Search

Injustices are prevalent in food systems, where the accumulation of vast wealth is possible for a few, yet one in ten people remain hungry. Here, for 194 countries we combine aquatic food production, distribution and consumption data with corresponding national policy documents and, drawing on theories of social justice, explore whether barriers to participation explain unequal distributions of benefits. Using Bayesian models, we find economic and political barriers are associated with lower wealth-based benefits; countries produce and consume less when wealth, formal education and voice and accountability are lacking. In contrast, social barriers are associated with lower welfare-based benefits; aquatic foods are less affordable where gender inequality is greater. Our analyses of policy documents reveal a frequent failure to address political and gender-based barriers. However, policies linked to more just food system outcomes centre principles of human rights, specify inclusive decision-making processes and identify and challenge drivers of injustice.

Atmospheric methane (CH4) concentration has more than doubled since per-industrial time. The contribution of natural wetland ecosystems, currently the largest natural emitters of CH4, to this increase is not well known. While temperature may have contributed to an growth in emissions, the extent wetlands has declined over this period due to drainage and land use conversion. In this study, we combine a new reconstruction of global wetland extent with simulated CH4 flux from nine land surface model to estimate wetland CH4 emissions over 1901-2020. Our analysis evaluates: 1) the uncertainty of modelled emissions over the century timescale, 2) the separate and combined effects of climate and land use change on emissions, and 3) the global and regional trends of wetland CH4 emissions. We show that prognostic outputs from model of wetland extent are highly uncertain over the century timescale although most prognostic models suggest an emission increase. We find that inclusion of wetland drainage reducing wetland area primarily in temperate latitudes is sufficient to offset the increase in modeled emission in the absence of land use, with an ensemble mean displaying no significant trend between 1901-1920 and 2001-2020. We evaluate the contribution of individual land uses to the decline in global emissions, in particular the conversion to irrigated rice and wet cultivation which are also methane emitting areas. These results diverge from previous source attributions at the century timescale and may require upward revisions to other biogenic sources to balance the budget and remain within δ13C isotope constraints. In the future, we will investigate the effect of wetland drainage on CO2, CH4 and aquatic carbon export for a more complete accounting of this land use change on global carbon fluxes.

The vast Amazon wetlands support multiple social-ecological systems basin-wide, and influence global water and carbon cycles. Recent environmental changes related to climate and infrastructure expansion have altered their rhythmicity and dynamics in many aspects, and understanding their impacts on hydrological variables such as river and floodplain water levels and discharges, inundation extent and storage, is urgent. The combination of new hydrodynamic modeling approaches with in situ and multisatellite data provides great opportunities to fostering this research agenda.Here, we present an unprecedented analysis of the Amazon’s surface water dynamics, its status and long-term trends, as well as perspectives with in situ and satellite data. We analyze inundation extent, water levels and river-floodplain interactions based on in situ (water levels across rivers and floodplains) and satellite data (radar altimetry, optical imagery, passive microwave and L-band SAR), as well as hydrodynamic modeling (MGB and CaMa-Flood models). Firstly, we present the outcomes of a recent intercomparison project where 29 inundation datasets were compared across the basin (Fleischmann et al., 2022; WebGIS at ). While a higher agreement was observed along the Amazon river floodplain, major discrepancies occurred for interfluvial wetlands, stressing the need of pursuing optimal merging techniques to improve local to large-scale inundation estimates. By looking at the dynamic inundation datasets, we were able to analyze long-term inundation trends, revealing a major increase of 26% in the maximum annual inundation across the Amazon River system since 1980, associated with longer flood duration and higher river-floodplain connectivity over multiple areas.While changes in regional-scale hydroclimatic processes have led to the intensification of the Amazon’s hydrological cycle, local geomorphological processes are able to largely alter river-floodplain interactions. To investigate it, we used long-term optical data from the Global Surface Water dataset to assess changes along the Amazon River channels and the associated erosion/sedimentation processes. Our results evidence major changes along the river over the last decades, and a mapping of the impacts on 238 riparian communities shows that 21% have been largely affected by bank erosion, damaging several properties, while 19% have been affected by sedimentation, impairing transportation and reducing access to the river waters.Finally, we present the first outcomes of a new floodplain hydrology monitoring network in the Mamirauá region of Central Amazon, which includes the widest floodplain reach of the Amazon. The network under development is the first of its kind in the Amazon, and aims at improving our understanding of river-floodplain dynamics through the optimal combination of in situ (more than 25 in situ water level loggers, several weather stations, among others) and satellite data, especially from current SAR altimetry missions such as Sentinel-3A/B and Sentinel6 and the forthcoming wide-swath SWOT mission. The presented results provide an important step towards a broad understanding of the Amazon surface water dynamics, from basin to local scales, and the sustainable use of the region’s river and wetland resources.

