Arctic rooting depth distribution influences modelled carbon emissions but cannot be inferred from aboveground vegetation type.

The distribution of roots throughout the soil drives depth-dependent plant-soil interactions and ecosystem processes, particularly in arctic tundra where plant biomass, is predominantly belowground. Vegetation is usually classified from aboveground, but it is unclear whether such classifications are suitable to estimate belowground attributes and their consequences, such as rooting depth distribution and its influence on carbon cycling. We performed a meta-analysis of 55 published arctic rooting depth profiles, testing for differences both between distributions based on aboveground vegetation types (Graminoid, Wetland, Erect-shrub, and Prostrate-shrub tundra) and between 'Root Profile Types' for which we defined three representative and contrasting clusters. We further analyzed potential impacts of these different rooting depth distributions on rhizosphere priming-induced carbon losses from tundra soils. Rooting depth distribution hardly differed between aboveground vegetation types but varied between Root Profile Types. Accordingly, modelled priming-induced carbon emissions were similar between aboveground vegetation types when they were applied to the entire tundra, but ranged from 7.2 to 17.6 Pg C cumulative emissions until 2100 between individual Root Profile Types. Variations in rooting depth distribution are important for the circumpolar tundra carbon-climate feedback but can currently not be inferred adequately from aboveground vegetation type classifications.

Global trends in grassland carrying capacity and relative stocking density of livestock.

Although the role of livestock in future food systems is debated, animal proteins are unlikely to completely disappear from our diet. Grasslands are a key source of primary productivity for livestock, and feed-food competition is often limited on such land. Previous research on the potential for sustainable grazing has focused on restricted geographical areas or does not consider inter-annual changes in grazing opportunities. Here, we developed a robust method to estimate trends and interannual variability (IV) in global livestock carrying capacity (number of grazing animals a piece of land can support) over 2001-2015, as well as relative stocking density (the reported livestock distribution relative to the estimated carrying capacity [CC]) in 2010. We first estimated the aboveground biomass that is available for grazers on global grasslands based on the MODIS Net Primary Production product. This was then used to calculate livestock carrying capacities using slopes, forest cover, and animal forage requirements as restrictions. We found that globally, CC decreased on 27% of total grasslands area, mostly in Europe and southeastern Brazil, while it increased on 15% of grasslands, particularly in Sudano-Sahel and some parts of South America. In 2010, livestock forage requirements exceeded forage availability in northwestern Europe, and southern and eastern Asia. Although our findings imply some opportunities to increase grazing pressures in cold regions, Central Africa, and Australia, the high IV or low biomass supply might prevent considerable increases in stocking densities. The approach and derived open access data sets can feed into global food system modelling, support conservation efforts to reduce land degradation associated with overgrazing, and help identify undergrazed areas for targeted sustainable intensification efforts or rewilding purposes.

Fluvial sediment supply to a mega-delta reduced by shifting tropical-cyclone activity.

The world's rivers deliver 19 billion tonnes of sediment to the coastal zone annually, with a considerable fraction being sequestered in large deltas, home to over 500 million people. Most (more than 70 per cent) large deltas are under threat from a combination of rising sea levels, ground surface subsidence and anthropogenic sediment trapping, and a sustainable supply of fluvial sediment is therefore critical to prevent deltas being 'drowned' by rising relative sea levels. Here we combine suspended sediment load data from the Mekong River with hydrological model simulations to isolate the role of tropical cyclones in transmitting suspended sediment to one of the world's great deltas. We demonstrate that spatial variations in the Mekong's suspended sediment load are correlated (r = 0.765, P < 0.1) with observed variations in tropical-cyclone climatology, and that a substantial portion (32 per cent) of the suspended sediment load reaching the delta is delivered by runoff generated by rainfall associated with tropical cyclones. Furthermore, we estimate that the suspended load to the delta has declined by 52.6 ± 10.2 megatonnes over recent years (1981-2005), of which 33.0 ± 7.1 megatonnes is due to a shift in tropical-cyclone climatology. Consequently, tropical cyclones have a key role in controlling the magnitude of, and variability in, transmission of suspended sediment to the coast. It is likely that anthropogenic sediment trapping in upstream reservoirs is a dominant factor in explaining past, and anticipating future, declines in suspended sediment loads reaching the world's major deltas. However, our study shows that changes in tropical-cyclone climatology affect trends in fluvial suspended sediment loads and thus are also key to fully assessing the risk posed to vulnerable coastal systems.

