Ecosystem prescription preparation: A Rocky Mountain high!

Forest Resources Management majors, during their junior year in the professional forestry curriculum at the University of Tennessee, participate in a field camp, Forestry Spring Block, for the entirety of the spring semester. Courses range from woodland surveying through silviculture and forest measurements. The final course, a capstone course, involves the development of an ecosystem prescription on a designated woodland. During the spring field camps, 2000 and 2001, the students were invited to the Manitou Experimental Forest (USDA Forest Service) north of Woodland Park, Colorado, to develop their ecosystem prescriptions. Four scenarios were used: pre-Columbian restoration of uneven-aged ponderosa pine; emphases on wildlife management or wildfire protection in a wildland/urban interface; wilderness recreation; and timber management in uneven-aged ponderosa pine. Students gathered data, completed analyses, used FVS and SUPPOSE models to project stand development, and drafted their prescription. On the last day each crew made a PowerPoint presentation to the audience for review and discussion. The presentation will relate some of the teaching and learning experiences of the students and the faculty

Use of Data Denial Experiments to Evaluate ESA Forecast Sensitivity Patterns

The overall goal of this multi-phased research project known as WindSENSE is to develop an observation system deployment strategy that would improve wind power generation forecasts. The objective of the deployment strategy is to produce the maximum benefit for 1- to 6-hour ahead forecasts of wind speed at hub-height ({approx}80 m). In this phase of the project the focus is on the Mid-Columbia Basin region which encompasses the Bonneville Power Administration (BPA) wind generation area shown in Figure 1 that includes Klondike, Stateline, and Hopkins Ridge wind plants. The Ensemble Sensitivity Analysis (ESA) approach uses data generated by a set (ensemble) of perturbed numerical weather prediction (NWP) simulations for a sample time period to statistically diagnose the sensitivity of a specified forecast variable (metric) for a target location to parameters at other locations and prior times referred to as the initial condition (IC) or state variables. The ESA approach was tested on the large-scale atmospheric prediction problem by Ancell and Hakim 2007 and Torn and Hakim 2008. ESA was adapted and applied at the mesoscale by Zack et al. (2010a, b, and c) to the Tehachapi Pass, CA (warm and cools seasons) and Mid-Colombia Basin (warm season only) wind generation regions. In order to apply the ESA approach at the resolution needed at the mesoscale, Zack et al. (2010a, b, and c) developed the Multiple Observation Optimization Algorithm (MOOA). MOOA uses a multivariate regression on a few select IC parameters at one location to determine the incremental improvement of measuring multiple variables (representative of the IC parameters) at various locations. MOOA also determines how much information from each IC parameter contributes to the change in the metric variable at the target location. The Zack et al. studies (2010a, b, and c), demonstrated that forecast sensitivity can be characterized by well-defined, localized patterns for a number of IC variables such as 80-m wind speed and vertical temperature difference. Ideally, the data assimilation scheme used in the experiments would have been based upon an ensemble Kalman filter (EnKF) that was similar to the ESA method used to diagnose the Mid-Colombia Basin sensitivity patterns in the previous studies. However, the use of an EnKF system at high resolution is impractical because of the very high computational cost. Thus, it was decided to use the three-dimensional variational analysis data assimilation that is less computationally intensive and more economically practical for generating operational forecasts. There are two tasks in the current project effort designed to validate the ESA observational system deployment approach in order to move closer to the overall goal: (1) Perform an Observing System Experiment (OSE) using a data denial approach which is the focus of this task and report; and (2) Conduct a set of Observing System Simulation Experiments (OSSE) for the Mid-Colombia basin region. The results of this task are presented in a separate report. The objective of the OSE task involves validating the ESA-MOOA results from the previous sensitivity studies for the Mid-Columbia Basin by testing the impact of existing meteorological tower measurements on the 0- to 6-hour ahead 80-m wind forecasts at the target locations. The testing of the ESA-MOOA method used a combination of data assimilation techniques and data denial experiments to accomplish the task objective

Predicting growth response of shrubs to clear-cutting and site preparation in coastal Oregon forests

Cover-projection models were developed based on algebraic difference formulations of an exponential-power function to describe shrub recovery and development patterns after clear-cutting and site preparation. We tested the effect of six treatments on shrub growth patterns by incorporating indicator variables into the rate and shape parameters of the models for salal (Gaultheria shallon Pursh), thimbleberry (Rubus parviflorus Nutt.), salmonberry (Rubus spectabilis Pursh), and all shrubs. For salal, the shape parameter included an adjustment for burning treatments that delayed maximum cover by several years as compared with unburned treatments. The rate parameter in the thimbleberry model was adjusted for burning treatments; maximum cover occurred about 2 years earlier in burned than in unburned treatments. Both rate and shape parameters in the salmonberry model were adjusted for burning treatments; delayed establishment but increased growth rate and less salmonberry cover are characteristic of burned treatments as compared with the unburned treatments. The rate and shape parameters in the model for the shrub group included adjustments for burning treatments. Overstory removal fostered shrub development, whereas site preparation treatments slowed and curtailed it. The final cover-projection models accounted for 68 92% of the total variation in cover, with the adjustments for burning accounting for 1.5 3.3% of the variation. The predicted growth patterns are consistent with trends in site occupancy and published autecological characteristics.Keywords: salal (Gaultheria shallon), overstory removal, clear-cutting, site preparation, salmonberry (Rubus spectabilis, shrub growth patterns, shrub development, burn treatment, cover-projection models, thimbleberry (Rubus parviflorus), shrub recover

