Challenging a Global Land Surface Model in a Local Socio-Environmental System
Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly us...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/9/10/398 |
| _version_ | 1797550316522045440 |
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| author | Kyla M. Dahlin Donald Akanga Danica L. Lombardozzi David E. Reed Gabriela Shirkey Cheyenne Lei Michael Abraha Jiquan Chen |
| author_facet | Kyla M. Dahlin Donald Akanga Danica L. Lombardozzi David E. Reed Gabriela Shirkey Cheyenne Lei Michael Abraha Jiquan Chen |
| author_sort | Kyla M. Dahlin |
| collection | DOAJ |
| description | Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson’s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses. |
| first_indexed | 2024-03-10T15:27:33Z |
| format | Article |
| id | doaj.art-6c062b4d10294f849535ceeb6fc0e361 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-10T15:27:33Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-6c062b4d10294f849535ceeb6fc0e3612023-11-20T17:57:00ZengMDPI AGLand2073-445X2020-10-0191039810.3390/land9100398Challenging a Global Land Surface Model in a Local Socio-Environmental SystemKyla M. Dahlin0Donald Akanga1Danica L. Lombardozzi2David E. Reed3Gabriela Shirkey4Cheyenne Lei5Michael Abraha6Jiquan Chen7Department of Geography, Environment, and Spatial Sciences, Michigan State University (MSU), East Lansing, MI 48824, USADepartment of Geography, Environment, and Spatial Sciences, Michigan State University (MSU), East Lansing, MI 48824, USANational Center for Atmospheric Research, Boulder, CO 80305, USAMSU Center for Global Change and Earth Observation, East Lansing, MI 48824, USADepartment of Geography, Environment, and Spatial Sciences, Michigan State University (MSU), East Lansing, MI 48824, USADepartment of Geography, Environment, and Spatial Sciences, Michigan State University (MSU), East Lansing, MI 48824, USAMSU Center for Global Change and Earth Observation, East Lansing, MI 48824, USADepartment of Geography, Environment, and Spatial Sciences, Michigan State University (MSU), East Lansing, MI 48824, USALand surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson’s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.https://www.mdpi.com/2073-445X/9/10/398Community Land Modelcarbon cyclelandscape ecologymodel benchmarking |
| spellingShingle | Kyla M. Dahlin Donald Akanga Danica L. Lombardozzi David E. Reed Gabriela Shirkey Cheyenne Lei Michael Abraha Jiquan Chen Challenging a Global Land Surface Model in a Local Socio-Environmental System Land Community Land Model carbon cycle landscape ecology model benchmarking |
| title | Challenging a Global Land Surface Model in a Local Socio-Environmental System |
| title_full | Challenging a Global Land Surface Model in a Local Socio-Environmental System |
| title_fullStr | Challenging a Global Land Surface Model in a Local Socio-Environmental System |
| title_full_unstemmed | Challenging a Global Land Surface Model in a Local Socio-Environmental System |
| title_short | Challenging a Global Land Surface Model in a Local Socio-Environmental System |
| title_sort | challenging a global land surface model in a local socio environmental system |
| topic | Community Land Model carbon cycle landscape ecology model benchmarking |
| url | https://www.mdpi.com/2073-445X/9/10/398 |
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