Geology topics

Kenneth J. Bagstad

Publications and source records attributed to Kenneth J. Bagstad.

At least 19 recordsLinked to original sources

USGS2025

Land use strategies for achieving Chile’s nationally determined contributions

Chile’s Nationally determined contributions (NDCs) commit to carbon neutrality by 2050, with measures to reduce emissions and natural hazards while enhancing water security. The Forestry and Other Land Uses (FOLU) sectors are critical to Chile’s goal of carbon neutrality, as they serve as a net carbon sink. In this paper, we conduct policy scenario analysis focusing on FOLU strategies for meeting the NDCs. We implement the Integrated Economic-Environmental Modeling framework linked with spatial Land Use-Land Cover and Ecosystem Services (ES) Modeling (IEEM + ESM) to assess impacts on economic, environmental and social indicators. Our results show that the implementation of Chile’s FOLU strategies would reduce emissions, enhance wealth and economic growth and increase future flows of ES. Carbon dioxide emissions would be reduced (by 151 million tons by 2050) to levels that would be considerably better than current Government expectations. Gross Domestic Product and wealth would be bolstered by US$16 065 million and US$22 731 million, respectively. Water-related ES would improve including the quality of potable water, while more water would be maintained within forested ecosystems, thereby reducing the future risk of natural hazards such as landslides and floods. The FOLU strategies would create 72 800 new jobs and reduce poverty by 15 586 individuals. Analysis with IEEM + ESM demonstrates that reducing wildfire-driven forest loss would have outsized impacts and be the most effective and expedient way to contribute to meeting NDC targets. The IEEM + ESM approach is an example of an analytical framework that is meeting growing demand from Government institutions and multilateral development banks for understanding the effects and transition pathways of NDC strategies on economic, social and environmental outcomes.

Environmental Research: Ecology↗

USGS2025

Scoping decision-maker needs and science availability to support regional natural capital accounting in the U.S. Colorado River Basin

Natural capital accounting has the potential to yield important policy insights at multiple scales, but there remains a disconnect between regional-scale natural capital accounts and their use for informing policy. In this paper, we propose a roadmap that could lead to the creation of policy-relevant regional accounts, with steps split across an initial scoping phase and a subsequent development phase. We demonstrate the scoping steps in action with an application to the Colorado River Basin (“Basin”), a large watershed in the southwestern United States (U.S.) that has faced aridification and substantial high-profile tradeoffs around the use of its water and other natural resources. Drawing on prior U.S. Geological Survey science co-production efforts, we conducted a series of eight discussion sessions with 41 scientists and science representatives whose work is relevant to Basin water, riparian and riverine ecosystems, upland ecosystems and energy and minerals. We summarise participants' thoughts on key topics and economic linkages, their insights and questions of interest and their recommendations on existing scientific data sources and gaps. We evaluate the suitability of the available data for construction of System of Environmental-Economic Accounting (SEEA) Central Framework and SEEA Ecosystem Accounting accounts, including those for land, water, forests, energy and minerals and ecosystems (covering extent, condition and ecosystem services). We present a series of lessons learned during the scoping phase, as well as lessons that could be relevant for future practitioners engaging in the development phase. The information can help guide the development of timely and relevant regional-scale environmental-economic accounts in the U.S. and beyond.

Arizona, California, Colorado, Nevada, New Mexico,↗

USGS2025

Integrating quantitative macroeconomic and ecosystem service modeling methods to assess conservation rograms in Mexico

