Drought Modeling Based on SPI Index using Satellite and Ground station Data via the Integrated GPR-CEEMD Model


Water Department, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran


Drought is one of the most important problems which affects agriculture section and water resources. Nowadays, the use of the remote sensing technique has been considered as useful tool for drought monitoring. This study aimed to predict the temporal drought using ground station and TRMM3B43 satellite data between the years of 1998-2017. Therefore, precipitation data were first converted to the SPI index, and then, using the intelligent Gaussian Process Regression (GPR) method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), Tabriz drought was investigated. Different models were defined and the impact of different input parameters were assessed. It was observed that the rainfall amounts from the TRMM satellite in the monthly scale had a good correlation with the Tabriz station precipitation and the results of drought analysis using satellite data were almost similar with ground station data. The obtained results proved the high capability and efficiency of the applied method in predicting the SPI drought index and it was observed that time series decomposition based on the complementary ensemble empirical mode decomposition led to more accurate outcomes. The input data decomposition increased the predictive accuracy by approximately 30 to 40 percent. It was observed that in prediction of drought the climatic elements including mean monthly temperature and relative humidity, as well as SPI indexes related to the previous months, were effective and by climatic parameters eliminating, the modeling error increased up to 15-20%. Also, the results of sensitivity analysis showed that SPIt-1 is the most effective parameter in modeling.


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