2021
Dralle, D. N.; Hahm, W. J.; Chadwick, K. D.; McCormick, E.; Rempe, D. M.
Technical note: Accounting for snow in the estimation of root zone water storage capacity from precipitation and evapotranspiration fluxes Journal Article
In: Hydrology and Earth System Sciences, vol. 25, no. 5, pp. 2861–2867, 2021.
Abstract | Links | BibTeX | Tags: ERCZO, evapotranspiration, hydrologic modeling, root zone, snow melt, water storage
@article{Dralle2021,
title = {Technical note: Accounting for snow in the estimation of root zone water storage capacity from precipitation and evapotranspiration fluxes},
author = {D. N. Dralle and W. J. Hahm and K. D. Chadwick and E. McCormick and D. M. Rempe},
doi = {10.5194/hess-25-2861-2021},
year = {2021},
date = {2021-05-27},
journal = {Hydrology and Earth System Sciences},
volume = {25},
number = {5},
pages = {2861–2867},
abstract = {A common parameter in hydrological modeling frameworks is root zone water storage capacity (SR[L]), which mediates plant water availability during dry periods as well as the partitioning of rainfall between runoff and evapotranspiration. Recently, a simple flux-tracking-based approach was introduced to estimate the value of SR (Wang-Erlandsson et al., 2016). Here, we build upon this original method, which we argue may overestimate SR in snow-dominated catchments due to snow melt and evaporation processes. We propose a simple extension to the method presented by Wang-Erlandsson et al. (2016) and show that the approach provides a lower estimate of SR in snow-dominated watersheds. This SR dataset is available at a 1 km resolution for the continental USA, along with the full analysis code, on the Google Colab and Earth Engine platforms. We highlight differences between the original and new methods across the rain–snow transition in the Southern Sierra Nevada, California, USA. As climate warms and precipitation increasingly arrives as rain instead of snow, the subsurface may be an increasingly important reservoir for storing plant-available water between wet and dry seasons; therefore, improved estimates of SR will better clarify the future role of the subsurface as a storage reservoir that can sustain forests during seasonal dry periods and episodic drought.},
keywords = {ERCZO, evapotranspiration, hydrologic modeling, root zone, snow melt, water storage},
pubstate = {published},
tppubtype = {article}
}
A common parameter in hydrological modeling frameworks is root zone water storage capacity (SR[L]), which mediates plant water availability during dry periods as well as the partitioning of rainfall between runoff and evapotranspiration. Recently, a simple flux-tracking-based approach was introduced to estimate the value of SR (Wang-Erlandsson et al., 2016). Here, we build upon this original method, which we argue may overestimate SR in snow-dominated catchments due to snow melt and evaporation processes. We propose a simple extension to the method presented by Wang-Erlandsson et al. (2016) and show that the approach provides a lower estimate of SR in snow-dominated watersheds. This SR dataset is available at a 1 km resolution for the continental USA, along with the full analysis code, on the Google Colab and Earth Engine platforms. We highlight differences between the original and new methods across the rain–snow transition in the Southern Sierra Nevada, California, USA. As climate warms and precipitation increasingly arrives as rain instead of snow, the subsurface may be an increasingly important reservoir for storing plant-available water between wet and dry seasons; therefore, improved estimates of SR will better clarify the future role of the subsurface as a storage reservoir that can sustain forests during seasonal dry periods and episodic drought.
2018
Rempe, Daniella; Dietrich, William
Direct observations of rock moisture, a hidden component of the hydrologic cycle Journal Article
In: Proceedings of the National Academy of Science of the United States of America, vol. 115, no. 11, pp. 2664-2669, 2018.
Abstract | Links | BibTeX | Tags: Critical Zone, deep vadose zone, ERCZO, evapotranspiration, rock moisture, water budget
@article{daniellaRempe2018,
title = {Direct observations of rock moisture, a hidden component of the hydrologic cycle},
author = {Daniella Rempe and William Dietrich},
url = {https://angelo.berkeley.edu/wp-content/uploads/sites/59/Proceedings-of-the-National-Academy-of-Sciences-2018-Rempe.pdf},
doi = {https://doi.org/10.1073/pnas.1800141115},
year = {2018},
date = {2018-03-13},
journal = {Proceedings of the National Academy of Science of the United States of America},
volume = {115},
number = {11},
pages = {2664-2669},
abstract = {Recent theory and field observations suggest that a systematically varying weathering zone, that can be tens of meters thick, commonly develops in the bedrock underlying hillslopes. Weathering turns otherwise poorly conductive bedrock into a dynamic water storage reservoir. Infiltrating precipitation typically will pass through unsaturated weathered bedrock before reaching groundwater and running off to streams. This invisible and difficult to access unsaturated zone is virtually unexplored compared with the surface soil mantle. We have proposed the term “rock moisture” to describe the exchangeable water stored in the unsaturated zone in weathered bedrock, purposely choosing a term parallel to, but distinct from, soil moisture, because weathered bedrock is a distinctly different material that is distributed across landscapes independently of soil thickness. Here, we report a multiyear intensive campaign of quantifying rock moisture across a hillslope underlain by a thick weathered bedrock zone using repeat neutron probe measurements in a suite of boreholes. Rock moisture storage accumulates in the wet season, reaches a characteristic upper value, and rapidly passes any additional rainfall downward to groundwater. Hence, rock moisture storage mediates the initiation and magnitude of recharge and runoff. In the dry season, rock moisture storage is gradually depleted by trees for transpiration, leading to a common lower value at the end of the dry season. Up to 27% of the annual rainfall is seasonally stored as rock moisture. Significant rock moisture storage is likely common, and yet it is missing from hydrologic and land-surface models used to predict regional and global climate.},
keywords = {Critical Zone, deep vadose zone, ERCZO, evapotranspiration, rock moisture, water budget},
pubstate = {published},
tppubtype = {article}
}
Recent theory and field observations suggest that a systematically varying weathering zone, that can be tens of meters thick, commonly develops in the bedrock underlying hillslopes. Weathering turns otherwise poorly conductive bedrock into a dynamic water storage reservoir. Infiltrating precipitation typically will pass through unsaturated weathered bedrock before reaching groundwater and running off to streams. This invisible and difficult to access unsaturated zone is virtually unexplored compared with the surface soil mantle. We have proposed the term “rock moisture” to describe the exchangeable water stored in the unsaturated zone in weathered bedrock, purposely choosing a term parallel to, but distinct from, soil moisture, because weathered bedrock is a distinctly different material that is distributed across landscapes independently of soil thickness. Here, we report a multiyear intensive campaign of quantifying rock moisture across a hillslope underlain by a thick weathered bedrock zone using repeat neutron probe measurements in a suite of boreholes. Rock moisture storage accumulates in the wet season, reaches a characteristic upper value, and rapidly passes any additional rainfall downward to groundwater. Hence, rock moisture storage mediates the initiation and magnitude of recharge and runoff. In the dry season, rock moisture storage is gradually depleted by trees for transpiration, leading to a common lower value at the end of the dry season. Up to 27% of the annual rainfall is seasonally stored as rock moisture. Significant rock moisture storage is likely common, and yet it is missing from hydrologic and land-surface models used to predict regional and global climate.