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Accurate characterization of precipitation P at subdaily temporal resolution is important for a wide range of hydrological applications, yet large-scale gridded observational datasets primarily contain daily total P. Unfortunately, a widely used deterministic approach that disaggregates P uniformly over the day grossly mischaracterizes the diurnal cycle of P, leading to potential biases in simulated runoff Q. Here we present Precipitation Isosceles Triangle (PITRI), a two-parameter deterministic approach in which the hourly hyetograph is modeled with an isosceles triangle with prescribed duration and time of peak intensity. Monthly duration and peak time were derived from meteorological observations at U.S. Climate Reference Network (USCRN) stations and extended across the United States, Mexico, and southern Canada at 6-km resolution via linear regression against historical climate statistics. Across the USCRN network (years 2000–13), simulations using the Variable Infiltration Capacity (VIC) model, driven by P disaggregated via PITRI, yielded nearly unbiased estimates of annual Q relative to simulations driven by observed P. In contrast, simulations using the uniform method had a Q bias of −11%, through overestimating canopy evaporation and underestimating throughfall. One limitation of the PITRI approach is a potential bias in snow accumulation when a high proportion of P falls on days with a mix of temperatures above and below freezing, for which the partitioning of P into rain and snow is sensitive to event timing within the diurnal cycle. Nevertheless, the good overall performance of PITRI suggests that a deterministic approach may be sufficiently accurate for large-scale hydrologic applications. [ABSTRACT FROM AUTHOR]

The Environmental Modeling Center (EMC) at the National Centers for Environmental Prediction (NCEP) and the University of Washington (UW) run parallel drought monitoring systems over the continental United States based on the North American Land Data Assimilation System (NLDAS). The NCEP system uses four land surface models (LSMs): Variable Infiltration Capacity (VIC), Noah, Mosaic, and Sacramento (SAC). The UW system uses VIC, SAC, Noah, and the Community Land Model (CLM). An assessment of differences in drought characteristics using both systems for the period 1979-2008 was performed. For soil moisture (SM) percentiles and runoff indices, differences are relatively small among different LSMs in the same system. However, the ensemble mean differences between the two systems are large over the western United States-in some cases exceeding 20% for SM and runoff percentile differences. These differences are most apparent after 2002 when the NCEP system transitioned to use the real-time North American Regional Reanalysis (NARR) and its precipitation gauge station data. (The UW system went into real-time operation in 2005.) Experiments were performed to address the sources of uncertainties. Comparison of simulations using the two systems with different model forcings indicates that the precipitation forcing differences are the primary source of the SM and runoff differences. While temperature, shortwave and longwave radiation, and wind speed forcing differences are also large after 2002, their contributions to SM and runoff differences are much smaller than precipitation. [ABSTRACT FROM AUTHOR]

Retrospectively simulated soil moisture from an ensemble of six land surface/hydrological models was used to reconstruct drought events over the continental United States for the period 1920–2003. The simulations were performed at one-half-degree spatial resolution, using a common set of atmospheric forcing data and model-specific soil and vegetation parameters. Monthly simulated soil moisture was converted to percentiles using Weibull plotting position statistics, and the percentiles were then used to represent drought severities and durations. An ensemble method, based on an inverse mapping of the average of the individual model’s soil moisture percentiles, was also used to combine all models’ simulations. Major results are 1) all models and the ensemble reconstruct the known severe drought events during the last century. The spatial extents and severities of drought are plausible for the individual models although substantial among-model disparities exist. 2) The simulations are in more agreement with each other over the eastern than over the western United States. 3) Most of the models show that soil moisture memory is much longer over the western than over the eastern United States. The results provide some insights into how a hydrological nowcast system can be developed, and also early results from a test application within the University of Washington’s real-time national Surface Water Monitor and a review of the multimodel nowcasts during the southeastern drought beginning in summer 2007 are included. [ABSTRACT FROM AUTHOR]

• Land surface model used to develop Colorado River projections under climate change. • Streamflow declines mostly due to warming-induced shift from snowfall to rainfall. • Higher precipitation scenarios did not offset warming-induced streamflow reduction. • Streamflow contributions from subbasins remain consistent until end of 21st century. • Climate change spatial attribution informs water management of the Colorado River. Studies of climate change impacts in the Colorado River Basin have focused on the Upper Basin, finding that warming has exacerbated streamflow declines since 2000. Presently, it is unclear if the predicted high degree of interannual precipitation variability across the basin could overwhelm impacts of future warming and how this might vary in space. To address this, we employed an updated Variable Infiltration Capacity model framework to generate streamflow projections across the entire basin. Meteorological data were drawn from downscaled historical and future climate projections of eight climate models that best represent climatology. Results showed a high confidence of Far-Future (2066–2095) mean annual streamflow declines (relative to the Baseline, 1976–2005) at the Upper Basin, Lower Basin, and basin-wide scales (ensemble median trends of −5 % to −25 %). Streamflow declines were attributed to warming that diminished snowfall and melt during April to September and year-round increased soil evaporation from the Upper Basin, and overall precipitation declines in the Lower Basin. Widespread reductions in annual runoff and baseflow efficiencies indicated that precipitation increases did not offset the noted warming effects. Spatial coherence tests indicated that streamflow correlations among subbasins remained relatively constant in the future scenarios, though correlations with the Green River increased. This spatial attribution analysis is providing actionable information for water management in the Colorado River Basin under current and future conditions. [ABSTRACT FROM AUTHOR]

A data set of observed daily precipitation, maximum and minimum temperature, gridded to a 1/16° (~6 km) resolution, is described that spans the entire country of Mexico, the conterminous U.S. (CONUS), and regions of Canada south of 53° N for the period 1950-2013. The dataset improves previous products in spatial extent, orographic precipitation adjustment over Mexico and parts of Canada, and reduction of transboundary discontinuities. The impacts of adjusting gridded precipitation for orographic effects are quantified by scaling precipitation to an elevation-aware 1981-2010 precipitation climatology in Mexico and Canada. Differences are evaluated in terms of total precipitation as well as by hydrologic quantities simulated with a land surface model. Overall, orographic correction impacts total precipitation by up to 50% in mountainous regions outside CONUS. Hydrologic fluxes show sensitivities of similar magnitude, with discharge more sensitive than evapotranspiration and soil moisture. Because of the consistent gridding methodology, the current product reduces transboundary discontinuities as compared with a commonly used reanalysis product, making it suitable for estimating large-scale hydrometeorologic phenomena.