The loss of hydrological connectivity and fragmentation of natural wetlands have driven widespread wetland degradation worldwide. Monitoring techniques are needed to assess the degree of fragmentation and to aid with the restoration of affected wetlands. Hydrogeodetic tools such as wetland Interferometric Synthetic Aperture Radar (InSAR) can be used to monitor wetland hydrology as it provides information on three-dimensional flow dynamics at a high spatial resolution. While this technique has been utilized previously for the manual assessment of hydrological connectivity in wetlands, this study proposes the first deep learning-based approach for the automated detection of barriers to the natural water flow that cannot otherwise be identified by conventional space imagery. To this end, a deep convolutional network is trained by segmenting edge features in ALOS PALSAR-1 L-Band InSAR images captured between 2006 and 2011. The training dataset consists of manually labelled and delineated barriers showing abrupt changes in water surface elevation and 22 wrapped interferograms with high coherence across several sample sites in the Everglades and the wetlands of southern Louisiana, United States. The scenes were processed in the Interferometric synthetic aperture radar Scientific Computing Environment (ISCE). The network is set up using a UNet structure with alternating convolutional and pooling or upsampling layers along a contracting and expanding part. The validation of the resulting pixel-wise segmentation shows that the network can successfully detect hydrological barriers in wetlands. Apart from identifying the location of barriers, the CNN can be applied to identify the type and persistence of the fragmentation over the entire wetland. Utilizing the multitemporal data additionally helps detect seasonal changes in the presence or absence of hydrological barriers in the sample sites. This study demonstrates the potential of deep learning techniques for the automated detection of hydrological parameters in InSAR imagery and sets the groundwork for the automated monitor of wetland fragmentation across the world.

Extensive floodplains throughout the Amazon basin support important ecosystem services and influence global water and carbon cycles. A recent change in the hydroclimatic regime of the region, with increased rainfall in the northern portions of the basin, has produced record-breaking high water levels on the Amazon River mainstem. Yet, the implications for the magnitude and duration of floodplain inundation across the basin remain unknown. Here we leverage state-of-the-art hydrological models, supported by in-situ and remote sensing observations, to show that the maximum annual inundation extent along the central Amazon increased by 26% since 1980. We further reveal increased flood duration and greater connectivity among open water areas in multiple Amazon floodplain regions. These changes in the hydrological regime of the world’s largest river system have major implications for ecology and biogeochemistry, and require rapid adaptation by vulnerable populations living along Amazonian rivers., Environmental Research Letters, 18 (3), ISSN:1748-9326, ISSN:1748-9318

Despite contributing to healthy diets for billions of people, aquatic foods are often undervalued as a nutritional solution because their diversity is often reduced to the protein and energy value of a single food type (‘seafood’ or ‘fish’)1–4. Here we create a cohesive model that unites terrestrial foods with nearly 3,000 taxa of aquatic foods to understand the future impact of aquatic foods on human nutrition. We project two plausible futures to 2030: a baseline scenario with moderate growth in aquatic animal-source food (AASF) production, and a high-production scenario with a 15-million-tonne increased supply of AASFs over the business-as-usual scenario in 2030, driven largely by investment and innovation in aquaculture production. By comparing changes in AASF consumption between the scenarios, we elucidate geographic and demographic vulnerabilities and estimate health impacts from diet-related causes. Globally, we find that a high-production scenario will decrease AASF prices by 26% and increase their consumption, thereby reducing the consumption of red and processed meats that can lead to diet-related non-communicable diseases5,6 while also preventing approximately 166 million cases of inadequate micronutrient intake. This finding provides a broad evidentiary basis for policy makers and development stakeholders to capitalize on the potential of aquatic foods to reduce food and nutrition insecurity and tackle malnutrition in all its forms. Data on the nutrient content of almost 3,000 aquatic animal-source foods is combined with a food-systems model to show that an increase in aquatic-food production could reduce the inadequate intake of most nutrients.

