A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing‐based runoff‐routing model to form the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real‐time Global Flood Monitoring System (GFMS). The GFMS uses real‐time satellite‐based precipitation to derive flood monitoring parameters for the latitude band 50°N–50°S at relatively high spatial (∼12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real‐time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research‐quality and real‐time satellite precipitation products. Evaluation results are slightly better for the research‐quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is ∼0.9 and the false alarm ratio is ∼0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi‐global domain. Validation using real‐time precipitation across the tropics (30°S–30°N) gives positive daily Nash‐Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

A new version of a real-time global flood monitoring system (GFMS) driven by Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall has been developed and implemented using a physically based hydrologic model. The purpose of this paper is to evaluate the performance of this new version of the GFMS in terms of flood event detection against flood event archives to establish a baseline of performance and directions for improvement. This new GFMS is quantitatively evaluated in terms of flood event detection during the TRMM era (1998–2010) using a global retrospective simulation (3-hourly and ⅛° spatial resolution) with the TMPA 3B42V6 rainfall. Four methods were explored to define flood thresholds from the model results, including three percentile-based statistical methods and a Log Pearson type-III flood frequency curve method. The evaluation showed the GFMS detection performance improves [increasing probability of detection (POD)] with longer flood durations and larger affected areas. The impact of dams was detected in the validation statistics, with the presence of dams tending to result in more false alarms and greater false-alarm duration. The GFMS validation statistics for flood durations >3 days and for areas without dams vary across the four methods, but center around a POD of ~0.70 and a false-alarm rate (FAR) of ~0.65. The generally positive results indicate the value of this approach for monitoring and researching floods on a global scale, but also indicate limitations and directions for improvement of such approaches. These directions include improving the rainfall estimates, utilizing higher resolution in the runoff-routing model, taking into account the presence of dams, and improving the method for flood identification.

We developed a hierarchical dominant river tracing (DRT) algorithm for automated extraction and spatial upscaling of basin flow directions and river networks using fine‐scale hydrography inputs (e.g., flow direction, river networks, and flow accumulation). In contrast with previous upscaling methods, the DRT algorithm utilizes information on global and local drainage patterns from baseline fine‐scale hydrography to determine upscaled flow directions and other critical variables including upscaled basin area, basin shape, and river lengths. The DRT algorithm preserves the original baseline hierarchical drainage structure by tracing each entire flow path from headwater to river mouth at fine scale while prioritizing successively higher order basins and rivers for tracing. We applied the algorithm to produce a series of global hydrography data sets from 1/16° to 2° spatial scales in two geographic projections (WGS84 and Lambert azimuthal equal area). The DRT results were evaluated against other alternative upscaling methods and hydrography data sets for continental U.S. and global domains. These results show favorable DRT upscaling performance in preserving baseline fine‐scale river network information including: (1) improved, automated extraction of flow directions and river networks at any spatial scale without the need for manual correction; (2) consistency of river network, basin shape, basin area, river length, and basin internal drainage structure between upscaled and baseline fine‐scale hydrography; and (3) performance largely independent of spatial scale, geographic region, and projection. The results of this study include an initial set of DRT upscaled global hydrography maps derived from HYDRO1K baseline fine‐scale hydrography inputs; these digital data are available online for public access at ftp://ftp.ntsg.umt.edu/pub/data/DRT/.

Hydrological models are usually calibrated against observed streamflow (Q_obs), which is not applicable for ungauged river basins. A few studies have exploited remotely sensed evapotranspiration (ET_RS) for model calibration but their effectiveness on streamflow simulation remains uncertain. This paper investigates the use of ET_RS in the hydrological calibration of a widely used land surface model coupled with a source–sink routing scheme and global optimization algorithm for 28 natural river basins. A baseline simulation is a setup based on the latest model developments and inputs. Sensitive parameters are determined for Q_obs and ET_RS-based model calibrations, respectively, through comprehensive sensitivity tests. The ET_RS-based model calibration results in a mean Kling–Gupta Efficiency (KGE) value of 0.54 for streamflow simulation; 61% of the river basins have KGE > 0.5 in the validation period, which is consistent with the calibration period and provides a significant improvement over the baseline. Compared to Q_obs, the ET_RS calibration produces better or similar streamflow simulations in 29% of the basins, while further significant improvements are achieved when either better ET or precipitation observations are used. Furthermore, the model results show better or similar performance in 68% of the basins and outperform the baseline simulations in 90% of the river basins using model parameters from the best ET_RS calibration runs. This study confirms that with reasonable precipitation input, the ET_RS-based spatially distributed calibration can efficiently tune parameters for better ET and streamflow simulations. The application of ET_RS for global scale hydrological model calibration promises even better streamflow accuracy as the satellite-based ET_RS observations continue to improve.