Numerical Modelling of Extreme Precipitation Events

This report was produced for the Urban Water Research Association of Australia, a now discontinued research program.

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Report no. UWRAA 131

December 1997

Background

The project investigates whether numerical models of the atmosphere may be used as tools for quantitative precipitation forecasting, over catchment size regions, for extreme precipitation events.

The concept of PMP, or Probable Maximum Precipitation, is used by hydrologists and meteorologists involved in the design of structures, such as dams or bridges, where the need is to compute extreme rainfall (and hence flood) events. The concept is that the PMP is an upper bound for rainfall at a site. It is thus seen as a single deterministic number (governed by physical principles) that would never be exceeded. However, there have been several occasions on which observed rainfalls have exceeded PMP estimates valid at the time; the occurrence of such events led to a re-evaluation of the PMP methodologies and resulted in the development of the generalised techniques that are now used throughout Australia.

Study Conclusions

Five conclusions with implications for the estimation of PMP may be drawn from the work presented in this report.

1. As the moisture availability is increased the precipitation efficiency of the storms does not change significantly. For each case study presented, the production of heavy rainfall (rainfall rates greater than 25 mm hr-1) is between 80% and100% efficient. This supports the simple model that assumes implicitly that extreme precipitation storms have the highest efficiency.

2. As moisture availability is increased the duration of the heavy rainfall increases, i.e. it begins earlier and is more continuous. Life cycles are not considered in the simple model; however, the results presented here and the recent paper of Zhao et al. (1997) suggest that the duration of the storm increases as the moisture availability increases. An increase in the duration of heavy rainfall will result in higher total rainfall.

3. As moisture availability changes, the spatial distribution of the area over which more than 50% of the total rainfall occurs as heavy rainfall changes. Zhao et al. (1997) also found that the areal coverage of rainfall varies nonlinearly with the precipitable water.

4. The control simulation may be thought of as giving the depth-area curve for the actual storm, while the enhanced-moisture simulation provides the depth-area curves for a storm maximised by the moisture while conserving its dynamic integrity. The enhanced moisture storm is associated with a moisture-adjustment factor and the current PMP methodology would multiply the depth-area curves of the control simulation by this factor. If the depth-area curve for the increased moisture simulation lies above that of the control simulation, then the maximisation relationship of the current PMP technique under-estimates the precipitation simulated by the model. The simulations reported here indicate that this may occur and hence the precipitation is not linearly related to the precipitable water. Where this was the case, for the case studies presented in Section 3,the model produces between 15% and 35% more precipitation than the current storm maximisation technique for areas of 50 to 70 km2. For areas of500 km2, the model produces between 5% and 15% more precipitation than the current storm maximisation technique.

5. The topography affects the distribution of the “convergence component” of the precipitation due to feedback effects to the dynamics of the storm system.

Despite these deficiencies in the assumptions used to estimate PMP, we believe that there is no operational replacement available at present for the current PMP methodology. However, improvements in the estimation of PMP may soon be possible if increased effort is placed on (amongst other things) the numerical modelling of extreme rainfall events. These improvements are only possible if the results of these efforts are communicated to, and accepted by, the hydrological community.

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