Estimation of Evapotranspiration is vital role for proper water management and efficient farming activities. A decision support system (DSS_ET) was developed which supports 22 ET0 estimation methods with varied options for calculation of various intermediate parameters. The objective of the study is to estimate ET0 in the North central Plateau zone of Odisha, using weather data of the respective locality and screening of methods to estimate ET0 close to FAO-56 Penman Monteith method. The FAO-24 Penman(c=1) and Turc methods yielded the highest (5.605 mm/day) and the lowest mean ET0 (4.201 mm/day) respectively. For this zone, the highest ET0 values was found to be 10.32 mm/d for FAO-24 Penman(c=1) method followed by Businger-van Bavel (9.73 mm/d) and FAO-PPP-17-Penman (9.68 mm/d) in the month of May, whereas, lowest ET0 value was found in the month of December (2.54 mm/d) for the Priestly-Taylor method followed by 1982 Kimberly-Penman method (3.07 mm/d). Among all the methods, Penman-Monteith and Priestley-Taylor methods were ranked first and tenth respectively. For this zone, correction factor for Penman-Monteith and 1982 Kimberly-Penman methods approaches to one. The FAO-24 Penman (c=1) and Businger-van Bavel methods give more diversion from FAO-56 Penman-Monteith method.

Cotton is an immensely important crop for the sustainable economy of India and livelihood of the Indian cotton farming community. Identification of potential regions would help in increasing the productivity, ensures better utilization of available resources and avoid wastage of resources in the inefficient zones. Efficient cropping zones of the Cotton crop of Tamil Nadu were keyed out with 30 years data (1985-2015) using Relative Yield Index (RYI) and Relative Spread Index (RSI). The results reveled that in Tamil Nadu, fifteen districts were found for MECZ and three for ECZ. Coimbatore is most essential area for cotton crop. Similarly eight districts are coming under LECZ because RSI were very less compare to RYI. Tiruchirapalli and Ramanathapuram both the district have less RYI and RSI indicating NECZ for cotton crop.

The study aims to the Hydro-Estimator Method (HEM) has been used as the noble approach for analysing the heavy rainfall episodes over entire Odisha using the INSAT-3D satellite- derived rainfall estimates. The findings demonstrate that, in terms of the frequency of rainfall occurrences, INSAT-3D satellite rainfall products clearly illustrated both the spatial and temporal variability in rainfall pattern of Odisha. The performance statistics with IMERG and daily merged satellite retrieved rainfall show that both the dataset corelated well with the HEM with a small deviation. For heavy rainfall events, HEM shows good skill and correlation in detecting heavy rainfall with an accuracy of 20 mm and good pattern matching with actual rainfall. Entire Odisha is considered as study area, which is located in the eastern part of India. It comprises of 30 districts and of 314 blocks spreading over an area of 155707 km2. The state has 30 districts, which are further divided into 314 revenue blocks. The rainfall data (1 year; 2016) for all the blocks of Odisha has been obtained from Special Relief Commissioner (SRC), Government of Odisha and subjected to further analysis process. The satellite derived rainfall estimates viz., HEM, were evaluated with rain  gauge based gridded data viz., IMD-GRIDDED Dataset for the year 2016 over the Odisha region at the summer monsoon period. The satellite derived rainfall estimates HE have exhibited some good results with the IMD-GRIDDED Dataset its average R2 on a daily basis for different blocks is 0.34 and on a monthly basis it is 0.44. RMSE has also been determined to different block for JJAS month. The average RMSE on daily basis is 17.7 and on monthly basis is 94.6 as shown in bias maps.

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