Towards harmonized standards for freshwater biodiversity monitoring and biological assessment using benthic macroinvertebrates

Monitoring programs at sub-national and national scales lack coordination, harmonization, and systematic review and analysis at continental and global scales, and thus fail to adequately assess and evaluate drivers of biodiversity and ecosystem degradation and loss at large spatial scales. Here we review the state of the art, gaps and challenges in the freshwater assessment programs for both the biological condition (bioassessment) and biodiversity monitoring of freshwater ecosystems using the benthic macroinvertebrate community. To assess the existence of nationally- and regionally- (sub-nationally-) accepted freshwater benthic macroinvertebrate protocols that are put in practice/used in each country, we conducted a survey from November 2022 to May 2023. Responses from 110 respondents based in 67 countries were received. Although the responses varied in their consistency, the responses clearly demonstrated a lack of biodiversity monitoring being done at both national and sub-national levels for lakes, rivers and artificial waterbodies. Programs for bioassessment were more widespread, and in some cases even harmonized among several countries. We identified 20 gaps and challenges, which we classed into five major categories, these being (a) field sampling, (b) sample processing and identification, (c) metrics and indices, (d) assessment, and (e) other gaps and challenges. Above all, we identify the lack of harmonization as one of the most important gaps, hindering efficient collaboration and communication. We identify the IUCN SSC Global Freshwater Macroinvertebrate Sampling Protocols Task Force (GLOSAM) as a means to address the lack of globally-harmonized biodiversity monitoring and biological assessment protocols.

Microplastic pollution in stream sediments discharging from Türkiye's eastern Black sea basin

Rivers are the principal route for terrestrial microplastics to reach the marine environment. The Black Sea exhibits a notable representation because it has a drainage zone almost six times the surface area and is semi-closed, meaning that microplastics tend to gather there. To mitigate MP pollution, it is necessary to identify the contamination sources and then raise public awareness. Thus, the current study focused on the MP presence in the sediment of streams running into the SE Black Sea. 594 MPs were observed in sediment samples from 16 stations along the 350 km coastline. The abundance of MP was higher, particularly in streams that pass through locations with high tourism and industrial activity levels. Detected MPs ranged between 0.1 and 5 mm, while the overall density was smaller than 1 mm. Fragments and fiber MPs were regularly detected, although the presence of films was rarely recorded. The polymer structures that were most commonly observed in the analyzed pollutants were PET and PE. The current study uncovered MP contamination in stream sediments originating from Türkiye's Eastern Black Sea basin and might be a baseline work for future inland water studies.

Investigation of heavy metals in tissues and habitats of three edible frogs from Türkiye

The soil, water, and organisms have been contaminated by heavy metals due to human activities and industrialization, which has produced a major environmental problem that has a deleterious effect on human health and food quality. Frogs, one of the good bioindicators for environmental pollution, are also among the alternative essential protein sources for humans. In Türkiye, three of these frogs are edible: Pelophylax ridibundus, Pelophylax bedriagae, and Pelophylax caralitanus, also known as Anatolian water frogs. Hence, to assess the possible health risks that might result from consuming frog legs in addition aquatic habitat of Anatolian water frogs, the water, sediment, and frog tissue samples (muscle and liver) were obtained from 11 different provinces covering all regions of Türkiye and analyzed to determine Cd, Cu, Cr, Zn, Pb, and As concentrations. The results revealed considerable variations in heavy metal concentrations among frog tissues, influenced by the sampling sites and species (ANOVA: p < 0.05). The Estimated Daily Intake (EDI) values, calculated based on the average serving size, were also lower than the Provisional Tolerable Daily Intake (PTDI) levels for adult consumers. Furthermore, the study computed the Target Hazard Quotient (THQ) values for heavy metals, all of which were below the critical value of 1, indicating that consuming the hind leg muscles from these frog species would not pose an adverse health risk for humans.

