Quantifying dynamic water storage in unsaturated bedrock with borehole nuclear magnetic resonance

Schmidt, Logan; Rempe, D. M.

doi:10.1002/essoar.10503052.1

Cited by 7 publications

(13 citation statements)

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“…(2021) first reported on the observed depth of dynamic water storage by contrasting a single pair of wet and dry season observations, following methodology and using instrumentation similar to Salve et al. (2012), Schmidt and Rempe (2020), Rempe and Dietrich (2018), and Hahm et al. (2020).…”

Section: Methodsmentioning

confidence: 99%

Bedrock Vadose Zone Storage Dynamics Under Extreme Drought: Consequences for Plant Water Availability, Recharge, and Runoff

Hahm

Dralle

Sanders

et al. 2022

Water Resources Research

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Bedrock vadose zone water storage (i.e., rock moisture) dynamics are rarely observed but potentially key to understanding drought responses. Exploiting a borehole network at a Mediterranean blue oak savanna site—Rancho Venada—we document how water storage capacity in deeply weathered bedrock profiles regulates woody plant water availability and groundwater recharge. The site is in the Northern California Coast Range within steeply dipping turbidites. In a wet year (water year 2019; 647 mm of precipitation), rock moisture was quickly replenished to a characteristic storage capacity, recharging groundwater that emerged at springs to generate streamflow. In the subsequent rainless summer growing season, rock moisture was depleted by about 93 mm. In two drought years that followed (212 and 121 mm of precipitation) the total amount of rock moisture gained each winter was about 54 and 20 mm, respectively, and declines were documented exceeding these amounts, resulting in progressively lower rock moisture content. Oaks, which are rooted into bedrock, demonstrated signs of water stress in drought, including reduced transpiration rates and extremely low water potentials. In the 2020–2021 drought, precipitation did not exceed storage capacity, resulting in variable belowground water storage, increased plant water stress, and no recharge or runoff. Rock moisture deficits (rather than soil moisture deficits) explain these responses.

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Section: Methodsmentioning

confidence: 99%

Bedrock Vadose Zone Storage Dynamics Under Extreme Drought: Consequences for Plant Water Availability, Recharge, and Runoff

Hahm

Dralle

Sanders

et al. 2022

Water Resources Research

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“…It is based on the resonance of the magnetic moment of the protons from water molecules. NMR can be used from the surface or boreholes to infer the water content in the vadose zone (e.g., Schmidt and Rempe, 2020). Recent works have also shown its value for monitoring the water dynamics through time-lapse measurements (e.g., Mazzili et al 2020, Lesparre et al 2020.…”

Section: Water Content Dynamics In the Vadose Zonementioning

confidence: 99%

Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards 4D hydrogeology

Hermans

Goderniaux

Jougnot

et al. 2022

Preprint

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Abstract. Essentially all hydrogeological processes are strongly influenced by the subsurface spatial heterogeneity and the temporal variation of environmental conditions, hydraulic properties, and solute concentrations. This spatial and temporal variability needs to be considered when studying hydrogeological processes in order to employ adequate mechanistic models or perform upscaling. The scale at which a hydrogeological system should be characterized in terms of its spatial heterogeneity and temporal dynamics depends on the studied process and it is not always necessary to consider the full complexity. In this paper, we identify a series of hydrogeological processes for which an approach coupling the monitoring of spatial and temporal variability, including 4D imaging, is often necessary: (1) groundwater fluxes that control (2) solute transport, mixing and reaction processes, (3) vadose zone dynamics, and (4) surface-subsurface water interaction occurring at the interface between different subsurface compartments. We first identify the main challenges related to the coupling of spatial and temporal fluctuations for these processes. Then, we highlight some recent innovations that have led to significant breakthroughs in this domain. We finally discuss how spatial and temporal fluctuations affect our ability to accurately model them and predict their behavior. We thus advocate a more systematic characterization of the dynamic nature of subsurface processes, and the harmonization of open databases to store hydrogeological data sets in their four-dimensional components, for answering emerging scientific question and addressing key societal issues.

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“…To accomplish this, we first averaged the TDT time series for sensors located in the unsaturated zone year‐round (1.95, 3.64 and 5.22 m depth) to capture the temporal pattern of storage accumulation and depletion. We then normalized this relative time series using the range of storage representative of the Rivendell hillslope (0–400 mm annual storage) (Schmidt & Rempe, 2020), which is measured via neutron probe in boreholes.…”

Section: Site Descriptionmentioning

confidence: 99%

Multicriteria analysis on rock moisture and streamflow in a rainfall‐runoff model improves accuracy of model results

Follette

Hahm

Rempe

et al. 2022

Hydrological Processes

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Although shallow (<1.5 m) soil water storage has been extensively studied, the significance of deeper unsaturated zone water storage to flow generation is poorly documented. However, a limited but growing body of empirical work shows that the weathered bedrock vadose zone, not soil, stores the majority of plant available water in many seasonally dry and semi‐arid landscapes. Moreover, this storage dynamic mediates recharge to hillslope groundwater systems that generate stream discharge and support ecologically significant baseflows. Explicit representations of bedrock vadose zone processes are rarely incorporated into runoff models, due in part to a paucity of observations that can constrain simulations. Here, we develop a simple representation of the weathered bedrock vadose zone that is guided by in situ field observations. We incorporate this representation into a rainfall‐runoff model, and calibrate it on streamflow alone, on rock moisture (i.e., weathered bedrock vadose zone moisture) alone, and on both using the concept of Pareto optimality. We find that the model is capable of accurately simultaneously simulating dynamics in rock moisture and streamflow, in terms of Kling‐Gupta Efficiency, when using Pareto optimal parameter sets. Calibration on streamflow alone, however, is insufficient to accurately simulate rock moisture dynamics. We further find that the posterior distributions of some model parameters are sensitive to choice of calibration scenario. The posterior distribution of high‐performing model parameters resulting from the streamflow only calibration scenario include physically unrealistic values that are not yielded by the rock moisture only or Pareto calibration strategies. These results suggest that the accuracy of some model results can be increased and parameter uncertainty decreased via incorporation of rock moisture data in calibration, without sacrificing streamflow simulation quality. Emerging recognition of the global significance of weathered bedrock water storage in seasonally dry and semi‐arid regions motivates more observations of weathered bedrock moisture and integration of this variable into earth system models.

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Quantifying dynamic water storage in unsaturated bedrock with borehole nuclear magnetic resonance

Cited by 7 publications

References 0 publications

Bedrock Vadose Zone Storage Dynamics Under Extreme Drought: Consequences for Plant Water Availability, Recharge, and Runoff

Bedrock Vadose Zone Storage Dynamics Under Extreme Drought: Consequences for Plant Water Availability, Recharge, and Runoff

Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards 4D hydrogeology

Multicriteria analysis on rock moisture and streamflow in a rainfall‐runoff model improves accuracy of model results

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