Abstract
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 (PET) 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 (AET), 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 PET 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 (R2). 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.
Highlights
The water balance model is considerably important for water resource management, irrigation scheduling, and crop pattern designing (Kang et al, 2003; Valipour, 2012)
The highest annual deficit of water was observed in Rajshahi, which is located in the central–western part of the study area, where the depth of groundwater below the surface increases rapidly (Shamsudduha et al, 2009; Rahman et al, 2016)
A wavelet-aided ARIMA model was used for forecasting the water balance components (WBCs)
Summary
The water balance model is considerably important for water resource management, irrigation scheduling, and crop pattern designing (Kang et al, 2003; Valipour, 2012). Accurately forecasting the water balance components (WBCs) and detecting the changes in them is important for achieving sustainable water resource management. Rahman et al.: Modeling the changes in water balance components taminated by noises from hydrophysical processes. This affects the accuracy of the analysis, simulation, and forecasting (Sang et al, 2013; Wang et al, 2014). Denoising the time series is essential for improving the accuracy of the obtained results. The wavelet denoising technique was coupled with the ARIMA (autoregressive integrated moving average) model for forecasting the WBCs after detecting the changes in them by using different forms of the Mann–Kendall (MK) test. The time period responsible for the trends in the WBC time series was identified using discrete wavelet transform (DWT) time series data
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