APPENDIX B. REVIEW OF SIMILAR & USEFUL PRODUCTS

Summary Report

Washington Department of Commerce has reviewed more than 40 products for their potential utility in our efforts to develop a web-based spatial decision support system for critical areas and land use planning. The goal of this research was to highlight the range of similar platforms and tools that could be utilized to develop the proposed tool, and show that there are existing products that can be adapted and reused, but decisions about which platforms and tools to incorporate will be made in the next phase. Some products provide example frameworks for achieving the functionality required for our tool, while others provide models and data that could potentially be included in our tool. We separated the reviewed products into four categories based on their primary uses for our purposes:

1. Web-Based Spatial Decision Support System Architecture: These research papers describe complete architectural frameworks for developing web-based decision support systems.

2. Decision Support Frameworks: These tools provide interactive decision support functionality, either via desktop Geographic Information Systems (GIS) applications or web GIS applications, and range from structured decision making frameworks to applications that calculate effects of land use decisions based on user inputs.

3. Models and Tools for Potential Inclusion: These tools could potentially be integrated as components of our decision support system for modeling ecosystem services, hydrology, and land cover change.

4. Integrated Web Mapping Applications: These tools allow users to display and overlay data layers, with limited or no additional analysis capability, and are primarily useful for identifying potential data sources for our tool and assessing added value that could be provided for the local counties and resource agencies that developed them.

This summary version of the report provides an overview of the products we reviewed and highlights their usefulness and limitations for our proposed tool. We highlighted the capabilities of each tool for key features related to landscape prioritization, scenario evaluation, model integration, data management, data processing, web user interface, web mapping application, map querying, map filtering, user modification and models, user criteria weighting, user selection or addition of data, report generation, data download, and security and sign in. A comparison table of features among each reviewed product can be found at the end of this document. The full report provides additional detail on each product, including images, descriptions of data included in each tool, and detailed descriptions of how each tool can be used. It can be used as a reference to learn more about any of the products described.

Section 1: Web-Based Spatial Decision Support System Architecture

We reviewed two research papers that line out system architecture for developing a web-based spatial decision support system. The first describes a system designed for site selection based on landscape characteristics, stakeholder concerns, and economic considerations in a multi criteria decision analysis. The second describes a system designed for watershed management by allowing users to delineate watersheds and run hydrologic and water quality assessments based on land use scenarios. Each of these tools describes a variation of a potential application of our tool (landscape prioritization and land use scenario assessment) and describes the development of a web-based system that meets those needs.

1.1 Multi Criteria Site Selection Tool Architecture

The first example of web-based spatial decision support system architecture was designed to select sites for freight lorry parking facilities in an urban area. It uses complex multi criteria decision analysis to evaluate alternative sites based on land use, infrastructure, costs, and stakeholder concerns in a participative decision making process. Because the site selection process involves a lot of interconnected variables, conflicting objectives, and subjective stakeholder judgements, it is an inherently complex strategic decision making problem. These factors make weighted multi criteria decision analysis an appropriate model component for building a decision support tool to solve the problem. A web based spatial decision support system was designed to help with handling and visualizing the spatial data associated with the problem and encourage stakeholder participation in the decision process. The authors describe the architecture of the tool in detail and provide recommendations for open source technology that can be used to implement each component.

To use the tool, a user selects an area of interest, the software identifies spatial data for that area and displays it on a web map, and the user selects the datasets they want to use or can add new datasets. Then the user can select and modify a decision model or create a new model, where each criterion in the model is linked to a spatial dataset. Then the software generates comparison tables that the user(s) fill in to weight the criteria. The software calculates and displays a results layer that shows the score of each alternative site on the web map, and the results, models, and datasets can be shared publicly online to ensure transparency in the decision process.

The conceptual design for this tool shows the potential for use of open source technologies when developing a web based spatial decision support system. While the paper describes one specific application, the proposed structure of the tool could be used to design similar decision support tools for other types of location problems (i.e. locating restoration or development areas). The article is very recent, published in October 2019, so the tool has likely not yet been implemented, but it lines out a solid plan and conceptual design with up to date technological recommendations that could be adapted for our purposes (i.e. prioritizing areas for protection, restoration, or development instead of prioritizing locations for a parking facility).

Key Features:

Landscape Prioritization Model Integration Data Management Data Processing Web User Interface Web Mapping Application User Modification of

Models User Criteria Weighting User Select or Add Data

1.2 Watershed Management Tool Architecture

The second example of web-based spatial decision support system architecture was designed for watershed management through watershed delineation and water quality and hydrologic impact analysis of land use scenarios. The user can view layers select a watershed outlet point on a web map, from which the software uses flow directions to delineate the watershed that contributes to that point. The tool automatically clips all of the other input grid layers (i.e. land use, soils) to the results of the watershed delineation and runs them through the L-THIA model, which calculates runoff and non point source pollution. The user can also edit the inputs to evaluate alternative land use scenarios. The authors describe the architecture of the tool and the technologies used to build it in detail.

This article is 15 years old, and many aspects of technology have changed since this prototype web based spatial decision support system was developed. However, much of the framework for putting together such a tool remains the same, even if the best technology to use to do so has changed. This is a relatively simple decision problem with few variables, but it demonstrates how a tool can be developed to process and calculate data on the server side based on a user selection on a web mapping interface, to generate inputs that can be fed into a model to calculate and display impacts of land use scenarios on hydrology. While our tool will be more complex, the basic process and components of the system will be similar.

Section 2: Decision Support Frameworks

We reviewed 20 tools that provide examples of decision support frameworks or functionality that could be adapted or used as an example for our web-based decision support system for critical areas and land use planning. Some are decision support frameworks that take users through a structured decision and/or scenario analysis process to evaluate benefits and tradeoffs by allowing them to input data and build models based on their priorities. Some do not provide a structured decision making process but provide the ability to query and display features in a layer based on their attributes or their spatial relationships with other layers, or filter geographic areas based on multiple criteria to prioritize, answer questions, and make decisions. Others allow users to input a spatial area representing a planned land use change on a map and simply calculate the effects based on the area’s overlap or relationship to other data layers. Many of these tools are open source and many contain features that would be useful for our tool.

2.1 Ecosystem Management Decision Support System (EMDS)

The Ecosystem Management Decision Support (EMDS) system is a freely available knowledge-based spatial decision support system that provides a framework for integrating ecological analysis and planning for adaptive ecosystem management. EMDS integrates GIS with logic programming and

Key Features:

Scenario Evaluation Model Integration Data Management Data Processing Web User Interface Web Mapping Application

decision modeling technologies in a framework that allows users to develop applications that answer questions related to landscape evaluation, at any appropriate scale.

EMDS is made up of a set of analytical engines linked to GIS, including a fuzzy logic engine, multi criteria decision analysis, Prolog-based decision trees, and Bayesian networks. The logic engine evaluates environmental data and then synthesizes those evaluations to assess the condition of landscape features. The decision engine can use those summarized outputs along with logistical information (i.e. cost, feasibility, social context, human values, etc.) to prioritize landscape features for management activities based on user-defined objectives. As an example at the watershed scale, the logic component could assess watershed condition, then the decision component could prioritize those watersheds for restoration activities based on their condition and other factors important to decision makers. Decision makers can review the results and see the relative contributions of ecological states and social contexts to alternative decisions, through sensitivity analysis or by developing alternative scenarios.

To assess landscape condition, the user designs a logic model based on the interconnections of all the relevant variables that influence landscape condition at a particular scale, with each variable linked to a spatial data layer. Data can come from multiple sources, including outputs from a variety of other models, and the model can prioritize which dataset to use for a given area. The logic engine evaluates the linked landscape data against the logic model, using fuzzy logic to measure the degree to which data for each variable matches desired conditions. For the watershed condition example, the model would evaluate the degree to which each watershed represents good conditions for that watershed, as specified by the user as a function in the model. After a logic analysis or scenario has been run, each data input and evaluated logic topic can be displayed on a map. After they are built, logic models can easily be adapted based on new information and the results of new management activities, making EMDS applications very useful for adaptive management and scenario planning. If changes under a given management or land use scenario can be translated into changes in the underlying input data for the analysis variables, the same model can be re-applied to compare current and alternative scenarios and see where a proposed change would improve or degrade the ecosystem over time.

A multi criteria decision analysis can prioritize features for management activities based on multiple competing criteria, taking the process from an assessment focus to a planning focus. The most typical application of the EMDS decision engine is prioritizing landscape areas for restoration work, conservation, or preservation by combining estimates of the current condition of landscape features and ecosystems with independent estimates of feasibility and effectiveness. Decision makers apply relative weights to competing needs, feasibilities, and efficacies. Then the decision model rates each alternative and shows the locations of map features with the highest priority. The output also shows the contributions made by each criterion to the overall ranking of each alternative, allows the ability to analyze model sensitivity/robustness to see which factors have the most influence, and facilitates tradeoff analysis between criteria by showing how unit changes in each data input change priority scores.

Key Features:

Landscape Prioritization Scenario Evaluation Model Integration Data Management Data Processing User Modification of

Models User Criteria Weighting User Select or Add Data

The other two processing engines in EMDS are for Bayesian networks and decision trees. The Bayesian network engine may be useful for answering questions related to wildlife populations and risk. Both engines can be stand-alone or integrated into larger decision support projects. For example, in a restoration decision, Bayesian networks could be used to assess population viability of a species, then the outputs could be integrated into a logic-based evaluation of ecosystem integrity, before applying a strategic decision model to identify high priority management units for restoration and a tactical decision model (decision tree) to identify high priority management activities. EMDS manages the interoperability of the analytical engines with each other and with the GIS environment. EMDS also integrates a workflow editor, so applications can automate complex sequences of analytical tasks and repetitive analyses for alternative scenarios, as well as tasks related to data assembly, pre-processing, and post-processing. Once created, EMDS can store libraries of workflows for future use.

EMDS is one of the most versatile and comprehensive software packages for ecosystem assessment and planning, and it can be used equally well for other aspects of land use planning. It allows users to reduce complex, multi-dimensional analysis and decision processes into manageable pieces that conceptually frame and transparently present results for planning. EMDS provides a solid framework for organizing criteria, expert knowledge, and spatial data to structure and assess decision problems. Benefits of using EMDS include following:

Transparency in decision-making processes by showing stakeholders the logic and data that decision makers use to solve problems and balance competing goals.

Models are fully self-documenting and show data limitations, underlying assumptions, and development specifications.

Models can be quickly modified during meetings to perform immediate analysis of new ideas with input from collaborators.

A formal logic system makes it easier for experts to integrate their knowledge and provides the structure needed to have productive discussions about model specifications.

Formalizing logic processes and data sources can preserve institutional knowledge to standardize assessments and decision processes over time.

Each analysis has defined criteria, data, and judgments, and can easily be repeated. Decisions can be adaptively managed by comparing future and past landscapes to assess

progress toward goals, as well as by looking at which parts of a previous decision worked well and which parts did not, and adapting the models accordingly.

Qualitative information from expert assessments can be used when sufficient data are unavailable to develop mathematical models for all the variables that are of interest.

The logic engine can provide clear reasoning with incomplete information, partially evaluating system states and processes and providing information about the influence of missing data.