This datasetincludes seven wetland CH4 model simulated CH4 flux (FCH4) per standard wetland areaat different time scales. The modeled FCH4 are extractedfrom grid-level simulations with gridded climatic forcingat the location of 83 eddy covariance sites from theFLUXNET-CH4 database. The climatic forcingsat site locations are provided. &nbsp

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

While wetlands are the largest natural source of methane (CH4 ) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17±11days, and lagged air and soil temperature by median values of 8±16 and 5±15days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

SummaryClimate change presents a particularly complex challenge in the context of flyway scale conservation of migratory bird species as it requires coordinated action by multiple countries along these species’ migratory routes. Coordinating conservation responses requires understanding the vulnerability of species and their habitats to climate change at the flyway scale throughout each species’ annual cycle. To contribute to such understanding, we used species distribution models to assess the exposure to climate change of waterbird species that are the focus of the Agreement on the Conservation of African-Eurasian Migratory Waterbirds (AEWA). We found that the species with the smallest proportion of their current range projected to be climatically suitable by 2050 (those whose distributions respond to changes in water availability but that do not perform synchronised migration) are dispersive species in the Afrotropical biogeographic realm, and migratory species in their breeding season, particularly Arctic breeding waders. These species also have the most limited availability of newly suitable areas. Projections for most other Palearctic migratory waterbird species suggest that losses of suitable areas in their current passage and wintering ranges may be largely offset by new areas becoming climatically suitable. The majority of migratory Palearctic waterbirds in the breeding season and Afrotropical waterbirds are widely dispersed with only a small proportion of their populations currently supported by ‘Critical Sites’ (i.e. sites that are either important for Globally Threatened Species or support 1% of the bioregional population of any waterbird species). This makes it unlikely that climate change adaptation measures focusing only on key sites will be sufficient to counter the predicted range losses. Therefore, climate change adaptation responses should also be implemented at the landscape scale for Afrotropical waterbirds and for breeding populations of Palearctic migrant waterbirds.

The prediction of streamflow in gauged and ungauged basins is a central challenge of hydrology and is increasingly being met by machine learning and deep learning models. With increase in data volume and advances in modeling techniques, the capacity for deep learning tools to compete and complement physics-based hydrological models over a variety of settings and scales is still being explored. Here, we present initial results of the MAchine learning for Swiss (CH) river FLOW estimation (MACH-Flow) project. We train machine learning models on daily discharge data from 260 gauging stations across Switzerland covering the 1980-2020 time window. The river gauging stations we included have catchment areas ranging between 0.1-3000 km2, and average streamflow between 0.1-100 m3/second. We also test a range of predictor features including: air temperature, precipitation, incoming radiation, relative humidity, as well as a number of static catchment variables. We evaluated multiple model architectures of ranging complexity, from models focusing on runoff predictions over individual headwater catchments, such as Neural Network, Long short-term memory (LSTM) cells. We also investigate Graph Neural Networks capable of leveraging information from neighbouring stations in making point location predictions. Predictions are generated at gauging locations as well as over 307 land units used for drought monitoring. We benchmark and compare deep learning methods against two process-based hydrological models: 1) the PREecipitation Runoff EVApotranspiration HRU Model (PREVAH) used operationally by Swiss federal agencies and 2) the comparatively streamlined Simple Water Balance Model (SWBM). We compared the deep learning and physics-based models with regards to predicting daily river discharge as well as of low-flows during drought conditions that are essential for water managers and planners in Switzerland. We find that most deep learning methods with sufficient tuning and lookback periods can compete with the streamflow predictions from process-based models, particularly at gauging stations on larger non-regulated rivers where hydro-dynamic time lags are significant. Finally, we discuss the prospects for generating discharge predictions across all river segments of Switzerland using deep learning methods, along with challenges and opportunities to achieve this goal.

