Spatiotemporal Patterns of Agricultural Drought in Sri Lanka: 1881–2010

Abstract

A spatiotemporal analysis of two well-known agricultural drought indices, the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Index at a 9-month scale (SPI-9), is presented for Sri Lanka. The analysis was conducted based on monthly precipitation and temperature data from January 1881 to December 2010 using 13 stations distributed across the three climatic zones of the country. Principal component analysis shows that the first two principal components of PDSI and SPI-9 are spatially comparable and could physically represent the two main monsoons. A wavelet analysis of these principal components' scores for both indices indicates a stronger association between the Northeastern monsoon and El-Niño in recent decades. Correlation analysis with agricultural metrics suggests that different indices might be appropriate for each of the climatic zones in Sri Lanka. PDSI correlated best with the intermediate zone districts; SPI-9 correlated best with the dry zone districts; but neither index correlated well with the wet zone districts.

Combined impact of local climate and soil properties on soil moisture patterns

“…The primary climate data were obtained from the Meteorological Department of Sri Lanka, which controls the distribution of the data. Missing data were infilled using methods described fully by Gunda et al (2016). Calculated soil moisture values can be reproduced using the PDSI Matlab code available as supplementary information in Jacobi et al (2013).…”

“…Long-term, monthly precipitation and average temperature data for the 13 stations, were obtained from the Meteorological Department of Sri Lanka and processed as outlined in Gunda et al (2016); the 13 stations capture the climate spatial variabilities of the country . PDSI was calculated on data from 1875 to 2016, with stable values achieved from Jan 1878 to Mar 2014.…”

Examining adaptations to water stress among farming households in Sri Lanka’s dry zone

“…We used stratified random sampling to select 30 Grama Niladhari ( GN ) divisions, the smallest administrative unit in Sri Lanka that typically comprises between 100 and 500 households living within 1–3 villages. Because of the potential regional variability in climate change impacts (Gunda et al 2016), the population of GN divisions was separated into three categories based on geographical location (i.e., North, North-Central, and Southeast). Weighting selection by sub-region size, eight to ten GN divisions were selected from each sub-region ( N  = 30); half of the GNs were selected from state-managed irrigation systems and half were not.…”

“…While increased drought severity is affecting the entire region (Disaster Information Management System 2012; Gunda et al 2016), access to irrigation water is expected to negatively correlate with farmer climate vulnerability and the need for adaptive farming strategies. Because irrigation water is used to mitigate climatic variability’s impacts on yields, our survey asked farmers about their levels of satisfaction with the irrigation water they received throughout the previous rice farming season (October 2014–February 2015).…”

“…Because each farmer’s contribution is important to the national rice supply, government programs provide fertilizer subsidies to all farmers (Davis et al 2016). Despite these efforts, however, farmers are challenged by an increase in climate change-related drought events (Gunda et al 2016). Our initial site survey, conducted from 2011 to 2012, found that dry zone farming households are employing a variety of adaptive strategies to buffer against climate change impacts.…”