Emergence from drought: Direct and remote observations of moisture storage deficits, plant water stress, and groundwater reveal ecohydrologic recovery
Bibliographic record
Abstract
Plant transpiration, groundwater, and streamflow responses to changes in precipitation rates are moderated by largely unobserved partitioning of unsaturated subsurface water storage. Direct observations of deep root-zone storage dynamics are therefore critical to understanding the processes driving frequently observed surficial responses to drought and drought recovery, namely stream discharge and plant stress. Here we present six years of direct measurements of plant water stress and storage in soil and weathered bedrock (approx. 1-5 m) from our rain-dominated oak savanna study site in the California Coast Range, providing a window into the subsurface during the wet 2019 water year (WY), through three consecutive dry years of severe drought, and concluding with two wet years of drought recovery. During the drought, precipitation was insufficient to replenish plant water uptake from the unsaturated root-zone underlying predominantly north-facing slopes with oaks and south-facing slopes without oaks resulting in the multiyear development of a storage deficit. Increased plant water stress led to dieback, and the persistent deficit did not permit substantial infiltration of precipitation beyond the unsaturated root-zone to recharge groundwater and generate streamflow. Substantial high-intensity rainfall during the wet water years following drought (2023 and 2024) quickly eliminated the accumulated storage deficits, leading to rising water tables and streamflow. Thus, we observe initiation of hydrologic drought recovery requires sufficient precipitation to replenish root-zone storage deficits. Furthermore, we advocate for the presence and magnitude of storage deficits (which can be estimated via distributed evapotranspiration and precipitation products) as a metric of drought presence and severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".