Evidence-based requirements for perceptualising intercatchment groundwater flow in hydrological models
Bibliographic record
Abstract
Groundwater-dominated catchments are often critical for nationally-important water resources. Many conceptual rainfall-runoff models used for the simulation of river flows tend to degrade in their model performance in groundwater-dominated catchments as they are rarely designed to simulate spatial groundwater behaviours or interactions with surface waters. Intercatchment groundwater flow is one such neglected variable. Efforts have been made to incorporate this process into existing models, but there is a need for greater emphasis on improving our perceptual models of groundwater-surface water interactions prior to any model edits. In this study, national meteorological, hydrological, hydrogeological, geological and artificial influence (characterising abstractions and return flows) datasets are used to develop a perceptual model of intercatchment groundwater flow and how it varies spatially and temporally across the River Thames. We characterise the water balance, presence of gaining/losing river reaches and intra-annual dynamics in 80 subcatchments of the River Thames in the UK, taking advantage of its wealth of data, densely gauged river network and geological variability. We show the prevalence of non-conservative river reaches across the study area, with heterogeneity both between, and within, geological units giving rise to a complex distribution of recharge and discharge points along the river network. We identify where non-conservative reaches can be attributed to intercatchment groundwater flow, and where other processes (e.g. human abstractions and discharge uncertainty) are likely the cause. Escarpments of Chalk and Jurassic Limestone show evidence of intercatchment groundwater flow both from headwater to downstream reaches, and out-of-catchment via springlines. We found temporal as well as spatial variability across the study area, with more seasonality and variability in river catchments on Jurassic Limestone outcrops and less on Chalk and Lower Greensand outcrops. Our results show the need for a degree of local investigation and hydrogeological perceptualisation within regional analysis, which we show to be achievable given relatively simple geological interpretation and data requirements. We then discuss the inclusion of external flow fluxes within existing models to enable calibration improvements in groundwater-dominated catchments, and, importantly, the characterisation of these fluxes given the temporal and spatial variability of intercatchment groundwater flow that our perceptual model has shown.
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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 | 0.008 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".