A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data

Chlorophyll-a (Chl-a) concentration is a reliable indicator of phytoplankton biomass and eutrophication, especially in inland waters. Remote sensing provides a means for large-scale Chl-a estimation by linking the spectral water-leaving signal from the water surface with in situ measured Chl-a concentrations. Single-sensor images cannot meet the practical needs for long-term monitoring of Chl-a concentrations due to cloud cover and satellite operational lifetimes. However, quantifying long-term inland water Chl-a concentrations using multi-source remote sensing data remains a problem, as improper input of satellite reflectance products will affect the accuracy of Chl-a over inland waters, as well as existing models cannot meet the need for multi-source remote sensing data to retrieve high precision Chl-a. To explore these problems towards a solution, four reflectance data derived from Ocean and Land Colour Instrument (OLCI), MultiSpectral Instrument (MSI), and Operational Land Imager (OLI) were evaluated against in situ measurements of Erhai Lake. Reflectance data from these sensors were assessed to determine their consistency. Results indicate that R_rhos products (i.e., surface reflectance, a semi-atmospheric correction reflectance) that controlled for the atmospheric diffuse transmittance were highly correlated with the measured reflectance values. The in situ reflectance also confirmed the higher fidelity of satellite reflectance in the green-red band. Subsequently, a new extreme gradient boosting (XGB) model applied to multi-source remote sensing data is proposed to estimate long-term inland water Chl-a concentrations. Comparative experiments showed the XGB model with R_rhos products outperformed other solutions, providing accurate estimates for daily, monthly, and long-term trends in Erhai Lake. The XGB model was finally processed 3954 R_rhos reflectance data derived from OLCI, ENVISAT Medium Resolution Imaging Spectrometer (MERIS), MSI, and OLI sensors, mapping Chl-a concentrations in Erhai Lake over a 20-year period. This study could serve as a reference for the long-term Chl-a monitoring using multi-source remote sensing data to support inland lake management and future water quality evaluation.

Long short-term memory models of water quality in inland water environments

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data. It is the most widely applied network used to predict the time series of water quality variables. First, we reviewed applications of a standalone LSTM and discussed its calculation time, prediction accuracy, and good robustness with process-driven numerical models and the other machine learning. This review was expanded into the LSTM model with data pre-processing techniques, including the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method and Synchrosqueezed Wavelet Transform. The review then focused on the coupling of LSTM with a convolutional neural network, attention network, and transfer learning. The coupled networks demonstrated their performance over the standalone LSTM model. We also emphasized the influence of the static variables in the model and used the transformation method on the dataset. Outlook and further challenges were addressed. The outlook for research and application of LSTM in hydrology concludes the review.

Agricultural ditches are hotspots of greenhouse gas emissions controlled by nutrient input

Agricultural ditches are pervasive in agricultural areas and are potential greenhouse gas (GHG) hotspots, since they directly receive abundant nutrients from neighboring farmlands. However, few studies measure GHG concentrations or fluxes in this particular water course, likely resulting in underestimations of GHG emissions from agricultural regions. Here we conducted a one-year field study to investigate the GHG concentrations and fluxes from typical agricultural ditch systems, which included four different types of ditches in an irrigation district located in the North China Plain. The results showed that almost all the ditches were large GHG sources. The mean fluxes were 333 μmol m-2 h-1 for CH4, 7.1 mmol m-2 h-1 for CO2, and 2.4 μmol m-2 h-1 for N2O, which were approximately 12, 5, and 2 times higher, respectively, than that in the river connecting to the ditch systems. Nutrient input was the primary driver stimulating GHG production and emissions, resulting in GHG concentrations and fluxes increasing from the river to ditches adjacent to farmlands, which potentially received more nutrients. Nevertheless, the ditches directly connected to farmlands showed lower GHG concentrations and fluxes compared to the ditches adjacent to farmlands, possibly due to seasonal dryness and occasional drainage. All the ditches covered approximately 3.3% of the 312 km2 farmland area in the study district, and the total GHG emission from the ditches in this area was estimated to be 26.6 Gg CO2-eq yr-1, with 17.5 Gg CO2, 0.27 Gg CH4, and 0.006 Gg N2O emitted annually. Overall, this study demonstrated that agricultural ditches were hotspots of GHG emissions, and future GHG estimations should incorporate this ubiquitous but underrepresented water course.

