Time series and stochastic analyses to study the hydrodynamic characteristics of karstic aquifers
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Bibliographic record
Abstract
Abstract The regional study of hydrodynamic characteristics of karstic aquifers is challenging because of the great variety of lithology and the structural complexity found in carbonate formations. In order to improve this situation, a combined approach of time series and stochastic analyses was adopted to assess the hydrodynamic behaviour of the karstic aquifers. To achieve this, daily flow rates of 20 springs were taken from the 11 most significant aquifer units of the Basque Country. The results demonstrate the presence of memory effects, which modulated the input rainfall for short‐, medium‐ and long‐term storage capacity, resulting in hydrodynamic properties such as system memory, response time and mean delay between input and output. They reflect the storage and the manner in which these are filled and emptied, thus indicating the karstification of the aquifer. Likewise, the hydrodynamic and hydraulic classification obtained from the stochastic analysis provides a complementary approach to characterize the hydraulic behaviour of the studied karstic aquifers. The discussed examples indicate that this approach provides an excellent method to research hydrological karst systems. It is also shown that the use of hydrologic time series, alone, does not lead to a satisfactory classification of the hydrodynamic characteristics. Therefore, the general approach to hydrological regionalization in karst areas should take into account the structural complexity, heterogeneity of the lithology and the degree of karstification. Only in this case will the regionalization be physically founded, leading to a regional understanding of the hydrodynamic characteristics and flow conditions in a karst aquifer. Copyright © 2009 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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