Assessing Soil Moisture Patterns Using a Soil Topographic Index in a Humid Region

Zeyuan Qiu (), Andrew Pennock, Subhasis Giri, Carole Trnka, Xu Du and Hongmei Wang
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Zeyuan Qiu: New Jersey Institute of Technology, University Heights
Andrew Pennock: New Jersey Institute of Technology, University Heights
Subhasis Giri: New Jersey Institute of Technology, University Heights
Carole Trnka: New Jersey Institute of Technology, University Heights
Xu Du: Montclair State University
Hongmei Wang: Northwest A&F University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 7, No 12, 2243-2255

Abstract: Abstract Knowledge of soil moisture is essential for soil conservation and efficient water resources management especially related to control nonpoint-source pollution. Soil topographic indices (STI) are often used to understand the soil moisture patterns in landscapes and make effective landscape management decisions. This study assessed the relationships between soil moisture measurements and STI values in two study sites in North-central New Jersey, USA. The soil moisture measurements were taken in these study sites using a time domain reflectometry probe during thirteen sampling events between April 2013 and July 2015. The STI values at the sampling points were derived from a 3-m LiDAR digital elevation model and SSURGO soil database. The Spearman’s correlation analysis based on these data in all sampling points identified a significant positive correlation between soil moisture and STI. Strong positive relationships between soil moisture and STI were also identified when using binned data to eliminate the impacts of unevenness in data distribution and the impacts of micro-variations in topography, vegetation, soil compaction, and instrumental errors. The linear mixed modeling results revealed significant and positive impacts of STI and precipitation, and significant but negative impacts of temperature on soil moisture. The degrees of these effects vary across two study sites, which reflect the complex and dynamic interactions among soils, topography and climate in landscapes that affect soil moisture. Given the stochastic nature of climate factors such as precipitation and temperature, the static STI would be a reliable factor to predict soil moisture patterns in the landscape. The findings support various STI-based conservation planning efforts in New Jersey and beyond such as targeting hydrologically sensitive areas for natural resources protection and preservation and best management practice implementation.

Keywords: Soil moisture; Soil topographic index; Linear mixed model; New Jersey (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11269-017-1640-7

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