Dry Weather Extremes of Sri Lanka and Impact of Southern Oscillation

Occurrence of lengthier dry spells is becoming a common phenomenon in most parts of theworld and it is an exigent challenge to adapt the changing climate. World MeteorologicalOrganisation defines an index to calculate the frequency of consecutive dry days (CDD) toidentify dry extremes and it is timely important to identify spatial and temporalcharacteristics of these dry extremes as a country.This study focused on identifying the spatial and temporal trends of consecutive dry days inSri Lanka from 1981 to 2010 and considers the impact of southern oscillation on theoccurrence of dry extremes of Sri Lanka as one of the causal factor. Extremes were identifiedusing the daily rainfall data using RClimDex 1.0 package. Dry extremes were mapped toderive the spatial and temporal characteristics. Non-parametric Mann Kendall test is used todetect the trends and their significance.Average frequencies of consecutive dry days in 1982, 1983, 1987, 1992 and 1998 were muchhigher than that of the other years. Inversely lower annual averages of frequency ofconsecutive dry days were seen in 1985, 1991, 2003 and 1981. Most of these extreme caseshighly coincided with the behavioral pattern of southern oscillation index. However, all thelengthier dry spells were not due to the southern oscillation and the causes should be furtherinvestigated for better decision making. Spatially there was a significant decreasing trend ofconsecutive dry days in Puttalam, Hambanthota, Nuwara Eliya and Rathnapura while thedecreasing trends of other regions were not statistically significant. According to the researchoutputs it is clear these decreasing trends of CDDs are fair signs for dry zonal regionsincluding Puttalam and Hambanthota. However, the significant decreasing trends of CDDs inRatnapura may let to consider whether this region is getting wetter and wetter incurringnumber of issues including floods and adverse impacts on plantations. Hence, appropriateadaptation methods should be rectified to cope with the changing climate.Keywords: Consecutive dry days, Spatial temporal trends, RClimDex, Non-parametricMann Kendall trend test, Southern oscillation

Identifying Spatial Clusters of Vulnerability Levels to Floods: An Initiative to Improve Disaster Resilience

Floods, the most frequent natural disaster in Sri Lanka, has become one of the significant barriers to the social and economic wellbeing of the country. Given that the frequency and intensity of the floods will be increased in the small island developing states in the tropical region as per the predictions of Intergovernmental Panel on climate change, it is timely essential to investigate how the vulnerability levels can be assessed as an initiative to strengthen the resilience of the communities. This study aims to identify spatial clusters of vulnerability levels of the flood-prone regions selecting Ihala Welgama Grama Niladhari division (GND) in Bulathsinhala divisional secretariat division in Kalutara district as the case study. This study uses multi-dimensional aspects of vulnerability, including social, physical, economic, institutional and attitudinal aspects of vulnerability to deepen the understanding of the vulnerability levels and to identify spatial clusters. Accordingly, indices were developed based on selected variables related to each of the above aspects to derive the multi-dimensional vulnerability levels. A household questionnaire survey was developed to get the data required for calculating above indices, and this survey covered the entire population of the GND (100 households). Then, the indices were calculated for each household unit of the GND. Standardised values of each sub-index ranging from 0-1 were clustered using multivariate clustering of ArcGIS pro to identify spatial clusters. Three clusters (high, moderate and low vulnerable spatial clusters) were identified based on the optimised Pseudo F-Statistics. Highly vulnerable cluster accounted for 28% of the total households, and 51% of the households are moderately vulnerable to floods. Only 21% showed a low vulnerability. Majority of the households in the highly and moderately vulnerable clusters were located within the closer proximity to the river compared to the low vulnerable cluster. Mapping spatial clusters based on multiple dimensions of vulnerability is an effective way to identify clusters that need to prioritise in enhancing the resilience of households in flood-prone areas.Keywords: Resilience, Vulnerability, Multivariate clustering, Spatial, Flood