The impact of land use on ecosystems has reached critical levels, jeopardizing biosphere integrity. A key indicator that quantifies, monitors, and analyses such impacts is the Human Appropriation of Net Primary Production (HANPP). Assessing HANPP consists of a comparison of actual net primary production and potential vegetation properties—i.e., vegetation properties that would prevail in the absence of land use (i.e., the NPP of the potential vegetation; NPPpot). While technical advancements in remote sensing have led to global land cover data at high spatial resolution up to 30 arcsec (i.e., 1 km at the equator) and higher, spatial explicit global NPPpot, mainly provided by Dynamic Global Vegetation Models (DGVMs), are usually generated at 30 arcmin (i.e., 55 km at the equator). This spatial mismatch causes uncertainties in HANPP assessments. We here present a workable solution for downscaling NPPpot until global, high‐resolution results, for example, from DGVMs, become feasible. This method uses a simple NPPpot model with readily available auxiliary information at higher spatial resolution to generate fine‐scale patterns to downscale high‐quality coarse scale NPPpot. It includes a smoothing procedure to reduce downscaling artifacts. We demonstrate this Smooth Auxiliary Data (SAD) downscaling by applying it to NPPpot results from the LPJ‐GUESS DGVM model for the year 2010 to downscale from 30 arcmin to 30 arcsec resolution. This approach generates fine‐scaled NPPpot patterns and aligns with alternative models for smaller geographic units while computational cost is acceptable.

Efficient monitoring of tree canopy mortality requires data that cover large areas and capture changes over time while being precise enough to detect changes at the canopy level. In the development of automated approaches, aerial images represent an under-exploited scale between high-resolution drone images and satellite data. Our aim herein was to use a deep learning model to automatically detect canopy mortality from high-resolution aerial images after severe drought events in the summers 2018–2020 in Luxembourg. We analysed canopy mortality for the years 2017–2020 using the EfficientUNet++, a state-of-the-art convolutional neural network. Training data were acquired for the years 2017 and 2019 only, in order to test the robustness of the model for years with no reference data. We found a severe increase in canopy mortality from 0.64 km2 in 2017 to 7.49 km2 in 2020, with conifers being affected at a much higher rate than broadleaf trees. The model was able to classify canopy mortality with an F1-score of 66%–71% and we found that for years without training data, we were able to transfer the model trained on other years to predict canopy mortality, if illumination conditions did not deviate severely. We conclude that aerial images hold much potential for automated regular monitoring of canopy mortality over large areas at canopy level when analysed with deep learning approaches. We consider the suggested approach a cost-efficient and -effective alternative to drone and field-based sampling.

Abstract Drainage and reduction in precipitation due to climate change have led to global declines in the water table (WT) of pristine peatlands, which have increased ecosystem releases of carbon dioxide (CO2) and nitrous oxide (N2O) and decreased emissions of methane (CH4). However, the trade-offs in these changes on net greenhouse gas (GHG) balances of peatlands globally remains unknown. Using meta-analysis and bootstrapped upscaling approaches, we assessed the global effect of WT decline on net GHG balances in peatlands, considering both on- and off-site (e.g., biomass removal, hydrological export, ditch emission) ecosystem fluxes of CO2, CH4 and N2O. We found that drainage caused a significant increase in net emissions of CO2 by 0.90 (95% confidence interval: 0.72–1.08) Pg CO2 yr–1 and of N2O by 0.09 (0.05–0.12) Pg CO2-eq. yr–1, while emissions of CH4 decreased by 0.30 (0.13–0.48) Pg CO2-eq. yr–1. The overall GHG balance significantly increased by 0.68 (0.43–0.94) Pg CO2-eq. yr–1. Climate, land use, and time since drainage introduced variability, with the conversion of (sub)tropical peatlands to agriculture and forestry dominating net global increases in peatland emissions (105%, range: 63–147%). In contrast, forestry in boreal peatlands reduced global GHG emissions by –4% (–8~–1%). Conversion of temperate and boreal peatlands to agriculture and forestry land uses was climate neutral overall, as increases in CO2 and N2O emissions were offset by reductions in CH4 emissions. Drained (sub)tropical peatlands for agriculture and forestry are global GHG emission hotspots. Conserving pristine peatlands and restoring degraded peatlands are needed particularly in the (sub)tropics for climate change mitigation.

