Daily gridded (0.5°× 0.5°) rainfall data between 1971 and 2005 were used to detect spatial patterns of trend in rainfall and rainy days during the Indian Summer Monsoon (June to September). A non-parametric (Mann-Kendall test) method was used to test for monotonic trend at each grid level. The magnitude of trend is estimated using Sen's method. Further, a field significance test was applied to assess significant trend at an aggregated level over each meteorological subdivision. A statistically significant (α = 0.1) increasing trend of both rainfall and rainy days during the monsoon season was found over the east coast and Deccan Plateau region of India. Meteorological subdivisions over the west coast, western arid region and northeastern humid region showed significantly decreasing trends in both rainfall and rainy days. The northern hilly parts of the Himalaya were found to have a significantly increasing trend of rainfall but decreasing trend of rainy days. The north and central plains of India showed a decreasing trend of rainy days and the eastern plain was found to have a decreasing trend of rainfall during the summer monsoon period.

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A b s t r a c t. Soil temperature is an important factor in biogeochemical processes. On-site monitoring of soil temperature is limited in spatio-temporal scale as compared to air temperature data inventories due to various management difficulties. Therefore, empirical models were developed by taking 30-year long-term (1985-2014) air and soil temperature data for prediction of soil temperatures at three depths (5, 15, 30 cm) in morning (0636 Indian standard time) and afternoon (1336 Indian standard time) for alluvial soils in lower Indo-Gangetic plain. At 5 cm depth, power and exponential regression models were best fitted for daily data in morning and afternoon, respectively, but it was reverse at 15 cm. However, at 30 cm, exponential models were best fitted for both the times. Regression analysis revealed that in morning for all three depths and in afternoon for 30 cm depth, soil temperatures (daily, weekly, and monthly) could be predicted more efficiently with the help of corresponding mean air temperature than that of maximum and minimum. However, in afternoon, prediction of soil temperature at 5 and 15 cm depths were more precised for all the time intervals when maximum air temperature was used, except for weekly soil temperature at 15 cm, where the use of mean air temperature gave better prediction.

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