InVEST Coastal Blue Carbon Model

By analyzing changes in carbon storage over time and comparing this across alternative management scenarios, the InVEST Blue Carbon model quantifies the value of carbon storage and sequestration services provided by coastal ecosystems.

CarbonCoastalecosystems

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Initial contribute: 2019-07-14

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Stanford University
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Application-focused categoriesNatural-perspectiveLand regions

Detailed Description

English {{currentDetailLanguage}} English

Quoted from: https://storage.googleapis.com/releases.naturalcapitalproject.org/invest-userguide/latest/coastal_blue_carbon.html#coastal-blue-carbon

Summary

Marine and terrestrial ecosystems help regulate Earth’s climate by adding and removing greenhouse gases (GHGs) such as carbon dioxide (CO2) to and from the atmosphere. Coastal marshes, mangroves, and seagrasses, in particular, store large amounts of carbon in their sediments, leaves, and other forms of biomass. In addition to storing carbon, marine ecosystems continually accumulate carbon in their sediments, creating large reservoirs of long-term sequestered carbon. By storing and sequestering carbon, marine ecosystems keep CO2 out of the atmosphere where it would otherwise contribute to climate change.

Management activities that change the cover of coastal vegetation, such as the restoration of seagrass beds and the clearing of mangrove forests, change the ability of coastal and marine areas to store and sequester carbon.

The InVEST Coastal Blue Carbon model attempts to predict the amount of carbon stored and sequestered over a coastal zone at particular points in time due to changes in land cover. Using an estimate of the monetary social value, or where available, a market price for stored and sequestered carbon, the InVEST Coastal Blue Carbon model also quantifies the marginal value of storage and sequestration.

Results of the InVEST Coastal Blue Carbon model can be used to compare current and future scenarios of carbon stock and net sequestration, as well as identify locations within the landscape where degradation of coastal ecosystems should be avoided and restoration of coastal ecosystems should be prioritized in order to preserve and enhance these carbon storage and sequestration services.

Introduction

This model makes use of a variety of information, including:

  • The distribution and abundance of coastal vegetation

  • Habitat-specific carbon stock data

  • Impact characteristics of various land-cover disturbances to biomass and soil carbon stock pools to predict carbon emission rates

  • Carbon accumulation rates to estimate carbon stock, net sequestration and value across a land or seascape

  • Estimates of the monetary social value or market price of carbon

To quantify the value of carbon storage and sequestration, the model focuses on changes in atmospheric carbon dioxide and other greenhouse gases as a result of changes caused by human activities that can affect marine ecosystems which store and sequester carbon. Changes to the composition of the atmosphere have wide-ranging effects on natural systems that can result in changes to agricultural productivity, air quality, sea levels, and more.

The Model

Modeling Considerations

Mapping and modeling changes in carbon storage and sequestration for coastal and marine habitats can present challenges. The types of spatial inputs and available information about the carbon cycle vary by location. Some study areas have high-quality data available for a detailed analysis while other locations do not have the information necessary to model changes in the position and function of coastal vegetation. Salt marsh, for example, is often studied in the context of migration due to sea-level rise. The combination of natural (e.g. sea-level rise) and anthropogenic (e.g. salt marsh migration blocked by roads) factors should be included in scenario maps and subsequent carbon modeling where possible. When exploring future land cover scenarios, land cover map outputs produced by the SLAMM model (Sea Level Affecting Marshes Model, developed by Warren Pinnacle) can be useful inputs to the InVEST Coastal Blue Carbon model (Clougheet et al. 2010). However, because not all sites have the detailed elevation and habitat information required to run SLAMM, this InVEST model provides a flexible approach that allows users to provide either detailed land use/land cover maps or maps indicating the presence of coastal and marine vegetation that can sequester carbon.

How it Works

InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in three main pools (biomass, sediment carbon (i.e. soil), and standing dead carbon (i.e. litter) see Figure 1). Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the three carbon pools and the rate of annual carbon accumulation in the biomass and sediments. If local information is not available, users can draw upon the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these three carbon pools.

_images/pools.png

Figure 1. Three carbon pools for marine ecosystems included in the InVEST blue carbon model (mangrove example).

模型元数据

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Natural Capital Project (2019). InVEST Coastal Blue Carbon Model, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/17ff9e4e-54cc-4dfc-ae95-fe9f343613fc
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Contributor(s)

Initial contribute : 2019-07-14

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Authorship

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Stanford University
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