An ecohydrological journey of 4500+ years reveals a surprisingly stable precipitation-aquifer recharge relation in the Jerusalem region
Bibliographic record
Abstract
The analyses of ecosystem response to climatic variability have been primarily concentrated on the last decades, due mainly to the lack of long-term meteorological records. Here, we assessed long-term precipitation-aquifer recharge dynamics in the Jerusalem region by exploring a unique 4500 years reconstructed annual precipitation time series (Morin et al 2019) and proxy information on air temperature, solar radiation, and atmospheric CO2 concentration [CO2]. We combined these data to reconstruct continuous hourly time series of climatic variables from 2500 B.C. to present using a weather generator model. The reconstructed climatic variables were then used to force the T&C mechanistic ecohydrological model (Fatichi and Pappas, 2017). Simulation results quantified the change in groundwater recharge, a key variable for water resource management in the region, which is simulated as deep drainage from the soil profile. For the recent years, modeled vegetation dynamics were evaluated with remote sensing observations of Leaf Area Index (LAI) while modeled recharge was validated with observed discharge from a number of local springs. The 4500 years of simulations revealed that groundwater recharge was strongly affected by precipitation not only at the annual scale, as expected, but also by a multi-decadal average, suggesting an important memory effect of soil moisture conditions on recharge. Almost the entire variability in groundwater recharge over 4500 years was explained by precipitation alone, with minor effects of temperature and [CO2], which both displayed significant changes in the last 50 years. The compensating biophysical and ecophysiological effects of [CO2] increase on plants could explain this pattern: while an increase in [CO2] stimulates productivity and LAI, increasing also evapotranspiration (ET) and decreasing recharge, it also improves water use efficiency, thus largely cancelling the aforementioned effect on ET. A sensitivity analysis to expected future levels of [CO2] and temperature clearly showed that elevated CO2 contributes to maintain current groundwater recharge values also in the future by closing stomata. However, a +2-3°C air temperature increase could reduce groundwater recharge of 30-40% due to enhanced ground evaporation and evaporation from interception, but also because of larger transpiration due to higher vapor pressure deficit, despite an enhanced plant water stress. The link between groundwater recharge and precipitation in the Jerusalem region has been very stable in the last 4500 years, but this stability is jeopardized in a warmer future, with potentially strong implications for water resources management. Morin, E., Ryb, T., Gavrieli, I., & Enzel, Y. (2019). Mean, variance, and trends of Levant precipitation over the past 4500 years from reconstructed Dead Sea levels and stochastic modeling. Quaternary Research, 91(2), 751-767. doi:10.1017/qua.2018.98 Fatichi S., and C. Pappas (2017). Constrained variability of modeled T:ET ratio across biomes. Geophysical Research Letters. 44(13), 6795-6803, doi:10.1002/2017GL074041
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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 | 0.001 |
| 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".