Comparison of mapping approaches for estimating extreme precipitation of any return period at ungauged locations

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Reliable estimation of return period values of extreme precipitation at ungauged locations is considered to be a key exercise in hydrometeorological studies. This study aims to identify an accurate approach in producing spatial maps (i.e. ungauged estimation) of extreme precipitation for any return period within a region. The study compares the following approaches: interpolation of summary of data as represented by L-moments, interpolation of parameters of an extreme value distribution and interpolation of return period quantile value. Several interpolation schemes are considered; however, the aim is to evaluate schemes that employ secondary data. The schemes compared are ordinary kriging, kriging with external drift (KED) and a more traditional, inverse distance weighting. The secondary data namely elevation, satellite based mean annual precipitation (MAP), distance from nearest coast (CD) and geographical coordinates are incorporated in the KED system. Annual maximum 1-day precipitation series at 76 gauging stations from the region of East China have been used to assess the performance. The generalized extreme value (GEV) distribution, appropriate for the study region, with the method of L-moments is used to analyze the frequency of extreme precipitation. It is found that either the approach of interpolating parameters of GEV distribution or L-moments should be the natural choice for estimating design value at any return period. However, in terms of error statistics the approach of interpolating parameters has given a lower RMSE value compared to the approach of interpolating L-moments. The approach of quantile interpolation performed worst and should not be used in practice in interpolating return period values. The KED is recognized as the most appropriate interpolation scheme when a significant covariate is identified. The MAP appears to be a suitable covariate in most cases when interpolating L moments (1st and 2nd L-moment) or GEV parameters (location and scale parameter). There is no spatial dependence identified for L-skewness or shape parameter of GEV distribution and in the future one should concentrate on how a superior spatial model can be identified in this context.