Abstract:Norochcholai coal power plant is the largest coal power plant in Sri Lanka and during the combustion of coalit annually generates about 250,000 metric tons of coal ash which consists of fly ash (FA) and bottom ash (BA). Almost all the generated ash is disposed into ash dumps except a small quantity of FA (30%) that is effectively utilized. Therefore, use of coal ash for construction purposes will offer a sustainable solution for reducing its by-products and overcoming the scarcity of raw materials required for construction work. The main aim of this research was therefore to determine the geotechnical engineering properties of FA and BA to find out the feasibility of using them as light weight embankment or backfill material. A series of laboratory experiments were conducted on FA, BA and FA-BA co-mixed samples to determine their particle size distribution, specific gravity, index properties, compaction characteristics, shear strength parameters and California bearing ratio (CBR). The experimental results reveal that the particle size of coal ash (FA and BA) is predominantly silt sized while containing some sand-sized fractions as well. This coal ash has a low maximum dry density (MDD) and a high optimum moisture content (OMC) compared to typical granular soils used in embankments. In addition, this ash has a higher friction angle and higher cohesion than most types of construction fills. Thus, both FA and BA can be used as light weight embankment or back fill material in civil engineering construction work.

Extreme rainfall events leading to severe hydrological impacts warrant an accurate prediction of such events not only on time but also in magnitude. Sri Lanka is a South Asian country that is frequently affected by severe tropical storms. The primary aim of this study was to improve heavy rainfall events forecast during the North-east monsoon over the Badulu Oya catchment, Sri Lanka. This aim was accomplished by simulating precipitation for two extreme North-East monsoon rainfall events using the Weather Research and Forecasting (WRF-ARW) model. A detailed comparison was made between the 24-h spatial distribution of model rainfall and observations obtained from rainfall gauges. Verification was evaluated based on three deterministic approaches. Each rainfall event was simulated multiple times using 15 different parameterization scheme combinations including six microphysics and four cumulus schemes at a 3 km grid resolution. The filtered best model combinations were validated using observations from another two heavy North-East monsoon rainfall events. The key finding from these evaluations was that model configurations with WSM5, WSM6, Kessler and WDM6 microphysics, and KF, BMJ and MKF cumulus schemes displayed the overall best performances. Therefore, these combinations have a good potential for operational use in numerical weather prediction over the said catchment.

Short-term intense precipitation is one of the hallmarks of climate change. Mi Oya River basin experiences severe seasonal floods annually, but the damage can be lessened by developing a numerical weather forecasting (NWF) model for the entire basin and incorporating it with effective reservoir management. Several areas in Sri Lanka have undergone NWF studies, however they are insufficient to determine the best physics schemes for the basin. This study investigates the Weather Research and Forecasting (WRF-ARW) model's predictability with varying three microphysics and two cumulus schemes to discover the optimal set of physics parameters for predicting heavy rainfall occurrences throughout the Southwest and Northeast monsoon seasons within a nested domain configuration. The WRF model's forecasting results at 3 km grid resolution were compared with four rainfall gauging stations in the basin for three rainfall events in May 2016, April 2018, and November 2015. Total Model Performance was derived for the evaluation utilizing bias, MAE, RMSE, Correlation Coefficient, and slope of each model's output data with observed rainfall data. After comparing the model output to data, WSM6 microphysics and Betts-Miller-Janjic cumulus with other default physics settings were determined to be the optimal physics combination to forecast weather across the region.