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Recent studies disagree on how rainfall extremes over India have changed in space and time over the past half century 1-4 , as well as on whether the changes observed are due to global warming 5,6 or regional urbanization 7 . Although a uniform and consistent decrease in moderate rainfall has been reported 1,3 , a lack of agreement about trends in heavy rainfall may be due in part to differences in the characterization and spatial averaging of extremes. Here we use extreme value theory [8][9][10][11][12][13][14][15] to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability. We show that when generalized extreme value theory 8,16-18 is applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches 2-4 . Furthermore, our space-time regression analysis of the return levels points to increasing spatial variability of rainfall extremes over India. Our findings highlight the need for systematic examination of global versus regional drivers of trends in Indian rainfall extremes, and may help to inform flood hazard preparedness and water resource management in the region.There is considerable debate in the recent literature about the nature of space-time trends in extreme rainfall over India 1-3 and their attribution to aspects of global change, specifically, global climate change 5,6 versus regional urbanization patterns 7 . Previous researchers have drawn a variety of conclusions 1-4 about trends in rainfall extremes during the Indian monsoon from a regular 1 • ×1 • (or similar) gridded daily rainfall dataset over India for 1951-2003. The differences can probably be attributed to the corresponding definitions of extremes, levels of spatial aggregation and areas of coverage. The use of fixed thresholds over a 12 • × 10 • box labelled as Central India suggested an increasing trend in rainfall extremes concurrent with decreasing moderate rainfall, resulting in no discernible net trends 1 . However, the use of variable percentile-based thresholds over each individual 1 • × 1 • grid 2 , analysis based on homogeneous regions 3 and analysis with a percentile-based definition of the frequency and intensity of rainfall extremes 4 showed no discernible spatially uniform trends in rainfall extremes over India. Field significance tests do not statistically support the hypothesis of increasing trends in heavy rain events 2,3 , and the region labelled as Central India in ref. 1 may not be meteorologically homogeneous 19 . However, the use of additional data (1901-2004; ref. 5) confirmed the findings of ref. 1 when identical definitions of extremes, aggregations and coverage were used. The often conflicting insights about Indian rainfall extremes in the recent literature point to the importance of effective characterization of extremes, especially for understanding and communicating their relevance to impacts and policy.Here we show that rainfall extremes over I...

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Access to daily high-resolution gridded surface weather data based on direct observations and over long time periods is essential for many studies and applications including vegetation, wildlife, soil health, hydrological modelling, and as driver data in Earth system models. We present Daymet V4, a 40-year daily meteorological dataset on a 1 km grid for North America, Hawaii, and Puerto Rico, providing temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset includes an objective quantification of uncertainty based on strict cross-validation analysis for temperature and precipitation results. The dataset represents several improvements from a previous version, and this data descriptor provides complete documentation for updated methods. Improvements include: reductions in the timing bias of input reporting weather station measurements; improvement to the three-dimensional regression model techniques in the core algorithm; and a novel approach to handling high elevation temperature measurement biases. We show cross-validation analyses with the underlying weather station data to demonstrate the technical validity of new dataset generation methods, and to quantify improved accuracy.

[1] Recent hydrologic studies on multivariate stochastic analysis have indicated that copulas perform well for bivariate problems. In particular, the Frank family of Archimedean copulas has been a popular choice for a dependence model. However, there are limitations to extending such Archimedean copulas to trivariate and higher dimensions, with very specific restrictions on the kinds of dependencies that can be modeled. In this study, we examine a non-Archimedean copula from the Plackett family that is founded on the theory of constant cross-product ratio. It is shown that the Plackett family not only performs well at the bivariate level, but also allows a trivariate stochastic analysis where the lower-level dependencies between variables can be fully preserved while allowing for specificity at the trivariate level as well. The feasible range of Plackett parameters that would result in valid 3-copulas is determined numerically. The trivariate Plackett family of copulas is then applied to the study of temporal distribution of extreme rainfall events for several stations in Indiana where the estimated parameters lie in the feasible region. On the basis of a given rainfall depth and duration, conditional expectations of rainfall features such as expected peak intensity, time to peak, and percentage cumulative rainfall at 10% cumulative time increments are evaluated. The results of this study suggest that while the constant cross-product ratio theory was conventionally applied to discrete type random variables, it is also applicable to continuous random variables, and that it provides further flexibility for multivariate stochastic analyses of rainfall.

Abstract. To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computationand data-intensive effort was conducted to organize a comprehensive hydrologic data set with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation -including meteorologic forcings, soil, land class, vegetation, and elevation -were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous US at refined 1/24 • (∼ 4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter data set was prepared for the macro-scale variable infiltration capacity (VIC) hydrologic model. The VIC simulation was driven by Daymet daily meteorological forcing and was calibrated against US Geological Survey (USGS) WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter data set may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous US. We anticipate that through this hydrologic parameter data set, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter data set will be provided to interested parties to support further hydro-climate impact assessment.

Despite the fact that Global Climate Model (GCM) outputs have been used to project hydrologic impacts of climate change using off-line hydrologic models for two decades, many of these efforts have been disjointed-applications or at least calibrations have been focused on individual river basins and using a few of the available GCMs. This study improves upon earlier attempts by systematically projecting hydrologic impacts for the entire conterminous United States (US), using outputs from ten GCMs from the latest Coupled Model Intercomparison Project phase 5 (CMIP5) archive, with seamless hydrologic model calibration and validation techniques to produce a spatially and temporally consistent set of current hydrologic projections. The Variable Infiltration Capacity (VIC) model was forced with ten-member ensemble projections of precipitation and air temperature that were dynamically downscaled using a regional climate model (RegCM4) and bias-corrected to 1/24° (~4 km) grid resolution for the baseline (1966-2005) and future (2011-2050) periods under the Representative Concentration Pathway 8.5. Based on regional analysis, the VIC model projections indicate an increase in winter and spring total runoff due to increases in winter precipitation of up to 20% in most regions of the US. However, decreases in snow water equivalent (SWE) and snowcovered days will lead to significant decreases in summer runoff with more pronounced shifts in the time of occurrence of annual peak runoff projected over the eastern and western US. In contrast, the central US will experience year-round increases in total runoff, mostly associated with increases in both extreme high and low runoff. The projected hydrological changes described in this study have implications for various 3 aspects of future water resource management, including water supply, flood and drought preparation, and reservoir operation.

Methodological choices can have strong effects on projections of climate change impacts on hydrology. In this study, we investigate the ways in which four different steps in the modeling chain influence the spread in projected changes of different aspects of hydrology. To form the basis of these analyses, we constructed an ensemble of 160 simulations from permutations of two Representative Concentration Pathways, 10 global climate models, two downscaling methods, and four hydrologic model implementations. The study is situated in the Pacific Northwest of North America, which has relevance to a diverse, multinational cast of stakeholders. We analyze the effects of each modeling decision on changes in gridded hydrologic variables of snow water equivalent and runoff, as well as streamflow at point locations. Results show that the choice of representative concentration pathway or global climate model is the driving contributor to the spread in annual streamflow volume and timing. On the other hand, hydrologic model implementation explains most of the spread in changes in low flows. Finally, by grouping the results by climate region the results have the potential to be generalized beyond the Pacific Northwest. Future hydrologic impact assessments can use these results to better tailor their modeling efforts.