---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Nadav
      foaf_name: Peleg, Nadav
      foaf_surname: Peleg
  - foaf_Person:
      foaf_givenName: Peter
      foaf_name: Molnar, Peter
      foaf_surname: Molnar
  - foaf_Person:
      foaf_givenName: Paolo
      foaf_name: Burlando, Paolo
      foaf_surname: Burlando
  - foaf_Person:
      foaf_givenName: Simone
      foaf_name: Fatichi, Simone
      foaf_surname: Fatichi
      foaf_workInfoHomepage: http://www.librecat.org/personId=cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  bibo_doi: 10.1016/j.jhydrol.2019.02.010
  bibo_volume: 571
  dct_date: 2019^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0022-1694
  - http://id.crossref.org/issn/1879-2707
  dct_language: eng
  dct_publisher: Elsevier@
  dct_subject:
  - Weather generator
  - Stochastic downscaling
  - Climate change
  - Internal climate variability
  - Climate uncertainty
  - High-resolution rainfall model
  dct_title: Exploring stochastic climate uncertainty in space and time using a gridded
    hourly weather generator@
...