Abstract. We present a community data set of daily forcing and hydrologic response data for 671 small-to mediumsized basins across the contiguous United States (median basin size of 336 km 2 ) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the period 1980-2010 was generated for three basin spatial configurations -basin mean, hydrologic response units (HRUs) and elevation bands -by mapping daily, gridded meteorological data sets to the subbasin (Daymet) and basin polygons (Daymet, Maurer and NLDAS). Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the data set for community use and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model, calibrated using the shuffled complex evolution global optimization routine. After optimization minimizing daily root mean squared error, 90 % of the basins have NashSutcliffe efficiency scores ≥ 0.55 for the calibration period and 34 % ≥ 0.8. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow and, for a given aridity, fewer extreme error days are present as the basin snow water equivalent increases.

This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.

Water resources management decisions commonly depend on monthly to seasonal streamflow forecasts, among other kinds of information. The skill of such predictions derives from the ability to estimate a watershed’s initial moisture and energy conditions and to forecast future weather and climate. These sources of predictability are investigated in an idealized (i.e., perfect model) experiment using calibrated hydrologic simulation models for 424 watersheds that span the continental United States. Prior work in this area also followed an ensemble-based strategy for attributing streamflow forecast uncertainty, but focused only on two end points representing zero and perfect information about future forcings and initial conditions. This study extends the prior approach to characterize the influence of varying levels of uncertainty in each area on streamflow prediction uncertainty. The sensitivities enable the calculation of flow forecast skill elasticities (i.e., derivatives) relative to skill in either predictability source, which are used to characterize the regional, seasonal, and predictand variations in flow forecast skill dependencies. The resulting analysis provides insights on the relative benefits of investments toward improving watershed monitoring (through modeling and measurement) versus improved climate forecasting. Among other key findings, the results suggest that climate forecast skill improvements can be amplified in streamflow prediction skill, which means that climate forecasts may have greater benefit for monthly-to-seasonal flow forecasting than is apparent from climate forecast skill considerations alone. The results also underscore the importance of advancing hydrologic modeling, expanding watershed observations, and leveraging data assimilation, all of which help capture initial hydrologic conditions that are often the dominant influence on hydrologic predictions.

This paper describes a fully automated scheme that has provided calibrated 1-10-day ensemble river discharge forecasts and predictions of severe flooding of the Brahmaputra and Ganges Rivers as they flow into Bangladesh; it has been operational since 2003. The Bangladesh forecasting problem poses unique challenges because of the frequent life-threatening flooding of the country and because of the absence of upstream flow data from India means that the Ganges and Brahmaputra basins must be treated as if they are ungauged. The meteorological-hydrological forecast model is a hydrologic multimodel initialized by NASA and NOAA precipitation products, whose states and fluxes are forecasted forward using calibrated European Centre for Medium-Range Weather Forecasts ensemble prediction system products, and conditionally postprocessed to produce calibrated probabilistic forecasts of river discharge at the entrance points of the Ganges and Brahmaputra into Bangladesh. Forecasts with 1-10-day horizons are presented for the summers of 2003-07. Objective verification shows that the forecast system significantly outperforms both a climatological and persistence forecast at all lead times. All severe flooding events were operationally forecast with significant probability at the 10-day horizon, including the extensive flooding of the Brahmaputra in 2004 and 2007, with the latter providing advanced lead-time warnings for the evacuation of vulnerable residents.

Many of the largest rivers on the planet emanate from the Tibetan Plateau and the Himalayas (Fig. 1a), fed by glacial and snow melting and monsoon rainfall. Nearly 25% of the global population reside in the vast agrarian societies in the Yellow, Yangtze, Mekong, Irrawaddy, Ganges, Brahmaputra, and Indus river basins, each of which is subject to periods of widespread and long-lived flooding. Flooding remains the greatest cause of death and destruction in the developing world, leading to catastrophic loss of life and property. While almost every government in Asia has made substantial progress over the past two decades in saving the lives of victims of slow-onset flood disasters, such events remain relentlessly impoverishing. In India alone, an average 6 million hectares ( A new ensemble flood prediction scheme, with skill to 10 to 15 days, allowed people along the Brahmaputra to evacuate well in advance of floods in 2007/08.