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Albedo can be used to quantify ecosystem and landscape contributions to local and global climate. Such contributions are conventionally expressed as radiative forcing (RF) and global warming impact (GWI). We contextualize our results within landscape carbon production and storage to highlight the importance of changes in albedo for landscape GWI from multiple causes, including net ecosystem production (NEP) and greenhouse gas (GHG) emissions. To examine the spatiotemporal changes in albedo (Δα) in contrasting managed landscapes through calculations of albedo-induced RF (RFΔα) and GWI (GWIΔα) under different climatic conditions. We selected five contrasting landscapes within the Kalamazoo River watershed in southern Michigan USA as proof of concept. The daily MCD43A3 MODIS (V006) product was used to analyze the inter- and intra-annual variations of growing season albedo. In addition, the variations of RFΔα and GWIΔα were computed based on landscape composition and climate. The RFΔα (− 5.6 W m−2) and GWIΔα (− 1.3 CO2eq ha−1 year−1) were high in forest-dominated landscapes, indicating cooling effects and CO2eq mitigation impacts similar to crops. The CO2eq mitigation of cropland-dominated landscapes was on average 52% stronger than forest-dominated landscapes. In the landscape with the highest proportion of forest, under dry and wet conditions CO2eq mitigation was reduced by up to 24% and ~ 30%, respectively; in one cropland-dominated landscape wet conditions reduced CO2eq mitigation by 23%. Findings demonstrate that quantifying spatiotemporal changes in albedo in managed landscapes and under different climatic conditions is essential to understand how landscape modification affects RFΔα and GWIΔα and thereby contributes to ecosystem-level GWI.

Surface energy partitioning directly connects to the urban heat island effect, which consequently changes regional climate, the health of the urban population, and anthropogenic energy use. In order to quantify land-atmosphere fluxes from urban areas and the impact of the level of intensity of development, we use seven site-years of land-atmosphere flux data from three locations averaged to seasonal timescales. Additionally, all three of our study sites include urban rivers, allowing us to examine urban areas with high and low amounts of potential evapotranspiration. As expected, the urban river decreases the Bowen Ratio of observed fluxes, primarily through lowering sensible heat fluxes. Urban density is positively correlated with sensible and latent heat fluxes when fluxes are coming from non-river areas and negatively correlated with heat fluxes when coming from the urban river. We conclude that effective urban redevelopment guidelines can adopt this knowledge to decrease the urban heat island effect and reach sustainability targets to counteract increased temperatures from climate change.

The rise of artificial intelligence (AI), blockchain (BC), and the internet of things (IoT) has had significant applications in the advancement of sustainability research. This review examines how these digital transformations drive natural and human systems, as well as which industry sectors have been applying them to advance sustainability. We adopted qualitative research methods, including a bibliometric analysis, in which we screened 960 publications to identify the leading sectors that apply AI/BC/IoT, and a content analysis to identify how each sector uses AI/BC/IoT to advance sustainability. We identified “smart city”, “energy system”, and “supply chain” as key leading sectors. Of these technologies, IoT received the most real-world applications in the “smart city” sector under the dimensions of “smart environment” and “smart mobility” and provided applications resolving energy consumption in the “energy system” sector. AI effectively resolved scheduling, prediction, and monitoring for both the “smart city” and “energy system” sectors. BC remained highly theoretical for “supply chain”, with limited applications. The technological integration of AI and IoT is a research trend for the “smart city” and “energy system” sectors, while BC and IoT is proposed for the “supply chain”. We observed a surge in AI/BC/IoT sustainability research since 2016 and a new research trend—technological integration—since 2020. Collectively, six of the United Nation’s seventeen sustainable development goals (i.e., 6, 7, 9, 11, 12, 13) have been the most widely involved with these technologies.

