Showing papers in "Environmental Research Letters in 2019"

Key indicators of Arctic climate change : 1971–2017

Abstract: Key observational indicators of climate change in the Arctic, most spanning a 47 year period (1971–2017) demonstrate fundamental changes among nine key elements of the Arctic system. We find that, coherent with increasing air temperature, there is an intensification of the hydrological cycle, evident from increases in humidity, precipitation, river discharge, glacier equilibrium line altitude and land ice wastage. Downward trends continue in sea ice thickness (and extent) and spring snow cover extent and duration, while near-surface permafrost continues to warm. Several of the climate indicators exhibit a significant statistical correlation with air temperature or precipitation, reinforcing the notion that increasing air temperatures and precipitation are drivers of major changes in various components of the Arctic system. To progress beyond a presentation of the Arctic physical climate changes, we find a correspondence between air temperature and biophysical indicators such as tundra biomass and identify numerous biophysical disruptions with cascading effects throughout the trophic levels. These include: increased delivery of organic matter and nutrients to Arctic near‐coastal zones; condensed flowering and pollination plant species periods; timing mismatch between plant flowering and pollinators; increased plant vulnerability to insect disturbance; increased shrub biomass; increased ignition of wildfires; increased growing season CO2 uptake, with counterbalancing increases in shoulder season and winter CO2 emissions; increased carbon cycling, regulated by local hydrology and permafrost thaw; conversion between terrestrial and aquatic ecosystems; and shifting animal distribution and demographics. The Arctic biophysical system is now clearly trending away from its 20th Century state and into an unprecedented state, with implications not only within but beyond the Arctic. The indicator time series of this study are freely downloadable at AMAP.no.

The effects of climate extremes on global agricultural yields

Abstract: Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors - including mean climate as well as climate extremes - explain 20%-49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%-43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.

International comparison of health care carbon footprints

Abstract: Climate change confronts the health care sector with a dual challenge. Accumulating climate impacts are putting an increased burden on the service provision of already stressed health care systems in many regions of the world. At the same time, the Paris agreement requires rapid emission reductions in all sectors of the global economy to stay well below the 2 °C target. This study shows that in OECD countries, China, and India, health care on average accounts for 5% of the national CO2 footprint making the sector comparable in importance to the food sector. Some countries have seen reduced CO2 emissions related to health care despite growing expenditures since 2000, mirroring their economy wide emission trends. The average per capita health carbon footprint across the country sample in 2014 was 0.6 tCO2, varying between 1.51 tCO2/cap in the US and 0.06 tCO2/cap in India. A statistical analysis shows that the carbon intensity of the domestic energy system, the energy intensity of the domestic economy, and health care expenditure together explain half of the variance in per capita health carbon footprints. Our results indicate that important leverage points exist inside and outside the health sector. We discuss our findings in the context of the existing literature on the potentials and challenges of reducing GHG emissions in the health and energy sector.

Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern

Abstract: The summer of 2018 witnessed a number of extreme weather events such as heatwaves in North America, Western Europe and the Caspian Sea region, and rainfall extremes in South-East Europe and Japan that occurred near-simultaneously. Here we show that some of these extremes were connected by an amplified hemisphere-wide wavenumber 7 circulation pattern. We show that this pattern constitutes an important teleconnection in Northern Hemisphere summer associated with prolonged and above-normal temperatures in North America, Western Europe and the Caspian Sea region. This pattern was also observed during the European heatwaves of 2003, 2006 and 2015 among others. We show that the occurrence of this wave 7 pattern has increased over recent decades.

Non-renewable groundwater use and groundwater depletion: a review

Abstract: Population growth, economic development, and dietary changes have drastically increased the demand for food and water. The resulting expansion of irrigated agriculture into semi-arid areas with limited precipitation and surface water has greatly increased the dependence of irrigated crops on groundwater withdrawal. Also, the increasing number of people living in mega-cities without access to clean surface water or piped drinking water has drastically increased urban groundwater use. The result of these trends has been the steady increase of the use of non-renewable groundwater resources and associated high rates of aquifer depletion around the globe. We present a comprehensive review of the state-of-the-art in research on non-renewable groundwater use and groundwater depletion. We start with a section defining the concepts of non-renewable groundwater, fossil groundwater and groundwater depletion and place these concepts in a hydrogeological perspective. We pay particular attention to the interaction between groundwater withdrawal, recharge and surface water which is critical to understanding sustainable groundwater withdrawal. We provide an overview of methods that have been used to estimate groundwater depletion, followed by an extensive review of global and regional depletion estimates, the adverse impacts of groundwater depletion and the hydroeconomics of groundwater use. We end this review with an outlook for future research based on main research gaps and challenges identified. This review shows that both the estimates of current depletion rates and the future availability of non-renewable groundwater are highly uncertain and that considerable data and research challenges need to be overcome if we hope to reduce this uncertainty in the near future.

