Climate change projection and drought characterization in Bangladesh

One of the biggest threats of the climatic change is aberrant pattern or distribution of rainfall that results to drought. The main objective of this research was to develop a methodological framework to assess the impacts of climate change on seasonal drought characteristics with uncertainty. Bangladesh, one of the most vulnerable countries in the world to climate change was considered as the study area for implementation of the framework. An ensemble of general circulation models (GCMs) of Coupled Model Intercomparison Project phase 5 (CMIP5) were used for downscaling and projection of rainfall and temperature under different Representative Concentration Pathways (RCP) scenarios. Two state of art data mining approaches known as Random Forest (RF) and Support Vector Machine (SVM) were used for the development of downscaling models and Quantile Mapping (QM) approach was used to remove biases in GCMs. The observed and future projected rainfall data were used to characterize the seasonal droughts using Severity-Area-Frequency (SAF) curves developed for different climatic and major crop growing seasons. The results revealed superior performance of SVM in downscaling rainfall and temperature in tropical climate in terms of all standard statistics. Downscaling of CMIP5 GCMs projections revealed a change in annual average rainfall in Bangladesh in the range of -8.6% in the northeast to +11.9% in the northwest, which indicates that spatial distribution of rainfall of Bangladesh will be more homogeneous in future. The maximum and minimum temperatures of Bangladesh were projected to increase in the range of 0.8 to 4.3ºC and 1.0 to 4.8ºC, respectively under different RCPs. Future projection of droughts revealed that affected areas will increase for higher severity and higher return period droughts. Overall, the country will be more affected by higher return period Kharif (May- October) and monsoon droughts, and lower return period pre-monsoon and postmonsoon droughts due to climate change

Historical trends and future projection of climate at Dhaka city of Bangladesh

Dhaka, the capital city of Bangladesh is considered as one of the most vulnerable cities of the world to climate change. A study has been carried out to assess the historical changes as well as future changes in the climate of Dhaka city in order to propose necessary mitigation and adaptation measures. Statistical downscaling model (SDSM) was used for the projection of future changes in daily rainfall and temperature and non-parametric trend analysis was used to assess the changes in rainfall, temperature and related extremes. The impacts of projected changes in climate on urban infrastructure and livelihood in Dhaka city was finally assessed to propose necessary adaptation measures. The study revealed that night time temperature in Dhaka city has increased significantly at a rate of 0.22ºC/decade in last fifty year, which is support to increase continually in the future. Different temperature related extreme events are also found to increase significantly in Dhaka. On the other hand, no significant change in rainfall or rainfall related extremes are observed. Therefore, it can be remarked that imminent impacts of climate change will be due to the increase in temperature and temperature related extremes. The public health and the water and energy supply are likely to be imminent affected sector in the city due to climate change

Bivariate frequency analysis of flood variables using copula in Kelantan river basin

A copula-based methodology is presented in this study for bivariate flood frequency analysis of Kelantan river basin located in Northeast Malaysia. The joint dependence structures of three flood characteristics, namely, peak flow (Q), flood volume (V) and flood duration (D) were modelled using t-Copula. Various univariate distribution functions of flood variables were fitted with observed flood variables to find the best distributions. Cumulative joint distribution functions (CDF) of peak flow and volume (Q-F), peak flow and duration (Q-D) and volume and duration (V-D) revealed that return period of joint return periods are much higher compared to univariate return period. The joint probabilities of occurrence of 0.8, 0.6, 0.4, 0.2 and 0 can be expected when flood duration greater than 65 h, 54 h, 46 h, and 32 h, and the flood volume higher than 0.62 km3, 0.33 km3, 0.25 km3, and 0.22 km3 respectively

Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques

The east coast of Peninsular Malaysia is one of the most vulnerable regions of Malaysia to hydrological disasters, which is believed to become more vulnerable due to climate change. Studies to have better understandings of the hydrological processes in the region are therefore, of paramount importance for disaster risk mitigation. However, unavailability of long-term river discharge data is one of the major constraints of hydrologic studies in the area. The major objective of this study is to predict river discharge in ungauged river basins in the study area. For this purpose, a set of multiple linear regression equations and exponential functions have been developed, which are expressed in the forms of multivariate equations. Available streamflow data along with other catchment characteristics from gauged catchments were used to develop the equations and were subsequently applied to the poorly gauged or ungauged catchments within the study area for prediction of streamflow. In this present study, 4 to 7 explanatory variables were selected as the input variables, which comprise of climatic, geomorphologic, geographic characteristics, soil properties, land use pattern and land cover of the area. Ten flow metrics as maximum, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95, mean and minimum were therefore predicted. Thus, the results of the developed multivariate equations revealed the model to be capable of predicting the desired flow metrics at ungauged catchments in the area under consideration with reasonable accuracy