Abstract

Spatially distributed historical meteorological forcings (temperature and precipitation) are commonly incorporated into modeling efforts for long-term natural resources planning. For water management decisions, it is critical to understand the uncertainty associated with the different choices mad... Show moreSpatially distributed historical meteorological forcings (temperature and precipitation) are commonly incorporated into modeling efforts for long-term natural resources planning. For water management decisions, it is critical to understand the uncertainty associated with the different choices made in hydrologic impact assessments (choice of hydrologic model, choice of forcing dataset, calibration strategy, etc.). This paper evaluates differences among four commonly used historical meteorological datasets and their impacts on streamflow simulations produced using the Variable Infiltration Capacity (VIC) model. The four meteorological datasets examined here have substantial differences, particularly in minimum and maximum temperatures in high-elevation regions such as the Rocky Mountains. The temperature differences among meteorological forcing datasets are generally larger than the differences between calibration and validation periods. Of the four meteorological forcing datasets considered, there are substantial differences in calibrated model parameters and simulations of the water balance. However, no single dataset is superior to the others with respect to VIC simulations of streamflow. Also, optimal calibration parameter values vary across case study watersheds and select meteorological datasets, suggesting that there is enough flexibility in the calibration parameters to compensate for the effects of using select meteorological datasets. Evaluation of runoff sensitivity to changes in climate indicates that the choice of meteorological dataset may be as important in characterizing changes in runoff as climate change, supporting consideration of multiple sources of uncertainty in long-term planning studies. Show less