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Register a new userSpatiotemporal Analysis of Meteorological Drought Variability in the Indian Region Using Standardized Precipitation Index
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Added by
Mallenahalli Naresh Kumar Prof. Dr.
Publication date
2015
fields
Physics
and research's language is
English
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Grid (1{deg} latitude x 1{deg} longitude) level daily rainfall data over India from June to September for the years 1951 to 2007, generated by India Meteorological Department, was analyzed to build monthly time series of Standardized Precipitation Index (SPI). Analysis of SPI was done to study the spatial and temporal patterns of drought occurrence in the country. Geographic spread of SPI derived Area under Dryness (AUD) in different years revealed the uniqueness of 2002 drought with wide spread dryness in July. Mann-Kendal trend analysis and moving average based trends performed on AUD indicated increasing trend in July. The area under moderate drought frequency has increased in the most recent decade. Ranking of years based on Drought Persistency Score (DPS) indicated that the year 1987 was the severe-most drought year in the country. The results of the study have revealed various aspects of drought climatology in India. A similar analysis with the SPI of finer spatial resolution and relating it to crop production would be useful in quantifying the impact of drought in economic terms.
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Monthly rainfall data from June to October for 39 years was used to generate Standardized Precipitation Index (SPI) values based on Gamma distribution for a low rainfall and a high rainfall district of Andhra Pradesh state, India. Comparison of SPI, with actual rainfall and rainfall deviation from the mean indicated that SPI values under-estimate the intensity of dryness/wetness when the rainfall is very low/very high respectively. As a result, the SPI in the worst drought years of 2002 and 2006 in the low rainfall district has indicated only moderate dryness instead of extreme dryness. The range of SPI values of the high rainfall district indicated better stretching, compared to that of the low rainfall district. Further, the SPI values of longer time scale (2-, 3- and 4- months) showed an extended range compared to 1-month, but the sensitivity in drought years has not improved significantly. To ascertain whether non normality of SPI is a possible reason, normality tests were conducted. The Shapiro-Wilk statistic, p-values and absolute value of the median confirmed normal distribution of SPI in both the districts whereas cumulative probability distribution of SPI indicated deviation from normal probability in the lower and upper ranges. Therefore, it is suggested that SPI as a stand alone indicator needs to be interpreted with caution to assess the intensity of drought. Further investigations should include; sensitivity of SPI to the estimated shape and scale at lower and upper bounds of gamma and impact of other distributions such as Pearson III on SPI computation, to complement the above results.
Assessments of impacts of climate change and future projections over the Indian region, have so far relied on a single regional climate model (RCM) - eg., the PRECIS RCM of the Hadley Centre, UK. While these assessments have provided inputs to various reports (e.g., INCCA 2010; NATCOMM2 2012), it is important to have an ensemble of climate projections drawn from multiple RCMs due to large uncertainties in regional-scale climate projections. Ensembles of multi-RCM projections driven under different perceivable socio-economic scenarios are required to capture the probable path of growth, and provide the behavior of future climate and impacts on various biophysical systems and economic sectors dependent on such systems. The Centre for Climate Change Research, Indian Institute of Tropical Meteorology (CCCR-IITM) has generated an ensemble of high resolution downscaled projections of regional climate and monsoon over South Asia until 2100 for the Intergovernmental Panel for Climate Change (IPCC)using a RCM (ICTP-RegCM4) at 50 km horizontal resolution, by driving the regional model with lateral and lower boundary conditions from multiple global atmosphere-ocean coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The future projections are based on three Representation Concentration Pathway (RCP) scenarios (viz., RCP2.6, RCP4.5, RCP8.5) of the IPCC.
Skilful prediction of the seasonal Indian summer monsoon (ISM) rainfall (ISMR) at least one season in advance has great socio-economic value. It represents a lifeline for about a sixth of the worlds population. The ISMR prediction remained a challenging problem with the sub-critical skills of the dynamical models attributable to limited understanding of the interaction among clouds, convection, and circulation. The variability of cloud hydrometeors (cloud ice and cloud water) in different time scales (3-7 days, 10-20 days and 30-60 days bands) are examined from re-analysis data during Indian summer monsoon (ISM). Here, we also show that the internal variability of cloud hydrometeors (particularly cloud ice) associated with the ISM sub-seasonal (synoptic + intra-seasonal) fluctuations is partly predictable as they are found to be tied with slowly varying forcing (e.g., El Ni~no and Southern Oscillation). The representation of deep convective clouds, which involve ice phase processes in a coupled climate model, strongly modulates ISMR variability in association with global predictors. The results from the two sensitivity simulations using coupled global climate model (CGCM) are provided to demonstrate the importance of the cloud hydrometeors on ISM rainfall predictability. Therefore, this study provides a scientific basis for improving the simulation of the seasonal ISMR by improving the physical processes of the cloud on a sub-seasonal time scale and motivating further research in this direction.
Tropical cyclones are one of the most powerful and destructive natural phenomena on earth. Tropical storms and heavy rains can cause floods, which lead to human lives and economic loss. Devastating winds accompanying cyclones heavily affect not only the coastal regions, even distant areas. Our study focuses on the intensity estimation, particularly cyclone grade and maximum sustained surface wind speed (MSWS) of a tropical cyclone over the North Indian Ocean. We use various machine learning algorithms to estimate cyclone grade and MSWS. We have used the basin of origin, date, time, latitude, longitude, estimated central pressure, and pressure drop as attributes of our models. We use multi-class classification models for the categorical outcome variable, cyclone grade, and regression models for MSWS as it is a continuous variable. Using the best track data of 28 years over the North Indian Ocean, we estimate grade with an accuracy of 88% and MSWS with a root mean square error (RMSE) of 2.3. For higher grade categories (5-7), accuracy improves to an average of 98.84%. We tested our model with two recent tropical cyclones in the North Indian Ocean, Vayu and Fani. For grade, we obtained an accuracy of 93.22% and 95.23% respectively, while for MSWS, we obtained RMSE of 2.2 and 3.4 and $R^2$ of 0.99 and 0.99, respectively.
Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Ni~no-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Also, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.
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