ARIES:
Adding Clarity to Ecosystem Services

Ecosystem services are often ignored because few people understand the value of what they do.   ARIES is one of many new resources being designed to change that.   For now, it maps and models the flow of services and takes stock of their impacts. The next step is to turn that into an economic evaluation.

20 October 2011| The Chehalis River Basin in Washington State is no stranger to catastrophic flooding.   In 2007, a flood in the region caused $166 million in damage—to everything from agricultural lands to personal property to transportation systems.   With this type of risk, the surrounding communities realized they needed to understand how flood protection functioned in the Basin.  

Working with the Chehalis River Basin Flood Authority, Earth Economics took on the task in 2010 to identify and estimate the value of this environmental service as well as model the landscape’s ecosystem service provisioners and beneficiaries.   Earth Economics used a new modeling software program called Artificial Intelligence for Ecosystem Services (ARIES) to help build landscape maps and models.   The Chehalis River Basin flood protection models became ARIES first case study.

ARIES uses the best available scientific data to build ecosystem service models and maps, which in turn help environmental asset decisions to be better informed, more effective, less costly and less contentious. The application was developed by a team of scientists, environmental modelers and economists led by Ferdinando Villa, a researcher at the Basque Center for Climate Change with grant funding from the National Science Foundation (NSF) of the Gund Institute at the University of Vermont.

ARIES’ partners on the grant included Conservation International and Earth Economics.   These NGOs have both performed ARIES case studies as well as done public relations work for the application.

What Does it Do?

Essentially, ARIES produces models and maps that illustrate the supply and demand of the ecosystem service, highlighting landscape factors that may inhibit the delivery.     In other words, in addition to identifying the service source, the application identifies how and where the ecosystem services are being used and whether there is an excess or need.

For the Chehalis River Basin flood protection case study, the models and maps describe where flood protection and other ecosystem services are provisioned, who benefits from these ecosystem services, how flooding is created, where flood protection and other ecosystem services are being impaired and how the service of flood protection is transferred to beneficiaries.  

Whatever ecosystem service they are modeling, it is essential to understand the location’s landscape attributes—such as forest cover, soil type and dams.

“Supply and demand is not performed by market forces in this case; it’s performed by nature” says Villa. “Modeling of that particular component is necessary because if you need the water for your purposes to drink or to run your hydroelectric plant—not only do you need nature to produce the water but also you need paths for the water to get to you.”

ARIES can essentially build models of these pathways anywhere in the world because it has the ability to connect to multiple publically available datasets to build these models.   These databases have a variety of focuses, including everything from biodiversity to forest cover to water flow.   Though these databases offer less detailed landscape and ecosystem service data, this ability enables ARIES to build models even where data and funds are lacking.

“It can connect on the backend of all of these other systems and pull in data we need,” says Jennifer Harrison-Cox, the Managing Director of Earth Economics.   “It is just a matter of coding for the backend data store you want to pull from.   That makes it a very powerful tool that can synthesize a lot of data sources.”

Because the data used in the production of ARIES maps and models varies in scale and resolution, the maps carry with them an uncertainty value and the models themselves are based on probabilistic and Bayesian in nature.   Unlike other modeling applications, this approach allows ARIES to build models in data-scarce areas by using the knowledge learned in building models data-rich areas.

For example, the data and models from the Chehalis River Basin case study and others will be built into the ARIES application once they are complete.

“As other analyses are done and information is incorporated from specific case studies into those analyses, ARIES incorporates that information in its modeling framework,” says Ladd Connell, the Director of Multilateral Relations for Conservation International.

Though these will inevitably be less reliable than those built for areas rich with data, they provide the justification for further analysis of ecosystem services—without high costs.

“It was impossible to build one model that would fit reality in a way which is precise enough to use it for policy making,” says Villa. “But on the other hand, it was impossible for decision makers to hire or pay for the experience needed to build custom models in every place.   In order to operationalize the notion of ecosystem services, there must be a middle ground.”

Understanding how changes in nature’s infrastructure impact human economies will be increasingly important as the impact of climate change and expanding populations continues to grow.

“We think this could be tremendously important because one of the problems we are facing in the world today is that the current development paradigm does not take into account ecosystem service values,” says Connell.

