Mizukami, NaokiÌý1Ìý;ÌýClark, MartynÌý2Ìý;ÌýMendoza, PabloÌý3Ìý;ÌýGutmann, EthanÌý4
1Ìý±·°ä´¡¸é
2Ìý±·°ä´¡¸é
3ÌýUniversity of Colorado at Â鶹¹ÙÍø
4Ìý±·°ä´¡¸é
Process-based hydrologic models are valuable tools to understand physical mechanism of hydrologic impact under climate change. However, such models require extensive meteorological forcing data, including precipitation, temperature, shortwave and longwave radiation, humidity, surface pressure and wind speed. Data on precipitation and temperature are more common than the other variables – consequently, radiation, humidity, pressure and wind speed often must be either estimated using empirical relationships with precipitation and temperature, or obtained from numerical weather prediction models. We examined two climate forcing datasets, which use different methods to estimate radiative energy fluxes and humidity, and investigated the impact of the choice of forcing data on hydrologic simulations over the mountainous Upper Colorado River basin. Comparisons of model simulations forced by two forcing data illustrate that the methods used to estimate shortwave radiation have a large impact on hydrologic states and fluxes particularly in high elevation (e.g., ~20% difference in runoff above 3000 m elevation), substantially altering the timing of snow melt and runoff (~20 days difference) and the partitioning of precipitation between evapotranspiration and runoff. The different forcing datasets also potentially exhibit large differences in hydrologic sensitivity to inter-annual temperature and precipitation. The results suggest that the choice of forcing dataset is an important consideration when conducting climate impact assessments, and subsequent applications in water resources planning and management. This presentation also discusses on-going study on impact of forcing datasets generated with different downscaling methods on hydrologic simulations.