Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.153
IF5.153
IF 5-year value: 5.460
IF 5-year
5.460
CiteScore value: 7.8
CiteScore
7.8
SNIP value: 1.623
SNIP1.623
IPP value: 4.91
IPP4.91
SJR value: 2.092
SJR2.092
Scimago H <br class='widget-line-break'>index value: 123
Scimago H
index
123
h5-index value: 65
h5-index65
Volume 18, issue 7
Hydrol. Earth Syst. Sci., 18, 2463–2483, 2014
https://doi.org/10.5194/hess-18-2463-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 18, 2463–2483, 2014
https://doi.org/10.5194/hess-18-2463-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Jul 2014

Research article | 03 Jul 2014

Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations

W. J. Riley1 and C. Shen2 W. J. Riley and C. Shen
  • 1Earth Systems Division, Climate and Carbon Department, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
  • 2Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA

Abstract. Watershed-scale hydrological and biogeochemical models are usually discretized at resolutions coarser than where significant heterogeneities in topography, abiotic factors (e.g., soil properties), and biotic (e.g., vegetation) factors exist. Here we report on a method to use fine-scale (220 m grid cells) hydrological model predictions to build reduced-order models of the statistical properties of near-surface soil moisture at coarse resolution (25 times coarser, ~7 km). We applied a watershed-scale hydrological model (PAWS-CLM4) that has been previously tested in several watersheds. Using these simulations, we developed simple, relatively accurate (R2 ~0.7–0.8), reduced-order models for the relationship between mean and higher-order moments of near-surface soil moisture during the nonfrozen periods over five years. When applied to transient predictions, soil moisture variance and skewness were relatively accurately predicted (R2 0.7–0.8), while the kurtosis was less accurately predicted (R2 ~0.5). We also tested 16 system attributes hypothesized to explain the negative relationship between soil moisture mean and variance toward the wetter end of the distribution and found that, in the model, 59% of the variance of this relationship can be explained by the elevation gradient convolved with mean evapotranspiration. We did not find significant relationships between the time rate of change of soil moisture variance and covariances between mean moisture and evapotranspiration, drainage, or soil properties, as has been reported in other modeling studies. As seen in previous observational studies, the predicted soil moisture skewness was predominantly positive and negative in drier and wetter regions, respectively. In individual coarse-resolution grid cells, the transition between positive and negative skewness occurred at a mean soil moisture of ~0.25–0.3. The type of numerical modeling experiments presented here can improve understanding of the causes of soil moisture heterogeneity across scales, and inform the types of observations required to more accurately represent what is often unresolved spatial heterogeneity in regional and global hydrological models.

Publications Copernicus
Download
Citation