Identifying the environmental drivers of the distribution of carbon isotopes in global inland waters.

Pollution by heavy metals (Cu, Zn, Cd, Cr) in global inland waters poses significant threats to freshwater biodiversity and ecosystem function, yet the global patterns and drivers of heavy metals remain poorly explored. Here, assessing using 11,573 data points collected from 514 peer-reviewed publications, key advances include: (1) quantification of median concentrations in global inland waters (Cu: 8.38, Zn: 30.00, Cd: 0.53, and Cr: 7.00 μg L−1), providing global-scale reference values to contextualize local water quality assessments; (2) anthropogenic activities, temperature, actual evapotranspiration, precipitation and runoff dominantly control the concentrations of heavy metals in inland waters, and (3) global heavy metal pollution hotspots were established, revealing significantly elevated concentrations in West and South Asia and Africa, followed by South America. Our study provides a comprehensive analysis of inland water for Cu, Zn, Cd, and Cr, offering a scientific foundation for targeting pollution control in vulnerable regions under climate change.

Inland waters (streams, rivers, lakes, and reservoirs) are important sources of greenhouse gases (GHGs), including carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), to the atmosphere. Their importance has been acknowledged in the IPCC assessment reports and in the regional and global greenhouse budgets coordinated by the Global Carbon Project (GCP). In the framework of the 2nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative of the GCP, a comprehensive synthesis of existing estimates of regional to global inland water GHG emissions was conducted (Lauerwald et al., 2023a, 2023b) to support the inclusion of these emissions in (sub-)continental GHG budgets. Although that synthesis was published only two years ago, a number of new global estimates have been published since. Here, we present an updated synthesis of recent, global inland water GHG emissions estimates. Moreover, we go beyond the scope of the RECCAP2 synthesis by analyzing regional patterns in more detail, and summarizing the state of knowledge about long-term trends of inland water GHG emissions in response to changes in climate, land use, wastewater management and river damming. Based on that, we discuss how contemporary inland water GHG emissions are impacted by anthropogenic activities and how they may evolve over the 21st century.We estimate that global inland water GHG emissions have a combined warming potential of 8 (5–13) Pg CO2-eq. yr⁻¹ for a 100-year time horizon (GWP100). CO2 emissions, primarily from tropical river systems, contribute approximately three-quarters of this total, while CH4, largely from lakes and reservoirs, accounts for most of the remainder. Notably, boreal and Arctic lakes are important emitters due to their large total area, while nutrient-rich lakes and reservoirs with warmer temperatures in the mid to low latitudes exhibit the highest per-area CH4 emission rates. Contributions from N2O emissions are relatively minor.About one third of CH4 emissions and about three quarters of N2O emissions from global inland waters can be attributed to anthropogenic perturbations, primarily through eutrophication. For inland water CO2 emissions, quantification of the anthropogenic component is more complex. Empirical and modelling studies suggest that global greening also increases terrestrial carbon deliveries to inland waters, and through that, emissions of CO2 from inland waters. Moreover, changes in streamflow are an uncertain, but very important driver. Most dramatic increases are expected for inland water CH4 and N2O emissions, which are projected to strongly increase in response to global warming, while changes in nutrient loads from agricultural runoff may offset or enhance that trend.ReferencesLauerwald et al. 2023a, GBC, https://doi.org/10.1029/2022GB007657Lauerwald et al. 2023b, GBC, https://doi.org/10.1029/2022GB007658 

Microplastics (MPs) contamination in global inland waters has raised concerns recently. However, quantitative research on the abundance of MP in these environments remains limited. Here we compiled a comprehensive dataset of MPs in global inland waters from 5365 observations across 301 publications, revealing a diverse distribution of MP abundance in inland waters, ranging from 0.00 to 4,275,800.70 items m−3 (mean: 25,255.47 ± 132,808.40 items m−3). Human development index, evapotranspiration, cropland, and land surface runoff emerged as the primary factors influencing MPs levels in water. The predicting map showed particularly high MP abundance in China, but also relatively high abundance in Asia, Europe, Africa, and the eastern United States.Urgent action is needed to monitor and manage MPs in inland waters, especially the small-sized MPs (<1 mm). Implementing effective strategies to regulate plastic production and waste management is imperative for protecting freshwater ecosystems, particularly in countries with high MP pollution.

