Geology Reports⌕ Search

USGS · 70015999

Monitoring moisture storage in trees using time domain reflectometry

Abstract

Laboratory and field tests were performed to examine the feasibility of using time domain reflectometry (TDR) to monitor changes in the moisture storage of the woody parts of trees. To serve as wave guides for the TDR signal, pairs of stainless steel rods (13 cm long, 0.32 cm in diameter, and 2.5 cm separation) were driven into parallel pilot holes drilled into the woody parts of trees, and a cable testing oscilloscope was used to determine the apparent dielectric constant. A laboratory calibration test was performed on two sapwood samples, so that the relation between the volumetric water content and the apparent dielectric constant of the sapwood could be determined over a range of water contents. The resulting calibration curve for these sapwood samples was significantly different than the general calibration curve used for soils, showing a smaller change in the apparent dielectric constant for a given change in the volumetric water content than is typical for soils. The calibration curve was used to estimate the average volumetric water content to a depth of 13 cm in living trees. One field experiment was conducted on an English walnut tree ( Juglans regia ) with a diameter of 40 cm, growing in a flood-irrigated orchard on a Hanford sandy loam near Modesto, California (U.S.A.). Rods were driven into the tree at about 50 cm above the soil surface and monitored hourly for the month of August, 1988. The moisture content determined by TDR showed a gradual decrease from 0.44 to 0.42 cm 3 cm −3 over a two week period prior to flood irrigation, followed by a rapid rise to 0.47 cm 3 cm −3 over a four day period after irrigation, then again a gradual decline approaching the next irrigation. A second field experiment was made on ten evergreen and deciduous trees with diameters ranging from 30 to 120 cm, growing in the foothills of the Coast Range of central California. Rods were driven into each tree at 50 to 100 cm above the soil surface and monitored on a biweekly to monthly basis for over a year. Most trees showed an early spring maximum in moisture content determined by TDR associated with leaf growth, and a late summer minimum in moisture content associated with the end of the dry season. Moisture contents ranged from 0.20 to 0.70 cm 3 cm −3 , with an annual percentage change in moisture of 15% to 70% depending on species and environmental conditions. A final field test was performed in northern New Mexico (U.S.A.) to examine the effect of trunk freezing on TDR measurements. This test confirmed that freezing conditions were recorded as a total loss of liquid water by the TDR method. These results suggest that further TDR calibration for wood, plus some understanding of the relation between tree moisture and physiological stress could be useful to several disciplines, ranging from irrigation scheduling to watershed management to forest ecology.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 37.570755481439576° to 37.72075117136899° latitude; -121.1140801245068° to -120.87833353846591° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J. Constantz, F. Murphy. 1990. Monitoring moisture storage in trees using time domain reflectometry. https://doi.org/10.1016/0022-1694(90)90032-s

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Inverse modeling for post-wildfire hydrologic process understanding over snow-dominated watersheds

Wildfire-induced changes to vegetation and soil properties can alter snowpack dynamics and hydrologic processes in ways that complicate predictions of post-wildfire water availability. To better understand these effects, we coupled the Parameter ESTimation in C++ (PEST++) software with the Weather Research & Forecasting Hydrologic model (WRF-Hydro) across three burned, snow-dominated watersheds in the western United States. We incorporated three burn-severity-based land cover categories into WRF-Hydro and used an iterative Ensemble Smoother (iES) within PEST++ to estimate model parameters controlling snow accumulation and melt, infiltration, and streamflow generation. This framework supported three complementary objectives: (1) assessing parameter sensitivities under pre- and post-wildfire conditions, (2) comparing pre- and post-wildfire parameter ensembles to identify wildfire-driven shifts in hydrologic processes, and (3) using counterfactual simulations to isolate the hydrologic response to wildfire from interannual climate variability. Our findings indicate that model parameter sensitivity varied by domain and shifted following wildfire. Maximum snow albedo and snow albedo decay were most influential in the higher-elevation East Troublesome domain, while the rain-snow partitioning temperature threshold dominated in the lower-elevation Chippy Creek and Las Conchas domains. Following wildfire, streamflow sensitivity shifted away from snowpack parameters toward soil hydraulic parameters, consistent with wildfire-driven reductions in evapotranspiration and increases in soil moisture. Incorporating wildfire effects led to complex changes in simulated snowpack accumulation and streamflow timing, generally leading to earlier snowmelt freshet initiation and cessation and reduced late season flows. Simulations were more sensitive to interannual climate variations than wildfire land cover changes. These results highlight both the potential and challenges of ensemble-based inverse modeling for understanding post-wildfire hydrologic change in snow-dominated watersheds.

