A wide range of planning activities, scientific research, and decision support tools require representations of future hydrology at local to regional scales. For example, understanding and predicting wildfire risk, drought, migration of vegetation, invasive species, water quality, agricultural and rangeland productivity, flood control, and many other practical applications require estimates of means and extremes of future streamflow, soil moisture, snowpack, and other hydrologic variables.
The state-of-the-art physically based, distributed Variable Infiltration Capacity (VIC) hydrologic model has been widely used for regional climate assessments. In its most commonly used form, the model is forced by gridded daily (or shorter time step) observations of six variables: precipitation, temperature, surface wind, downward solar and longwave radiation, and dew point. We plan to evaluate the separate but related problems concerning the implications of a) alternative downscaling and bias correction assumptions on hydrologic simulations using the VIC model, and b) the use of climate model output for variables other than precipitation and temperature.
We propose herein to test alternate approaches to estimating these variables via a new multivariate statistical approach that links them more directly to climate model predictions at the daily time step than has previously been possible. This project will evaluate four different downscaling methods in conjunction with both temperature indexing approaches for estimation of solar and downward longwave radiation and humidity outlined above. Our primary focus will be on the Columbia River Basin, however, we will consider a broader domain as well.
The downscaling methods include (i) bias correction and spatial downscaling (BCSD); (ii) Multivariate Adaptive Constructed Analogs (MACA); (iii) direct interpolation of RCM output (most likely from the North American Regional Climate Change Assessment Program (NARCCAP); and (iv) bias corrected and interpolated RCM (probably NARCCAP) output.