---
OA_place: publisher
OA_type: free access
_id: '22508'
abstract:
- lang: eng
  text: High-resolution space-time stochastic models for precipitation are crucial
    for hydrological applications related to flood risk and water resources management.
    In this study, we present a new stochastic space-time model, STREAP, which is
    capable of reproducing essential features of the statistical structure of precipitation
    in space and time for a wide range of scales, and at the same time can be used
    for continuous simulation. The model is based on a three-stage hierarchical structure
    that mimics the precipitation formation process. The stages describe the storm
    arrival process, the temporal evolution of areal mean precipitation intensity
    and wet area, and the evolution in time of the two-dimensional storm structure.
    Each stage of the model is based on appropriate stochastic modeling techniques
    spanning from point processes, multivariate stochastic simulation and random fields.
    Details of the calibration and simulation procedures in each stage are provided
    so that they can be easily reproduced. STREAP is applied to a case study in Switzerland
    using 7 years of high-resolution (2 × 2 km2; 5 min) data from weather radars.
    The model is also compared with a popular parsimonious space-time stochastic model
    based on point processes (space-time Neyman-Scott) which it outperforms mainly
    because of a better description of spatial precipitation. The model validation
    and comparison is based on an extensive evaluation of both areal and point scale
    statistics at hydrologically relevant temporal scales, focusing mainly on the
    reproduction of the probability distributions of rainfall intensities, correlation
    structure, and the reproduction of intermittency and wet spell duration statistics.
    The results shows that a more accurate description of the space-time structure
    of precipitation fields in stochastic models such as STREAP does indeed lead to
    a better performance for properties and at scales which are not used in model
    calibration.
alternative_title:
- A Stochastic Model for Space-Time Precipitation
article_processing_charge: No
article_type: original
author:
- first_name: Athanasios
  full_name: Paschalis, Athanasios
  last_name: Paschalis
- first_name: Peter
  full_name: Molnar, Peter
  last_name: Molnar
- first_name: Simone
  full_name: Fatichi, Simone
  id: cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  last_name: Fatichi
- first_name: Paolo
  full_name: Burlando, Paolo
  last_name: Burlando
citation:
  ama: Paschalis A, Molnar P, Fatichi S, Burlando P. A stochastic model for high-resolution
    space-time precipitation simulation. <i>Water Resources Research</i>. 2013;49(12):8400-8417.
    doi:<a href="https://doi.org/10.1002/2013wr014437">10.1002/2013wr014437</a>
  apa: Paschalis, A., Molnar, P., Fatichi, S., &#38; Burlando, P. (2013). A stochastic
    model for high-resolution space-time precipitation simulation. <i>Water Resources
    Research</i>. American Geophysical Union. <a href="https://doi.org/10.1002/2013wr014437">https://doi.org/10.1002/2013wr014437</a>
  chicago: Paschalis, Athanasios, Peter Molnar, Simone Fatichi, and Paolo Burlando.
    “A Stochastic Model for High-Resolution Space-Time Precipitation Simulation.”
    <i>Water Resources Research</i>. American Geophysical Union, 2013. <a href="https://doi.org/10.1002/2013wr014437">https://doi.org/10.1002/2013wr014437</a>.
  ieee: A. Paschalis, P. Molnar, S. Fatichi, and P. Burlando, “A stochastic model
    for high-resolution space-time precipitation simulation,” <i>Water Resources Research</i>,
    vol. 49, no. 12. American Geophysical Union, pp. 8400–8417, 2013.
  ista: Paschalis A, Molnar P, Fatichi S, Burlando P. 2013. A stochastic model for
    high-resolution space-time precipitation simulation. Water Resources Research.
    49(12), 8400–8417.
  mla: Paschalis, Athanasios, et al. “A Stochastic Model for High-Resolution Space-Time
    Precipitation Simulation.” <i>Water Resources Research</i>, vol. 49, no. 12, American
    Geophysical Union, 2013, pp. 8400–17, doi:<a href="https://doi.org/10.1002/2013wr014437">10.1002/2013wr014437</a>.
  short: A. Paschalis, P. Molnar, S. Fatichi, P. Burlando, Water Resources Research
    49 (2013) 8400–8417.
das_tickbox: '1'
date_created: 2026-07-27T12:30:24Z
date_published: 2013-12-01T00:00:00Z
date_updated: 2026-08-06T08:09:42Z
day: '01'
doi: 10.1002/2013wr014437
extern: '1'
intvolume: '        49'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: ' https://doi.org/10.1002/2013WR014437'
month: '12'
oa: 1
oa_version: Published Version
page: 8400-8417
publication: Water Resources Research
publication_identifier:
  eissn:
  - 1944-7973
  issn:
  - 0043-1397
publication_status: published
publisher: American Geophysical Union
quality_controlled: '1'
scopus_import: '1'
status: public
title: A stochastic model for high-resolution space-time precipitation simulation
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 49
year: '2013'
...