Accurate quantification of landscape soil greenhouse gas (GHG) exchange from chamber measurements is challenging due to the high spatial‐temporal variability of fluxes, which results in large uncertainties in upscaled regional and global flux estimates. We quantified landscape‐scale (6 km 2 in central Germany) soil/ecosystem respiration (SR/ER‐CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) fluxes at stratified sites with contrasting landscape characteristics using the fast‐box chamber technique. We assessed the influence of land use (forest, arable, and grassland), seasonality (spring, summer, and autumn), soil types, and slope on the fluxes. We also evaluated the number of chamber measurement locations required to estimate landscape fluxes within globally significant uncertainty thresholds. The GHG fluxes were strongly influenced by seasonality and land use rather than soil type and slope. The number of chamber measurement locations required for robust landscape‐scale flux estimates depended on the magnitude of fluxes, which varied with season, land use, and GHG type. Significant N 2 O‐N flux uncertainties greater than the global mean flux (0.67 kg ha −1 yr −1 ) occurred if landscape measurements were done at <4 and <22 chamber locations (per km 2 ) in forest and arable ecosystems, respectively, in summer. For CO 2 and CH 4 fluxes, uncertainties greater than the global median CO 2 ‐C flux (7,500 kg ha −1 yr −1 ) and the global mean forest CH 4 ‐C uptake rate (2.81 kg ha −1 yr −1 ) occurred at <2 forest and <6 arable chamber locations. This finding suggests that more chamber measurement locations are required to assess landscape‐scale N 2 O fluxes than CO 2 and CH 4, based on these GHG‐specific uncertainty thresholds.

Worldwide, rice production contributes about 10% of total greenhouse gas (GHG) emissions from the agricultural sector, mainly due to CH 4 emissions from continuously flooded fields. Alternate Wetting and Drying (AWD) is a promising crop technology for mitigating CH 4 emissions and reducing the irrigation water currently being applied in many of the world's top rice‐producing countries. However, decreased emissions of CH 4 may be partially counterbalanced by increased N 2 O emissions. In this case study for the Philippines, the national mitigation potential of AWD is explored using the process‐based biogeochemical model LandscapeDNDC. Simulated mean annual CH 4 emissions under conventional rice production for the time period 2000–2011 are estimated as 1,180 ± 163 Gg CH 4 yr −1 . During the cropping season, this is about +16% higher than a former estimate using emission factors. Scenario simulations of nationwide introduction of AWD in irrigated landscapes suggest a considerable decrease in CH 4 emissions by −23%, while N 2 O emissions are only increased by +8%. Irrespective of field management, at national scale, the radiative forcing of irrigated rice production is always dominated by CH 4 (>95%). The reduction potential of GHG emissions depends on, for example, number of crops per year, residue management, amount of applied irrigation water, and sand content. Seasonal weather conditions also play an important role since the mitigation potential of AWD is almost double as high in dry as compared to wet seasons. Furthermore, this study demonstrates the importance of temporal continuity, considering off‐season emissions and the long‐term development of GHG emissions across multiple years.

Background: Globally, rice systems are a major source of atmospheric CH 4 and for major rice-producing countries, such as Vietnam, CH 4 as well as N 2 O emissions from agricultural land used for rice production may represent about one-fourth of total national anthropogenic greenhouse gas (GHG) emissions. However, national-scale estimates of GHG emissions from rice systems are uncertain with regard to its magnitude, spatial distribution, and seasonality. Aims: Here, we used the biogeochemical model LandscapeDNDC to calculate emissions ofCH 4 andN 2 OfromricesystemsinVietnam(Tier3IPCCapproach).Ourobjectiveswere to

The paradigm that permafrost-affected soils show restricted mineral nitrogen (N) cycling in favor of organic N compounds is based on the observation that net N mineralization rates in these cold climates are negligible. However, we find here that this perception is wrong. By synthesizing published data on N cycling in the plant-soil-microbe system of permafrost ecosystems we show that gross ammonification and nitrification rates in active layers were of similar magnitude and showed a similar dependence on soil organic carbon (C) and total N concentrations as observed in temperate and tropical systems. Moreover, high protein depolymerization rates and only marginal effects of C:N stoichiometry on gross N turnover provided little evidence for N limitation. Instead, the rather short period when soils are not frozen is the single main factor limiting N turnover. High gross rates of mineral N cycling are thus facilitated by released protection of organic matter in active layers with nitrification gaining particular importance in N-rich soils, such as organic soils without vegetation. Our finding that permafrost-affected soils show vigorous N cycling activity is confirmed by the rich functional microbial community which can be found both in active and permafrost layers. The high rates of N cycling and soil N availability are supported by biological N fixation, while atmospheric N deposition in the Arctic still is marginal except for fire-affected areas. In line with high soil mineral N production, recent plant physiological research indicates a higher importance of mineral plant N nutrition than previously thought. Our synthesis shows that mineral N production and turnover rates in active layers of permafrost-affected soils do not generally differ from those observed in temperate or tropical soils. We therefore suggest to adjust the permafrost N cycle paradigm, assigning a generally important role to mineral N cycling. This new paradigm suggests larger permafrost N climate feedbacks than assumed previously.