Strong influence of El Niño Southern Oscillation on flood risk around the world.

El Niño Southern Oscillation (ENSO) is the most dominant interannual signal of climate variability and has a strong influence on climate over large parts of the world. In turn, it strongly influences many natural hazards (such as hurricanes and droughts) and their resulting socioeconomic impacts, including economic damage and loss of life. However, although ENSO is known to influence hydrology in many regions of the world, little is known about its influence on the socioeconomic impacts of floods (i.e., flood risk). To address this, we developed a modeling framework to assess ENSO's influence on flood risk at the global scale, expressed in terms of affected population and gross domestic product and economic damages. We show that ENSO exerts strong and widespread influences on both flood hazard and risk. Reliable anomalies of flood risk exist during El Niño or La Niña years, or both, in basins spanning almost half (44%) of Earth's land surface. Our results show that climate variability, especially from ENSO, should be incorporated into disaster-risk analyses and policies. Because ENSO has some predictive skill with lead times of several seasons, the findings suggest the possibility to develop probabilistic flood-risk projections, which could be used for improved disaster planning. The findings are also relevant in the context of climate change. If the frequency and/or magnitude of ENSO events were to change in the future, this finding could imply changes in flood-risk variations across almost half of the world's terrestrial regions.

Decoding the drivers of bank erosion on the Mekong river: The roles of the Asian monsoon, tropical storms, and snowmelt.

We evaluate links between climate and simulated river bank erosion for one of the world's largest rivers, the Mekong. We employ a process-based model to reconstruct multidecadal time series of bank erosion at study sites within the Mekong's two main hydrological response zones, defining a new parameter, accumulated excess runoff (AER), pertinent to bank erosion. We employ a hydrological model to isolate how snowmelt, tropical storms and monsoon precipitation each contribute to AER and thus modeled bank erosion. Our results show that melt (23.9% at the upstream study site, declining to 11.1% downstream) and tropical cyclones (17.5% and 26.4% at the upstream and downstream sites, respectively) both force significant fractions of bank erosion on the Mekong. We also show (i) small, but significant, declines in AER and hence assumed bank erosion during the 20th century, and; (ii) that significant correlations exist between AER and the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). Of these modes of climate variability, we find that IOD events exert a greater control on simulated bank erosion than ENSO events; but the influences of both ENSO and IOD when averaged over several decades are found to be relatively weak. However, importantly, relationships between ENSO, IOD, and AER and hence inferred river bank erosion are not time invariant. Specifically, we show that there is an intense and prolonged epoch of strong coherence between ENSO and AER from the early 1980s to present, such that in recent decades derived Mekong River bank erosion has been more strongly affected by ENSO.

Using global datasets to create environmental profiles for data-poor regions: a case from the Irrawaddy and Salween River Basins.

