The analytical framework that was demonstrated combines quantitative and spatial modeling to evaluate RS on and near DoD lands. Ecosystem service terminology varies widely among authors. Throughout this demonstration, the adopted terminology is derived from the current scientific literature. The approach, described below, incorporates widely accepted hydrologic models and equations, remote sensing, geographic information system (GIS) analysis, as well as stakeholder involvement. Although GISs are commonly used to assess RS via simple land cover proxies, this project’s approach enables the project team to separately estimate the capacity and flow of RS by incorporating multiple layers of information, thereby increasing the resolution and accuracy of the analysis as well as its applicability to specific management questions.

Technical objectives were translated into 12 performance objectives (POs). The first four POs sought to improve production function details and spatial resolution of GIS-based analyses of the focal RS (i.e., surface water regulation, and sediment and nitrogen regulation). The next two POs sought to demonstrate transferability of the frameworks for measuring capacity of, demand on, demand for, and flow of RS. POs 7 and 8 sought to demonstrate how to rank ecological pressures on RS and measure flow of RS to beneficiaries. The next two POs sought to demonstrate how to minimize propagation errors in geospatial data and enhance RS-based decision support systems. POs 11 and 12 sought to demonstrate the utility of the framework for integrating RS into installation planning decisions and improve projections of RS to be used in that planning.

Baseline data required for the RS framework include land cover, land use, soil type and hydrologic characteristics, precipitation, and air temperature. These data are publicly available; however, site-specific data also were incorporated when the resolution was significantly better or when the data were more up-to-date. The preceding data were integrated into several hydrological equations, including: (1) Surface Curve Number Method for estimating surface runoff based on land cover, soil type, and precipitation patterns; and (2) Revised Universal Soil Loss Equation, based on land cover, soil erodibility, slope, and management practices. These equations were used to estimate capacity of sediment, nitrogen (N), and surface water regulation. Nitrogen regulation capacity was assessed in two phases—leaching and riparian filtration, which together reduce the nitrogen loading into streams, rivers, and lakes. Nitrogen lost from surface water via infiltration was calculated using the New York Nitrogen Leaching Index. Nitrogen removed via filtration was calculated from published nitrogen-removal efficiencies associated with agricultural and riparian zone best management practices.

The innovative framework proposed here distinguishes RS capacity from RS flow by mapping the hydrologic flow of disservices and services from source to stream and beneficiaries, respectively. Ecological pressures (sometimes called disservices) were mapped using three methods (including field-collected data) and the results were compared to determine the most cost- and time-effective strategy for land-use planning. The three methods were established to reflect three levels of time and computer processing investment. Because the demand for RS depends largely on the magnitude and location of ecological pressures (e.g., sediment, nitrogen, and excessive surface water), the flow paths of known pressures were mapped as well as potentially unknown pressures from training areas. Finally, this project demonstrated a method to quantify the magnitude of RS generated on military installations by comparing water quality monitoring data to modeled estimates of upstream soil loss and ranking the watersheds based on their ecological pressure and ambient condition.

The geospatial analysis associated with this project was initially conducted within the ArcGIS environment—first in Economic and Social Research Institute version 9.3, and later adapted for ArcMap version 10.2 to correspond to updated systems used on military installations. Presentations of results and scenario planning meetings were conducted several times at two military installations (Army National Guard Maneuver Training Center [ANG-MTC] Fort Pickett and Marine Corps Air Station [MCAS] Cherry Point) throughout the demonstration, and a field validation of land cover data was conducted on both installations.

The analytical framework that was demonstrated combines quantitative and spatial modeling to evaluate RS on and near DoD lands. Ecosystem service terminology varies widely among authors. Throughout this demonstration, the adopted terminology is derived from the current scientific literature. The approach, described below, incorporates widely accepted hydrologic models and equations, remote sensing, geographic information system (GIS) analysis, as well as stakeholder involvement. Although GISs are commonly used to assess RS via simple land cover proxies, this project’s approach enables the project team to separately estimate the capacity and flow of RS by incorporating multiple layers of information, thereby increasing the resolution and accuracy of the analysis as well as its applicability to specific management questions.

Technical objectives were translated into 12 performance objectives (POs). The first four POs sought to improve production function details and spatial resolution of GIS-based analyses of the focal RS (i.e., surface water regulation, and sediment and nitrogen regulation). The next two POs sought to demonstrate transferability of the frameworks for measuring capacity of, demand on, demand for, and flow of RS. POs 7 and 8 sought to demonstrate how to rank ecological pressures on RS and measure flow of RS to beneficiaries. The next two POs sought to demonstrate how to minimize propagation errors in geospatial data and enhance RS-based decision support systems. POs 11 and 12 sought to demonstrate the utility of the framework for integrating RS into installation planning decisions and improve projections of RS to be used in that planning.

