40-Years of Lake Urmia Restoration Research: Synthesis and Next Steps

Lake Urmia’s desiccation and recent nascent recovery have garnered international and Iranian attention. Lake restoration at this scale requires integration across many sciences, technology, engineering, management, and governance topics. Here, we synthesized 544 peer-reviewed articles on Lake Urmia indexed in the Scopus database, answered nine restoration questions of scientific and popular interest, and recommended next steps for consequential lake restoration. We find: (1) research on diverse topics is fragmented and needs more integration. (2) Ecological and limnological studies have mostly focused on salinity, Artemia, and Flamingos. (3) Dust from the dry lakebed and nearby regions has negatively impacted human health. (4) Most research seeks to restore the lake to a single, uniform level of 1274.1 m thought to recover Artemia. (5) The lake’s north and south arms have different chemical and physical properties but researchers disagree on how newly breaching the causeway that separates the arms will impact salinities, evaporation, and ecosystems. (6) Expanding irrigated agriculture, dam construction, and mismanagement had a larger impact on lake decline than temperature increases and precipitation decreases. (7) The Iranian government’s 5-year recovery effort helped raise lake level about 1 m and immobilize lakebed dust. (8) Only one study publicly shared data, and only three studies described engagement with stakeholders or managers. (9) Numerous suggestions to improve economic conditions, work with farmers, or change farmer-government processes require varying effort and most still require implementation. We see next steps for lake recovery to monitor ungauged or poorly characterized water flows throughout the basin; develop alternative livelihoods beyond agriculture; describe the entire food web that supports migratory birds; manage for diverse ecosystem objectives and their associated lake levels; adapt basin water management to available water and lake evaporation; build capacity to share data, models, and code; train researchers in data-sharing tools and best practices; and better connect research topics, researchers, stakeholders, and managers. All of our findings and next steps encourage Lake Urmia managers to extend restoration efforts beyond five years and cultivate more public support

Probabilistic modeling framework for flood risk assessment: A case study of Poldokhtar city

Study region: Poldokhtar City is located on the bank of the Kashkan river in Iran. Study focus: This study presents a probabilistic modeling framework for flood risk assessment using Monte Carlo simulations. We developed a Machine Learning (ML)-based flood depth prediction model for Poldokhtar city using the Least Squares Support Vector Machine. HEC-RAS was utilized for 2D flood modeling, and its performance was evaluated by comparing simulated and remote sensing-derived flood extent maps. The simulated results were used to develop a surrogate ML-based model that predicts flood depth maps. Finally, we used this model to estimate the flood risk of Poldokhtar city as a combination of hazard, exposure, and vulnerability for 10000 flood scenarios. New hydrological insights for the region: The mean annual flood damage of the city based on the proposed framework is US$1177034, which is about three times lower than that calculated using the simplified method used in the classical risk analysis (US$ 3455400). Buildings near the floodwalls of the river and in the southwestern parts of the city have higher mean flood losses than those in other areas. Risk index frequencies of buildings reveal the most at-risk zones in the city, where there have been building constructions without considering flood hazards. The proposed framework would be of use to stakeholders such as policymakers to develop effective flood management strategie

Comparative Evaluation of Statistical and Mechanistic Models of <i>Escherichia coli</i> at Beaches in Southern Lake Michigan

Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures