A remote sensing tool for near real-time monitoring of harmful algal blooms and turbidity in reservoirs

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R² values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R² values of 0.29, 0.51, and 0.46 and SVR had R² values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality.     

A systematic review of animal personality in conservation science

Although animal personality research may have applied uses, this suggestion has yet to be evaluated by assessing empirical studies examining animal personality and conservation. To address this knowledge gap, we performed a systematic review of the peer-reviewed literature relating to conservation science and animal personality. Criteria for inclusion in our review included access to full text, primary research articles, and relevant animal conservation or personality focus (i.e., not human personality studies). Ninety-two articles met these criteria. We summarized the conservation contexts, testing procedures (including species and sample size), analytical approach, claimed personality traits (activity, aggression, boldness, exploration, and sociability), and each report's key findings and conservation-focused suggestions. Although providing evidence for repeatability in behavior is crucial for personality studies, repeatability quantification was implemented in only half of the reports. Nonetheless, each of the 5 personality traits were investigated to some extent in a range of conservations contexts. The most robust studies in the field showed variance in how personality relates to other ecologically important variables across species and contexts. Moreover, many studies were first attempts at using personality for conservation purposes in a given study system. Overall, it appears personality is not yet a fully realized tool for conservation. To apply personality research to conservation problems, we suggest researchers think about where individual differences in behavior may affect conservation outcomes in their system, assess where there are opportunities for repeated measures, and follow the most current methodological guides on quantifying personality.     

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.     

Investigation of long-term river water quality variations using different urbanization indices and assessment of common scientific perspectives of urbanization on water quality

This study investigated the water quality variation spanning 30 years (1986–2017) in 16 catchments of Hong Kong against different urbanization indices, namely, built area fraction; population; and product of population and built area fraction. Pearson correlations of three different periods of time (1988–1990, 1998–2000, and 2015–2017) indicated that water quality trends were dependent on the urbanization index. Total solids, nitrite-nitrogen, total phosphorus, electrical conductivity, dissolved oxygen, and flow rate had significant deteriorative trends (Pearson r > 0.5 and p < 0.05) with population and product of built area and population. Results also interpreted that built area fraction and product of built area and population were the worst and best indices that represented urbanization and/or its impacts, respectively. Mann-Kendall test for the entire 30 year period showed that water quality had improved with time with respect to certain water quality parameters (e.g., dissolved oxygen, ammoniacal nitrogen and total suspended solids). The results portrayed that although the urbanization of catchments had increased with time, the river water quality with respect to many parameters showed signs of improvement and the legislative measures implemented seemed to be effective in controlling pollution.     

Inexpensive substrate sampler for macroinvertebrates

Assessment of aquatic macroinvertebrates is a critical component of many watershed monitoring programs and passive samplers are often used to collect long-term site data, especially in environments where active sampling is not possible. However, standard passive samplers can be expensive and lost in extreme conditions. We developed a sampler using plastic soda bottles (PSB) filled with river rock and compared its effectiveness with standard Hester-Dendy samplers in both lotic and lentic environments. Abundance, taxa richness, and macroinvertebrate composition showed no significant differences between sampler types in either habitat type. PSB samplers, which can be constructed for less than one dollar each, collected the same number of organisms and represented the same diversity as Hester-Dendy devices that cost around $38 each. In studies where funds are limited, PSB samplers appear to be suitable for passive monitoring.     

Correction to: Impact of precipitation disparity on groundwater fluctuation in a semi-arid region (Vellore district) of southern India using geospatial techniques

Environmental Science and Pollution Research - A Correction to this paper has been published: https://doi.org/10.1007/s11356-021-13406-7     

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.     

Carbon Emission from the Surface of Coarse Woody Debris in Korean Pine Forests of Southern Primorye

Carbon dioxide fluxes from the surface of coarse woody debris (CWD) have been measured in Korean pine forests of the southern Sikhote-Alin mountain range. The seasonal dynamics of oxidative conversion of CWD carbon have been evaluated, and average values of the CO₂ emission rate have been determined for CWD fragments of three tree species at different stages of decomposition. The degree of decomposition is an important factor of spatial variation in CO₂ emission rate, and temporal variation in this parameter is adequately described by an exponential function of both CWD temperature and air temperature (R² = 0.65–0.75).     

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.