The increasing availability of spatial data inspires the exploration of previously less-studied, yet regionally and nationally important areas, such as the Irrawaddy and Salween River Basins in Southeast Asia. This article documents our experience using global datasets to create environmental basin profiles in these two basins. Our approach draws on the concepts of freshwater vulnerability assessments that guided the selection of indicators. Data on land use, population distribution and fertilizer load were used. The unit of analysis was chosen to distinguish areas with similar bio-geographical characteristics, such as the critical delta areas. Results were further discussed for sub-areas that experience relatively the most pressure in terms of examined indicators within the studied area. The river mouths of both rivers had the most intensive land use and high population density. They are also home to important ecosystems and are sensitive to changes in upstream areas. Our study presents a concise and spatially distributed view of the environmental basin profiles of the Irrawaddy and Salween River Basins. The analysis also provides some interesting methodological insights about the potential of public macro-scale datasets for environmental assessment. The spatial approach allowed the analysis of different indicators, providing a platform for data integration as well as a visually powerful overview of the study area. Yet, the use of macro-scale datasets entails challenges. Despite improvements, the assessment process tends to be driven by the availability and quality of data, rather than by the actual research and management needs. The greatest utility of macro-scale datasets lies-at least in data-poor areas-in larger scale comparative analyses between the basins and their different sub-areas.

Agricultural input shocks affect crop yields more in the high-yielding areas of the world.

The industrialization of agriculture has led to an increasing dependence on non-locally sourced agricultural inputs. Hence, shocks in the availability of agricultural inputs can be devastating to food crop production. There is also a pressure to decrease the use of synthetic fertilizers and pesticides in many areas. However, the combined impact of the agricultural input shocks on crop yields has not yet been systematically assessed globally. Here we modelled the effects of agricultural input shocks using a random forest machine learning algorithm. We show that shocks in fertilizers cause the most drastic yield losses. Under the scenario of 50% shock in all studied agricultural inputs, global maize production could decrease up to 26%, and global wheat production up to 21%, impacting particularly the high-yielding 'breadbasket' areas of the world. Our study provides insights into global food system resilience and can be useful for preparing for potential future shocks or agricultural input availability decreases at local and global scales.

Increased probability of hot and dry weather extremes during the growing season threatens global crop yields.

Although extreme weather events recur periodically everywhere, the impacts of their simultaneous occurrence on crop yields are globally unknown. In this study, we estimate the impacts of combined hot and dry extremes as well as cold and wet extremes on maize, rice, soybean, and wheat yields using gridded weather data and reported crop yield data at the global scale for 1980-2009. Our results show that co-occurring extremely hot and dry events have globally consistent negative effects on the yields of all inspected crop types. Extremely cold and wet conditions were observed to reduce crop yields globally too, although to a lesser extent and the impacts being more uncertain and inconsistent. Critically, we found that over the study period, the probability of co-occurring extreme hot and dry events during the growing season increased across all inspected crop types; wheat showing the largest, up to a six-fold, increase. Hence, our study highlights the potentially detrimental impacts that increasing climate variability can have on global food production.

World's human migration patterns in 2000-2019 unveiled by high-resolution data.

Despite being a topical issue in public debate and on the political agenda for many countries, a global-scale, high-resolution quantification of migration and its major drivers for the recent decades remained missing. We created a global dataset of annual net migration between 2000 and 2019 (~10 km grid, covering the areas of 216 countries or sovereign states), based on reported and downscaled subnational birth (2,555 administrative units) and death (2,067 administrative units) rates. We show that, globally, around 50% of the world's urban population lived in areas where migration accelerated urban population growth, while a third of the global population lived in provinces where rural areas experienced positive net migration. Finally, we show that, globally, socioeconomic factors are more strongly associated with migration patterns than climatic factors. While our method is dependent on census data, incurring notable uncertainties in regions where census data coverage or quality is low, we were able to capture migration patterns not only between but also within countries, as well as by socioeconomic and geophysical zonings. Our results highlight the importance of subnational analysis of migration-a necessity for policy design, international cooperation and shared responsibility for managing internal and international migration.

Earth beyond six of nine planetary boundaries.

This planetary boundaries framework update finds that six of the nine boundaries are transgressed, suggesting that Earth is now well outside of the safe operating space for humanity. Ocean acidification is close to being breached, while aerosol loading regionally exceeds the boundary. Stratospheric ozone levels have slightly recovered. The transgression level has increased for all boundaries earlier identified as overstepped. As primary production drives Earth system biosphere functions, human appropriation of net primary production is proposed as a control variable for functional biosphere integrity. This boundary is also transgressed. Earth system modeling of different levels of the transgression of the climate and land system change boundaries illustrates that these anthropogenic impacts on Earth system must be considered in a systemic context.