The Amazon River basin harbors some of the world's largest wetland complexes, which are of major importance for biodiversity, the water cycle and climate, and human activities. Accurate estimates of inundation extent and its variations across spatial and temporal scales are therefore fundamental to understand and manage the basin's resources. More than fifty inundation estimates have been generated for this region, yet major differences exist among the datasets, and a comprehensive assessment of them is lacking. Here we present an intercomparison of 29 inundation datasets for the Amazon basin, based on remote sensing only, hydrological modeling, or multi-source datasets, with 18 covering the lowland Amazon basin (elevation 1000 km2) is 323,700 km2. The highest spatial agreement is observed for floodplains dominated by open water such as along the lower Amazon River, whereas intermediate agreement is found along major vegetated floodplains fringing larger rivers (e.g., Amazon mainstem floodplain). Especially large disagreements exist among estimates for interfluvial wetlands (Llanos de Moxos, Pacaya-Samiria, Negro, Roraima), where inundation tends to be shallower and more variable in time. Our data intercomparison helps identify the current major knowledge gaps regarding inundation mapping in the Amazon and their implications for multiple applications. In the context of forthcoming hydrology-oriented satellite missions, we make recommendations for future developments of inundation estimates in the Amazon and present a WebGIS application (https://amazon-inundation.herokuapp.com/) we developed to provide user-friendly visualization and data acquisition of current Amazon inundation datasets. Ministry of Education (MOE) Nanyang Technological University The work was part of the SABERES project financed by the BNPParibas Foundation as part of its “Climate & Biodiversity Initiative” program 2019. A.S.F. was supported by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnologico, ´ Brazil) [grant number 141161/2017-5]. F.P., J.F.F., M.P.B. and F.A. received support from CNES (SWOT-ST project SWOT for SOUTH AMERICA, ID: 6018-4500066497). F.P. and M.P.B. also received support from CNES (SWOT-ST project SWOT Wetlands Hydrology Monitoring). F.P. is supported by the IRD Groupement De Recherche International (GDRI) SCaHyLab. J.M.M. received support from NASA IDS grant NNX17AK49G and the US National Science Foundation (Division of Environmental Biology, grant 1753856). E.P. acknowledges Nanyang Technological University (SUG-NAP EP3/19) and Ministry of Education of Singapore (AcRF Tier1 RT 06/19 and AcRF Tier2 RT 11/ 21). A.F.R. acknowledges the Research Foundation of Sao ˜ Paulo (FAPESP, grant #2019/24049-5). S.W. has been supported by the French AMANECER-MOPGA project funded by ANR and IRD (ref. ANR18-MPGA-0008). M.C. received funding from NASA IDS grant NNX17AK49G. The SWAF dataset development was financed by the CATDS and the SWOT-AVAL programs by CNES.

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years.

Sustaining the organisms, ecosystems, and processes that underpin human well-being is necessary to achieve sustainable development. Here we analyze 14 of nature’s contributions to people (NCP) for food, water, and climate security. Using spatial optimization, we identify critical natural assets, the most important ecosystems for providing NCP, comprising 30% (for local benefits) to 44% (for local and global benefits) of total global land area and 24% of national territorial waters. Many of these NCP are left out of international agreements focused on conserving species or mitigating climate change, yet our analysis shows that explicitly prioritizing critical natural assets jointly advances development, climate, and conservation goals. Crafting policy and investment strategies that protect critical natural assets is essential for sustaining human well-being and securing Earth’s life support systems.

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET). ISSN:0168-1923 ISSN:1873-2240

Atmospheric methane (CH4) concentrations have shown a puzzling resumption in growth since 2007 following a period of stabilization from 2000 to 2006. Multiple hypotheses have been proposed to explain the temporal variations in CH4 growth, and attribute the rise of atmospheric CH4 either to increases in emissions from fossil fuel activities, agriculture and natural wetlands, or to a decrease in the atmospheric chemical sink. Here, we use a comprehensive ensemble of CH4 source estimates and isotopic δ13C-CH4 source signature data to show that the resumption of CH4 growth is most likely due to increased anthropogenic emissions. Our emission scenarios that have the fewest biases with respect to isotopic composition suggest that the agriculture, landfill and waste sectors were responsible for 53 ± 13% of the renewed growth over the period 2007–2017 compared to 2000–2006; industrial fossil fuel sources explained an additional 34 ± 24%, and wetland sources contributed the least at 13 ± 9%. The hypothesis that a large increase in emissions from natural wetlands drove the decrease in atmospheric δ13C-CH4 values cannot be reconciled with current process-based wetland CH4 models. This finding suggests the need for increased wetland measurements to better understand the contemporary and future role of wetlands in the rise of atmospheric methane and climate feedback. Our findings highlight the predominant role of anthropogenic activities in driving the growth of atmospheric CH4 concentrations.

