---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Athanasios
      foaf_name: Paschalis, Athanasios
      foaf_surname: Paschalis
  - foaf_Person:
      foaf_givenName: Peter
      foaf_name: Molnar, Peter
      foaf_surname: Molnar
  - foaf_Person:
      foaf_givenName: Simone
      foaf_name: Fatichi, Simone
      foaf_surname: Fatichi
      foaf_workInfoHomepage: http://www.librecat.org/personId=cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  - foaf_Person:
      foaf_givenName: Paolo
      foaf_name: Burlando, Paolo
      foaf_surname: Burlando
  bibo_doi: 10.1002/2013wr014437
  bibo_issue: '12'
  bibo_volume: 49
  dct_date: 2013^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0043-1397
  - http://id.crossref.org/issn/1944-7973
  dct_language: eng
  dct_publisher: American Geophysical Union@
  dct_title: A stochastic model for high-resolution space-time precipitation simulation@
...