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Abstract: BigData andmachine learning (ML) technologies have the potential to impactmany facets of environment andwatermanagement (EWM). BigData are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and socialmedia have contributed to the proliferation of BigData inmany EWMfields, such asweather forecasting, disastermanagement, smart water and energymanagement systems, and remote sensing. BigData brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics.ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn fromdata.MLmay help unlock the power of BigData if properly integratedwith data analytics. Recent breakthroughs inAI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features fromdata, with better predictive performance and less human intervention. Collectively BigData andML techniques have shown great potential for data-driven decisionmaking, scientific discovery, and process optimization. These technological advancesmay greatly benefit EWM, especially because (1)many EWMapplications (e.g. early floodwarning) require the capability to extract useful information from a large amount of data in autonomousmanner and in real time, (2)EWMresearches have become highlymultidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge aboutmany EWMprocesses is still incomplete, but whichmay now be complemented through data-driven discovery. A large number of applications onBigData andML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in BigData andML, (3) provide a systematic review of current applications, andfinally (4) discussmajor issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM,with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWMresearch.

Are e-scooters polluters? The environmental impacts of shared dockless electric scooters

Abstract: Shared stand-up electric scooters are nowoffered inmany cities as an option for short-term rental, and marketed for short-distance travel. Using life cycle assessment, we quantify the total environmental impacts of thismobility option associatedwith global warming, acidification, eutrophication, and respiratory impacts.Wefind that environmental burdens associatedwith charging the e-scooter are small relative tomaterials andmanufacturing burdens of the e-scooters and the impacts associated with transporting the scooters to overnight charging stations. The results of aMonteCarlo analysis show an average value of life cycle global warming impacts of 202 gCO2-eq/passenger-mile, driven by materials andmanufacturing (50%), followed by daily collection for charging (43%of impact).We illustrate the potential to reduce life cycle global warming impacts through improved scooter collection and charging approaches, including the use of fuel-efficient vehicles for collection (yielding 177 gCO2-eq/passenger-mile), limiting scooter collection to thosewith a lowbattery state of charge (164 gCO2-eq/passenger-mile), and reducing the driving distance per scooter for e-scooter collection and distribution (147 gCO2-eq/passenger-mile). The results prove to be highly sensitive to e-scooter lifetime; ensuring that the shared e-scooters are used for two years decreases the average life cycle emissions to 141 gCO2-eq/passenger-mile. Under our BaseCase assumptions, wefind that the life cycle greenhouse gas emissions associatedwith e-scooter use is higher in 65%of ourMonteCarlo simulations than the suite ofmodes of transportation that are displaced. This likelihood drops to 35%–50%under our improved and efficient e-scooter collection processes and only 4%whenwe assume two-year e-scooter lifetimes.When e-scooter usage replaces average personal automobile travel, we nearly universally realize a net reduction in environmental impacts.

What causes deforestation in Indonesia

Abstract: We investigate the causes of deforestation in Indonesia, a country with one of the highest rates of primary natural forest loss in the tropics, annually between 2001 and 2016. We use high spatial resolution imagery made available on Google Earth to characterize the land cover types following a random selection of deforestation events, drawn from the Global Forest Change dataset. Notorious in the region, large-scale oil palm and timber plantations together contributed more than two-fifths of nationwide deforestation over our study period, with a peak in late aughts followed by a notable decline up to 2016. Conversion of forests to grasslands, which comprised an average of one-fifth of national deforestation, rose sharply in dominance in years following periods of considerable fire activity, particularly in 2016. Small-scale agriculture and small-scale plantations also contributed one-fifth of nationwide forest loss and were the dominant drivers of loss outside the major islands of Indonesia. Although relatively small contributors to total deforestation, logging roads were responsible for a declining share of deforestation, and mining activities were responsible for an increasing share, over the study period. Direct drivers of deforestation in Indonesia are thus spatially and temporally dynamic, suggesting the need for forest conservation policy responses tailored at the subnational level, and new methods for monitoring the causes of deforestation over time.