Best available science can be built into the logic models, promoting its use in decision processes. Multiple data sources can be included for each variable with designated prioritization, so

regional datasets can be used as needed for coverage, while more accurate local datasets can be prioritized where available to ensure that the best available data is consistently applied.

Models can be designed to interpret less reliable data more conservatively if necessary, and are robust to missing data.

Users can easily update analyses as datasets are refined or developed.

Fuzzy logic captures biological and land use effects that are graduated along gradients, facilitates interpretation of imprecise criteria, standardizes all criteria to a common measurement scale, and allows the degree of uncertainty to be evaluated on a spectrum.

Intelligent prioritization of data and results and by combining locally determined feasibility factors with a consistent identification of value and risk factors.

Consistent analyses across a landscape and over time allow analysis of statistical changes for comparing geographies or effects over time.

Supports multiscale analysis and is well suited to hierarchical planning so that outcomes benefit both local and regional goals.

Workflows allow automation of analyses for alternative scenarios, complex sequences of analytical tasks, and pre- and post-processing steps.

New version will be delivered as a web service, facilitating development of web applications.

To use the EMDS framework in our decision support tool, we would need to build logic and decision models at appropriate scales and populate them with data links. This would require input from subject matter experts and modelers. Preparing the raw data for model input could be time-consuming, as data may need to be transformed and concatenated to be formatted correctly as EMDS inputs; this also presents a potential barrier for local jurisdictions sharing new data with the tool. However, the pre-processing steps can likely be automated using EMDS’s workflow capabilities.

To run an analysis, the end user would need an interface to select variables from the logic and decision models to include, and add their own if necessary. They would also need to be able to review the data sources for each variable to select those most appropriate for their context, and they would need to be able to add new datalinks to the model for datasets that were not initially included. This would likely require some guidance on how to properly format datasets for use in the model. For the logic model structure, the end user would need an easy way to change the logic operators and the numerical threshold functions against which each piece of data is evaluated. For the decision models, end users would need to be able to adjust the weights and priorities according to local contexts. Users would also need to be able to save their changes to the models to be able to re-run scenarios as new data becomes available or new management decisions are required. An intuitive front end interface that connects to the EMDS framework and allows users to quickly make these changes to the models would need to be developed.

The EMDS framework would provide the back end programming and functionality required to organize and process data for scenario assessment. The models can be developed by a non-programmer in an intuitive graphical format and they are modular and can be designed to be interoperable at the data level. This makes it easy to start small and build up a system over time, and to start with simple models that contain a limited number of variables and dependency networks and gradually develop them into large, complex systems. Building the models can be time consuming, and requires expert knowledge as well as information from stakeholders. Once they are built, however, allowing end users to change parameters and data sources within them is relatively easy. A defined application programming interface (API) has also been developed to allow extension of the EMDS framework with additional data formats or analytical and modeling engines. Because not all of the functionality needed for the more specialized applications of our tool is available out of the box, a critical component of EMDS for our needs is the workflow editor, which can be used to program workflows that handle the data assembly, pre-processing, and post-processing required to adapt and run the models, and then calculate and

render the results in a way that allows end users to explore alternative scenarios and answer their key questions.

Our application will need to be web-based and provide users the ability to modify models to fit their decision contexts. The upcoming version of EMDS will be delivered as a web service which supports multiple users and is multi-thread safe, allowing client web applications to be developed. Data storage and processing are kept on the server, and the user interface can be on a web browser. Data can reside independently of EMDS as services and used directly without the need to import into the EMDS format, which is stricter and more difficult to maintain. While the EMDS framework is open source and freely available, the engines built into it require purchase of licenses to access model developer functionality. License costs have not yet been finalized for the new web service version of EMDS, but will likely include an annual fee of around a thousand dollars for setting it up on as a web service on the hosting agency’s website. Development to transform EMDS into an enterprise system with all functionality delivered via web services is well underway, with expected completion in late 2020 or early 2021. This can facilitate development of web-based applications and provide a complete design, analytical, and dynamic scenario planning framework for agencies and large organizations.

The EMDS Consortium includes the U.S. Forest Service and five private companies, each with different areas of expertise related to EMDS capabilities. The primary contractor is Mountain View Business Group, and they can support programming workflows based on well-defined processes. Other contractors can support logic modeling, decision modeling, and Bayesian networks. To pursue development of an EMDS application for our tool, we could work with the U.S. Forest Service Research Station through a cost reimbursable agreement covering their staff time and the time of needed subcontractors with specific modeling expertise. However, we would likely still need to contract with a web developer to develop the front end user interface.

2.2 Envision

Envision is a robust GIS-based modeling platform designed for alternative futures analysis and scenario exploration of coupled human and natural systems for integrated planning and environmental assessments at community and regional scales. It integrates a variety of spatially explicit models of landscape change processes and production, and it can integrate traditional simulation models with a “multi-agent modeling system” that represents human decision-makers and incorporates policies in landscape simulations. The interactions of actors, who have decision making authority over parcels and landscapes, the landscape (which changes as decisions are implemented), and the policies that guide and constrain decisions are central to Envision. As actors assess alternative management options, they weigh the relative utility of potentially relevant policies against their own values, landscape production and scarcities expressed as feedbacks, and global policy preferences to determine which policies to apply. This triggers a policy outcome that modifies site attributes representing landscape change, and those changes accumulate through space and time to provide a set of landscape trajectories. This functionality allows the user to define different policy scenarios and play those scenarios out on the landscape in a decision making framework that can look at many factors on the landscape and make decisions about landscape change processes in response to decision rules.

Key Features:

Scenario Evaluation Model Integration Data Management Data Processing User Modification of

Models User Select or Add Data

Envision has been applied to a wide variety of systems, problems, and geographies since the early 1990’s, including some in the Puget Sound area. Envision applications have been designed to broadly assess the effects of scenarios that reflect visions of the future. For example, applications have explored the effects of climate change, population growth, and development patterns on communities, land use, agriculture, water resources, and fire management over time. These scenario assessments have typically been used as a way for jurisdictions to develop a preferred vision for the future that balances ecosystem, land use, and community needs. The most similar application of Envision to our project was an alternative futures analysis in Skagit County, which was intended to inform comprehensive plan updates and develop a 50 year plan to protect environmental values, maintain natural resource industries, and accommodate growth. The application evaluated a number of scenarios against indicators for the built environment, natural environment, and working land.

Envision is open source and freely available. The framework consists of a dynamic spatial engine that represents polygonal, network, point, and grid-based landscape characterizations; a multi-agent modeling framework for representing values and behaviors of decision makers on the landscape; an extensible plug-in architecture for including process models describing landscape change dynamics, evaluative models describing landscape production metrics, visualizers for visual representation of data, and analysis modules for processing data or generating models; and a rich representation of policies that guide and constrain decision making and alternative land management scenarios. The developers have paid a lot of attention to the software engineering and software architecture so that it can integrate models and be applied in many different ways. The open architecture provides the ability to define and plug in a variety of models related to landscape processes (i.e. modules that describe land use change, transportation, urban growth boundary developments, etc.), as well as key metrics to be calculated (i.e. ecosystem services, availability of lands for different uses, critical areas). The variety of models and metrics that can be incorporated is essentially limitless. Models can generally operate on any version of a dataset added as long as it has a certain structure, which is important for data updates. Applications are developed using plug-in modules that simulate various aspects of the system being analyzed and model a variety of situations. Some plug-ins are pre-built to simplify application development for common simulation needs, and functionality can be extended using custom plug-ins for specific modeling needs. Prebuilt plug-ins do not require any programming and include capabilities for modeling population growth and allocation processes, state transition models, wildfire dynamics, hydrologic processes, habitat suitability, ecosystem services, and output reporting. New modules can easily be created using Envision’s Application Programming Interface (API). The API allows developers to develop modules that can access Envision’s spatial datasets, communicate with other modules, and extend Envision’s functionality.

For our tool, the Envision framework can provide spatial data management, model integration, and sophisticated decision making capability for analysis of alternative scenarios. One thing Envision does very well is allow the user to represent different types of scenarios in terms of the important dials and knobs that should be incorporated, and then play those out on the landscape with anywhere from simple to sophisticated models depending on needs and computational requirements; it is likely that our models will be on the simpler side. The developers have confirmed that the conceptual design for the scenario analysis component of our tool is something that could be done well using Envision, where dials and knobs can be turned by the end users and results reported back as change maps, other mapping products, tabular data, and time series. There is a lot that goes into defining scenarios in a

structured way that allows them to be computationally represented, and Envision has built a lot of that into its architecture. They suggested taking advantage of the pieces of Envision that are ready to go, especially for things like model integration. Our tool would likely have several underlying models related to land use change, habitat provision, ecosystem services, and other factors, as well as some datasets. Many relevant models have already been used in Envision and could be brought to bear quickly for our tool. Because the framework of Envision is very flexible and open, it can also be extended in the future without having to do a rearchitecture of the underlying engine. It is easy snap new models in as they are needed and the needs become more complex. The developers said that a relatively simple Envision application could be developed for our purposes using the technology they have.

The aspect of our tool that Envision does not attempt to do particularly well is provide a Graphic User Interface (GUI). A custom front end web interface would need to be developed for user input and display of results in our tool. Some significant effort would be needed initially to work with a set of potential end users to go through the requirements specification process for the front end. The developers are currently finishing up the process of moving Envision to a cloud server architecture where it will run as an exposed web service that web applications can access. Once complete, connecting a front end interface to Envision will be relatively straightforward. The developers suggest using an HTML and Javascript environment on a web server to provide a GUI, while using Envision as a set of back end services for performing the more computationally intensive tasks that cannot be done as well on a client machine like a web application. They also run a consulting firm that could provide assistance with both the back end and front end development needed for our tool.

2.3 NatureServe Vista

NatureServe Vista is a broadly capable decision support tool that supports complex assessment to integrate conservation with planning and management. It allows planners and managers to assess impacts related to natural, cultural, and development objectives and provides tools to create well-documented and defensible plans for sites and landscapes based on integrated data, expert knowledge, and stakeholder values. It can be used at any scale and in any environment, and it allows users to test “what-if” scenarios on the fly. Vista supports complete life cycle planning assessment to develop alternatives, implement a plan, monitor, and conduct adaptive management. Its core components are “conservation elements”, “scenarios”, and “scenario evaluation and site explorer”. The “conservation elements” component analyzes the locations of features and assesses their condition, distribution, patterns, and conservation requirements. The “scenario” component assesses the effects of land use patterns and other factors on conservation elements based on their locations and conservation policies. The “scenario evaluation and site explorer” component evaluates current and future condition of elements, where actions should occur, and what actions should be taken.

Conservation elements are planning elements, which can include human-based elements (i.e. habitats, ecosystems, species, human population densities, facilities). The ability of Vista to characterize elements and how they respond scenarios is an aspect we may want to consider for our tool. For example, on the species or ecological side, the user would specify a certain impact on

Key Features:

Landscape Prioritization Scenario Evaluation Model Integration Data Management Data Processing User Modification of

Models User Select or Add Data Report Generation

habitat condition from intense land use; the opposite can be done for desirable human activities, where conservation actions can range from precluding human activities altogether to allowing lower density or recreational uses.

