Geology topics

Rachel Sleeter

Publications and source records attributed to Rachel Sleeter.

10 recordsLinked to original sources

USGS2024

Opportunities and challenges for precipitation forcing data in post-wildfire hydrologic modeling applications

The frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post-wildfire hydrologic hazards including debris flows, flash floods, water-quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data-sparse, headwater catchments in complex terrain, and post-wildfire hydrologic effects are particularly sensitive to high-intensity, short-duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire-induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade-offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post-wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire-prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.

WIREs Water↗

USGS2021

Modeling the impacts of hydrology and management on carbon balance at the Great Dismal Swamp, Virginia and North Carolina, USA

The impact of drainage on the stability of peatland carbon sinks is well known; however, much less is understood regarding the way active management of the water-table affects carbon balance. In this study, we determined the carbon balance in the Great Dismal Swamp, a large, forested peatland in the southeastern USA, which has been drained for over two hundred years and is now being restored through hydrologic management. We modeled future net ecosystem carbon balance over 100 years (2012 to 2112) using in situ field observations paired with simulations of water-table depth. The three scenarios used in the model were baseline conditions, flooded/wet conditions, and drained/dry conditions, which represent a range of potential management actions and climate conditions at the Great Dismal Swamp. In the Baseline scenario, results show a carbon sink of 0.7 Tg, or an average annual rate of 0.23 Mg C/ha/yr. The Flooded/Wet scenario produced a net ecosystem carbon balance of 4.6 Tg C or an average annual rate of 1.06 Mg C/ha/yr. For the Drained/Dry scenario, under which no management was conducted, and typically dry conditions were assumed, the Great Dismal Swamp becomes a net carbon source at –2.07 Tg C or an average annual rate of –0.38 Mg C/ha/yr.

North Carolina, Virginia↗

USGS2018

Estimating the societal benefits of carbon dioxide sequestration through peatland restoration

The Great Dismal Swamp National Wildlife Refuge (GDS) is a forested peatland that provides a number of ecosystem services including carbon (C) sequestration. We modeled and analyzed the potential capacity of the GDS to sequester C under four management scenarios: no management, no management with catastrophic fire, current management, and increased management. The analysis uses the Land Use and Carbon Scenario Simulator developed for the GDS to estimate net ecosystem C balance. The model simulates net C gains and losses on an annual time-step from 2013 through 2062 which is converted to carbon dioxide equivalent (CO2-eq) and monetized using the Interagency Working Group's Social Cost of Carbon. Our analysis incorporates compounded uncertainty including variation in ecological processes, temporal and spatial heterogeneity, and uncertainty in the discount rate. The no management scenario results in 2.4 million tons of CO2 emissions with a Net Present Value (NPV) under a 3% discount rate of −\$67 million. No management with catastrophic fires emits 6.5 million tons of CO2 with an NPV of −\$232 million. Current management avoids 9.9 million tons of emissions (via sequestration) with an NPV of \$326 million. Increased management avoids 16.5 million tons of emissions with an NPV of \$524 million.

North Carolina, Virginia↗

USGS2017

A carbon balance model for the great dismal swamp ecosystem

Background Carbon storage potential has become an important consideration for land management and planning in the United States. The ability to assess ecosystem carbon balance can help land managers understand the benefits and tradeoffs between different management strategies. This paper demonstrates an application of the Land Use and Carbon Scenario Simulator (LUCAS) model developed for local-scale land management at the Great Dismal Swamp National Wildlife Refuge. We estimate the net ecosystem carbon balance by considering past ecosystem disturbances resulting from storm damage, fire, and land management actions including hydrologic inundation, vegetation clearing, and replanting. Results We modeled the annual ecosystem carbon stock and flow rates for the 30-year historic time period of 1985–2015, using age-structured forest growth curves and known data for disturbance events and management activities. The 30-year total net ecosystem production was estimated to be a net sink of 0.97 Tg C. When a hurricane and six historic fire events were considered in the simulation, the Great Dismal Swamp became a net source of 0.89 Tg C. The cumulative above and below-ground carbon loss estimated from the South One and Lateral West fire events totaled 1.70 Tg C, while management activities removed an additional 0.01 Tg C. The carbon loss in below-ground biomass alone totaled 1.38 Tg C, with the balance (0.31 Tg C) coming from above-ground biomass and detritus. Conclusions Natural disturbances substantially impact net ecosystem carbon balance in the Great Dismal Swamp. Through alternative management actions such as re-wetting, below-ground biomass loss may have been avoided, resulting in the added carbon storage capacity of 1.38 Tg. Based on two model assumptions used to simulate the peat system, (a burn scar totaling 70 cm in depth, and the soil carbon accumulation rate of 0.36 t C/ha −1 /year −1 for Atlantic white cedar), the total soil carbon loss from the South One and Lateral West fires would take approximately 1740 years to re-amass. Due to the impractical time horizon this presents for land managers, this particular loss is considered permanent. Going forward, the baseline carbon stock and flow parameters presented here will be used as reference conditions to model future scenarios of land management and disturbance.

