Spatiotemporal Rainfall Variability and Trend Analysis of Shimsha River Basin, India

Karnataka state has the second highest rainfed agricultural land in India, where agricultural output relies heavily on rainfall. The Shimsha basin, a sub-basin of Cauvery in the state, comes under a semi-arid region and predominantly consists of rainfed agricultural land. Rainfall patterns have changed dramatically with time resulting in frequent floods and droughts. Understanding the spatiotemporal distribution of rainfall and its change patterns in the area would benefit sustainable agriculture planning and water resources management practices. The current study aims to determine the variability and trend in rainfall. The daily rainfall data of the Shimsha basin from 1989 to 2018 is collected, and the annual, seasonal, and monthly rainfall totals and the number of rainy days are derived. All the time series are subjected to statistical methods to examine rainfall variability and trend. The mean, standard deviation, coefficient of variation (CV), and Standardized Anomaly Index are used for the preliminary and variability analysis, while the coefficient of skewness and kurtosis are used to understand the rainfall distribution characteristics. The homogenous and serially independent series are identified by homogeneity and serial correlation tests. The trend in the homogenous and serially independent series is identified by Mann-Kendall and Spearman's rank correlation tests, while the magnitude of the trend is quantified using the Sen's slope technique, and the trend change point is evaluated using the sequential Mann-Kendall test. Based on the study, the average rainfall in the study area is 801.86 mm, with CV ranging from 43.3 to 22.27%. The southwest monsoon (SWM) season brings the greatest rain to the basin, followed by the post-monsoon (PM), summer, and winter seasons. In the annual time frame, except one station, all other stations have shown significant or insignificant increasing trends. The seasonal rainfall has shown insignificant rising trends during the summer and winter seasons while insignificant increasing and decreasing trends during the PM season. The SWM season has indicated significant increasing trends, insignificant increasing and decreasing trends. Overall, the study area has noticed an increased annual and seasonal rainfall except for the post-monsoon season, during which the rainfall showed a considerable decline. The findings of the study are helpful in water resource management, agricultural planning, and socioeconomic development in the study area.

The detection and monitoring of pollution caused by gold mining using a vegetation cover index

This study explores how a vegetation cover (VC) index can be employed as a pollution warning tool in gold mining areas in the Northwest of Iran. The analysis included the following: (a) the extraction of normalized difference vegetation index (NDVI) maps from Landsat images in three zones, i.e., mining operations, upstream areas without any exploration, and the downstream area of the mining activities, (b) calculation of the zones' VC, (c) investigation of transformation trends in each pixel of VC time series using the Mann-Kendall trend test, (d) determination of the pixels with significant VC reduction and the significant starting points of the trend using the sequential Mann-Kendall test, (e) assessment of the correlation between the zones with significantly reduced VC, and (f) a correlation test between average monthly and annual climate parameters and VC. Our results indicate that although 51 ha of VC has been demolished around the mining activities areas (i.e., zone 1), an overall upward trend in vegetation with no chemical leakage is observed into the downstream area of the basin (i.e., zone 3). This upward trend can be mostly attributed to the increasing precipitation and decreasing temperature in the study period. The fact that the area downstream of the mine shows that the heap leaching method for gold mining in Andaryan mine is currently not damaging the vegetation, this likely means that there is no leakage to the surrounding environment from the mine. Our results further show that using NDVI in a pixel-based scale and statistical methods has a high potential to quantify the effects of human activities on surface biophysical characteristics.