The scenario building capability in Vista can represent cumulative impacts of human uses or restrictions. Instead of limiting polygons or pixels to one designation, scenarios are represented by stacks of layers. For example, one pixel could contain low density residential land use, agricultural activities like grazing, modeled invasive species presence, a modeled fire regime, and overhead power lines, and the cumulative effects model can drill down to calculate the impact of each of those factors and multiply them together to produce a condition score. The cumulative condition model was built as a modular function in Vista, so the code could be enacted in another application. The scenario capability also allows users to override certain elements of scenarios while keeping others in place. For example, the user could change a one unit per acre residential zoning area to 5 acre zoning, but keep all of the roads that are already there.

In scenario evaluation, Vista intersects elements with scenarios to evaluate how scenarios affect each element, generating maps and quantitative reports. There are two models for evaluating scenarios. One is a simple categorical model that calculates whether the intersections of scenarios with elements are negative, neutral, or beneficial, and it can generate value indices to create richness maps and weight elements based on condition, importance, or confidence. An average confidence layer indicates where there is more uncertainty around decisions. Vista also has a landscape condition gradient model that is very habitat or species specific and builds in expert knowledge-based nuance to bring ecological meaning to results by showing how many occurrences of each ecological (or human) feature are no longer viable under different scenarios. There is also a sophisticated off-site effect model, which can detect impacts of scenarios (i.e. road construction or intense development) on not only the area that they intersect, but also in a zone around them. The model uses a fall-off curve where the impact is very high close to the development and drops off.

The site explorer function can drill down at individual sites or groups of sites to understand how the elements are performing within that site, what scenario features are present, and how the elements are responding to those features in terms of impacts and drivers. Then the user can try out options for changing or mitigating the impact, including choosing from available actions such as zoning, restoration, or relocating a development with undesirable impacts. Because there is an expert database behind it, it can do instantaneous, on the fly what if testing that allows users to change parts of the scenario and preview the results. After a plan has been developed, Vista supports ongoing implementation and adaptive management. New information can be added at any time (i.e. a new element distribution map, a proposed highway, or new expert knowledge) and used to update analyses. If a conservation priority area is lost, Vista can be used to find an alternative location to meet goals and update plans.

Vista’s focus is on biodiversity applications, but it has flexibility for supporting broader multi-objective planning. Other valued factors (i.e. historic sites, agricultural areas, recreation areas, hazard areas, climate refugia) can be considered alongside biodiversity. Non-conservation land uses or features can be input as conservation elements to assess scenarios that meet multiple objectives and avoid land use conflicts. However, Vista is not designed to address all land use concerns, and interoperating Vista with specialized tools for those purposes is recommended. A “toolkit approach” typically uses Vista as a framework integration tool that can exchange information with other tools.

Vista has been applied by many organizations for projects worldwide, including watershed vulnerability assessments, prioritizing areas where conservation can benefit habitat alongside human communities, prioritizing ecological corridors, adapting land use plans to retain ecosystem services, integrating transportation planning with land use and conservation planning, identifying offsite mitigation locations, assessing cumulative effects of stressors, and investigating alternative sites for renewable energy. These applications have occurred at many scales, and at all levels of government. Many applications integrate Vista with other models and tools to develop new software packages that meet specific user needs.

While NatureServe continues to provide Vista (and consulting firm PlanIt Forward continues to provide services and support for Vista applications), they are no longer investing in updates or delivery as a web service, limiting its utility for our purposes. Their consultant suggested using Vista as a guide for functionality that could be replicated in our tool, and we could work with them to get algorithms and source code used to build particular features of interest. Some of the functions (i.e. cumulative condition model) were built as modular functions with standalone code that could be plugged into another application. Before NatureServe stopped providing Vista, the ArcGIS Pro version was under development, which would have included capability to interface online. The engineering team provided an estimate of $475,000 to $550,000 and 9 months of time from approximately two full time engineers to build the entire tool, which was starting from scratch and would have been able to do a lot more than the current version of Vista. Our tool would probably require a similar amount of cost and effort, unless we use more off the shelf interfaces and engines with less engineering of new analytics.

2.4 Sacramento I-PLACE3S

Sacramento Region Blueprint’s web-based Planning for Community Energy, Environmental, and Economic Sustainability (I-PLACE3S) model is a scenario planning tool that looks at the effects of planning decisions on the community. The user can evaluate how well a community integrates a variety of land use effects, identifies redevelopment and infill potential, provides housing and jobs, and provides transportation. Users can apply a variety of land use designations to potential development areas, each of which has characteristics like number of housing units per acre, number of employees commercial areas can support, and number of parking spaces that can be placed on the land. Each user can customize I-PLACE3S’s formulas for local contexts (i.e. housing costs, demographics, vehicle characteristics). The tool shows the effects of changing land use designations on traffic, agricultural land, open space, and economic impacts over 50 years. The tool can be used to create multiple future scenarios and show results as maps and tables that effectively communicate information to stakeholders and decision makers.

I-PLACE3S is transportation and development focused, with limited ability to assess impacts on environmental conditions. It could be used as an example of how transportation and built environment considerations have been effectively developed in a web-based scenario planning tool, but we would need to add a lot of ecological models for the purposes of a critical areas tool. It is also an example of a web-based system that successfully uses powerful database servers to analyze very large datasets. Moving the PLACE3S tool from its previous form as a desktop extension to an internet

Key Features:

Scenario Evaluation Model Integration Data Management Data Processing Web User Interface Web Mapping Application User Modification of

Models User Select or Add Data

version reduced processing time from hours to seconds and enabled it to be used efficiently during planning workshops.

2.5 EPA Decision Analysis for a Sustainable Environment, Economy, and Society (DASEES)

The Environmental Protection Agency’s Decision Analysis for a Sustainable Environment, Economy, and Society (DASEES) is an open source, web-based, deliberative-analytic framework of decision analysis tools designed to support and provide a logical process for structured stakeholder-driven group decision making in communities. The deliberative side of DASEES supports common understanding, development of objectives, and identification of measures. The analytic side supports the integration of data, models and tools necessary to solve the decision problem at hand and assess consequences of actions. DASEES facilitates the application of structured decision making through organizing and processing information used for identifying common goals, and creating, evaluating, and implementing alternatives for complex multi-objective environmental management and policy problems. It explicitly links values with actions and helps keep track of tradeoffs among alternatives.

Structured decision making is a generic name for any organized approach to integrate facts (scientific knowledge) and values (stakeholder concerns) into a decision-making process. The basic steps in a structured decision making process are 1) Clarify the Decision Context, 2) Define Objectives and Measures, 3) Develop Alternatives, 4) Estimate Consequences, 5) Evaluate Tradeoffs and Select a Decision, and 6) Implement and Monitor. A key point is that understanding the decision context and defining values and objectives comes before developing alternatives. This considered a ‘value-based’ approach, as compared with more traditional ‘alternatives-based’ approaches. SDM and tools like DASEES help to manage information, data, and analyses, but they do not provide the answer and people still make the decisions. DASEES is user-friendly, but technical analyses brought to the tool may require specialized expertise. Understanding the process of deliberate structuring of decisions is also necessary for using DASEES.

DASEES can be implemented across multiple scales, locations, and issues, and it has adaptable data and information needs that can come from expert judgement and various data sources. DASEES uses a combination of embedded decision-analytic tools and user-selected external resources. Several important features include web-based functionality that facilitates work by dispersed groups, a common platform for documenting and sharing decision data and records, system visualization tools, a framework for integrating disparate metrics in decision making, and effective communication of information to stakeholders. The scope of application can vary, and it has been used at neighborhood, community, city, and regional levels in a variety of locations and for a variety of problem types.

DASEES is not a spatial decision support tool, but it can complement such tools. DASEES builds a model of the decision and accepts modeled results of management action consequences from other tools, studies, and experts to evaluate proposed actions and integrate all the pieces of the decision problem. For complex problems with uncertainty, it establishes causality with network models that capture probability of success. A feature is in development to allow import of data layers for calculation and

Key Features:

Scenario Evaluation Model Integration Data Management Data Processing Web User Interface User Modification of

Models User Select or Add Data

display of spatial results of decisions. When this feature is complete, there may be more opportunity to use DASEES in conjunction with spatial data for land use and critical areas planning decision making.

Our tool could use DASEES or a similar a decision framework as part of its front end, to guide users through a logical process for making land use decisions. Users would start by identifying their values and desired outcomes (i.e. critical areas protection, working lands, housing density, etc) before developing alternative scenarios that could achieve those outcomes based on spatial data. Maps, models, and tools would need to be included to facilitate developing these scenarios based on critical areas regulations, buildable land, best available science, watershed functions, and other factors. Ecological and economic models could then link to each scenario to evaluate its consequences and ability to achieve the desired outcomes. From there, decision-makers could evaluate tradeoffs between scenarios and select the best one. After implementation, the framework could provide a process to support monitoring and adaptive management.

2.6 King County’s Water Quality Benefits Evaluation

King County is currently working on developing new integrated modeling tools to help determine the most effective ways to leverage water quality improvement efforts and strategically prioritize actions. The tools will show the relative benefit of actions that reduce pollutants, as well as the combination of actions that are likely to be most effective in reducing threats to people and aquatic animals.

The first tool contains pollutant loading models which characterize current pollutant loading to King County water bodies. These models show loading from non-point pollution across the landscape by assessing landscape characteristics (i.e. impervious surface, land use, geology) that could change water quantity and quality and producing a heat map for areas of high pollutant loads. The models will also need to include point sources such as superfund contaminated sites, areas with large amounts of creosote treated pilings, and combined sewer overflows. The output from the models will be average annual loads for major King County waterbodies for about ten major pollutants.

The outputs of the pollutant loading models can then be coupled with the next tool, which models and optimizes cost effectiveness of actions for reducing pollutants using the SUSTAIN modeling framework. It will be used for repeating analysis for different points on the map for several pollutants to look at potential action packages normalized by cost. The initial focus will be on areas that drain to wastewater service areas to make the results most useful for near-term planning efforts, but the tool could be used to look at other points in the future. The combined outputs of these first two tools show different packages of actions optimized for different pollutants, and they can be normalized by cost to facilitate comparison.

The third tool is a causal model which connects the potential actions to five priority water quality outcomes and uses Bayesian networks to demonstrate the most influential factors for each goal. The model produces a table describing each factor and its relationship to other factors. The goal is to compare actions to see what might be the most useful, rather than looking at the actual numbers.

Key Features:

Landscape Prioritization Scenario Evaluation Model Integration Data Management Data Processing

Sensitivity and influence analyses will be needed to test which factors the endpoint is most sensitive to, and models could be adjusted and improved over time.

The completed toolset will provide a systematic method to help decision makers see benefits of actions and potential tradeoffs between actions. This is similar to the goal for our critical areas decision support tool, though King County’s project focuses on a more specific part of the problem. Our tool could include similar nutrient loading models that link the effects of land use decisions to critical areas, as well as optimization and causal models that help to prioritize actions to reduce those loads. However, our tool would also need to include many other models and goals. Bayesian networks could provide a framework for demonstrating the relationships between scenarios and variables, and their influence on meeting critical areas and land use goals.

2.7 Maryland Development Management Assessment Tool (DMAT)

The Maryland National Capital Parks and Planning Commission’s Development Management Assessment Tool (DMAT) is a simple decision support tool designed to help planners with their countywide master planning process. At the time of tool development, there was no collective understanding of the total permitted development capacity within the county and master plans were not being designed to work together or consider the larger consequences for the system. The tool shows where development of land is constrained by mapping existing regulations relating to both environmental sensitivities and man-made development controls. The resulting maps show which lands have the most unconstrained development potential. The tool’s process is simple, and consists of overlaying GIS layers for each constraining factor to cumulatively build up a consolidated map of regulatory lands. By progressively subtracting the regulated lands, the user can see and quantify the remaining amount and location of relatively unconstrained land.

Areas were shown as least suitable, marginally suitable, or most suitable for development based on the presence of various regulatory factors. Environmental regulatory layers include hydrological water areas, streams/wetlands buffers, erodible soils, parks, biodiversity areas, agricultural reserves, special protection areas, and forest conservation easements. Man-made regulatory layers include utility sites and transmission line buffers, water main buffers, railroads, road/highway buffers, government ownership, public education sites, historic preservation areas, exhausted TDR areas, quarries, regulated affordable housing, private institutional sites, HOA common ownership areas, and single family land use. Several qualifiers were included in addition to regulations for areas that were not economically viable or suitable for development, to give a more realistic picture of development constraints. The tool was able to show stakeholders a visual progression of regulatory constraints that added up to about 85 percent regulated land and a remaining 15 percent of relatively unconstrained land. They could then examine the distribution and development potential on those unconstrained lands and see which areas would benefit from more planning.

Although the tool was designed for countywide processes in Maryland, most planning environments could benefit from at least some aspects of its conceptual structure and approach. It provided a fast and open way to understand complex regulatory conditions, helped prioritize the planning department’s

Key Features:

Landscape Prioritization Data Management Data Processing

workload, showed where change is possible, highlighted areas that were likely to experience the most change due to having fewer constraints, provided a clear basis for scenario generation to evaluate development alternatives, and improved community processes by allowing more focused debate and encouraging more informed and thoughtful choices by those in both leadership and stakeholder positions.

The DMAT provides a simple framework for buildable lands assessment that could be incorporated into our tool, if we are able to map all of the environmental and man-made regulatory constraints for each jurisdiction. Mapping critical areas regulations is a priority that has been expressed by stakeholders. If we were able to incorporate regulatory datasets into our tool, we would be able to show the amount of remaining buildable land in each Puget Sound jurisdiction, and cumulatively for the region. Then we could use this as the basis for developing buildout scenarios (considering hydrology, climate change, and other additional factors that should guide development locations). These scenarios could then be linked with other datasets and models to evaluate the ecological and economic impacts of each scenario and help local governments plan their growth in a way that protects critical area and watershed functions while maximizing economic development.

2.8 Model My Watershed

Model My Watershed is a web-based modeling and scenario evaluation tool that guides users through a process to analyze mapped watershed data, visualize monitoring data, and run model simulations of human impacts on water quality. The user selects an area of interest based on boundary layers, drawing on the map, delineating a watershed from a point on the map, or uploading a file. Once an area has been selected, the tool displays data, statistics, and layers for that area in tables and charts. Users can navigate between topics using tabs in the display window, including stream networks, land cover distribution, hydrologic soil group distribution, terrain, climate, point sources, farm animals, and water quality. Users can display the related map layers that statistics are based on, and the tool provides information about the data source and a download option.

The monitoring section allows users to search for additional monitoring data for their area of interest from a database. Data can be filtered by date. Users can also contribute by adding their own datasets to the repositories using the WikiWatershed Data Sharing Portal or the HydroShare online collaboration environment.

The modeling section includes two models that simulate stormwater runoff and water quality and allows users to run those models for their area of interest, and also create and evaluate the human impacts of alternative conservation and development scenarios. The “Site Storm Model” is designed for use in smaller, more developed areas. It simulates a hypothetical 24-hour storm by a hybrid of SLAMM, TR-55, and EPA’s STEP-L model algorithms. The “Watershed Multi-Year Model” is designed for use in larger, more rural areas. It simulates 30 years of daily data by the GWLF-E (MapShed) model. The user simply selects a model and the tool calculates results for the selected area of interest. Hydrology results include streamflow, surface runoff, subsurface

Key Features:

Scenario Evaluation Data Management Data Processing User Select or Add Data Model Integration User Modification of

Models Web User Interface Web Mapping Application Report Generation Data Download Security/Sign In

flow, and point source flow, and can be displayed on a line graph as well as a table. Water quality results include tables of total loads, loading rates, and mean concentrations of sediment, nitrogen, and phosphorous, broken down by source (i.e. land cover/land use type, farm animals, stream bank erosion, point sources, and septic systems).

The user can then also add changes to their area of interest to see how they affect the modeled results. Changes that can be made include changing areas of land cover from one type to another, with unlimited possible changes as long as the total area remains the same. Conservation practices can also be added. Rural conservation practices include cover crops, no till agriculture, conservation tillage, reduced tillage, nutrient management, livestock waste management, poultry waste management, veg buffer strips, streambank fencing, and streambank stabilization. Urban conservation practices include veg buffer strips, streambank stabilization, surface water retention, and infiltration/bioretention. The user selects which practices to apply and how much area they should cover, and the model automatically recalculates the hydrology and water quality results based on that scenario. Users can also manipulate the default settings for any of the model parameters calculated. The tool also allows users to sign in and save scenarios.

This tool is an example of a very user-friendly scenario assessment tool that integrates multiple models and data layers to generate quantified output metrics for watershed condition. We could use this as an example to develop a similar tool that integrates other models for ecosystem services, habitat, hydrology, critical areas, and other factors, and allows users to evaluate results for alternative land use scenarios. However, the scenarios used in this tool are limited in that they are not spatially explicit: users specify how much to change, but not where the changes occur within the area of interest. Our tool would need to include spatial inputs of scenarios to more accurately model results, because the effects of implemented changes may vary by location. We may also be able to learn from the output metric display, sign in, and data sharing features found in this tool.

2.9 Pollination Mapper

The Pollination Mapper was built by the same developers as Model My Watershed, described in the previous section, but has a different interface and some different features. It walks users through the process of comparing pollination management scenarios for farming in a very user-friendly interface. The user can zoom in to their farm on the map and outline their crop field. The tool calculates existing crops based on the available data, and the user can update the crop map by drawing areas of different crop types on the map. Then they can create pollination management scenarios by adjusting honey bee stocking rates or the size and location of pollinator plantings on the farm to compare how different scenarios impact crop pollination and yield. The outputs show crop yield on a bar chart as a percentage of maximum yield, and there is a table that compares results under the pollinator planting scenario to baseline yields. The user can define many different scenarios, and there is a user-friendly comparison tool to compare outputs for each scenario side by side.

Key Features:

Scenario Evaluation Data Management Data Processing User Select or Add Data Model Integration User Modification of

Models Web User Interface Web Mapping Application Report Generation Data Download Security/Sign In

This tool is an example of a very user-friendly scenario assessment tool that integrates a pollinator model with land cover and crop data layers to generate quantified output metrics for crop yields. Users can spatially input scenarios on the map to recalculate the model outputs and easily compare results. We could use this as an example to develop a similar tool that integrates other models and allows users to spatially input and evaluate results for alternative land use scenarios. We may also be able to learn from the output metric display, scenario comparison, sign in, and data sharing features found in this tool.

2.10 SeaSketch

SeaSketch is a collaborative web-based decision support tool for marine planning. It is designed to be easy to use and users can easily create hundreds of alternative proposals that represent a variety of interests. Users select layers to display on a web map, and then draw plans (i.e. for marine reserves) and view reports that provide information on the consequences of those plans based on intersections with other layers (i.e. habitat distribution, species, energy, human activities). Reports can be viewed side by side to evaluate tradeoffs between alternatives. Groups of sketches can be analyzed together as collections and shapefiles of plans can be exported for further analysis in ArcMap. Users can also collaborate via forum discussions and surveys.

Our tool could use similar functionality to assess the impacts and benefits of land use proposals, such as restoring or developing an area. Users could draw the scenario they are interested in on the map, and then the tool could generate reports of the effects on critical areas, land use, or other factors. Depending on available models and datasets, this impact assessment could be tied to complex modeling for hydrology, habitat, or other factors in addition to intersections with natural resource layers to further quantify impacts.

Users can see public information without signing in, but must sign in to use most features of the web tool. Project administrators can set restrictions on access to data, sketch creation, forums, and surveys for users. This could potentially be used as a model for sign in functionality for our tool.

2.11 NOAA OceanReports

NOAA’s OceanReports tool is a web-based tool that generates high level analysis reports for a user’s custom-drawn area of interest. It produces summary statistics and infographics related to general information, energy and minerals, natural resources and conservation, oceanographic and biophysical, transportation and infrastructure, and economics and commerce. Our tool could include similar functionality to generate reports on attributes of a user’s area of interest related to critical areas and land use.

Key Features:

Scenario Evaluation Data Management Data Processing User Select or Add Data Web User Interface Web Mapping Application Report Generation Data Download Security/Sign In

Key Features:

Scenario Evaluation Data Management Data Processing Web User Interface Web Mapping Application Report Generation

2.12 USFWS IPaC

The U.S. Fish and Wildlife Service’s IPaC is a project planning tool designed to streamline the USFWS environmental review process and integrate it into project design. The tool allows users to quickly and easily identify USFWS managed resources and suggested conservation measures for their projects. It shows if any listed species, migratory birds, or other natural resources may be impacted. The map also shows other resources in an area of interest, such as wetlands, wildlife refuges, GAP land cover, and other important biological resources.

The user can explore the map and sketch or select an area of interest to bring up a list of endangered or threatened species that could be affected by activities in that area, based on species range information. To define a project or request an official species list, the user needs to create an account and log in. Accounts are used for security, saving projects, and pre-filling forms. Account generation includes agreement to a use policy and consent to monitoring of activities. After defining a project, the tool shows how many and which types of USFWS resources are affected and provides a step by step project review process. The first step is to request an official species list, if required for the project. The next step guides the user through the regulatory review process for endangered species, migratory birds, refuge/fish hatchery compatibility, and wetlands (based on National Wetlands Inventory data). Each process includes contact information for the appropriate agency and many provide recommendations for mitigating potential impacts of projects on species.

This is a simple tool that simply looks up information based on a spatial area input. While our tool will be more complicated and involve a lot more data, something like this could be used for looking up critical area impacts for parcels. Our tool could show landowners which critical areas are present and provide recommendations for how to manage the land accordingly. It could also show conservation opportunities based on location and provide contact information for local agency resources. Another part of this tool that could be useful for our purposes is the user profile sign in. This could be used to track who is using the tool for what, and different levels of access could be granted to different user profiles based on data security needs.

