Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor and Random Forest ). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed.
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Quantifying the Precipitation, Evapotranspiration, and Soil Moisture Network's Interaction Over Global Land Surface Hydrological Cycle
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).
more » « less- NSF-PAR ID:
- 10489910
- 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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