The Weight of Water: Using a Geological Weighing Lysimeter to Quantify the Field-Scale Water Balance
Bibliographic record
Abstract
Quantifying water and energy fluxes are critical to understand how water is moved and stored on the landscape. These measurements are important for flood and drought forecasting, water resources management, and large-scale numerical weather prediction models. Moreover, land surface model’s (LSMs) which are hydrological tools used to predict and forecast water and energy fluxes, rely on these measurements to calibrate and validate their predictions. To evaluate hydrological fluxes and in turn water storage, representative observations are needed to capture the temporal and spatial dynamics of water on the landscape. However, hydrological fluxes are often difficult to measure and are limited to specific fluxes and spatial resolutions. Geological Weighing Lysimeters (GWL) are novel instruments that provide measurements of total integrated water storage at scales of 102 m2 and 106 m2 (field-scale). These tools use a saturated formations response to changes in mechanical loading, to estimate the change of water storage on the land surface. This research assessed the efficacy of a GWL in a deep confined aquifer at a research site in Duck Lake, Saskatchewan, to measure total water storage and partition individual stores from field-scale water balance. We found when coupled with supplementary observations of shallow groundwater and snow storage, the GWL provided a reliable record of temporal storage dynamics observed in point scale dielectric probes. Inconsistencies in soil moisture storage were from the dielectric probes inability to measure ice content in the soils and different estimates of hydrological fluxes between scales. We then used these storage estimates to critically assess the performance of two LSMs: the Canadian Land Surface Scheme (CLASS) and the Structure for Unifying Multiple Modeling Alternatives: (SUMMA). We found each LSM was able to reproduce total water storage and subsurface storage dynamics well, however they both had major inconsistencies simulating snowpack dynamics and hydrological fluxes. We speculate these inconsistencies are the result of differences in soil hydraulic property representations. The outcome of this research is two-fold. First, GWL and supplementary observations can be used to partition individual storage components from the water balance providing insight into hydrological fluxes; and secondly, small differences in soil hydraulic properties may largely influence Land Surface Schemes (LSSS’s) simulated fluxes, more research is needed to assess the influence have.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".