1a.Objectives (from AD-416):
1) Consolidation of a regional database of historical meteorological information in support of adaptive-management planning and validation of forecast-modeling applications.
2) Development and validation of a seasonal forecasting tool for rangeland restoration planning.
3) Determination of climatic thresholds for successful rangeland seeding practices using historical weather data and BLM
seeding records contained in the USGS Landscape Treatment Digital Library.
4) Determination of weather and climatic thresholds for soil stability and wind erosion for different vegetation states and
previous and current planting treatments.
5) Supplementation of Ecological Site Descriptions with restoration-specific climatological, weather and soil stability
information, for adaptive management planning.
6) Identification of strategies to facilitate adoption of weather-centric management and forecasting strategies by land
management agencies and professional restoration planners.
1b.Approach (from AD-416):
The current suite of Global Circulation Model (GCM) scenarios utilized by the NOAA Climate Prediction Center (CPC) will be evaluated for their individual utility in predicting seasonal probability of exceedence values (PoE) for temperature and precipitation across a regional selection of meteorological sites in the western US. Seasonal predictions will be customized for traditional rangeland seeding seasons for weather estimation during critical seasons for establishment in the first year after planting. In the Great Basin region, most post-fire and restoration seeding occurs in mid to late fall (October-November).
Predictive accuracy of GCM model output will be evaluated for subsequent winter (soil moisture storage at deeper soil layers),spring (critical for seedling establishment and near-surface microclimate) and summer (potential for juvenile seedling survival). PoE information will be converted to stochastic weather scenarios and seedbed modeling used to conduct sensitivity analysis on the degree to which forecast conditions need to exceed climate norms in order to justify forecast-induced changes in management. Land management and restoration professionals in BLM and NRCS will be included in the management team evaluating the utility of forecast tools, and manager surveys conducted to evaluate current barriers to use of active weather information in real-time management, and design decision support systems that can be used for effective technology transfer. Specific additional weather tools to be developed include a weather supplement that can be linked to specific Ecological Site Descriptions, and guidance for use of weather data in adaptive management planning for inclusion in revised NRCS conservation practice standards for rangeland seeding.
This Reimbursable Cooperative Agreement by ARS and USDA-National Institute of Food and Agriculture (NIFA) Rangeland Research Program is in support of ARS and USDA-NIFA rangeland research, technology-transfer and educational outreach programs. In 2013 we began consolidation of daily regional weather records from national and state databases and from two high-resolution modeled-gridded weather datasets. We developed point-access applications for extracting gridded weather data for period-of-record input datasets for seedbed microclimatic modeling, and tested topographic effects on seasonal temperature and moisture at seeding depth in the Boise Foothills East experimental area. We tested alternative methods for quantifying historical variability in seedbed microclimate and conducted field surveys to correlate vegetation-topographic relationships with predicted patterns of seedbed microclimate. This agreement was established in support of Objective 2 of the in-house project, the goals being to develop decision-support tools that will improve the success of rangeland restoration projects in the Great Basin by integrating weather, climate, micro-climate and forecast data into ecological site descriptions and conservation practice models to reduce the risks of climatic uncertainties.