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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]

The objective of this study is to apply and test a simple parametric water balance model for prediction of soil moisture regime in the presence of vegetation. The intention was to evaluate the differences in model parameterization and performance when applied to small watersheds under three different types of land covers ( Acacia, degraded forest and natural forest). The watersheds selected for this purpose are located in the sub-humid climate within the Western Ghats, Karnataka, India. Model calibration and validation were performed using a dataset comprising depth-averaged soil moisture content measurements made at weekly time steps from October 2004 to December 2008. In addition to this, a sensitivity analysis was carried out with respect to the water-holding capacity of the soils with the aim of explaining the suitability and adaptation of exotic vegetation types under the prevailing climatic conditions. Results indicated reasonably good performance of the model in simulating the pattern and magnitude of weekly average soil moisture content in 150 cm deep soil layer under all three land covers. This study demonstrates that a simple, robust and parametrically parsimonious model is capable of simulating the temporal dynamics of soil moisture content under distinctly different land covers. Also, results of sensitivity analysis revealed that exotic plant species such as Acacia have adapted themselves effectively to the local climate. [ABSTRACT FROM AUTHOR]

Summary: An understanding of the soil moisture variability is necessary to characterize the linkages between a region’s hydrology, ecology and physiography. In the changing land use scenario of Western Ghats, India, where deforestation along with extensive afforestation with exotic species is being undertaken, there is an urgent need to evaluate the impacts of these changes on regional hydrology. The objectives of the present study were: (a) to understand spatio-temporal variability of soil water potential and soil moisture content under different land covers in the humid tropical Western Ghats region and (b) to evaluate differences if any in spatial and temporal patterns of soil moisture content as influenced by nature of land cover. To this end, experimental watersheds located in the Western Ghats of Uttara Kannada District, Karnataka State, India, were established for monitoring of soil moisture. These watersheds possessed homogenous land covers of acacia plantation, natural forest and degraded forest. In addition to the measurements of hydro-meteorological parameters, soil matric potential measurements were made at four locations in each watershed at 50cm, 100cm and 150cm depths at weekly time intervals during the period October 2004–December 2008. Soil moisture contents derived from potential measurements collected were analyzed to characterize the spatial and temporal variations across the three land covers. The results of ANOVA (p <0.01, LSD) test indicated that there was no significant change in the mean soil moisture across land covers. However, significant differences in soil moisture with depth were observed under forested watershed, whereas no such changes with depth were noticed under acacia and degraded land covers. Also, relationships between soil moisture at different depths were evaluated using correlation analysis and multiple linear regression models for prediction of soil moisture from climatic variables and antecedent moisture condition were developed and tested. A regression model relating near-surface soil moisture (50cm) with profile soil moisture content was developed which may prove useful when surface soil moisture contents derived from satellite remote sensing are available. Overall results of this study indicate that while the nature of land cover has an influence on the spatio-temporal variability of soil moisture, other variables related to topography may have a more dominant effect. [Copyright &y& Elsevier]

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]