LS Land Issue 29 ~UPD~ Full Version
Leticia Troung
Abstract. We implement and analyze 13 different metrics (4 moist thermodynamic quantities and 9 heat stress metrics) in the Community Land Model (CLM4.5), the land surface component of the Community Earth System Model (CESM). We call these routines the HumanIndexMod. We limit the algorithms of the HumanIndexMod to meteorological inputs of temperature, moisture, and pressure for their calculation. All metrics assume no direct sunlight exposure. The goal of this project is to implement a common framework for calculating operationally used heat stress metrics, in climate models, offline output, and locally sourced weather data sets, with the intent that the HumanIndexMod may be used with the broadest of applications. The thermodynamic quantities use the latest, most accurate and efficient algorithms available, which in turn are used as inputs to the heat stress metrics. There are three advantages of adding these metrics to CLM4.5: (1) improved moist thermodynamic quantities; (2) quantifying heat stress in every available environment within CLM4.5; and (3) these metrics may be used with human, animal, and industrial applications.
We demonstrate the capabilities of the HumanIndexMod in a default configuration simulation using CLM4.5. We output 4 daily temporal resolution globally. We show that the advantage of implementing these routines into CLM4.5 is capturing the nonlinearity of the covariation of temperature and moisture conditions. For example, we show that there are systematic biases of up to 1.5 C between monthly and 0.5 C between 4 daily offline calculations and the online instantaneous calculation, respectively. Additionally, we show that the differences between an inaccurate wet bulb calculation and the improved wet bulb calculation are 1.5 C. These differences are important due to human responses to heat stress being nonlinear. Furthermore, we show heat stress has unique regional characteristics. Some metrics have a strong dependency on regionally extreme moisture, while others have a strong dependency on regionally extreme temperature.
Land degradation adversely affects the businesses, communities, and species that depend on that land. It also contributes significantly to climate change and reduces land productivity. And it is the communities that are often already exposed to climate-related risks that are made even more vulnerable when the land they depend on is degraded. For instance, competition for scarce land resources is projected to increase social instability like conflict and migration (UNCCD, 2017).
By setting SBTs for nature, companies and cities can consider their impacts on land in relation to biodiversity, water, ocean, freshwater, and climate. Doing so will increase the return on investments made in the process of setting and taking action to achieve their science-based targets, thus supporting their goals in other areas, as well.
In May 2023, the Science Based Targets Network issued detailed methodologies for companies to assess and prioritize their impacts on nature, and enable them to progress to setting initial target-setting resources on freshwater quality and quantity as well as land to complement those on climate from the Science Based Targets initiative.
The first land targets are at a beta stage and have undergone an internal consultation with experts across industry, academia and NGOs, followed by both expert review panel and public consultations for broader representation. They are currently being piloted in 2023 by the initial group of target-setting companies.
The Terrestrial Resources GIS Map Viewer (TRGIS) allows users to explore the CLC map as well as maps of species observations, land cover, conservation lands, park boundaries, and critical wildlife habitat.
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National Park Service Expedition Series (Video) - Developed for students by the National Parks Service, this fun and informative five-part video series explores the history and significance of Ellis Island.
The exact structure depended on the platform. On Realms and Bedrock Dedicated Servers, the Far Lands generated the same as on Windows 10, Xbox One, and PlayStation 4: only the nothingness generated. On mobile devices and Nintendo Switch, the Edge Far Lands would generate, depending on the coordinates. In multiplayer, the structure would depend on the platform used by the owner of the world.In Minecraft Education, what happened at the Far lands depends on the world. Sometimes it is a skygrid, and sometimes it is a plain ocean/bedrock with different biomes.The features of the Far Lands in other Bedrock ports remain unknown.
The End Far Lands are made up almost exclusively of end stone and appear a bit more squashed and stretched horizontally than the Overworld Far Lands. Micro-end islands still generate inside the Far Lands, even after the latter dissipates. Since there is no signature liquid of the End, they just generate down to a dry void; similarly, there is no bedrock floor.
The world at excessive coordinates is not supported, and as such certain issues related to the Far Lands may never be fixed. This is because such issues would affect only players who intentionally teleport to high coordinates, and exist as a limitation of the game engine itself.[6]
The Multi-Resolution Land Characteristics (MRLC) consortium is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. The creation of this consortium has resulted in the mapping of the lower 48 United States, Hawaii, Alaska and Puerto Rico into a comprehensive land cover product termed, the National Land Cover Database (NLCD), from decadal Landsat satellite imagery and other supplementary datasets.
MRLC hosts land cover and land condition data from various sources, including NLCD and Rangeland Condition Monitoring Assessment and Projection (RCMAP) time-series, Ecological Potential, and projections of future fractional rangeland components. Data are offered for download, as WMS services, and in applications.
With the latest release, NLCD now includes map products characterizing land cover and land cover change across nine epochs from 2001 to 2021 (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021). The 2021 suite of NLCD products follow the same protocols and procedures of the previously released NLCD epochs (2001-2019), are directly comparable to the 2019 release across the full time series, and are suitable for multi-temporal analysis. Science products and the change index, however, will need to be reacquired for the additional 2021 change information.
The United States Geological Survey (USGS), in collaboration with the Multi-resolution Landscape Consortium (MRLC), has provided the community with the National Land Cover Database (NLCD), a detailed land cover database for more than 30 years. To produce the next generation of USGS land cover, with moderate thematic detail at low latency and annual frequency, research is well underway for single stream land cover products. In the initial 2024 product release, users can expect Anderson Level II type land cover classes (for example, the 16 land cover categories used in the current NLCD classification typology) at 30-meter spatial resolution on an annual time step for the years 1985-2023 for the conterminous United States. Research is underway to improve the methodology for producing and validating land cover and change related components included in the next-generation product suite and follow-on products. The results will provide USGS with leading-edge capabilities for land cover monitoring, assessments, and projections.
In the coming months, USGS in collaboration with the MRLC, will publish a 2021 CONUS land cover suite (NLCD 2021). Later in 2023, we will release the Conterminous United States (CONUS) Reference Data product updated through 2021, followed by a validation assessment of the Collection 1.3 LCMAP CONUS Science Products. After Collection 1.3, no further production of LCMAP Science Products will occur.
Land cover refers to the classification of surface cover on the ground, whether forest, urban infrastructure, bodies of water or agricultural land, etc., helping to distinguish natural and anthropogenic features. Identifying, delineating, and mapping land cover (or land use) is important for global, regional and local monitoring studies, resource management and planning activities. Land cover classes can include natural features such as tropical forest, shrubland, grassland, and water bodies, but also human-made features such as urban areas and cropland.
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