This paper describes a publicly available, long-term , hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of 1 /168 latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915-2011 versus 1950-2000), 2) increased the spatial resolution from 1 /88 to 1 /168, and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community.

Stormwater management facilities are important elements of the civil infrastructure that can be sensitive to climate change, particularly to precipitation extremes that generate peak runoff flows. The design and anticipated performance of stormwater infrastructure is based on either the presumed characteristics of a "design rainstorm" or the continuous simulation of streamflow driven by a time series of precipitation. Under either approach, a frequency distribution of precipitation is required, either directly or indirectly, together with an underlying assumption that the probability distribution of precipitation extremes is statistically stationary. This assumption, and hence both approaches, are called into question by climate change. We therefore examined both historical precipitation records and simulations of future rainfall to evaluate past and prospective changes in the probability distributions of precipitation extremes across Washington State. The historical analyses were based on hourly precipitation records for the time period 1949-2007 from weather stations surrounding three major metropolitan areas of the state: the Puget Sound region (including Seattle, Tacoma, and Olympia), the Vancouver (WA) region (including Portland, OR), and the Spokane region. Changes in future precipitation were simulated using two runs of the Weather Research and Forecast regional climate model (RCM) for the time periods 1970-2000 and 2020-2050, statistically downscaled from the ECHAM5 and CCSM3 Global Climate Model and biascorrected against the SeaTac Airport rainfall record. Downscaled and bias-corrected hourly precipitation sequences were then used as input to the HSPF hydrologic model to simulate streamflow in two urban watersheds in central Puget Sound. Few statistically significant changes in extreme precipitation were observed in the historical records, with the possible exception of the Puget Sound. RCM simulations generally indicate increases in extreme rainfall magnitudes throughout the state, but the range of projections is too large to predicate engineering design, and actual changes could be difficult to distinguish from natural variability. Nonetheless, the evidence suggests that drainage infrastructure designed using mid-20 th century rainfall records may be subject to a future rainfall regime that differs from current design standards.

[1] Despite advances in physically based hydrologic models and prediction systems, longstanding statistical methods remain a fundamental component in most operational forecasts of seasonal streamflow. We develop a hybrid framework that employs gridded observed precipitation and model-simulated snow water equivalent (SWE) data as predictors in regression equations adapted from an operational forecasting environment. We test the modified approach using the semidistributed variable infiltration capacity hydrologic model in a case study of California's Sacramento River, San Joaquin River, and Tulare Lake hydrologic regions. The approach employs a principal components regression methodology, adapted from the Natural Resources Conservation Service, which leverages the ability of the distributed model to provide an added dimension to SWE predictors in a statistical framework. Hybrid forecasts based on data simulated at grid points acting as surrogates for ground-based observing stations are found to perform comparably to those based on their observed counterparts. When a larger selection of grid points are considered as potential predictors, hybrid forecasts achieve superior skill, with the largest benefits in watersheds that are poorly represented in terms of ground-based observations. Forecasts are also found to offer overall improvement over those officially issued by California's Department of Water Resources, although their specific performance in dry years is less consistent. The study demonstrates the utility of physically based models within an operational statistical framework, as well as the ability of the approach to identify locations with strong predictive skill for potential ground station implementation.Citation: Rosenberg, E. A., A. W. Wood, and A. C. Steinemann (2011), Statistical applications of physically based hydrologic models to seasonal streamflow forecasts, Water Resour. Res., 47, W00H14,

Abstract. We assess the significance of groundwater storage for seasonal streamflow forecasts by evaluating its contribution to interannual streamflow anomalies in the 29 tributary sub-basins of the Colorado River. Monthly and annual changes in total basin storage are simulated by two implementations of the Variable Infiltration Capacity (VIC) macroscale hydrology model -the standard release of the model, and an alternate version that has been modified to include the SIMple Groundwater Model (SIMGM), which represents an unconfined aquifer underlying the soil column. These estimates are compared to those resulting from basinscale water balances derived exclusively from observational data and changes in terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) satellites. Changes in simulated groundwater storage are then compared to those derived via baseflow recession analysis for 72 reference-quality watersheds. Finally, estimates are statistically analyzed for relationships to interannual streamflow anomalies, and predictive capacities are compared across storage terms. We find that both model simulations result in similar estimates of total basin storage change, that these estimates compare favorably with those obtained from basinscale water balances and GRACE data, and that baseflow recession analyses are consistent with simulated changes in groundwater storage. Statistical analyses reveal essentially no relationship between groundwater storage and interannual streamflow anomalies, suggesting that operational seasonal streamflow forecasts, which do not account for groundwater conditions implicitly or explicitly, are likely not detrimentally affected by this omission in the Colorado River basin.