Europe-LAND Databases of existing LU/LC patterns and spatial modelling tools, Cards and Success Stories for modelling tools

Funded by the European Union (10108307). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EC-CINEA. Neither the European Union nor the granting authority can be held responsible for them. Europe-LAND cards and Success Stories for modelling tools Complementing the deliverable D4.1 “Databases of existing LU/LC patterns and spatial modelling tools” we provide user-friendly, hands-on guidance tools for selected models/modelling tools for environmental domains related to the project focus in the form of compact information cards, and related success stories that illustrate how the respective model can be applied in both research and practice. These tools are provided for five environmental domains covering LULC, Climate, Hydrology, Forest, and Biodiversity (see Tables 1 –5). Cards for modelling tools For modelling tools each of five above mentioned domains key environmental standardized model cards were developed to summarise the model technical, functional, and practical characteristics. The purpose of the modelling card is to provide a concise and accessible overview that facilitates comparison across models. Each card includes key details such as the model’s full name, developer/institution, year/version, software environment (e.g., TerrSet, R, QGIS, Python), and modelling principles (e.g., rule-based, stochastic, machine learning). It also details the types of input data required (e.g., land cover maps, drivers like slope or population density), core modelling capabilities, and scenario development functions. In addition to technical information, the cards highlight intended users (e.g., planners, academics, NGOs), hardware and software requirements, and extensions or plug-ins if applicable. Where helpful, we included a schematic diagram of the modelling process and links to online documentation or tutorials. These cards serve as essential open access reference tools for users seeking to understand or apply LUCC models in research or planning, and are especially useful for selecting a model based on project needs, data availability, or spatial scale. Success Stories for modelling tools The cards are further complemented by selected success stories based on open access scientific papers that showcase its practical application in a European and selected non-European case studies. These stories were extracted from recent high-quality peer-reviewed publications and are meant to illustrate how each model has been effectively used to simulate or analyse land change in real-world settings. The success stories include essential details such as the study area (e.g., Romania, Hungary, Italy), the modelling timeframe, the types of land cover analysed, and the goals of the project (e.g., predicting deforestation, assessing urban sprawl, evaluating climate-related land change). Furthermore, each success story summarises the modelling process (e.g., calibration methods, transition rules, scenario assumptions), validation results (e.g., Kappa, ROC, FOM), and policy or management implications. Funded by the European Union (10108307). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EC-CINEA. Neither the European Union nor the granting authority can be held responsible for them. For example, the CLUE-S model was used in Romania to predict forest cover until 2050 under different scenarios, providing actionable insights for forest management and carbon sequestration. SECLAND model in Austria to project future land-use until 2050 under climate and socioeconomic change in the LTSER region Eisenwurzen. These narratives not only demonstrate the strengths and limitations of each model but also enhance understanding of their real-world utility, guiding future users in how to design and implement LUCC modelling projects effectively. A summary of the included modelling tools and their key characteristics is provided in tables 1 –5. Table 1. Overview and key characteristics of LULC models Model Use of LULC Suitable For CA-Markov (Cellular Automata– Markov Chain) Uses transition probabilities and neighbourhood rules to simulate spatial dynamics Urban expansion, deforestation, and long-term LU/LC trends CLUE-S (Conversion of Land Use & Effects – Small scale) Spatial allocation model combining demand with statistical suitability maps Regional/national scenario-based land use projections GEOMOD (Geographic Modelling System) Predicts land change with logistic regression and suitability maps Simple land use prediction with limited data requirements LCM-Climate (Land Change Modeler with Climate) Integrates LULC transitions with climate variables Coupled land–climate studies, mitigation/adaptation LSTM (neural net) Deep learning model using past land cover and drivers Data-driven LULC prediction with high spatio-temporal accuracy MOLUSCE (Modules for Land Use Change Evaluation) Uses multiple ML algorithms plus CA for transitions Urban dynamics, spatio-temporal modelling, scenario analysis SEC-LAND (Socio-Ecosystem Change) Spatially explicit scenario modelling of socioecological systems Participatory scenario development, landscape transitions SLEUTH (Slope, Land use, Exclusion, Urban extent, Transportation, Hillshade) CA model using physical parameters to model urban growth Historical trend-calibrated urban expansion FLUS (Future Land Use Simulation) Integrates human/environmental drivers in CA framework Scenario analysis, sustainable landuse strategy development Funded by the European Union (10108307). