Understanding low-flow periods based on river and aquifer recessions using a sequential groundwater-surface water modelling approach
Bibliographic record
Abstract
Low-flow periods are a seasonal component of a river regime characterized by a reduction in discharge. This recession phenomenon is associated with water shortages and quality problems that are detrimental to communities and ecosystems that rely on groundwater-fed rivers. This paper presents a methodology to understand the dynamics of low-flow periods by utilizing the information contained in the response of the river-aquifer system during recessions. The methodology is developed and applied to the 5000 km2 Yamaska River watershed in Quebec (Canada), where critical low-flow conditions are frequently observed in winter and summer, and where the heterogeneous nature of the geology can lead to complex interactions between the rivers and the aquifer. Multiple water table and streamflow recession events recorded over a period of 20-50 years at 16 monitoring wells and 22 gauging stations were combined to obtain an averaged recession response at each location, referred to as the master recession curve (MRC). An MRC, which is minimally influenced by precipitation and evapotranspiration processes, contains important information about the flow and storage characteristics of an aquifer and its connection to rivers. Moreover, MRCs from wells and gauging stations provide complementary information. The recession-based analysis provided a tenable framework to disregard the surface modelling component at this stage since, during the depletion periods, the system is minimally influenced by atmospheric processes. A sequential modelling approach was devised to construct an integrated hydrological model using the HydroGeoSphere simulator to capture the groundwater-surface water interactions during low flows. First, the hydraulic characteristics of the subsurface were derived from the MRCs by history matching with the model in fully saturated mode. With the subsurface domain characterized, the rest of the processes were parameterized to capture the observed groundwater and surface water hydrographs using the fully integrated model. Beyond elucidating the low-flow dynamics, this methodology showcases efficiency due to its sequential strategy, alleviating the inherent computational burdens of setting up integrated models. This communication presents the outcomes from conceptual and numerical analyses, contributing to understanding hydrologic systems under low-flow conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".