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The Hargreaves Samani (HS) equation is one of the most promising approaches for estimation of reference evapotranspiration under data-scarce conditions. Many modifications of the HS equation have been performed under different climatic conditions using different approaches to improve the precision of the evapotranspiration (ET 0 ) estimates for use at different locations; the results have not been consistent.The purpose of this study was to review and to evaluate the two most promising parameters used for the calibration of the HS evapotranspiration equation, under the climatic conditions of Gangtok, East Sikkim, India. The calibration was based on solar radiation and temperature difference. The equation was calibrated for solar radiation coefficient, C H first and then for temperature constant, T H and temperature exponent, E H to estimate the extent of improvement in estimation. The resulting modification shows a significant reduction in error. When only C H is modified, the percentage bias error (PBIAS) value was reduced from 23.3 to 0.8%, 26.1 to 10.19% and 30.1 to 3.6% for daily, weekly and monthly scales respectively. Consistent results for all three time spaces were observed, when, C H = 0.001 and T H = 13.9 and E H = 0.56. The PBIAS value was À1.9, 7.7 and 1.3% respectively for the three time spans, with index of agreement near to 1. The proposed equation may be an alternative to the Penman-Monteith method under limiting data conditions.

The characterization of temporal and spatial variability of soil moisture is highly relevant for understanding the many hydrological processes, to model the processes better and to apply them to conservation planning. Considerable variability in space and time coupled with inadequate and uneven distribution of irrigation water results in uneven yield in an area Spatial and temporal variability highly affect the heterogeneity of soil water, solute transport and leaching of chemicals to ground water. Spatial variability of soil moisture helps in mapping soil properties across the field and variability in irrigation requirement. While the temporal variability of water content and infiltration helps in irrigation management, the temporal correlation structure helps in forecasting next irrigation. Kriging is a geostatistical technique for interpolation that takes into account the spatial auto-correlation of a variable to produce the best linear unbiased estimate. The same has been used for data interpolation for the C. T. A. E. Udaipur India. These interpolated data were plotted against distance to show variability between the krigged value and observed value. The range of krigged soil moisture values was smaller than the observed one. The goal of this study was to map layer-wise soil moisture up to 60 cm depth which is useful for irrigation planning

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