Inland waters, characterized by small scale but a large number, play an important role in the carbon budget and global carbon cycle. Colored dissolved organic matter (CDOM) is a significant indicator used for tracing dissolved organic carbon (DOC) in inland waters. Accurate remote-sensing estimation of CDOM concentration is still a challenge due to complex optical properties of inland waters. Many efforts have been made to estimate high concentration CDOM, leading to a knowledge gap in using remote sensing to estimate low concentration CDOM, which results in difficulty and uncertainty for estimating total carbon storage in global inland waters. Currently, few studies are devoted to estimating low concentration CDOM, while Landsat-8’s applicability for estimating low concentration CDOM is still unknown. In this study, two datasets, NRL_SFE and Lake_Erie, were collected to represent extremely low CDOM conditions that aCDOM (440) (the absorptions of CDOM at 440 nm) ranges 0.215–1.165 m–1, and 0.066–1.242 m–1, respectively. The best CDOM retrieval model (validation results: R2 = 0.78; RMSE = 0.161 m–1; MRE = 26.02%), aCDOM (440) = 0.483x –1.776, x = Rrs (B2)/Rrs (B4), was developed for monitoring CDOM in the two regions. Results show that Landsat-8 and the best model work well for estimating low concentration CDOM in inland waters. In addition, we have proved that Landsat-8 surface reflectance products, which are freely provided by USGS, are convenient and useful for developing remote sensing algorithm of CDOM estimation after correcting water surface reflectance. The image-derived CDOM’s spatial patterns in Lake Erie demonstrate the Landsat-8’s applicability to observe spatiotemporal variations of low concentration CDOM.

Inland waters are important sources of the greenhouse gasses (GHGs) carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) to the atmosphere. While a growing number of global estimates of inland water GHG emissions exists, the integration of inland waters into regional GHG budgets is often hampered by the lack of adequate geo-spatial datasets. Moreover, existing estimates diverge substantially, in part due to persisting uncertainties related to the size and distribution of effective inland water surface areas. &amp;#160;In the framework of the 2nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative, we synthesize existing estimates of GHG emissions from streams, rivers, lakes and reservoirs, and homogenize them with regard to underlying global maps of inland water surface areas and the effects of seasonal ice cover. We then produce estimates of inland water GHG emissions for 10 extensive land regions that are used for the regional land budgets of RECCAP2. According to our synthesis, global inland waters emit 5.6 (3.5-9.1) Pg CO2 yr-1, 101 (83-135) Tg CH4 yr-1 and 326 (254-592) Gg N2O yr-1. South American rivers contribute about one third of global inland water CO2 emissions. North-American and Russian lakes contribute together one third of global inland water CH4 emissions. Finally, North America alone contributes one fourth of global inland water N2O emissions.The global inland water emissions sum up to a global warming potential (GWP) of an equivalent emission of 13.6 (10.0-20.3) and 8.3 (5.8-12.7) Pg CO2 yr-1 at a 20- and 100-year horizon, respectively. At 100-year horizon, the contribution of CO2 dominates the GWP of global inland water GHG emissions, with rivers being the largest emitters. At the 20-year horizon, on the contrary, lakes and rivers are equally important emitters, and the contributions of CH4 to the GWP of inland water GHG emissions even exceed those of CO2. Contributions of N2O to the GWP appear to be less significant at both time horizons. Normalized to the area of the RECCAP-2 land regions, South America and South East Asia show the highest inland water emission rates in terms of GWP, dominated by riverine CO2 emissions.

