Downscaling of Regional Climate Model Precipitation for Urban ...

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11 th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008 RESULTS Figures 3-5 show some examples of results from the simple downscaling scheme, for the Kalmar gauge (nr. 1 in Figure 1b). The parameters were roughly optimised by trial-and-error and the final values obtained were cl=8% and tl=0.18 mm. All results have been averaged over 10 realizations from the stochastic scheme, to obtain stable estimates Figure 3 shows the result in terms of IDF curves, return periods 15 (top left), 5 (top right), 3 (bottom left) and 1 (bottom right) year(s). As expected, the short-term extremes in the grid box averages are substantially lower than in the point observations for short durations. For the maximum duration considered (1 day), however, the grid box values are in fact rather close to the observed IDF curves. Thus, at a daily scale the local (i.e. grid box) precipitation extremes simulated by the RCA3 model appear to well agree with point observations, which indicates that the RCA3 precipitation is realistic with respect to local extremes. Overall, the IDF curves obtained from the downscaled time series very well reproduce the observed curves, for all durations and return periods considered. There is a weak tendency towards underestimation, but only with a small amount. Figure 3. IDF curves for Kalmar from observed point series (solid), RCA3 grid box average series (dashed) and downscaled point series (dotted). 4 Downscaling of climate model precipitation
11 th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008 Figure 4a shows the result in terms of four general statistical properties of the time series: (1) mean 30-min volume (mm), (2) standard deviation (mm), (3) mean non-zero 30-min volume (mm) and (4) probability of non-zero 30-min volume (-). For the grid box averages, the nonzero probability is substantially higher than in the observations. A higher value is expected for spatial averages than for point values, but the difference is likely exacerbated by the overestimated frequency of wet 30-min periods in the RCA3 data discussed above. The mean 30-min volume is also overestimated in the grid box averages but to a lesser degree. In the downscaled series, all properties are close to the observed. Figure 4b shows the autocorrelation function for lags 1-6. In the observations, the autocorrelation is ~0.5 for lag 1 and then decreases to ~0.25 for lag 6. For the time series of grid box averages, the value at lag 1 is ~0.8 and at lag 6 ~0.6. It is expected to have a higher autocorrelation in spatial averages, but also in this case the difference compared with the point observations is probably amplified by the inaccuracies in the RCA3 data. The erroneously generated precipitation in the RCA3 model often occurs in the form of long, uninterrupted sequences of similar (small) values, which increases autocorrelation. The autocorrelations in the downscaled series does not perfectly reproduce the shape of the observed function, but overall the level is very reasonable. a b Figure 4. General descriptive statistics (a) and autocorrelation functions (b) for Kalmar from observed point series (black bar, solid line), RCA3 grid box average series (grey bar, dashed line) and downscaled point series (white bar, dotted line). Figure 5 shows the result in terms of some event-related properties, mean and standard deviation of event duration (Figure 5a), event volume (Figure 5b) and length of dry period between events (Figure 5c). As expected from the results shown in Figures 3 and 4, and the associated discussion, as compared with point observations the events are longer and the dry periods shorter in the time series of grid box averages. The mean event volume is, however, well reproduces. After downscaling, also the properties of event durations are well reproduced. The dry periods lengths become somewhat overestimated though, possibly because the thresholding removes also some minor precipitation events that should have been regarded as “true”. a b c Figure 5. Statistical properties of event duration (a), event volume (b) and dry period length (c) for Kalmar from observed point series (black), RCA3 grid box average series (grey) and downscaled point series (white). Olsson et al. 5
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