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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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Quantifying the Precipitation, Evapotranspiration, and Soil Moisture Network's Interaction Over Global Land Surface Hydrological Cycle
Abstract Enhancing our understanding of the intricate interplay among hydro‐climatic processes is crucial for a comprehensive assessment of water availability and climate extremes across global land regions. Here, we propose an integrated framework to investigate networks of the global fields of multiple hydrological variables (Precipitation, Evapotranspiration, Soil Moisture). We apply a two‐layer complex network concept to formulate the independent networks of each hydrological variable and their interactions. Intra‐ (Single‐layer) and cross‐ (two‐layer) network coefficients are derived from the formulated hydrological network to quantify the linkage, spatial connection density, and scale for the independent hydrological fields (or variables) and their interactions. The joint distribution of the intra‐network coefficients reveals multiple spatial scales of connectivity for a moderately well‐connected location in case of evapotranspiration and soil moisture. With increasing global mean temperature, spatially synchronized evapotranspiration over such a large scale may lead to multi‐continental droughts and heatwaves. Furthermore, the (cross‐) network coefficients have identified regions acting as “bottlenecks” for moisture flow and the water‐dominated areas with less evaporative actions. The contrasting features of two‐layer network coefficients have provided a qualitative picture of moisture circulation and recirculation over many hydrological hotspot regions, such as the Amazonian basin, Indian subcontinents, and the Sahel region. The derived results can be employed to gain insights into the global water cycle’s multiple interacting processes (e.g., land‐atmosphere interactions).
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- Award ID(s):
- 2422542
- PAR ID:
- 10489909
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 60
- Issue:
- 2
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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