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Climate models project changing patterns of precipitation and increases in temperature that modify soil moisture dynamics. Land use and changing frequency and intensity of precipitation can induce changes in soil structure and rooting abundances at timescales shorter than commonly considered. Soil structure is a critical ecosystem that governs water flow through soil profiles and across landscapes, and can influence weathering rates and thus solute release and soil development. We hypothesize that the altered soil structure and modification of rooting depth distributions linked to land use change can influence soil solute concentrations, and that those shifts in solute release are dependent on patterns of precipitation. We installed suction lysimeters to collect soil water for ~3 y in two grassland regions with distinct mean annual precipitation (800 mm y-1, 1100 mm y-1) in native prairie, agriculture, and post-agriculture land uses at depths of 10, 40, and 120 cm. We linked solute concentrations to soil moisture, aggregate-size distribution, pore geometry, and rooting depth distributions to assess how land use change and the altered rooting abundance it imposes can modify soil structure and hydrologic fluxes, and to infer how soil weathering can shift deep in the subsurface. We reveal how soil moisture residence time and the soil pore network can govern solute production, and the importance of precipitation and thus of soil moisture accumulation over growing seasons for mineral weathering and solute production. Specifically, we find that the solubility potential of multiple weathering products and organic carbon increases with precipitation, dominance of relatively small aggregates at the surface, and fewer coarse roots. Enhanced solute concentrations at depth may also reflect transport down-profile. Our findings reveal unintended consequences of land use change that influence important hydrologic dynamics and nutrient cycling in the vadose zone and how deeply and how persistently unexpected consequences of changes in land cover can propagate.
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Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning
This study explores machine learning for estimating soil moisture at multiple depths (0–5 cm, 0–10 cm, 0–20 cm, 0–50 cm, and 0–100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation from the Global Precipitation Measurement (GPM), evapotranspiration from the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS), soil properties from gridded National Soil Survey Geographic (gNATSGO), and land cover information from the National Land Cover Database (NLCD). Five machine learning algorithms are evaluated including the feed-forward artificial neural network, random forest, extreme gradient boosting (XGBoost), Categorical Boosting, and Light Gradient Boosting Machine. The methods are tested by comparing to in situ soil moisture observations from several national and regional networks. XGBoost exhibits the best performance for estimating soil moisture, achieving higher correlation coefficients (ranging from 0.76 at 0–5 cm depth to 0.86 at 0–100 cm depth), lower root mean squared errors (from 0.024 cm3/cm3 at 0–100 cm depth to 0.039 cm3/cm3 at 0–5 cm depth), higher Nash–Sutcliffe Efficiencies (from 0.551 at 0–5 cm depth to 0.694 at 0–100 cm depth), and higher Kling–Gupta Efficiencies (0.511 at 0–5 cm depth to 0.696 at 0–100 cm depth). Additionally, XGBoost outperforms the SMAP Level 4 product in representing the time series of soil moisture for the networks. Key factors influencing the soil moisture estimation are elevation, clay content, aridity index, and antecedent soil moisture derived from SMAP.
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- Award ID(s):
- 2312319
- PAR ID:
- 10614768
- Publisher / Repository:
- Remote Sensing
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 16
- Issue:
- 19
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 3699
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs16193699
