Climate change or irrigated agriculture – what drives the water level decline of Lake Urmia?
Bibliographic record
Abstract
<p>Lake Urmia is one of the largest hypersaline lakes on earth with a unique biodiversity. Over the past two decades the lake water level declined dramatically, threatening the functionality of the lake’s ecosystems. There is a controversial debate about the reasons for this decline, with either mismanagement of the water resources, or climatic changes assumed to be the main cause.</p><p>During this study we gathered an extensive hydro-meteorological data set, information about the reservoirs and the lake bathymetry. This data served for a quantification of the water budget components of Lake Urmia over the last five decades. Interestingly, a comparison of the temporal patterns of the principal natural boundary conditions of streamflow (precipitation and evaporation) with the inflow to the lake revealed that the variability of the inflow can be well explained its natural drivers. With this we can show that variations of Lake Urmia’s water level during the analyzed period were mainly triggered by climatic changes.</p><p>However, under the current climatic conditions agricultural water extraction volumes are significant and often exceed the remaining surface water inflow volumes. This rather simple observation shows that something deeper needs to be dug here. Therefore, we performed a parsimonious hindcast experiment and run a set of development scenarios based on the previously developed water balance. This helped us to better quantify the human impact on the development of the water volume of Lake Urmia. We could show that changes in agricultural water withdrawal would have a significant impact on the lake volume and could either stabilize the lake, or lead to its complete collapse (Schulz et al., 2020).</p><p> </p><p><strong>References</strong></p><p>Schulz, S., Darehshouri, S., Hassanzadeh, E., Tajrishy, M. and Schüth, C.: Climate change or irrigated agriculture – what drives the water level decline of Lake Urmia, Sci. Rep., 10(1), 236, doi:10.1038/s41598-019-57150-y, 2020.</p>
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".