Conveying the importance of public conservation programs to Government decision makers is challenging given the long-term nature of investments in conservation and competition for scarce public resources. The effectiveness of conservation programs in terms of their impact on ecological and socioeconomic outcomes has been evaluated in the literature though not typically in an integrated way that enables the assessment of trade-offs across environmental, economic and social objectives. Through a novel approach that uses macroeconomic tools that incorporate land use-land cover change and ecosystem services, we conduct policy scenario analysis to investigate the economic, natural capital and ecosystem service impacts of three conservation programs in Mexico. We show their combined impact on Gross Domestic Product and wealth in 2035 to be US$856.9 and US$492.3 million, respectively and that the programs reduce poverty by 1,800 individuals. In addition to macroeconomic effects, our approach illustrates that by accounting for changes in regulating ecosystem service flows, cumulative Gross Domestic Product would be 1.34 times higher. Our results build a business case for ongoing investment and expansion of these conservation programs by demonstrating the benefits to biodiversity, natural capital and the economic well-being of Mexican society. The case study allows for replication in other countries as the tools and data employed are openly available for much of Latin America and the Caribbean, Southeast Asia and increasingly worldwide.

Environmental and Resource Economics↗

USGS2025

An enhanced national-scale urban tree canopy cover dataset for the United States

Moderate-resolution (30-m) national map products have limited capacity to represent fine-scale, heterogeneous urban forms and processes, yet improvements from incorporating higher resolution predictor data remain rare. In this study, we applied random forest models to high-resolution land cover data for 71 U.S. urban areas, moderate-resolution National Land Cover Database (NLCD) Tree Canopy Cover (TCC), and additional explanatory climatic and structural data to develop an enhanced urban TCC dataset for U.S. urban areas. With a coefficient of determination (R 2 ) of 0.747, our model estimated TCC within 3% for 62 urban areas and added 13.4% more city-level TCC on average, compared to the native NLCD TCC product. Cross validations indicated model stability suitable for building a national-scale TCC dataset (median R 2 of 0.752, 0.675, and 0.743 for 1,000-fold cross validation, urban area leave-one-out cross validation, and cross validation by Census block group median year built, respectively). Additionally, our model code can be used to improve moderate-resolution TCC in other parts of the world where high-resolution land cover data have limited spatiotemporal availability.

conterminous United States↗

USGS2025

A tiered assessment framework for interregional flows of ecosystem services from migratory species

The Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services has called for assessments explicitly accounting for interregional flows of ecosystem services (ESs) across geographic scales. An important type of interregional ES flow is generated by the long-distance movements of migratory species. Many migratory species provide important benefits to people, and due to migration dynamics, ESs provided in one location may be affected by habitat conservation, or lack thereof, in other locations. The state of the science on interregional flows of ESs from migratory species, however, is nascent and lacks structure needed to consistently characterize flows. We developed a 4-tiered system for categorizing assessments and the conclusions they can support based on 4 levels of ecological and socioeconomic information, ranging from incomplete to high, and how they are combined. The 4 tiers of assessment are based on differing levels of detail in the estimation of system-level ecological and socioeconomic information on a species and the services it provides: telecoupled ESs, qualitative flows, quantitative static flows, and quantitative dynamic flows. Recent assessment studies largely fall within the first tier, which does not quantify flows. Socioeconomic and ecological information are needed to achieve each tier. Our framework can be used to identify and classify a range of methods, with varying time and data requirements, that can be used to maximize the information content and relevance of ES assessments for migratory species based on available resources.

Conservation Biology↗

USGS2025

Land-use and socioeconomic time-series reveal legacy of redlining on present-day gentrification within a growing United States city

Home Owners’ Loan Corporation (HOLC) maps illustrated patterns of segregation in United States cites in the 1930s. As the causes and drivers of demographic and land-use segregation vary over years, these maps provide an important spatial lens in determining how patterns of segregation spatially and temporally developed during the past century. Using a high-resolution land-use time series (1937-2018) of Denver, Colorado, USA, in conjunction with 80 years of U.S. Census data, we found divergent land-use and demographics patterns across HOLC categories were both pre-existent to the establishment of HOLC mapping and continued to develop over time. Over this period, areas deemed “declining” or “hazardous” had more diverse land use compared to “desirable” areas. “Desirable” areas were dominated by one land-use type (single-family residential), while single-family residential diminished in prominence in the “declining/hazardous” areas. This divergence became more established decades after HOLC mapping, with impact to racial metrics and low-income households. We found changes in these demographic patterns also occurred between 2000 and 2019, highlighting how processes like gentrification can develop from both rapid demographic and land-use changes. This study demonstrates how the legacy of urban segregation develops over decades and can simultaneously persist in some neighborhoods while providing openings for fast-paced gentrification in others.