“ARIES is becoming a valuable tool for spatial policy planning, conservation planning, spatial assessment and distribution of ecosystem services, and optimization of payment schemes for ecosystem services (PES) – among other uses,” says Harrison-Cox.   “To be successful, markets need to be designed in such a way that the flow of ecosystem services, their costs and benefits are well understood by both regulators and investors.”

Right now, the ARIES’ models do not assign actual economic values to ecosystem service benefits; however, Earth Economics is developing an application to incorporate actual economic values.     Similar to the connection ARIES’ has with other databases, Earth Economics’ Simple Effective Resource for Valuing Ecosystem Services or SERVES will allow ARIES to incorporate valuation data into resulting models.

“Soon ARIES will be able to consume valuation data through the backend of SERVES, allowing economic analysis of ecosystem service benefits,” says Harrison-Cox.   “The ecosystem service maps, along with dollar values can be used to establish market prices based on real world data.”

With these values incorporated, Connell thinks ARIES will have a large impact on policy making.   “Our working hypothesis that this would result in an economy that is much more sustainable because it does take into account these values and ensures protection where protection is warranted and where there is opportunity for sustainable use,” he says.

Earth Economics has been utilizing SERVES valuation data with FEMA to support their cost-benefit analysis for flood insurance rates nationwide.   The application is meant to be utilized anytime a user needs an economic value, according to Harrison-Cox, like in appraisals, determining market value or doing a cost-benefit analysis.
 
Even without this specific economic data, ARIES provides a structure to build payment for ecosystem service (PES) schemes because the unique flow maps ARIES produces identify both the beneficiaries and provision of ecosystem services.   For example, when considering flood protection, ARIES can identify the patches of forest that are providing some form of flood protection for specific communities.  

“Then you can tax them for the protection of that forest because that forest is actually what gives them the benefit,” says Villa.  

Finally, in the context of land use decisions for public lands, the maps and models identify whether an extractive resource, like cut timber, is more valuable than a fully functional ecosystem service.

How Does All That Info Come Together?

Potentially, these types of models can be built anywhere in the world.   Users would select a portion of the geography, select ecosystem service priorities and ARIES will then produce maps and models utilizing data from the publically available datasets and any data they input themselves.

Currently, however, building these maps and models requires working with the ARIES modeling team—like in the Chehalis River Basin case study.  

To produce the Chehalis River Basin maps and models, the system began by building a map focused on land use and land cover of the specified area.   This includes topography, vegetation, impermeable surfaces, levees, dams, residents and public infrastructure.  

ARIES then is able to model each ecosystem service into a storyline – or a set of assumptions from a scenario to modeled in ARIES – which include models of the provisioning, beneficiaries, use and sink flow of that service.

“We wanted to model the whole process, not just the provision of the service, (but also) the usage and the need, the actual supply and demand parts,” says Villa.

Typically, users do not compute storylines for all of the ecosystem services.   Instead, they prioritize specific ecosystem services.   Then, the user can focus on gathering the related local models and data.   For this case study, it was gathered from stakeholders, local governments, agencies, the State of Washington and the Chehalis River Basin Flood Authority.

The locally-sourced and publically-available data is integrated into Bayesian algorithms to produce ARIES ecosystem service models.

For the Chehalis River Basin flood protection, the ARIES models utilized models and data describing the capacity of different land cover types to deliver reduction in flows and the erosion and sedimentation module as well as the rainfall, slope and vegetation.

Because the data is gathered from such a variety of sources, the next step for modelers is to bring it into compatibility and code in ARIES language.

“How you put all of these models together and have them all work to answer a more complex question is really the trick,” says Harrison-Cox.   To work through that process, her team used a spreadsheet to identify the models and data that are available, the sources of this data, the temporal and spatial aspects as well as the resolution.

This type of information is also utilized to identify the certainty and reliability of the input data.   Understanding this uncertainty is necessary because “ARIES is built on basic science and our understanding of how natural systems work.   The better the data is, and modeling of hydrology, topography, soils and other determining factors are, the better ARIES can perform.”

“That uncertainty is based on several things, but one is not having all the data necessary for the assessment.   If you are using a probabilistic model, you don’t need all of the data.   You can have some of the data, not all of them,” says Villa.   “If you have less than the optimum, you will get higher uncertainty but at least as a decision maker, you will know that uncertainty… where that uncertainty is high, you have to be more careful when you use those numbers to make any decision.”

