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The study area, Nagadeepa is situated in Badulla District within the dryer parts of the country. Groundwater is the main drinking water source in the area. The study was carried out from 2013 to 2016 and the main objective of the study was to examine the chemical status and pollution levels by examining of 25 water quality parameters namely Na+, K+, Mg2+, Ca2+, Pb Mo, Cr, Cd, Mg, Hg, As, Al, Zn, Se, Cl-, F-, NO3-, HCO3-, SO42-, PO43-, DO, pH, temperature, electrical conductivity and water hardness in 28 wells. The results of the factor analysis indicated that conductivity, alkalinity, hardness, Calcium, Fluoride, Mo, Cr and Cd have a strong impact on the water quality compared to the categories of moderate and weak loadings. The parameters namely; Mg, Na, As and Al, Zn, Se and Water Level, Se, Cl, pH and DO, Pb have a moderate impact on water quality based on the moderate loadings indicated by the PC analysis. The weak impact on water quality is indicated by Cl-, SO22- and K, PO4, Hg, Se and Hg, Se, Al and Temperature, Cl-, pH and PO43-. Analysis indicated that; (i) significant and positive correlation with alkalinity, hardness, Ca2+ and Mg2+, (ii) positive and significant correlation only with hardness and Ca2+, (iii) Hardness shows positive and significant correlation only with Ca2+, (iv) Fluoride has a positive and significant correlation with Mo2+ and (v) Na+ has a positive and significant correlation only with Mg2+. Cluster analysis indicated, cluster 1 with less polluted water in 9 wells, cluster 2 with moderately polluted water in 11 wells, cluster 3 with highly polluted water in 7 wells, and cluster 4 with very highly polluted water in one well. Factor analysis indicated that pH, conductivity, alkalinity, hardness, dissolved oxygen, chloride, and sulfate are the major factors that affect the quality of the water. In the majority of the sampling wells, the correlation coefficient revealed a geogenic nature. Cluster analysis revealed that 29% of wells are unsuitable for drinking purposes.

Japan International Cooperation Agency (JICA) and National Building Research Organisation (NBRO) have been conducting projects to prevent and minimize landslide damages. Landslide early warning is one of the targets of the JICA-NBRO projects based on the analysis of rainfall indices. The analysis of these indices, such as working rainfall and Soil Water Index (SWI), was carried out. The Soil Water Index is an output of conceptual hydrological model representing soil water content. Time series of observed rainfall at 25 gauging stations from 2014 to 2020 were utilized to calculate rainfall indices. Moreover, past landslide records were utilized to analyze the correlation between landslide occurrences and the calculated rainfall indices. Results showed that most of the past landslides were caused by severe rainfall events in which Soil Water Index exceeded 112. Results also showed that small-scale slope failures were caused by relatively minor rainfall events. The critical values of rainfall indices causing landslides depend on the regional characteristics. The critical value in the south-western region tends to be higher than in the northern and southern regions. It seems that the higher rainfall in the south-western region increases the critical rainfall value of landslide occurrence. There is a possibility to improve the accuracy of landslide early warning by using the rainfall indices considering regional characteristics.

Fourteen districts were identified as landslide prone in Sri Lanka. These districts fall in the wet and intermediate climatological zones where annual rainfall is over 2000 mm. Short term heavy rainfall and long term cumulative rainfalls are active factors for landslide occurrences. In this study, to enhance the landslide prediction capacity and accuracy, two indices were analyzed; Short Term Rainfall Index—1.5 h. half period Working Rainfall and Long-Term Rainfall Index—Soil Water Index. Also, regional critical Soil Water Index values were evaluated for the analysis of regional geological and other parametric impact on landslides. Hydrological Tank Model simulations were used to calculate Soil Water Index and to predict landslides with snake curves and probability curves. In regional aspect, tank model parametric analysis was carried in determination of suitable parameter set for each soil conditions. Rainfall intensity acts as the main contributing factor for Soil Water Index. With probability curves, better predictions were obtained on regional based soil water index of greater than 100 values to identify the landslide occurrences even though the Short-Term rainfall did not exceed 10 mm. Region-wise Critical Soil Water Indexes were obtained by critical lines with soil parameters derived for Sri Lankan soil conditions. Better practices on landslide early warnings could be performed through these derived regional long-term Soil Water indexes. As a future development, it is expected to develop warning levels based on these critical values.

Victoria reservoir is located at an elevation of340 m to 440 m with a geographical position of7° 15'to T" 19' Nand 800 39' to 800 48' E which has been constructed by damming the River Mahaweli atVictoria fall. in Sri Lanka in 1983. The reservoir storage capacity is 72 1.2 MCM and upstream damsite comprises 1338 km2 in the districts of Kandy, Nuwara-Eliya and Matale. The elevation of thecatchment ranges from 340 m to 2 I00 m. Soil erosion is a major water quality issue in the upland reservoirs. The objective of this paper is toanalyze catchment issues contributing to soil erosion in the Victoria reservoir and to evaluate the soilerosion risk areas in the catchment. Study was carried out from 2002 to 2004. Soil erosion occurs due to natural causes such as rain fall, rainfall runoff and wind, and due to humanactivities. Universal Soil Loss Equation (A = RKLSCP) introduced by Wischmeier and Smith in1965 is a most widely used method for estimating soil erosion. This encounters detachment of soilparticles and its transport by raindrops and surface runoff, which depends on the rainfall erosivity (R),erodibility of soil (K), slope length factor (LS), cover and management factor (C) and the supportpractice factor of the equation. The data on erosivity points were interpolated with SO rn resolution grid cells. The erodibility valuerelevant to each soil group was entered into the attribute table, which was converted into grid cellswith 50 III resolution, containing soil erodibility values. The Triangulated Irregular Network (TIN) wascreated by contour interpolating with 20 m interval, and grid cell was 50 m. Using TIN, slopepercentages map was derived, which was used to obtain LS factor. The C factor values relevant toeach landuse type were entered into the attribute table. The map was converted into grid cells with SOm resolution, containing C factor values The soil erosion of the Victoria catchment was categorized into five erosion categories, namely; low,moderate, high, very high and extremely high, which extend within Kandy, Nuwara-Eliya and Mataledistricts. All categories were spread both in the left and right banks. Extremely high erosion areas extend over 24.34km2 with a percentage of 1.82, Very high erosionareas extend oyer i21 24 km2 with a percentage of 9 06, High erosion areas extend over 302 91 km2 with a percentage of 22.63, Moderate erosion areas extend over 434.01 km2 with a percentage of32.43 and low erosion area extends over 454.97 km with a percentage of 33 .99. The results of mapanalysis were confirmed through field verifications. The soil erosion is high in the high slope regionsand in the areas where soil conservation methods are inadequate or poor.