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EC-CINEA. Neither the European Union nor the granting authority can be held responsible for them. Table 2. Overview and key characteristics of included climate models Model Focus Suitable For LULC Effect on Outputs JULES (Joint UK Land Environment Simulator) Represents land use change via Plant Functional Types (PFTs) Land surface interactions in Earth system models Alters energy, carbon, nitrogen, and water fluxes LPJ-GUESS (LundPotsdam-Jena General Ecosystem Simulator) Simulates vegetation dynamics and land management Ecosystem dynamics, climate and land-use impact studies Influences biogeochemical cycles, water balance, vegetation MEGAN (Model of Emissions of Gases and Aerosols from Nature) Requires vegetation types and LAI for emissions modelling Biogenic emissions, atmospheric chemistry, air quality Affects emissions of isoprene, BVOCs, aerosols ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) Maps LULC to PFTs and includes land use change dynamics Carbon, nitrogen and water cycling; Earth system modelling Drives fluxes of carbon, water, and energy GCAM (Global Change Analysis Model) Detailed LULC component; models land allocation and changes IAMs, climate-energyland policy assessments Impacts emissions, energy, food, land, water sectors CLM (Community Land Model) Uses sub-grid land units and PFTs, includes urban and managed lands Land surface simulations in CESM; biogeochemical processes Impacts surface fluxes, water, energy, carbon Biome-BGC (Biogeochemical Cycles Model) Uses site and land cover parameters for PFT-specific simulations Ecosystem productivity, climate change impact, biogeochemical fluxes Affects GPP, NPP, ET, LAI, NEE etc. WRF (Weather Research and Forecasting Model) Includes urban canopy models and land surface processes Weather prediction, regional climate, air quality, urban climate Influences heat island effect, rainfall, wind fields REMO (Regional Climate Model) Couples with land surface models and includes LULC dynamics Regional climate simulations, highresolution impact modelling Affects rainfall, evapotranspiration, hydrological feedback RegCM (Regional Climate Model) Supports land surface schemes (e.g. CLM, BATS), models LU change Regional downscaling, climate impact studies, CORDEX Alters land-atmosphere feedbacks and surface climate DANUBIA (Modular Environmental Simulation Framework) Includes detailed land use data for spatially explicit scenario analysis Water governance, landuse/climate interaction, socio-environmental modelling Affects water demand, hydrological processes, and land management responses Funded by the European Union (10108307). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EC-CINEA. Neither the European Union nor the granting authority can be held responsible for them. Table 3. Overview and key characteristics of included hydrological models Model Focus Suitable For LULC Effect on Outputs SWAT (Soil & Water Assessment Tool) Uses land use data for hydrological simulation Basin-scale water quantity and quality Changes in land cover affect runoff, erosion, nutrients PRECIS (Providing REgional Climates for Impacts Studies) Uses LULC for downscaling boundaries Regional climate impact assessments Alters localised climate variables (rainfall, extremes) EPIC (Erosion-Productivity Impact Calculator) Integrates land management and crop rotations Field-scale crop yield, soil, and erosion Land use changes affect irrigation, runoff, and erosion MIKE SHE Integrates land cover into coupled surface-groundwater processes Integrated surfacegroundwater watershed modelling Land use influences evapotranspiration, infiltration, runoff VIC (Variable Infiltration Capacity) Uses LULC in surface-water parameterisation Large-scale hydrological modelling Land cover changes affect soil moisture and streamflow HBV (Hydrologiska Byråns Vattenbalansavdelning) Integrates LULC in runoff simulation Catchment water balance and streamflow LULC changes influence runoff and flow patterns TOPMODEL Uses LULC in topographydriven hydrology Saturated groundwater flow and runoff Land cover affects soil moisture distribution and runoff HEC-HMS (Hydrologic Modelling System) Uses LULC for flow simulations Flood forecasting and water resources management Land use changes affect runoff volume and flood peaks RHESSys (Regional HydroEcologic Simulation System) Integrates LULC in ecohydrological processes Integrated carbon-water dynamics at catchment scale LULC changes influence evapotranspiration, runoff, nutrients Funded by the European Union (10108307). