Changes in monsoon precipitation patterns over Bangladesh and its teleconnections with global climate

Hysteresis effect and seasonal step-like creep deformation of the Jiuxianping landslide in the Three Gorges Reservoir region

The present study examines the effects of convective available potential energy (CAPE), temperature and humidity on the spatiotemporal variation of thunderstorm (TS) frequency over Bangladesh using an Empirical Orthogonal Function (EOF) approach. TS and CAPE, temperature, humidity data have been used for 38-year (1980–2017) and collected from the Bangladesh Meteorological Department and ERA-40 reanalysis, respectively. Mann-Kendall nonparametric trend test, Sen’s slope, Pearson’s correlation and EOF modes are applied to understand the variability of TS and the effects of CAPE, temperature and humidity on it. Results show a decrease in TS frequency in Bangladesh at a rate of −0.408/year annually and − 0.384/year during pre-monsoon, when it is most prevalent. The TS frequency spatially varies widely over Bangladesh. The present study shows an increase in TS in the areas where it is less prevalent and a decrease in the areas where it occurs more. CAPE shows a decreasing trend during winter and an increasing trend during post-monsoon and at an annual scale. TS frequency is positively correlated with CAPE and humidity and negatively with temperature. Therefore, the rising temperature may be the cause of decreasing TS frequency in Bangladesh. The findings can be useful for hazard management planning in Bangladesh.

Effects of convective available potential energy, temperature and humidity on the variability of thunderstorm frequency over Bangladesh

Application of novel hybrid machine learning techniques for particle Froude number estimation in sewer pipes

• For the first time, rainfall erosivity (RE) in Bangladesh has been calculated to be 43 MJmmha -1 h −1 year 1 . • Summer and monsoon are 16 to 25 times more erosive than winter. • The highest RE value was in the southeastern part and the lowest RE value was in the western part of the country. • DMI and SASMI had positive impacts on RE. Understanding the spatiotemporal variability of annual, monthly, and seasonal rainfall erosivity (RE) is crucial for determining the risk of soil erosion and developing soil conservation plans. However, little knowledge is available on RE at the monthly, seasonal, and annual scales in monsoon-driven climate regions like Bangladesh. Therefore, we aimed to assess the spatiotemporal variability of RE in Bangladesh from 1980 to 2017 and analyze teleconnection of RE with large-scale atmospheric circulation indices. The detrended fluctuation analysis (DFA) and cross wavelet analysis were employed to evaluate the potential future trends and interpret the periodic cycles in the RE, respectively. The results demonstrate that mean annual RE values fluctuated from 12574.16 M J . m m . h a - 1 . h - 1 to 123914.76 M J . m m . h a - 1 . h - 1 , while RE has been lower in recent decades than in the earlier decades. The mean annual RE is decreasing all over the country, and significant abrupt change points occurred in the early 2000 s. The highest mean annual RE was observed in the southernmost part of Bangladesh and the lowest in the western part of the country. The results of DFA indicated that the RE trend would sustain its present trend in the upcoming period, except for May and July. The effects of Niño3.4 SST and IOD, SASMI, and EASMI have almost no to minimal influence on the RE trend in Bangladesh. However, a noticeable impact was found on the monthly scale, which could be why the observed intra-annual variability of RE was observed. We found that the average RE has reduced significantly with increasing distance from the sea, latitude, and elevation in Bangladesh. The study found that increasing geopotential height in the summer and decreasing geopotential height in the winter with enough moisture divergence, decreasing convective precipitation, decreasing large-scale rain rate, decreasing specific cloud liquid water content, and decreasing explicit rainwater content all contributed to RE variability and change in the country. 

Spatiotemporal variability of rainfall erosivity and its teleconnection with atmospheric circulation in monsoon-driven climate region

Thunderstorm frequency (TSF) prediction with higher accuracy is of great significance under climate extremes for reducing potential damages However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict To close this gap, we proposed two novel hybrid machine learning models through hybridization of data pre-processing ensemble empirical mode decomposition (EEMD) with two state-of-arts models, namely artificial neural network (ANN), support vector machine for TSF prediction at three categories over Bangladesh We have demarcated the yearly TSF datasets into three categories for the period 1981–2016 recorded at 28 sites; high (March–June), moderate (July–October), and low (November–February) TSF months The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared with classical ANN, SVM, and autoregressive integrated moving average EEMD-ANN and EEMD-SVM hybrid models showed 802–2248% higher performance precision in terms of root mean square error compared to other models at high-, moderate-, and low-frequency categories Eleven out of 21 input parameters were selected based on the random forest variable importance analysis The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category, and thunderstorm days are the most contributing parameters influencing TSF prediction The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision The hybrid models developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world

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Kurratul Ayen

Papers

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Changes in monsoon precipitation patterns over Bangladesh and its teleconnections with global climate