In the Chehalis River Basin, ARIES ability to utilize global datasets to fill in data gaps helped policy makers and others form a basic understanding of the flood risk and the need for flood protection, even though there were some gaps in the available locally sourced data.

The beneficiary models had the highest uncertainty value in the case study.     These are the models that describe the people who reside, work, own or otherwise gain from reductions in flood damage or hazards as well as a physical map of the area under threat.  

A more complete and reliable beneficiary analysis would have included data on individuals from Vancouver to Los Angeles that economically benefit from the transportation of goods as well as those who enjoy the benefits of the recreation areas.   The models could also be strengthened if GIS data about exact locations of property assets was available and included because one area of a property by flooding might be affected while another may not.

Scenario Building

A key part of this type of case study is understanding how floods might occur in region in the future.

To do that, ARIES utilized GIS data to describe how flooding can occur in the Chehalis River Basin—including the areas where flooding is most likely to occur and how the flood would move across the geography.   Included in this modeling was data on precipitation, snow, slope, soils, vegetation cover and forest successional stages as well as levees, bridges and dams.  

“By providing provisioning, beneficiary and benefit flow maps, ARIES provides a framework whereby multiple scenarios can be run and calibrated to help ensure maximum benefit from investment,” says Harrison-Cox.

Policy makers can use ARIES to scenario-test their decisions to see how they will impact ecosystem services.   For example, how flood protection would be affected by the transition of a forest into a parking lot.   A user can highlight a specific section of the forest and change it to urban land use, which in the system will result in a parking lot.

When this change is saved, all ecosystem service storylines are recomputed.   The user can now return to the models and maps to understand the total impact of this scenario alternative.   For example, how lives were impacted by the change in flood routes due to the change in the forest landscape.

Currently, this scenario testing can only be done in conjunction with the ARIES team.   Even then it purposefully allows only a limited number of modeled parameters to be changed.   This is primarily because scenario changes can be complex and the ARIES team would prefer that the user completely understand the change before testing it.

 “Things like the scenarios, for example, we have disabled because they can be misused,” says Villa.   In other words, the ARIES team does not want the system utilized to justify environmental destruction; instead, their central purpose is to provide data support for policy decisions.

Not Without its Challenges

Working with real-world users has led to some challenges that the ARIES team has had to address.
     
In the case of the Chehalis River basin, one of the difficulties was explaining the concept of Bayesian networks to the policy makers.   These models hide the complexities of the science and math for something easy to understand—but this means it also hides what it is exactly analyzing.

In addition, the ARIES terminology may not translate to policy makers.   The terms ‘source,’ ‘sink’ and ‘use’ models do not really mean anything to decision makers.   Instead, Earth Economis uses the terms beneficiaries, provisioners, impairments and flow models.

“We are finding that adjusting the language to different audiences is necessary,” says Harrison-Cox.

In addition to language, policy makers will most likely find it a challenge to create storylines and build models on their own.

“I don’t see there is a time in the near future that a city council person, for example, could go in and run ARIES to ask question and solve a problem,” says Harrison-Cox. “It is going to require an intermediary, somebody who knows how to pull these models together, the assumptions, the suite of questions to ask and the data inputs to set ARIES up in such a way that results in actionable answers.”

However, if they do partner with someone who is able to build the models, they will be able to perform all of the analysis online.   Then, it will be accessible at any time for the policy maker to run scenarios.

Completing the System

Along with input from continued case study work, ARIES will continue to evolve with input from users and case studies.    

ARIES seeks to have free and unsupervised access for all non-profit users, including NGOS, academics and governments.   These users will be asked to share their data and thereby support the continued growth of the system.

Corporate users, though, will not be required to share the results of their application.   Instead, they will be asked to pay for the use of ARIES; this money will be utilized to further develop the system.     Villa thinks that companies will have a specific way they want to use ARIES.   The system was built with this in mind, so that companies could have a specific interface and utilize classified data.

With this vision in mind, ARIES continues to develop the system.   The Beta system is scheduled to be released later this fall.

With this release, policy makers around the world will have the opportunity to understand their region’s basic flow of ecosystem services.     Like in the Chehalis River Basin, these decision makers can consider how policy choices will impact the environment and the economics of the region.

October 20, 2011: Some sections of this article were altered shortly after publication to provide clarity

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