A method to downscale soil moisture to fine resolutions using topographic, vegetation, and soil data

Abstract

Additional testing is performed using the Tarrawarra and Nerrigundah catchments where vegetation is relatively homogeneous and limited soil data are available for interpolation. For Cache la Poudre, the estimated soil moisture patterns improve substantially when the vegetation and soil data are used in addition to topographic data, and the performance is similar for the iii EMT+VS and EOF models. Adding spatially-interpolated soil data to the topographic data at Tarrawarra and Nerrigundah decreases model performance and results in worse performance than the EOF method, in which the soil data are not highly weighted. These results suggest that the soil data must have greater spatial detail to be useful to the EMT+VS model. iv ACKNOWLEDGEMENTS

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

“…a coarse grid of soil moisture values rather than a single average soil moisture value). Some studies have not encountered this issue because they have focused on downscaling within an area that falls within a single coarse grid cell [Busch et al, 2012, Coleman and Niemann, 2013, Pellenq et al, 2003, Perry and Niemann, 2007, Ranney et al, 2015, Wilson et al, 2005. Other studies have downscaled multiple coarse grid values but have not considered this issue in depth.…”

“…That layer begins at the ground surface and ends at the depth where the hydraulic conductivity begins to decrease significantly due to a lower permeability soil layer or bedrock. The hydrologically active layer has ranged from 5 cm and 30 cm depth in past model applications [Ranney et al, 2015]. Over this range of depths, soil moisture is assumed to be constant.…”

“…More details can be found in Coleman and Niemann [2013] and Ranney et al [2015]. The EMT+VS model downscales soil moisture using a water balance of the hydrologically active soil layer.…”

“…In a typical application,  is the soil moisture that is being downscaled, V is fine-resolution vegetation cover data, and the other variables are typically either specified or calibrated constants. However, the model derivation allows all variables in Equation (3) except v  to vary at the fine resolution if desired [Ranney et al, 2015]. The variable DDI is calculated by averaging the fine resolution data.…”

“…Temimi et al [2010] used an index that combines topographic attributes and the leaf area index (LAI) to estimate soil moisture at a 90 m resolution. Ranney et al [2015] generalized the Coleman and Niemann [2013] model to accept fine scale soil and vegetation data and called it the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model. Using this approach, vegetation data were found to provide more value for downscaling than soil data, particularly if the soil data are sparse or uncertain.…”