Quantifying changes in flooding and habitats in the Tonle Sap Lake (Cambodia) caused by water infrastructure development and climate change in the Mekong Basin.

The economic value of the Tonle Sap Lake Floodplain to Cambodia is arguably among the highest provided to a nation by a single ecosystem around the world. Nonetheless, the Mekong River Basin is changing rapidly due to accelerating water infrastructure development (hydropower, irrigation, flood control, and water supply) and climate change, bringing considerable modifications to the flood pulse of the Tonle Sap Lake in the foreseeable future. This paper presents research conducted to determine how the historical flooding regime, together with human action, influenced landscape patterns of habitats in the Tonle Sap Lake, and how these habitats might shift as a result of hydrological changes. Maps of water depth, annual flood duration, and flood frequency were created for recent historical hydrological conditions and for simulated future scenarios of water infrastructure development and climate change. Relationships were then established between the historical flood maps and land cover, and these were subsequently applied to assess potential changes to habitat cover in future decades. Five habitat groups were clearly distinguishable based on flood regime, physiognomic patterns, and human activity: (1) Open water, flooded for 12 months in an average hydrological year; (2) Gallery forest, with flood duration of 9 months annually; (3) Seasonally flooded habitats, flooded 5-8 months and dominated by shrublands and grasslands; (4) transitional habitats, flooded 1-5 months and dominated by abandoned agricultural fields, receding rice/floating rice, and lowland grasslands; and (5) Rainfed habitats, flooded up to 1 month and consisting mainly of wet season rice fields and village crops. It was found that water infrastructure development could increase the area of open water (+18 to +21%) and the area of rainfed habitats (+10 to +14%), while reducing the area covered with seasonally flooded habitats (-13 to -22%) and gallery forest (-75 to -83%). Habitat cover shifts as a result of climate change include a net increase of open water (2-21%), as well as a reduction of rainfed habitats by 2-5% and seasonally flooded habitats by 5-11%. Findings from this study will help guide on-going and future conservation and restoration efforts throughout this unique and critical ecosystem.

Crop Yield Loss Risk Is Modulated by Anthropogenic Factors.

High crop yield variation between years-caused by extreme shocks on the food production system such as extreme weather-can have substantial effects on food production. This in turn introduces vulnerabilities into the global food system. To mitigate the effects of these shocks, there is a clear need to understand how different adaptive capacity measures link to crop yield variability. While existing literature provides many local-scale studies on this linkage, no comprehensive global assessment yet exists. We assessed reported crop yield variation for wheat, maize, soybean, and rice for the time period 1981-2009 by measuring both yield loss risk (variation in negative yield anomalies considering all years) and changes in yields during "dry" shock and "hot" shock years. We used the machine learning algorithm XGBoost to assess the explanatory power of selected gridded indicators of anthropogenic factors globally (i.e., adaptive capacity measures such as the human development index, irrigation infrastructure, and fertilizer use) on yield variation at a 0.5° resolution within climatically similar regions (to rule out the role of average climate conditions). We found that the anthropogenic factors explained 40%-60% of yield loss risk variation across the whole time period, whereas the factors provided noticeably lower (5%-20%) explanatory power during shock years. On a continental scale, especially in Europe and Africa, the factors explained a high proportion of the yield loss risk variation (up to around 80%). Assessing crop production vulnerabilities on global scale provides supporting knowledge to target specific adaptation measures, thus contributing to global food security.

Hotspots for social and ecological impacts from freshwater stress and storage loss.

