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.
Highlights
Precipitation is one of most critical input variables for accurate hydrological simulation [1,2]
This study systematically analyzed the associated uncertainties originating from two popular multi-sensor and multi-satellite precipitation estimates (i.e., Tropical Rainfall Measurement Mission (TRMM) Version-7 real-time product 3B42RTv7 and post-real-time research product 3B42v7) and gauge-based precipitation in hydrological utility using two hydrological models
To quantify the relative contribution of input uncertainty and its interactions with parameter uncertainty and model structure uncertainty in streamflow modeling driven by the three precipitation datasets one by one as input, the variance decomposition method was applied to disaggregate the total uncertainty to seven components of potential sources
Summary
Precipitation is one of most critical input variables for accurate hydrological simulation [1,2]. Studies on the precipitation input impacts on the performance of hydrological models are fewer, compared to the attention paid to sophisticated rainfall-runoff modeling approaches [3]. SPE are capable of reproducing spatial-temporal precipitation at high resolutions, the accuracy in supporting reliable hydrological modeling is limited [12,13]. This is primarily because of the indirect observation and noisy retrieval of such high-resolution satellite precipitation [14] and resampling errors from coarse to fine resolution in hydrological utility [15]. SPE with random errors and biases could induce input uncertainty in streamflow and flood simulation, resulting in unreliable decision making or guidance in engineering and policy
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