1a.Objectives (from AD-416):
Development and validation of a seasonal forecasting tool for rangeland restoration planning.
1b.Approach (from AD-416):
We will utilize output from the Climate Forecast System (CFS), the main dynamical model used by NOAA’s Climate Prediction Center (CPC). We propose to use statistical downscaling methods to generate forecasts for relatively local-scale rangeland restoration applications. We will evaluate the accuracy of CFS forecasts with historical weather data and hindcasts for the years 1982-2010. These hindcasts include seasonal outlooks for a 9-month period subsequent to the fall planting season in the Intermountain western US. Forecast skill will be assessed using well-established statistical methods, including the Brier Skill Score, and Heidke Skill Score for variables of temperature and precipitation. We will specifically assess the skill of such forecasts for seasonal and monthly time periods associated with winter soil moisture storage, active spring germination and seedling establishment, and seedling persistence through the summer. We will also evaluate conditional skill during strong phases of the El-Nino Southern-Oscillation. This analysis will allow for the creation of skill masks that will be used with seasonal climate projections to highlight regions of greater confidence. This analysis may yield criteria for threshold forecast probabilities that trigger active management/non-management planning decisions in the fall planting season.
This Specific Cooperative Agreement by ARS and the University of Idaho (UI) in support of ARS and UI rangeland restoration research, technology-transfer and educational outreach programs. In this Fiscal Year (FY) the distributed gridded databases developed by a scientist at UI, and the Daymet gridded database were extracted into discrete files on a four km2 and one km2 grid, respectively, and converted to input parameter files for the Simultaneous Heat and Water (SHAW) microclimatic model for sensitivity analysis on weather effects on seedbed microclimate. Database extraction tools were developed to customize parameter output for the SHAW model, microclimatic summaries for restoration applications, and for evaluation of topographic effects on seedbed temperature and moisture. A database structure was also set up for accepting regional meteorological data in a similar format for hind-casting applications and web access tools. 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.