Geology topics

Brian Voigt

Publications and source records attributed to Brian Voigt.

11 recordsLinked to original sources

USGS2021

Lessons learned from development of natural capital accounts in the United States and European Union

The United States and European Union (EU) face common challenges in managing natural capital and balancing conservation and resource use with consumption of other forms of capital. This paper synthesizes findings from 11 individual application papers from a special issue of Ecosystem Services on natural capital accounting (NCA) and their application to the public and private sectors in the EU and U.S. NCA is inherently a data-integration centered exercise, aiming to draw new insights by realigning environmental and economic data into a consistent framework. Drawing primarily on papers from the special issue and other key NCA literature, we identify lessons learned and gaps remaining for NCA’s development and application to decision making. In doing so, we identify eight key similarities and three major differences in NCA development, status, and application between the U.S. and EU. NCA can be highly policy relevant: special issue papers address critical issues including agriculture, water, conservation/land-use planning, climate, and corporate decision making. In both the U.S. and EU, further application is needed to drive demand for the accounts’ production. Based on these experiences, the U.S. and EU can be important leaders in cross-sector, international collaboration toward next-generation environmental economic accounts that advance global NCA practice.

Ecosystem Services↗

USGS2020

Sustaining Environmental Capital Initiative summary report

Federal agencies need credible scientific information to determine the production and value of ecosystem services in an efficient and timely manner. The U.S. Geological Survey addresses this scientific information need through the Sustaining Environmental Capital Initiative project. The project has relied on U.S. Geological Survey expertise related to water, fisheries, advanced modeling, and economics and other social sciences to conduct eight case studies across a range of environment types, including water-based environments, deserts, sagebrush ecosystems, floodplains, and forests. The Sustaining Environmental Capital Initiative also supported the development and expansion of four tools with the intent of adding content and usability for partners’ decision-making needs. The tools are the Natural Value Resource Center, Benefit Transfer Toolkit, Riverine Environmental Flow Decision Support System, and Artificial Intelligence for Ecosystem Services modeling platform.

Open-File Report↗

USGS2019

Towards globally customizable ecosystem service models

Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.

Science of the Total Environment↗

USGS2019

Reimagining the potential of Earth observations for ecosystem service assessments

The benefits nature provides to people, called ecosystem services, are increasingly recognized and accounted for in assessments of infrastructure development, agricultural management , conservation prioritization, and sustainable sourcing. These assessments are often limited by data, however, a gap with tremendous potential to be filled through Earth observations (EO), which produce a variety of data across spatial and temporal extents and resolutions. Despite widespread recognition of this potential, in practice few ecosystem service studies use EO. Here, we identify challenges and opportunities to using EO in ecosystem service modeling and assessment. Some challenges are technical, related to data awareness, processing, and access. These challenges require systematic investment in model platforms and data management. Other challenges are more conceptual but still systemic; they are byproducts of the structure of existing ecosystem service models and addressing them requires scientific investment in solutions and tools applicable to a wide range of models and approaches. We also highlight new ways in which EO can be leveraged for ecosystem service assessments, identifying promising new areas of research. More widespread use of EO for ecosystem service assessment will only be achieved if all of these types of challenges are addressed. This will require non-traditional funding and partnering opportunities from private and public agencies to promote data exploration, sharing, and archiving. Investing in this integration will be reflected in better and more accurate ecosystem service assessments worldwide.

Science of the Total Environment↗

USGS2018

The natural capital accounting opportunity: Let's really do the numbers

The nation’s economic accounts provide objective, regular, and standardized information routinely relied upon by public and private decision makers. But they are incomplete. The U.S. and many other nations currently do not account for the natural capital — such as the wildlife, forests, grasslands, soils, and water bodies—upon which all other economic activity rests. By creating formal natural capital accounts (NCA) and ecosystem goods and services (EGS) accounts, governments and businesses can better understand the past, peer into the future, innovate, conserve, and plan for environmental shocks. They would standardize, regularly repeat, and aggregate diverse natural resource, environmental, and social and economic data and could thereby play a significant role in advancing the science of coupled biophysical and social systems.

BioScience↗

USGS2018

Machine learning for ecosystem services

Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54–77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.

