Predicting wildfire occurrence distribution with spatial point process models and its uncertainty assessment: A case study in the Lake Tahoe Basin, USA

Strategic fire and fuel management planning benefits from detailed understanding of how wildfire occurrences are distributed spatially under current climate, and from predictive models of future wildfire occurrence given climate change scenarios. In this study, we fitted historical wildfire occurrence data from 1986 to 2009 to a suite of spatial point process (SPP) models with a model averaging approach. We then predicted human- and lightning-caused wildfire occurrence over the 2010-2100 period in the Lake Tahoe Basin, a forested watershed in the western US with an extensive wildland-urban interface. The purpose of our research was threefold, including (1) to quantify the influence of biophysical and anthropogenic explanatory variables on spatial patterns of wildfire occurrence, (2) to model current and future spatial distribution of wildfire occurrence under two carbon emission scenarios (A2 and B1), and (3) to assess prediction uncertainty due to model selection. We found that climate variables exerted stronger influences on lightning-caused fires, with climatic water deficit the most important climatic variable for both human- and lightning-caused fires. The recent spatial distribution of wildfire hotspots was mainly constrained by anthropogenic factors because most wildfires were human-caused. The future distribution of hotspots (i.e. places with high fire occurrence density), however, was predicted to shift to higher elevations and ridge tops due to a more rapid increase of lightning-caused fires. Landscape-scale mean fire occurrence density, averaged from our top SPP models with similar empirical support, was predicted to increase by 210% and 70% of the current level under the A2 and B1 scenarios. However, individual top SPP models could lead to substantially different predictions including a small decrease, a moderate increase, and a very large increase, demonstrating the critical need to account for model uncertainty.