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Assessment of land surface temperature dynamics over the Bharathapuzha River Basin, India

John Jisha, Chithra N. R., Thampi Santosh G.

Acta Geophysica

2021

Vol. 69, no. 3

855--876

Anthropogenic interventions have altered the natural environment and afected many of its physical, chemical, and biological characteristics. Changes in land use-land cover (LULC) are one of the main drivers that alter the hydrologic cycle and cause signifcant impacts on local, regional, and even the global climate system. It is now widely recognised and accepted that climate change is one of the gravest problems that our planet Earth is facing at present. This study analyses the impact of LULC dynamics on the spatial and temporal variation of land surface temperature (LST) in an inter-state river basin, which also happens to be the largest river basin in the state of Kerala, India, viz. the Bharathapuzha river basin, during the period 1990–2017. LST time-series analysis (derived from Landsat) revealed that 98% of the river basin experienced LST less than 298 K in January 1990. Over time, along with changes in LULC, LST also increased; in 2017, about 7.82% of the river basin experienced LST greater than 312 K. A notable change in LULC that occurred during this period was the drastic increase in areas with high albedo. The seasonal curves of LST derived from MODIS data are strong evidence of the devastating impacts of change in LULC on LST and, in turn, on climate change. The major spatial and temporal components of change in LST in the study region were identifed by principal component analysis (PCA). The results of this spatiotemporal analysis spread over a period of 28 years can be used for formulating sustainable development policies and mitigation strategies against extreme climatic events in the river basin.

Trend analysis and SARIMA forecasting of mean maximum and mean minimum monthly temperature for the state of Kerala, India

Kabbilawsh P., Sathish Kumar D., Chithra N. R.

Acta Geophysica

2020

Vol. 68, no. 4

1161--1174

The development of temperature forecasting models for the state of Kerala using Seasonal Autoregressive Integrated Moving Average (SARIMA) method is presented in this article. Mean maximum and mean minimum monthly temperature data, for a period of 47 years, from seven stations, are studied and applied to develop the model. It is expected that the time-series datasets of temperature to display seasonality (and hence non-stationary), and a possible trend (due to the fact that the data spans 5 decades). Hence, the key step in the development of the models is the determination of the non-stationarity of the temperature time-series, and the transformation of the non-stationary time-series into a stationary time-series. This is carried out using the Seasonal and Trend decomposition using Loess technique and Kwiatkowski–Phillips–Schmidt–Shin test. Before carrying out this process, several preliminary tests are conducted for (1) fnding and flling the missing values, (2) studying the characteristics of the data, and (3) investigating the presence of the trend and seasonality. The non-stationary temperature time-series are transformed to stationary temperature time-series, by one seasonal diferencing and one frstorder diferencing. This information, along with the original time-series, is further utilized to develop the models using the SARIMA method. The parsimonious and best-ft SARIMA models are developed for each of the fourteen variables. The study revealed that SARIMA(2, 1, 1)(1, 1, 1)12 as the ideal forecasting model for eight out of the fourteen time-series datasets.