North Carolina, Virginia↗

USGS2016

Regional analysis of social characteristics for evacuation resource planning: ARkStorm scenario

Local planning is insufficient for regional catastrophes; regional exercises are needed to test emergency plans and decision-making structures. The ARkStorm scenario would trigger a mass evacuation that would be complicated by the social characteristics of populations [e.g., vehicle ownership, age, poverty, English language limitation (ELL), and shelter needs]. Land cover data and dasymetric mapping improves the allocation of residential populations and their social characteristics to the ARkStorm flood zone in 21 counties in California. Numbers and concentrations of county, urban, and rural residents exposed to flooding as well as populations in and out of the scenario flood zone are profiled. The results inform mass evacuation planning by providing a means to (1) examine the sufficiency of mutual aid agreements, (2) underscore planning for carless populations, and (3) tailor multilingual communication strategies. The various geographical distinctions emphasize different challenges throughout the region. It will be important to investigate behavioral responses to warnings, identify evacuation constraints (e.g., shelter capacity versus need), and obtain comparable data on transient populations.

Natural Hazards Review↗

USGS2015

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

AIMS Environmental Science↗

USGS2012

Mapping socio-environmentally vulnerable populations access and exposure to ecosystem services at the U.S.-Mexico borderlands

Socio-environmental vulnerable populations are often unrepresented in land-use planning yet have great potential for loss when exposed to changes in ecosystem services. Administrative boundaries, cultural differences, and language barriers increase the disassociation between land-use management and marginalized populations living in the U.S.–Mexico borderlands. This paper describes the development of a Modified Socio-Environmental Vulnerability Index (M-SEVI), using determinants from binational census and neighborhood data that describe levels of education, access to resources, migratory status, housing, and number of dependents, to provide a simplified snapshot of the region's populace that can be used in binational planning efforts. We apply this index at the SCW, located on the border between Arizona, USA and Sonora, Mexico. For comparison, the Soil and Water Assessment Tool is concurrently applied to assess the provision of erosion- and flood control services over a 9-year period. We describe how this coupling of data can form the base for an ecosystem services assessment across political boundaries that can be used by land-use planners. Results reveal potential disparities in environmental risks and burdens throughout the binational watershed in residential districts surrounding and between urban centers. The M-SEVI can be used as an important first step in addressing environmental justice for binational decision-making.

Arizona↗

USGS2012

Future scenarios of land-use and land-cover change in the United States--the Marine West Coast Forests Ecoregion

Detecting, quantifying, and projecting historical and future changes in land use and land cover (LULC) has emerged as a core research area for the U.S. Geological Survey (USGS). Changes in LULC are important drivers of changes to biogeochemical cycles, the exchange of energy between the Earth’s surface and atmosphere, biodiversity, water quality, and climate change. To quantify the rates of recent historical LULC change, the USGS Land Cover Trends project recently completed a unique ecoregion-based assessment of late 20th century LULC change for the western United States. To characterize present LULC, the USGS and partners have created the National Land Cover Database (NLCD) for the years 1992, 2001, and 2006. Both Land Cover Trends and NLCD projects continue to evolve in an effort to better characterize historical and present LULC conditions and are the foundation of the data presented in this report. Projecting future changes in LULC requires an understanding of the rates and patterns of change, the major driving forces, and the socioeconomic and biophysical determinants and capacities of regions. The data presented in this report is the result of an effort by USGS scientists to downscale the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) to ecoregions of the conterminous United States as part of the USGS Biological Carbon Sequestration Assessment. The USGS biological carbon assessment was mandated by Section 712 of the Energy Independence and Security Act of 2007. As part of the legislative mandate, the USGS is required to publish a methodology describing, in detail, the approach to be used for the assessment. The development of future LULC scenarios is described in chapter 3.2 and appendix A. Spatial modeling is described in chapter 3.3.2 and appendix B and in Sohl and others (2011). In this report, we briefly summarize the major components and methods used to downscale IPCC-SRES scenarios to ecoregions of the conterminous United States, followed by a description of the Marine West Coast Forests Ecoregion, and lastly a description of the data being published as part of this report.

California;Oregon;Washington↗

USGS2008

A New Method for Mapping Population Distribution

Human population counts are collected and commonly displayed uniformly across areas, such as U.S. Census Bureau block-groups and tracts. The limitation inherent in this type of representation is the assumption that people are evenly distributed across each areal unit, when in actuality many areas such as parks, open spaces, and industrial zones are uninhabited. The U.S. Geological Survey (USGS) has developed a geospatial tool that uses a 'dasymetric' mapping method to redistribute census population values to homogenous, inhabited, zones, enhancing visualization and accuracy.

Fact Sheet↗

USGS2007

Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods

The U.S. Census Bureau provides decadal demographic data collected at the household level and aggregated to larger enumeration units for anonymity purposes. Although this system is appropriate for the dissemination of large amounts of national demographic data, often the boundaries of the enumeration units do not reflect the distribution of the underlying statistical phenomena. Conventional mapping methods such as choropleth mapping, are primarily employed due to their ease of use. However, the analytical drawbacks of choropleth methods are well known ranging from (1) the artificial transition of population at the boundaries of mapping units to (2) the assumption that the phenomena is evenly distributed across the enumeration unit (when in actuality there can be significant variation). Many methods to map population distribution have been practiced in geographic information systems (GIS) and remote sensing fields. Many cartographers prefer dasymetric mapping to map population because of its ability to more accurately distribute data over geographic space. Similar to ?choropleth maps?, a dasymetric map utilizes standardized data (for example, census data). However, rather than using arbitrary enumeration zones to symbolize population distribution, a dasymetric approach introduces ancillary information to redistribute the standardized data into zones relative to land use and land cover (LULC), taking into consideration actual changing densities within the boundaries of the enumeration unit. Thus, new zones are created that correlate to the function of the map, capturing spatial variations in population density. The transfer of data from census enumeration units to ancillary-driven homogenous zones is performed by a process called areal interpolation.

Techniques and Methods↗