2.13 TNC Resilient Land Mapping Tool

The Nature Conservancy’s Resilient Land Mapping Tool is a web-based tool that shows climate-resilient land in much of the United States, but does not cover Washington State. The user can display map layers that show resilient and connected networks, resilient sites, landscape diversity, local connectedness, geophysical setting, landforms, regional flow, and climate flow. The user can draw a shape on the map or upload a shapefile of an area of interest to calculate results for that area. The results include how much of the area is outside a prioritized network, and how it compares to average terrestrial resilience based on resilience, landscape diversity, and local connectedness. There are no recommendations or functionality for planning based on those

Key Features:

Scenario Evaluation Data Management Data Processing Web User Interface Web Mapping Application Report Generation Security/Sign In

Key Features:

Scenario Evaluation Data Management Data Processing User Select or Add Data Web User Interface Web Mapping Application Report Generation

results. This is another example of a tool that looks up and calculates information based on the user’s spatial input and this functionality could be adapted for our purposes.

2.14 Open Space Assessment Tool

Trust for Public Land and Puget Sound Regional Council’s Open Space Assessment Tool (OSAT) is a state of the art GIS decision support tool that is the first of its kind and addresses twelve ecosystem services over four counties. It is an effort to baseline the region’s critical open space services to inform conservation, enhancement, and protection priorities. The tool integrates regional data to understand opportunities and tradeoffs in regional decision making across communities, the landscape, and challenges related to open space planning, climate change, biodiversity, human health, social equity, and economic development. The tool is password protected due to the sensitivity of parcel data and an account must be requested to use the tool.

Open space services layers generally show how much each area of open space contributes to ecosystem services or which areas contribute the most. There are layers that describe services related to health opportunities, air, water, play, social vulnerability, health indicators, food, materials, work, energy, habitat, transport, and disaster mitigation. In addition to basic mapping functionality, users can generate reports that summarize baseline open space conditions and services for different geographies. Clicking on a geography displays a report of its name, acreage, percent open space, canopy cover, impervious surface, and demographics. The downloadable PDF report also contains information on how values for the area compare with regional values, and it breaks down amount of open space by type and provides the acreage of land in that area that provides each open space service.

Users can also query the parcel layer based on its relationship to the open space service layers. The user specifies a set of criteria (i.e. in an area of park need, providing significant carbon storage, providing water quality benefits) and the query tool returns the parcels that match those criteria. OSAT allows users to query based on multiple attributes simultaneously, making it one of the more advanced querying tools found in web mapping applications. Users can also assess open space projects by drawing an area on the map, such as a planned park. The tool calculates the area of the project, the service area (within a 10 minute walk), total population and households served, demographics of population served, income of households served, and race of people served. The training documentation for OSAT also describes a scenario development feature that is not currently available in the tool. This allowed users to combine open space service objective priorities in different ways to meet goals. Users could adjust scenario weights using slider bars and the tool would produce an output showing which areas meet the priorities. Users could run and compare multiple alternative scenarios.

Several features of OSAT are important for our efforts. Our tool will need advanced querying capability similar to that provided in OSAT and we may be able to learn from their efforts to implement this functionality. Including a tool similar to the project impact tool could also be important for calculating

Key Features:

Landscape Prioritization Scenario Evaluation Data Management Data Processing User Select or Add Data User Criteria Weighting Web User Interface Web Mapping Application Map Querying Report Generation Security/Sign In Data Download

things like impacts of development projects or benefits of restoration projects for critical areas. The scenario weighting feature could potentially also be useful as a way to prioritize areas on the landscape for protection, restoration, or development based on a jurisdiction’s goals. The login feature may be useful for our tool as a way to save scenarios and restrict access to certain features based on data security. Data sources for OSAT may be worth reviewing for possible inclusion in our tool. In addition to OSAT, Trust for Public Lands has a series of Climate Smart Cities decision support tools for various cities that use the exact same interface and have the same functionality.

2.15 Chesapeake Conservancy Geospatial Web Applications

Chesapeake Conservancy’s Conservation Innovation Center has developed several custom web applications that can be used to process and render large amounts of data to support conservation and restoration decisions. These include the Nature’s Network Restoration Prioritization Tool, the Capital Region Land Conservancy Parcel Viewer, and the Greater Baltimore Wilderness Coalition Coastal Resiliency Tool.

The most relevant for our purposes is the Nature’s Network Restoration Prioritization Tool, which ranks watersheds for restoration and conservation based on more than 200 different user-selected and weighted metrics related to ecological, species, land use, and conservation factors. Results can be stratified by watershed or by state to rank the units for each geographic area separately. The tool takes all the input parameters and generates a prioritization map, which can take up to a minute of processing time. On the prioritization map, dark areas are high priority and light areas are low priority. The results can be filtered by percentile rank, and clicking on a watershed displays information on its rank and how much of each of the selected parameters it contains. Results can be exported and models can be saved. This tool provides an example of a web-based tool that uses multiple user-specified weighted criteria to prioritize areas on a landscape for restoration action. We could develop a similar tool for prioritizing areas on the landscape for development, protection, and restoration in a comprehensive planning context by allowing users to select and weight relevant criteria in a similar web interface. This tool also provides an innovative example for saving user-defined models for future use and for exporting results.

The Capital Region Land Conservancy Parcel Viewer shows parcels, major roads, and NHD streams with 35 foot or 100 foot buffers. The user can visually see how much each parcel is affected and where construction and roads have been built within the buffer area. This is a simple viewer with no analysis capabilities, which limits its utility.

The Greater Baltimore Wilderness Coalition’s Coastal Resiliency Tool shows green infrastructure strategies to increase regional resilience to storms, sea level rise, and other impacts. The map shows where the opportunities are for implementing each strategy and the user can swipe between two strategies on the map to compare them. There are also reference maps that show boundaries and roads, stormwater BMP sites, state and county green infrastructure, forest cores/corridors, floodplains, sea level rise, and other natural features (i.e. critical areas). This is a map viewer that shows pre-analyzed results with no new analysis capabilities, which limits its utility.

Key Features:

Landscape Prioritization Data Management Data Processing User Select or Add Data User Criteria Weighting Web User Interface Web Mapping Application Map Filtering Report Generation Data Download

2.16 Data Basin & Environmental Evaluation Modeling System (EEMS)

The Kresge Foundation’s Data Basin is a science-based mapping and analysis platform that supports learning, research, and sustainable environmental stewardship. The four basic design principles for Data Basin include 1) improved data access and science transparency, 2) highly functioning data integration, 3) easy-to-use design, and 4) time and cost-effective, collaborative workspaces and tools. It allows users to explore data; create custom visualizations, drawings, and analyses; use collaborative tools in groups; publish datasets, maps, and galleries; and develop decision support and custom tools. Data Basin provides access to thousands of reliable, well-documented, authoritative conservation datasets.

Data Basin’s mapping tools allow users to view and analyze data using overlays, filters, and styling tools. User data can be combined with data from reliable conservation sources and viewed as maps, graphs, and animations or exported in various formats. Data Basin’s analysis tools allow users to explore attribute data, find intersections between datasets, view animations of datasets over time, and generate custom analyses and summary reports. Maps also allow users identify features or groups of features by clicking on the map and to swipe between visible layers to compare them. Premium mapping and analysis features (require ng a paid subscription) include select by attribute, select by intersection, filter layer by selection, export selection to drawing, export selection or drawing to shapefile, buffer, and site assessment to intersect a project area with other datasets and generate a report. Data Basin may provide access to useful datasets for our tool related to habitat, conservation, and connectivity. There are approximately 2,000 data layers on the platform that include Washington State. The premium analysis features would also be useful to include in our tool, and this appears to be one of the more advanced web-based platforms for providing those features.

Another important aspect of Data Basin that is relevant to our project is the creation and maintenance of customized Data Basin platforms called gateways, which are branded interfaces for a particular region or topic that can be integrated into existing websites to add powerful new ways to explore, access, and interpret information. More than 30 gateways have already been developed for all levels of government, universities, and other organizations. Gateways provide integrated data management and can include specialized software applications that address specific topics, including conservation and land use planning, biodiversity and ecosystem valuation, environmental risk avoidance, renewable energy planning, affordable housing, infrastructure planning, climate change adaptation and resilience, and others.

Conservation Biology Institute has also developed a logic modeling program called the Environmental Evaluation Modeling System (EEMS), which is now available online. EEMS is a tree-based, fuzzy logic modeling system used to develop complex spatial models that are highly transparent and can be explored and modified by users without much technical or science expertise. The software is open source and a good candidate for scenario testing and landscape prioritization. Data Basin provides special support for EEMS model outputs, so that users can explore the model and its results in detail

Key Features:

Landscape Prioritization Scenario Evaluation Model Integration Data Management Data Processing User Modification of

Models User Select or Add Data Web User Interface Web Mapping Application Map Querying Map Filtering Report Generation Data Download Security/Sign In

while combining mapped outputs with other available datasets. CBI staff are currently working on integrating Data Basin (as a workbench content management and planning platform) with specific decision support applications developed by CBI and others, with other modeling software such as EEMS, hydrologic, fire, water, and climate. EEMS plus Data Basin and specialized applications would enable ready integration of existing databases and planning initiatives in the region.

The Data Basin team can build custom tools to meet specific needs, including decision support and reporting tools. Conservation Biology Institute has reviewed our conceptual design for the tool and confirmed that they would be able to build the proposed system using the Data Basin platform and their application development expertise. Their existing map and decision support interfaces are attractive and accessible and provide most of the front end viewing functionality we would need for filtering, querying, layer intersection, and buffering, as well as guiding users through decision processes. EEMS can be used for prioritizing areas on the landscape for development, protection and restoration. Other models can be integrated to evaluate other scenarios. Data Basin provides data management, including data hosting via cloud services, access to existing databases via map services, standard metadata, and data security and sharing features.

2.16.1 Florida Resource and Assistance Simple Map Viewer: Data Basin Gateway

Peninsular Florida LLC’s Florida Resource and Assistance Simple Map Viewer is a Data Basin gateway interface designed to inform conservation decisions by allowing the user to explore watershed-scale information on priority natural resources, biodiversity richness, landscape connectivity and integrity, surface waters, and factors contributing to potential landscape change, such as sea level rise and urban development. The viewer allows users to identify their own priorities across the landscape, based on the conservation priorities, species, land use categories, and threats that are meaningful to their objectives. It is connected to the LCC’s Conservation Planning Atlas, which contains all the data layers and information used in the map interface.

Users can select criteria for priority resources, amount of species habitat, amount of land use types, projected sea level rise, and urban development population growth to apply filters and display the watersheds on the map that meet those criteria. The tool provides a user friendly interface with slider bars and input boxes for applying filter criteria and helps users keep track of which information has been filtered. The user can show the effect of each individual factor on the map by clicking on the map icon next to its name in the window.

To find out more information about a given watershed (i.e. a range of information on priority resources, land use, threats, partners), the user can click directly on the watershed on the map. The tool shows how well each watershed aligns with each resource priority, the percentage of the watershed in each land use category, how much of the watershed is affected by sea level rise projections, and how much of the watershed will be developed under future growth scenarios. It also shows the breakdown of overall ownership of land in the watershed, partner conservation efforts in that watershed, and links to private landowner opportunities for conservation or incentive programs that operate in that area based on land cover type and species presence. Aggregated data can also be displayed for multiple watersheds.