Ecosystem Services↗

USGS2016

Modeling the effects of urban expansion on natural capital stocks and ecosystem service flows: A case study in the Puget Sound, Washington, USA

Urban expansion and its associated landscape modifications are important drivers of changes in ecosystem service (ES). This study examined the effects of two alternative land use-change development scenarios in the Puget Sound region of Washington State on natural capital stocks and ES flows. Land-use change model outputs served as inputs to five ES models developed using the Artificial Intelligence for Ecosystem Services (ARIES) platform. While natural capital stocks declined under managed (1.3–5.8%) and unmanaged (2.8–11.8%) development scenarios, ES flows increased by 18.5–56% and 23.2–55.7%, respectively. Human development of natural landscapes reduced their capacity for service provision, while simultaneously adding beneficiaries, particularly along the urban fringe. Using global and local Moran’s I, we identified three distinct patterns of change in ES due to projected landuse change. For services with location-dependent beneficiaries – open space proximity, viewsheds, and flood regulation – urbanization led to increased clustering and hot-spot intensities. ES flows were greatest in the managed land-use change scenario for open space proximity and flood regulation, and in the unmanaged land-use change scenario for viewsheds—a consequence of the differing ES flow mechanisms underpinning these services. We observed a third pattern – general declines in service provision – for carbon storage and sediment retention, where beneficiaries in our analysis were not location dependent. Contrary to past authors’ finding of ES declines under urbanization, a more nuanced analysis that maps and quantifies ES provision, beneficiaries, and flows better identifies gains and losses for specific ES beneficiaries as urban areas expand.

Washington↗

USGS2014

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.

PLoS ONE↗

USGS2014

From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments

Ecosystem services mapping and modeling has focused more on supply than demand, until recently. Whereas the potential provision of economic benefits from ecosystems to people is often quantified through ecological production functions, the use of and demand for ecosystem services has received less attention, as have the spatial flows of services from ecosystems to people. However, new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems. Our analysis includes five ecosystem services: carbon sequestration and storage, riverine flood regulation, sediment regulation for reservoirs, open space proximity, and scenic viewsheds. Each ecosystem service is characterized by different beneficiary groups and means of service flow. Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use. With the exception of the carbon sequestration service, regions that actually provided services to people, i.e., connected to beneficiaries via flow paths, amounted to 16-66% of those theoretically capable of supplying services, i.e., all ecosystems across the landscape. These results offer a more complete understanding of the spatial dynamics of ecosystem services and their effects, and may provide a sounder basis for economic valuation and policy applications than studies that consider only theoretical service provision and/or use.

Washington↗

USGS2013

Spatial dynamics of ecosystem service flows: a comprehensive approach to quantifying actual services

Recent ecosystem services research has highlighted the importance of spatial connectivity between ecosystems and their beneficiaries. Despite this need, a systematic approach to ecosystem service flow quantification has not yet emerged. In this article, we present such an approach, which we formalize as a class of agent-based models termed “Service Path Attribution Networks” (SPANs). These models, developed as part of the Artificial Intelligence for Ecosystem Services (ARIES) project, expand on ecosystem services classification terminology introduced by other authors. Conceptual elements needed to support flow modeling include a service's rivalness, its flow routing type (e.g., through hydrologic or transportation networks, lines of sight, or other approaches), and whether the benefit is supplied by an ecosystem's provision of a beneficial flow to people or by absorption of a detrimental flow before it reaches them. We describe our implementation of the SPAN framework for five ecosystem services and discuss how to generalize the approach to additional services. SPAN model outputs include maps of ecosystem service provision, use, depletion, and flows under theoretical, possible, actual, inaccessible, and blocked conditions. We highlight how these different ecosystem service flow maps could be used to support various types of decision making for conservation and resource management planning.

Ecosystem Services↗

USGS2007

Beach morphology monitoring in the Columbia River Littoral Cell: 1997-2005

This report describes methods used, data collected, and results of the Beach Morphology Monitoring Program in the Columbia River Littoral Cell (CRLC) from 1997 to 2005. A collaborative group primarily consisting of the US Geological Survey and the Washington State Department of Ecology performed this work. Beach Monitoring efforts consisted of collecting topographic and bathymetric horizontal and vertical position data using a Real Time Kinematic Differential Global Positioning System (RTK-DGPS). Sediment size distribution data was also collected as part of this effort. The monitoring program was designed to: 1) quantify the short- to medium-term (seasonal to interannual) beach change rates and morphological variability along the CRLC and assess the processes responsible for these changes; 2) collect beach state data (i.e., grain size, beach slope, and dune/sandbar height/position) to enhance the conceptual understanding of CRLC functioning and refine predictions of future coastal change and hazards; 3) compare and contrast the scales of environmental forcing and beach morphodynamics in the CRLC to other coastlines of the world; and 4) provide beach change data in a useful format to land use managers.

Oregon, Washington↗