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Algal blooms constitute an emerging threat to global inland water quality, yet their spatial and temporal distribution at the global scale remains largely unknown. Here we establish a global bloom database, using 2.91 million Landsat satellite images from 1982 to 2019 to characterize algal blooms in 248,243 freshwater lakes, representing 57.1% of the global lake area. We show that 21,878 lakes (8.8%) spread across six continents have experienced algal blooms. The median bloom occurrence of affected lakes was 4.6%, but this frequency is increasing; we found increased bloom risks in the 2010s, globally (except for Oceania). The most pronounced increases were found in Asia and Africa, mostly in developing countries that remain reliant on agricultural fertilizer. As algal blooms continue to expand in scale and magnitude, this baseline census will be vital towards future risk assessments and mitigation efforts. Algal blooms are occurring more frequently, as seen in a global database compiled from satellite imagery from the past few decades.

Global impervious surface area (ISA) has more than doubled over the last three decades, but the associated carbon emissions resulting from the depletion of pre-existing land carbon stores remain unknown. Here, we report that the carbon losses from biomass and top soil (0–30 cm) due to global ISA expansion reached 46–75 Tg C per year over 1993–2018, accounting for 3.7–6.0% of the concurrent human land-use change emissions. For the Annex I countries of UNFCCC, our estimated emissions are comparable to the carbon emissions arising from settlement expansion as reported by the national greenhouse gas inventories, providing independent validation of this kind. The contrast between growing emissions in non-Annex I countries and declining ones in Annex I countries over the study period can be explained by an observed emerging pattern of emissions evolution dependent on the economic development stage. Our study has implications for international carbon accounting and climate mitigation as it reveals previously ignored but substantial contributions of ISA expansion to anthropogenic carbon emissions through land-use effects. Terrestrial carbon emissions from global expansion of impervious surface area have been long overlooked in global carbon budget assessments. Qiu et al. report that these emissions account for up to 6.0–12.9% of those from human land use change.

Groundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and management. However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. In this work, we introduce a new attention‐based graph neural network (aGNN) for modeling contaminant transport with limited monitoring data and quantifying causal connections between contaminant sources (drivers) and their spreading (outcomes). In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). It also demonstrates a high level of applicability in inferring observations for unmonitored sites (i.e., inductive learning). Furthermore, an explanatory analysis based on aGNN quantifies the influence of each contaminant source, which has been validated by a physics‐based model with consistent outcomes with an R2 value exceeding 92%. The major advantage of aGNN is that it not only has a high level of predictive power in multiple scenario evaluations but also substantially reduces computational cost. Overall, this study shows that aGNN is efficient and robust for highly nonlinear spatiotemporal learning in subsurface contaminant transport, and provides a promising tool for groundwater management involving contaminant source attribution. Plain Language Summary Groundwater contamination caused by human activities is a longstanding global challenge. Accurately characterizing and modeling the movement of contaminants is crucial for the protection and management of groundwater resources. However, the complexity of the processes, limitations in data availability, and high computational demands pose significant challenges. In the age of big data, machine learning offers new avenues for exploring contaminant transport in groundwater. In this study, we introduce a novel machine learning model called an attention‐based graph neural network (aGNN) designed to model contaminant transport with sparse monitoring data and to analyze the causal relationships between contaminant sources and observed concentrations at specific locations. We conducted five synthetic case studies across diverse aquifer systems with varying monitoring setups, where aGNN demonstrated superior performance over models based on other approaches. It also proved highly capable of making inferences about pollution levels at unmonitored sites. Moreover, an explanatory analysis using aGNN effectively quantified the impact of each contaminant source, with results validated by a physics‐based model. Overall, this study establishes aGNN as an efficient and robust method for complex spatiotemporal learning in subsurface contaminant transport, making it a valuable tool for groundwater management and contaminant source identification. Key Points A novel graph‐based deep learning method is proposed for modeling contaminant transport constrained by monitoring data The proposed model quantifies the contribution of each potential contaminant source to the observed concentration at an arbitrary location The deep learning method substantially reduces the computational cost compared with a physics‐based contaminant transport model