Humans and ecosystems are deeply connected to, and through, the hydrological cycle. However, impacts of hydrological change on social and ecological systems are infrequently evaluated together at the global scale. Here, we focus on the potential for social and ecological impacts from freshwater stress and storage loss. We find basins with existing freshwater stress are drying (losing storage) disproportionately, exacerbating the challenges facing the water stressed versus non-stressed basins of the world. We map the global gradient in social-ecological vulnerability to freshwater stress and storage loss and identify hotspot basins for prioritization (n = 168). These most-vulnerable basins encompass over 1.5 billion people, 17% of global food crop production, 13% of global gross domestic product, and hundreds of significant wetlands. There are thus substantial social and ecological benefits to reducing vulnerability in hotspot basins, which can be achieved through hydro-diplomacy, social adaptive capacity building, and integrated water resources management practices.

Food system by-products upcycled in livestock and aquaculture feeds can increase global food supply.

Many livestock and aquaculture feeds compete for resources with food production. Increasing the use of food system by-products and residues as feed could reduce this competition. We gathered data on global food system material flows for crop, livestock and aquaculture production, focusing on feed use and the availability of by-products and residues. We then analysed the potential of replacing food-competing feedstuff-here cereals, whole fish, vegetable oils and pulses that account for 15% of total feed use-with food system by-products and residues. Considering the nutritional requirements of food-producing animals, including farmed aquatic species, this replacement could increase the current global food supply by up to 13% (10-16%) in terms of kcal and 15% (12-19%) in terms of protein content. Increasing the use of food system by-products as feed has considerable potential, particularly when combined with other measures, in the much-needed transition towards circular food systems.

Climate change risks pushing one-third of global food production outside the safe climatic space.

Food production on our planet is dominantly based on agricultural practices developed during stable Holocene climatic conditions. Although it is widely accepted that climate change perturbs these conditions, no systematic understanding exists on where and how the major risks for entering unprecedented conditions may occur. Here, we address this gap by introducing the concept of safe climatic space (SCS), which incorporates the decisive climatic factors of agricultural production: precipitation, temperature, and aridity. We show that a rapid and unhalted growth of greenhouse gas emissions (SSP5-8.5) could force 31% of the global food crop and 34% of livestock production beyond the SCS by 2081-2100. The most vulnerable areas are South and Southeast Asia and Africa's Sudano-Sahelian Zone, which have low resilience to cope with these changes. Our results underpin the importance of committing to a low-emissions scenario (SSP1-2.6), whereupon the extent of food production facing unprecedented conditions would be a fraction.

Identifying Key Drivers of Peatland Fires Across Kalimantan's Ex-Mega Rice Project Using Machine Learning.

Throughout Indonesia ecological degradation, agricultural expansion, and the digging of drainage canals has compromised the integrity and functioning of peatland forests. Fragmented landscapes of scrubland, cultivation, degraded forest, and newly established plantations are then susceptible to extensive fires that recur each year. However, a comprehensive understanding of all the drivers of fire distribution and the conditions of initiation is still absent. Here we show the first analysis in the region that encompasses a wide range of driving factors within a single model that captures the inter-annual variation, as well as the spatial distribution of peatland fires. We developed a fire susceptibility model using machine learning (XGBoost random forest) that characterizes the relationships between key predictor variables and the distribution of historic fire locations. We then determined the relative importance of each predictor variable in controlling the initiation and spread of fires. The model included land-cover classifications, a forest clearance index, vegetation indices, drought indices, distances to infrastructure, topography, and peat depth, as well as the Oceanic Niño Index (ONI). The model performance consistently scores highly in both accuracy and precision across all years (>75% and >67.5% respectively), though recall metrics are much lower (>25%). Our results confirm the anthropogenic dependence of extreme fires in the region, with distance to settlements and distance to canals consistently weighted the most important driving factors within the model structure. Our results may help target the root causes of fire initiation and propagation to better construct regulation and rehabilitation efforts to mitigate future fires.

Grain export restrictions during COVID-19 risk food insecurity in many low- and middle-income countries.

