Precipitation or rainfall (in tropics) is an important climatic parameter and the studies on rainfall are commonly hampered due to lack of continuous data. To fill the gaps (missing observations) in data, several interpolation techniques are currently used. However, the lack of knowledge on the suitability of these methods for Sri Lanka is a practical problem. In view of this problem, this study is aimed at comparing a few selected methods used for the estimation of missing rainfall data with a new method introduced by the authors to determine their suitability in Sri Lankan context. The methods studied were Arithmetic Mean (Local Mean) method, Normal Ratio method and Inverse Distance method. The new method introduced by the authors is named as Aerial Precipitation Ratio method. In this approach, rain gauging stations where complete monthly rainfall data sets are available were selected in such a way that the selected stations represent each of the seven major Agro-ecological zones of Sri Lanka. This selection procedure of stations makes it possible to generalize the results to the entire country. The period of data ranged from 15 years in the case of mid country intermediate zone to 28 years Up country intermediate zone and Mid country wet zone. Subsequently, monthly rainfall data of each station were estimated using the data of surrounding stations based on the above selected methods so that actual data and the estimated data can be compared. Each estimated series was compared with the actual data series using different statistical comparison techniques. These comparisons include Descriptive Statistics of Error, Root Mean Square Error, Mean Absolute Percentage of Error and Correlation Coefficient. Results of the study show that the Inverse Distance method is the most suitable method for all three Low-country zones (wet, intermediate, and dry).

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In view of the significance in terms of agricultural production and its dependency on rainfall, the DL, region of the North Central Province (NCP) was chosen to study the influence of El NifiolLa Nifia episodes on the rainfall regime. The rainfall data of two representative stations as mean seasonal rainfall and mean number of rainy days were analyzed based on four seasons i.e. First Inter-Monsoon (FIM), South West Monsoon (SWM), Second Inter-Monsoon (SIM) and North East Monsoon (NEM). Seasonal time series of rainfall data from 1906 to 2000 were divided into El Niiio, La Nifia and Neutral years. Student t-test and chi-square test were carried out to determine whether there were significant differences among the mean seasonal rainfall and mean number ofrainy days in the time series. Both rainfall amounts and the number ofrainy days in SIM seasons showed a statistically significant increase with respect to the situation in Neutral years. Even though i t is not statistically significant, a n apparent increase of mean seasonal rainfall and t h e mean number of rainy days i n NEM seasons have also been evident during El Nifio years. Thus, El Nifio years are likely to produce above normal or a t least near normal rains during the Maha season in the NCP. Hence, appearance and progression of El Nifio type circulation i n the Pacific ocean could be safely used as a long-range forecasting tool for rainfall of the Maha season in the NCP. Although there were obvious differences of seasonal time series of FIM, SIM and NEM between La Nifia and Neutral years, statistically significant relationships could not be established. However, the teleconnection between seasonal rainfall during SWM period and La Niria events was positive a t both locations. This study reveals that there is a n influence of El Niiio and La Nifia episodes on the seasonal rainfall regime of DL, region of the NCP.

Batticaloa Lagoon is one of the estuaries in the country which is frequently affected by floating aquatic plants; mainly Eichhornia crassipes. The present study aimed to develop a relationship between field measured and satellite derived biomass that can satisfactorily estimate the spatial distribution of green and dry biomass of the floating aquatic plants in Batticaloa Lagoon. Cloud free six Sentinel-2A images were acquired for the period of March 2017 to February 2018. Real time field measurements of biomass of floating aquatic plants were obtained in 12 locations in two weeks interval. A buffer zone of 3 km was created around the lagoon to obtain Land Use/Land Cover (LULC) distribution to study the influence of surrounding LULC on floating aquatic plants. A number of band ratios and indices were developed using Sentinel-2A images to establish relationships with the field estimated biomass. The LULC analysis revealed that paddy was the abundant land use in the study area and the cultivation was highly seasonal which impacts the distribution of floating aquatic plants in dry and wet seasons. Among 21 tested band ratios and indices, normalized difference red edge index (NDREI, r 2 =0.78) and band ratio B8/B (r 2 =0.67) for the green biomass and band ratio B3/B4 (r 2 =0.73) and NDREI-Narrow (r 2 =0.61) for the dry biomass in dry and wet season showed strong positive correlation with field biomass. The temporal distribution of the estimated biomass also confirmed the potential of Sentinel-2A images to be used as a source of data for monitoring of floating aquatic plants in the lagoon due to high spatial and spectral resolution of NIR and Red edge bands. These estimated biomass maps can be used to identify the locations which are affected by aquatic plants in order to take proper control measures.

Wilpattu Forest complex is the largest protected area in Sri Lanka and it is a designated Ramsar wetland cluster. It contains a range of diverse terrestrial habitats including various forest types and a coastline with large coastal sand dunes. However, this vital ecosystem is under threat of fragmentation and degradation due to increasing human interventions. Habitat fragmentation is a serious threat to the rich biodiversity in this ecosystem. The objective of this study was to assess the loss of forest cover within the region, to examine the spatial pattern of forest cover fragmentation and to predict the potential forest cover change in 2025 using remote sensing and GIS techniques. Two Landsat 5 TM satellite images of 1992 and Landsat 8 OLI-TIRS image of 2018 were classified using unsupervised and supervised techniques to obtain three land use/ land cover classes namely, forest, nonforest and water. Forest cover change during 1992 and 2018 was analysed using classified images. Finally, future changes were modelled to assess possible threat to the forest cover. The analysis revealed that Wilpattu forest complex has lost 19,524 ha of its forest reserves to other land uses within the last 26 years and the highest impact was seen in the upper Wilpattu forest where about 7.48% area has lost from forest to other land uses. Study also proved that the total forest area is becoming more fragmented affecting the balance of the valuable natural eco-system of the area. Lack of availability in proper forest boundaries and classification issues were identified as main limitations in this study. However, it must be emphasized that immediate actions are needed to prevent further degradation of this sensitive ecosystem.