Material efficiency strategies to reducing greenhouse gas emissions associated with buildings, vehicles, and electronics—a review

Abstract: As one quarter of global energy use serves the production of materials, the more efficient use of these materials presents a significant opportunity for the mitigation of greenhouse gas (GHG) emissions. With the renewed interest of policy makers in the circular economy, material efficiency (ME) strategies such as light-weighting and downsizing of and lifetime extension for products, reuse and recycling of materials, and appropriate material choice are being promoted. Yet, the emissions savings from ME remain poorly understood, owing in part to the multitude of material uses and diversity of circumstances and in part to a lack of analytical effort. We have reviewed emissions reductions from ME strategies applied to buildings, cars, and electronics. We find that there can be a systematic trade-off between material use in the production of buildings, vehicles, and appliances and energy use in their operation, requiring a careful life cycle assessment of ME strategies. We find that the largest potential emission reductions quantified in the literature result from more intensive use of and lifetime extension for buildings and the light-weighting and reduced size of vehicles. Replacing metals and concrete with timber in construction can result in significant GHG benefits, but trade-offs and limitations to the potential supply of timber need to be recognized. Repair and remanufacturing of products can also result in emission reductions, which have been quantified only on a case-by-case basis and are difficult to generalize. The recovery of steel, aluminum, and copper from building demolition waste and the end-of-life vehicles and appliances already results in the recycling of base metals, which achieves significant emission reductions. Higher collection rates, sorting efficiencies, and the alloy-specific sorting of metals to preserve the function of alloying elements while avoiding the contamination of base metals are important steps to further reduce emissions.

Machine learning and artificial intelligence to aid climate change research and preparedness

Abstract: Climate change challenges societal functioning, likely requiring considerable adaptation to cope with future altered weather patterns. Machine learning (ML) algorithms have advanced dramatically, triggering breakthroughs in other research sectors, and recently suggested as aiding climate analysis (Reichstein et al 2019 Nature 566 195–204, Schneider et al 2017 Geophys. Res. Lett. 44 12396–417). Although a considerable number of isolated Earth System features have been analysed with ML techniques, more generic application to understand better the full climate system has not occurred. For instance, ML may aid teleconnection identification, where complex feedbacks make characterisation difficult from direct equation analysis or visualisation of measurements and Earth System model (ESM) diagnostics. Artificial intelligence (AI) can then build on discovered climate connections to provide enhanced warnings of approaching weather features, including extreme events. While ESM development is of paramount importance, we suggest a parallel emphasis on utilising ML and AI to understand and capitalise far more on existing data and simulations.

Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition

Abstract: While many developed countries are increasing their forest cover, deforestation is still rife in the tropics and subtropics. With international trade in forest-risk commodities on the rise, it is becoming increasingly important to consider between-country trade linkages in assessing the drivers of-and possible connections between-forest loss and gain across countries. Previous studies have shown that countries that have undergone a forest transition (and are now increasing their forest cover) tend to displace land use outside their borders. However, lack of comprehensive data on deforestation drivers imply that it has not been possible to ascertain whether this has accelerated forest loss in sourcing countries. To remedy this, we present a land-balance model that quantifies deforestation embodied in production of agricultural and forestry commodities at country level across the tropics and subtropics, subsequently tracing embodied deforestation to countries of apparent consumption using a physical, country-to-country trade model. We find that in the period 2005-2013,62% (5.5 Mha yr(-1)) of forest loss could be attributed to expanding commercial cropland, pastures and tree plantations. The commodity groups most commonly associated with deforestation were cattle meat, forestry products, oil palm, cereals and soybeans, though variation between countries and regions was large. Alarge (26%) and slightly increasing share of deforestation was attributed to international demand, the bulk of which (87%) was exported to countries that either exhibit decreasing deforestation rates or increasing forest cover (late-or post-forest transition countries), particularly in Europe and Asia (China, India, and Russia). About a third of the net forest gains in post-forest transition countries was in this way offset by imports of commodities causing deforestation elsewhere, suggesting that achieving a global forest transition will be substantially more challenging than achieving national or regional ones.