Analysis of spatiotemporal variation in dissolved organic carbon concentrations for streams with cropland-dominated watersheds

It remains a challenge to understand how dissolved organic carbon (DOC) is cycled from farmlands to rivers due to the complex interaction between farming practices, the baseflow hydrology of predominantly flat lowlands, and seasonal environmental influences such as snowpack. To address this, field DOC concentrations were measured monthly throughout the year at sub-basin scales across the Chippewa River Watershed, which falls within the Corn Belt of the Midwestern United States. These DOC dynamics in stream water from croplands were benchmarked against the data sampled from hilly forested areas in the Connecticut River Watershed. The Soil Water Assessment Tool (SWAT) simulation was applied to provide potential predictive variables associated with daily baseflow. Our study outlines a framework using the combination of primary field data, hydrological modeling, and knowledge-based reclassification of Land Use/Land Cover (LULC) data to analyze the viability of modeling the spatial and temporal variations of cropland stream DOC concentrations. Calibration of the SWAT model resulted in the overall daily Nash-Sutcliffe model efficiency coefficient (NSE) of 0.67 and the corresponding R² = 0.89. Our main results show: 1) baseflow DOC concentrations from croplands were substantially higher throughout the year relative to other landcover areas, especially for spring runoff/snowmelt scenarios, 2) an empirical analysis explained ~82 % of the spatial gradient of annual mean observed DOC concentrations, and 3) with the addition of hydrological simulated variables, a linear model explained ~81 % of monthly and 54 % of daily variations of observed DOC concentrations for cropland sub-basins. Our study identified key factors regulating the spatiotemporal DOC concentrations in cropland streamflow; the contribution here promotes to strengthen future analytical models that link watershed characteristics to carbon cycling processes in a large freshwater ecosystem.

Multi-temporal analysis of inland water level change using ICESat-2 ATL-13 data in lakes and dams

Accurate and frequent monitoring of inland water body levels is essential for water sources and their ecological protection and management. Water bodies such as natural lakes and manmade dams provide an ecological environment for endangered animals and endemic plants and serve as irrigation and water source for human activities. However, in situ measurements or fixed observation stations are not always available for small to large water bodies. Here, we investigate water level changes at a regional level where endangered endemic species live in the Ramsar site, a nationally protected park, and inland waters using the National Aeronautics and Space Administration's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) satellite launched in 2018. Most of the inland water bodies show high R² values ranging from 0.28 to 0.99 and the root mean square error values are widely distributed lower than 1 m. This study shows the potential and limitations of the new altimetry data source ICESat-2 ATL13 product for monitoring the long-term behavior of inland water reservoirs for sustainable monitoring.

Dynamics and controls of inland water CH4 emissions across the Conterminous United States: 1860-2019

Inland waters (rivers, lakes, and reservoirs) have been recognized as hotspots of methane (CH₄) emissions. However, the magnitude and spatiotemporal pattern of CH₄ emissions and their underlying mechanisms remain largely unknown due to a lack of process-based quantification of CH₄ production, consumption, and evasion within the aquatic ecosystem. Here we developed a process-based aquatic CH₄ module within the framework of the Dynamic Land Ecosystem Model (DLEM) to explicitly simulate inland water carbon fluxes and the associated CH₄ processes. We further applied this model to assess the inland-water CH₄ emissions across the conterminous United States (CONUS) as affected by the climate variability, land use, fertilizer nitrogen (N) application, atmospheric N deposition, and rising atmospheric CO₂ concentration during 1860-2019. The inland water CH₄ emissions across the CONUS had doubled from the 1860s (1.65±0.18 Tg CH₄-C∙yr^-1) to the 2010s (3.73±0.36 Tg CH₄-C∙yr^-1). In the 2000s, inland water accounts for 8% of the regional CH₄ budget that offsets 11∼14% of the terrestrial C uptake across the CONUS. Our study showed that the small headwater streams (1^st -3^rd order) account for 49% of the diffusive CH₄, and reservoirs constitute 50% of the ebullitive CH₄ emissions during the 2010s. Climate change and variability played a dominant role in the increased CH₄ emissions from rivers and lakes. This study implies that effective mitigation strategies to reduce CH4 emissions should pay much attention to global climate change and headwater stream management.

Short-term rainfall limits cyanobacterial bloom formation in a shallow eutrophic subtropical urban reservoir in warm season

