Unraveling the hydrological dynamics of Lake Urmia: A comprehensive analysis of the impact of climatic changes and agricultural water extraction on lake level decline
Bibliographic record
Abstract
Lake Urmia, one of the largest hypersaline lakes on earth, known for its unique biodiversity, has experienced a profound and alarming decline in water levels over the last two decades, posing a huge threat to the lake's complex ecosystems. The causes of this decline are subject to controversy and vary between blaming mismanagement of water resources and attributing it to climate change. In order to find out the reasons for the drying up of Lake Urmia, we have conducted a series of studies to quantify the water balance components of Lake Urmia and analyze their temporal evolution and interaction over the last five decades. These studies encompass various methods, including the development of an improved bathymetric model using remote sensing data (Schröder et al., 2022), laboratory experiments to estimate the evaporation of the dried-up lake bed (Darehshouri et al., 2020, 2023) as well as setting up a water balance model, accompanied by a statistical analysis of lake inflow and meteorological variables (Schulz et al., 2020). Our results show that the fluctuations in the water levels of Lake Urmia during the study period are mainly related to weather conditions. Nevertheless, scenario simulations also revealed that agricultural water extraction, which has even exceeded the residual lake inflow in recent years, is also a decisive factor. The influence of irrigation water withdrawal on the volume of the lake can thus either strengthen the stability of the lake or accelerate its collapse. This differentiated understanding is essential for informed decision-making and sustainable management strategies to preserve or restore the ecological functioning of Lake Urmia.Darehshouri, S., Michelsen, N., Schüth, C., and Schulz, S.: A low‐cost environmental chamber to simulate warm climatic conditions, Vadose Zone Journal, 19, https://doi.org/10.1002/vzj2.20023, 2020.Darehshouri, S., Michelsen, N., Schüth, C., Tajrishy, M., and Schulz, S.: Evaporation from the dried-up lake bed of Lake Urmia, Iran, Science of The Total Environment, 858, 159960, https://doi.org/10.1016/j.scitotenv.2022.159960, 2023.Schröder, T., Hassanzadeh, E., Darehshouri, S., Tajrishy, M., and Schulz, S.: Satellite based lake bed elevation model of Lake Urmia using time series of Landsat imagery, Journal of Great Lakes Research, 48, 1710–1717, https://doi.org/10.1016/j.jglr.2022.08.016, 2022.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, Scientific Reports, 10, 236, https://doi.org/10.1038/s41598-019-57150-y, 2020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".