Geology topics

Travis Nauman

Publications and source records attributed to Travis Nauman.

3 recordsLinked to original sources

USGS2019

Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests

Salinity loading in the Upper Colorado River Basin (UCRB) costs local economies upwards of $300 million US dollars annually. Salinity source models have generally included coarse spatial data to represent non‐agriculture sources. We developed new predictive soil property and cover maps at 30 m resolution to improve source representation in salinity modeling. Salinity loading erosion risk indices were also created based on soil properties, remotely sensed bare ground exposure, and topographic factors to examine potential surface soil erosion drivers. These new maps and data from previous SPARROW models were related to recently updated records of salinity at 309 stream gauges in the UCRB using random forest regressions. Resulting salinity yield predictions indicate more diffuse salinity sources, with slightly higher yields in more arid portions of the UCRB, and less overall load coming from irrigated agricultural sources. Model simulations still indicate irrigation to be the major human source of salinity (661,000 Mg, or 12%), but also suggest that 75,000 Mg (1.4%) of annual salinity in the UCRB is coming from areas with excessive exposed bare ground in high elevation mountain areas. Model inputs allow for field scale screening of locations that could be targeted for salinity control projects. Results confirm recent studies indicating limited surface erosional influence on salinity loading in UCRB surface waters, but impacts of monsoonal runoff events are still not fully understood, particularly in drylands. The study highlights the utility of new predictive soil maps and machine learning for environmental modeling.

Arizona, Colorado, New Mexico, Utah, Wyoming↗

USGS2019

Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data

Fine scale maps of soil properties enable efficient land management and inform earth system models. Recent efforts to create soil property maps from field observations tend to use similar tree-based machine learning interpolation approaches, but often deal with depth of predictions, validation, and uncertainty differently. One of the main differences in approaches is whether to model individual depths of interest separately as ‘2D’ models, or to create models that incorporate depth as a predictor variable creating a ‘3D’ model that can make pre-dictions for all depths. It is unclear how choice of 2D or 3D approach influences model accuracy and uncertainty due to lack of direct comparison and inconsistent presentation of results in past studies. This study compares 2D and 3D methods for mapping soil electrical conductivity (salinity), pH, sum of fine and very fine sands, and organic carbon at 30 m resolution for the upper 432,000 km 2 of the Colorado River Watershed of the United States of America. A new, simple, model-agnostic relative prediction interval (RPI) approach to report un-certainty is presented that scales prediction interval width to the 95% interquantile width of the original training sample distribution. The RPI approach enables direct comparison of uncertainty between properties and depths and is easily interpretable by end users. Results indicate that 3D mapping of soil properties with strong variation with depth can result in substantial areas with much higher uncertainty that coincide with unrealistic predictions relative to 2D models, even though 3D models had slightly better global cross-validation scores. Maps and global model summaries of RPI proved helpful in identifying these issues with 3D models. These results suggest that the use of RPI or similar approaches to evaluate models can identify accuracy problems not evident in global va-lidation diagnostics.

Geoderma↗

USGS2011

An inventory and monitoring plan for a Sonoran Desert ecosystem; Barry M. Goldwater Range-West

Marine Corps Air Station Yuma manages the Barry M. Goldwater Range-West, which encompasses approximately 2,800 square kilometers of Sonoran Desert habitat in southwestern Arizona. The Barry M. Goldwater Range is a major U.S. military installation designed as an air combat training location for the U.S. Marine Corps and U.S. Air Force, but it also includes some of the most pristine desert habitat in the United States. In an effort to ensure the long-term viability of this unique natural resource, the U.S. Geological Survey (USGS) has developed an Integrated Natural Resources Management Plan and Inventory and Monitoring Plan to guide natural resource management of the Barry M. Goldwater Range-West. This Inventory and Monitoring Plan provides a framework for long-term ecosystem monitoring on Barry M. Goldwater Range-West lands by identifying existing and potential threats to ecosystem function, prioritizing resources for monitoring, and providing information and protocols necessary to initiate a long-term ecosystem monitoring program. The Inventory and Monitoring Plan and related protocols were developed through extensive review of existing Sonoran Desert monitoring programs and monitoring literature and through a 2-day workshop with resource managers, monitoring experts, and other stakeholders. The Barry M. Goldwater Range-West Inventory and Monitoring Plan stresses the importance of regional monitoring partnerships and protocol standardization for understanding landscape-scale ecosystem changes in the Sonoran Desert; information and protocols contained within the plan may also be of interest to land managers engaged in large-scale ecosystem monitoring and adaptive management of other arid regions.

Open-File Report↗