Evaluating the Temporal Dynamics of Uncertainty Contribution from Satellite Precipitation Input in Rainfall-Runoff Modeling Using the Variance Decomposition Method

Abstract

Satellite precipitation estimates (SPE), characterized by high spatial-temporal resolution, have been increasingly applied to hydrological modeling. However, the errors and bias inherent in SPE are broadly recognized. Yet, it remains unclear to what extent input uncertainty in hydrological models driven by SPE contributes to the total prediction uncertainty, resulting from difficulties in uncertainty partitioning. This study comprehensively quantified the input uncertainty contribution of three precipitation inputs (Tropical Rainfall Measurement Mission (TRMM) near-real-time 3B42RTv7 product, TRMM post-real-time 3B42v7 product and gauge-based precipitation) in rainfall-runoff simulation, using two hydrological models, the lumped daily Ge´nie Rural (GR) and distributed Coupled Routing and Excess STorage (CREST) models. For this purpose, the variance decomposition method was applied to disaggregate the total streamflow modeling uncertainty into seven components (uncertainties in model input, parameter, structure and their three first-order interaction effects, and residual error). The results showed that the total uncertainty in GR was lowest, moderate and highest when forced by gauge precipitation, 3B42v7 and 3B42RTv7, respectively. While the total uncertainty in CREST driven by 3B42v7 was lowest among the three input data sources. These results highlighted the superiority of post-real-time 3B42v7 in hydrological modeling as compared to real-time 3B42RTv7. All the input uncertainties in CREST driven by 3B42v7, 3B42RTv7 and gauge-based precipitation were lower than those in GR correspondingly. In addition, the input uncertainty was lowest in 3B42v7-driven CREST model while highest in gauge precipitation-driven GR model among the six combination schemes (two models combined with three precipitation inputs abovementioned). The distributed CREST model was capable of making better use of the spatial distribution advantage of SPE especially for the TRMM post-real-time 3B42v7 product. This study provided new insights into the SPE’s hydrological utility in the context of uncertainty, being significant for improving the suitability and adequacy of SPE to hydrological application.

Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China

“…In addition, we would like to characterize the influence of the input parameters on the hydrological simulation errors, which had also been reported in [51][52][53]. However, as the true values of the non-precipitation parameters are missing, it is hard to analyze the contributions of their errors to the final simulation errors.…”

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

“…For SRPs that have multiple versions, the gauge-corrected version was selected to avoid the known systematic biases found in the SRPs as compared to ground measurements (Jiang and Wang, 2019;Pellarin et al, 2020). The selected rainfall datasets include single and multi-sensor, with various merged and gauge-corrected products obtained from rain gauges, microwave sensors in low Earth orbits and infrared sensors on geostationary satellites (Maggioni and Massari, 2018;Thiemig et al, 2013;Golian et al, 2019). Moreover, six different datasets of air temperature (at 2 m above ground) are used for the calculation of potential evaporation, and they are obtained from the following reanalysis products: JRA-55, EWEMBI, WFDEI, MERRA-2, PGF and ERA5.…”

Suitability of 17 rainfall and temperature gridded datasets for largescale hydrological modelling in West Africa

“…CC BY 4.0 License. Gebremichael, 2011;Ma et al, 2018;Liu et al, 2017;Bhattacharya et al, 2019). Usually, the influence of temperature datasets in combination with rainfall datasets is not tested (e.g.…”