Abstract:Satellite-based and reanalysis quantitative precipitation estimates are attractive for hydrologic prediction or forecasting and reliable water resources management, especially for ungauged regions. This study evaluates three widely used global highresolution precipitation products [Precipitation Estimation from Remotely Sensed Information using Artificial Neural NetworksClimate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42V7), and National Centers for Environment Prediction-Climate Forecast System Reanalysis (NCEP-CFSR)] against gauge observations with seven statistical indices over two humid regions in China. Furthermore, the study investigates whether the three precipitation products can be reliably utilized as inputs in Soil and Water Assessment Tool, a semi-distributed hydrological model, to simulate streamflows. Results show that the precipitation estimates derived from TRMM 3B42V7 outperform the other two products with the smallest errors and bias, and highest correlation at monthly scale, which is followed by PERSIANN-CDR and NCEP-CFSR in this rank. However, the superiority of TRMM 3B42V7 in errors, bias, and correlations is not warranted at daily scale. PERSIANN-CDR and 3B42V7 present encouraging potential for streamflow prediction at daily and monthly scale respectively over the two humid regions, whilst the performance of NCEP-CFSR for hydrological applications varies from basin to basin. Simulations forced with 3B42V7 are the best among the three precipitation products in capturing daily measured streamflows, whilst PERSIANN-CDR-driven simulations underestimate high streamflows and high streamflow simulations driven by NCEP-CFSR mostly are overestimated significantly. In terms of extreme events analysis, PERSIANN-CDR often underestimates the extreme precipitation, so do extreme streamflow simulations forced with it. NCEP-CFSR performs just the reverse, compared with PERSIANN-CDR. The performance pattern of TRMM 3B42V7 on extremes is not certain, with coexisting underestimation and overestimation.
This paper aims to investigate the uncertainty in simulated extreme low and high flows originating from hydrological model structure and parameters. To this end, three different rainfall-runoff models, namely GR4J, HBV and Xinanjiang, are applied to two subbasins of Qiantang River basin, eastern China. The Generalised Likelihood Uncertainty Estimation approach is used for estimating the uncertainty of the three models due to parameter values, henceforth referred as parameter uncertainty. Uncertainty in simulated extreme flows is evaluated by means of the annual maximum discharge and mean annual 7-day minimum discharge. The results show that although the models have good performance for the daily flows, the uncertainty in the extreme flows could not be neglected. The uncertainty originating from parameters is larger than uncertainty due to model structure. The parameter uncertainty of the extreme flows increases with the observed discharge. The parameter uncertainty in both the extreme high flows and the extreme low flows is the largest for the HBV model and the smallest for the Xinanjiang model. It is noted that the extreme low flows are mostly underestimated by all models with optimum parameter sets for both subbasins. The largest underestimation is from Xinanjiang model. Therefore it is not reliable enough to use only one set of the parameters to make the prediction and carrying out the uncertainty study in the extreme discharge simulation could give an overall picture for the planners.
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