The global increase in dominance of toxic blooms of cyanobacteria has severely impacted aquatic ecosystems and threatened human health for decades. Although it has been shown that high levels of rainfall may inhibit the growth of bloom-forming cyanobacteria, it is still unclear how cyanobacteria respond to short-term rainfall events. Based on five-year (2016-2020) high-frequency (half-week) sampling data from a shallow eutrophic urban reservoir in subtropical China, we explored the short-term effects of rainfall events on cyanobacterial biomass (CBB) by constructing generalized additive models of CBB in rainy periods during warm (April to September) and cool (December and January) months, respectively. We find evidence in support of the hypotheses that short-term rainfall events significantly reduce CBB in warm months, but the opposite response was observed in the cool months. We also highlight a difference in the factors explaining CBB decreases in warm months (precipitation, air temperature, relative humidity, dissolved oxygen and total phosphorus) compared with factors explaining the response of CBB in cool months (sunshine hours, pH and total carbon). In particular, meteorological factors (precipitation, wind speed and sunlight) might drive changes in water temperature and hydro-dynamics of the reservoir, thereby causing a rapid reduction of CBB after rainfall events in warm months. This varying response of cyanobacteria to short-term rainfall events in the shallow eutrophic subtropical reservoir may also be expected in temperate or cool lakes as climate change effects become stronger.

Accuracy assessment of remotely sensed data to analyze lake water balance in semi-arid region

Lake water level fluctuation is a function of hydro-meteorological components, namely input, and output to the system. The combination of these components from in-situ and remote sensing sources has been used in this study to define multiple scenarios, which are the major explanatory pathways to assess lake water levels. The goal is to analyze each scenario through the application of the water balance equation to simulate lake water levels. The largest lake in Iran, Lake Urmia, has been selected in this study as it needs a great deal of attention in terms of water management issues. We ran a monthly water balance simulation of nineteen scenarios for Lake Urmia from 2003 to 2007 by applying different combinations of data, including observed and remotely sensed water level, flow, evaporation, and rainfall. We used readily available water level data from Hydrosat, Hydroweb, and DAHITI platforms; evapotranspiration from MODIS and rainfall from TRMM. The analysis suggests that the consideration of field data in the algorithm as the initial water level can reproduce the fluctuation of Lake Urmia water level in the best way. The scenario that combines in-situ meteorological components is the closest match to the observed water level of Lake Urmia. Almost all scenarios showed good dynamics with the field water level, but we found that nine out of nineteen scenarios did not vary significantly in terms of dynamics. The results also reveal that, even without any field data, the proposed scenario, which consists entirely of remote sensing components, is capable of estimating water level fluctuation in a lake. The analysis also explains the necessity of using proper data sources to act on water regulations and managerial decisions to understand the temporal phenomenon not only for Lake Urmia but also for other lakes in semi-arid regions.

Behavior of Microplastics in Inland Waters: Aggregation, Settlement, and Transport

Inland waters are the main medium transporting microplastics to the ocean. Aggregation, vertical settlement, and horizontal transport will occur when microplastics enter the inland waterbodies. This paper reviews these behaviors of microplastics in inland waters and their influencing factors. The aggregation of microplastics were divided into homogeneous aggregation and heterogeneous aggregation, which are critical for the settlement of microplastics. The settlement of microplastics in inland water bodies is influenced by microplastic properties (size, density, and shapes) and environmental conditions (microorganisms, sedimental properties, hydraulic conditions, and so on). Horizontal transport of microplastics in water is influenced by hydrologic conditions, rainfall, river morphologies, dams, vegetation, etc. Future perspectives including laboratory simulations and numerical models involving multiple factors, the behaviors of degradable plastics, and the influence of hydrologic conditions have been proposed.

Assessing cyanobacterial frequency and abundance at surface waters near drinking water intakes across the United States

This study presents the first large-scale assessment of cyanobacterial frequency and abundance of surface water near drinking water intakes across the United States. Public water systems serve drinking water to nearly 90% of the United States population. Cyanobacteria and their toxins may degrade the quality of finished drinking water and can lead to negative health consequences. Satellite imagery can serve as a cost-effective and consistent monitoring technique for surface cyanobacterial blooms in source waters and can provide drinking water treatment operators information for managing their systems. This study uses satellite imagery from the European Space Agency's Ocean and Land Colour Instrument (OLCI) spanning June 2016 through April 2020. At 300-m spatial resolution, OLCI imagery can be used to monitor cyanobacteria in 685 drinking water sources across 285 lakes in 44 states, referred to here as resolvable drinking water sources. First, a subset of satellite data was compared to a subset of responses (n = 84) submitted as part of the U.S. Environmental Protection Agency's fourth Unregulated Contaminant Monitoring Rule (UCMR 4). These UCMR 4 qualitative responses included visual observations of algal bloom presence and absence near drinking water intakes from March 2018 through November 2019. Overall agreement between satellite imagery and UCMR 4 qualitative responses was 94% with a Kappa coefficient of 0.70. Next, temporal frequency of cyanobacterial blooms at all resolvable drinking water sources was assessed. In 2019, bloom frequency averaged 2% and peaked at 100%, where 100% indicated a bloom was always present at the source waters when satellite imagery was available. Monthly cyanobacterial abundances were used to assess short-term trends across all resolvable drinking water sources and effect size was computed to provide insight on the number of years of data that must be obtained to increase confidence in an observed change. Generally, 2016 through 2020 was an insufficient time period for confidently observing changes at these source waters; on average, a decade of satellite imagery would be required for observed environmental trends to outweigh variability in the data. However, five source waters did demonstrate a sustained short-term trend, with one increasing in cyanobacterial abundance from June 2016 to April 2020 and four decreasing.