Globally, about 50% of all arable soils are classified as acidic. As crop and plant growth are significantly hampered under acidic soil conditions, many farmers, but increasingly as well forest managers, apply lime to raise the soil pH. Besides its direct effect on soil pH, liming also affects soil C and nutrient cycles and associated greenhouse gas (GHG) fluxes. In this meta‐analysis, we reviewed 1570 observations reported in 121 field‐based studies worldwide, to assess liming effects on soil GHG fluxes and plant productivity. We found that liming significantly increases crop yield by 36.3%. Also, soil organic C (SOC) stocks were found to increase by 4.51% annually, though soil respiration is stimulated too (7.57%). Moreover, liming was found to reduce soil N 2 O emission by 21.3%, yield‐scaled N 2 O emission by 21.5%, and CH 4 emission and yield‐scaled CH 4 emission from rice paddies by 19.0% and 12.4%, respectively. Assuming that all acid agricultural soils are limed periodically, liming results in a total GHG balance benefit of 633−749 Tg CO 2 ‐eq year −1 due to reductions in soil N 2 O emissions (0.60−0.67 Tg N 2 O‐N year −1 ) and paddy soil CH 4 emissions (1.75−2.21 Tg CH 4 year −1 ) and increases in SOC stocks (65.7–110 Tg C year −1 ). However, this comes at the cost of an additional CO 2 release (c. 624–656 Tg CO 2 year −1 ) deriving from lime mining, transport and application, and lime dissolution, so that the overall GHG balance is likely neutral. Nevertheless, liming of acid agricultural soils will increase yields by at least 6.64 × 10 8 Mg year −1 , covering the food supply of 876 million people. Overall, our study shows for the first time that a general strategy of liming of acid agricultural soils is likely to result in an increasing sustainability of global agricultural production, indicating the potential benefit of liming acid soils for climate change mitigation and food security.

We can observe a global decrease of well maintained weather stations by meteorological services and governmental institutes. At the same time, environmental sensor data is increasing through the use of opportunistic or remote sensing approaches. Overall, the trend for environmental sensor networks is strongly going towards automated routines, especially for quality-control (QC) to provide usable data in near real-time. A common QC scenario is that data is being flagged manually using expert knowledge and visual inspection by humans. To reduce this tedious process and to enable near-real time data provision, machine-learning (ML) algorithms exhibit a high potential as they can be designed to imitate the experts actions.Here we address these three common challenges when applying ML for QC: 1) Robustness to missing values in the input data. 2) Availability of training data, i.e. manual quality flags that mark erroneous data points. And 3) Generalization of the model regarding non-stationary behavior of one experimental system or changes in the experimental setup when applied to a different study area. We approach the QC problem and the related issues both as a supervised and an unsupervised learning problem using deep neural networks on the one hand and dimensionality reduction combined with clustering algorithms on the other.We compare the different ML algorithms on two time-series datasets to test their applicability across scales and domains. One dataset consists of signal levels of 4000 commercial microwave links distributed all over Germany that can be used to monitor precipitation. The second dataset contains time-series of soil moisture and temperature from 120 sensors deployed at a small-scale measurement plot at the TERENO site “Hohes Holz”.First results show that supervised ML provides an optimized performance for QC for an experimental system not subject to change and at the cost of a laborious preparation of the training data. The unsupervised approach is also able to separate valid from erroneous data at reasonable accuracy. However, it provides the additional benefit that it does not require manual flags and can thus be retrained more easily in case the system is subject to significant changes.In this presentation, we discuss the performance, advantages and drawbacks of the proposed ML routines to tackle the aforementioned challenges. Thus, we aim to provide a starting point for researchers in the promising field of ML application for automated QC of environmental sensor data.

