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Impact of rainfall and slopes on soil organic carbon (SOC) dynamics was assessed under tea and coffee plantations, land covers, in Western Ghats Mountain (WGM). Severe water erosion in the western side of WGM by heavy rainfall and land sloppiness recorded less SOC (3.07%) compared to the eastern side (4.03%) in tea plantations. Similar trend in SOC was observed in coffee plantations, with 1.98% in western side and 3.25% in eastern side. Loss of soil organic carbon stock under tea plantations in western sides was 0.346 million tonnes and in coffee plantations it was 0.190 million tonnes. Hence, the total organic carbon loss was 0.536 million tonnes. Highest SOC content and stocks were recorded in steep slopes in tea and coffee plantations due to dense biomass cover and adoption of soil and water conservation measures. [ABSTRACT FROM AUTHOR]

Analysis of soil samples collected from 16 rice paddy fields located in the Western Ghats region was performed to quantify the concentration of Cu, Zn, Mn, Fe, Ni, Cr, Cd, and Pb using atomic absorption spectroscopy. High concentrations of these heavy metals were found in rice paddy fields regularly cultivated using agrochemicals. We compared this concentration with soils of rice paddy field that was not under cultivation. Cu, Zn, Mn, Fe, Cr, Ni, Pb, and Cd showed increases of 1.2, 1.3, 2.3, 2.2, 1.8, 2.8, 1.8, and 8.5 times, respectively, in the rice paddy fields cultivated with synthetic fertilizers such as NPK, urea, potash, diammonium phosphate, etc., and several categories of pesticides belonging to the class organophosphates, carbamates, and acetanilide herbicide. In contaminated sites, the heavy metals exhibited maximum correlation with soil moisture content (SMC) (Zn, Fe, Cr, Ni, and Cd), soil organic content (SOC) (Fe, Cr, Ni, and Cd), and water holding capacity (WHC) (Cu, Pb, and Cd) than those observed for the reference site. The principal component analysis (PCA) revealed a total of 77.944% variance of heavy metals contributed from WHC (40.259%), SMC (20.854%), and SOC (16.832%). This indicates the build-up of heavy metals in rice paddy soils under the strong influence of moisture content, water holding capacity, and organic carbon content of the soil. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were analyzed to study the vegetation dynamics over different meteorological subdivisions in India for the period 2000–2016. Soil moisture (SM), rainfall (RF), and land use land cover (LULC) data were analyzed to identify the climatic and anthropogenic drivers that cause vegetation changes at the subdivision scale. Principal component analysis and MK (Mann-Kendall) test showed significant greening trend over semi-arid regions of Northwest India (NWI) and South India (SI) while slight browning trend seen over some of the subdivisions in Indo-Gangetic (IG) plains and Western Ghats (WG). It is found that the NDVI has superior correlation with soil moisture compared with rainfall and the croplands (CL) found to have significant increasing trend over the NWI and SI. Increasing trend in soil moisture over the NWI and SI may have contributed to increase in CL area and the greening trend. Over IG plains, the NDVI showed moderate correlation with SM and RF, and the greening trend (browning trend) in some regions can be attributed to increase in natural vegetation mosaic (decrease of CL). The NDVI has shown browning trend over the core monsoon regions of Madhya Pradesh (an increase of barren lands over west MP and decrease of CL over east MP) and Western Ghats (significant decrease of CL over Konkan and Goa). This study revealed that the soil moisture and LULC changes are the major driving factors for the vegetation changes over majority of the subdivisions in India. [ABSTRACT FROM AUTHOR]

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Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and −14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs. [ABSTRACT FROM AUTHOR]

It is imperative to know the spatial distribution of vegetation trends in India and its responses to both climatic and non-climatic drivers because many ecoregions are vulnerable to global climate change. Here we employed the NDVI3g satellite data over the span of 35 years (1981/82–2015) to estimate vegetation trends and corresponding climatic variables trends (i.e., precipitation, temperature, solar radiation and soil moisture) by using the Mann–Kendall test (τ) and the Theil–Sen median trend. Analysis was performed separately for the two focal periods—(i) the earlier period (1981/82–2000) and (ii) later period (2000–2015)—because many ecoregions experienced more warming after 2000 than the 1980s and 1990s. Our results revealed that a prominent large-scale greening trend (47% of area) of vegetation continued from the earlier period to the later period (80% of area) across the northwestern Plain and Central India. Despite climatologically drier regions, the stronger greening trend was also evident over croplands which was attributed to moisture-induced greening combined with cooling trends of temperature. However, greening trends of vegetation and croplands diminished (i.e., from 84% to 40% of area in kharif season), especially over the southern peninsula, including the west-central area. Such changes were mostly attributed to warming trends and declined soil moisture trends, a phenomenon known as temperature-induced moisture stress. This effect has an adverse impact on vegetation growth in the Himalayas, Northeast India, the Western Ghats and the southern peninsula, which was further exaggerated by human-induced land-use change. Therefore, it can be concluded that vegetation trend analysis from NDVI3g data provides vital information on two mechanisms (i.e., temperature-induced moisture stress and moisture-induced greening) operating in India. In particular, the temperature-induced moisture stress is alarming, and may be exacerbated in the future under accelerated warming as it may have potential implications on forest and agriculture ecosystems, including societal impacts (e.g., food security, employment, wealth). These findings are very valuable to policymakers and climate change awareness-raising campaigns at the national level. [ABSTRACT FROM AUTHOR]