Baseline data required for the RS framework include land cover, land use, soil type and hydrologic characteristics, precipitation, and air temperature. These data are publicly available; however, site-specific data also were incorporated when the resolution was significantly better or when the data were more up-to-date. The preceding data were integrated into several hydrological equations, including: (1) Surface Curve Number Method for estimating surface runoff based on land cover, soil type, and precipitation patterns; and (2) Revised Universal Soil Loss Equation, based on land cover, soil erodibility, slope, and management practices. These equations were used to estimate capacity of sediment, nitrogen (N), and surface water regulation. Nitrogen regulation capacity was assessed in two phases—leaching and riparian filtration, which together reduce the nitrogen loading into streams, rivers, and lakes. Nitrogen lost from surface water via infiltration was calculated using the New York Nitrogen Leaching Index. Nitrogen removed via filtration was calculated from published nitrogen-removal efficiencies associated with agricultural and riparian zone best management practices.

The innovative framework proposed here distinguishes RS capacity from RS flow by mapping the hydrologic flow of disservices and services from source to stream and beneficiaries, respectively. Ecological pressures (sometimes called disservices) were mapped using three methods (including field-collected data) and the results were compared to determine the most cost- and time-effective strategy for land-use planning. The three methods were established to reflect three levels of time and computer processing investment. Because the demand for RS depends largely on the magnitude and location of ecological pressures (e.g., sediment, nitrogen, and excessive surface water), the flow paths of known pressures were mapped as well as potentially unknown pressures from training areas. Finally, this project demonstrated a method to quantify the magnitude of RS generated on military installations by comparing water quality monitoring data to modeled estimates of upstream soil loss and ranking the watersheds based on their ecological pressure and ambient condition.

The geospatial analysis associated with this project was initially conducted within the ArcGIS environment—first in Economic and Social Research Institute version 9.3, and later adapted for ArcMap version 10.2 to correspond to updated systems used on military installations. Presentations of results and scenario planning meetings were conducted several times at two military installations (Army National Guard Maneuver Training Center [ANG-MTC] Fort Pickett and Marine Corps Air Station [MCAS] Cherry Point) throughout the demonstration, and a field validation of land cover data was conducted on both installations.

This demonstration showed that many environmental issues (e.g., compliance with National Environmental Policy Act, Endangered Species Act, and Clean Water Act; suburban encroachment) facing military installations can be analyzed as tradeoffs among ESs. For example, this project’s approach to ES analysis can inform planners regarding how dedicating a land parcel to training, housing, or stewardship will influence surface water quality or flooding. Capacities and flows of ES vary greatly across landscapes and are likely to vary as climate changes or development occurs. For example, climate change may increase nitrogen leaching if precipitation increases and off-installation development may impact on-installation water quality. The GIS maps developed via the approach herein are instructive in showing variation in ES capacities and flows. ES capacities often can be estimated via existing data but a need exists to validate data and recognize resolution limits; in some situations, new kinds of data are needed. For example, adequate data on ambient water quality were sometimes lacking and some land cover data were out of date. Analyses of ES capacity and flow are useful to managers and planners by helping them identify and prioritize management targets. For example, flow-path analysis helps identify trouble spots to guide effective implementation of best management practices, and ES analysis can inform prioritization of compatible use buffers. Responses to the end-of-project surveys of installation staff likely to use this framework or tools indicated that the demonstrated approach was informative, useful, and easy to use in the context of installation environmental compliance and land-use planning. Because the analytical approach is new, much room for improvement remains. Refining the models and tools demonstrated herein will lead to better management choices and outcomes. The new tools that were developed are accessible to on-installation GIS analysts or hired consultants.

This demonstration included 12 POs—10 quantitative and 2 qualitative—that were initially designed to evaluate the success of the demonstration. Success was achieved on PO 1–4, 11, and 12, and partial success on performance objectives 5–7 and 9–10. Due to changes in the scope of the demonstration and data available during the demonstration, PO 8 was modified to better inform the impacts of regulating service capacity within installation boundaries and on lands in the corresponding encroachment buffer program. Limited success on POs was largely attributed to the lack of on-the-ground water quality monitoring data that would be needed to quantify the actual flow of regulating services occurring (e.g., surface water retention).

Few future issues, especially technological constraints, limit the implementation of the demonstrated framework for using RS to evaluate ecological resilience. The GIS tools that were developed can be used within the ArcGIS version 10.2 environment and require no further licenses beyond those already owned. End products, along with an End-User Guide, will enable GIS analysts to conduct the same analyses described in this report as well as adapt and update the underlying models as needed (through Python scripting or in ModelBuilder). The tools demonstrated in this project were developed to facilitate assessment of baseline and future changes to the landscapes of specific installations and surrounding areas. With such assessments, however, comes the need for (1) accurate information that drives the specification of model parameters, and (2) time for staff to conduct the analyses. On-installation personnel time was the most limited resource, followed by on-the-ground data from water quality monitoring; both limited the success of the demonstration and implications for future implementation. Implementation of the methodology herein may lead to re-assessments of installation tradeoffs in prioritizing their limited resources for environmental management. Even so, the work shows that implementing an RS-based assessment framework and methodology can provide insight into future land management on military installations, including decisions related to encroachment buffers, stewardship, and regulatory compliance.