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| Titel |
Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts |
| VerfasserIn |
L. Vasiliades, A. Loukas, G. Patsonas |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 9, no. 3 ; Nr. 9, no. 3 (2009-06-17), S.879-894 |
| Datensatznummer |
250006795
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| Publikation (Nr.) |
 copernicus.org/nhess-9-879-2009.pdf |
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| Zusammenfassung |
| Despite uncertainties in future climates, there is considerable evidence
that there will be substantial impacts on the environment and human
interests. Climate change will affect the hydrology of a region through
changes in the timing, amount, and form of precipitation, evaporation and
transpiration rates, and soil moisture, which in turn affect also the
drought characteristics in a region. Droughts are long-term phenomena
affecting large regions causing significant damages both in human lives and
economic losses. The most widely used approach in regional climate impact
studies is to combine the output of the General Circulation Models (GCMs)
with an impact model. The outputs of Global Circulation Model CGCMa2 were
applied for two socioeconomic scenarios, namely, SRES A2 and SRES B2 for the
assessment of climate change impact on droughts. In this study, a
statistical downscaling method has been applied for monthly precipitation.
The methodology is based on multiple regression of GCM predictant variables
with observed precipitation developed in an earlier paper (Loukas et al.,
2008) and the application of a stochastic timeseries model for precipitation
residuals simulation (white noise). The methodology was developed for
historical period (1960–1990) and validated against observed monthly
precipitation for period 1990–2002 in Lake Karla watershed, Thessaly,
Greece. The validation indicated the accuracy of the methodology and the
uncertainties propagated by the downscaling procedure in the estimation of a
meteorological drought index the Standardized Precipitation Index (SPI) at
multiple timescales. Subsequently, monthly precipitation and SPI were
estimated for two future periods 2020–2050 and 2070–2100. The results of the
present study indicate the accuracy, reliability and uncertainty of the
statistical downscaling method for the assessment of climate change on
hydrological, agricultural and water resources droughts. Results show that
climate change will have a major impact on droughts but the uncertainty
introduced is quite large and is increasing as SPI timescale increases.
Larger timescales of SPI, which, are used to monitor hydrological and water
resources droughts, are more sensitive to climate change than smaller
timescales, which, are used to monitor meteorological and agricultural
droughts. Future drought predictions should be handled with caution and
their uncertainty should always be evaluated as results demonstrate. |
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