---
OA_type: closed access
_id: '22551'
abstract:
- lang: eng
  text: Exploring the effects of climate change on the hydrological response at the
    local scale requires climate data at high spatial and temporal resolutions. This
    is best achieved by generating downscaled ensembles of future climate variables
    derived from climate models. For this purpose we present a methodology to re-parameterize
    the AWE-GEN-2d model (Advanced WEather GENerator for a two-dimensional grid).
    The model simulates key meteorological variables needed by hydrological models
    and is particularly suitable to explore the effects of stochastic (natural) climatic
    uncertainty, which is fundamental for hydrological applications, especially at
    sub-kilometer and hourly scales. Factors of change for different climate statistics
    are calculated from climate model simulations of present and future climates and
    subsequently applied to the statistics derived from observations to re-parameterize
    AWE-GEN-2d. The model abilities in generating an ensemble of future climate variables
    for the transient period 2020–2089 is presented with examples of precipitation
    and near-surface air temperature fields from hourly to multi-annual scales for
    a small mountainous region in the Swiss Alps. The stochastic uncertainty is examined
    for present and future periods and for spatial scales from the RCM scale (12-km,
    daily) to 2-km demonstrating the potential use of AWE-GEN-2d outputs. At the RCM
    scale, model results yield a small increase in annual precipitation (4%) which
    is within the stochastic uncertainty range for present and future periods (7%).
    At the fine scale of 2-km, the increase in annual precipitation can exceed the
    stochastic uncertainty, but for less than 10% of the domain area. On the contrary,
    changes in annual near-surface air temperature exceed stochastic uncertainty both
    at the RCM and finer scales. Stochastic climate uncertainty was concluded to be
    very similar when comparing present and future periods and 12-km and 2-km scales.
    The benefits of using AWE-GEN-2d in hydrological climate change impact assessments
    are finally discussed.
article_processing_charge: No
article_type: original
author:
- first_name: Nadav
  full_name: Peleg, Nadav
  last_name: Peleg
- first_name: Peter
  full_name: Molnar, Peter
  last_name: Molnar
- first_name: Paolo
  full_name: Burlando, Paolo
  last_name: Burlando
- first_name: Simone
  full_name: Fatichi, Simone
  id: cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  last_name: Fatichi
citation:
  ama: Peleg N, Molnar P, Burlando P, Fatichi S. Exploring stochastic climate uncertainty
    in space and time using a gridded hourly weather generator. <i>Journal of Hydrology</i>.
    2019;571:627-641. doi:<a href="https://doi.org/10.1016/j.jhydrol.2019.02.010">10.1016/j.jhydrol.2019.02.010</a>
  apa: Peleg, N., Molnar, P., Burlando, P., &#38; Fatichi, S. (2019). Exploring stochastic
    climate uncertainty in space and time using a gridded hourly weather generator.
    <i>Journal of Hydrology</i>. Elsevier. <a href="https://doi.org/10.1016/j.jhydrol.2019.02.010">https://doi.org/10.1016/j.jhydrol.2019.02.010</a>
  chicago: Peleg, Nadav, Peter Molnar, Paolo Burlando, and Simone Fatichi. “Exploring
    Stochastic Climate Uncertainty in Space and Time Using a Gridded Hourly Weather
    Generator.” <i>Journal of Hydrology</i>. Elsevier, 2019. <a href="https://doi.org/10.1016/j.jhydrol.2019.02.010">https://doi.org/10.1016/j.jhydrol.2019.02.010</a>.
  ieee: N. Peleg, P. Molnar, P. Burlando, and S. Fatichi, “Exploring stochastic climate
    uncertainty in space and time using a gridded hourly weather generator,” <i>Journal
    of Hydrology</i>, vol. 571. Elsevier, pp. 627–641, 2019.
  ista: Peleg N, Molnar P, Burlando P, Fatichi S. 2019. Exploring stochastic climate
    uncertainty in space and time using a gridded hourly weather generator. Journal
    of Hydrology. 571, 627–641.
  mla: Peleg, Nadav, et al. “Exploring Stochastic Climate Uncertainty in Space and
    Time Using a Gridded Hourly Weather Generator.” <i>Journal of Hydrology</i>, vol.
    571, Elsevier, 2019, pp. 627–41, doi:<a href="https://doi.org/10.1016/j.jhydrol.2019.02.010">10.1016/j.jhydrol.2019.02.010</a>.
  short: N. Peleg, P. Molnar, P. Burlando, S. Fatichi, Journal of Hydrology 571 (2019)
    627–641.
das_tickbox: '1'
date_created: 2026-07-27T12:30:24Z
date_published: 2019-04-01T00:00:00Z
date_updated: 2026-08-06T08:45:41Z
day: '01'
doi: 10.1016/j.jhydrol.2019.02.010
extern: '1'
intvolume: '       571'
keyword:
- Weather generator
- Stochastic downscaling
- Climate change
- Internal climate variability
- Climate uncertainty
- High-resolution rainfall model
language:
- iso: eng
month: '04'
oa_version: None
page: 627-641
publication: Journal of Hydrology
publication_identifier:
  eissn:
  - 1879-2707
  issn:
  - 0022-1694
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Exploring stochastic climate uncertainty in space and time using a gridded
  hourly weather generator
type: journal_article
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 571
year: '2019'
...