Colorado↗

USGS2025

Interoperability for ecosystem service assessments: Why, how, who, and for whom?

Despite continued, rapid growth in the literature, the fragmentation of information is a major barrier to more timely and credible ecosystem services (ES) assessments. A major reason for this fragmentation is the currently limited state of interoperability of ES data, models, and software. The FAIR Principles, a recent reformulation of long-standing open science goals, highlight the importance of making scientific knowledge Findable, Accessible, Interoperable, and Reusable . Critically, FAIR aims to make science more transparent and transferable by both people and computers . However, it is easier to make data and models findable and accessible through data and code repositories than to achieve interoperability and reusability. Achieving interoperability will require more consistent adherence to current technical best practices and, more critically, to build consensus about and consistently use semantics that can represent ES-relevant phenomena. Building on recent examples from major international initiatives for ES (IPBES, SEEA, GEO BON), we illustrate strategies to address interoperability, discuss their importance, and describe potential gains for individual researchers and practitioners and the field of ES. Although interoperability comes with many challenges, including greater scientific coordination than today’s status quo, it is technically achievable and offers potentially transformative advantages to ES assessments needed to mainstream their use by decision makers. Individuals and organizations active in ES research and practice can play critical roles in creating widespread interoperability and reusability of ES science. A representative community of practice targeting interoperability for ES would help advance these goals.

Ecosystem Services↗

USGS2025

Natural capital accounting on forested lands: An application to the Colorado River basin

This paper creates a first set of forest natural capital accounts and demonstrates how these accounts can be integrated with general equilibrium models of the economy. Focusing on the Colorado River Basin, we show that deforestation has direct implications for the forest industry and indirect impacts on the economy through water treatment costs and carbon stock. 327,000 acres of forest are projected to be lost to development by 2100, representing a loss of 1.3 million tons of carbon stored in forests. The direct economic impacts associated with forest loss are estimated to be over $30 million, with $28 million of that coming directly from the value of lost carbon.

Conference Paper↗

USGS2024

Leveraging natural capital accounting to support businesses with nature-related risk assessments and disclosures

Nature loss threatens businesses, the global economy and financial stability. Understanding and addressing these risks for business will require credible measurement approaches and data. This paper explores how natural capital accounting (NCA) can support business data and information needs related to nature, including disclosures aligned with the Taskforce on Nature-related Financial Disclosures recommendations. As businesses seek to measure, manage and disclose their nature-related risks and opportunities, they will need well-organized, consistent and high-quality information regarding their dependencies and impacts on nature, which few businesses currently collect or track in-house. NCA may be useful for these purposes but has not been widely used or applied by businesses. National NCA guided by the U.N. System of Environmental-Economic Accounting may provide: (i) a useful framework for businesses in conceptualizing, organizing and managing nature-related data and statistics; and (ii) data and information that can directly support business disclosures, corporate NCA and other business applications. This paper explores these opportunities as well as synergies between national and corporate natural capital accounts. In addition, the paper discusses key barriers to advancing the wider use and benefits of NCA for business, including: awareness of NCA, data access, business capabilities related to NCA, spatial and temporal scales of data, audit and assurance considerations, potential risks, and costs and incentives.

