Abstract
Most studies of input data used in hydrological models have focused on flow; however, point discharge data negligibly reflect deviations in spatial input data. To study the effects of different input data sources on hydrological processes at the catchment scale, eight MIKE SHE models driven by station-based data (SBD) and remote sensing data (RSD) were implemented. The significant influences of input variables on water components were examined using an analysis of the variance model (ANOVA) with the hydrologic catchment response quantified based on different water components. The results suggest that compared with SBD, RSD precipitation resulted in greater differences in snow storage in the different elevation bands and RSD temperatures led to more snowpack areas with thinner depths. These changes in snowpack provided an appropriate interpretation of precipitation and temperature distinctions between RSD and SBD. For potential evapotranspiration (PET), the larger RSD value caused less plant transpiration because parameters were adjusted to satisfy the outflow. At the catchment scale, the spatiotemporal distributions of sensitive water components, which can be defined by the ANOVA model, indicate that this approach is rational for assessing the impacts of input data on hydrological processes.
Highlights
Model simulation is a principal approach for studying hydrological processes at the catchment scale; the accuracy of modelling results is dwarfed owing to the uncertainties of model parameterizations, model structures and input data [1]
These were revealed by comparing parameter values under STA to the three models setting with only one remote sensing input in table
Model using dissimilar data sources, results peaks and the simulated discharge model indicated that precipitation, temperature and had significant effects on barely explained deviations in input data reflected by flow hydrography peaks at the outlet station
Summary
Model simulation is a principal approach for studying hydrological processes at the catchment scale; the accuracy of modelling results is dwarfed owing to the uncertainties of model parameterizations, model structures and input data [1]. Kavetski et al [8] demonstrated a multitude of distinctions in the predicted hydrographs and calibrated parameters, with or without consideration of the input uncertainty of precipitation data with similar conclusions drawn by Xu et al [9]. A number of studies have illustrated that input data may profoundly influence predicted river runoff [11,12,13,14]. Little attention has been paid to the effect of different input data on the entire hydrological cycle at the catchment scale
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