Long-Term Variability of Soil Moisture in the Southern Sierra: Measurement and Prediction
Abstract
Using 6 yr (Water Year [WY] 2009–WY 2014) of hourly in situ measurements from a spatially distributed water-balance cluster, we quantified the long-term accuracy of an algorithm used to predict spatial patterns of depth-integrated soil-water storage within the rain–snow transition zone of the southern Sierra Nevada. The algorithm—the multivariate, non-parametric regression-tree estimator Random Forest—was used to predict soil-water storage based on a combination of attributes at each instrument cluster (soil texture, topographic wetness index, elevation, northness, and canopy cover). Out-of-bag R2 (similar to cross-validation for Random Forest) was used to quantify the accuracy of the estimator for unobserved data. Accuracy was consistently high during the wet-up, snow-cover, and early recession periods of average and wet years. The accuracy declined at the end of a 3-yr dry period, and the relative rank of the independent variables in the model shifted. Soil texture was the highest-ranked independent variable across all years, followed by elevation and northness. Topographic wetness increased in importance during dry periods. Northness exhibited high importance during the wet-up and early recession periods of most water years. As a result, during dry years, the importance of elevation declined. In dry years, notable differences in soil-water storage at each depth includemore »
- Authors:
-
- Univ. of California, Berkeley, CA (United States)
- Univ. of California, Berkeley, CA (United States); Univ. of California, Merced, CA (United States)
- Univ. of California, Merced, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1479384
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Vadose Zone Journal
- Additional Journal Information:
- Journal Volume: 17; Journal Issue: 1; Journal ID: ISSN 1539-1663
- Publisher:
- Soil Science Society of America
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Oroza, Carlos A., Bales, Roger C., Stacy, Erin M., Zheng, Zeshi, and Glaser, Steven D. Long-Term Variability of Soil Moisture in the Southern Sierra: Measurement and Prediction. United States: N. p., 2018.
Web. doi:10.2136/vzj2017.10.0178.
Oroza, Carlos A., Bales, Roger C., Stacy, Erin M., Zheng, Zeshi, & Glaser, Steven D. Long-Term Variability of Soil Moisture in the Southern Sierra: Measurement and Prediction. United States. https://doi.org/10.2136/vzj2017.10.0178
Oroza, Carlos A., Bales, Roger C., Stacy, Erin M., Zheng, Zeshi, and Glaser, Steven D. Thu .
"Long-Term Variability of Soil Moisture in the Southern Sierra: Measurement and Prediction". United States. https://doi.org/10.2136/vzj2017.10.0178. https://www.osti.gov/servlets/purl/1479384.
@article{osti_1479384,
title = {Long-Term Variability of Soil Moisture in the Southern Sierra: Measurement and Prediction},
author = {Oroza, Carlos A. and Bales, Roger C. and Stacy, Erin M. and Zheng, Zeshi and Glaser, Steven D.},
abstractNote = {Using 6 yr (Water Year [WY] 2009–WY 2014) of hourly in situ measurements from a spatially distributed water-balance cluster, we quantified the long-term accuracy of an algorithm used to predict spatial patterns of depth-integrated soil-water storage within the rain–snow transition zone of the southern Sierra Nevada. The algorithm—the multivariate, non-parametric regression-tree estimator Random Forest—was used to predict soil-water storage based on a combination of attributes at each instrument cluster (soil texture, topographic wetness index, elevation, northness, and canopy cover). Out-of-bag R2 (similar to cross-validation for Random Forest) was used to quantify the accuracy of the estimator for unobserved data. Accuracy was consistently high during the wet-up, snow-cover, and early recession periods of average and wet years. The accuracy declined at the end of a 3-yr dry period, and the relative rank of the independent variables in the model shifted. Soil texture was the highest-ranked independent variable across all years, followed by elevation and northness. Topographic wetness increased in importance during dry periods. Northness exhibited high importance during the wet-up and early recession periods of most water years. As a result, during dry years, the importance of elevation declined. In dry years, notable differences in soil-water storage at each depth include lower-than-average storage in the deeper regolith at the beginning of the water year and lower storage in near-surface layers during the winter resulting from transient snow cover.},
doi = {10.2136/vzj2017.10.0178},
journal = {Vadose Zone Journal},
number = 1,
volume = 17,
place = {United States},
year = {Thu Jul 05 00:00:00 EDT 2018},
month = {Thu Jul 05 00:00:00 EDT 2018}
}
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Works referencing / citing this record:
Land carbon models underestimate the severity and duration of drought’s impact on plant productivity
journal, February 2019
- Kolus, Hannah R.; Huntzinger, Deborah N.; Schwalm, Christopher R.
- Scientific Reports, Vol. 9, Issue 1
Subsurface Water Dominates Sierra Nevada Seasonal Hydrologic Storage
journal, November 2019
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Spatially distributed water-balance and meteorological data from the rain–snow transition, southern Sierra Nevada, California
journal, January 2018
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Land carbon models underestimate the severity and duration of drought’s impact on plant productivity
journal, February 2019
- Kolus, Hannah R.; Huntzinger, Deborah N.; Schwalm, Christopher R.
- Scientific Reports, Vol. 9, Issue 1
Cosmogenic Isotopes Unravel the Hydrochronology and Water Storage Dynamics of the Southern Sierra Critical Zone
journal, February 2019
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