 |
| Titel |
Stochastic generation of annual, monthly and daily climate data: A review |
| VerfasserIn |
R. Srikanthan, T. A. McMahon |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 5, no. 4 ; Nr. 5, no. 4, S.653-670 |
| Datensatznummer |
250002715
|
| Publikation (Nr.) |
 copernicus.org/hess-5-653-2001.pdf |
|
|
|
|
|
| Zusammenfassung |
| The generation of
rainfall and other climate data needs a range of models depending on the time
and spatial scales involved. Most of the models used
previously do not take into account year to year variations in the model
parameters. Long periods of wet and dry years were observed
in the past but were not taken into account. Recently, Thyer and Kuczera (1999)
developed a hidden state Markov model to account for the
wet and dry spells explicitly in annual rainfall. This review looks firstly at
traditional time series models and then at the more complex models
which take account of the pseudo-cycles in the data. Monthly rainfall data have
been generated successfully by using the method of fragments.
The main criticism of this approach is the repetitions of the same yearly
pattern when only a limited number of years of historical data
are available. This deficiency has been overcome by using synthetic fragments
but this brings an additional problem of generating the right
number of months with zero rainfall. Disaggregation schemes are effective in
obtaining monthly data but the main problem is the large number
of parameters to be estimated when dealing with many sites. Several
simplifications have been proposed to overcome this problem. Models
for generating daily rainfall are well developed. The transition probability
matrix method preserves most of the characteristics of daily,
monthly and annual characteristics and is shown to be the best performing model.
The two-part model has been shown by many researchers to
perform well across a range of climates at the daily level but has not been
tested adequately at monthly or annual levels. A shortcoming
of the existing models is the consistent underestimation of the variances of the
simulated monthly and annual totals. As an alternative,
conditioning model parameters on monthly amounts or perturbing the model
parameters with the Southern Oscillation Index (SOI)
result in better agreement between the variance of the simulated and observed
annual rainfall and these approaches should be investigated further.
As climate data are less variable than rainfall, but are correlated among
themselves and with rainfall, multisite-type models have been used
successfully to generate annual data. The monthly climate data can be obtained
by disaggregating these annual data. On a daily time step at
a site, climate data have been generated using a multisite type model
conditional on the state of the present and previous days. The generation
of daily climate data at a number of sites remains a challenging problem. If
daily rainfall can be modelled successfully by a censored
power of normal distribution then the model can be extended easily to generate
daily climate data at several sites simultaneously. Most
of the early work on the impacts of climate change used historical data adjusted
for the climate change. In recent studies, stochastic daily
weather generation models are used to compute climate data by adjusting the
parameters appropriately for the future climates assumed. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|