Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems
Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/11/10/1856 |
| _version_ | 1827649360681238528 |
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| author | Michael S. O’Donnell Daniel J. Manier |
| author_facet | Michael S. O’Donnell Daniel J. Manier |
| author_sort | Michael S. O’Donnell |
| collection | DOAJ |
| description | Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate spatiotemporal-varying soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes as gridded surfaces by enhancing the Newhall simulation model. Importantly, our approach allows for the substitution of data and parameters, such as climate, snowmelt, soil properties, alternative potential evapotranspiration equations and air-soil temperature offsets. We applied the model across the western United States using monthly climate averages (1981–2010). The resulting data are intended to help improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), the spread of invasive species and wildfire risk. The demonstrated modeled results had significant correlations with vegetation patterns—for example, soil moisture variables predicted sagebrush (R<sup>2</sup> = 0.51), annual herbaceous plant cover (R<sup>2</sup> = 0.687), exposed soil (R<sup>2</sup> = 0.656) and fire occurrence (R<sup>2</sup> = 0.343). Using our framework, we have the flexibility to assess dynamic climate conditions (historical, contemporary or projected) that could improve the knowledge of changing spatiotemporal biotic patterns and be applied to other geographic regions. |
| first_indexed | 2024-03-09T19:56:43Z |
| format | Article |
| id | doaj.art-2062535fbb06457fbf4999c034e12c7c |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T19:56:43Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-2062535fbb06457fbf4999c034e12c7c2023-11-24T00:54:52ZengMDPI AGLand2073-445X2022-10-011110185610.3390/land11101856Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid EcosystemsMichael S. O’Donnell0Daniel J. Manier1U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Ave, Bldg. C, Fort Collins, CO 80526, USAU.S. Geological Survey, Fort Collins Science Center, 2150 Centre Ave, Bldg. C, Fort Collins, CO 80526, USASoil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate spatiotemporal-varying soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes as gridded surfaces by enhancing the Newhall simulation model. Importantly, our approach allows for the substitution of data and parameters, such as climate, snowmelt, soil properties, alternative potential evapotranspiration equations and air-soil temperature offsets. We applied the model across the western United States using monthly climate averages (1981–2010). The resulting data are intended to help improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), the spread of invasive species and wildfire risk. The demonstrated modeled results had significant correlations with vegetation patterns—for example, soil moisture variables predicted sagebrush (R<sup>2</sup> = 0.51), annual herbaceous plant cover (R<sup>2</sup> = 0.687), exposed soil (R<sup>2</sup> = 0.656) and fire occurrence (R<sup>2</sup> = 0.343). Using our framework, we have the flexibility to assess dynamic climate conditions (historical, contemporary or projected) that could improve the knowledge of changing spatiotemporal biotic patterns and be applied to other geographic regions.https://www.mdpi.com/2073-445X/11/10/1856Newhall simulation modelsagebrush biomeseasonalitysite potentialsnowmeltsoil temperature and moisture regimes |
| spellingShingle | Michael S. O’Donnell Daniel J. Manier Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems Land Newhall simulation model sagebrush biome seasonality site potential snowmelt soil temperature and moisture regimes |
| title | Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems |
| title_full | Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems |
| title_fullStr | Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems |
| title_full_unstemmed | Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems |
| title_short | Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems |
| title_sort | spatial estimates of soil moisture for understanding ecological potential and risk a case study for arid and semi arid ecosystems |
| topic | Newhall simulation model sagebrush biome seasonality site potential snowmelt soil temperature and moisture regimes |
| url | https://www.mdpi.com/2073-445X/11/10/1856 |
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