Philosophical Transactions of the Royal Society B:↗

USGS2024

The economics of decarbonizing Costa Rica's agriculture, forestry and other land uses sectors

In 2018, Costa Rica demonstrated its commitment to the Paris Agreement and published its Decarbonization Plan for achieving zero net emissions by the year 2050. We evaluate the impacts of the country's strategy for decarbonizing its Agriculture, Forestry and Other Land Uses (AFOLU) sectors by coupling the Integrated Economic-Environmental Modeling framework with high-resolution spatial land use-land cover change and ecosystem services modeling (IEEM+ESM). Our results show that decarbonization of AFOLU would simultaneously enhance carbon storage, water purification, water regulation and erosion mitigation ecosystem services. Moreover, the positive cumulative wealth impact of decarbonization would be approximately US$7.27 billion by 2050 while lifting an additional 3810 individuals out of poverty. From a public investment perspective, decarbonization would have a fiscally neutral impact with the economic benefits sufficient in magnitude to off-set policy implementation costs and generate economic returns of over US$852 million when changes in natural capital stocks and environmental quality are considered. This application to Costa Rica is the first integrated economy-wide analysis of a growing number of decarbonization plans globally. The IEEM+ESM approach provides an integrated framework for analyzing decarbonization plans and can be used to refine AFOLU mitigation strategies to capitalize on synergies and minimize negative trade-offs across the three dimensions of wealth and sustainable economic development, namely economy, society and the environment.

Ecological Economics↗

USGS2024

Urban tree cover provides consistent mitigation of extreme heat in arid but not humid cities

Urban land cover types influence the urban microclimates. However, recent work indicates the magnitude of land cover's microclimate influence is affected by aridity. Moreover, this variation in cooling and warming potentials of urban land cover types can substantially alter the exposure of urban areas to extreme heat. Our goal is to understand both the relative influences of urban land cover on local air temperature, as well as how these influences vary during periods of extreme heat. To do so we apply predictive machine learning models to an extensive in-situ microclimate and 1 m land cover dataset across eight U.S. cities spanning a wide aridity gradient during typical and extreme heat conditions. We demonstrate how the cooling influence of tree canopy and the warming influence of buildings on microclimate linearly scales with regional aridity, while the influence of turf and impervious surfaces does not. These interactions lead tree canopy to consistently mitigate to air temperature increases during periods extreme heat in arid cities, while the influence of urban tree canopy on extreme heat in humid regions is varied, suggesting that mitigation is possible, but tree canopy can also aggravate extreme heat or have no significant effect.

Sustainable Cities and Society↗

USGS2023

Banking on strong rural livelihoods and the sustainable use of natural capital in post-conflict Colombia

In post-conflict Colombia, the government has prioritized resettlement of displaced people through development of strong rural livelihoods and the sustainable use of natural capital. In this paper, we considered government proposals for expanding payment for ecosystem services (PES) and sustainable silvopastoral systems, and private-sector investment in habitat banking. We coupled the Integrated Economic-Environmental Model (IEEM) with spatially explicit land use and land cover change and ecosystem services models to assess the potential impacts of these programs through the lens of wealth and sustainable economic development. This innovative workflow integrates dynamic endogenous feedbacks between natural capital, ecosystem services and the economic system, and can be applied to other country contexts. Results show that PES and habitat banking programs are strong investment propositions (Net Present Value of US$4.4 and $4.9 billion, respectively), but only when moving beyond conventional economic analysis to include non-market ecosystem services. Where a portfolio investment approach is taken and PES is implemented with sustainable silvopastoral systems, investment returns would reach US$7.1 billion. This paper provides a detailed evaluation of the benefits of investing in rural livelihoods and enhancing Colombia’s natural capital base, with empirical evidence to inform the spatial targeting of policies to maximize economic, environmental and social outcomes.

Environment, Development, and Sustainability↗

USGS2023

The future of ecosystem assessments is automation, collaboration, and artificial intelligence

Robust and routine ecosystem assessments will be fundamental to track progress towards achieving this decade’s global environmental and sustainability goals. Here we examine four needs that address common failure points of ecosystem assessments. These are (1) developing rapid, reproducible, and repeatable ecological data workflows, (2) harmonizing in situ and remotely sensed data, (3) integrating socioeconomic and biophysical data, and (4) increasing access to the digital resources and cyberinfrastructure needed to perform assessments. These four needs have profound potential to help us achieve our environmental objectives through cross-sector collaborations that leverage advancements in digital resources, remote data streams, and data science.

