2. Thesis and Dissertations
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Item Effect of Urbanization on Extreme Climate Indices and Compound Events in Kerala(National Institute of Technology Karnataka, Surathkal, 2024) VIJAY, ANJALIStudies 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° x 0.25° spatial resolution), temperature (1° x 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 and 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. Land Use and Land Cover (LULC) analysis gives essential information on how the region has evolved over time. Due to adverse environmental effects, the significant and widespread changes in the LULC resulting from human activities have become a pressing issue for decision planners and the Government. Kerala, an ecologically diverse state in India characterized by complex topography, has experienced substantial LULC transformations due to rapid urbanization. These changes were assessed by analyzing Landsat images from 1990 to 2020, utilizing two distinct machine learning classification techniques, namely Random Forest (RF) and Classification And Regression Trees (CART), within the Google Earth Engine (GEE) platform. ii Normalized Difference Vegetation Index (NDVI), Normalized Differences Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and bare soil index (BSI) are the indices used in addition to aid the accurate LULC classification. Results showed that the performance of RF is better than CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values indicate the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren plains. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted that the area of the barren class and the water class decreased steadily from 1990 to 2020. The study adopted a dynamic classification method using time-varying land cover data to classify the urban and rural grids. This approach provided a more comprehensive understanding of the urbanization process in Kerala over the past three decades. The study incorporated 24 extreme climate indices, including 12 temperature extremes and 12 precipitation extremes, tailored to the unique climatic features of Kerala state. Various statistical methods were applied to investigate changes in long-term trends and spatial variation of heat wave characteristics. Additionally, the research aimed to examine the spatio-temporal variation of heat wave-drought compound events in Kerala from 1951 to 2020. A comparative analysis was conducted to assess the intensity and duration of heat waves within compound events compared to individual heat wave events, providing insights into their distinct characteristics and patterns. By comparing the trends between urban and nearby rural grids, the study employed a method to estimate the impact of urbanization on climate extremes. This approach yielded valuable insights into the changes occurring in extreme weather events in Kerala and their connection to urbanization, enhancing our understanding of this relationship. Long-term trend analysis of extreme climate indices showed that more than 60% of the grids in the region had experienced a decrease in indices such as CWD, R10, R20, R25, RX5 day, PRCPTOT, and SDII, whereas 80% of grid points showed an increasing trend in indices like R95p, R99p, RX1 day, and R50. The rising incidence of CDD, coupled iii with a declining number of CWD occurrences in the state, signifies an extended period of drought conditions in Kerala. An increasing trend is observed in the extreme hot temperature indices. In contrast, all extreme cold indices demonstrate decreasing trends, indicating a concerning rate of decline in cold extremes alongside the rise in warm extremes. Results pointed out that urbanization has decreased light rain and increased extreme precipitation in urban areas. It is seen that urbanization has a statically significant positive effect on heat waves and compound events, with more substantial impacts observed for HWD, HWF, and HWL compared to HWA.Item Analysis of Influence of Land Use Land Cover and Climate Changes on Streamflow of Netravati Basin, India(National Institute Of Technology Karnataka Surathkal, 2023) Jose, Dinu Maria; G S, DwarakishMassive Land Use/Land Cover (LULC) change is a result of human activities. These changes have, in turn, affected the stationarity of climate, i.e., climate change is beyond the past variability. Studies indicate the effect of LULC change and climate change on the hydrological regime and mark the necessity of its timely detection at watershed/basin scales for efficient water resource management. This study aims to analyse and predict the influence of climate change and LULC change on streamflow of Netravati basin, a tropical river basin on the south-west coast of India. For future climate data, researchers depend on general circulation models (GCMs) outputs. However, significant biases exist in GCM outputs when considered at a regional scale. Hence, six bias correction (BC) methods were used to correct the biases of high-resolution daily maximum and minimum temperature simulations. Considerable reduction in the bias was observed for all the BC methods employed except for the Linear Scaling method. While there are several BC methods, a BC considering frequency, intensity and distribution of rainfall are few. This study used an effective bias correction method which considers these characteristics of rainfall. This study also assessed and ranked the performance of 21 GCMs from the National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and bias-corrected outputs of 13 Coupled Model Inter-comparison Project, Phase 6 (CMIP6) GCMs in reproducing precipitation and temperature in the basin. Four multiple-criteria decision-making (MCDM) methods were used to identify the best GCMs for precipitation and temperature projections. For the CMIP6 dataset, BCC-CSM2-MR was seen as the best GCM for precipitation, while INM-CM5-0 and MPIESM1-2-HR were found to be the best for minimum and maximum temperature in the basin by group ranking procedure. However, the best GCMs for precipitation and temperature projections of the NEX-GDDP dataset were found to be MIROCESM-CHEM and IPSL-CM5A-LR, respectively. Multi-Model Ensembles (MMEs) are used to improve the performance of GCM simulations. This study also evaluates the performance of MMEs of precipitation and temperature developed by six methods, including mean and Machine Learning (ML) techniques.The results of the study reveal that the application of an LSTM model for ensembling performs significantly better than models. In general, all ML approaches performed better than the mean ensemble approach. Analysis and mapping of LULC is essential to improve our understanding of the human-nature interactions and their effects on land-use changes. The effects of topographic information and spectral indices on the accuracy of LULC classification were investigated in this study. Further, a comparison of the performance of Support Vector Machine (SVM) and Random Forest (RF) classifiers was evaluated. The RF classifier outperformed SVM in terms of accuracy. Finally, the classified maps by RF classifier using reflectance values, topographic factors and spectral indices, along with other driving factors are used for making the future projections of LULC in the Land Change Modeler (LCM) module of TerrSet software. The results reveal that the area of built-up is expected to increase in the future. In contrast, a drop in forest and barren land is expected. The SWAT model is used to study the impacts of LULC and climate change on streamflow. The results indicate a reduction in annual streamflow by 2100 due to climate change. While an increase in streamflow of 13.4 % is expected due to LULC change by the year 2100 when compared to the year 2020. The effect of climate change on streamflow is more compared to LULC change. A reduction in change is seen in the streamflow from near to far future.