Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China

Abstract

Satellite and reanalysis precipitation estimates of high quality are widely used for hydrological modeling, especially in ungauged or data-scarce regions. To improve flood simulations by merging different precipitation inputs or directly merging streamflow outputs, this study comprehensively evaluates the accuracy and hydrological utility of nine corrected and uncorrected precipitation products (TMPA-3B42V7, TMPA-3B42RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, and PERSIANN-CCS) from 2006 to 2018 on a daily timescale using the Coupled Routing and Excess Storage (CREST) hydrological model in two flood-prone tributaries, the Beijiang and Dongjiang Rivers, of the Pearl River Basin, China. The results indicate that (1) all the corrected precipitation products had better performance (higher CC, CSI, KGE’, and NSCE values) than the uncorrected ones, particularly in the Beijiang River, which has a larger drainage area; (2) after re-calibration under Scenario II, the two daily merged precipitation products (NSCE values: 0.73–0.87 and 0.69–0.82 over the Beijiang and Dongjiang Rivers, respectively) outperformed their original members for hydrological modeling in terms of BIAS and RMSE values; (3) in Scenario III, four evaluation metrics illustrated that merging multi-source streamflow simulations achieved better performance in streamflow simulation than merging multi-source precipitation products; and (4) under increasing flood levels, almost all the performances of streamflow simulations were reduced, and the two merging schemes had a similar performance. These findings will provide valuable information for improving flood simulations and will also be useful for further hydrometeorological applications of remote sensing data.

Inter-Comparison of Multiple Gridded Precipitation Datasets over Different Climates at Global Scale

“…Concurrently, JRA55, NCEP-NCAR and NCEP-DOE were adjusted to a 0.5 • resolution through the application of the inverse distance weighting interpolation method. These methods have been extensively utilized in prior studies and have shown a high level of effectiveness [11,15,53,54]. The daily rain gauge observations used in this paper were collected from the Global Summary of the Day database (https://data.noaa.gov, accessed on 1 April 2024) and the China Meteorological Administration website (http://data.cma.cn/, accessed on 1 April 2024).…”

“…These datasets exhibit substantial variability with respect to data sources, spatial coverage and resolution, temporal span and resolution, among other factors [8][9][10]. According to data sources, these precipitation datasets fall into four categories: (1) gauge-based, (2) satellite-based, (3) reanalysis and (4) merged [8,[11][12][13].…”

Space–Time Characterization of Extreme Precipitation Indices for the Semiarid Region of Brazil

“…Regions with large bodies of water, such as Sobradinho, have a considerable capacity to retain solar radiation, which makes these areas more suitable for data capture due to the occurrence of optical processes relevant to remote sensing [82]. This renders such data highly reliable for estimations regarding water bodies, such as hydrographic basins [83,84]. Throughout the period represented by the CDD (Figure 3a), precipitation occurred in a well-distributed manner.…”