Despite their importance for biodiversity and ecosystem services, wetlands are among the most threatened ecosystems globally. The conservation of many migratory waterbirds depends on the conservation of a network of key sites along their flyways. However, the suitability of these sites is changing under climate change, and it is important that management of individual sites in the network adapts to these changes. Using bioclimatic models that also account for changes in inundation, we found that projected climate change will reduce habitat suitability for waterbirds at 57.5% of existing Critical Sites within Africa-Eurasia, varying from 20.1% in Eastern Europe to 87.0% in Africa. African and Middle East sites are particularly threatened, comprising 71 of the 100 most vulnerable sites. By highlighting priority sites for conservation and classifying Critical Sites into Climate Change Adaptation Strategy (CCAS) classes, our results can be used to support the climate change adaptation of both individual sites and the entire site network.

In many wetlands the timing and duration of inundation determine ecological characteristics and the provision of ecosystem services; however, wetland conservation decisions often rely on static maps of wetland boundaries that do not capture their dynamic hydrological variability and connectivity. The Amazon River basin contains some of the world's most extensive wetlands, many of which are floodplains where seasonal flood pulses result in a temporally varying inundation area and hydrological connectivity with river systems. This study classified Amazon wetlands according to the timing and duration (months per year) of inundation detected by remote sensing, and also investigated the contribution of precipitation regimes in affecting wetland distribution and hydrological dynamics. Permanently inundated wetlands account for the largest area and are mainly floodplains located in the lowlands of the catchment. Seasonally inundated wetlands varied greatly in the duration of inundation over the course of the year, ranging from 1 to 9 months. Distinct seasonal timing was detected among the large wetland complexes, reflecting rainfall regimes as well as time lags for drainage and drying. For example, inundation in the extensive Llanos de Moxos region of the southern Amazon was protracted and lasted well after the rainy season, compared with the Roraima region of the northern Amazon, where inundation was shorter and tracked the rainy season. The integration of inundation dynamics into wetland classification captures regional differences in timing and duration of inundation in the major wetlands of the basin that should be considered for conservation planning and other ecological applications. This information can aid regional wetland management and planning, especially with regards to minimizing the effects of dam and waterway construction that can directly affect the natural wetland dynamics. The use of global remotely sensed inundation data makes this approach easily transferable to other large tropical wetlands.

Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

Sustaining the organisms, ecosystems, and processes that underpin human well-being is necessary to achieve sustainable development. Here we identify critical natural assets, natural and semi-natural ecosystems that provide 90% of the total current magnitude of 14 types of nature’s contributions to people (NCP). Critical natural assets for maintaining local-scale NCP (12 of the 14 NCP mapped) comprise 30% of total global land area and 24% of national territorial waters, while 44% of land area is required for maintaining all NCP (including those that accrue at the global scale, carbon storage and moisture recycling). At least 87% of the world’s population lives in the areas benefiting from critical natural assets for local-scale NCP, while only 16% lives on the lands containing these assets. Critical natural assets also overlap substantially with areas important for biodiversity (covering area requirements for 73% of birds and 66% of mammals) and cultural diversity (representing 96% of global Indigenous and non-migrant languages). Many of the NCP mapped here are left out of international agreements focused on conserving species or mitigating climate change, yet this analysis shows that explicitly prioritizing critical natural assets for NCP could simultaneously advance development, climate, and conservation goals. Crafting policy and investment strategies that protect critical natural assets is essential for sustaining human well-being and securing Earth’s life support systems.

