Faculty Publications
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Item Machine learning–based assessment of long-term climate variability of Kerala(Springer Science and Business Media Deutschland GmbH, 2022) Vijay, A.; Varija, K.Studies on historical patterns of climate variables and climate indices have attained significant importance because of the increasing frequency and severity of extreme events worldwide. While the recent events in the tropical state of Kerala (India) have drawn attention to the catastrophic impacts of extreme rainfall events leading to landslides and loss of human lives, a comprehensive and long-term spatiotemporal assessment of climate variables is still lacking. This study investigates the long-term trend analysis (119 years) of climate variables at 5% significance level over the state using gridded datasets of daily rainfall (0.25° × 0.25° spatial resolution) and temperature (1° × 1° spatial resolution) at annual and seasonal scales (south-west monsoon, north-east monsoon, winter and summer). Five trend analysis techniques including the Mann–Kendall test (MK), three modified Mann–Kendall tests and innovative trend analysis (ITA) test were performed in the study. It is evident from the trend analysis results that more than 83% of grid points were showing negative trends in annual and south-west monsoon season rainfall series (at a mean rate of 39.70 mm and 28.30 mm per decade respectively). All the trend analysis tests identified statistically significant increasing trends in mean and maximum temperature at annual and seasonal scales (0.10 to 0.20 °C/decade) for all grids. The K-means clustering algorithm delineated 59 grid points into five clusters for annual rainfall, illustrating a clear geographical pattern over the study area. There is a clear gradient in rainfall distribution and concentration inside the state at annual as well as seasonal scales. The majority of annual rainfall is concentrated in a few months of the year. That may lead the state vulnerable to water scarcity in non-rainy seasons. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Assessing the changing pattern of hydro-climatic variables in the Aghanashini River watershed, India(Springer Science and Business Media Deutschland GmbH, 2023) Yashas Kumar, H.K.; Varija, K.Growing population and climate change have altered the hydro-climatic trend from past decades. This manuscript analyses the abrupt shift in these time series and their changing pattern using historical data sets. The Pettitt test and the Standard Normal Homogeneity Test were used to evaluate the time series' homogeneity. The Concentration Index, Precipitation Concentration Index and Seasonality Index were employed to analyse the spatial variability of daily, monthly and seasonal rainfall patterns over the Aghanashini River watershed. Furthermore, the temporal trend in the rainfall, streamflow, and temperature time series was investigated using Mann–Kendall (MK) and the graphical Innovative-Şen (IŞ) test. Clear evidence of climate change impact on the rainfall and streamflow pattern was recognized, as there is an upward shift in the maximum temperature time series and a downward shift in the rainfall and streamflow time series after 2001. The rainfall indices showed that the watershed has fewer percentage of rainy days and stronger rainfall seasonality, indicating a possible risk of flash floods in the downstream of the watershed. Additionally, the results of the MK and IŞ trend tests paralleled each other and provided support for the findings emphasized by rainfall indices. © 2023, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.