Abstract. The objectives of the present study were to explore the changes in the water balance components (WBCs) by co-utilizing the discrete wavelet transform (DWT) and different forms of the Mann-Kendall (MK) test and develop a wavelet denoise autoregressive integrated moving average (WD-ARIMA) model for forecasting the WBCs. The results revealed that most of the potential evapotranspiration (P ET ) trends (approximately 73 %) had a decreasing tendency from 1981-1982 to 2012-2013 in the western part of Bangladesh. However, most of the trends (approximately 82 %) were not statistically significant at a 5 % significance level. The actual evapotranspiration (A ET ), annual deficit, and annual surplus also exhibited a similar tendency. The rainfall and temperature exhibited increasing trends. However, the WBCs exhibited an inverse trend, which suggested that the P ET changes associated with temperature changes could not explain the change in the WBCs. Moreover, the 8-year (D3) and 16-year (D4) periodic components were generally responsible for the trends found in the original WBC data for the study area. The actual data was affected by noise, which resulted in the ARIMA model exhibiting an unsatisfactory performance. Therefore, wavelet denoising of the WBC time series was conducted to improve the performance of the ARIMA model. The quality of the denoising time series data was ensured using relevant statistical analysis. The performance of the WD-ARIMA model was assessed using the Nash-Sutcliffe efficiency (NSE) coefficient and coefficient of determination (R 2 ). The WD-ARIMA model exhibited very good performance, which clearly demonstrated the advantages of denoising the time series data for forecasting the WBCs. The validation results of the model revealed that the forecasted values were very close to actual values, with an acceptable mean percentage error. The residuals also followed a normal distribution. The performance and validation results indicated that models can be used for the short-term forecasting of WBCs. Further studies on different combinations of wavelet analysis are required to develop a superior model for the hydrological forecasting in the context of climate change. The findings of this study can be used to improve water resource management in the highly irrigated western part of Bangladesh.

Abstract. The objectives of the study are to explore the changes in water balance components (WBC) by co-utilizing discrete wavelet transformation (DWT) and different forms of Mann–Kendal (MK) test; and to develop wavelet autoregressive moving average (ARIMA) models for forecasting the WBC. Trend test results reveal that the most of the trends (about 73 %) identified in potential evapotranspiration (PET) show decreasing tendency during the hydrological year 1981–82 to 2012–13 in the western part of Bangladesh, however most of the changes (about 82 %) are insignificant at 5 % significant level. Actual evapotranspiration (AET), annual deficit and annual surplus also show the almost similar tendency. Rainfall and temperature show increasing trends, but WBC show inverse of this tendency and suggesting that traditional concept of changes in PET associated with changes in temperature cannot explain the changes in WBC. Moreover, it is found that generally 8-years (D3) to 16-years (D4) periodic components are the effective components and are responsible for trends found in original data of WBC in western part of Bangladesh. Wavelet denoising of WBC time series has been done to improve the performance of models as actual data affected by noise and show unsatisfactory performances. The quality of denoised data has been ensured by relevant statistical analysis. Performance of wavelet ARIMA models have been assessed by Nash–Sutcliffe Efficiency (NSE) coefficient and coefficient of determination (R2). The obtained results indicate that performances of wavelet ARIMA models of WBC are acceptable to very good and clearly demonstrate the advantages of denoising over actual data. The models validation results reveal that the forecasted values are very close to actual values with acceptable mean percentage error and residuals also follow normally distribution. Performances and validation results indicate that models can be used for short term forecasting of WBC. Further studies on different combinations of wavelet analysis would be facilitated to develop better models for WBC in context of climate change and findings of study can be used to improve water resources management in highly irrigated western part of Bangladesh.

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