ABSTRACTFreshwater ecosystems play a critical role in sustaining biodiversity, but with increasing anthropogenic disturbances in recent years, issues such as water degradation and biotic community decline have become increasingly severe. Although existing research has explored the impact of water quality factors and land use on macroinvertebrate communities, the specific mechanisms by which human activities indirectly influence macroinvertebrate diversity by altering the aquatic environment are still underexplored. Therefore, this study, on the basis of land use, aquatic environmental, and macroinvertebrate survey data, employed a Partial Least Squares Path Model (PLS‐SEM) to elucidate the mechanisms by which land use and water quality factors jointly drive changes in macroinvertebrate communities. Our results demonstrate that macroinvertebrate community structure varied significantly among disturbance levels, with biodiversity indices increasing progressively as disturbance intensity lessened. Specifically, pollution‐tolerant taxa dominated in high disturbance areas, Bellamya and Alocinma were predominant in moderate disturbance areas, whereas sensitive species were dominant in low disturbance areas. Land use explained 11.1% of the variation in diversity, about 2.6 times that of water quality factors (4.3%), indicating clear differences in their driving effects on macroinvertebrate diversity, as shown by variance partitioning analysis (VPA). According to the PLS‐SEM results, built‐up land, as the main negative factor, exerted combined effects on diversity both directly and indirectly by causing water quality deterioration. Water quality factors exhibited spatially differentiated effects: ammonia nitrogen and total nitrogen strongly inhibited diversity in high and moderate disturbance areas, whereas in low disturbance areas, total hardness and water temperature emerged as the primary positive drivers. This study elucidates the dynamic response mechanisms of macroinvertebrate communities to changes in land use and water quality, offering new perspectives for freshwater ecosystem health assessments and biodiversity conservation.

<p indent="0mm">The Beijing-Tianjin sandstorm source control project (BTSSCP) is a large and famous ecological construction project with enormous investment that aims to improve and optimize the ecological condition of Beijing-Tianjin and the surrounding areas. Since the implementation of the BTSSCP in 2000, profound changes have occurred in the region regarding land use, vegetation cover, soil, wind and water erosion, and the supply of regional ecosystem services (ESs). Changes in regional ESs are influenced not only by human activities but also by natural factors such as climate change. Distinguishing and quantifying the roles of climate change and land-use change on regional ES changes can better serve sustainable land-use management decisions at the regional and national levels. Additionally, information is needed on the economic benefits caused by sandstorm control and ecological restoration, such as the value change of the regulating ESs since the BTSSCP began, the variation in the spatial patterns of ESs and their integration, and the contribution of sand control and vegetation restoration to regional ES changes. Moreover, a landscape-level ES multifunctionality concept and measurement framework provided deep insight into the spatial supply-benefit relationship of ESs, and this information is crucial for sustainable ecosystem management and land-use planning. In this study, a set of ES indicators that represent ecological provisioning (<italic>n</italic>=2), regulating (<italic>n</italic>=4), and supporting (<italic>n</italic>=2) services at the county scale in the BTSSCP were calculated based on a field survey, Normalized difference vegetation index (NDVI) data, land-use data, meteorological data (i.e., temperature, precipitation, wind speed, and direction) and socioeconomic census data. Then, the ES multifunctionality of each county was assessed using the multiple ES landscape index (MESLI) and the ES Shannon diversity index (ESHDI). Variation partitioning was used to distinguish the contributions of land-use area composition and major climatic conditions (annual average temperature, annual precipitation, and average wind speed) to various ESs, and the MESLI and ESHDI in different periods were measured and compared. Partial least squares path modeling (PLS-PM) was applied to determine the direct and indirect effects of the BTSSCP on the regional ecological supply, ecological regulation, habitat support services and multifunctional indicators of ESs and their changes. The results showed that (1) the implementation of the BTSSCP effectively improved the regulating ESs in Beijing, Tianjin and surrounding areas and promoted the overall improvement of the comprehensive benefits of regional ESs while effectively improving the integration and diversity of ESs in the core areas of wind and sand management. The MESLI increased in 94.8% of all counties in 2015 compared to 2000. (2) Both land-use composition and climatic factors had impacts on the regional ES provisions, but the influence of land use on the regulating and provisioning ESs was significantly higher than that of climatic factors. The direct effect of land use on ES provision, regulation, and support was enhanced, the multifunctionality of regional ESs was dramatically improved, and the role of climatic factors markedly declined as the project proceeded. (3) The economic benefits of the BTSSCP were positive, with a 415300 yuan/km² increase in the content of regulating ESs, and 82.7% of counties had reduced wind erosion after BTSSCP implementation, which indicated that the comprehensive economic benefits of BTSSCP implementation were significant. These results provide a scientific basis for evaluating the benefits of the BTSSCP and serve as a reference for developing new national management strategies.

