Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6

Abstract

Abstract. Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

Predicting long-term dynamics of soil salinity and sodicity on a global scale

“…They were all derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Database v4.1 (resampled to 250-m resolution) ( 69 ) and computed in the System for Automated Geoscientific Analyses geographic information system Terrain Analysis-Hydrology and Morphometry libraries (except elevation and aspect) ( 70 ). Other static predictors were sample upper and lower depths from the surface (centimeters), soil classes based on the WRB ( 34 ) soil classification system, groundwater table depth at equilibrium (meters) ( 71 ), the average of annual fertilizer input rate (1980 to 2018) for C3 annual and perennial crops (kilograms of nitrogen per hectare per year of crop season; for definition of C3 crops see SI Appendix , Table S1 ) ( 72 ), plant rooting depth (meters) ( 73 ), average soil and sedimentary thickness (meters) ( 74 ), topographic index ( 75 ), and parent material lithological classes ( 76 ).…”

Using natural history collections to investigate changes in pangolin (Pholidota: Manidae) geographic ranges through time

“…We downloaded historical and current human population size data as population count and population density from the History Database of the Global Environment (HYDE v3.2.1; Goldewijk et al, 2017 ) at 5′ or 0.083 degree resolution. For land-use change, we downloaded historical land-use states data at 0.25 × 0.25 degree spatial resolution from the Land-Use Harmonization (LUH2) project (LUH2 v2 h; http://luh.umd.edu/data.shtml ; Hurtt et al, 2020 ). These data represent the fraction of each 0.25 × 0.25 degree grid cell covered by each land-use type.…”

“…For the human population size and the land-use data, we calculated the change in population size or land-use cover (but not both) for each 0.25 × 0.25 degree grid cell between 2015 (present-day) and 1850, 1900 or 1950 in turn. We used 2015 as our present-day baseline as these were the most recent data available ( Goldewijk et al, 2017 ; Hurtt et al, 2020 ), and used 1850 as our earliest historical date as most specimens were collected between 1850 and 1950 (ideally we would extract values for the year each specimen was collected, but we do not have year data for all specimens). For each specimen and time period, we then extracted the mean change in every population size or land-use variable across the 0.25 × 0.25 degree grid cells occupied by the specimen’s point radius polygon.…”

Land-Use Harmonization Datasets for Annual Global Carbon Budgets

“…The Land-Use Harmonization 2 dataset (Hurtt et al 2019a(Hurtt et al , 2019b provides global, annual, gridded land-use states and all associated land-use transitions between those states, fractionally, at 0.25° spatial resolution for the years 850-2100 . The twelve LUH2 land-use states include gridded fractions of cropland (which is further sub-divided into fractions of C3 annuals, C4 annuals, C3 perennials, C4 perennials, and C3 N-fixers), grazing land (sub-divided into fractions of managed pasture and rangeland), urban land, primary land (both forested and non-forested) and secondary land (both potentially forested and potentially non-forested).…”

“…ELUC is computed by the DGVMs as the difference between two simulations, one with land use and one without, and as a result it includes the loss of additional sink capacity from reduced forest cover, that is not included in the estimates of ELUC from book-keeping models. One of the ways in which BLUE differs from H&N2017 is that it utilizes gridded historical land-use and land-use change maps from the Land-Use Harmonization 2 (LUH2) dataset (Hurtt et al, 2019a(Hurtt et al, , 2019b, whereas H&N2017 makes use of national-level land-use data, primarily from FAO. Many of the https://doi.org/10.5194/essd-2020-388 DGVMs also utilize the LUH2 dataset to prescribe the gridded historical land-use and land-use changes used by those models.…”