The current state of the viewer can be saved and shared, including extent, filters, and selected watersheds. A PDF report can also be configured and saved, including the map, current filters, watershed details, resources for additional information, and a link to reopen the map with all the filters

applied in the same way. There are also crosslinks to the associated datasets in the LCC’s Conservation Planning Atlas that can be downloaded.

This is an example of an interface that allows the user to select, explore, and combine the effects of selections on different data layers to match their own priorities and goals, which aligns well with the vision for our tool. The main features found in this interface that could be applied to our interface include:

Filtering Multiple Layers: Many interfaces provide the ability to filter layers individually, but most do not allow the user to combine the effects of filters on different layers. Including a function to combine the effects of filters between layer types would be very useful for site selection applications, such as prioritizing habitat and critical areas to protect or restore. For example, it could be designed to select an appropriate site based on any combination of how much habitat it provides for given species of concern, the planned land use of the area, development pressure, watershed sensitivity, ecosystem services, and other factors.

Viewing Information by Area: This interface provides the ability to view detailed information for each watershed by clicking on it on the map. This kind of location-based information viewing could be built into our map as well, allowing the user to select a watershed, county, city, urban growth area, parcel, or some other boundary on the map to pull up information on the critical areas, land use, land cover, housing growth, habitat and species presence, natural hazards, climate change threats, ecosystem services, watershed functions, and conservation opportunities and programs for that area. The ability to aggregate the information for multiple areas is also useful. For example, the user might be interested in aggregating the information for each UGA in a county, or each watershed or parcel with a certain characteristic to look at cumulative properties and effects. The location-based landowner conservation opportunities feature is also something that could be adapted for our purposes.

Report Generating: The report generating feature is well designed, and incorporating a similar feature into our interface would provide an easy way of getting the relevant information from the maps to the people who need to use it to make decisions.

2.16.2 RePlan Regional Conservation and Development Planning Tool: EEMS-Based Application

Conservation Biology Institute developed the RePlan Regional Conservation and Development Planning Tool, a California application that has a fully operational EEMS model built in to give users freedom in choosing planning outcome targets. The model is run on-the-fly and outputs are viewed inside the application for analysis. The tool integrates natural resource and planning datasets with analytical and reporting tools for regional conservation and development planning and siting. Users can display and overlay data layers, screen the landscape for areas that meet multiple criteria related to the environment, land use, land designation, climate change, and ecosystem services, and then select sites for further analysis. Sites and information can be downloaded as reports.

The user begins the process by selecting an area of interest based on the entire state, ecoregions, counties, watersheds, or a user-drawn region. They can then screen the landscape for that area based on multiple criteria. The tool provides a very user-friendly interface for selecting screening criteria that includes slider bars, check boxes, input boxes, and dropdown menus. The screening results show up as a set of highlighted polygons on the map that meet all the selected criteria. The user can then select one

or multiple sites from the screening results for analysis and reporting based on its intersection with a selected set of environmental, land designation and use, climate change, and ecosystem services data layers. The information displayed is generally the acreage of land in the selected site that meets each criterion. More detailed reports can be downloaded. Alternatively, users can skip the landscape screening process and analyze any area drawn on the map.

This tool provides an example of how an interface could be set up to guide users through a landscape prioritization and scenario assessment process and display results in our tool, though our application would need more complex scenario assessment capabilities. It also has useful features for report generation, data downloads, and saving maps for future use. A similar example of a Data Basin application with a slightly different interface is the California Energy Infrastructure Planning Analyst, which was designed to assist with planning energy development while avoiding environmental risks based on spatial data.

2.17 The Nature Conservancy Atlas of Ocean Wealth Explorer

The Nature Conservancy’s Atlas of Ocean Wealth Explorer is a global scale mapping interface for information about ocean ecosystem services. Users can explore maps and information on recreation and tourism, natural coastal protection, blue carbon, fisheries, and mangrove restoration. The tool displays maps, as well as a window that shows quantified estimates, economic values, and other statistics. Clicking on each statistic in the window changes the map to display information related to that statistic. The statistics for natural coastal protection can be adjusted based on different types of storms. There is also information on restoration potential and the ecosystem service value of restoration work, and restoration scores can be viewed by region. Fisheries statistics also include box and whisker plots showing the distribution of data. Users can also select and overlay information from all the different categories on the map, along with other regional datasets and reference data.

This interface could be used as an example for how some parts of our critical areas tool could be laid out to display information. The output information calculated by the interface for an area is displayed clearly and provides information like restorable area, degraded area, and restoration potential, as well as ecosystem services value of that area and its restoration potential. Each statistic links easily to the layer on the map that can be viewed to explore it further. This layout could be adapted to display information for critical areas protection or restoration, and could aid in selecting protection or restoration sites, as well as assessing the impacts of expanding critical areas buffers.

2.18 Esri Decision Support Web Application Frameworks & Tools

Esri has a suite of tools and frameworks that can be used to build decision support tools in web applications. These include the GeoPlanner scenario evaluation framework, widgets for suitability modeling and other geoprocessing tasks that can be customized within WebApp Builder, and portals

Key Features:

Scenario Evaluation Data Management User Select or Add Data Web User Interface Web Mapping Application Report Generation

that can be set up to organize shared data. Esri’s frameworks and tools provide a lot of functionality off the shelf, and they can be customized and extended using Python and Javascript APIs.

GeoPlanner is the most relevant tool for off the shelf scenario evaluation features, but it requires a special license for users and use of spatial analysis tools by users costs credits. It has several key tools and features:

“Explore” tools allow users to visualize their planning area by displaying and adding data, using spatial analysis tools like “Create Buffers”, “Create Travel Time Areas”, and “Enrich Layer,” and using suitability modeling that assesses suitability for development or other activities by overlaying layers and ranking outputs.

“Add Data” tools allow users to pull in data from ArcGIS Online or a portal. They can also share and access suitability models created by others.

“Analysis” tools allow users to create new layers that show how an area functions or performs and show how that performance relates to a scenario (i.e. suitability layers). Tools include suitability analysis, spatial analysis tools hosted on ArcGIS Online, classification, and custom geoprocessing.

“Modeler” uses weighted raster overlay to analyze site conditions and develop a suitability layer based on multiple mapped criteria. Users can select from available models, and choose and weight the variables they want to include in the model.

“Design” tools allow users to sketch, import, and modify design features, including scenarios that represent land use planning alternatives.

“Evaluation” tools allow users to assess performance of planning scenarios by comparing them to suitability criteria and key performance indicators. There are comparison tools for visualizing tradeoffs, comparing multiple scenarios, comparing scenarios with other layers, identifying areas of agreement or competition between scenarios, and generating reports.

“Dashboards” display key metrics as gauges and charts to allow users to quickly visualize and understand the impacts of planning scenarios in real time. It can track metrics for multiple variables and automatically updates as scenarios are modified.

This platform allows for landscape prioritization, scenario evaluation, geoprocessing, data sharing, and data visualization, which are all key features needed in our decision support tool. Several vendors have suggested using these products for tool development, and extending them through custom development as needed. Further analysis is needed on the potential for integrating other models to generate metric outputs (i.e. hydrologic condition, landscape change processes, ecosystem services, habitat suitability) in addition to metrics based solely on map layers. Further assessment of licensing requirements for end users is also needed.

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Landscape Prioritization Scenario Evaluation Model Integration Data Management Data Processing User Select or Add Data User Criteria Weighting Web User Interface Web Mapping Application Map Filtering Map Querying Report Generation Security/Sign In

Section 3: Models & Tools for Potential Inclusion

We reviewed six modeling tools for potential inclusion in our web-based decision support system for critical areas and land use planning. These include ecosystem services modeling, hydrologic modeling, and land cover change detection. All of these components will be important for inclusion in our tool to assess the effects of land use decisions, and these tools represent potential frameworks for calculating that information if sufficient data inputs are available. We will further assess these models and others alongside data inputs in the next phase.

3.1 InVEST

InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) is a set of free, open source, spatially explicit software models that can be used to map and value ecosystem services. Users can explore how changes in ecosystems can cause changes in the flows of a variety of ecosystem services and quantify tradeoffs among natural resource priorities due to alternative management decisions. It also identifies areas where investment in natural capital would benefit humans and conservation. The tool includes separate models for terrestrial, freshwater, marine, and coastal ecosystems, as well as some additional tools that help with locating and processing input data and with displaying and interpreting outputs.

InVEST models work at multiple scales and users can select only the ecosystem services they are interested in modeling. They use maps as input information and also produce maps as outputs. Results can be produced in biophysical terms (i.e. tons of carbon sequestered) or economic terms (i.e. net present value of that sequestered carbon). The models are based on production functions that define how changes in an ecosystem’s structure and function are likely to affect the flows and values of ecosystem services across a landscape or seascape. They account for both service supply (i.e. living habitats as buffers for storm waves) and the location and activities of humans that benefit from the service (i.e. location of people and infrastructure that could be affected by coastal storms).

Relevant InVEST models for our purposes may include carbon storage and sequestration, coastal blue carbon, habitat quality, habitat risk assessment, sediment retention, water purification, coastal vulnerability, urban flood risk mitigation, urban cooling, urban resilience, recreation, scenic quality, fisheries production, crop pollination, and crop yield. Each model typically requires land use/land cover (LULC) maps with some associated data. The tool’s website provides guidance on where to find appropriate data sources.

3.2 i-Tree Tools

USDA Forest Service’s i-Tree is a peer-reviewed software suite that provides urban and rural forestry analysis and benefits assessment tools. These tools can help strengthen forest management and advocacy efforts by quantifying forest structure and the ecosystem services and other environmental benefits that trees provide. i-Tree provides baseline data that can be used to demonstrate value and set

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priorities for more effective decision making. The tools work at multiple scales, from individual trees to entire landscapes or regions. Most of the current i-Tree tools are web apps, but there are also several desktop apps.

3.2.1 i-Tree Landscape

i-Tree Landscape allows the user to rapidly assess human and forest population information and threats to help prioritize areas for tree planting or protection. Users can explore tree canopy, land cover, and basic demographic information in a location of their choosing to learn about the benefits of trees, see how planting trees will increase the benefits provided, and map areas to prioritize tree planting efforts.

An i-Tree Landscape analysis is broken into five guided steps. To find locations, the user can search or select areas on the map while viewing data layers for boundaries, canopy and land cover, forest risk, health risk, and future climate. For the selected areas, the user can then explore a variety of land cover, demographic, and climate and forest vulnerability data for that region. The next step shows tree benefits based on the tree cover in the selected areas, which include amounts and dollar values of carbon, air pollution, and hydrologic benefits provided by trees, and can be viewed in table or chart form. If multiple areas are selected users can prioritize them for tree planting to promote sustainable tree benefits where they will be most valuable by choosing and weighting multiple criteria including various land cover, demographic, risk, and tree benefit data to create a custom prioritization index. There are also some predefined scenarios provided that assess tree cover, available planting space, and demographic information to maximize benefits in areas with high population density, high minority populations, or high levels of poverty. The last step shows the results of the analysis in a standard report or custom outputs.

