Short term forecasting of dry spells in dry zone of Sri Lanka

Abstract

Droughts and dry spells are a recurrent feature of the natural climate in the dry zone of Sri Lanka. The unpredictable pattern of dry spells cause significant damages to the agricultural system, livelihood of people and the economy of the country. This research was initiated to investigate the temporal and spatial variability of the starting time and the lengths of dry spells in the dry zone (DZ) of Sri Lanka using daily rainfall data (1950-2005) in 11 rain gauge locations and to explore the possibility of forecasting properties of critical dry spells. A review on statistical anlysis on dry spells noted that no studies were reported to predict the starting date or length of dry spells. The mean number of dry spells (≥ 7 dry days) per year, irrespective of locations, was 12 while the duration varied from 15 to 23 days with a mean of 19 days. The four longest dry spells within a year according to the time of occurrence were considered as critical dry spells. The mean lengths of such critical dry spells in the dry zone were 31, 33, 38 and 33 days respectively. The mean length of the critical dry spell increased from the first to the fourth in some locations while it decreased in some locations. In a few locations the longest spell occurred during the middle of the year, i.e. the third spell. Based on the results obtained on the temporal and spatial variability of critical dry spells, climate charts were developed to be used by the decision makers in the respective locations. Linear and non linear regression with or without autoregressive error models (p<0.05) were developed to forecast the starting dates of second, third and fourth critical dry spells separately for all locations. Validity of models were confirmed using various statistical indicators and they were also validated using an independant data set ( 2000-2005). It was not possible to develop standard models for the four critical dry spell length series separately. Thus one critical length series was formed by pooling all four series for a given location. New types of models known as non linear bilinear type with one, two or three customer-specific input variables were developed for each location separately. A new approach was developed to identify customer-specific input variables using the same series. The prediction performance of the proposed models was demonstrated using a real data set of 12 individual points. The results obtained in this study will be helpful in minimizing unexpected damage due to droughts and will help effective and efficient planning for farmers, irrigation engineers, coconut growers, policy makers and researchers.