ABSTRACTThe Moderate Resolution Imaging Spectroradiometer (MODIS) has the advantage of providing continuous, global, near-daily spatial measurements, and has greatly aided in understanding physical, optical, and biological processes in the global ocean biosphere. However, little research has been implemented for the remote-sensing monitoring of global inland waters. One important factor is that there is no operational atmospheric correction method designed for global inland waters. The MODIS surface reflectance product (MOD09) provides surface reflectance data for land at the global scale, but it does not offer accurate atmospheric correction over inland waters because of the constraints of its primary correction algorithm. The purpose of this article is to provide a simple and operational correction method for the MOD09 product to retrieve the water-leaving reflectance for large inland waters larger than 25 km2. The correction method is based on an analysis of additive noises in MOD09 data over inland waters and on the adoption of two assumptions. Field-measured data collected in three typical inland waters in China were used to assess the performance of the correction method to ensure its applicability for waters in different conditions. The results show acceptable agreement with field data over the three inland waterbodies, with a mean relative error of 17.1% in visible bands. Our study demonstrates that the MOD09 correction method is moderately accurate when compared with the optimal method for specific waterbodies, but it has the potential for use in operational data-processing systems to derive water-leaving reflectance data from MOD09 data over inland waters in a variety of conditions and large regions.

River systems connect the terrestrial biosphere, the atmosphere and the ocean in the global carbon cycle. A recent estimate suggests that up to 3 petagrams of carbon per year could be emitted as carbon dioxide (CO2) from global inland waters, offsetting the carbon uptake by terrestrial ecosystems. It is generally assumed that inland waters emit carbon that has been previously fixed upstream by land plant photosynthesis, then transferred to soils, and subsequently transported downstream in run-off. But at the scale of entire drainage basins, the lateral carbon fluxes carried by small rivers upstream do not account for all of the CO2 emitted from inundated areas downstream. Three-quarters of the world's flooded land consists of temporary wetlands, but the contribution of these productive ecosystems to the inland water carbon budget has been largely overlooked. Here we show that wetlands pump large amounts of atmospheric CO2 into river waters in the floodplains of the central Amazon. Flooded forests and floating vegetation export large amounts of carbon to river waters and the dissolved CO2 can be transported dozens to hundreds of kilometres downstream before being emitted. We estimate that Amazonian wetlands export half of their gross primary production to river waters as dissolved CO2 and organic carbon, compared with only a few per cent of gross primary production exported in upland (not flooded) ecosystems. Moreover, we suggest that wetland carbon export is potentially large enough to account for at least the 0.21 petagrams of carbon emitted per year as CO2 from the central Amazon River and its floodplains. Global carbon budgets should explicitly address temporary or vegetated flooded areas, because these ecosystems combine high aerial primary production with large, fast carbon export, potentially supporting a substantial fraction of CO2 evasion from inland waters.

