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Poor quality water is a potential source for demand management in irrigated agriculture after essential treatment. Magnetic water treatment (MWT) is one of the emerging technologies among water treatment methods for the above-cited problems without harming the environment. The optimum configuration of the 7000 gauss magnetic treatment device and the effect of magnetized water on the yield parameters of the eggplant in a pot culture experiment were examined in this study. The maximum significant (P< 0.05) changes in physicochemical properties (pH, EC, total hardness, Langelier Saturation Index, Na, etc.) of water occurred at an optimized velocity of 0.8 m/s and less. The mean plant height was maximum under magnetized bore water treatment compared to all other treatments (P< 0.05). In particular, the average yield per plant was increased by 17 per cent in magnetized bore water treatment compared to the control. Overall, the results have shown that an optimum configuration of the water treatment device is needed before the start of the experiment based on magnetic strength. Moreover, magnetically treated irrigation water positively impacted plant growth parameters and the yield of the eggplant. Keywords: Irrigation, Magnetic water, Total hardness, Langelier Saturation Index, yield per plant

Water is a scarce, limited and precious resource and one of the costliest inputs in agriculture and horticulture. Most of the irrigation is by open flow systems having relatively low efficiencies of water application. Due to scarcity of water and latest advancements in technology, farmers have started using micro irrigation systems i.e. drips and micro sprinklers. Micro sprinklers are a modification of conventional sprinklers and drip The success of micro-irrigation depends on the capacity and the ability of the system to optimize distribution of water resulting in high yields and better quality. The micro-irrigation system has complete adaptability to automation. Hence it is needed to know the performance of different micro-sprinkler systems at changing pressure heads and at different overlaps. The success of micro-irrigation depends on the capacity and the ability of the system to optimize distribution of water resulting in high yields and better quality. The micro-irrigation system has complete adaptability to automation. Hence it is needed to know the performance of different micro-sprinkler systems at changing pressure heads and at different overlaps.

As a natural hazard, drought is a complex multivariate phenomenon that does not depend on only one hydrometeorological variable, as well as, considering a particular kind of drought may not be effective for drought management. Considering this, many multivariate drought indices have been developed based on linearity assumptions or conventional copulas assuming symmetric relationships among univariate drought indices. In this study, D-vine copula was applied to construct a four-dimensional index, named as Integrated Drought Index (IDI), by combining four univariate drought indices (Standardized Precipitation Index [SPI], Reconnaissance Drought Index [RDI], Standardized Soil moisture Index [SSI] and Standardized stream flow Drought Index [SDI]) to better reflect many hydrometeorological variables (precipitation, evapotranspiration, soil moisture and stream flow) and different kinds of drought (meteorological, agricultural and hydrological) simultaneously. Vine copula was used to solve nonlinear and asymmetric relationships among drought indices due to its flexibility over the free selection of copula(s) in each step of hierarchical structure in high dimensional modelling. The IDI was constructed for 1-and 4-month timescales for the upper Tapti basin of the central region in India. The performance of IDI was tested with dependence measures (Pearson's correlation coefficient, Mutual Information) and evaluated against the Terrestrial Water Storage Anomaly data derived from the Gravity Recovery and Climate Experiment (GRACE) mission. Spatial analysis of drought was carried out by fuzzy c-means (FCM) clustering algorithm with IDI. IDI based on vine copula solved the nonlinear and asymmetric relationships among different variables associated with the occurrence of droughts effectively with a reduction of uncertainty as compared to the single drought indices for different kinds of droughts. Analysis revealed spatially different drought risks in the upper and lower river basins. In general, the vine copula addresses nonlinear and asymmetric relationships that exist between the variables associated with natural hazards like drought.