This project was designed and proposed by the scientist from Tropical Climate and Foundation for Environment, Climate and Technology (FECT),together with our partners at the University of Peradeniya, to assess the impacts of climate on the Tea Plantation Systems in Sri Lanka. The project focuses primarily on the impacts on tea production, yield and quality. The research will take place in the hill country estates that are under Dilmah for a detailed study. The duration of this project will be from December 2017 to December 2018 and be sponsored by Dilmah Conservation for this period
Project title:Impacts of climate variability and change on tea plantations in the hill country
Future work: The project will also focus on the impacts on water resources, renewable energy supply, health, land management, logistics, wildlife and disaster risk of the plantation system.
About Dilmah: Dilmah Tea, one of Sri Lanka’s most recognized Tea Brands, has recently developed a climate observatory and Climate Research Program under Dilmah Conservation Center for Climate Change Research & Adaptation.
Objectives:
1. Assessing climate information needs/resources for Tea sector
2. Obtaining Tea sector data for climate analysis
3. Obtaining climate data for Tea sector needs
4. Understanding climate diagnostics
5. Assessing climate impacts on Tea
6. Communication and dissemination of output
Expected outputs:
1.Producing reports on needs assessment, knowledge resources and gaps in the Tea Sector
2. Collection, compilation and assessment of available and essential tea related data, metadata on plantations.
3. Collection, compilation and assessment of available metadata on climate, environment and tea.
4. Reports on climatological analysis on historical seasonal and inter-annual variability of rainfall and temperature.
5. Statistical model on climate impact and tea production.
6.Weekly climate reports and customized weather (climate histories) for early adopters.
Assessing and Monitoring the Impact of Deforestation, Forest and Plantation Degradation and Rehabilitation on Carbon Sequestration and Hydrology in South Asia
1. Research Idea
Implementation of policies that are intended to reduce emissions from deforestation, and to estimate sequestration of carbon due to forests, depends on accurate and precise estimates of emissions averted. Remote sensing in combination with ground based work can help monitor forest systems at sub-national scales to complement global efforts but much further ground based work is urgently required. Land use change is related to hydro-climate and hydrology has to be considered alongside.
2. Objectives
The objectives of the proposed collaboration are to: [1] Compile fine scale multi-decadal database of forest inventories, satellite imagery, and hydro-climatic histories,
[2] Undertake pilot projects to understand impacts of deforestation, forest and land degradation for key forest types in South Asia.[3] Inventory carbon stocks under forest and land degradation or rehabilitation in time and use it as ground-truth to refine methodologies for remote estimation of carbon stock and its variability.[4] Quantify hydrological change due to land use change and its interrelationship carbon sequestration.[5] Develop capacity in partner institutions, and communicate results especially to national policy makers.
Enabling the use of epidemiological, environmental and societal information by the public and stakeholders to anticipate, manage and mitigate emerging and re-emerging infectious diseases in Kandy, Sri Lanka.
Project title: Enabling the use of epidemiological, environmental and societal information by the public and stakeholders to anticipate, manage and mitigate emerging and re-emerging infectious diseases in Kandy, Sri Lanka.
Problem: In recent years, there is evidence of emergence of new diseases such as Chikungunya, Dengue, Leishmaniases, Japanese Encephalitis and Leptospirosis and re-emergence of diseases such as Malaria in the hill district of Kandy in Sri Lanka. Kandy is the largest metropolis in Sri Lanka after Colombo (1.4 million people in 1,917 sq. km) and it has been attracting increasing migration leading to rapid urbanization and land use change. Climate change is leading to both an alarming and unusual decline in rainfall and streamflow in the rivers through Kandy district (by about 10-20% over 3 decades) and a rise in temperature that is four times the global rate of warming. The hill region is particularly susceptible for the migration of disease risks to higher altitudes as temperature increases.
Objectives:
1. The overall goal is to promote multiple stakeholder participation in data generation, management, risk prediction and communication related to (re-) emerging infectious disease decisions.
2. To engage and mobilize multiple agencies, community groups and local government in a program to use information about epidemiology, environmental and societal factors to control and prevent emerging and re-emerging diseases.
3. To bring together expertise in the epidemiological and entomological, environmental, socio-economic and ICT fields to interrogate existing data to monitor epidemiological, land use, hydrological and climate and socio-economic information for interpreting hazard, exposure and vulnerability to EID at the sub-district and village scale in Kandy.
4. To develop a system of risk prediction for water-related vector-borne diseases based on the epidemiological, environmental and vulnerability information that already exists and can be routinely collected.
5. To enable stakeholder participation in assessing risks of disease and the social, environmental and vulnerability conditions that contribute to disease risk and for managing and mitigating these conditions.
