A workshop training session designed to provide the workshop participants background on dominant hydrologic and erosion processes on rangelands, which are the basis of the Rangeland Hydrology and Erosion Model (RHEM). The RHEM model is a new technology from the USDA research groups that is designed specifically for application to grazing lands and grazing land management for soil erosion. The workshop will show users how to access and interpret model predictions using examples for different ecological and climatic conditions.
Participants will need to bring a windows based laptop to run the model during the workshop. If the user wants to become familiar with the model and background science before the workshop, the RHEM model can be accessed at <http//dss tucson ars ag gov/rhem/> and RHEM scientific publications at <http/apps Tucson ars ag gov/rhem/docs>.
The workshop will be held at the Agriculture and Forestry campus of Udl (ETSEA), probably in a room with computers facilities* for all participants, the evening of Sunday 11 June 2017, from 14-18h.
(*If such is the case, there would not be required participant laptops to run the model).
The workshop will be conducted by 5-6 instructors from the USDA-ARS, the University of Nevada and the Desert Research Institute of Nevada (USA), belonging to the RHEM developing team. Participants will receive digital copy of lecture, worksheets and other documents required to understand and use the erosion prediction tool.
APPLICATION FOR REGISTRATION IN THE WORKSHOP RHEM
(11 June 2017)
Only participants already registered in CONSOWA2017 may apply.
Participants in the Workshop have to plan to reach Lleida in the morning of Sunday 11 June 2017 the latest.
The maximum number of participants will be 30, selected in strict order of reception of applications.
The applications have to be addressed by email to Rosa M. Poch (firstname.lastname@example.org) with the following information:
- Subject: I wish to participate in the Workshop RHEM
- Address email:
- Date of registration in CONSOWA2017: