---
res:
  bibo_abstract:
  - 'Extreme rainfall is quantified in engineering practice using Intensity–Duration–Frequency
    curves (IDF) that are traditionally derived from rain-gauges and more recently
    also from remote sensing instruments, such as weather radars. These instruments
    measure rainfall at different spatial scales: rain-gauge samples rainfall at the
    point scale while weather radar averages precipitation on a relatively large area,
    generally around 1 km2. As such, a radar derived IDF curve is representative of
    the mean areal rainfall over a given radar pixel and neglects the within-pixel
    rainfall variability. In this study, we quantify subpixel variability of extreme
    rainfall by using a novel space–time rainfall generator (STREAP model) that downscales
    in space the rainfall within a given radar pixel. The study was conducted using
    a unique radar data record (23 years) and a very dense rain-gauge network in the
    Eastern Mediterranean area (northern Israel). Radar–IDF curves, together with
    an ensemble of point-based IDF curves representing the radar subpixel extreme
    rainfall variability, were developed fitting Generalized Extreme Value (GEV) distributions
    to annual rainfall maxima. It was found that the mean areal extreme rainfall derived
    from the radar underestimate most of the extreme values computed for point locations
    within the radar pixel (on average, ∼70%). The subpixel variability of rainfall
    extreme was found to increase with longer return periods and shorter durations
    (e.g. from a maximum variability of 10% for a return period of 2 years and a duration
    of 4 h to 30% for 50 years return period and 20 min duration). For the longer
    return periods, a considerable enhancement of extreme rainfall variability was
    found when stochastic (natural) climate variability was taken into account. Bounding
    the range of the subpixel extreme rainfall derived from radar–IDF can be of major
    importance for different applications that require very local estimates of rainfall
    extremes.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Nadav
      foaf_name: Peleg, Nadav
      foaf_surname: Peleg
  - foaf_Person:
      foaf_givenName: Francesco
      foaf_name: Marra, Francesco
      foaf_surname: Marra
  - 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: Athanasios
      foaf_name: Paschalis, Athanasios
      foaf_surname: Paschalis
  - foaf_Person:
      foaf_givenName: Peter
      foaf_name: Molnar, Peter
      foaf_surname: Molnar
  - foaf_Person:
      foaf_givenName: Paolo
      foaf_name: Burlando, Paolo
      foaf_surname: Burlando
  bibo_doi: 10.1016/j.jhydrol.2016.05.033
  bibo_volume: 556
  dct_date: 2018^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:
  - Extreme rainfall variability
  - High resolution rainfall modeling
  - IDF curves
  - Precipitation downscaling
  - Subpixel scale
  - Weather radar
  dct_title: Spatial variability of extreme rainfall at radar subpixel scale@
...