Advancing Tools to Understand and Adapt to Hydroclimatic Variability and Change in Alaska and Hawaii

The majority of the work conducted to understand the impacts of climate variability and change on water resources, and work to develop effective climate adaptation strategies in the water sector in the USA, is focused on the contiguous USA (CONUS). These tools do not currently address the unique modeling problems in Alaska and Hawaii (oCONUS), as characterized by intense spatial climatic gradients, sparse station networks, and dominance of distinctive hydrologic processes that are considered peculiar in the CONUS (e.g., glaciers and permafrost in Alaska; volcanic geology, intense rainfall, and high rates of evapotranspiration in Hawaii). To address these knowledge and capability gaps the US Army Corps of Engineers (USACE) and the National Center of Atmospheric Research (NCAR) have been developing a suite of new tools to support climate risk assessments in Alaska and Hawaii.

Three main elements are discussed here, 1) climate mapping; 2) climate downscaling; and 3) hydrologic modeling. This collaborative effort has resulted in new daily, gridded, meteorological datasets and methodologies for oCONUS. These datasets have incorporated local knowledge, gauge networks, estimate uncertainty, and account for frozen precipitation gauge under catch in Alaska. Further, new ensemble climatologically aided interpolation and gauge under catch methodologies have been developed. For climate downscaling, we have developed multiple downscaled scenarios using CMIP5 climate models, First, pseudo-global warming (PGW) dynamically downscaled simulations at high resolution over both domains have been completed to enable process studies under one future change scenario. Additionally, downscaling for multiple CMIP5 models using the intermediate complexity atmospheric model (ICAR) and multiple statistical methods for the 1950-2100 time period are available. Finally, we have initial hydrologic projections for oCONUS using the Variable Infiltration Capacity (VIC) model across all ICAR and statistically downscaled permutations. This talk will summarize the new publicly available tools, data sets, and results that have resulted from this extensive initial effort.