Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

Boosting large-scale river connectivity restoration by planning for the presence of unrecorded barriers

Conservation decisions are invariably made with incomplete data on species’ distributions, habitats, and threats, but frameworks for allocating conservation investments rarely account for missing data. We examined how explicit consideration of missing data can boost return on investment in ecosystem restoration, focusing on the challenge of restoring aquatic ecosystem connectivity by removing dams and road crossings from rivers. A novel way of integrating the presence of unmapped barriers into a barrier optimization model was developed and applied to the U.S. state of Maine to maximize expected habitat gain for migratory fish. Failing to account for unmapped barriers during prioritization led to nearly 50% lower habitat gain than was anticipated using a conventional barrier optimization approach. Explicitly acknowledging that data are incomplete during project selection, however, boosted expected habitat gains by 20–273% on average, depending on the true number of unmapped barriers. Importantly, these gains occurred without additional data. Simply acknowledging that some barriers were unmapped, regardless of their precise number and location, improved conservation outcomes. Given incomplete data on ecosystems worldwide, our results demonstrate the value of accounting for data shortcomings during project selection.     

Five-wheel framework for system-based monitoring of heavy metal pollution in rivers

Sectorial approach for monitoring heavy metal pollution in rivers has failed to report realistic pollution status and associated ecological and human health risks. The increasing spread of heavy metals from different sources and emerging risks to human and environmental health call for reexamining heavy metal pollution monitoring frameworks. Also, the sources, spread, and load of heavy metals in the environment have changed significantly over time, requiring consequent modification in the monitoring frameworks. Therefore, studies on heavy metal monitoring in rivers conducted in the last decade were evaluated for experimental designs, research frameworks, and data presentations. Most studies (∼99%) (i) lacked inclusiveness of all environmental compartments; (ii) focused on “one pollutant – one/two compartment” or sometimes “one pollutant – one compartment – one effect” approach; and (iii) remained “data-rich but information poor.” An ecological approach with integrative system thinking is proposed to develop a holistic approach for monitoring river pollution. It is visualized that heavy metal monitoring, risk analyses, and water management must incorporate tracking pollutants in different environmental compartments of a river (water, sediment, and floodplain/bank soil) and consider correlating it with riverbank land use. The systems-based pollution monitoring and assessment studies will reveal the critical factors that drive heavy metals pollutant movement in ecosystems and associated potential risks to the environment, wildlife, and humans. Also, water quality and pollution indexing tools would help better communicate complex pollution data and associated risks among all stakeholders. Therefore, integrating systems approaches in scientific- and policy-based tools would help sustainably manage the health of rivers, wildlife, and humans.     

Microwave-assisted synthesis of biodegradable and antibacterial thiophene,furan and thiazole derivatives

Environmental Chemistry Letters - The rise of bacterial resistance to common clinical antibiotics is calling for alternative techniques to synthesize antibacterial drugs with high biodegradability....     

The need for spatially explicit quantification of benefits in invasive‐species management

Worldwide, invasive species are a leading driver of environmental change across terrestrial, marine, and freshwater environments and cost billions of dollars annually in ecological damages and economic losses. Resources limit invasive‐species control, and planning processes are needed to identify cost‐effective solutions. Thus, studies are increasingly considering spatially variable natural and socioeconomic assets (e.g., species persistence, recreational fishing) when planning the allocation of actions for invasive‐species management. There is a need to improve understanding of how such assets are considered in invasive‐species management. We reviewed over 1600 studies focused on management of invasive species, including flora and fauna. Eighty‐four of these studies were included in our final analysis because they focused on the prioritization of actions for invasive species management. Forty‐five percent (n = 38) of these studies were based on spatial optimization methods, and 35% (n = 13) accounted for spatially variable assets. Across all 84 optimization studies considered, 27% (n = 23) explicitly accounted for spatially variable assets. Based on our findings, we further explored the potential costs and benefits to invasive species management when spatially variable assets are explicitly considered or not. To include spatially variable assets in decision‐making processes that guide invasive‐species management there is a need to quantify environmental responses to invasive species and to enhance understanding of potential impacts of invasive species on different natural or socioeconomic assets. We suggest these gaps could be filled by systematic reviews, quantifying invasive species impacts on native species at different periods, and broadening sources and enhancing sharing of knowledge.     

