Dr. Eric McDonald, Associate Research Professor at the Desert Research Institute (DRI), is leading a $2.6M, multi-institution effort to support military operations by developing improved methods of forecasting desert terrain conditions. With funding from the Army Research Office of the U.S. Department of Defense, Dr. McDonald is collaborating with Dr. Alan Gillespie, University of Washington; Dr. Yehouda Enzel, Hebrew University of Jerusalem; and Rivka Amit, Geological Survey of Israel. Their overall goal is to develop an integrated, predictive tool for forecasting desert terrain conditions. This research will support tactical operations, testing, and training conducted by the military in arid environments.

Identifying and avoiding surfaces where helicopter pilots can lose visibility due to excessive dust emissions is amilitary challenge.

Recent world events have emphasized the importance of warfare and intelligence-gathering capabilities in desert environments. Characterized by diverse and extreme conditions, desert environments can change rapidly, impacting visibility and vehicle trafficability. Further, operations in desert conditions can disclose the location of military operations. New technologies are required to address these particular challenges.

Advances in earth science research have shown that unique, predictable relations exist among landscape position, soil, vegetation, and geology. Further, new instrumentation is providing a wide range of environmental information to enhance the ability to characterize ground and subsurface conditions. Models and methods from geomorphology, soil science, climatology, and atmospheric science will be integrated with remote-sensing techniques and other technologies to develop a new predictive model. Satellites and unmanned aerial vehicles will be used to remotely collect new information.

The predictive model will be developed through implementation of the following objectives:

• Evaluating current scientific understanding of surficial processes, assessing remote-sensing technologies and capabilities, and compiling existing data to predict desert terrain conditions.
  
• Integrating knowledge of desert surficial processes and remote-sensing technologies to develop a dynamic model for predicting desert terrain conditions.
  
• Validating the predictive model across diverse desert terrain.

Infiltrometer set up at the Yuma Proving Ground. Various techniques will be used in characterizing the spatial distribution of soil properties to predict potential for dust emissions and determine suitability for passage by vehicles and troops.

Current available information is insufficient for predicting desert terrain conditions suitable for military traffic and operations and for identifying potential hazards. Spatial distribution, age, and geology of desert landforms will be systematically integrated into geomorphic models, providing a platform for predicting terrain conditions. Information collected remotely will be integrated into models to link observable surface conditions to wide-scale subsurface conditions in near real-time. Using these models, DRI scientists can “invert” the processes that drive surface evolution to determine subsurface conditions from surface measurements. Additionally, integrating weather information and weather forecasts with information about soil hydrologic properties is expected to increase the predictive capabilities of the model.

Aerial view of the Yuma Proving Ground showing features ranging from smooth surfaces covered by desert pavement to surfaces that are highly dissected by deep ephemeral washes.

Up to four principal study areas will be used to develop and initially test the predictive model: U.S. Army Yuma Proving Ground, U.S. Army National Training Center, Nevada Test Site, and Negev Desert in Israel. The model will be validated across diverse desert terrains including alluvial fans, alluvial terraces, dunes, active channels, playas, and other features common to military operations. Early input about desert terrain conditions from military personnel will be used to identify strategies for training military personnel in predicting desert terrain conditions. In addition to supporting battlefield readiness requirements, this project will contribute to overall stewardship of U.S. Department of Defense installations in arid regions.