40-years of Lake Urmia restoration research: Review, synthesis and next steps.

Public concern over environmental issues such as ecosystem degradation is high. However, restoring coupled human-natural systems requires integration across many science, technology, engineering, management, and governance topics that are presently fragmented. Here, we synthesized 544 peer-reviewed articles published through September 2020 on the desiccation and nascent recovery of Lake Urmia in northwest Iran. We answered nine questions of scientific and popular interest about causes, impacts, stabilization, recovery, and next steps. We find: (1) Expansion of irrigated agriculture, dam construction, and mismanagement impacted the lake more than temperature increases and precipitation decreases. (2) Aerosols from Lake Urmia's exposed lakebed are negatively impacting human health. (3) Researchers disagree on how a new causeway breach will impact salinity, evaporation, and ecosystems in the lake's north and south arms. (4) Most researchers tried to restore to a single, uniform, government specified lake level of 1274.1 m intended to recover Artemia. (5) The Iranian government motivated and funded a large and growing body of lake research. (6) Ecological and limnological studies mostly focused on salinity, Artemia, and Flamingos. (7) Few studies shared data, and only three studies reported engagement with stakeholders or managers. (8) Researchers focused on an integration pathway of climate downscaling, reservoirs, agricultural water releases, and lake level. (9) Numerous suggestions to improve farmer livelihoods and governance require implementation. We see an overarching next step for lake recovery is to couple human and natural system components. Examples include: (a) describe and monitor the system food webs, hydrologic, and human components; (b) adapt management to monitored conditions such as lake level, lake evaporation, lake salinity, and migratory bird populations; (c) improve livelihoods for poor, chronically stressed farmers beyond agriculture; (d) manage for diverse ecosystem services and lake levels; (e) engage all segments of society; (f) integrate across restoration topics while building capacity to share data, models, and code; and (g) cultivate longer-term two-way exchanges and public support. These restoration steps apply in different degrees to other Iranian ecosystems and lakes worldwide.

Evaluating the impacts of foreshore sand and birds on microbiological contamination at a freshwater beach.

Beaches along the Great Lakes shorelines are important recreational and economic resources. However, contamination at the beaches can threaten their usage during the swimming season, potentially resulting in beach closures and/or advisories. Thus, understanding the dynamics that control nearshore water quality is integral to effective beach management. There have been significant improvements in this effort, including incorporating modeling (empirical, mechanistic) in recent years. Mechanistic modeling frameworks can contribute to this understanding of dynamics by determining sources and interactions that substantially impact fecal indicator bacteria concentrations, an index routinely used in water quality monitoring programs. To simulate E. coli concentrations at Jeorse Park beaches in southwest Lake Michigan, a coupled hydrodynamic and wave-current interaction model was developed that progressively added contaminant sources from river inputs, avian presence, bacteria-sediment interactions, and bacteria-sand-sediment interactions. Results indicated that riverine inputs affected E. coli concentrations at Jeorse Park beaches only marginally, while avian, shoreline sand, and sediment sources were much more substantial drivers of E. coli contamination at the beach. By including avian and riverine inputs, as well as bacteria-sand-sediment interactions at the beach, models can reasonably capture the variability in observed E. coli concentrations in nearshore water and bed sediments at Jeorse Park beaches. Consequently, it will be crucial to consider avian contamination sources and water-sand-sediment interactions in effective management of the beach for public health and as a recreational resource and to extend these findings to similar beaches affected by shoreline embayment.

Modeling the photoinactivation and transport of somatic and F-specific coliphages at a Great Lakes beach.

Fecal indicator organisms (FIOs), such as Escherichia coli and enterococci, are often used as surrogates of contamination in the context of beach management; however, bacteriophages may be more reliable indicators than FIO due to their similarity to viral pathogens in terms of size and persistence in the environment. In the past, mechanistic modeling of environmental contamination has focused on FIOs, with virus and bacteriophage modeling efforts remaining limited. In this paper, we describe the development and application of a fate and transport model of somatic and F-specific coliphages for the Washington Park beach in Lake Michigan, which is affected by riverine outputs from the nearby Trail Creek. A three-dimensional model of coliphage transport and photoinactivation was tested and compared with a previously reported E. coli fate and transport model. The light-based inactivation of the phages was modeled using organism-specific action spectra. Results indicate that the coliphage models outperformed the E. coli model in terms of reliably predicting observed E. coli/coliphage concentrations at the beach. This is possibly due to the presence of additional E. coli sources that were not accounted for in the modeling. The coliphage models can be used to test hypotheses about potential sources and their behavior and for predictive modeling.

Comparative Evaluation of Statistical and Mechanistic Models of Escherichia coli 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.