The present study measures management of flash flood to avoid the devastating impacts on boro rice production at Sunamganj district. This study interacts for meteorological parameter assessment with global climate data. Three major climatic parameters (precipitation, temperature and cloud fraction) have been considered for this study. These three climatic data were analyzed using numerical software EdGCM for the period 1920 to 2020 and then downscaled by Transform software. Flash floods have been classified as general flash floods and devastating flash flood. Flash floods have been reported to be severe in March and April. A trend was evaluated for the detection of flash flooding occurrences. The study concludes that when temperature is high, a small amount of rainfall can cause a slight cloud fraction to cause flooding. Flash flooding occurs when temperature is above 76oF(24.44oC), rainfall is at least 3 mm and cloud fraction is at most 46%. Furthermore, for validation Mann-Kendall trend analysis have been done. The test result discovered increasing trend for rainfall and decreasing trend for temperature and cloud fraction. Finally, an algorithm was developed in C++ program as a flash flood precaution tool which helped to prepare strategy as well as to adapt with the flash flood.

An attempt was made to develop low cost porous evaporative cooling storage structures for extending the shelf life of citrus fruits and vegetables at the Sylhet Agricultural University campus, Bangladesh. Clay soil, bamboo and straw were used as a wall material. Sand, clay, zeolite, rice husk and charcoal etc. were used as a pad material. But the mixture of sand and clay was found as the most efficient pad materials for lowering temperature. Porous evaporative cooling storage structure (PECSS) was developed to reduce the problems of post-harvest losses at farmer level. It is eco-friendly and no energy requirements for storage of vegetables and fruits. PECSS improves the quality and productivity of vegetables and citrus fruits by reducing temperature, prolonging shelf life and reducing post-harvest losses respectively. The study revealed that shelf life of egg-plant (Solanum melongena) was 11 days in PECSS condition and it was 6 days in ambient condition. Therefore, weight loss was 4.07% for PECSS and 11.84% in room condition respectively. Storage life of Ladies finger (Abelmoschus esculentus) was 6 days more in PECSS condition than room condition. Weight loss was 6.62% in PECSS condition and 17.47% loss in ambient condition. In case of Malabar Spinach (Basella alba) it was 6 days for PECSS condition and 3 days for room condition and weight loss was found to be 9.48% and 16.17% respectively. The shelf life of stem amaranth (Amaranthus cruentus) was 5 days in PECSS condition and 2 days in ambient condition. Weight loss was found 7.05% at PECSS condition and 28.62% as in-room condition. By chemical analysis for fruits lemon (Citrus limon) and orange (Citrus sinensis) found that pH and TSS were increased both ambient and PECSS condition but in PECSS condition this rate was less than ambient condition. Vitamin C, percentage juice content, citric acid values all were decrease at both condition but in PECSS condition its rate was the less ambient condition. There is scope for intensive study to improve the firmness of the porous evaporative cooling storage structure (PECSS) to reduce the storage loss of vegetables and citrus fruits for different region and its suitability for large scale design.

Downscaling is a state-of-the-art technique to generate fine-resolution climate change prediction and an obvious tool for forecasting future climate scenarios for many data-scarce areas like Bangladesh. The Educational Global Climate Model (EdGCM) predicts numerically and its performance was not evaluated for Bangladesh earlier. Due to this reason, an attempt has been made to apply a new geostatistical approach with the help of transform software to downscale EdGCM for identifying the trend of surface air temperature at the Sylhet district. Both Doubled_CO² and Global_Warming_01 are simulated from EdGCM and maps are generated to depict global temperature variations. Downscaling is applied to the outputs from Doubled_CO² scenario. Percent of bias (PBIAS), Nash-Sutcliffe efficiency (NSE) and the ratio of root mean square error to the standard deviation of measured data (RSR) values are satisfactory and acceptable. The trend analysis was performed using the Mann-Kendall Trend test and Sen’s slope estimator. Temperature changes are significant for both downscaled and observed results of p-value which is less than alpha = 0.05. Mann-Kendall Z tests for annual downscaled and IPCC during (2006-2020) show a positive trend. Downscaled predicted annual average temperature (simulations by Doubled_CO²) for 2020 is 21.67˚C for the Sylhet district.

The study is conducted to determine the correlation between climatic parameters and rice yield. The present study is also undertaken to analyze the land cover change in Sylhet district between 2013 and 2018 using LANDSAT-8 images. Local climate and rice yield data are collected from BMD (Bangladesh Meteorological Department) and BRRI (Bangladesh Rice Research Institute) and BBS (Bangladesh Bureau of Statistics). ArcGIS 10.5 and SPSS software are used to show the vegetation condition and correlation coefficient between rice yield and climatic variables respectively. It is revealed from the result that rainfall is negatively correlated with Aman and Boro (local and HYV) rice whereas temperature and relative humidity showed a positive correlation with local Aman and Boro rice. On the other hand, relative humidity showed a strong linear relationship with HYV Boro rice. Finally, both temperature and relative humidity have substantial effects on yields in the Boro rice. Furthermore, vegetation condition is observed through NDVI and found the moderate-high vegetation in 2013. After that NDVI value is fluctuating which evidently signifies the rapid vegetation cover change due to a flash flood, flood and other climate changing aspects. Additionally, Forested and high land vegetation’s are endangered rapidly. Some adaptation strategies should be followed to minimize the effects of natural calamities for improving better vegetation condition.

The overall goal of this study was to examine the effects of climate change on the yield of four distinct crops (Aus, Aman, Boro and Wheat) in Sylhet by using secondary climate data from 1970 to 2020. The study's other goal is to assess the impact of river water levels on crop productivity in Sylhet over time. Data on crop productivity, weather variability and river water levels were gathered from the various fields. Yield vs. climatic correlation was discovered in the study, and this correlation varied according to season. To estimate the impact of climate change on rice yield, a multiple regression model is used. Climate variables in the model were found to account for 11% of the overall variation in Aus rice yield. The relationship between relative humidity and maximum temperature is positive and statistically significant. Other variables had no effect on yield because they were not significant. Furthermore, Regression results indicated that climate variables account for 60.6 percent of the overall variation in Aman rice output. Relative humidity, on the other hand, can undermine the yield. Climate variables account for 53.5 percent of the overall variation in Boro rice output, according to the findings. As a result, an increase in rainfall may have a negative impact on Boro rice yield. Maximum and minimum temperatures might have a favorable impact on Boro rice yield. Increases in maximum temperature, on the other hand, can considerably boost Wheat yield while decreases in minimum temperature can diminish Wheat yield. On the other side, the results of the regression analysis suggest that river water level has a minor impact on Aus, Aman, and Boro yield. However, as the model demonstrates, the river water level can have an impact on wheat yield. The impact of temperature and rainfall on water level was also investigated in this study because the regression model failed to produce positive results. Surprisingly, the model performs well, despite the fact that maximum temperatures have a negative impact on water levels in the Aus and Aman seasons. This shows that if warmer temperatures aid raises Aus and Aman yields, then the water level cannot sabotage the yield rise. Rainfall has a favorable impact on the water levels in the Aus, Aman, and Boro seasons, but has a negative impact on the water levels in the Wheat season. Int. J. Agril. Res. Innov. Tech. 12(2): 18-26, December 2022