Quantifying flood-water impacts on a lake water budget via volume-dependent transient stable isotope mass balance
Bibliographic record
Abstract
Abstract. Interactions between groundwater and surface water are often overlooked in lake water budgets, even though groundwater can significantly contribute to the total annual water inputs to a lake. Isotope mass balance models have seen significant developments in the last decade for assessing the spatial and temporal variability of hydrological processes in lakes but are generally applied assuming steady-state. While this assumption is generally acceptable for long-term water balances of large lakes, it may be less appropriate for lakes which undergo strong seasonality of hydrological processes and meteorological conditions. In this study, a volume-dependent transient isotopic mass balance model was developed for an artificial lake (named Lake A) in Canada, and in a context where direct measurement of surface water fluxes is difficult, if not impossible. This lake typically receives important inputs of flood-water during the spring freshet period, as a hydraulic connection with a large watershed establishes each year. Quantification of the water fluxes to Lake A allowed to highlight the impacts of flood-water inputs over the annual water budget. The isotopic mass balance model revealed that groundwater and surface water inputs respectively account for 71 % and 28 % of the total annual water inputs to Lake A, which demonstrates its dependence on groundwater. An important part of these groundwater inputs is likely to correspond to flood-derived surface water due to bank storage. On an annual timescale, Lake A was found to be resilient to surface water pollution and sensitive to groundwater quantity and quality changes. There is however a likelihood that the resilience to surface water pollution is lower from April to August, as important water inputs originating from Lake DM contribute to the water balance via direct and indirect inputs (i.e., from bank storage). This suggests that the surface water fluxes between Lake DM and Lake A did not only have an impact on the dynamic of Lake A during springtime but also significantly influenced the long-term dynamics of Lake A. These findings can help anticipating the impacts of variation in the intensity and/or duration of future flooding events on lakes' water quality. From a more global perspective, this knowledge is useful for establishing regional-scale management strategies for maintaining water quality at flood-affected lakes in a context of land-use and climate changes.
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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 | 0.000 | 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".