Global virtual water trade and the hydrological cycle: Patterns, drivers, and socio-environmental impacts

Abstract: The increasing global demand for farmland products is placing unprecedented pressure on the global agricultural system and its water resources. Many regions of the world, that are affected by a chronic water scarcity relative to their population, strongly depend on the import of agricultural commodities and associated embodied (or virtual) water. The globalization of water through virtual water trade (VWT) is leading to a displacement of water use and a disconnection between human populations and the water resources they rely on. Despite the recognized importance of these phenomena in reshaping the patterns of water dependence through teleconnections between consumers and producers, their effect on global and regional water resources has just started to be quantified. This review investigates the global spatiotemporal dynamics, drivers, and impacts of VWT through an integrated analysis of surface water, groundwater, and root-zone soil moisture consumption for agricultural production; it evaluates how virtual water flows compare to the major 'physical water fluxes' in the Earth System; and provides a new reconceptualization of the hydrologic cycle to account also for the role of water redistribution by the hidden 'virtual water cycle'.

Assessment of climate change impacts on buildings, structures and infrastructure in the Russian regions on permafrost

Abstract: Russian regions containing permafrost play an important role in the Russian economy, containing vast reserves of natural resources and hosting large-scale infrastructure to facilitate these resources' exploitation. Rapidly changing climatic conditions are a major concern for the future economic development of these regions. This study examines the extent to which infrastructure and housing are affected by permafrost in Russia and estimates the associated value of these assets. An ensemble of climate projections is used as a forcing to a permafrost-geotechnical model, in order to estimate the cost of buildings and infrastructure affected by permafrost degradation by mid-21st century under RCP 8.5 scenario. The total value of fixed assets on permafrost was estimated at 248.6 bln USD. Projected climatic changes will affect 20% of structures and 19% of infrastructure assets, costing 16.7 bln USD and 67.7 bln USD respectively to mitigate. The total cost of residential real estate on permafrost was estimated at 52.6 bln USD, with 54% buildings affected by significant permafrost degradation by the mid-21st century. The paper discusses the variability in climate-change projections and the ability of Russia's administrative regions containing permafrost to cope with projected climate-change impacts. The study can be used in land use planning and to promote the development of adaptation and mitigation strategies for addressing the climate-change impacts of permafrost degradation on infrastructure and housing.

Drought vulnerability and risk assessments: state of the art, persistent gaps, and research agenda

Abstract: Reducing the social, environmental, and economic impacts of droughts and identifying pathways towards drought resilient societies remains a global priority. A common understanding of the drivers of drought risk and ways in which drought impacts materialize is crucial for improved assessments and for the identification and (spatial) planning of targeted drought risk reduction and adaptation options. Over the past two decades, we have witnessed an increase in drought risk assessments across spatial and temporal scales drawing on a multitude of conceptual foundations and methodological approaches. Recognizing the diversity of approaches in science and practice as well as the associated opportunities and challenges, we present the outcomes of a systematic literature review of the state of the art of people-centered drought vulnerability and risk conceptualization and assessments, and identify persisting gaps. Our analysis shows that, of the reviewed assessments, (i) more than 60% do not explicitly specify the type of drought hazard that is addressed, (ii) 42% do not provide a clear definition of drought risk, (iii) 62% apply static, index-based approaches, (iv) 57% of the indicator-based assessments do not specify their weighting methods, (v) only 11% conduct any form of validation, (vi) only ten percent develop future scenarios of drought risk, and (vii) only about 40% of the assessments establish a direct link to drought risk reduction or adaptation strategies, i.e. consider solutions. We discuss the challenges associated with these findings for both assessment and identification of drought risk reduction measures and identify research needs to inform future research and policy agendas in order to advance the understanding of drought risk and support pathways towards more drought resilient societies.

