2022
LaFollette, PT; Hahm, WJ; Rempe, DM; Dietrich, WE; Brauer, CC; Weerts, AH; Dralle, DN
Multicriteria analysis on rock moisture and streamflow in a rainfall-runoff model improves accuracy of model results Journal Article
In: Hydrological Processes, vol. 36, iss. 3, 2022.
Links | BibTeX | Tags: ERCZO, hydrologic modeling, rainfall, rock moisture, Stream Flow
@article{LaFollette2022,
title = {Multicriteria analysis on rock moisture and streamflow in a rainfall-runoff model improves accuracy of model results},
author = {PT LaFollette and WJ Hahm and DM Rempe and WE Dietrich and CC Brauer and AH Weerts and DN Dralle},
url = {https://angelo.berkeley.edu/lafollette_rockmoisture_streamflow_model/},
doi = {https://doi.org/10.1002/hyp.14536},
year = {2022},
date = {2022-03-09},
journal = {Hydrological Processes},
volume = {36},
issue = {3},
keywords = {ERCZO, hydrologic modeling, rainfall, rock moisture, Stream Flow},
pubstate = {published},
tppubtype = {article}
}
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.