Phytoplankton blooms in coastal oceans can be beneficial to coastal fisheries production and ecosystem function, but can also cause major environmental problems 1 , 2 —yet detailed characterizations of bloom incidence and distribution are not available worldwide. Here we map daily marine coastal algal blooms between 2003 and 2020 using global satellite observations at 1-km spatial resolution. We found that algal blooms occurred in 126 out of the 153 coastal countries examined. Globally, the spatial extent (+13.2%) and frequency (+59.2%) of blooms increased significantly ( P  < 0.05) over the study period, whereas blooms weakened in tropical and subtropical areas of the Northern Hemisphere. We documented the relationship between the bloom trends and ocean circulation, and identified the stimulatory effects of recent increases in sea surface temperature. Our compilation of daily mapped coastal phytoplankton blooms provides the basis for global assessments of bloom risks and benefits, and for the formulation or evaluation of management or policy actions. Satellite observations reveal global increases in the extent and frequency of phytoplankton blooms between 2003 and 2020 and provide insights into the relationship between blooms, ocean circulation and sea surface temperature.

Lakes are important natural resources and carbon gas emitters and are undergoing rapid changes worldwide in response to climate change and human activities. A detailed global characterization of lakes and their long-term dynamics does not exist, which is however crucial for evaluating the associated impacts on water availability and carbon emissions. Here, we map 3.4 million lakes on a global scale, including their explicit maximum extents and probability-weighted area changes over the past four decades. From the beginning period (1984–1999) to the end (2010–2019), the lake area increased across all six continents analyzed, with a net change of +46,278 km 2 , and 56% of the expansion was attributed to reservoirs. Interestingly, although small lakes (<1 km 2 ) accounted for just 15% of the global lake area, they dominated the variability in total lake size in half of the global inland lake regions. The identified lake area increase over time led to higher lacustrine carbon emissions, mostly attributed to small lakes. Our findings illustrate the emerging roles of small lakes in regulating not only local inland water variability, but also the global trends of surface water extent and carbon emissions. Lakes are essential components of the hydrological and biogeochemical cycles. Here, Pi et al develop a global lake dataset called GLAKES via high-resolution satellite images and deep learning to examine global lake changes over four decades.

Natural aquifers usually exhibit complex physical and chemical heterogeneities, which are key factors complicating kinetic processes, such as contaminant transport and transformation, posing a great challenge in the remediation of contaminated groundwater. Aquifer heterogeneity usually leads to a distinct feature, the so-called “anomalous transport” in groundwater, which deviates from the phenomenon described by the classical advection-dispersion equation (ADE) based on Fick’s Law. Anomalous transport, also known as non-Fickian dispersion or “anomalous dispersion” in a broad sense, can explain the hydrogeological mechanism that leads to the temporally continuous deterioration of water quality and rapid spatial expansion of pollutant plumes. Contaminants enter and then are retained in the low-permeability matrix from the high-permeability zone via molecular diffusion, chemical adsorption, and other mass exchange effects. This process can be reversed when the concentration of pollutants in high-permeability zones is relatively low. The contaminants slowly return to the high-permeability zones through reverse molecular diffusion, resulting in sub-dispersive anomalous transport leading to the chronic gradual deterioration of water quality. Meanwhile, some contaminants are rapidly transported along the interconnected preferential flow paths, resulting in super-dispersive anomalous transport, which leads to the rapid spread of contaminants. Aquifer heterogeneity is also an important factor that constrains the efficacy of groundwater remediation, while the development, application, and evaluation of groundwater remediation technologies are usually based on the Fickian dispersion process predicted by the ADE equation. Comprehensive studies of the impacts of non-Fickian dispersion on contaminant transport and remediation are still needed. This article reviews the non-Fickian dispersion phenomenon caused by the heterogeneity of geological media, summarizes the processes and current understanding of contaminant migration and transformation in highly heterogeneous aquifers, and evaluates mathematical methods describing the main non-Fickian dispersion features. This critical review also discusses the limitations of existing research and outlines potential future research areas to advance the understanding of mechanisms and modeling of non-Fickian dispersion in heterogeneous media.

More than two billion people worldwide have suffered thyroid disorders from either iodine deficiency or excess. By creating the national map of groundwater iodine throughout China, we reveal the spatial responses of diverse health risks to iodine in continental groundwater. Greater non-carcinogenic risks relevant to lower iodine more likely occur in the areas of higher altitude, while those associated with high groundwater iodine are concentrated in the areas suffered from transgressions enhanced by land over-use and intensive anthropogenic overexploitation. The potential roles of groundwater iodine species are also explored: iodide might be associated with subclinical hypothyroidism particularly in higher iodine regions, whereas iodate impacts on thyroid risks in presence of universal salt iodization exhibit high uncertainties in lower iodine regions. This implies that accurate iodine supply depending on spatial heterogeneity and dietary iodine structure optimization are highly needed to mitigate thyroid risks in iodine-deficient and -excess areas globally. Both iodine deficiency and excess could cause thyroid disorders. By creating a national map of groundwater iodine throughout China, the authors reveal the spatial responses of diverse health risks to iodine in continental groundwater.