The aridity Index under global warming

Abstract: Aridity is a complex concept that ideally requires a comprehensive assessment of hydroclimatological and hydroecological variables to fully understand anticipated changes. A widely used (offline) impact model to assess projected changes in aridity is the Aridity Index (defined as the ratio of potential evaporation to precipitation), summarizing the aridity concept into a single number. Based on the aridity index, it was shown that aridity will generally increase under conditions of increased CO2 and associated global warming. However, assessing the same climate model output directly, suggests a more nuanced response of aridity to global warming, raising the question if the Aridity Index provides a good representation of the complex nature of anticipated aridity changes. By systematically comparing projections of the Aridity Index against projections for various hydroclimatological and ecohydrological variables, we show that the Aridity Index generally provides a rather poor proxy for projected aridity conditions. Direct climate model output is shown to contradict signals of increasing aridity obtained from the Aridity Index in at least half of the global land area with robust change. We further show that part of this discrepancy can be related to the parameterization of potential evaporation. Especially the most commonly used potential evaporation model likely leads to an overestimation of future aridity due to incorrect assumptions under increasing atmospheric CO2. Our results show that AI-based approaches do not correctly communicate changes projected by the fully coupled climate models. The solution is to directly analyse the model outputs rather than use a separate offline impact model. We thus urge for a direct and joint assessment of climate model output when assessing future aridity changes rather than using simple index-based impact models that use climate model output as input and are potentially subject to significant biases.

Persistent fossil fuel growth threatens the Paris Agreement and planetary health

Abstract: Amidst declarations of planetary emergency and reports that the window for limiting climate change to 1.5 °C is rapidly closing, global average temperatures and fossil fuel emissions continue to rise. Global fossil CO2 emissions have grown three years consecutively: +1.5% in 2017, +2.1% in 2018, and our slower central projection of +0.6% in 2019 (range of –0.32% to 1.5%) to 37 ± 2 Gt CO2 (Friedlingstein et al 2019 Earth Syst. Sci. Data accepted), after a temporary growth hiatus from 2014 to 2016. Economic indicators and trends in global natural gas and oil use suggest a further rise in emissions in 2020 is likely. CO2 emissions are decreasing slowly in many industrialized regions, including the European Union (preliminary estimate of −1.7% [–3.4% to +0.1%] for 2019, −0.8%/yr for 2003–2018) and United States (−1.7% [–3.7% to +0.3%] in 2019, −0.8%/yr for 2003–2018), while emissions continue growing in India (+1.8% [+0.7% to 3.7%] in 2019, +5.1%/yr for 2003–2018), China (+2.6% [+0.7% to 4.4%] in 2019, +0.4%/yr for 2003–2018), and rest of the world ((+0.5% [−0.8% to 1.8%] in 2019, +1.4%/yr for 2003–2018). Two under-appreciated trends suggest continued long-term growth in both oil and natural gas use is likely. Because per capita oil consumption in the US and Europe remains 5- to 20-fold higher than in China and India, increasing vehicle ownership and air travel in Asia are poised to increase global CO2 emissions from oil over the next decade or more. Liquified natural gas exports from Australia and the United States are surging, lowering natural gas prices in Asia and increasing global access to this fossil resource. To counterbalance increasing emissions, we need accelerated energy efficiency improvements and reduced consumption, rapid deployment of electric vehicles, carbon capture and storage technologies, and a decarbonized electricity grid, with new renewable capacities replacing fossil fuels, not supplementing them. Stronger global commitments and carbon pricing would help implement such policies at scale and in time.

The unprecedented coupled ocean-atmosphere summer heatwave in the New Zealand region 2017/18: drivers, mechanisms and impacts

Abstract: During austral summer (DJF) 2017/18, the New Zealand region experienced an unprecedented coupled ocean-atmosphere heatwave, covering an area of 4 million km2. Regional average air temperature anomalies over land were +2.2oC, and sea surface temperature anomalies reached +3.7oC in the eastern Tasman Sea. This paper discusses the event, including atmospheric and oceanic drivers, the role of anthropogenic warming, and terrestrial and marine impacts. The heatwave was associated with very low wind speeds, reducing upper ocean mixing and allowing heat fluxes from the atmosphere to the ocean to cause substantial warming of the stratified surface layers of the Tasman Sea. The event persisted for the entire austral summer resulting in a 3.8±0.6 km3 loss of glacier ice in the Southern Alps (the largest annual loss in records back to 1962), very early Sauvignon Blanc wine-grape maturation in Marlborough, and major species disruption in marine ecosystems. The dominant driver was positive Southern Annular Mode (SAM) conditions, with a smaller contribution from La Nina. The long-term trend towards positive SAM conditions, a result of stratospheric ozone depletion and greenhouse gas increase, is thought to have contributed through association with more frequent anticyclonic "blocking" conditions in the New Zealand region and a more poleward average latitude for the Southern Ocean storm track. The unprecedented heatwave provides a good analogue for possible mean conditions in the late 21st century. The best match suggests this extreme summer may be typical of average New Zealand summer climate for 2081-2100, under the RCP4.5 or RCP6.0 scenario.

Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms

Abstract: Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression, random forests, Extreme Gradient Boosting, and their ensembles) as meta-models for a cropping systems simulator (APSIM) to inform future decision support tool development. We asked: (1) How well do ML meta-models predict maize yield and N losses using pre-season information? (2) How many data are needed to train ML algorithms to achieve acceptable predictions? (3) Which input data variables are most important for accurate prediction? And (4) do ensembles of ML meta-models improve prediction? The simulated dataset included more than three million data including genotype, environment and management scenarios. XGBoost was the most accurate ML model in predicting yields with a relative mean square error (RRMSE) of 13.5%, and Random forests most accurately predicted N loss at planting time, with a RRMSE of 54%. ML meta-models reasonably reproduced simulated maize yields using the information available at planting, but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10%–40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables (weather, soil properties, management, initial conditions), thus depending on the data availability researchers may use a different ML model. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre-season management.

Projections of declining fluvial sediment delivery to major deltas worldwide in response to climate change and anthropogenic stress

Abstract: Deltas are resource rich, low-lying areas where vulnerability to flooding is exacerbated by natural and anthropogenically induced subsidence and geocentric sea-level rise, threatening the large populations often found in these settings Delta 'drowning' is potentially offset by deposition of sediment on the delta surface, making the delivery of fluvial sediment to the delta a key balancing control in offsetting relative sea-level rise, provided that sediment can be dispersed across the subaerial delta Here we analyse projected changes in fluvial sediment flux over the 21st century to 47 of the world's major deltas under 12 environmental change scenarios The 12 scenarios were constructed using four climate pathways (Representative Concentration Pathways 26, 45, 60 and 85), three socioeconomic pathways (Shared Socioeconomic Pathways 1, 2 and 3), and one reservoir construction timeline A majority (33/47) of the investigated deltas are projected to experience reductions in sediment flux by the end of the century, when considering the average of the scenarios, with mean and maximum declines of 38% and 83%, respectively, between 1990–2019 and 2070–2099 These declines are driven by the effects of anthropogenic activities (changing land management practices and dam construction) overwhelming the effects of future climate change The results frame the extent and magnitude of future sustainability of major global deltas They highlight the consequences of direct (eg damming) and indirect (eg climate change) alteration of fluvial sediment flux dynamics and stress the need for further in-depth analysis for individual deltas to aid in developing appropriate management measures

Global unsustainable virtual water flows in agricultural trade

Abstract: Author(s): Rosa, Lorenzo; Chiarelli, Davide Danilo; Tu, Chengyi; Rulli, Maria Cristina; D'Odorico, Paolo | Abstract: Recent studies have highlighted the reliance of global food production on unsustainable irrigation practices, which deplete freshwater stocks and environmental flows, and consequently impair aquatic ecosystems. Unsustainable irrigation is driven by domestic and international demand for agricultural products. Research on the environmental consequences of trade has often concentrated on the global displacement of pollution and land use, while the effect of trade on water sustainability and the drying of over-depleted watercourses has seldom been recognized and quantified. Here we evaluate unsustainable irrigation water consumption (UWC) associated with global crop production and determine the share of UWC embedded in international trade. We find that, while about 52% of global irrigation is unsustainable, 15% of it is virtually exported, with an average 18% increase between year 2000 and 2015. About 60% of global virtual transfers of UWC are driven by exports of cotton, sugar cane, fruits, and vegetables. One third of UWC in Mexico, Spain, Turkmenistan, South Africa, Morocco, and Australia is associated with demand from the export markets. The globalization of water through trade contributes to running rivers dry, an environmental externality commonly overlooked by trade policies. By identifying the producing and consuming countries that are responsible for unsustainable irrigation embedded in virtual water trade, this study highlights trade links in which policies are needed to achieve sustainable water and food security goals in the coming decades.

