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Accurate estimation of ET0, and determination of its trend/variability and sensitivity to changes in climate variables is essential at regional scale for different applications. These are attempted for India by considering 18 homogeneous ET0 regions formed in companion paper. Food and Agriculture Organization (FAO) recommends Penman–Monteith (PM) equation for ET0 estimation, which requires information on several climate variables. The equation cannot be used in data‐sparse areas where information/forecasts on one or more required climate variables is unavailable/unreliable. To address this, relevance vector regression (RVR) relationships are developed in this paper for the 18 homogeneous regions to arrive at FAO‐PM estimate of ET0 from subsets of its predictor climate variables, which could be typically expected in data‐sparse areas. The developed relationships are shown to be better in arriving at ET0 estimates when compared to multiple linear regression (MLR) relationships and three widely used empirical equations (Hargreaves, Mcguinness–Bordne, Priestly–Taylor). Regional trend analysis revealed that ET0 is significantly decreasing (increasing) in most regions located in north (south) India. Sensitivity of ET0 and surface runoff to changes in their predictor climate variables is determined for each region by considering third‐order Taylor series approximation of their functional relationships. Key climate variable(s) that govern ET0 changes in each region are identified. Results indicate that at annual scale solar radiation and relative humidity govern ET0 changes in regions located in south and north‐east India, whereas wind speed followed by relative humidity mostly influence the ET0 changes over other regions. Existence of evaporation paradox in four regions and validity of Bouchet's complementary relationship between ET0, actual evapotranspiration (ETa), and precipitation in 11 regions is also established. Significant divergence in trend of ET0 and ETa was evident in north‐east India. [ABSTRACT FROM AUTHOR]

Urban lakes in many places around the world are rapidly becoming vulnerable because of such factors as urbanization, climate change, anthropogenic pollutant inputs, etc. The influence of such forcing factors on lakes hydrology must be correctly recognized and addressed in order to protect them over the long term. Facing similar challenges, Sukhna Lake, an urban lake in northern India, has apparently dried up frequently in the recent past. Numerous hypotheses were subsequently proposed to isolate the possible factors affecting the lake and its water budget, including the potential impacts of land use changes, climate change, anthropogenic activities and other natural processes. Using meteorological data, lake‐catchment information and a hydrologic model, these hypotheses were comprehensively analysed. Relevant data on rainfall, wind, temperature, lake inflows, groundwater, lake physical characteristics, catchment land uses, soil texture, etc., were gathered for the analysis. A temporal trend analysis of factors relevant to these hypotheses was undertaken to identify critical drivers of hydrological changes. A sensitivity analysis also was performed, using the lake water budget, to determine and prioritize the predominant factors affecting the lake, leading to the creation of an annual lake water budget for the period from 1971 to 2013, highlighting the lake inflows and outflows. The lake annual inflow (catchment run‐off) was computed by adopting a rainfall–run‐off model based on the SCS‐curve number. Lacking any anthropogenic water withdrawals, the outflow was quantified by estimating the evaporation loss (using the FAO‐based Penman–Monteith Equation). The results of the present study  indicate that the process of siltation and the construction of check dams in the catchment, rather than urbanization and climate change, were the dominating reasons contributing to changes in the lake hydrology, and affecting the lake most in recent years. [ABSTRACT FROM AUTHOR]

The article reports on the study of the historical climate variations and climate change in the Brahmaputra basin such as Tibetan plateau (TP), Himalayan belt and the floodplains (FP). The study analyses the historical trends in temperature and precipitation of the TP, FP, and Himalayan belt through a global 100 year monthly high resolution dataset. It shows that precipitation in the basin is determined by the strength by the monsoons. It expresses that El Niño Southern Oscillation teleconnection does not have an important role in the changes in monsoon precipitation. The study also demonstrates the relationship among air temperature, FP, TP, and the strength of the monsoon.