In October 2020, under COVID‐19 quarantine, AmeriFlux held its largest and one of its most successful annual meetings Historically, ∼100 scientists attend;this meeting had over 400 registrants and participants Participants expressed that this was among the best virtual meetings that they had ever attended, and 100% of post‐meeting survey respondents stated that they would attend again Feedback revealed the meeting fostered a strong sense of connection to the AmeriFlux community, especially among early career and international scientists How did a feeling of strong connection to the community arise from the seemingly cold and isolated structures of virtual meetings?The meeting emphasized Diversity, Equity, and Inclusion (DEI), which resulted in an unexpected enhancement of science communication and community connections Additionally, the meeting experimented with an online virtual gaming‐like world (Gather town), where users controlled video/audio‐enabled avatars in a conference center and poster hall environment to create spontaneous conversations and discussions In lieu of a social‐bonding field trip, participants showed videos of their field sites accompanied by informal banter, which were watched in group settings in Gather Social mixers were structured over Zoom breakout rooms that were limited in size to promote participation with accessible games Science talks were selected based on appeal to a demographically diverse organizing committee, which enhanced appeal to a broad meeting audience Finally, breakout reports were given not in the format of bullet point slides, but instead as creatively improvized fairytales, which dramatically enhanced engagement Here, we describe some of the process that went into the AmeriFlux 2020 meeting Keeping with the theme of experimenting and fairytales, we present this narrative in the form of a fairytale;and, without further ado…

Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson&rsquo, s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI, phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.

Temperate and semiarid grasslands comprise 80% of the land area on the Mongolian Plateau and environs, which includes Mongolia (MG), and the province of Inner Mongolia (IM), China. Substantial land cover/use change in the last few decades, driven by a combination of post-liberalization socioeconomic changes and extreme climatic events, has degraded these water-limited grassland's structure and function. Hence, a precise estimation of canopy cover (CC, %) and aboveground biomass (AGB, g m−2) is needed. In this study, we analyzed >1000 field observations with sampling during June, July and August (JJA) in 2006, 2007, 2010 and 2016 in IM and 2010–2012 and 2014–2016 in MG. The field sampling was stratified by the dominant vegetation types on the plateau, including the meadow steppe, the typical steppe, and the desert steppe. Here we used Moderate Resolution Imaging Spectroradiometer (MODIS) derived surface reflectance and vegetation indices optimized for low cover conditions to develop and test predictive models of CC and AGB using observed samples as training and validation data through rule-based regression tree models. We then used the predictive models to estimate spatially-explicit CC and AGB for the plateau over the last decade (2000–2016). Our study demonstrated the effectiveness of our predictive models in up-scaling ground observations to the regional scale across steppe types. Our results showed that model R2 and RMSE for CC and AGB were 0.74 (13.1%) and 0.62 (85.9 g m−2), respectively. The validation R2 and RMSE for CC and AGB were 0.67 (14.4%) and 0.68 (76.9 g m−2), respectively. The mean ± SD for CC and AGB were 24.9 ± 23.4% and 155.2 ± 115.2 g m−2, respectively. We also found that our scaled up estimates were significantly related to inter-annual climatic variability and anthropogenic drivers especially distance to urban/built-up areas and livestock density. In addition to their direct use in quantifying the spatiotemporal changes in the terrestrial carbon budget, results from these predictive models can help decision makers and rangeland managers plan sustainable livestock practices in the future.

Integrating the dynamics and interconnections of natural and human system properties into a single measure would make it simpler to reliably and repeatedly assess and compare different social-environmental systems (SES). We propose a novel metric to assess the magnitudes and variations in SES dynamics by integrating longitudinal gross domestic product, population, and ecosystem net primary production. We use annual public data across the Asian Drylands Belt (ADB) from 1992 through 2016 for 18 political entities as our testbed for assessing the efficacy of the metric. We perform cross-comparisons with existing natural and social science metrics to demonstrate the validity of the proposed metric, including the Human Development Index and the Palmer Drought Severity Index. The new metric demonstrates notable and meaningful differences in trends among the political entities that reflect major social, economic and environmental events over the 25-year period. It provides unique perspectives about the three pillar components (social, economic and environmental systems) in each of the 18 political entities (PE) of the ADB. The metric also shows meaningful associations with key economic and environmental indicators and great potential for broader application and evaluation, given additional testing in other countries, regions, and biomes.

