Hydrological Drought Class Transition Using SPI and SRI Time Series by Loglinear Regression

Loglinear models for three-dimensional contingency tables was used with data from 21 rainfall stations and 7 hydrometric stations in the Luanhe river basin, northeast China, for short term prediction of drought severity class. Loglinear models were fitted to drought class transitions derived from standardized precipitation index (SPI) and standardized runoff index (SRI) time series to find which series was more suitable for hydrological drought class prediction 1 and 2 months ahead, respectively. Expected frequencies for two consecutive transitions between drought classes were first calculated, and based on this the predicted drought classes 1 and 2 months ahead were obtained. The results showed that despite the contingency tables of drought class transitions presented the maintenance of the precedent drought class, results of three-dimensional loglinear modeling presented good results when comparing predicted and observed drought classes. Only for a few cases predictions did not fully match the observed drought class, mainly for 2-month lead and when the SRI values are near the limit of the severity class predicted by SRI time series. Based on the correlation analysis of SPI and SRI, we presented the well-known method of hydrological drought class prediction by SPI time series. It was found that, using loglinear regression method, the accuracy of predictions for 2-month lead predicted by SPI time series was higher than those predicted by SRI time series. When we divided the SPI and SRI time series into 2 sub-periods (pre- and post-1980 where land cover changed), we got the same drought class prediction as that predicted by the entire SPI and SRI time series, which illustrated that changes in land use did not affect predictions of hydrological drought classes in the Luanhe river basin. It could be concluded that loglinear prediction of drought class transitions is a useful tool for short term hydrological drought warning, and the results could provide significant information for water resources managers and policy makers to mitigate drought effects.