Bibliographic record
Abstract
Hydrogeologists should understand how groundwater recharges aquifers, the methods available for quantifying this component of the water budget, and the strengths and weaknesses of the different methods. Understanding the sources of recharge can be important for predicting the impacts of land use change and climate change on groundwater resources and for determining the vulnerability of groundwater resources to contamination by human activity. Recharge rates are also important input parameters for many groundwater models—models that are essential tools for predicting the impacts of groundwater extraction. Changes in recharge rates can have important implications for groundwater resources and groundwater dependent ecosystems. Decreases in recharge rates—for example, during drought or due to climate change—can lead to declines in groundwater levels, leading to reductions in spring and river flows and adverse impacts on groundwater-dependent ecosystems. Increases in recharge rates—such as those due to urban developments, land clearance or the development of irrigated agriculture—are often linked to rising groundwater levels and to groundwater flooding and the development of land and river salinity. In arid regions, increases in recharge can cause leaching of salts stored in deep unsaturated profiles, which can increase groundwater salinity. Quantifying rates of recharge and the timescale between changes in land use and changes in groundwater recharge are key to predicting impacts on groundwater systems. This book begins by describing the relevant recharge processes, some of the principal methods for estimating recharge, and how the recharge rate is affected by rainfall, snowmelt, evapotranspiration, soil type and land use. It also examines the spatial and temporal variability of recharge, and the spatial and temporal scales at which recharge can be measured. Case studies from around the globe are presented to illustrate the diversity of approaches used to understand and quantify recharge processes. The final chapter discusses climate change impacts on recharge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".