Anzali Wetland Crisis: Unraveling the Decline of Iran's Ecological Gem

Journal of Geophysical Research Atmospheres, Journal Year: 2024, Volume and Issue: 129(4)

Published: Feb. 12, 2024

Abstract The wetland loss rate in Iran is faster than the global average. Comprehending shrinkage Iranian wetlands and identifying underlying drivers of these changes essential for safeguarding their ecosystems' health services. This study proposes a novel gray‐box modeling framework to quantify effects climate change anthropogenic activities on wetlands, by combining process‐based machine learning models. developed model utilized project Anzali coastal simulating complex interaction between meteorological, hydrological, sea water level characteristics, surface area. Our aggregates Soil Water Assessment Tool model, 12 General Circulation Models Coupled Model Intercomparison Project Phase 6, Landsat imagery, Long Short‐Term Memory till 2100. A comprehensive range Land Use/Cover scenarios are analyzed. results show that will seasonally desiccate 2058, mainly due increasing air temperature, reduction precipitation inflow, excessive sediment loading wetland, decline Caspian Sea level. For optimistic scenarios, where no considered, gradually diminish become seasonal waterbody outcomes this highlight desiccation has profound implications regional‐scale ecological balance, ecosystem function, public health, local economy. Robust environmental interventions sustainable development strategies urgently needed mitigate detrimental impacts wetland.

Language: Английский

---

Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems

Environmental Pollution, Journal Year: 2024, Volume and Issue: 355, P. 124242 - 124242

Published: May 27, 2024

Language: Английский

---

Environmental Controls on the Conversion of Nutrients to Chlorophyll in Lakes

Water Research, Journal Year: 2025, Volume and Issue: 274, P. 123094 - 123094

Published: Jan. 4, 2025

Language: Английский

---

Predicting Chlorophyll-a Concentrations in the World’s Largest Lakes Using Kolmogorov-Arnold Networks

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

Accurate prediction of chlorophyll-a (Chl-a) concentrations, a key indicator eutrophication, is essential for the sustainable management lake ecosystems. This study evaluated performance Kolmogorov-Arnold Networks (KANs) along with three neural network models (MLP-NN, LSTM, and GRU) traditional machine learning tools (RF, SVR, GPR) predicting time-series Chl-a concentrations in large lakes. Monthly remote-sensed data derived from Aqua-MODIS spanning September 2002 to April 2024 were used. The based on their forecasting capabilities March August 2024. KAN consistently outperformed others both test forecast (unseen data) phases demonstrated superior accuracy capturing trends, dynamic fluctuations, peak concentrations. Statistical evaluation using ranking metrics critical difference diagrams confirmed KAN's robust across diverse sites, further emphasizing its predictive power. Our findings suggest that KAN, which leverages KA representation theorem, offers improved handling nonlinearity long-term dependencies data, outperforming grounded universal approximation theorem algorithms.

Language: Английский

---

Pivotal role of snow depth, local atmospheric conditions, and large-scale climate signals on ice thinning in Finnish lakes

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 966, P. 178715 - 178715

Published: Feb. 1, 2025

Language: Английский

---

Impact of seasonal climate variability on constructed wetland treatment efficiency

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 72, P. 107350 - 107350

Published: March 13, 2025

Language: Английский

---

Human health risk of nitrate in groundwater of Tehran–Karaj plain, Iran

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 3, 2024

Groundwater pollution by nitrate has is a major concern in the Tehran-Karaj aquifer, Iran, where wells provide up to 80% of water supply for population more than 18 million-yet detailed human health risks associated with are unknown due lack accessible data adequately cover aquifer both place and time. Here, using rich dataset measured annually 75 wells, we mapped non-carcinogenic risk between 2007 2018, window most extensive anthropogenic activities this region. Nitrate concentration varied from ~ 6 150 mg/L, around three times greater standard level drinking use, i.e. 50 mg/L. Samples nitrate, which mainly located eastern parts study region, threatened children's health, vulnerable age group, almost all years during period. Our findings revealed that number samples positive adults decreased (17 wells) 2018 (6 wells). Although hypothesized unsustainable agricultural practices, growing population, increased industrial could have improved sanitation infrastructures helped prevent intensification compilation beneficial local authorities understand high-risk zones formulation policy actions protect people who use groundwater other purposes densely populated

Language: Английский

---

Thermal stratification and mixing of dam reservoirs in Iran

Watershed Ecology and the Environment, Journal Year: 2024, Volume and Issue: 6, P. 138 - 145

Published: Jan. 1, 2024

Although numerical water quality models offer valuable insights into thermal stratification (TSn) and mixing dynamics in lakes, they are often resource time consuming, limiting their applications for investigating a large number of lakes over wide geographical area. An alternative approach is using well-known classification systems, which require minimum data to provide acceptable information on TSn patterns lakes. This study investigates the regimes 198 dam reservoirs located Iran, Lewis's method analysis. The results highlight that all investigated Iran can be represented by six out eight possible classifications. majority northeastern categorized as "warm monomictic". For north northwest regions, classes observed. However, southern part only "continuous warm polymictic", monomictic", "discontinuous cold polymictic" types located. Our findings reveal 35.4%, 21.2%, 17.2%, 13.1%, 6.6%, 5.6% were classified "dimictic", respectively. authorities with initial further in-depth studies decision-making management enhancement strategies country.

Language: Английский

---

Human-induced N-P imbalances will aggravate GHG emissions from lakes and reservoirs under persisting eutrophication

Water Research, Journal Year: 2025, Volume and Issue: 276, P. 123240 - 123240

Published: Feb. 2, 2025

Language: Английский

---

Exploring the Impact of Land Use Scales on Water Quality Based on the Random Forest Model: A Case Study of the Shaying River Basin, China

Water, Journal Year: 2024, Volume and Issue: 16(3), P. 420 - 420

Published: Jan. 27, 2024

Optimizing the land use structure is one of most effective means improving surface water aquatic environment. The relationship between patterns and quality complex due to influence dams sluices. To further investigate impact on in different basins, we Shaying River as an example, which a typical tributary Huai Basin. Utilizing 2020 data monitoring from two periods, this study employs GIS spatial analysis, Random Forest Model, redundancy Partial Least-Squares Regression quantitatively explore how different-scale buffer zone quality. key findings include: (1) notable seasonal differences indicators within basin. Water Quality Index (WQI) significantly better non-flood season compared flood season, with deteriorating towards lower reaches. Key affecting include dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), turbidity (Tur) NH3-N, permanganate index (CODMn), electrical conductivity (EC) season. (2) Cultivated construction are main uses sub-basin was identified scale for River. (3) (PLSR) analysis revealed that cultivated land, grass primary types influencing changes, PLSR model during lands show positive correlation indicators, while forest bodies, grasslands correlate positively DO negatively other indicators. underscores rational planning crucial enhancing

Language: Английский

---