309-1708
PURE UUID: 4d9ca911-f6ee-4b33-a171-9fad31a179b7
ORCID for Athanasios Paschalis: ORCID iD orcid.org/0000-0003-4833-9962

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Date deposited: 18 Jan 2016 16:51
Last modified: 14 Mar 2024 22:14

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Contributors

Author: Athanasios Paschalis ORCID iD
Author: Peter Molnar
Author: Simone Fatichi
Author: Paolo Burlando

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