Temporal variation in total phosphorus concentrations revealed from a multidecadal monitoring program on Big Platte Lake,Michigan

Effective water quality management depends on enactment of appropriately designed monitoring programs to reveal current and forecasted conditions. Because water quality conditions are influenced by numerous factors, commonly measured attributes such as total phosphorus (TP) can be highly temporally varying. For highly varying processes, monitoring programs should be long-term and periodic quantitative analyses are needed so that temporal trends can be distinguished from stochastic variation, which can yield insights into potential modifications to the program. Using generalized additive mixed modeling, we assessed temporal (yearly and monthly) trends and quantified other sources of variation (daily and subsampling) in TP concentrations from a multidecadal depth-specific monitoring program on Big Platte Lake, Michigan. Yearly TP concentrations decreased from the late 1980s to late 1990s before rebounding through the early 2000s. At depths of 2.29 to 13.72 m, TP concentrations have cycled around stationary points since the early 2000s, while at the surface and depths ≥?18.29 concentrations have continued declining. Summer and fall peaks in TP concentrations were observed at most depths, with the fall peak at deeper depths occurring 1 month earlier than shallower depths. Daily sampling variation (i.e., variation within a given month and year) was greatest at shallowest and deepest depths. Variation in subsamples collected from depth-specific water samples constituted a small fraction of total variation. Based on model results, cost-saving measures to consider for the monitoring program include reducing subsampling of depth-specific concentrations and reducing the number of sampling depths given observed consistencies across the program period.     

Assessment of the State of Lung Lichen, <Emphasis Type="Italic">Lobaria pulmonaria</Emphasis> (L.) Hoffm., in Forest Communities with Different Times Since Disturbance in the Northeast of European Russia

Quantitative parameters of cenopopulations of Lobaria pulmonaria, an endangered cyanolichen, have been studied in spruce phytocenoses of Karelia recovering after disturbance through the successional stage of aspen stands for a period ranging from 80 to 450 years. The results show that, as the time since disturbance increases, the total number of L. pulmonaria thalli and the number of colonized substrate units increase as well, with no stabilization of these parameters being observed in the series of communities studied. The total area of thalli in the phytocenoses is restored within approximately 200 years after the last disturbance. In old-growth forests (>400 years), L. pulmonaria thalli colonize a broad spectrum of tree species in different life states, including lower branches of young spruce trees. Even if they serve as temporary substrates, this can markedly strengthen the potential of the species for further expansion within the community in case of shortage in the main phorophyte (aspen). Thus, the proportion of regressive populations decreases, while that of colonizing populations increases, which is indicative of successful reproduction of the species.     

Investigating the oil curse in OECD and Non-OECD oil-exporting economies using green measures of income

Empirical studies investigating the natural resource curse theory mostly employ cross-country and panel regression techniques subject to endogeneity bias. Also, most of these studies employ GDP in its aggregate or per-capita terms as the outcome variable in their analyses. However, the use of GDP measures of income for resource curse investigations may not portray the true incomes of resource-intensive economies. Standard national accounts treat natural resource rents as a positive contribution to income without adjusting for the value of depleted natural resource stock. This treatment often leads to a positive bias in the national income computations of resource-rich economies. Unlike previous studies, we test the robustness of the curse in the predominantly used measures of national income, GDP, by investigating the theme in genuine income measures of economic output as well. Also, the paper deviates from most empirical studies in the literature by using the Arellano–Bond difference GMM method in investigating the oil curse in OECD and Non-OECD oil-exporting economies. Additionally, we employ two alternative measures of resource intensity in our explorations; the share of oil rents in GDP and per-capita oil reserves. Our results provide evidence of the curse in Non-OECD countries employing aggregate and per-capita measures of genuine income.     

From vineyards to feedlots: a fund-flow scanning of sociometabolic transition in the Vallès County (Catalonia) 1860–1956–1999

Regional Environmental Change - We analyse the changes to agricultural metabolism in four municipalities of Vallès County (Catalonia, Iberia) by accounting for their agroecosystem funds and...