In economically developed countries, mortality increases distinctly during winter. Many causes have been suggested, including light-dark cycles, temperature/weather, and infectious agents. The authors analyzed monthly mortality in the United States during the period 1959-1999 for four major disease classes. The authors isolated the seasonal component of mortality by removing trends and standardizing the time series. They evaluated four properties: coincidence in mortality peaks, autocorrelation structure and autoregressive integrated moving average (ARIMA) models, magnitude, and age distribution. Peak months of mortality for ischemic heart disease, cerebrovascular disease, and diabetes mellitus coincided appropriately with peaks in pneumonia and influenza, and coefficients of autocorrelation and ARIMA models were essentially indistinguishable. The magnitude of the seasonal component was highly correlated with traditional measures of excess mortality and was significantly larger in seasons dominated by influenza A(H2N2) and A(H3N2) viruses than in seasons dominated by A(H1N1) or B viruses. There was an age shift in mortality during and after the 1968/69 pandemic in each disease class, with features specific to influenza A(H3N2). These findings suggest that the cause of the winter increase in US mortality is singular and probably influenza. Weather and other factors may determine the timing and modulate the magnitude of the winter-season increase in mortality, but the primary determinant appears to be the influenza virus.

This report first assesses the scale of the energy-irrigation nexus in South Asia. This is followed by a section describing what it would take to make a metered tariff regime work, the main comparison being with North China where such a regime does seem to work. The potential for indirect management of the groundwater economy through the specific mechanism of electricity pricing and supply policies is discussed.

Recent studies have shown an increasing trend in hydroclimatic disturbances like droughts, which are anticipated to become more frequent and intense under global warming and climate change. Droughts adversely affect the vegetation growth and crop yield, which enhances the risks to food security for a country like India with over 1.2 billion people to feed. Here, we compared the response of terrestrial net primary productivity (NPP) to hydroclimatic disturbances in India at different scales (i.e., at river basins, land covers, and climate types) to examine the ecosystems' resilience to such adverse conditions. The ecosystem water use efficiency (WUE : NPP/Evapotranspiration) is an effective indicator of ecosystem productivity, linking carbon (C) and water cycles. We found a significant difference (p < .05) in WUE across India at different scales. The ecosystem resilience analysis indicated that most of the river basins were not resilient enough to hydroclimatic disturbances. Drastic reduction in WUE under dry conditions was observed for some basins, which highlighted the cross-biome incapability to withstand such conditions. The ecosystem resilience at land cover and climate type scale did not completely relate to the basin-scale ecosystem resilience, which indicated that ecosystem resilience at basin scale is controlled by some other ecohydrological processes. Our results facilitate the identification of the most sensitive regions in the country for ecosystem management and climate policy making, and highlight the need for taking sufficient adaptation measures to ensure sustainability of ecosystems.

There is a great deal of geographic imbalance in global hydrologic data sets. Outside of the US and parts of Europe, there are many parts of the world that have only sparsely available streamflow gauge networks with only a few years' worth of data (Do et al., 2017;Fekete & Vörösmarty, 2007). Besides streamflow gauges, these regions also lack data on physiographic attributes such as geology and soil depth. Nevertheless, climate change is stressing these parts of the world, and accurate hydrologic simulations are needed for these regions just as much, or even more than for data-rich regions.Catchments across the world are often perceived as being unique from each other, requiring customized model development for each basin (Teutschbein & Seibert, 2012). As a rule of thumb, when we create process-based hydrologic models, our development effort scales roughly linearly to the modeled area, computational effort scales linearly at best, and accuracy is unrelated to the number of basins modeled. It is typically difficult to apply knowledge gained from one basin to another, as parameters or experiences do not transfer easily. As a result, although there have been calls for hydrologic studies to transcend the uniqueness

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