Predicting Dengue Risk from Environmental, Entomological, and Societal Information in Kandy and Colombo
Introduction
Problem: In recent years, dengue cases have been rising dramatically in the Central Province. Our work mainly focuses on obtaining dengue data from Kandy, Matale and Nuwara Eliya districts. It is useful to study the spread of dengue and the role of climate in Central Province. Dengue case data and entomology data is collected through MOH divisions for each district as well through the Regional Director of Health Services (RDHS) where daily, weekly and monthly data are being obtained.
Methods
Data Collection
Dengue Cases
We are collecting data on fine scale on Dengue cases and climate. We have collected data from 12 MOH divisions in Kandy district until 2018 April and obtained up to April 2018 data from the RDHS Matale. The table below summarizes the data that was accessed.
Table 1: Dengue case data accessed from MOH divisions in Central Province
Weekly
Monthly
Yearly
Districts
2005-2014
1997-2017
1997-2017
Kandy MOHs
2001-2010
2001-2010
Matale MOHs
2004-2018April
2004-2018April
2004-2018April
Nuwara Eliya MOHs
2004-2008
2004-2008
Entomological Data
Entomological data is crucial to analyze and identify the relationship with the temporal and spatial distribution of dengue incidences. The data collected includes the dengue vector indices, namely, container index, house index and breteau index for Ae. aegypti and Ae. albopictus and the different breeding sites of these vectors. Entomological investigations are being carried out at 10 sentinel sites covering 10 high dengue risk areas in the Kandy district .Monthly sampling data on vector density of these high dengue transmission MOH areas are available at the Regional Office of the Anti-malaria campaign in Kandy and at the relevant MOH Offices.
Table 2: Entomological data accessed from MOH divisions in Central Province
Monthly
Yearly
Kandy
2001-2017
2001-2017
Matale
2015-2018
2015-2018
Nuwara Eliya
–
–
Quality Assessment
Where possible data shall be verified against the reports with health officials. We shall be cross checking meteorological data with those in neighboring stations and with satellite data for accuracy.
Exploratory Data Analysis
Time Series
To look at the evolution of dengue cases over the record we plot a time series. the time series from 1997-2017 is shown in Figure 1 and the data is complete and consistent with neighboring districts and with aggregates of MOH level data. The highest dengue cases were observed in the year 2017.
Figure 1: Dengue cases in Sri Lanka from 1997-2017
Yearly Cycle
Yearly cycle was used to identify relationship between dengue cases and climate variables for Kandy district from 2008-2017 (Figure 2).
Figure 2: Average number of cases of Dengue by month for Kandy is shown as the black line. The monthly average Rainfall is shown as the gray shading; the monthly average Minimum temperature is shown as the yellow bar; the monthly average Maximum temperature is shown as the orange bar from 2008 to 2017.
According to the above graph the month with the highest dengue incidence are closely clustered at a maximum temperature 29-30 °C of and a minimum temperature of 23-24 °C. The peak month for rainfall is November and the peak month for maximum and minimum temperature is March.
Scatter Plots
We use scatter plots to reveal relationships between variables of interest such as dengue cases and climatic factors such as rainfall and temperature in an exploratory mode. We have monthly estimates for cases for Kandy and rainfall, minimum and maximum temperature estimated at Katugastota The scatter plots in Figure 3 show the relationship between dengue incidents and climatic properties (Monthly rainfall, Min/Max temperature) from 2008-2017 (Figure 2).
Figure 3: The monthly number of Dengue cases in Kandy district is plotted against Minimum temperature as yellow squares; Maximum temperature as orange triangle; Rainfall as blue dots from 2008 to 2017.
Correlation Analysis Composite Analysis
Virology and Immunology
Scientific literature is being carried out to assess changing serotypes in Sri Lanka and changes in Human immunity (Figure 4). We plan to identify the relationships between prevalence of different species of dengue vectors and dengue incidence by sub-district (MOH), district, region and season and to quantify human immunity to different strains of dengue.
Figure 4: The changing percentage of the DENV-1, DENV-2, DENV-3 and DENV-4 serotypes from 1989 to 2006 (Raheel et al., 2011).
Future Work
We expect to compile data for dengue analysis at district, sub-district scale for dengue incidence, interpolated climate, entomology and its quality control, gap filling, interpolation, and exploratory data analysis. We shall review local history of human immunity and quality control. We will be carrying out vulnerability analysis using demographic data and look into history of control programs. We shall be implementing a model for vector abundance assuming there is a relationship between vector prevalence and dengue risk as literature has proven this in many regions. We shall implement this model for the target sub-districts. Such understanding will assist with dengue control and policy making in advance. It is necessary to collect data in areas where there is inadequate sufficient entomological data. Therefore we have purchased microscopes and other equipment for entomological surveillance. We will also undertake household larval surveys and weather observations.
Flyer- Climate Sensitivity of Dengue in the Central Province
