Simulating overland flow following wildfire: mapping vulnerability to landscape disturbance

The probability of landscape-scale disturbances such as fire are expected to increase in the future due to anticipated climate changes and past land management practices. These disturbances can produce dramatic changes in hydrologic responses (e.g. overland flow) that can pose risks to human life, infrastructure, and the environment. Assessing these risks and associated remediation strategies requires spatially explicit evaluation of upland hydrology. However, most current evaluation methods focus on a specified location within a watershed, precluding estimation of spatially distributed, upland, hydrological response; and those that do consider spatial variability usually do not account for redistribution of overland flow among adjacent subunits. Here we highlight the use of a spatially distributed model for assessing spatial changes in upland hydrologic response following landscape-scale disturbance. Using a distributed model called SPLASH (Simulator for Processes of Landscapes: Surface/Subsurface Hydrology), we simulated pre- and post-fire scenarios based on the Cerro Grande fire (Los Alamos, NM, USA; May 2000) over 17 300 ha (resolution of 30 m ð 30 m) for 2 year and 100 year design storms. For the 2 year storm, maximum overland flow rates for burned cells in the post-fire scenario greatly exceeded those for pre-fire conditions (modes: pre-fire, 3Ð25 ð 10 � 10 m 3 s � 1 ; post-fire, 7Ð0 ð 10 � 10 m 3 s � 1 ). For the 100 year storm, maximum overland flow was much greater than for the 2 year storm (modal pre-fire: 31Ð8 ð 10 � 10 m 3 s � 1 ), with the difference between pre- and post-fire simulations being less dramatic (modal post-fire: 48Ð6 ð 10 � 10 m 3 s � 1 ). Mapped differences between pre- and post-fire provide a means for prioritizing upland areas for remediation using an approach that accounts not only for topography, soils, and plant cover, but also for the redistribution of overland flow. More generally, our results highlight the potential utility of spatially distributed models to focus and prioritize rehabilitation efforts for future assessments of risk following landscape-scale disturbance. Copyright  2001 John Wiley & Sons, Ltd.