Abstract
Abstract. Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be downscaled to a spatial and temporal resolution applicable for flood modeling. This study evaluates such downscaling and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation data set and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002–2011) in the Susquehanna River basin located in the northeastern United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity, and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall data set, while runoff simulations forced with the stage IV precipitation data set were considered as the runoff reference. Results show that the generated rainfall ensembles from the downscaled reanalysis product encapsulate the reference rainfall. The statistical analysis consists of frequency and quantile plots plus mean relative error and root-mean-square error statistics. The results demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven downscaled reanalysis data set compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the downscaled product has 3 times lower mean relative error than the original product; this ratio increases to 4 times for the simulated runoff values. The proposed downscaling scheme is modular in design and can be applied on any gridded satellite and reanalysis data set.
Highlights
Flooding is one of the costliest natural hazards, occurring repeatedly around the globe (e.g., Sampson et al, 2014; Hagen and Lu, 2011)
To assess the correspondence between sensor and reference rainfall data, we plotted the quantile values from TRMM3B42V7, Global Land Data Assimilation System (GLDAS), and downscaled ensemble-mean www.hydrol-earth-syst-sci.net/18/5077/2014/
GLDAS is available over a relatively long time period, which provides a good source of precipitation data for hydrological analyses and global flood hazard mapping
Summary
Flooding is one of the costliest natural hazards, occurring repeatedly around the globe (e.g., Sampson et al, 2014; Hagen and Lu, 2011). Flood frequency maps are not available for most regions around the world (Hagen and Lu, 2011) due to limited economic resources to support long-term observations; this results in lack of knowledge and data (e.g., groundbased rain gauge measurements). Global gridded precipitation data sets from satellites and reanalysis data sets derived from data assimilation systems are two main sources for deriving global flood hazard maps (Cloke et al, 2013; Kappes et al, 2012). Global reanalysis products can provide long-term precipitation data sets for frequency analyses of hydrologic extremes (e.g., floods, droughts). Used reanalysis products include the JRA-25 (Onogi et al, 2005), ERA40 (Bosilovich et al, 2008; Uppala et al, 2005), ERAInterim (Dee et al, 2011), GLDAS
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