Environmental Research Letters↗

USGS2023

An interoperability strategy for the next generation of SEEA accounting

The System of Environmental-Economic Accounting (SEEA) is a set of international environmental-economic standards, adopted by the UN Statistical Commission in 2012 (SEEA Central Framework) and 2021 (SEEA Ecosystem Accounting); the latter in particular requires the integration of large and diverse data streams. These include geospatial and other data sources, which have proven challenging for some National Statistical Offices (NSOs) to implement. Although a variety of ecosystem service modelling platforms have been built over the last 15 years to meet various user demands, they often duplicate efforts, rely on data that are siloed, and rarely effectively reuse the knowledge gained from past modelling efforts. By making the data and models that underlie SEEA interoperable, NSOs and the scientific community can advance the accessibility, speed, quality, and transparency of SEEA accounts by making it possible to rapidly integrate and share new scientific data and models. Doing so requires an understanding of the benefits of interoperability, the costs of the status quo, and concrete pathways toward community-endorsed approaches for interoperability. The ARIES Network, which powers the ARIES for SEEA Explorer web application, offers such a path toward interoperability, providing substantial benefits to NSOs and scientific and policy communities.

Report↗

USGS2022

Can we avert an Amazon tipping point? The economic and environmental costs

The Amazon biome is being pushed by unsustainable economic drivers towards an ecological tipping point where restoration to its previous state may no longer be possible. This degradation is the result of self-reinforcing interactions between deforestation, climate change and fire. We assess the economic, natural capital and ecosystem services impacts and trade-offs of scenarios representing movement towards an Amazon tipping point and strategies to avert one using the Integrated Economic-Environmental Modeling (IEEM) Platform linked with spatial land use-land cover change and ecosystem services modeling (IEEM + ESM). Our approach provides the first approximation of the economic, natural capital and ecosystem services impacts of a tipping point, and evidence to build the economic case for strategies to avert it. For the five Amazon focal countries, namely, Brazil, Peru, Colombia, Bolivia and Ecuador, we find that a tipping point would create economic losses of US$256.6 billion in cumulative gross domestic product by 2050. Policies that would contribute to averting a tipping point, including strongly reducing deforestation, investing in intensifying agriculture in cleared lands, climate-adapted agriculture and improving fire management, would generate approximately US$339.3 billion in additional wealth and a return on investment of US$29.5 billion. Quantifying the costs, benefits and trade-offs of policies to avert a tipping point in a transparent and replicable manner can support the design of regional development strategies for the Amazon biome, build the business case for action and catalyze global cooperation and financing to enable policy implementation.

Amazon↗

USGS2022

Water-use data in the United States: Challenges and future directions

In the United States, greater attention has been given to developing water supplies and quantifying available waters than determining who uses water, how much they withdraw and consume, and how and where water use occurs. As water supplies are stressed due to an increasingly variable climate, changing land-use, and growing water needs, greater consideration of the demand side of the water balance equation is essential. Data about the spatial and temporal aspects of water use for different purposes are now critical to long-term water supply planning and resource management. We detail the current state of water-use data, the major stakeholders involved in their collection and applications, and the challenges in obtaining high-quality nationally consistent data applicable to a range of scales and purposes. Opportunities to improve access, use, and sharing of water-use data are outlined. We cast a vision for a world-class national water-use data product that is accessible, timely, and spatially detailed. Our vision will leverage the strengths of existing local, state, and federal agencies to facilitate rapid and informed decision-making, modeling, and science for water resources. To inform future decision-making regarding water supplies and uses, we must coordinate efforts to substantially improve our capacity to collect, model, and disseminate water-use data.

Journal of the American Water Resources Associatio↗

USGS2022

Assessing the accuracy and potential for improvement of the national land cover database’s tree canopy cover dataset in urban areas of the conterminous United States

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.

Remote Sensing↗

USGS2022

The global environmental agenda urgently needs a semantic web of knowledge

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

Environmental Evidence↗