Application of multi-Site stochastic daily Climate Generation to assess the
Impact of Climate Change in the eastern Seaboard of Thailand
Werapol Bejranonda and Manfred Koch
Department of Geohydraulics and Engineering Hydrology, University of Kassel
In the assessment of climate change impacts on future meteorological and/or hydrological regimes,
downscaling of large-scale climate/weather variables from GCMs or RCMs is widely applied. Since
the daily climate can describe the particular impact related to shorter-term behavior, e.g., storms and
floods, that monthly and seasonal statistics cannot achieve, a projection of climate variables on a daily
scale is required in many impact studies. However, the direct use of daily climate predictions from one
GCM is usually not reliable enough to represent the full variability of the climate variable's time series, namely, extreme behavior. Notwithstanding that daily GCM climate predictor variables are available for some models, the downscaling outcome is very sensitive to small changes in the daily predictor. For this reason, downscaling of monthly predictor data may be more recommendable, as it is
more reliable. However, the subsequent step to generate daily series from this downscaled monthly
climate series becomes then more tricky. Here we present a novel "daily-climate-regenerationapproach" to do this properly.
The regeneration of daily climate series which is able to reflect the full variability of a climate variable
on a daily scale, while still respecting its intra-month variability, is essentially done by re-sampling
the monthly series on a daily scale. This is achieved by using various stochastic techniques which basically synthesize daily climate from several ensembles of daily series which respect the relevant statistical attributes of the monthly climate series, such as its probability distribution, as well as mean and
variance. This multi-realization of synthetic daily climate can exhibit a broad spectrum of climate variability that can be useful in a practical climate assessment, as this approach provides also some uncertainty measure.
The daily weather (climate) -generator (DWG) proposed here processes the daily precipitation (PCP)and temperature- (T) series separately, wherefore for PCP both the monthly downscaled rainfall intensity and the probability of rainfall occurrence and for T the monthly temperatures are employed. In
addition, the observed daily time series of precipitation and temperatures are used to define the basic
statistical parameters of the respective climate variable's distribution. During this generation process,
firstly, the sequences of rainfall occurrence, either wet or dry state (%Wet) on each day, are synthesized. Then, secondly, the amount of daily rainfall is generated on the synthetic wet days. Afterwards,
the maximum and minimum temperatures are generated, whereby, depending of the rainfall state, i.e.,
wet or dry days, two different temperature distributions are used.
One particularly unique feature of the DWG proposed here is that it can, unlike other methods, which
are usually applied to a single-site weather station, also be applied to a multi-site climate station network across a region, taking into consideration the spatial correlation of the observed weather pattern
between the individual stations. Doing so results in a better representation of the regional climate, than
would possible by a single-site weather-generation approach.
This novel multi-site daily weather generator (DWG) is applied to climate time series' of daily and
monthly rainfall and maximum and minimum temperatures observed at a station network with 24 rainfall- and 4 temperature sites in the Eastern Seaboard industrial zone of Thailand (EST) between 19712000. More specifically, the method is calibrated for the 1971-1985 time period and validated during
1986-2000, by examining the synthetically generated data for %Wet, PCP, Tmax and Tmin against the
respective observed ones. A good agreement between the distributions of the two is obtained. Moreover, the analyses of the extremes of the daily synthetic and observed rainfalls result, for the 100-year
return period - critical values, in a bias of only 15% for the simulated one.
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Finally, the novel DWG is combined with a multiple-linear regression (MLR) downscaling method to
downscale years 1971-2000 monthly GCM-model climate (past) predictions in the study region; i.e.
first, spatially, with MLR, on the monthly scale, and then, temporarily, with DWG, to daily scale. The
results show that, compared with the well-known classical SDSM and LARS-WG downscaling models, this combined MLR+DWG method shows the best performance in downscaling daily climate.