Global food security is threatened by the effects of COVID-19 on international agricultural supply chains and locusts destroying crops and livelihoods in the Horn of Africa and South Asia. We quantify the possible impacts on global supplies and prices of wheat, rice and maize. We show that local production declines have moderate impacts on global prices and supply-but trade restrictions and precautionary purchases by a few key actors could create global food price spikes and severe local food shortages.

Future Transboundary Water Stress and Its Drivers Under Climate Change: A Global Study.

Various transboundary river basins are facing increased pressure on water resources in near future. However, little is known ab out the future drivers globally, namely, changes in natural local runoff and natural inflows from upstream parts of a basin, as well as local and upstream water consumption. Here we use an ensemble of four global hydrological models forced by five global climate models and the latest greenhouse-gas concentration (RCP) and socioeconomic pathway (SSP) scenarios to assess the impact of these drivers on transboundary water stress in the past and future. Our results show that population under water stress is expected to increase by 50% under a low population growth and emissions scenario (SSP1-RCP2.6) and double under a high population growth and emission scenario (SSP3-RCP6.0), compared to the year 2010. As changes in water availability have a smaller effect when water is not yet scarce, changes in water stress globally are dominated by local water consumption-managing local demand is thus necessary in order to avoid future stress. Focusing then on the role of upstream changes, we identified upstream availability (i.e., less natural runoff or increased water consumption) as the dominant driver of changes in net water availability in most downstream areas. Moreover, an increased number of people will be living in areas dependent on upstream originating water in 2050. International water treaties and management will therefore have an increasingly crucial role in these hot spot regions to ensure fair management of transboundary water resources.

Future projections of flood dynamics in the Vietnamese Mekong Delta.

The annual flood pulse of the Mekong River is crucial to sustain agriculture production, nutrition, and the livelihood of millions of people living in the Vietnamese part of the Mekong Delta (VMD). However, climate change impacts on precipitation, temperature and sea-level combined with land subsidence, upstream hydropower development, and water infrastructures (i.e. high-dykes construction) are altering the hydrological regime of the VMD. This study investigates future changes in flood hazard and agricultural production caused by these different scales of human-induced stresses. A quasi- two-dimensional (quasi-2D) hydrodynamic model was used to simulate eight scenarios representing the individual and compound impacts of these drivers for a baseline (1971-2000) and future (2036-2065) period. The scenarios map the most likely future pathway of climate change (RCP 4.5) combined with the best available Mekong upstream hydropower development, and land subsidence scenarios as well as the current delta development plan. We found that sea-level rise and land subsidence would cause the highest changes in flood hazard and damage to rice crop, followed by hydropower and climate change impacts. Expansion of high-dyke areas in two northernmost delta provinces (An Giang and Dong Thap) would have the smallest impact. The combination of all modelled drivers is projected to increase delta inundation extent by 20%, accompanied with prolonging submergence of 1-2 months, and 2-3 times increase in annual flood damage to rice crops in the flood-prone areas of the VMD. These findings of likely increasing risk of tidal induced flood hazard and damage call for well-planned adaptation and mitigation measures, both structural and non-structural.

Global priorities of environmental issues to combat food insecurity and biodiversity loss.

Various environmental challenges are rapidly threatening ecosystems and societies globally. Major interventions and a strategic approach are required to minimize harm and to avoid reaching catastrophic tipping points. Setting evidence-based priorities aids maximizing the impact of the limited resources available for environmental interventions. Focusing on protecting both food security and biodiversity, international experts prioritized major environmental challenges for intervention based on three comprehensive criteria - importance, neglect, and tractability. The top priorities differ between food security and biodiversity. For food security, the top priorities are pollinator loss, soil compaction, and nutrient depletion, and for biodiversity conservation, ocean acidification and land and sea use (especially habitat degradation) are the main concerns. While climate change might be the most pressing environmental challenge and mitigation is clearly off-track, other issues rank higher because of climate change's high attention in research. Research and policy agendas do not yet consistently cover these priorities. Thus, a shift in attention towards the high-priority environmental challenges, identified here, is needed to increase the effectiveness of global environmental protection.