Regenerating coastal forests in Oregon : an annotated bibliography of selected ecological literature

This annotated bibliography was compiled to provide a comprehensive list of sources on the ecological factors that affect forest regeneration. Abstracts from 494 publications are indexed by author, species, and subject, and are arranged into 4 major sections. Topics include the effects of biotic factors, abiotic factors, and stand and site history on vegetation, effects of vegetation on aerial and soil resources, effects of vegetation on stand development, vegetation management, plant succession, and response of vegetation to silvicultural activities and disturbances. The citations are predominately for coastal Douglas-fir occurring west of the Cascade Range in Oregon and Washington; however, applicable citations for associated conifers, hardwoods, shrubs, and herbs and for Alaska, California, and British Columbia are also included. Citations on forest genetics, seeds, seedling physiology, and insects are not included in the bibliography

Overstory influences on light attenuation patterns and understory plant community diversity and composition in southern boreal forests of Quebec

We have characterized overstory light transmission, understory light levels, and plant communities in mixedwood boreal forests of northwestern Quebec with the objective of understanding how overstory light transmission interacts with composition and time since disturbance to influence the diversity and composition of understory vegetation, and, in turn, the further attenuation of light to the forest floor by the understory. Overstory light transmission differed among three forest types (aspen, mixed deciduous-conifer, and old cedar-dominated), with old forests having higher proportions of high light levels than aspen and mixed forests, which were characterized by intermediate light levels. The composition of the understory plant communities in old forests showed the weakest correlation to overstory light transmission, although those forests had the largest range of light transmission. The strongest correlation between characteristics of overstory light transmission and understory communities was found in aspen forests. Species diversity indices were consistently higher in aspen forests but showed weak relationships with overstory light transmission. Light attenuation by the understory vegetation and total height of the understory vegetation were strongly and positively related to overstory light transmission but not forest type. Therefore, light transmission through the overstory influenced the structure and function of understory plants more than their diversity and composition. This is likely due to the strong effect of the upper understory layers, which tend to homogenize light levels at the forest floor regardless of forest type. The understory plant community acts as a filter, thereby reducing light levels at the forest floor to uniformly low levels

Use of Data Denial Experiments to Evaluate ESA Forecast Sensitivity Patterns

The overall goal of this multi-phased research project known as WindSENSE is to develop an observation system deployment strategy that would improve wind power generation forecasts. The objective of the deployment strategy is to produce the maximum benefit for 1- to 6-hour ahead forecasts of wind speed at hub-height ({approx}80 m). In this phase of the project the focus is on the Mid-Columbia Basin region which encompasses the Bonneville Power Administration (BPA) wind generation area shown in Figure 1 that includes Klondike, Stateline, and Hopkins Ridge wind plants. The Ensemble Sensitivity Analysis (ESA) approach uses data generated by a set (ensemble) of perturbed numerical weather prediction (NWP) simulations for a sample time period to statistically diagnose the sensitivity of a specified forecast variable (metric) for a target location to parameters at other locations and prior times referred to as the initial condition (IC) or state variables. The ESA approach was tested on the large-scale atmospheric prediction problem by Ancell and Hakim 2007 and Torn and Hakim 2008. ESA was adapted and applied at the mesoscale by Zack et al. (2010a, b, and c) to the Tehachapi Pass, CA (warm and cools seasons) and Mid-Colombia Basin (warm season only) wind generation regions. In order to apply the ESA approach at the resolution needed at the mesoscale, Zack et al. (2010a, b, and c) developed the Multiple Observation Optimization Algorithm (MOOA). MOOA uses a multivariate regression on a few select IC parameters at one location to determine the incremental improvement of measuring multiple variables (representative of the IC parameters) at various locations. MOOA also determines how much information from each IC parameter contributes to the change in the metric variable at the target location. The Zack et al. studies (2010a, b, and c), demonstrated that forecast sensitivity can be characterized by well-defined, localized patterns for a number of IC variables such as 80-m wind speed and vertical temperature difference. Ideally, the data assimilation scheme used in the experiments would have been based upon an ensemble Kalman filter (EnKF) that was similar to the ESA method used to diagnose the Mid-Colombia Basin sensitivity patterns in the previous studies. However, the use of an EnKF system at high resolution is impractical because of the very high computational cost. Thus, it was decided to use the three-dimensional variational analysis data assimilation that is less computationally intensive and more economically practical for generating operational forecasts. There are two tasks in the current project effort designed to validate the ESA observational system deployment approach in order to move closer to the overall goal: (1) Perform an Observing System Experiment (OSE) using a data denial approach which is the focus of this task and report; and (2) Conduct a set of Observing System Simulation Experiments (OSSE) for the Mid-Colombia basin region. The results of this task are presented in a separate report. The objective of the OSE task involves validating the ESA-MOOA results from the previous sensitivity studies for the Mid-Columbia Basin by testing the impact of existing meteorological tower measurements on the 0- to 6-hour ahead 80-m wind forecasts at the target locations. The testing of the ESA-MOOA method used a combination of data assimilation techniques and data denial experiments to accomplish the task objective