Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.
In this paper, spatiotemporal analysis of groundwater level fluctuations of 32 piezometric wells using geostatistical analysis was done for Mymensingh district. A total nine years of weekly ground water level data were used for the analysis. Geostatistical analysis was performed using ordinary kriging and empirical Bayesian kriging (EBK) methods. The semivariogram models called spherical, exponential and Gaussian model were fitted with the experimental semivariogram in ordinary kriging while semivariogram fitting is automatic in EBK. Model performances were tested using root mean square standardised error (RMSSE), root mean square error (RMSE) and average standard error (ASE). The cross-validation results indicate that EBK performs better comparing to ordinary kriging in representing the spatial groundwater level fluctuation in the study area. The geostatistical analysis result shows that the Phulbaria, Trisal, Muktagachha, Bhaluka, Gafargaon and Mymensingh Sadar Upazila is comparatively more vulnerable than other parts of the district.
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