C. T. Dhanya

Increasing probability of mortality during Indian heat waves

An increase of 0.5°C in summer mean temperatures increases the probability of mass heat-related mortality in India by 146%. Rising global temperatures are causing increases in the frequency and severity of extreme climatic events, such as floods, droughts, and heat waves. We analyze changes in summer temperatures, the frequency, severity, and duration of heat waves, and heat-related mortality in India between 1960 and 2009 using data from the India Meteorological Department. Mean temperatures across India have risen by more than 0.5°C over this period, with statistically significant increases in heat waves. Using a novel probabilistic model, we further show that the increase in summer mean temperatures in India over this period corresponds to a 146% increase in the probability of heat-related mortality events of more than 100 people. In turn, our results suggest that future climate warming will lead to substantial increases in heat-related mortality, particularly in developing low-latitude countries, such as India, where heat waves will become more frequent and populations are especially vulnerable to these extreme temperatures. Our findings indicate that even moderate increases in mean temperatures may cause great increases in heat-related mortality and support the efforts of governments and international organizations to build up the resilience of these vulnerable regions to more severe heat waves.

Changing characteristics of extreme wet and dry spells of Indian monsoon rainfall

Modeling of extreme events and its dynamic behavior have always been an intriguing topic. Increase in the magnitude and frequency of extreme events has widely been reported in recent decades, which is attributed to abrupt changes in climate. Numerous studies on extreme Indian monsoon characteristics, using a coarse-resolution data set, have pointed out significant changes in heavy precipitation pattern over India. However, these studies differ in their conclusions, emphasizing the need for a fine-resolution analysis. The present study aims to analyze the spatiotemporal variations and trends in the extreme (wet and dry) Indian monsoon precipitation, using 0.25° × 0.25° high-resolution gridded data for a period of 113 years (1901–2013). Significant increase in the maximum intensity of rainfall and spatial heterogeneity is observed over the past half century. In addition, significant negative trends in wet spell durations and positive trends in dry spell durations are observed over wet regions; whereas contrasting trends are observed over dry regions. A shift in the frequency distribution of extreme events during the monsoon period is also noticed. The 50 year return level of maximum intensity clearly shows positive trends over the past century. Though characteristics of extremes are observed to be highly localized, apparent signs of wet regions turning drier and dry regions turning wetter are obtained. A comprehensive insight into different characteristics (intensity, spell, onset, and frequency) of Indian monsoon extremes is provided, which will help in effective water resources management and flood/drought hazard preparedness.

Data Mining for Evolving Fuzzy Association Rules for Predicting Monsoon Rainfall of India

We used a data mining algorithm to evolve fuzzy association rules between the atmospheric indices and the Summer Monsoon Rainfall of All-India and two homogenous regions (Peninsular and West central). El Nino and Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation zonal wind index (EQWIN) indices are used as the causative variables. Rules extracted are showing a negative relation with ENSO index and a positive relation with the EQWIN index. A fuzzy rule based prediction technique is also implemented on the same indices to predict the summer monsoon rainfall of All-India, Peninsular, and West central regions. Rules are defined using a training dataset for the period 1958-1999 and validated for the period 20002006. The fuzzy outputs of the defined rules are converted into crisp outputs using the weighted counting algorithm. The variability of the summer monsoon rainfall over the years is well captured by this technique, thus proving to be efficient even when the linear statistical relation between the indices is weak.

Downscaling of Precipitation in Mahanadi Basin, India Using Support Vector Machine, K-Nearest Neighbour and Hybrid of Support Vector Machine with K-Nearest Neighbour

The climate impact studies in hydrology often rely on climate change information at fine spatial resolution. Downscaling is a practice for obtaining local-scale hydrological variables from regional-scale atmospheric data that are provided by General Circulation Models. Among two downscaling methods, Statistical Downscaling is taken into account, as it offers less computational work as compared to Dynamic Downscaling and also provides us with a platform to use ensemble GCM outputs. In the present study, a Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Hybrid of Support Vector Machine (SVM) with K-Nearest Neighbor (KNN) approaches are proposed for Statistical Downscaling of precipitation at monthly time scale. To reduce the dimensionality of the dataset, the Principal Component Analysis (PCA) is also performed. The CanCM4 simulations are run through the calibrated and validated SVM, KNN and hybrid of SVM with KNN downscaling models to obtain future projections of precipitation values. A comparison is made between the models in this study.

Predictability and chaotic nature of daily streamflow

The predictability of a chaotic series is limited to a few future time steps due to its sensitivity to initial conditions and the exponential divergence of the trajectories. Over the years, streamflow has been considered as a stochastic system. In this study, the chaotic nature of daily streamflow is investigated using autocorrelation function, Fourier spectrum, correlation dimension method (Grassberger-Procaccia algorithm) and false nearest neighbour method. Embedding dimensions of 6-7 obtained, indicate the possible presence of low-dimensional chaotic behaviour. The predictability of the system is estimated by calculating the systems Lyapunov exponent. A positive maximum Lyapunov exponent of 0.167 indicates that the system is chaotic and unstable with a maximum predictability of only 6 days. These results give a positive indication towards considering streamflow as a low dimensional chaotic system than as a stochastic system. Prediction is done using local polynomial method for a range of embedding dimensions and delay times. The uncertainty in the chaotic streamflow series is reasonably captured through the ensemble approach using local polynomial method.

