On the characterization of the fineâ€scale intermittency of turbulence

Some characterization of the fine-scale intermittency of turbulence is attempted utilizing the method of Kuo and Corrsin [J. Fluid Mech. 50, 285 (1971)] and the "envelope method" of Sreenivasan [J. Fluid Mech. 151, 81 (1985)]. It is found that the outcomes of these techniques are sensitively dependent on the details of the methods, and hence cannot be interpreted with complete confidence

Predictability of stream flow and rainfall based on ENSO for water resources management in Sri Lanka

We investigate the viability of using El Niño–Southern Oscillation (ENSO) and sea surface temperature (SST) data to predict seasonal streamflow for one of the major rivers in Sri Lanka, the Kelani, using correlation analysis, contingency tables, and principal component analysis. The agricultural seasons in Sri Lanka are Yala (April–September) and Maha (October–March). The correlation between the Kelani River streamflow during Yala and ENSO indices (r = −0.41) is significant at 99% level. In addition, the Kelani streamflow during Yala has a correlation with the Central Indian Ocean SST (r = −0.40) that is also significant at the 99% level. The first principal component of the Indo-Pacific Ocean SST is reminiscent of the SST associated with the ENSO mode. A prediction scheme based on this mode for the streamflow during Yala has a skill characterized by a correlation of 0.5 in a cross-validated mode. The prediction of streamflow during Maha is best carried out separately for the two halves of the season. During the El Niño phase, the rainfall during Maha is enhanced during the first half of the season (October–December) and diminished in the second half (January–February). Rainfall rather than streamflow has a better relationship with ENSO from October to December. During the second half of the Maha season, rainfall declines with both warm and cold ENSO phases and any prediction scheme has to take into account this non-linear relationship. Overall, useful skill for seasonal streamflow predictions has been demonstrated for the Yala season and skill for seasonal rainfall predictions for the first and second half of the Maha season has been elucidated

Epochal Changes in ENSO–Streamflow Relationships in Sri Lanka

In an effort to use climate predictions for streamflow and malaria hazard prediction, the decadal variability of the El Niño–Southern Oscillation (ENSO) influence on streamflow and rainfall in the Kelani River in Sri Lanka was investigated based on records from 1925 to 1995. In the last half century, the warm ENSO phase was associated with decreased annual streamflow and the cold ENSO phase with increased streamflow. The annual streamflow had a negative correlation (warm ENSO associated with low streamflow) with the concurrent ENSO index of Niño-3.4 that was significant at the 5% level. This negative correlation with Niño-3.4 is enhanced to a 1% significance level if the aggregate streamflow from January to September alone is considered. There has been a transition in correlation between January–September streamflow and ENSO between the 1950s and 1970s from near or above zero to negative values that have 95% significance levels reminiscent of an epochal shift. This shift was evident when considering the period when the southwest monsoon dominates (April–September) or when correlations were undertaken between the seasonal streamflow and rainfall and the ENSO index in the month prior to each season. Since the relationship between ENSO and Sri Lankan streamflow has strengthened in recent decades the potential for ENSO-based prediction is retained. The epochal shift may also explain why malaria epidemics ceased to co-occur frequently with El Niño episodes after 1945

The Strengthening Relationship between ENSO and Northeast Monsoon Rainfall over Sri Lanka and Southern India

Recently, it was reported that the relationship of the Indian southwest monsoon rainfall with El Niño–Southern Oscillation (ENSO) has weakened since around 1980. Here, it is reported that in contrast, the relationship between ENSO and the northeast monsoon (NEM) in south peninsular India and Sri Lanka from October to December has not weakened. The mean circulation associated with ENSO over this region during October to December does not show the weakening evident in the summer and indeed is modestly intensified so as to augment convection. The intensification of the ENSO–NEM rainfall relationship is modest and within the historical record but stands in contrast to the weakening relationship in summer. The intensification of the circulation is consistent with the warming of surface temperatures over the tropical Indian Ocean in recent decades. There is modestly intensified convection over the Indian Ocean, strengthening of the circulation associated with ENSO (Walker circulation), and enhanced rainfall during El Niño episodes in a manner consistent with an augmented ENSO–NEM relationship

Modulation of Sri Lankan Maha rainfall by the Indian Ocean Dipole

Investigating the September to December rainy season in Sri Lanka associated with the Maha rice growing season provides insights into the Asian monsoon during the boreal fall. Here, the modulation of the Maha rainfall by the tropical air-sea coupled phenomenon referred to as the Indian Ocean Dipole (IOD) is documented. The Maha rainfall has a strong and robust association with the IOD from 1869 to 2000. The anomalously warm sea surface in the western Indian Ocean associated with the positive IOD phase induces large scale convergence in the lower troposphere extending to Sri Lanka leading to the preponderant enhancement of Maha rainfall

Predictability of Sri Lankan rainfall based on ENSO

Investigating the year-round rainfall of Sri Lanka provides understanding into the South Asian monsoon system as it compliments studies on the Indian summer monsoon. The El Niño-Southern Oscillation (ENSO) is a primary mode of climate variability of this area. Here, the predictability of Sri Lanka rainfall based on ENSO is quantified based on composite analysis, correlations and contingency tables. The rainfall is modestly predictable based on ENSO during January-March, July-August and October-December. El Niño typically leads to wetter conditions during October to December and drier conditions during January to March and July to August on average. The correlations of ENSO indices with rainfall are statistically significant for October to December, January to March and July to August and an analysis based on contingency tables shows modest predictability. The use of ENSO indices derived from the central Pacific sea surfaces improves the predictability from January to June. The predictability in the mountain regions is diminished when garnering orographic rainfall. The predictability in the east is diminished during the cyclone season. The predictability based on ENSO for October to December rainfall is robust on a decadal scale while the predictability of January to March and July to August rainfall has acquired significance in recent decades. An ENSO-based scheme that is adapted to each season and region, and takes account of decadal variations can thus provide skillful rainfall predictions

