Evaluation of sampling techniques to characterize topographically-dependent variability for soil moisture downscaling
Date
2013
Authors
Werbylo, Kevin, author
Niemann, Jeff, advisor
Green, Tim, committee member
Kampf, Stephanie, committee member
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Abstract
Soil moisture patterns are an important consideration in many catchment-scale hydrologic applications. Unfortunately, estimating soil moisture patterns at resolutions that are appropriate for these applications (e.g., grid cells with a linear dimension of 10 to 50 m) is difficult. Downscaling methods can be used to estimate catchment-scale soil moisture patterns from coarser resolution estimates or spatial average soil moisture values. These methods usually infer the fine-scale variability in soil moisture using variations in ancillary variables like topographic attributes that have relationships to soil moisture. Previously, such relationships have been observed in catchments using soil moisture observations taken on uniform grids at hundreds of locations on multiple dates, but collecting data in this manner limits the applicability of this approach. The objective of this paper is to evaluate the effectiveness of two strategic sampling techniques for characterizing the relationships between topographic attributes and soil moisture for the purpose of constraining downscaling methods. The strategic sampling methods considered are conditioned Latin hypercube sampling (cLHS) and stratified random sampling (SRS). Each sampling method is used to select a limited number of locations and/or dates for soil moisture monitoring at three catchments with detailed soil moisture datasets (Tarrawarra, Satellite Station, and Cache la Poudre). These samples are then used to calibrate two available downscaling methods, and the effectiveness of the sampling methods is evaluated by the ability of the downscaling methods to reproduce the known soil moisture patterns at the catchments. The results show that cLHS and SRS can characterize the relationships between soil moisture and ancillary topographic variables with many fewer locations and dates than previously used. For example, when the number of locations for soil moisture monitoring is reduced by 82-90% and these locations are only monitored on 3 dates, the explanatory power of the downscaling methods frequently only reduces by less than 50%. Furthermore, both strategic sampling methods can substantially outperform random sampling when the number of samples is limited.
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Subject
conditioned Latin hypercube sampling
downscaling
efficient sampling
soil moisture
spatial variability
stratified random sampling