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Fire regimes are now recognized as the product of social processes whereby fire on any landscape is the product of human-generated drivers: climate change, historical patterns of vegetation manipulation, invasive species, active fire suppression, ongoing fuel management efforts, prescribed burning, and accidental ignitions. We developed a new fire model (Social-Climate Related Pyrogenic Processes and their Landscape Effects: SCRPPLE) that emphasizes the social dimensions of fire and enables simulation of fuel-treatment effects, fire suppression, and prescribed fires. Fire behavior was parameterized with daily fire weather, ignition, and fire-boundary data. SCRPPLE was initially parameterized and developed for the Lake Tahoe Basin (LTB) in California and Nevada, USA although its behavior is general and could be applied worldwide. We demonstrate the behavior and utility of our model via four simple scenarios that emphasize the social dimensions of fire regimes: a) Recent Historical: simulated recent historical patterns of lightning and accidental fires and current patterns of fire suppression, b) Natural-Fire-Regime: simulated wildfire without suppression, accidental fires, or prescribed fires, holding all other factors the same as Recent Historical, c) Enhanced Suppression: simulated a doubling of the effectiveness of suppression, holding all other factors the same as Recent Historical, and d) Reduced Accidental Ignitions: within which the number of accidental fires was reduced by half, holding all other factors the same as Recent Historical. Results indicate that SCRPPLE can recreate past fire regimes, including size, intensity, and locations. Furthermore, our results indicate that the 'Enhanced Suppression' and 'Reduced Accidental Ignitions' scenarios had similar capacity to reduce fire and related tree mortality over time, suggesting that within the broad outlines of the scenarios, reducing accidental fires can be as effective as substantially increasing resources for suppression.