A multi‐model ensemble provides useful information about the uncertainty of the future changes of climate. High‐emission scenarios using representative concentration pathways (RCP8.5) of the Fifth Phase Coupled Model Inter‐comparison Project (CMIP5) in the Intergovernmental Panel on Climate Change (IPCC) also aids to capture the possible extremity of the climate change. Using the CMIP5 regional climate modelling predictions, this study analyses the distribution of the temperature and precipitation in Bangladesh in the recent years (1971–2000) and in three future periods (2010–2040, 2041–2070 and 2070–2100) considering RCP8.5 scenarios. Climate changes are expressed in terms of 30‐year return values of annual near‐surface temperature and 24‐h precipitation amounts. At the end of the century, the mean temperature increase over Bangladesh among the 11 RCMs will vary from 5.77 to 3.24 °C. Spatial analysis of the 11 RCMs exhibited that the southwest and the south central parts of Bangladesh will experience a greater temperature rise in the future. Possible changes in rainfall are also exhibited both temporally and spatially. Based on the analysis of all the RCMs, a significant increase of rainfall in the pre‐ and post‐monsoon period is observed. It is also evident that monsoon rainfall will not increase in comparison with pre‐monsoon season. Zonal statistics of 64 districts of Bangladesh are also conducted for the 2020s, 2050s and 2080s to find out the most exposed regions in terms of the highest rise in temperature and changes in precipitation.
The Ganges–Brahmaputra–Meghna river system carries the world's third-largest fresh water discharge and Brahmaputra alone carries about 67% of the total annual flow of Bangladesh. Climate change will be expected to alter the hydrological cycles and the flow regime of these basins. Assessment of the fresh water availability of the Brahmaputra Basin in the future under climate change condition is crucial for both society and the ecosystem. SWAT, a semi-distributed physically based hydrological model, has been applied to investigate hydrological response of the basin. However, it is a challenging task to calibrate and validate models over this ungauged and poor data basin. A model derived by using gridded rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite and temperature data from reanalysis product ERA-Interim provides acceptable calibration and validation. Using the SWAT-CUP with SUFI-2 algorithm, sensitivity analysis of model parameters was examined. A calibrated model was derived using new climate change projection data from the multi-model ensemble CMIP5 Project over the South Asia CORDEX domain. The uncertainty of predicting monsoon flow is less than that of pre-monsoon flow. Most of the regional climate models (RCMs) show an increasing tendency of the discharge of Brahmaputra River at Bahadurabad station during monsoon, when flood usually occurs in Bangladesh.
Subsurface fires and smoldering events at landfills can present serious health hazards and threats to the environment. These fires are much more costly and difficult to extinguish than open fires at the landfill surface. The initiation of a subsurface fire may go unnoticed for a long period of time and undetected fires may spread over a large area. Unfortunately, not all landfill operators keep or publish heat elevation data and many landfills are not equipped with a landfill gas extraction system to control subsurface temperatures generated from the chemical reactions within. The timely and cost-effective identification of subsurface fires is an important and pressing issue. In this work, we describe a method for using satellite thermal infrared imagery at a moderate spatial resolution to identify the locations of subsurface fires and monitor their migration within landfills. The focus of this study was the Bridgeton Sanitary Landfill in Bridgeton, MO, USA where a subsurface fire was first identified in 2010 and continues to burn today. Observations from Landsat satellites over the last seventeen years were examined for surface temperature anomalies (or hot spots) that may be associated with subsurface fires. The results showed that the locations of hot spots identified in satellite imagery match the known locations of the subsurface fires. Changes in the hot-spot locations with time, as determined by in situ measurements, correspond to the spreading routes of the subsurface fires. These results indicate that the proposed approach based on satellite observations can be used as a tool for the identification of landfill subsurface fires by landfill owners/operators to monitor landfills and minimize the expenses associated with extinguishing landfill fires.
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