Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consistent, gridded dataset defining HSGs from soil texture, bedrock depth, and groundwater. The resulting data product—HYSOGs250m—represents runoff potential at 250 m spatial resolution. Our analysis indicates that the global distribution of soil is dominated by moderately high runoff potential, followed by moderately low, high, and low runoff potential. Low runoff potential, sandy soils are found primarily in parts of the Sahara and Arabian Deserts. High runoff potential soils occur predominantly within tropical and sub-tropical regions. No clear pattern could be discerned for moderately low runoff potential soils, as they occur in arid and humid environments and at both high and low elevations. Potential applications of this data include CN-based runoff modeling, flood risk assessment, and as a covariate for biogeographical analysis of vegetation distributions.
The cover of woody perennial plants (trees and shrubs) in arid ecosystems is at least partially constrained by water availability. However, the extent to which maximum canopy cover is limited by rainfall and the degree to which soil water holding capacity and topography impacts maximum shrub cover are not well understood. Similar to other deserts in the U.S. southwest, plant communities at the Jornada Basin Long‐Term Ecological Research site in the northern Chihuahuan Desert have experienced a long‐term state change from perennial grassland to shrubland dominated by woody plants. To better understand this transformation, and the environmental controls and constraints on shrub cover, we created a shrub cover map using high spatial resolution images and explored how maximum shrub cover varies with landform, water availability, and soil characteristics. Our results indicate that when clay content is below ~18%, the upper limit of shrub cover is positively correlated with plant available water as mediated by surface soil clay influence on water retention. At surface soil clay contents >18%, maximum shrub cover decreases, presumably because the amount of water percolating to depths preferentially used by deep‐rooted shrubs is diminished. In addition, the relationship between shrub cover and density suggests that self‐thinning occurs in denser stands in most landforms of the Jornada Basin, indicating that shrub–shrub competition interacts with soil properties to constrain maximum shrub cover in the northern Chihuahuan Desert.
Savanna woody plants can store significant amounts of carbon while also providing numerous other ecological and socio-economic benefits. However, they are significantly under-represented in widely used tree cover datasets, due to mapping challenges presented by their complex landscapes, and the underestimation of woody plants by methods that exclude short stature trees and shrubs. In this study, we describe a Google Earth Engine (GEE) application and present test case results for mapping percent woody canopy cover (%WCC) over a large savanna area. Relevant predictors of %WCC include information derived from radar backscatter (Sentinel-1) and optical reflectance (Sentinel-2), which are used in conjunction with plot level %WCC measurements to train and evaluate random forest models. We can predict %WCC at 40 m pixel resolution for the full extent of Senegal with a root mean square error of ∼8% (based on independent sample evaluation). Further examination of model results provides insights into method stability and potential generalizability. Annual median radar backscatter intensity is determined to be the most important satellite-based predictor of %WCC in savannas, likely due to its relatively strong response to non-leaf structural components of small woody plants which remain mostly constant across the wet and dry season. However, the best performing model combines radar backscatter metrics with optical reflectance indices that serve as proxies for greenness, dry biomass, burn incidence, plant water content, chlorophyll content, and seasonality. The primary use of GEE in the methodology makes it scalable and replicable by end-users with limited infrastructure for processing large remote sensing data.
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