An important component of developing a critical areas web tool will likely be quantifying the ecosystem services and other benefits of trees within critical areas. For this, we will need to link land cover maps with ecosystem services modeling, which is exactly what i-Tree Landscape does. The models and data used in the i-Tree Landscape app could potentially be connected with higher resolution, more up-to-date land cover mapping available locally such as that being done by the Washington Department of Fish and Wildlife to provide better estimates of tree benefits, and ideally expand the scale of the analyses down to the critical area or parcel level. The scenario development tool for prioritizing areas for tree planting could also be incorporated to aid in restoration planning. Additionally, it could be used as an example for designing similar prioritization scenario tools for critical areas and habitat protection and restoration sites, development sites, and other planning activities.

3.2.2 i-Tree Canopy

i-Tree Canopy lets users estimate tree cover and tree benefits for a given area using a random sampling process to classify ground cover types. The user defines a study area, then the app randomly generates sample points, for which the user selects the land cover type at that point based on aerial imagery. The more points completed, the better the land cover estimate becomes, and 500 to 1000 survey points are recommended. The app shows the percent cover estimate along with the standard error the entire time points are identified, so the user can use that information to decide how many points are enough. When

the user feels the survey is complete, they can generate a report, which includes a plot of the percent cover with standard error bars, a table that shows each cover class at its percent cover, and a table of tree benefit estimates for air quality and carbon sequestration.

iTree Canopy is a useful tool if a good land cover map does not exist for an area. It can also provide a more up-to-date estimate of land cover, as it uses current Google Maps aerial imagery. However, it is time consuming to designate all the points needed to generate an accurate estimate. For a critical areas web tool, it is likely that pre-analyzed land cover maps would be the main way for users to get information, but adding a tool like this as a supplementary tool, or simply linking to the iTree Canopy app within our interface could provide an additional option. Either way, the tree benefits conversion factors used to convert acreage of tree cover to tree benefits and value in i-Tree Canopy could be an easy way to calculate these outputs in our interface as well, but we will need to assess whether any local models are available that could be more accurate.

3.2.3 i-Tree Design

i-Tree Design is a parcel-level analysis tool that calculates current and future benefits of individual trees. This app allows inputs of location, species, tree size, and condition, and so can produce more accurate results that account for those factors which might cause variability in benefits provided between trees. Tree benefits are calculated for greenhouse gas mitigation, air quality improvements, and stormwater interception. With the additional step of drawing a building footprint and placing a tree on the map, the tree’s effects on building energy use can also be evaluated. Tree estimates are estimated for the current year, a user-specified forecast year sometime in the future, the projected total benefits across that future timespan, and the total benefits provided to date based on estimated tree age. Multiple trees and buildings can be added to compare benefits or to provide a full accounting of a property’s trees.

i-Tree Design is only useful at small scales, as the user must specify information for all trees and structures. It also requires specific information about tree species and size. As such, it probably would not be easy to integrate into our critical areas tool. However, it may be useful to link to the i-Tree Design app within any small-scale tree analysis applications of our tool so landowners have easy access to the tool if they do want to perform this type of analysis. We have regional scale maps of tree cover and structures, so perhaps the models used to calculate benefits in the i-Tree Design app could be scaled up to calculate energy savings for buildings and other ecosystem services in an area on a larger scale, though benefits would need to be based on canopy cover rather than on specified tree age and size. This concept could also be adapted for calculating the benefits of trees (and potential trees that could be planted) on temperatures for other things, such as salmon habitat in a stream.

3.2.4 i-Tree Species

i-Tree Species is an app that helps urban foresters select the most appropriate tree species to plant based on the species potential environmental services and geographic area. The user can select and rank the importance of each environmental service desired from trees, and the program calculates the best tree species to achieve those goals. Species are selected based on hardiness, mature height, and ranked environmental factors, including air pollution removal, air temperature reduction, ultraviolet radiation reduction, carbon storage, pollen allergenicity, building energy conservation, wind reduction, and stream flow reduction/stormwater management. The output is a ranked list of recommended species suited for local use that maximizes environmental services. This is another tool that would likely not be

fully integrated into our critical areas web interface, but any of our applications that involve scenarios for restoration and tree planting could link to this tool as a next step for planners.

3.2.5 i-Tree Eco

i-Tree Eco is a software application that uses tree measurements and other data to estimate ecosystem services and structural characteristics of urban or rural forests. The model uses data collected in the field from single trees, complete inventories, or randomly located plots throughout a study area along with local hourly air pollution and meteorological data to quantify forest structure, environmental effects, and value to communities. It provides sampling and data collection protocols, mobile data collection options, and summary reports. A central computing engine performs automated processing to estimate forest effects based on peer-reviewed scientific equations that predict environmental and economic benefits.

Estimates provided by the i-Tree Eco model include urban forest structure, pollution reduction, public health impacts, carbon, energy effects, avoided runoff, forecasting, bio-emissions, values, and potential pest impacts. However, because individual tree measurements are critical for accuracy of results, it is unlikely that we would be able to integrate this model into a web-based tool unless tree measurement databases already exist for our coverage area. It could instead be provided as an additional linked resource for planners who want to conduct a more detailed analysis of tree benefits in their jurisdictions.

3.3 Hydrologic Modeling: Hydrologic Condition Index & Puget Sound Watershed Characterization

King County and Ecology have done extensive work on watershed assessment and development of a hydrologic condition index. These models and data layers have been calibrated to the Puget Sound region and can be readily included in our decision support tool to assess the impacts of development, regulatory, and restoration scenarios on watershed condition.

3.3.1 King County Hydrologic Modeling for CAO Monitoring

King County assessed land use effects and regulatory effectiveness on streams in rural watersheds through a grant provided by the U.S. Environmental Protection Agency from 2007 to 2012. To measure environmental response to development with current regulations in place, researchers recorded changes in land cover and environmental variables (i.e. water quality, stream flow levels, insect populations, habitat complexity), along with permitting and regulatory compliance information, for a set of rural, forested, and urban watersheds with small streams. They also modeled historic conditions and full future build-out scenarios. To review and estimate compliance, they examined changes in land use to determine whether property owners would have needed a permit and, if so, whether they got one. This study was the first known attempt to assess the effectiveness of land use and development regulations in the Puget Sound region.

The study developed a new Hydrologic Condition Index, which allowed the hydrologic effect of any spatial configuration of land covers and geology to be indexed. This provided a precise measure of

Key Features:

Landscape Prioritization Scenario Evaluation Data Management Data Processing Web Mapping Application User Select or Add Data Data Download

hydrologic condition for each watershed and scenario. The project assessed land cover in every year during the study period, and under a full buildout scenario, as calculated based on amount and location of undeveloped land and zoning. The project also assessed historic land cover change to provide context and assess potential influences of past land uses. The Hydrologic Condition Index was estimated for a range of combinations of forest, grass, and impervious land cover conditions. It was also assessed for its ability to predict environmental response variables, including hydrology, water quality, biology, and channel complexity. The framework developed for this research is useful for continued monitoring of the impacts of development and effectiveness of regulations. The models should get better over time as flow routing, land cover mapping, hydrologic modeling, and understanding of watershed hydrology improves.

Because our focus area for developing our web tool for critical areas planning is mainly in the Puget Lowland Ecoregion with common geology, the hydrologic models should be applicable and could be integrated. Running the models would require an input of mapped land covers (forest, shrub, grass, buildings, etc) broken into grid cells. The modeled effect of geology and land cover on high pulse counts would then be weighted by the land cover grid cell’s inverted distance from the watershed monitoring point (the point on a stream downstream of the watershed) to make the effect of short distances large and the effect of long distances small. The results would then be summed to generate a watershed score, which would be divided by the score for the worst possible, all paved condition to calculate a Hydrologic Condition Index. The Hydrologic Condition Index could then be used to estimate environmental response variables in selected streams, such as hydrology, water quality, biology, and channel complexity.

Another aspect of this work that could be applied for the critical areas web tool is generating a full build-out scenario to estimate future conditions in streams or other critical areas. This defines the potential worst case scenario for development based on current zoning and available undeveloped land, assuming full compliance with development regulations. Future land covers under this scenario can be mapped using a standardized development template of building, clearing, and road applied to each undeveloped parcel. An additional interesting component could be mapping permit data to measure the amount of land use change in the region that is not in compliance with regulations. However, Commerce’s Puget Sound Mapping Project initially attempted to obtain and map permit data for the region and found this to be too difficult, ultimately using Office of Financial Management housing growth data as a proxy instead. This suggests that mapping the permit data that would be needed for this application on a regional scale may not be feasible.

3.3.2 Puget Sound Watershed Characterization

The Washington Department of Ecology’s Puget Sound Watershed Characterization (PSWC) is a tool that allows planners to identify the most important areas to protect and restore for watershed resources, and the most suitable areas for development. The project covers the entire Puget Sound drainage area at a coarse scale. The PSWC includes water assessments for water flow and water quality, as well as habitat assessments. The most recent addition is a decision support framework that integrates a Hydrologic Condition Index (a mid scale assessment that builds off of King County’s work described in the previous section) with the broad scale watershed characterization indices. This allows users to assess hydrologic condition in a more granular, spatially explicit way and can be used to assess the effects of land use and land cover changes on hydrology. The GIS data that were used for the water flow

and water quality assessments is available for download, including final results, source data, and intermediate data layers.

The Hydrologic Condition Index (HCI) is a mid scale hydrologic modeling tool that predicts the flashiness of stream flows (which can degrade downstream hydrologic processes, habitats, and biota) produced by combinations of land cover and surficial geology in watersheds. PSWC tested the potential application of the HCI for alternative futures applications to assess the effects of development patterns on the hydrologic health of a watershed. The HCI is calculated for a watershed from spatial data and a High Pulse Count coefficient for every possible combination of surficial geology and land use/land cover. The HCI shows the condition of the watershed for hydrologic processes on a scale from 0 to 1, where 1 is the worst possible condition (i.e. all impervious surface). Ecology developed preliminary categories to describe HCI ranges as good, moderate, and poor.

To integrate the HCI with the previously developed broad scale PSWC indices, they should be applied hierarchically. The process starts by looking at the broad scale assessment results at either a WRIA or sub-basin scale, then evaluating HCI results at the sub-basin and reach scales. Using this framework is intended for users to evaluate land use proposals for comprehensive planning or buildable lands analysis. For example, if a planner wants to figure out the best location for new development in a watershed to minimize degradation of hydrology, they would start by looking at the broad scale water flow results at the sub-basin scale. This shows which parts of the watershed are most important to protect and which parts are more suitable for development. Then they can calculate the HCI results for the watershed under current conditions, and under a future buildout scenario which shows the risk of future degradation. To prevent this future degradation, the HCI results can then be combined with the broad scale results to identify general management actions to prioritize in different parts of the watershed (i.e. where to increase development density through upzoning and where to limit development density). For the sub-basins where development density is appropriate, HCI can be used to look more closely at hydrologic impacts of upzoning, and compare the impacts of alternative development scenarios (i.e. current land cover, traditional development scenario, increased riparian buffer scenario, green development scenario). The alternative development scenarios translate into different patterns of development and land cover in the sub-basins, which have different HCI scores, as well as different numbers of potential housing units. Those results can show which scenarios are best hydrologically and best to meet land use needs. Additionally, for sub-basins that are most important for maintaining good hydrology, there are land use recommendations for limiting development and restoring certain areas based on maps of High Pulse Count coefficients.