In sub-Saharan Africa (SSA), the most important staple crop is maize; the production of which is dominated by smallholder farming systems using low external inputs (<10 kg N ha(-1)) resulting in low crop yields and large yield gaps (difference between actual and potential yields). To assess increases in soil N2O emissions when closing maize yield gaps by increased fertilizer use, we reviewed the literature, developed a relationship between yield gaps and soil N2O emissions, and used it to scale across SSA. According to our analysis, N2O emissions from maize production will increase from currently 255 to 1755 1226 Gg N2O-N year(-1) (+589%) if existing maize yield gaps are closed by 75%, increasing total anthropogenic N2O emissions for SSA by c. 50%.

Wildfire occurrence is influenced by climate, vegetation and human activities. A key challenge for understanding the risk of fires is quantifying the mediating effect of vegetation on fire regimes. Here, we explore the relative importance of Holocene land cover, land use, dominant functional forest type, and climate dynamics on biomass burning in temperate and boreo-nemoral regions of central and eastern Europe over the past 12 kyr. We used an extensive data set of Holocene pollen and sedimentary charcoal records, in combination with climate simulations and statistical modelling. Biomass burning was highest during the early Holocene and lowest during the mid-Holocene in all three ecoregions (Atlantic, continental and boreo-nemoral) but was more spatially variable over the past 3–4 kyr. Although climate explained a significant variance in biomass burning during the early Holocene, tree cover was consistently the highest predictor of past biomass burning over the past 8 kyr. In temperate forests, biomass burning was high at ∼45 % tree cover and decreased to a minimum at between 60 % and 70 % tree cover. In needleleaf-dominated forests, biomass burning was highest at ∼ 60 %–65 % tree cover and steeply declined at >65 % tree cover. Biomass burning also increased when arable lands and grasslands reached ∼ 15 %–20 %, although this relationship was variable depending on land use practice via ignition sources, fuel type and quantities. Higher tree cover reduced the amount of solar radiation reaching the forest floor and could provide moister, more wind-protected microclimates underneath canopies, thereby decreasing fuel flammability. Tree cover at which biomass burning increased appears to be driven by warmer and drier summer conditions during the early Holocene and by increasing human influence on land cover during the late Holocene. We suggest that long-term fire hazard may be effectively reduced through land cover management, given that land cover has controlled fire regimes under the dynamic climates of the Holocene.

Sub-Saharan Africa (SSA) is home to approximately ¼ of the global livestock population, which in the last 60 years has increased by factors of 2.5–4 times for cattle, goats and sheep. An important resource for pastoralists, most livestock live in semi-arid and arid environments, where they roam during the day and are kept in enclosures (or bomas) during the night. Manure, although rich in nitrogen, is rarely used, and therefore accumulates in bomas over time. Here we present in-situ measurements of N 2 O fluxes from 46 bomas in Kenya and show that even after 40 years following abandonment, fluxes are still ~one magnitude higher than those from adjacent savanna sites. Using maps of livestock distribution, we scaled our finding to SSA and found that abandoned bomas are significant hotspots for atmospheric N 2 O at the continental scale, contributing ~5% of the current estimate of total anthropogenic N 2 O emissions for all of Africa.

Increasing atmospheric CO 2 stimulates photosynthesis which can increase net primary production (NPP), but at longer timescales may not necessarily increase plant biomass. Here we analyse the four decade-long CO 2 -enrichment experiments in woody ecosystems that measured total NPP and biomass. CO 2 enrichment increased biomass increment by 1.05 ± 0.26 kg C m −2 over a full decade, a 29.1 ± 11.7% stimulation of biomass gain in these early-secondary-succession temperate ecosystems. This response is predictable by combining the CO 2 response of NPP (0.16 ± 0.03 kg C m −2 y −1 ) and the CO 2 -independent, linear slope between biomass increment and cumulative NPP (0.55 ± 0.17). An ensemble of terrestrial ecosystem models fail to predict both terms correctly. Allocation to wood was a driver of across-site, and across-model, response variability and together with CO 2 -independence of biomass retention highlights the value of understanding drivers of wood allocation under ambient conditions to correctly interpret and predict CO 2 responses.

One of the challenges facing the industrial adoption of additively manufactured parts is the surface roughness on the as-built part. The surface roughness of parts is frequently characterized by metrics specified by international standards organizations. However, these standards list many surface metrics that can make it unclear which to use to best describe the surface. In this work, the ability of the various surface metrics to successfully classify the as-built and post-processed surfaces is studied using linear classification models. Laser polishing via remelting and manual grinding are the post-processing techniques used to smooth the as-built surface. The ability of the linear classifier to successfully categorize the various surfaces is demonstrated, and the various surface metrics are ranked according to the strength of their individual ability to classify the surfaces. This work promotes the method as a potential way to autonomously classify as-built and laser polished surfaces.