Intensive fisheries have reduced fish biodiversity and abundance in aquatic ecosystems worldwide1-3. 'No-take' marine reserves have become a cornerstone of marine ecosystem-based fisheries management4-6, and their benefits for adjacent fisheries are maximized when reserve design fosters synergies among nearby reserves7,8. The applicability of this marine reserve network paradigm to riverine biodiversity and inland fisheries remains largely untested. Here we show that reserves created by 23 separate communities in Thailand's Salween basin have markedly increased fish richness, density, and biomass relative to adjacent areas. Moreover, key correlates of the success of protected areas in marine ecosystems-particularly reserve size and enforcement-predict differences in ecological benefits among riverine reserves. Occupying a central position in the network confers additional gains, underscoring the importance of connectivity within dendritic river systems. The emergence of network-based benefits is remarkable given that these reserves are young (less than 25 years old) and arose without formal coordination. Freshwater ecosystems are under-represented among the world's protected areas9, and our findings suggest that networks of small, community-based reserves offer a generalizable model for protecting biodiversity and augmenting fisheries as the world's rivers face unprecedented pressures10,11.

Global wetlands and floodplains offer benefits and perils alike for human society. For example, humans rely on natural flood cycles for fisheries and agriculture, yet flooding also caused nearly one trillion USD in damage in the past 30 years and impacts millions of people every year. Looking forward, altered flow regimes or increased drought conditions are expected to affect the natural inundation cycle and its ecosystem services. The current and potential future impacts of flooding and drying events warrant increasing efforts to quantify our dependence and exposure within flooded areas, since any change from current inundation patterns is expected to have consequences for those who rely on regular flood occurrences. This paper provides a baseline global assessment of the dependence and exposure of human populations, urban areas, roads, and agriculture on current inundation patterns. The analysis uses a spatially explicit inundation map at ∼500 m resolution (GIEMS-D15) derived from satellite remote sensing to represent flooding extents and overlays it with current population and land use maps. We find that 35% of the analyzed population, or 2.0 billion people, live inside areas that are prone to inland flooding, which comprise only 12% of the land surface area (excluding marine coastal areas), confirming that population densities within inundation zones are about three-times above global average. Likewise, 35% of urban areas potentially experience regular, seasonal, or infrequent flooding. Agriculture shows a similar pattern with 24% of the world’s cropland in areas of recurring inundation. Finally, we estimate that 18% of the global road network is exposed to inundation during high water periods. These global estimates demonstrate a preferential tendency of human populations, infrastructure, and agriculture to be co-located within inundation areas, making related anthropogenic activities highly susceptible to future changes in flood regimes. The results are intended to offer a suite of first-order estimates as partial input to more holistic risk and vulnerability assessments and to ultimately improve environmental planning and policy at large scales.

The United Nations’ (UN) 2030 Agenda for Sustainable Development defines the formidable challenge of integrating historically separate economic, social, and environmental goals into a unified ‘plan of action for people, planet, and prosperity.’ We highlight the substantial contribution inland fisheries can make towards preventing increased poverty and, in some cases, alleviating poverty (i.e. addressing Sustainable Development Goal [SDG] 1: No Poverty) as an opportunity to inform the next set of development agendas and their associated budgets and priorities. Overlooking the contribution of inland fisheries to poverty prevention and alleviation may undermine the capacity to successfully meet the development goals, especially in rural communities in Low-Income Food-Deficit countries. Inland fisheries are essential for food and economic security as the vast majority are small-scale operations or subsistence, predominantly used by poorer groups. Protecting inland fisheries from diverse threats from other water users and associated sectors requires robust, multi-sectoral, and multinational policies that can be brought about by global initiatives like the SDGs. Without such protection, their vital contribution towards sustainable livelihoods and poverty issues becomes uncertain. Further, integrating inland fisheries into sustainable development frameworks strengthens the likelihood of achieving the UN Agenda for Sustainable Development. In this perspective article, we posit that including inland fisheries in national policy statements and programs can prove beneficial to promoting economic and social growth for the poor, preventing further poverty, and achieving SDG 1 and other SDG targets, especially those related to food security.

Wetlands have been extensively modified by human activities worldwide. We provide a global-scale portrait of the threats and protection status of the world's inland wetlands by combining a global map of inundation extent derived from satellite images with data on threats from human influence and on protected areas. Currently, seasonal inland wetlands represent approximately 6% of the world's land surface, and about 89% of these are unprotected (as defined by protected areas IUCN I–VI and Ramsar sites). Wetland protection ranges from 20% in Central and 18% in South America to only 8% in Asia. Particularly high human influence was found in Asia, which contains the largest wetland area of the world. High human influence on wetlands even within protected areas underscores the urgent need for more effective conservation measures. The information provided here is important for wetland conservation planning and reveals that the current paradigm of wetland protection may be inadequate.

