SIMULATION OF SOIL MOISTURE BUDGETS: SPATIALLY REFINED PROJECTIONS OF SOIL-CLIMATE FOR ENVIRONMENTAL APPLICATIONS

Soil moisture, a product of climatic, geologic and other environmental factors, has important effects on plant distributions, growth rates, and associated habitat conditions. Existing soil moisture data have been inadequate to explain variability in vegetation patterns, habitat conditions, and fuel conditions and spatial and temporal variability in these factors continue to present noteworthy management challenges. Building on existing data and models, we developed a framework, SpatialNSM, that uses spatially explicit estimates of climate, soil properties, microtopography, and snowmelt in a monthly soil-water accounting system (Newhall soil simulation model). SpatialNSM produced moderate-resolution (30-m) continuous estimates of soil moisture (monthly, seasonal, and annual) and a moisture-temperature classification (akin to Soil Taxonomy) across the sagebrush biome and surrounding systems, including extensive forested areas and alpine environments. Current output using mean climate conditions (1981-2010) for temperature and precipitation inputs produced strong correlations between soil-climate factors and sagebrush cover, bare ground, and annual herbaceous cover using generalized additive models (GAM). The continuous estimates of soil moisture offer novel information as environmental predictors for habitat and wildlife population models—current applications include sagebrush restoration and recovery, and sage-grouse responses to habitat disturbance. Soil moisture is expected to be valuable for projecting primary production (of forage and other biomass) and fuel moisture (which has a strong effect on flamability)- two conditions that are very important for land management. Currently, the model conducts independent simulation of each grid-cell, without accounting for lateral flows of water (or materials) or infiltration to deeper geologic layers—this is an opportunity for model integration. The detailed information from SpatialNSM can be applied for management using similar expectations for similar conditions (described by soil-climate variables), spatial differentiation of drought effects, and implications of soil-climate trends for resource conditions.