Advanced Rainfall Trend Analysis of 117 Years over West Coast Plain and Hill Agro-Climatic Region of India

Abstract

In this paper, the rainfall trend of the West Coast Plain and Hill Agro-Climatic Region is analyzed for 117 years (1901–2017). This region is a globally recognized biodiversity hotspot and known for one of the highest rainfall receiving regions in India. Rainfall grid dataset is used for the analysis of rainfall trends on monthly, seasonal, and decadal time scales. Modified Mann–Kendall’s test, Linear Regression, Innovative Trend Analysis, Sen’s Slope test, Weibull’s Recurrence Interval, Pearson’s Coefficient of Skewness, Consecutive Disparity Index, Kurtosis, and some other important statistical techniques are employed for trend analysis. Results indicate that the rainfall trend is significant in January, July, August, September as well as the Winter season. Among all the significant trends, January and July showed a decreasing rainfall trend. July has the highest contribution (30%) among all the obtained monotonic trend to annual rainfall and coincidentally has the highest trend magnitude. August and September months with a combined contribution of 30% to annual rainfall, show an increasing monotonic trend with high magnitude whereas Winter season shows a monotonic decreasing rainfall trend with comparatively low magnitudes. Decadal analysis along with the study of recurrence interval of excess and deficit years helps to understand the decadal rhythm of trend and the magnitude of extreme monthly and seasonal events. Skewness reveals that rainfall dataset of all the periodic results is right-skewed and the recurrence interval also supports the skewness results. Sharply decreasing rainfall in July and rising rainfall in August and September is predictive of the impact on agriculture, biodiversity and indicates the rainfall regime shift in the region.

Spatio-Temporal Trend Mapping of Precipitation and its Extremes Across Afghanistan (1951-2010)

“…Positive value of Kurtosis indicates the peakedness in data while negative value indicates flatness of the distribution. The CV, Skewness, and Kurtosis are used to describe the precipitation statistics for monthly, seasonal and annual timeseries (Li-Ge et al 2013;Saini et al 2020) . Higher the CV higher is the variability in temporal distribution and lower the value more sTable is the time series.…”

“…The presence of serial autocorrelation (correlation between n and n-k time series, where k is the lag) in the time series would sometimes mislead the trend results i.e., increase in the chances in rejecting null hypothesis of no trend (Piyoosh and Ghosh 2017). To overcome this issue, Modified Mann-Kendall Test (MMKT) devised by Hamed and Ramachandra Rao (1998) is used in this study (Hamed and Ramachandra Rao 1998;Saini et al 2020). The MMKT follows similar approach to Mann-Kendall test with variance correction in addition.…”

“…S)* is the corrected variance obtained after correction and cf represents the correction factor proposed by Rao et al (Saini et al 2020).…”

“…Trend analysis involves long term time series from a dense network of rain gauge stations. In order to fill the gap, the use of gridded precipitation data along with the station data are seeking wider attention (Aich et al 2017;Nashwan et al 2019;Saini et al 2020) of researchers. Constrained to data availability, very few studies are available on climate variability analysis in Afghanistan.…”

Spatio-temporal trend mapping of precipitation and its extremes across Afghanistan (1951–2010)

“…Higher value indicates data is wide spread from the mean and lower value indicates the data is concentrated around the mean value. (Li-Ge et al 2013;Saini et al 2020) . Higher the CV higher is the variability in temporal distribution and lower the value more sTable is the time series.…”