Anthropogenic changes in land use and land cover (LULC) during the pre-industrial Holocene could have affected regional and global climate. Current LULC scenarios are based on relatively simple assumptions and highly uncertain estimates of population changes through time. Archaeological and palaeoenvironmental reconstructions have the potential to refine these assumptions and estimates. The Past Global Changes (PAGES) LandCover6k initiative is working towards improved reconstructions of LULC globally. In this paper, we document the types of archaeological data that are being collated and how they will be used to improve LULC reconstructions. Given the large methodological uncertainties involved, we propose methods to evaluate the revised scenarios by using independent pollen-based reconstructions of land cover and of climate. A further test involves carbon-cycle simulations to determine whether the LULC reconstructions are consistent with constraints provided by ice-core records of CO2 evolution and modern-day LULC. Finally, we outline a protocol for using the improved LULC reconstructions in palaeoclimate simulations within the framework of the Palaeoclimate Modelling Intercomparison Project in order to quantify the magnitude of anthropogenic impacts on climate through time and ultimately to improve the realism of Holocene climate simulations., Geoscientific Model Development Discussions, ISSN:1991-962X, ISSN:1991-9611

Aquatic ecosystems are exposed to a host of anthropogenic stressors whose combined effect can be synthesized with cumulative stress indices. The reliability of cumulative stress indices depends primarily on: 1) stressor incidence maps derived from remote sensing or modeling but rarely validated against on-the-ground observations, and 2) the weighting scheme used to combine multiple stressors into a cumulative index typically based on expert opinion. In this paper, we evaluate the exposure and weights for 13 aquatic stressor maps of the world’s rivers with a comparison against local stress reporting across 1018 inland and coastal sites from the Ramsar Wetlands of International Importance. We found that globally-mapped and locally-reported stressors are poorly aligned overall (AUC-ROC = 0.50–0.63), and that concordance did not improve when stratifying by ecosystem types or continents. Agreement varied across individual stressors, was highest for hydrological stressors and lowest for habitat disconnectivity stressors. We estimated stressor weights from the comparison and found them to be remarkably aligned well with expert-generated weights, suggesting there is convergence on a stressor hierarchy across local and global scales. Our comparison illustrates the value of integrating data across scales to inform the calculation of global stressor indices. Continued systematic stressor monitoring across environmental observation networks is central to benchmarking maps of ecosystem stress globally.

Consumption of wild-caught freshwater fish is concentrated in low-income countries, where it makes a critical contribution to food security and livelihoods. Underestimation of inland harvests in official statistics has long been suspected due to unmonitored subsistence fisheries. To overcome the lack of data from extensive small-scale harvests, we used household consumption surveys to estimate freshwater fish catches in 42 low- and middle-income countries between 1997 and 2014. After accounting for trade and aquaculture, these countries collectively consumed 3.6 MT (CI, 1.5-5.8) more wild-caught freshwater fish than officially reported, reflecting a net underreporting of 64.8% (CI, 27.1-103.9%). Individual countries were more likely to underestimate (n = 31) than overestimate (n = 11) catches, despite conservative assumptions in our calculations. Extrapolating our findings suggests that the global inland catch reported as 10.3 MT in 2008 was more likely 16.6 MT (CI, 2.3-30.9), which accords with recent independent predictions for rivers and lakes. In human terms, these hidden harvests are equivalent to the total animal protein consumption of 36.9 (CI, 30.8-43.4) million people, including many who rely upon wild fish to achieve even minimal protein intake. The widespread underreporting uncovered by household consumption surveys indicates that inland fisheries contribute far more to global food security than has been recognized previously. Our findings also amplify concerns about the sustainability of intensive fishery exploitation as degradation of rivers, lakes, and wetlands continues apace.

