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About National Soil Moisture Network

Objective

This project utilizes in situ measurements of soil moisture and NRCS SSURGO soil characteristics and PRISM data to develop high-resolution gridded soil moisture products. These data will be disseminated through this web portal.

Background

Spatial interpolation of in situ soil moisture is challenging because there are many factors that influence how soil moisture varies at regional scales including soil properties, topography, vegetation/land cover/land use and climate (Crow et al., 2012). Heterogeneity of soil properties influences all components of soil water balance and therefore has a significant impact on soil moisture. Rodriguez-Iturbe et al. (1995) found soil properties affect soil moisture distribution through commanding infiltration of moisture. Famiglietti et al. (1998) and Vereecken et al. (2007) concluded that soil hydraulic conductivities and porosity jointly influence the variability in surface soil moisture content. However, to date, no studies have developed gridded soil moisture products that account for the spatial variations in soil properties. The NRCS SSURGO data provides a unique gridded database of soil properties (gSSURGO) that is ideally suited for this project.
Soil moisture is also influenced by other factors. For example, routing processes that are affected by topography influence near-surface soil moisture. Numerous studies have demonstrated that soil moisture is generally lower at locations that are further upslope (Champagne et al., 2010; de Rosnay et al., 2009; Mohanty et al., 2000b). Land cover also plays an important role in determining soil moisture through modifying infiltration and evapotranspiration. Mohanty et al. (2000a) found that vegetation dynamics had a significant impact on intra-seasonal spatial patterns of soil moisture, which is consistent with other studies (Jacobs et al., 2004; Vinnikov et al., 1996). Isham et al. (2005) used a stochastic rainfall model and to demonstrate that the spatial variability of soil moisture is not only controlled by the spatial variability of vegetation, but also by rainfall. Precipitation is the dominant meteorological control of soil moisture (Seneviratne et al., 2010). The impact that all of these factors have on soil moisture varies depending on the spatial scale. Therefore, accurately interpolating soil moisture based on in situ (i.e., point) measurements is challenging and requires consideration of many different factors. .

Methods

This work builds on the North American Soil Moisture Database (NASMD) (Quiring et al., 2016). This project collects daily soil moisture observations from the NASMD stations that provide data in near real-time. These data are collected, harmonized and quality controlled using the North American Soil Moisture Database QAQC algorithm (Quiring et al., 2016). Our central hypothesis is that NRCS SSURGO data, along with PRISM climate data and other ancillary data (e.g., land use/land cover and elevation) can be used to develop more accurate gridded soil moisture surfaces. This approach has previously been applied to produce more accurate estimates of soil carbon. A similar smart-interpolation approach is also used by PRISM to develop gridded climate data from station-based measurements.

Cooperators, Partners, and Subject Matter Experts

USGS-CIDA Center for Integrated Data Analytics
USDA-NRCS National Soil Survey Center
USDA-NRCS National Water and Climate Center
The National Integrated Drought Information System (NIDIS)
USDA Southern Plains Climate Hub
U.S. Climate Reference Network


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Questions and Comments? quiring.10@osu.edu