Abstract. A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect the meteorological uncertainty. The methodology is illustrated by an application to generate temperature and precipitation ensemble forecasts for the American River in California. Parameter estimation and dependent validation results are presented based on operational single-value forecasts archives of short-range River Forecast Center (RFC) forecasts and medium-range ensemble mean forecasts from the National Weather Service (NWS) Global Forecast System (GFS).

A multisensor applications development and evaluation system at the National Severe Storms Laboratory addresses significant gaps in both our knowledge and capabilities for accurate high-resolution precipitation estimates at the national scale. W ater is a precious resource and, when excessive or in short supply, a source of many hazards. It is essential to monitor and predict water-related hazards, such as floods, droughts, debris flows, and water quality, and to determine current and future availability of water resources. Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) provide key input to these assessments. [QPE and VSTQPF are hereafter referred to collectively as quantitative precipitation information (QPl}.] To meet these needs at the national scale, accurate QPI is needed at various temporal and spatial scales for the entire United States, its territories, and immediate surrounding areas. Temporal scales range from minutes to several hours for tiash flood prediction. QPI products can then be aggregated to support longer-term applications for water supply prediction. Spatial scales range from a few square kilometers or less for urban flash flood predictit)n.

Compared to conventional rain gauge networks, the Weather Surveillance Radar-1988 Doppler provides precipitation estimates at enhanced spatial and temporal resolution that River Forecast Centers can use to improve streamflow forecasts. This study documents differences between radar-derived (stage III) mean areal precipitation (MAPX) and rain gauge-derived mean areal precipitation (MAP). The area of study is the headwaters of the Flint River basin, specifically the Culloden basin located in central Georgia south of Atlanta, with a drainage area of 1853 mi 2. The timing of radar installations in the southeast United States provided overlapping data for only 2 yr (Jun 1996-Jul 1998). The MAP and MAPX products being examined were prepared using procedures identical to those employed operationally at the National Weather Service's Southeast River Forecast Center. Results show that the radar (MAPX) underestimates gauge-derived rainfall (MAP) by ϳ38% at the end of the 2-yr period. This underestimate is most pronounced during the winter months of November-April when MAPX underestimates MAP by ϳ50%. Comparisons during the summer (May-Oct) indicate that MAPX is similar to MAP. The underestimation of winter rainfall likely is due to several factors: the inappropriate combination of radar values in areas of overlapping coverage, the radar beam overshooting the tops of stratiform rainfall, an inappropriate Z-R relationship, faulty radar calibration, and too few hourly rain gauges to prepare an accurate stage II bias adjustment factor and quality control the stage III product.

Abstract. Data from the regulated 14,000 km 2 upper Des Moines River basin and a coupled forecast-control model are used to study the sensitivity of flow forecasts and reservoir management to climatic variability over scales ranging from daily to interdecadal. Robust coupled forecast-control methodologies are employed to minimize reservoir system sensitivity to climate variability and change. Large-scale hydrologic-hydraulic prediction models, models for forecast uncertainty, and models for reservoir control are the building blocks of the methodology. The case study concerns the 833.8 x 10 6 m 3 Saylorville reservoir on the upper Des Moines River. The reservoir is operated by the U.S. Corps of Engineers for flood control, low-flow augmentation, and water supply. The total record of 64 years of daily data is divided into three periods, each with distinct characteristics of atmospheric forcing. For each climatic period the coupled forecast-control methodology is simulated with a maximum forecast lead time of 4 months and daily resolution. For comparison, the results of operation using current reservoir control practices were obtained for the historical periods of study. Large differences are found to exist between the probabilistic long-term predictions of the forecast component when using warm or cool and wet or dry initial conditions in the spring and late summer. Using ensemble input corresponding to warm or cool and wet or dry years increases these differences. Current reservoir management practices cannot accommodate historical climate variability. Substantial gain in resilience to climate variability is shown to result when the reservoir is operated by a control scheme which uses reliable forecasts and accounts for their uncertainty. This study shows that such coupled forecast-control decision systems can mitigate adverse effects of climatic forcing on regional water resources.