Brian Voigt

A Methodology for Adaptable and Robust Ecosystem Services Assessment

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant “one model fits all” paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES - both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.

New perspectives in ecosystem services science as instruments to understand environmental securities

As societal demand for food, water and other life-sustaining resources grows, the science of ecosystem services (ES) is seen as a promising tool to improve our understanding, and ultimately the management, of increasingly uncertain supplies of critical goods provided or supported by natural ecosystems. This promise, however, is tempered by a relatively primitive understanding of the complex systems supporting ES, which as a result are often quantified as static resources rather than as the dynamic expression of human–natural systems. This article attempts to pinpoint the minimum level of detail that ES science needs to achieve in order to usefully inform the debate on environmental securities, and discusses both the state of the art and recent methodological developments in ES in this light. We briefly review the field of ES accounting methods and list some desiderata that we deem necessary, reachable and relevant to address environmental securities through an improved science of ES. We then discuss a methodological innovation that, while only addressing these needs partially, can improve our understanding of ES dynamics in data-scarce situations. The methodology is illustrated and discussed through an application related to water security in the semi-arid landscape of the Great Ruaha river of Tanzania.

Integrating a traffic router and microsimulator into a land use and travel demand model

Abstract This paper describes one of the first known attempts at integrating a dynamic and disaggregated land-use model with a traffic microsimulator and compares its predictions of land use to those from an integration of the same land-use model with a more traditional four-step travel demand model. For our study area of Chittenden County, Vermont, we used a 40-year simulation beginning in 1990. Predicted differences in residential units between models for 2030 broken down by town correlated significantly with predicted differences in accessibility. The two towns with the greatest predicted differences in land use and accessibility are also the towns that currently have the most severe traffic bottlenecks and poorest route redundancy. Our results suggest that this particular integration of a microsimulator with a disaggregated land-use model is technically feasible, but that in the context of an isolated, small metropolitan area, the differences in predicted land use are small.

Testing an Integrated Land Use and Transportation Modeling Framework for a Small Metropolitan Area

This paper describes the implementation of a land use and transportation modeling framework developed for Chittenden County, Vermont, to test for differences in modeled output when employing a dynamically linked travel demand model (TDM) versus an assumption of static regional accessibilities over time. With the use of the land use model UrbanSim, two versions of a 40-year simulation for the county, one with a TDM and one without, were compared. In the first version, UrbanSim was integrated with the TransCAD four-step TDM; this allowed regional accessibilities to be recalculated at regularly scheduled intervals. In the second version, TransCAD was used to compute year 2000 accessibilities; these values were held constant for the duration of the model run. The results indicated some significant differences in the modeled outputs. In particular, although centrally located traffic analysis zones (TAZs) reveal relatively little difference between the two models, the differential within peripheral TAZs is both more pronounced and more heterogeneous. The pattern displayed suggests that some peripheral TAZs have higher modeled development with a TDM because the TDM accounts for the increased proximity of destinations, thereby making them amenable to development. Meanwhile, some peripheral TAZs have lower modeled development with a TDM because they already have good accessibility (e.g., access via Interstate), but the model without the TDM does not account for increased congestion.

Modeling the Effects of an Urban Growth Boundary on Vehicle Travel in a Small Metropolitan Area

An integrated land-use–transportation model was used to simulate the impact that an urban growth boundary would have on vehicle miles of travel in a small metropolitan community over a forty-year modeling horizon. The results of the modeling effort indicate that even in an area with low to moderate population growth, there is the potential to reduce vehicle miles of travel per person by as much as 25% from a business-as-usual scenario over a forty-year period. The reduction would result primarily from a shift of driving alone to carpooling or walking for many trips. A scenario in which growth is concentrated in a single urban core would also benefit from shorter average trip lengths; a scenario with multiple village centers would not have shorter trip lengths, but would still have significant improvements in total vehicle miles of travel.