For our critical area decision support tool, the PSWC can provide an example framework for prioritizing subwatersheds for protection, restoration, or development. PSWC bases its prioritizations on water flow and water quality parameters, which could be included in our decision tool alongside other important considerations for land use planning to identify areas on the landscape that are most suitable for protection, restoration, or development. PSWC also provides an example of hierarchical planning, going from coarse scale landscape prioritization to hydrologic condition assessments at the reach scale. Similar to PSWC, our tool could use landscape prioritization as the basis for developing alternative land use scenarios that match the identified prioritizations. The HCI would be an important response variable to assess for each scenario, and Ecology has already done much of the needed work to calibrate the model for the Puget Sound region. In addition to impacts on hydrology, our tool would need to assess the impacts of land use scenarios on habitat, ecosystem services, water quality, and other variables for

which models or data are available. PSWC also provides other data layers and indices that could be useful to include in our tool.

3.4 VELMA Eco-Hydrological Model

The Environmental Protection Agency’s Visualizing Ecosystem Land Management Assessments (VELMA) is “a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model to simulate the integrated responses of vegetation, soil, and water resources to interacting stressors.” The model can simulate how changes in climate and land use interact to affect soil water storage, runoff, drainage, evapotranspiration, carbon and nitrogen dynamics, and transport of nutrients to water bodies. VELMA was developed to support decision support goals related to assessing the effectiveness of natural and engineered green infrastructure (i.e. riparian buffers, cover crops, constructed wetlands, and other measures that intercept nutrients and contaminants that would otherwise end up in surface and ground water) for protecting water quality and quantifying ecosystem services.

VELMA can predict the effectiveness of alternative green infrastructure scenarios for protecting water quality in streams, rivers, and estuaries. It shows how natural and engineered green infrastructure options affect transport of water, nutrients, and toxics across multiple spatial and temporal scales. It also estimates potential ecosystem service co-benefits and tradeoffs (i.e. capacity to simultaneously provide clean water, flood control, food, climate regulation, habitat, etc) related to land use and policy decisions. The model provides a transferable and consistent framework for comparing green infrastructure benefits across landscapes, and has been linked with other tools (i.e. air quality models) to better understand tradeoffs of alternative decision scenarios for human health.

VELMA’s hydrological and biogeochemical submodels have been validated for simulating the effects of climate and land use changes on streamflow, stream chemistry, and ecosystem carbon and nitrogen dynamics. VELMA has been calibrated for a wide range of ecosystem types, focusing mainly on data-rich sites in the National Science Foundation’s Long Term Ecological Research network, including a temperate forest site in Oregon and an agricultural watershed in the Willamette River Basin. The model has been validated for green infrastructure in the Pacific Northwest in applications that focus on riparian buffers, cover crops, and other green infrastructure practices in agricultural and forest watersheds. Applications have shown how stream nutrient loads can be reduced by locating riparian buffers above a particular threshold width in areas with shallow groundwater flow, as well as how riparian buffers can fail due to contaminant loads, soil characteristics, climate change, and other factors. The model can be used efficiently at multiple spatial and temporal scales.

VELMA is simple and flexible. It has a user-friendly Graphic User Interface (GUI) that allows users to easily input parameter values and helps users with scenario development and model calibration. It also provides advanced visualization of simulation results to improve understanding. Both of these features are packaged in an open source programming environment. For users with sufficient GIS expertise (i.e. land managers, tribes, community groups), the user can assemble GIS data, create GIS and climate scenarios, run simulations, and analyze data, while the VELMA team can provide model input files and

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calibrated parameters. More advanced users can do all of this work independently. The model website provides a user manual and example. If a user wants to develop new algorithms or other features for VELMA, the authors of the manual can be contacted for access to the VELMA program code.

VELMA is useful on its own for quantifying how ecosystem services interact and respond to environmental changes, and it has been linked with Envision to quantify the economic and social impacts. A prototype was developed for the Willamette River Basin to assess the impacts of alternative land use and population growth scenarios on ecosystem services. It shows the capacity of the landscape to support projected growth under alternative growth management strategies and the resulting tradeoffs in services related to water quality, carbon sequestration, habitat, and agricultural and forest products. Outputs of the linked tools are visualizations of predicted changes in multiple ecosystem services, both in biophysical and economic terms. The goal is to create “a framework for integrated assessments that identify policy and management strategies for entire ecosystems and their bundled services, rather than piecemeal assessments of individual services”.

Running a VELMA simulation requires a simulator configuration file that specifies the names for all the other input data files. The simulation also requires complete data files for a flat-processed DEM, cover species ID map, cover species age map, soil parameters ID map, precipitation driver data, and air temperature driver data. To use the optional weather model, the simulation also requires a head index map, precipitation coefficients, and air temperature coefficients. Data for observed runoff and observed stream chemistry are not required to run the simulation, but are recommended to provide more information. Use of the VELMA model in our tool may be impractical due to its data requirements, as detailed information is required for each type of data input and some inputs such as species ID and species age maps are likely unavailable for most of our area of interest. This will require further assessment in the next phase.

3.5 WDFW High Resolution Change Detection and High Resolution Land Cover

Washington Department of Fish and Wildlife’s High Resolution Change Detection is a project that tracks land use/land cover change using high resolution 1-m National Agriculture Imagery Program (NAIP) imagery. Change is analyzed on a 2 year timeframe and the datasets provide polygons representing areas of change during that time, as well as information on the type of change and likely cause. A high resolution land cover dataset is also in development, though it is still in the experimental phase. It has been used by several organizations and generally maps canopy cover and impervious surfaces accurately. However, there are still some errors caused by shadows and bare earth.

Both the High Resolution Change Detection and High Resolution Land Cover datasets would be very useful to include in our tool. The High Resolution Change Detection data would be especially useful for compliance monitoring applications of the tool and can show where land cover has changed in or near critical areas or other sensitive areas. However, it is limited in that it takes about two years to process and develop a new dataset. The High Resolution Land Cover data would be especially useful for mapping canopy cover and impervious surfaces, and that information could be used as an input to other models that describe the landscape and inform management decisions.

Key Features:

Web Mapping Application Data Download

Section 4: Integrated Web Mapping Applications

We have reviewed 14 integrated web mapping applications that are used by Puget Sound counties and state agencies. These tools allow users to display and overlay data layers, with limited or no additional analysis capability, and are primarily useful for identifying potential data sources for our tool and assessing added value that could be provided for the local counties and resource agencies that developed them. We have included three state agency web mapping applications, as well as the integrated web mapping applications used by each Puget Sound county.

4.1 DNR Geologic Information Portal

The Washington Department of Natural Resources’ Geologic Information Portal is a web viewer for spatial data on geologic features and hazards. The portal includes a lot of data that can be overlaid in the map viewer. The viewer also includes features for adding data, identifying features on the map, reordering layers, viewing attribute tables for layers, selecting basemaps, querying data (including advanced querying using SQL expressions), setting bookmarks, printing maps, measuring, drawing, help, and feedback. Layers in the portal include geologic mapping at several scales, earthquake, seismic scenarios, landslide, tsunami, volcanoes, subsurface, earth resources permit locations, geothermal, minerals, coal, geophysical, and other geologic data. Many of the layers in this portal should be included in our critical areas tool because they relate to natural hazards. The map interface may also be useful as an example for developing query functionality for the web map in our tool.

4.2 Ecology’s Coastal Atlas

Ecology’s Coastal Atlas is a map viewer that allows users to view layers related to shorelines, ocean resources, administrative boundaries and regulations, and land cover. The viewer also has tools for navigation, identifying features on the map, selecting features on the map, drawing, and measuring. For our purposes, this viewer is primarily useful as a data source for layers that we may include.

4.3 WSDOT Community Planning Portal

The Washington Department of Transportation’s Community Planning Portal is a web viewer for spatial data for transportation planning. The viewer includes a lot of data that can be overlaid, but it is very basic and the only other features are selecting basemaps, measuring, sharing, and

Key Features:

Data Management User Select or Add Data Web Mapping Application Map Querying

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Data Management User Select or Add Data Web Mapping Application

bookmarking. Some of these layers will be useful in our critical areas tool for evaluating land use decisions and natural hazards in relation to existing infrastructure.

4.4 County Web Mapping Applications

Each of Puget Sound’s 12 counties manages its own GIS layers related to critical areas and land use planning, and most of these counties have integrated some or all of this data into interactive web mapping applications. County web mapping applications generally allow users to view and overlay data, and some include additional basic analysis functions like querying or filtering individual layers, selecting features, measuring distances, or drawing on the map. Functionality and layout is generally similar between each county’s applications, but data content and quality varies greatly between counties. None of these applications facilitate regional scale analysis or comparison of trends between counties, and they do not allow the user to easily integrate other regional datasets. The primary uses of these web mapping applications for our critical areas web tool will be to give us a sense of the data and functionality local government planners are currently working with in web tools, and to show which data sources they are currently using for their critical areas mapping. It will also help us to assess the added value that could be gained through our web tool, as compared with what each county is currently using.

Section 5: Next Steps

The array of tools and applications that we reviewed for utility in our critical areas and land use decision support tool demonstrates that all the proposed functionality for our tool has been successfully implemented by others in past efforts, even if none of them have put all of the pieces together yet. We have reviewed examples of web-based decision support system architecture; decision support frameworks and tools that can prioritize landscape areas based on multiple criteria and calculate the effects of alternative land use scenarios; models that quantify ecosystem services, hydrologic impacts, and land cover change; and web portals that provide access to locally available spatial data. Many of the developed decision support frameworks are open source and freely available, and there is strong potential for us to use and adapt this existing work to meet the needs of our project, saving time and money over developing a completely new system from scratch. In addition to decision support platforms, there are many aspects of other tools included in our review that will be useful for designing specific features of our tool, or for including within our tool. The tables on the following pages can be used as a reference for which tools can provide examples of each key feature.

Key Features:

Data Management User Select or Add Data Web Mapping Application Map Querying Map Filtering

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1.Web-Based Decision Support System Architectures

1.1 Multi Criteria Site Selection Tool 1.2 Watershed Management Tool 2.Decision Support Frameworks

2.1 EMDS 2.2 Envision 2.3 NatureServe Vista 2.4 Sacramento I-PLACE3S 2.5 DASEES 2.6 Water Quality Benefits Evaluation 2.7 DMAT 2.8 Model My Watershed 2.9 Pollination Mapper 2.10 SeaSketch

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2.11 OceanReports 2.12 IPaC 2.13 Resilient Land Mapping 2.14 Open Space Assessment Tool 2.15 Chesapeake Conservancy Tools 2.16 Data Basin & EEMS 2.17 Ocean Wealth Explorer 2.18 Esri Web Application Frameworks 3. Models & Tools for Potential Inclusion

3.1 inVEST 3.2 i-Tree Tools 3.3 Hydrologic Condition Index & PSWC 3.4 VELMA 3.5 High Resolution Change Detection 4.Integrated Web Mapping Applications

4.1 DNR Geologic Information Portal

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4.2 Ecology’s Coastal Atlas 4.3 WSDOT Community Planning Portal 4.4 County Web Mapping Applications