Description:

Climate and weather models usually span a global domain, necessitating a coarse spatial resolution; however, most applications need data at highly local scales. In addition, these models have biases that preclude their direct application for some uses. Different approaches have been developed to make climate and weather model output more useful and usable for hydrologic purposes. Statistical and dynamical downscaling methods – and hybrid methods – allow these model outputs to better resolve features such as mountains, coastal boundaries, land cover heterogeneity, and other hydroclimatic features. In addition, careful evaluation of downscaling methods in historical and current climate, statistical reliability for weather forecasts, and diagnosis of their representation of future changes are critical, and bias correction may be necessary for their application. Finally, understanding gained through applications of downscaled products provides key feedback between users and developers of the models and methods.  We invite presentations on the development of downscaling models and datasets, bias correction techniques, evaluations of different methods, and applications of downscaled products.