Sustainable inland waterways should meet the needs of navigation without compromising the health of riverine ecosystems. Here we propose a hierarchical model to describe sustainable development of the Golden Inland Waterways (GIWs) which are characterized by great bearing capacity and transport need. Based on datasets from 66 large rivers (basin area > 100,000 km 2 ) worldwide, we identify 34 GIWs, mostly distributed in Asia, Europe, North America, and South America, typically following a three-stage development path from the initial, through to the developing and on to the developed stage. For most GIWs, the exploitation ratio, defined as the ratio of actual to idealized bearing capacity, should be less than 80% due to ecological considerations. Combined with the indices of regional development, GIWs exploitation, and riverine ecosystem, we reveal the global diversity and evolution of GIWs’ sustainability from 2015 to 2050, which highlights the importance of river-specific strategies for waterway exploitation worldwide. The exploitation of rivers has been at the detriment of river ecosystems. Here the authors propose a concept of Golden Inland Waterways (GIWs) to represent large waterways and find that the exploitation ratio threshold around the turning point for most GIWs appear to be less than 80%, subject to ecological constraints.

Increased human water use combined with climate change have aggravated water scarcity from the regional to global scales. However, the lack of spatially detailed datasets limits our understanding of the historical water use trend and its key drivers. Here, we present a survey-based reconstruction of China’s sectoral water use in 341 prefectures during 1965 to 2013. The data indicate that water use has doubled during the entire study period, yet with a widespread slowdown of the growth rates from 10.66 km³·y−2 before 1975 to 6.23 km³·y−2 in 1975 to 1992, and further down to 3.59 km³·y−2 afterward. These decelerations were attributed to reduced water use intensities of irrigation and industry, which partly offset the increase driven by pronounced socioeconomic development (i.e., economic growth, population growth, and structural transitions) by 55% in 1975 to 1992 and 83% after 1992. Adoptions for highly efficient irrigation and industrial water recycling technologies explained most of the observed reduction of water use intensities across China. These findings challenge conventional views about an acceleration in water use in China and highlight the opposing roles of different drivers for water use projections.

The hydrological and biogeochemical properties of the hyporheic zone in stream and riverine ecosystems have been extensively studied over the past two decades. Although it is widely acknowledged that sediment heterogeneity can influence biogeochemical reactions, little effort has been made to understand the role of heterogeneity on the spatiotemporal variability of riverbed redox conditions under changing flow dynamics at different timescales. Here we integrate a mechanistic model and field data to demonstrate that heterogeneity in permeability plays a vital role in modulating sediment redox conditions at both seasonal (annual) and event (daily‐to‐weekly) timescales, whereas heterogeneity in particulate organic carbon (POC) content only has a comparable influence on redox conditions at the seasonal timescale. These findings underscore the importance of accurately characterizing sediment heterogeneity, in terms of permeability and POC content, in quantifying biogeochemical dynamics in the riverbed and hyporheic zones of riverine ecosystems. Plain Language Summary The redox condition of riverbed sediments is subject to the combined influence of hydrologic exchange flow and biogeochemical processes and is important for regulating the functioning of riverine ecosystems. Current understanding of the spatiotemporal pattern of sediment redox conditions especially with heterogeneity in consideration is limited, partially due to the lack of measurements and quantitative models. In this study, we integrate a mechanistic model and field data to reveal the role of sediment heterogeneity in controlling the redox condition under dynamic flow conditions. We demonstrate that heterogeneity in permeability modulates sediment redox condition at both seasonal and event timescales, and heterogeneity in particulate organic carbon is most prominent over multi‐month time intervals that reflect the balance between particulate organic carbon (POC) metabolism and time‐integrated oxygen influx. These findings highlight the importance of accurate characterization of sediment heterogeneity in both permeability and POC for predicting the dynamic redox shifts in riverbed sediments. Key Points A reactive transport model was developed to quantify the impact of heterogeneity in permeability and particulate organic carbon (POC) concentration on sediment redox conditions Heterogeneity in permeability controls sediment redox conditions at both seasonal (annual) and event (daily‐to‐weekly) timescales The effects of heterogeneity in POC occur over the monthly timescale, reflecting a balance between POC metabolism and the influx of oxygen