Finescale Evaluation of Drought in a Tropical Setting: Case Study in Sri Lanka

In regions of climatic heterogeneity, finescale assessment of drought risk is needed for policy making and drought management, mitigation, and adaptation. The relationship between drought relief payments (a proxy for drought risk) and meteorological drought indicators is examined through a retrospective analysis for Sri Lanka (1960–2000) based on records of district-level drought relief payments and a dense network of 284 rainfall stations. The standardized precipitation index and a percent-of-annual-average index for rainfall accumulated over 3, 6, 9, and 12 months were used, gridded to a spatial resolution of 10 km. An encouraging correspondence was identified between the spatial distribution of meteorological drought occurrence and historical drought relief payments at the district scale. Time series of drought indices averaged roughly over the four main climatic zones of Sri Lanka showed statistically significant (p < 0.01) relationships with the occurrence of drought relief. The 9-month cumulative drought index provided the strongest relationships overall, although 6- and 12-month indicators provided generally similar results. Some cases of appreciable drought without corresponding relief payments could be attributed to fiscal pressures, as during the 1970s. Statistically significant relationships between drought indicators and relief payments point to the potential utility of meteorological drought assessments for disaster risk management. In addition, the study provides an empirical approach to testing which meteorological drought indicators bear a statistically significant relationship to drought relief across a wide range of tropical climates

Use of seasonal climate information to predict coconut production in Sri Lanka

Accurate forecasting of annual national coconut production (ANCP) is important for national agricultural planning and negotiating forward contracts. Climate and the long-term trends (attributed to 'technology') are major factors that determine ANCP. The effect of climate on ANCP of the following year was studied for the seven agro-ecological regions (AER's) in the principal coconut growing areas for the period 1950-2002. Climate was studied based on seasons aggregated by the monsoon calendar and by quarters that are consistent with the agricultural calendar. The use of quarterly seasons explained more of the variability of ANCP than the use of monsoon based seasons. January-March rainfall in all AER's and July-September rainfall in the wetter regions are positively correlated with the ANCP (p < 0.005). The technology effect was estimated using a log-linear trend model. The regression model integrates both climate and technology effects developed to predict ANCP with high fidelity (R2 = 0.94). The climate effect was estimated by regressing production data that had been de-trended to remove the technology effects with quarterly rainfall in the year prior to harvest. The most significant predictors were found to be the quarterly rainfall from the AER's in the coconut growing regions that are designated as wet and intermediate. Representative rainfall from each quarter was used in a regression model with corrections for the technology effect. The correlation between observed and predicted values of the ANCP was 0.83 (p < 0.001). The prediction of ANCP for 2003 and 2004 gave errors of only 6.5 and 7.0%. The estimated value of ANCP for 2005 is 2715 million nuts, which is 12% higher than the mean. The lead time of the prediction extends to 15 months but it may be extended with the use of seasonal climate forecasts to 24 months

Economic Value of Climate Variability Impacts on Coconut Production in Sri Lanka

This paper assesses the economic value of climate variability, employing a percentile analysis on an array of 31-years national annual coconut production data from 1971 to 2001. Of the production array, 10% and 90% percentiles have been considered respectively as lower and upper production extremes. The 60% of production departures of each year of extremes with respect to the mean production of 10% to 90% percentile were attributed to climate variability because studies show that the 60% of the variation of coconut production is explained by climate. These production deviations were then valued multiplying by free-on-board (FOB) prices of fresh coconuts. Results show that the foregone income from coconuts due to low rainfall varied between US$32 million to US$ 73 million while the incremental coconut income in crop glut extremes due to high rainfall varied between US$42 million and US$ 87 million. Results show that the climate variability causes income losses to the economy estimated at US$32 million to US$ 73 million in years of extreme crop shortage. And in years of extreme crop surplus, the economy realises income gains of US$42 million to US$ 87 million. These indicate the potential for significant economic benefits from investments in adaptations that would reduce variability in nut production which is caused by variations in climate. Further work is however needed to estimate the effectiveness and economic benefits that might be achieved from investments in adaptation

The role of soil moisture initialization in subseasonal and seasonal streamflow prediction: A case study in Sri Lanka

The two main contributors to streamflow predictability at subseasonal to seasonal timescales in tropical regions are: (i) the predictability of meteorologic (particularly precipitation) anomalies, and (ii) the land surface soil moisture state at the start of the forecast period. Meteorological predictions at subseasonal timescale are usually fraught with error and may not be dependable. The accurate initialization of soil moisture, as obtained through real-time land data analysis, may provide skill in subseasonal to seasonal streamflow prediction, even when the prediction skill for rainfall is small. A series of experiments using the Catchment Land Surface Model (CLSM) is performed to characterize the contribution of accurate soil moisture initialization to the skill of streamflow prediction in Sri Lanka at timescales up to 2 months. We find that at the monthly timescale, accurate soil moisture initialization provides between 10% and 60% of the total runoff prediction skill that could be obtained under a perfect prediction of meteorological forcing. Some contributions to streamflow forecast skill are also found for the second month of forecast