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EC-CINEA. Neither the European Union nor the granting authority can be held responsible for them. Table 4. Overview and key characteristics of included forest modelling tools Model Focus Suitable For LULC Effect on Outputs iLand (forest LANDscape and disturbance) Forest landscape dynamics and disturbance Resilience, biodiversity, ecosystem services Changes in land use alter regeneration, carbon storage, fire spread, diversity LANDIS-II (LANdscape DIsturbance & Succession) Forest succession and disturbance modelling Policy evaluation, forest planning LULC shapes disturbance spread, species mix, age structure 3-PG (Physiological Principles Predicting Growth) Forest growth and productivity Yield modelling, carbon accounting Land use shifts growth potential, productivity and carbon fluxes FORMIND (Forest Model for INdividual-based Dynamics) Tree competition in tropical forests Tropical forest management, climate change Affects biomass recovery, species composition, regeneration capacity SORTIE-ND (Spatially Explicit Individual-based Forest Dynamics) Tree-level spatial dynamics Fine-scale forest ecology Land cover influences species coexistence and succession dynamics ForClim (Forest succession & climate change model) Forest succession and climate impacts Temperate forest development, climate impacts Land cover influences long-term species composition and structure EFISCEN (European Forest Information Scenario model) Forest resource projections Policy scenarios, wood supply LULC changes affect harvestable wood and age dynamics PICUS (Patch model for forest dynamics) Spatially explicit patchbased dynamics Forest structure and biodiversity studies Land cover affects regeneration, competition, growth heterogeneity CARAIB (CARbon Assimilation in the Biosphere) Vegetation–climate interactions Global carbon and water flux modelling LULC changes impact carbon, water, and energy fluxes FORMOSAIC (Forest mosaic dynamics simulator) Spatio-temporal forest dynamics Disturbance dynamics, succession Land cover determines gap dynamics, succession, and diversity ForestGALES (Forest GALE and windthrow model) Wind damage risk assessment Windthrow vulnerability, management LULC influences exposure, damage extent, and stability Funded by the European Union (10108307). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EC-CINEA. Neither the European Union nor the granting authority can be held responsible for them. Table 5. Overview and key characteristics of included biodiversity modelling tools for species and ecosystem assessments Model (Name) Focus Suitable For LULC Effect on Outputs MaxEnt (Maximum Entropy) Species distribution modelling (SDM) Species range predictions, conservation planning LULC shifts predicted habitat suitability and ranges BIOMOD2 (R package) Ensemble species distribution models Biodiversity forecasting, conservation LULC determines habitat availability and affects accuracy InVEST (Integrated Valuation of Ecosystem Services) Ecosystem services and habitat quality Land-use planning, tradeoff analysis Land-use patterns drive habitat fragmentation and quality GLOBIO (Global Biodiversity Model) Biodiversity intactness (Mean Species Abundance) Global biodiversity policy support (e.g., IPBES) Reduced habitat lowers biodiversity intactness AIM-biodiversity (Asia-Pacific Integrated Model) Global/regional biodiversity under future scenarios Scenario analysis, extinction risk assessment Land-use changes drive species loss and extinction risk SAR Models (Species–Area Relationship) Empirical species-area predictions Estimating species loss from habitat conversion Habitat loss leads to proportional species extinction Madingley (General Ecosystem Model) Trait-based ecosystem model Global ecosystem structure and function forecasts Land cover change alters habitat and trophic energy flows RangeShifter Spatially explicit dispersal and demography Species range-shift simulations, connectivity planning Fragmentation and land cover change affect dispersal PREDICTS (Projecting Responses of Ecological Diversity in Changing Terrestrial Systems) Global impacts of land use Global biodiversity projections, SDG/IPBES/CBD reporting Changes in land use directly alter species richness, abundance, and intactness BILBI (Biogeographic Modelling) Beta-diversity and turnover modelling Landscape to global-scale diversity assessments Land cover change shifts community composition and turnover MOL (Map of Life) Species distribution and range mapping Conservation prioritisation and range mapping LULC change shifts occurrence probabilities and range boundaries