DATA MINING AND ITS APPLICATIONS FOR MODELLING RAINFALL EXTREMES

ABSTRACT Data mining is a new powerful technology which helps in extracting hidden predictive information (future trends and behaviours) from large databases and thus facilitating decision makers to make proactive, knowledge-driven decisions. In this paper, a brief overview of various data mining functionalities, and an extensive review of the works done on temporal data mining are discussed. Of the two frameworks of temporal data mining, one that of frequent episodes is discussed in detail by explicating the various algorithms developed so far. Also, a case study using one of the algorithms, Minimal Occurrences With Constraints And Time Lags (MOWCATL), for extracting the rules to explain the spatial and temporal variation for extreme events in India is discussed and the results are shown.

Regionalization of rainfall characteristics in India incorporating climatic variables and using self-organizing maps

Abstract Regionalization of rainfall or delineation of a region into areas having similar rainfall characteristics is useful for many hydrologic applications and water resources management. In this study, we aim to regionalize India into regions having similar rainfall and climatic characteristics using self-organizing maps (SOM), an artificial neural network algorithm derived clustering technique. The SOM algorithm is applied to observed gauge-based gridded Indian Meteorological Department (IMD) rainfall data (0.25° × 0.25°) for 34 years duration (1980–2013). Information about climatic variables like air temperature, specific humidity, geo-potential height, surface pressure, etc., which directly influence the rainfall characteristics is derived from the Modern Era-Retrospective Analysis for Research and Applications reanalysis data-set. Four cluster validity indices are used to identify the optimal number of clusters. While, 10 homogeneous regions are identified over India when accounting rainfall characteristics only, incorporation of climatic variables added more heterogeneity dividing India into 15 homogeneous rainfall regions. These 15 homogeneous rainfall zones effectively capture the spatial variability of rainfall and its spell-characteristics over India. Moreover, none of the 15 delineated regions are found to be heterogeneous when subjected to regional homogeneity test. The present study will aid in regional frequency analysis, forecasting and downscaling of rainfall, land-use management and agriculture planning.

Unravelling Diurnal Asymmetry of Surface Temperature in Different Climate Zones

Understanding the evolution of Diurnal Temperature Range (DTR), which has contradicting global and regional trends, is crucial because it influences environmental and human health. Here, we analyse the regional evolution of DTR trend over different climatic zones in India using a non-stationary approach known as the Multidimensional Ensemble Empirical Mode Decomposition (MEEMD) method, to explore the generalized influence of regional climate on DTR, if any. We report a 0.36 °C increase in overall mean of DTR till 1980, however, the rate has declined since then. Further, arid deserts and warm-temperate grasslands exhibit negative DTR trends, while the west coast and sub-tropical forest in the north-east show positive trends. This transition predominantly begins with a 0.5 °C increase from the west coast and spreads with an increase of 0.25 °C per decade. These changes are more pronounced during winter and post-monsoon, especially in the arid desert and warm-temperate grasslands, the DTR decreased up to 2 °C, where the rate of increase in minimum temperature is higher than the maximum temperature. We conclude that both maximum and minimum temperature increase in response to the global climate change, however, their rates of increase are highly local and depend on the underlying climatic zone.

Examination of mean precipitation and moisture transport in reanalysis products over India

Abstract Different reanalyses have been applied widely for various climate-related researches around the globe. However, the skill of these reanalyses to simulate the precipitation patterns at regional scales needs to be carefully assessed due to their known susceptibility to significant biases and inability to capture extremes, especially over complex climatic regions such as India. Considering the significance of extreme precipitation and flood events, this study attempts to relate atmospheric moisture transport to the occurrence of extreme precipitation events using reanalyses. The performance of six global reanalyses is analysed over India in examining the annual and seasonal precipitation characteristics from 1980 to 2013 using various statistical indices. MERRA-Land reasonably estimated precipitation over India, while NCEP data-sets overestimated significantly. The role of integrated water vapour transport (IVT) in two recent extreme precipitation events i.e. over Mumbai in the year 2005 and over Uttarakhand in 2013 was examined. The results point out to the presence of significantly large amount of atmospheric moisture over the corresponding regions, days before the extreme precipitation events. Analysis of IVT patterns can provide indication for occurrence of extreme precipitation in advance. This study will be helpful in establishing the reliability of reanalyses for hydrological and climate-related applications.

A constrained tuning approach for optimal pump operation

Water supply systems transport drinking water from a treatment plant and make it available to users’ taps. The main concern in the operation of a water supply system is to guarantee consumer demands under a choice of quantity and quality throughout the entire life span for the possible loading circumstances. However, in some circumstances, the present infrastructure may not be adequate to meet the customer’s requirements. In such a scenario, system modelling plays a significant role in suitable management of water supply systems. From the perspective of taking management decisions, valve throttling control and pump speed control are very significant. These operational complications can be addressed by manual control or by automatic control. The difficulty is the use of manual controls that bring down the efficiency of the system. An automatic control–based skill has been developed that links the process of the variable speed pump control or valve throttling control. By employing an automatic control, the pump can regulate its speed at all times to meet the real flow requirements of each load served. A pump operational policy is established by which all the reservoirs can be fed concurrently to meet their requirements without making excessive transients. The gain of non-linear controllers is tuned by using different tuning methods such as the Ziegler-Nichols and constraint tuning and the best tuning method estimated. A significant additional objective was developed, namely, the search of optimal pump speed using the Monte Carlo method.