Large hydropower schemes have recently gained renewed interest as a provider of efficient and renewable energy, particularly in developing countries. However, some dams may have widespread effects on hydrological and ecosystem integrity, which reach beyond the scales addressed by typical environmental impact assessments. In this paper we address two main ecological impacts—reduced river connectivity and changes in the natural flow regime—at the scale of the entire Mekong River Basin as an important component of dam evaluations. The goal is to improve our understanding of the effect of individual dams as well as clusters of dams at a very large scale. We introduce two new indices, the River Connectivity Index (RCI) as a tool to measure network connectivity, and the River Regulation Index (RRI) as a measure of flow alteration, and calculate the individual and cumulative impact of 81 proposed dams using HydroROUT, a graph-theory based river routing model. Furthermore, we demonstrate how quantitative weighting, e.g. based on river habitat characterizations or species distribution models, may be included in dam impact assessments. A global comparison of large rivers shows that the Mekong would experience strong deterioration in the fragmentation and flow regulation indices if all dams that are currently under consideration in the basin were built, placing it among other heavily impounded rivers in the world. The results illustrate the importance of considering the location of dams, both relative in the network and relative to other already existing dams. Our approach may be used as an index-based ranking system for individual dams, or to compare basin-wide development scenarios, with the goal of providing guidance for decision makers wishing to select locations for future dams with less environmental impacts and to identify and develop potential mitigation strategies.

The world's most biodiverse river basins—the Amazon, Congo, and Mekong—are experiencing an unprecedented boom in construction of hydropower dams. These projects address important energy needs, but advocates often overestimate economic benefits and underestimate far-reaching effects on biodiversity and critically important fisheries. Powerful new analytical tools and high-resolution environmental data can clarify trade-offs between engineering and environmental goals and can enable governments and funding institutions to compare alternative sites for dam building. Current site-specific assessment protocols largely ignore cumulative impacts on hydrology and ecosystem services as ever more dams are constructed within a watershed ( 1 ). To achieve true sustainability, assessments of new projects must go beyond local impacts by accounting for synergies with existing dams, as well as land cover changes and likely climatic shifts ( 2 , 3 ). We call for more sophisticated and holistic hydropower planning, including validation of technologies intended to mitigate environmental impacts. Should anything less be required when tampering with the world's great river ecosystems?

Herein we review estimates of global and regional wetland area from ‘bottom-up’ approaches of site or national wetland inventories and ‘top-down’ approaches from global mapping and remote sensing. The trend for increasing wetland extent reported in the literature over time is a consequence of improved mapping technologies and methods rather than a real increase in wetland area, because a continuing trend for natural wetland loss and conversion is documented over the same time period. The most recent high-resolution estimate of global wetland area is in excess of 12.1×106km2, of which 54% is permanently inundated and 46% is temporarily inundated. Globally, 92.8% of continental wetland area is inland and only 7.2% is coastal. Regionally, the largest wetland areas are in Asia (31.8%), North America (27.1%) and Latin America and the Caribbean (Neotropics; 15.8%), with smaller areas in Europe (12.5%), Africa (9.9%) and Oceania (2.9%). It is likely that estimates of global wetland area published to date persist in underestimating the true wetland area. The ‘grand challenge’ of a global inventory integrating all types of permanent and temporary wetlands at high spatial resolution has yet to be fully achieved.

Large-scale estimates of the area of terrestrial surface waters have greatly improved over time, in particular through the development of multi-satellite methodologies, but the generally coarse spatial resolution (tens of kms) of global observations is still inadequate for many ecological applications. The goal of this study is to introduce a new, globally applicable downscaling method and to demonstrate its applicability to derive fine resolution results from coarse global inundation estimates. The downscaling procedure predicts the location of surface water cover with an inundation probability map that was generated by bagged derision trees using globally available topographic and hydrographic information from the SRTM-derived HydroSHEDS database and trained on the wetland extent of the GLC2000 global land cover map. We applied the downscaling technique to the Global Inundation Extent from Multi-Satellites (GIEMS) dataset to produce a new high-resolution inundation map at a pixel size of 15 arc-seconds, termed GIEMS-D15. GIEMS-D15 represents three states of land surface inundation extents: mean annual minimum (total area, 6.5 x 10(6) km(2)), mean annual maximum (12.1 x 10(6) km(2)), and long-term maximum (173 x 10(6) km(2)); the latter depicts the largest surface water area of any global map to date. While the accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates, overall accuracy is good yet spatially variable. A comparison against regional wetland cover maps generated by independent observations shows that the results adequately represent large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than previously available for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. The technique of applying inundation probabilities also allows for coupling with coarse-scale hydro-climatological model simulations. (C) 2014 Elsevier Inc All rights reserved.