Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

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)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(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.

In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics

“…The kinematic wave method is used for lateral transport of surface and subsurface runoff, while the diffusion wave (Muskingum-Cunge) method is used for river routing. The model has been applied in many regions, such as the Amazon (Beighley et al, 2009), Congo (Beighley et al, 2011), Susquehanna (Ray et al, 2016;Seyyedi et al, 2014Seyyedi et al, , 2015 and Ohio River Basin (Zhao & Beighley, 2016). In this application, the NLDAS domain was delineated into 74,075 river reaches and catchments based on a derived river network using a threshold area of roughly 100 km 2 .…”

Assessment and Comparison of TMPA Satellite Precipitation Products in Varying Climatic and Topographic Regimes in Morocco

“…Fortunately, recent advances in remote sensing have shown promise in addressing these inadequacies [9][10][11][12][13][14][15]. Satellite remote sensing datasets have been increasingly employed as an ancillary source of essential hydrologic measurements used for understanding and modeling hydrologic fluxes.…”

“…It is known that the precision of satellite products is dependent on a number of factors such as the terrain, precipitation type, and climate [15,27,28]. A number of studies have investigated the uncertainties of satellite precipitation over varying climatic, geographic, and topographic regimes, where it is known to both under-or overestimate actual precipitation [29][30][31][32][33][34][35][36].…”

Using high‐resolution satellite precipitation for flood frequency analysis: case study over the Connecticut River Basin

“…spatial variability, rain rate distributions, missing or falsely detecting rainfall areas), which lead to propagation in flow simulations. There are several studies (Brocca et al , ; Khan et al , ; Seyyedi et al , ) in the literature that have reported on these error differences in both precipitation and hydrological simulations. The added value of this study is that it examined those differences in the flood frequency domain rather than evaluating coincidental flow time series.…”