How will climate change shape climate opinion

Abstract: As climate change intensifies, global publics will experiencemore unusual weather and extreme weather events. Howwill individual experiences with theseweather trends shape climate change beliefs, attitudes, and behaviors? In this article, we review 73 papers that have studied the relationship between climate change experiences and public opinion. Overall, wefindmixed evidence that weather shapes climate opinions. Although there is some support for aweak effect of local temperature and extremeweather events on climate opinion, the heterogeneity of independent variables, dependent variables, study populations, and research designs complicate systematic comparison. To advance research on this critical topic, we suggest that future studies pay careful attention to differences between self-reported and objective weather data, causal identification, and the presence of spatial autocorrelation inweather and climate data. Refining research designs andmethods in future studies will help us understand the discrepancies in results, and allow better detection of effects, which have important practical implications for climate communication. As the global population increasingly experiences weather conditions outside the range of historical experience, researchers, communicators, and policymakers need to understand how these experiences shape-and are shaped by-public opinions and behaviors.

Global soil acidification impacts on belowground processes

Abstract: With continuous nitrogen (N) enrichment and sulfur (S) deposition, soil acidification has accelerated and become a global environmental issue. However, a full understanding of the general pattern of ecosystem belowground processes in response to soil acidification due to the impacting factors remains elusive. We conducted a meta-analysis of soil acidification impacts on belowground functions using 304 observations from 49 independent studies, mainly including soil cations, soil nutrient, respiration, root and microbial biomass. Our results show that acid addition significantly reduced soil pH by 0.24 on average, with less pH decrease in forest than non-forest ecosystems. The response ratio of soil pH was positively correlated with site precipitation and temperature, but negatively with initial soil pH. Soil base cations (Ca2+, Mg2+, Na+) decreased while non-base cations (Al3+, Fe3+) increased with soil acidification. Soil respiration, fine root biomass, microbial biomass carbon and nitrogen were significantly reduced by 14.7%, 19.1%, 9.6% and 12.1%, respectively, under acid addition. These indicate that soil carbon processes are sensitive to soil acidification. Overall, our meta-analysis suggests a strong negative impact of soil acidification on belowground functions, with the potential to suppress soil carbon emission. It also arouses our attention to the toxic effects of soil ions on terrestrial ecosystems.

A 40-year review of food–energy–water nexus literature and its application to the urban scale

Abstract: Essential for society to function, the production and consumption of food, energy, and water (FEW) are deeply intertwined, leading to calls for a nexus approach to understand and manage the complex tradeoffs and cascading effects. What research exists to date on this FEW nexus? How have scholars conceptualized these interactions at the urban scale? What are some promising approaches? Where are the research gaps? To answer these questions, we conducted a quantitative review of the academic literature on the FEW nexus (1399 publications) over more than four decades (1973–2017), followed by in-depth analysis of the most influential papers using an evaluation matrix that examined four components: 1) modeling approach; 2) scale; 3) nexus ‘trigger’; and 4) governance and policy. Scholars in the fields of environmental science predominated, while social science domains were under-represented. Most papers used quantitative rather than qualitative approaches, especially integrated assessment and systems dynamics modeling although spatial scale was generally recognized, explicit consideration of multi-scalar interactions was limited. Issues of institutional structure, governance, equity, resource access, and behavior were also underdeveloped. Bibliometric analysis of this literature revealed six distinct research communities, including a nascent urban FEW community. We replicated the analysis for this urban group, finding it to be just emerging (80% of papers have been published since 2010) and dominated by scholars in industrial ecology. These scholars focus on quantifying FEW flows of the urban metabolism in isolation rather than as a nexus, largely ignoring the political and socio-economic factors shaping these flows. We propose the urban FEW metabolism as a boundary object to draw in diverse scholarly and practitioner communities. This will advance research on complex FEW systems in four key areas: (1) integration of heterogeneous models and approaches; (2) scalar linkages between urban consumption and trans-boundary resource flows; (3) how actors and institutions shape resource access, distribution and use; and (4) co-production of knowledge with stakeholders.