Colorado, Montana, New Mexico↗

Reservoir releases and land cover interact to drive event-scale nitrate export in a large agricultural basin

Understanding the drivers of nitrate export in rivers is critical for developing effective nutrient management strategies. However, few studies have explored event-scale drivers of export in large river basins with human modifications like reservoirs. Here, we analyzed nitrate concentration-discharge (C-Q) relationships from 215 events at the outlet of the Kansas River Basin, USA (155,690 km 2 ) from 2014 to 2022 to (1) characterize event-scale nitrate export behaviors in a large agricultural basin, and (2) determine how different event characteristics are linked to these behaviors. We found that C-Q behaviors varied greatly, with 60% of events producing nitrate enrichment (n = 130) and 40% of events producing nitrate dilution (n = 85). These behaviors were correlated with complex spatial interlinkages between climate and land cover: across most of the basin, nitrate enrichment was correlated with drier antecedent conditions, but in wetter areas with higher proportions of urban and forested land cover, enrichment was more strongly correlated with precipitation magnitude/intensity. This difference in hydroclimatic controls on nitrate export might be related to differential distributions of nitrate sources within these land covers. Upstream of major reservoirs, however, neither variable was strongly correlated with C-Q behavior, suggesting that nitrate attenuation within reservoirs decouples event-scale concentration signals in upstream waters from those downstream. Reservoir outflows had variable impacts on C-Q behavior, reflecting reservoir-specific variations in nitrate attenuation efficiency. Together, these results identify specific complex interactions between hydroclimate, land cover, reservoir positioning, and individual reservoir properties that control event-scale nitrate export from large basins.

Colorado, Kansas, Nebraska↗

Deep learning error post-processing improves stochastic watershed modeling

Hydrologic extremes, including floods and droughts, pose substantial societal risks that are expected to intensify with climate change. Deterministic watershed models (DWMs) remain a mainstay for modeling these extremes, but lack explicit representation of uncertainty, limiting their utility for risk-informed planning. Stochastic watershed models (SWMs) address this limitation by generating ensembles of streamflow via models of observed DWM residuals. However, most SWMs struggle with the complex dependence between DWM residuals and the underlying hydrologic state, which can complicate stochastic simulations under nonstationary climates. Deep learning (DL) models, whether used as standalone models or post-processors for process-based DWMs, offer a pathway to address this challenge by reducing conditional dependence. In this study, we evaluate SWMs applied to seven models: three process-based models (PRMS, Hymod, and HBV), their hybrid process-DL counterparts, and a pure DL DWM, focusing on daily simulations and extremes under both historical conditions and synthetic climate change scenarios. Results for a case study watershed in Massachusetts show that SWMs applied to hybrid or pure DL DWMs consistently outperform those applied to process-based DWMs. However, an SWM applied to the pure DL model exhibits weaknesses at low flows for this study basin, underscoring the value of hybrid approaches. Extending the analysis across 73 additional basins demonstrates that these improvements are robust and generalizable statewide. This work highlights the potential of a DL-enhanced stochastic watershed modeling framework to advance hydrologic risk prediction under changing climate conditions, offering a scalable methodology for integrating uncertainty into watershed modeling for long-term planning.

Journal of Hydrology↗