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This dataset contains a projection of land use and land cover for the conterminous United States for the period 2001 - 2061. This projection used the USGS's LUCAS (Land Use and Carbon Scenario Simulator) model to project a business as usual scenario of land cover and land use change. By running the LUCAS model on the USGS's YETI high performance computer and parallelizing the computation, we ran 100 Monte Carlo simulations based on empirically observed rates of change at a relatively fine scale (270m). We sampled from multiple observed rates of change at the county level to introduce heterogeneity into the Monte Carlo simulations. Using this approach allowed the model to project different outcomes that were summarized...
Policy-relevant flood risk modeling must capture interactions between physical and social processes to accurately project impacts from scenarios of sea level rise and inland flooding due to climate change. Here we simultaneously model urban growth, flood hazard change, and adaptive response using the FUTure Urban-Regional Environment Simulation (FUTURES) version 3 framework (Sanchez et al., 2023). FUTURES is an open source urban growth model designed to address the regional-scale ecological and environmental impacts of urbanization; it is one of the few land change models that explicitly captures the spatial structure of development in response to user-specified scenarios. We present probabilistic land change projections...
The USGS Forecasting Scenarios of Land-use Change (FORE-SCE) model was used to produce an agricultural biofuel scenarios for the Northern Glaciated Plains, from 2012 to 2030. The modeling used parcel data from the USDA's Common Land Unit (CLU) data set to represent real, contiguous ownership and land management units. A Monte Carlo approach was used to create 50 unique replicates of potential landscape conditions in the future, based on a agricultural scenario from the U.S. Department of Energy's Billion Ton Update. The data are spatially explicit, covering the entire Northern Glaciated Plains ecoregions (an EPA Level III ecoregion), with a spatial resolution of 30-meters and 22 unique land-cover classes (including...
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: FORE-SCE,
Great Plains,
Land Cover,
Minnesota,
Model,
The purpose of this project is to use existing climate change datasets from the Climate Impacts Group (CIG) to summarize the the projected climate change impacts to United States Forest Service (USFS) lands in Oregon and Washington (Figure 1). Stakeholders in the Forest Service of this region were particularly interested in the variables that are likely to impact freshwater aquatic species, including projected changes in water availability, snowpack, and flood and low flow severities. Our objective is to summarize climate and hydrologic projections for USFS lands in Oregon and Washington. Since individual national forests may contain numerous distinct ecological regimes and cross hydrologic boundaries, averaging...
We simulated future patterns of urban growth using the FUTure Urban-Regional Environment Simulation (FUTURES; Meentemeyer et al., 2013) version 2 framework. FUTURES is an open source urban growth model designed to address the regional-scale ecological and environmental impacts of urbanization; it is one of the few land change models that explicitly captures the spatial structure of development in response to user-specified scenarios. We present probabilistic land change projections that predict urban growth under a Status Quo scenarios of growth. We computed each scenario for 50 stochastic iterations from 2020 through 2100 at annual time steps.
Risk assessment includes both risk estimation (identifying hazards and estimating their outcomes and probabilities) and risk evaluation (determining the significance or value of risks to those concerned with or affected by the decision). Risk estimation is about situations, and risk evaluation about the effect on people. Few situations are absolutely safe. Risks need to be estimated, and for many kinds of risk (e.g., exposures to potentially toxic substances or to potentially catastrophic situations) an expert view has to be formed, which must take account of associated uncertainties. Different sections of the public perceive risk in different ways, and regard some risks more seriously than the expert estimates....
Categories: Publication;
Types: Citation;
Tags: Competition,
Energy efficiency,
Projection,
Stochastic MARKAL
Urban growth and climate change together complicate planning efforts meant to adapt to increasingly scarce water supplies. Several studies have shown the impacts of urban planning and climate change separately, but little attention has been given to their combined impact on long-term urban water demand forecasting. Here we coupled land and climate change projections with empirically-derived coefficient estimates of urban water use (sum of public supply, industrial, and domestic use) to forecast water demand under scenarios of future population densities and climate warming. We simulated two scenarios of urban growth from 2012 to 2065 using the FUTure Urban-Regional Environment Simulation (FUTURES) framework. FUTURES...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Land Use Change,
Land use change,
North Carolina,
Projection,
Scenario,
Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges....
