How Do Modeling Decisions Affect the Spread Among Hydrologic Climate Change Projections? Exploring a Large Ensemble of Simulations Across a Diversity of Hydroclimates

Methodological choices can have strong effects on projections of climate change impacts on hydrology. In this study, we investigate the ways in which four different steps in the modeling chain influence the spread in projected changes of different aspects of hydrology. To form the basis of these analyses, we constructed an ensemble of 160 simulations from permutations of two Representative Concentration Pathways, 10 global climate models, two downscaling methods, and four hydrologic model implementations. The study is situated in the Pacific Northwest of North America, which has relevance to a diverse, multinational cast of stakeholders. We analyze the effects of each modeling decision on changes in gridded hydrologic variables of snow water equivalent and runoff, as well as streamflow at point locations. Results show that the choice of representative concentration pathway or global climate model is the driving contributor to the spread in annual streamflow volume and timing. On the other hand, hydrologic model implementation explains most of the spread in changes in low flows. Finally, by grouping the results by climate region the results have the potential to be generalized beyond the Pacific Northwest. Future hydrologic impact assessments can use these results to better tailor their modeling efforts. Patient Language Summary Future climate change will affect water resources throughout the Pacific Northwest of North America. Simulation experiments and recent observations agree that there will be less snow and it will melt earlier, which will impact the timing and amount of streamflow. However, the magnitudes of these changes are uncertain. In this study, we analyzed the spread among 160 different simulated scenarios of the hydrologic future. We show that the ways we represent the future atmosphere and land surface can have strong effects on our final predictions. Specifically, the way that we model the land surface has a large impact on predictions in arid zones or during dry periods. However, the way we model the atmosphere affects our predictions of changes in snow, snowmelt, and streamflow timing. Our findings are helpful for understanding future hydrologic change more thoroughly, which is of particular importance given international agreements in the Columbia River Basin.