Microplastics as an emerging threat to the freshwater ecosystems of Veeranam lake in south India: A multidimensional approach

In the current scenario, microplastic, as a contaminant, is becoming an ecological threat to the freshwater ecosystem. The present study attempted to determine the quality and quantity of microplastic contaminants in water and soil samples at Veeranam lake in Tamil Nadu, India. It is very important to mention that the Veeranam lake in Tamil Nadu, is a major urban water source of the capital district of Tamil Nadu. Using Van Veen grab-sampling equipment and trawl methods, the study detected the presence of microplastics in 28 sediment samples and 31 water samples from the collected samples. In addition to this, the density separation was performed with zinc chloride solution using the Sediment-Microplastic Isolation (SMI) unit. The quantum of total plastic particle present in surface water were in the range of 13-54 items/km2 with a mean value of 28 items/km2. In the case of sediment samples, the amount of total plastic particle was found in the range of 92-604 items/kg with a mean value of 309 items/kg. The abundance of microplastic particles in water and sediments in various shape, colour, and composition as in the order of nylons > polythene > fibres/PVC > fragments > foam > pellets; dominant colours as white > red > black > green > blue and yellow at the sampling sites. In term of percentage of contaminant distribution, the study found that the collected water and sediment samples deposited with polymer type of plastic particles were nylon (39%), polyethylene (23%), polystyrene (19%), polypropylene (15%), and polyvinyl chloride (4%). The research work is a baseline study for the proposed site of Veeranam lake for microplastics contamination.

Quantifying total suspended matter (TSM) in waters using Landsat images during 1984-2018 across the Songnen Plain, Northeast China

Understanding the spatiotemporal dynamics of total suspended matter (TSM) in waters is necessary to promote efficient water resource management. In our study, we have estimated the spatiotemporal pattern of TSM with the combination of time-series Landsat images and field survey. Among various remote sensing-derived parameters, the red/blue band turns to be robust and the most sensitive to the TSM from field measurements. In Songnen Plain, the mean annual TSM in 60.5% of the water bodies decreased from 1984 to 2018. The decreasing of TSM is likely due to the increasing of vegetation in the area. The TSM concentration in waters declined from April to July, and then increased from September onwards. We also found the TSM in water bodies in Songnen Plain has very high spatial variation. Our results indicated that the meteorological factors such as wind and precipitation may affect the variation of TSM. Our results demonstrate that long-term Landsat data are useful to examine TSM in inland waters. Our findings can support for water resource management under human activities and climate change.

Microplastic pollution in China's inland water systems: A review of findings, methods, characteristics, effects, and management

The pollution of marine environments and inland waters by plastic debris has raised increasing concerns worldwide in recent years. China is the world's largest developing country and the largest plastic producer. In this review, we gather available information on microplastic pollution in China's inland water systems. The results show that microplastics are ubiquitous in the investigated inland water systems, and high microplastic abundances were observed in developed areas. Although similar sampling and analytical methods were used for microplastic research in inland water and marine systems, methods of investigation should be standardized in the future. The characteristics of the detected microplastics suggest secondary sources as their major sources. The biological and ecological effects of microplastics have been demonstrated, but their risks are difficult to determine at this stage due to the discrepancy between the field-collected microplastics and microplastics used in ecotoxicological studies. Although many laws and regulations have already been established to manage and control plastic waste in China, the implementation of these laws and regulations has been ineffective and sometimes difficult. Several research priorities are identified, and we suggest that the Chinese government should be more proactive in tackling plastic pollution problems to protect the environment and fulfill international responsibilities.