The seasonal and interannual variability of the terrestrial carbon cycle is regulated by the interactions of climate and ecosystem function. However, the key factors and processes determining the interannual variability of net ecosystem productivity (NEP) in different biomes are far from clear. Here, we quantified yearly anomalies of seasonal and annual NEP, net carbon uptake period (CUP), and the maximum daily NEP (NEPmax) in response to climatic variables in 24 deciduous broadleaf forest (DBF), evergreen forest (EF), and grassland (GRA) ecosystems that include at least eight years of eddy covariance observations. Over the 228 site-years studied, interannual variations in NEP were mostly explained by anomalies of CUP and NEPmax. CUP was determined by spring and autumn net carbon uptake phenology, which were sensitive to annual meteorological variability. Warmer spring temperatures led to an earlier start of net carbon uptake activity and higher spring and annual NEP values in DBF and EF, while warmer autumn temperatures in DBF, higher autumn radiation in EF, and more summer and autumn precipitation in GRA resulted in a later ending date of net carbon uptake and associated higher autumn and annual NEP. Anomalies in NEPmax s were determined by summer precipitation in DBF and GRA, and explained more than 50% of variation in summer NEP anomalies for all the three biomes. Results demonstrate the role of meteorological variability in controlling CUP and NEPmax, which in turn help describe the seasonal and interannual variability of NEP.

Urban parks and forests provide many services to society and are becoming essential components within urban landscapes worldwide. While substantial research and actions have been taken to understand various ecosystem services of urban forests and parks, significantly less effort has been made on people's perceptions toward the effectiveness of these services. In addressing the above research needs, we conducted a field survey and hypothesized that an individual's knowledge will lead to different pro-environmental behaviors in urban forests and parks. Using the Toledo, Ohio, USA as our study site, we collected 267 interviews from five of the area's most frequented urban parks. A three-way ANOVA and two Partial Least Square Structural Equation Models quantified the causal relationship among demography, plant knowledge, environmental knowledge, and pro-environmental behaviors. We found that: (1) different levels of plant knowledge will have different influences on environmental behaviors; (2) pro-environmental behavioral models can be based on planned behaviors or habitual behaviors; and that (3) gender may not be an influential factor in determining pro-environmental behaviors. Environmental knowledge, especially plant knowledge, plays a key role in fostering pro-environmental behaviors. Therefore, we reason that disseminating plant knowledge education materials will profoundly raise visitors' pro-environmental behaviors. University of Toledo ; Sustainable Energy Pathways (CHE) of the US National Science Foundation (NSF) Publisher versiyon

This review was developed to introduce the essential components and variants of structural equation modeling (SEM), synthesize the common issues in SEM applications, and share our views on SEM’s future in ecological research. We searched the Web of Science on SEM applications in ecological studies from 1999 through 2016 and summarized the potential of SEMs, with a special focus on unexplored uses in ecology. We also analyzed and discussed the common issues with SEM applications in previous publications and presented our view for its future applications. We searched and found 146 relevant publications on SEM applications in ecological studies. We found that five SEM variants had not commenly been applied in ecology, including the latent growth curve model, Bayesian SEM, partial least square SEM, hierarchical SEM, and variable/model selection. We identified ten common issues in SEM applications including strength of causal assumption, specification of feedback loops, selection of models and variables, identification of models, methods of estimation, explanation of latent variables, selection of fit indices, report of results, estimation of sample size, and the fit of model. In previous ecological studies, measurements of latent variables, explanations of model parameters, and reports of key statistics were commonly overlooked, while several advanced uses of SEM had been ignored overall. With the increasing availability of data, the use of SEM holds immense potential for ecologists in the future.