The objective of this study is to quantify the melt pool dynamics during continuous wave laser remelting of a Co-Cr alloy manufactured using selective laser melting. This knowledge will inform process improvement and numerical modeling of laser remelting. A high-intensity X-ray beam imaged a 2 mm x 0.5 mm area of the surface with a 50 kHz framerate. Analysis of these videos quantified the melt pool surface wave movement and compared this to the initial surface features. The results indicate that the keyhole and its characteristic oscillations can suppress large wavelength features on the initial surface. (C) 2019 Published by Elsevier Ltd on behalf of CIRP.

Abstract. We present a numerical modelling investigation into the interactions between transient climate and vegetation cover with hillslope and fluvial processes. Model simulations were designed to investigate the effects of climate change and associated changes in surface vegetation cover on topographic basin metrics such as: slope, relief and channel steepness. The Landlab surface process model was used to evaluate the effects of temporal variations in vegetation cover on hillslope diffusion and detachment limited fluvial erosion. A suite of simulations were conducted to represent present-day climatic conditions and satellite-derived vegetation cover at the four EarthShape study areas as well hypothetical transient long term changes. Two different transient variations in climate and vegetation cover include a step change in climate or vegetation, as well as 100 kyr oscillations over 5 Myr. Results indicate that the coupled influence of surface vegetation cover and mean annual precipitation shifts basin landforms towards a new steady state, with the magnitude of change highly sensitive to the initial vegetation and climate conditions of the basin. Dry, non-vegetated basins show higher magnitudes of adjustment than basins that are situated in wetter conditions with higher vegetation cover. For coupled conditions when surface vegetation cover and mean annual precipitation change simultaneously, the landscape response tends to be weaker. When vegetation cover and mean annual precipitation change independently from each other, higher magnitude shifts in topographic metrics are simulated. Changes in vegetation cover show a higher impact on topography for low initial surface cover values whereas for areas with high initial surface cover, the effect of changes in precipitation dominate the formation of landscapes. This study demonstrates a sensitivity of catchment characteristics to different transient forcings in vegetation cover and mean annual precipitation, with a crucial role for initial vegetation and climate conditions. Ongoing research is developing fully-coupled landscape evolution and dynamic vegetation model (see companion paper) forced with predicted paleoclimate histories from an atmospheric general circulation model.

We present a numerical modeling investigation into the interactions between transient climate and vegetation cover with hillslope and detachment limited fluvial processes. Model simulations were designed to investigate topographic patterns and behavior resulting from changing climate and the associated changes in surface vegetation cover. The Landlab surface process model was modified to evaluate the effects of temporal variations in vegetation cover on hillslope diffusion and fluvial erosion. A suite of simulations were conducted to represent present-day climatic conditions and satellite derived vegetation cover at four different research areas in the Chilean Coastal Cordillera. These simulations included steady-state simulations as well as transient simulations with forcings in either climate or vegetation cover over millennial to million-year timescales. Two different transient variations in climate and vegetation cover including a step change in climate or vegetation were used, as well as 100 kyr oscillations over 5 Myr. We conducted eight different step-change simulations for positive and negative perturbations in either vegetation cover or climate and six simulations with oscillating transient forcings for either vegetation cover, climate, or oscillations in both vegetation cover and climate. Results indicate that the coupled influence of surface vegetation cover and mean annual precipitation shifts basin landforms towards a new steady state, with the magnitude of the change being highly sensitive to the initial vegetation and climate conditions of the basin. Dry, non-vegetated basins show higher magnitudes of adjustment than basins that are situated in wetter conditions with higher vegetation cover. For coupled conditions when surface vegetation cover and mean annual precipitation change simultaneously, the landscape response tends to be weaker. When vegetation cover and mean annual precipitation change independently from one another, higher magnitude shifts in topographic metrics are predicted. Changes in vegetation cover show a higher impact on topography for low initial surface cover values; however, for areas with high initial surface cover, the effect of changes in precipitation dominate the formation of landscapes. This study demonstrates the sensitivity of catchment characteristics to different transient forcings in vegetation cover and mean annual precipitation, with initial vegetation and climate conditions playing a crucial role.