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: CONUS,
CONUS,
Change,
Climate,
Climatology,
Categories: Publication;
Types: Citation;
Tags: Data Visualization & Tools,
Model,
Projection,
Science Tools For Managers,
South Central CASC,
All the growth-oriented sectors in a developing economy consume enormous energy in their production processes. Steel, aluminium and cement are the key manufacturing industries in India which provide inputs to various other sectors such as construction, transportation, power transmission, etc. As a result, their demand is consistently rising. These industries are heavily energy-intensive and use raw materials such as iron ore, coal, electricity, steam, and fuel oil, whose supply can act as severe production constraints over a period of time and can hinder sustainable development. Hence it becomes imperative for these industries to continuously innovate more energy efficient techniques. This paper makes a foray into...
Categories: Publication;
Types: Citation;
Tags: Competition,
Energy efficiency,
Projection,
Stochastic MARKAL
This dataset consists of modeled projections of land use and land cover for the State of California for the period 2001-2101. The Land Use and Carbon Scenario Simulator (LUCAS) model was initialized in 2001 and run forward on an annual time step to 2100. In total 9 simulations were run with 10 Monte Carlo replications of each simulation. Two base scenarios were selected from Sleeter et al., 2017 (http://onlinelibrary.wiley.com/doi/10.1002/2017EF000560/full) for analysis, including a "business-as-usual" (BAU) land use scenario and a scenario based on "medium" population projections. For each base scenario we ran three alternative conservation scenarios where we simulated conversion of lands into conservation easements....
Land Change Monitoring, Assessment, and Projection (LCMAP) represents a new generation of land cover mapping and change monitoring from the U.S. Geological Survey’s Earth Resources Observation and Science (EROS) Center. LCMAP answers a need for higher quality results at greater frequency with additional land cover and change variables than previous efforts. By utilizing a suite of operational automated algorithms to identify different forms of change and to characterize the large variety of land cover types, uses, and conditions that exist across the United States and beyond, LCMAP products provide land change science information in understanding changes in the type, intensity, condition, location, and time of...
This dataset consists of modeled projections of land use and land cover and population for the State of California for the period 1970-2101. For the 1970-2001 period, we used the USGS's LUCAS model to "backcast" LULC, beginning with the 2001 initial conditions and ending with 1970. For future projections, the model was initialized in 2001 and run forward on an annual time step to 2100. In total 5 simulations were run with 10 Monte Carlo replications of each simulation. The simulations include: 1) Historical backcast from 2001-1970, 2) Business-as-usual (BAU) projection from 2001-2101, and 3) three modified BAU projections based on California Department of Finance population projections based on high, medium, and...
Categories: Data;
Tags: California,
California,
LUCAS model,
Land Use Change,
USGS Science Data Catalog (SDC),
Planning for the effects of climate change on natural resources often requires detailed projections of future climate at finer spatial scales consistent with the processes managers typically consider. While it is numerically possible to produce downscaled climate at very fine scales (< 5km), accurate estimation at these scales is difficult and less certain without very detailed local information. Both the absence of a sufficiently dense network of long-term climate observations and the presence of local factors such as topography and land surface feedbacks from vegetation and snowpack contribute to the uncertainties of localized projections. To meet the needs of managers for developing adaptation strategies, vulnerability...
The USGS’s FORE-SCE model was used to produce a long-term landscape dataset for the Delaware River Basin (DRB). Using historical landscape reconstruction and scenario-based future projections, the data provided land-use and land-cover (LULC) data for the DRB from year 1680 through 2100, with future projections from 2020-2100 modeled for 7 different socioeconomic-based scenarios, and 3 climate realizations for each socioeconomic scenario (21 scenario combinations in total). The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (20 land use and land cover classes), 3) broad spatial extent (covering the entirety of the Delaware River basin, corresponding to USGS...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Delaware River,
Delaware River Basin,
Delaware River Basin,
FORE-SCE,
Future,
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