What water color parameters could be mapped using MODIS land reflectance products: A global evaluation over coastal and inland waters

Abstract. Land use and water resources are closely linked. Every single type of land use has a different influence on the hydrologic cycle, consequently impacting the people and the natural resources. The use of advanced technologies, for example monitoring the agricultural resources with remote sensing, offers the possibility to assess the water demand, to know the total cultivated area with the precise distribution of crops and enables the regularly acquisition of data distributed in space and time. The citrus sub-sector is of paramount importance in the Tunisian agricultural sector. The Cap Bon region has the main production area with 75 % of the total citrus area. The possibility of classifying citrus crops is important for water resource management at regional scale and for economic stability. Given the socio-economic importance of the citrus sector in the Cap Bon region, it is very important to have accurate estimation of the total area of citrus plots in this region. Therefore, the main objectives of this current work are: To integrate multitemporal synthetic aperture radar SAR data, Sentinel-1, and optical data Sentinel-2, together to determine the best machine learning algorithm that allowed obtaining the most accurate citrus crop classification in the region. To study and analyze the temporal signatures of the Normalized Difference Vegetation Index (NDVI) of the classified crops, mainly the citrus, with the purpose to provide the maximum amount of information that allow the differentiation between the crops. To study the potential relation between NDVI and Actual Evapotranspiration (ETa) fluxes measured with the eddy covariance method for a citrus orchard to extrapolate the eddy tower measurements to greater scales. To achieve these objectives, we evaluated the performance of 22 nonparametric classifiers during the period September 2020–June 2021. Additionally, ET measured by the eddy covariance method was available for the same period, so we tried to find the potential relation between NDVI and Actual Evapotranspiration (ETa). The results revealed that the best performing classifier is the Support Vector Machine SVM with an accuracy around 91 %. Consequently, our results provided a significant contribution to the citrus classification in the Cap Bon region but can be further improved. Also, the obtained results highlighted the potential to extrapolate accurate ET estimation to larger scales using the vegetation index obtained from Sentinel-2 data.

The modeling of pasture conservation and of its impact on stream water quality using Partial Least Squares-Path Modeling

Spatiotemporal vegetation changes serve as a key indicator of regional ecological environmental quality and provide crucial guidance for developing strategies for regional ecological protection and sustainable development. Currently, vegetation change studies in the Yangtze River Basin primarily rely on the Normalized Difference Vegetation Index (NDVI). However, the NDVI is susceptible to atmospheric and soil conditions and exhibits saturation phenomena in areas with high vegetation coverage. In contrast, the kernel NDVI (kNDVI) demonstrates significant advantages in suppressing background noise and improving saturation thresholds through nonlinear kernel transformation, thereby enhancing sensitivity to vegetation changes. To elucidate the spatiotemporal characteristics and driving mechanisms of vegetation changes in the Yangtze River Basin, this study constructed a temporal kNDVI using MOD09GA data from 2000 to 2022. Considering sectional heterogeneity, rather than analyzing the entire region as a whole as in previous studies, this research examined spatiotemporal evolution characteristics by sections using four statistical metrics. Subsequently, Partial Least Squares Path Modeling (PLSPM) was innovatively introduced to quantitatively analyze the influence mechanisms of topographic, climatic, pedological, and socioeconomic factors. Compared to traditional correlation analysis and the geographical detector method, PLSPM, as a theoretically driven statistical method, can simultaneously process path relationships among multiple latent variables, effectively revealing the intensity and pathways of driving factors’ influences, while providing more credible and interpretable explanations for kNDVI variation mechanisms. Results indicate that the overall kNDVI in the Yangtze River Basin exhibited an upward trend, with the midstream demonstrating the most significant improvement with minimal interannual fluctuations, the upstream displaying an east-increasing and west-stable spatial pattern, and the downstream demonstrating coexisting improvement and degradation characteristics, with these trends expected to persist. Driving mechanism analysis reveals that the upstream was predominantly influenced by the climatic factor, the midstream was dominated by terrain, and the downstream displayed terrain–soil coupling effects. Based on these findings, it is recommended that the upstream focus on enhancing vegetation adaptation management to climate change, the midstream need to coordinate the relationship between topography and human activities, and the downstream should concentrate on controlling the negative impacts of urban expansion on vegetation.

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

Agricultural land use changes stream dissolved organic matter via altering soil inputs to streams