Kosi-Ganga-River-Creek 35 years' Additive Time Series and Seasonal Analysis Using Remote Sensing Data

Abstract

Climate change effect can be observed around the globe but the most devastation is faced by economically weak and farmers in India. Kosi-Ganga-River-Creek area has witnessed frequent floods and heavy rainfall over the years. The study area is the creek where Kosi and Ganga river joins together in the Katihar district of Bihar, India. Two variables, total daily precipitation (PTot) and max daily air temperature (Tmax) (remote sensing climatological data) were fetched from ERA5 dataset using Google Earth Engine Coder to assess the climate change in the study area. The data shows stochastically fitting in further forecasting methods is as important to conduct as settings the approach reliability. This study applied exclusively time series analysis (such as decomposing a time series, seasonal subseries, and autocorrelation function (ACF) and lag time series) along with descriptive statistical analysis for both parameters of the dataset. The study found the changes in Tmax over the 30-year time period show significant variability in temperature. Tmax peaked during the year of 1990, 1995, 1998, 2004, 2008, 2012 and 2014 whereas a drop in Tmax before and after such rise was observed in the series pattern. The exponential increase in the seasonal monthly precipitation (Ptot) also correlates with the temperature increase. However, the increase is more during non-monsoon seasons like January, February and March. Although significant reduction in Ptot can be observed during May, June, July, August and September. The changes in Ptot and Tmax have caused severe damage to the agriculture and economy of the area. Thus it is essential to study climate change and forecast the probable changes in future along with other climatological conditions to mitigate the extreme weather effect. Without proper study, monitoring, assessment and management policies in Bihar will most likely continue to suffer due to agricultural losses, lively hood, life, economic losses and infrastructure.