Predicting Snow Water Equivalence (SWE) and Soil Moisture Response to Restoration Treatments in Headwater Ponderosa Pine Forests of the Desert LCC
The U.S. Forest Service is poised to conduct forest restoration treatments through the Four Forest Restoration Initiative (4FRI) on hundreds of thousands of acres of ponderosa pine forest along the Mogollon Rim of Arizona with the goals of reducing wildfire hazard and improving forest health. One of the 4FRI’s objectives is to thin and burn to accomplish within-stand openings that “promote snowpack accumulation and retention which benefits groundwater recharge and watershed processes at the fine (1 to 10 acres) scale.” However, little is known about how the openings created by restoration treatments affect snow water equivalence and soil moisture, which are key parts of the water balance that greatly influence water availability for healthy trees and for downstream water users in the Sonoran Desert.
This project is funded by the Bureau of Reclamation for 3 years. In this project, we have 3 main objectives:
1. Couple remotely sensed satellite data analysis with field measurements.
2. Analyze spatial relationships among forest pattern, topography, SWE, and soil moisture using geospatial
statistics.
3. Develop a predictive model of SWE and soil moisture responses to various forest restoration treatments and the consequent patterns of canopy openings.
Five existing forest restoration research sites on the Coconino National Forest and NAU’s Centennial Forest are revisited; these sites received restoration treatments within the past 5 to 20 years as part of scientific studies conducted by NAU School of Forestry, Rocky Mountain Research Station, and the Ecological Restoration Institute. At “control”, “thinned” and “thinned and burned” treatment units, we systematically measure soil texture, soil bulk density, soil moisture, and snow water equivalence through the use of in-situ soil moisture sensors, field sampling, and snow course data collection.
WorldView-2 satellite data with 2.4 m resolution and approximately monthly frequency is analyzed to evaluate surface moisture, snow extent, and forest pattern during the winter-spring seasons. This high spatial resolution satellite data is complimented with the analysis of coarser spatial resolution, but higher temporal resolution satellite data from the Landsat 8 (30 m resolution and 16-day interval) and MODIS (250 m resolution with 2-day interval) sensors. All of the geospatial data is managed within a GIS environment and analyzed using geospatial statistics such as Moran’s spatial autocorrelation test.
We hypothesize that forest restoration treatments that create a heterogeneous pattern of tree groups and openings, with average opening sizes ranging from ¾ to 1½ times as wide as the adjacent tree height, generate the greatest SWE and May/June deep rooting zone (50 cm) soil moisture. Findings of this study will inform adaptive management and help guide silviculturalists in selecting restoration treatments that are optimal for preserving precious moisture to promote a more resilient forest. By promoting forest resilience we can help ensure healthy forests and healthy watersheds for decades to come.