Elevated ozone (O3) pollution in the warm season is an emerging environmental concern affecting global highly urbanized megacities. In southwestern China, full characterization of causes for O3 pollution has been stymied by limited observations and the dominant factors that influence O3 variability on a long-term basis still lack understanding. Herein, we identified O3 variations and inferred trends in precursor emissions in Chengdu over 2013–2020 based on extensive ambient measurements, emission inventory, and satellite data. Numerical models were used to investigate the changes in meteorological variability and biogenic emissions. Trends of O3 in urban areas show deterioration (+14.0% yr−1) between 2013 and 2016 followed by a slight decrease over 2017–2020, while O3 levels in rural areas generally show a downward trend (−2.9% yr−1) during 2014–2020. Both emission inventory (−3.7% yr−1) and OMI satellite columns (−4.5% yr−1) depict strong decline trends in NOx emissions, while satellite HCHO columns exhibit a flattened downward trend of VOC emissions (−1.8% yr−1), which caused rural areas shifted from VOCs-limited to transitional or NOx-limited regime since 2016. Considering metropolitan Chengdu remains VOCs-limited regime over time, the existing regulatory framework involving simultaneous NOx and VOCs control would result in evident O3 improvements in the near future. Despite benefits from anthropogenic emission reductions, we demonstrate that meteorological conditions and enhanced biogenic emissions over the warm season could partially or even fully offset effects attributed to emission changes, making the net effects obscure. This finding provides robust evidence of reductions in NOx and VOCs emission and informs effective O3 mitigation policies for megacities which undergo similar emission pathways in Chengdu.

Recent years have witnessed devastating weather extremes, so it is no surprise that people have started wondering, “what’s up with the weather?” The potential role of climate change in making these extremes worse has gained traction not only in the scientific community but in public discourse a well. While scientists have made considerable progress on statistical “climate attribution”—a way of assessing the probability that climate change is influencing the character of some extreme weather events—public understanding has not kept pace. However, members of society do not need to know everything about climate and its related changes to make decisions. Rather, building climate literacy across society is paramount to better inform those decisions. There are many facets of climate literacy and many ways to help society become comfortable with the concepts. While riddled with challenges, one way of explaining the statistical methods around climate attribution is through carefully designed spinner boards. This is just one demonstration of how to explain a difficult subject, and if properly developed and used, this method holds the potential to significantly improve climate literacy.

Detection and monitoring of tropical forest degradation is crucial to climate change mitigation and biodiversity conservation efforts. Several algorithms have been recently developed to monitor forest degradation and disturbance using remote sensing. However, these algorithms differ in local predictions due to the variation in the biogeophysical parameters used as degradation proxies. It is crucial to assess their relative performance and shortcomings in order to develop a clear understanding of the conditions under which each algorithm will detect a disturbance. In this study, we used GEDI lidar data on forest structure to examine the sensitivity of widely used forest disturbance and degradation products in a frontier tropical forest landscape in the Peruvian Amazon. We compared a leading spectral-based degradation algorithm (Continuous Degradation Detection (CODED)) with a radar-based algorithm (ALOS-2 PalSAR-2 based Radar Forest degradation Index (RFDI)). Given the sensitivity of radar to canopy cover and volume, we hypothesized that a single radar observation may detect degradation better than a long spectral time series. We first identified stable forests for reference structure in two ways: using disturbance stratification data from CODED, and using Peruvian protected areas. Our analysis showed that CODED performed below expectations in detecting forest degradation, often including patches that were regrowing after clear-felling in its “degraded” class. As CODED classified spectral changes over time rather than capturing structural variability, it classified 82% of palm plantations area as “degraded.” CODED also failed to detect degradation in forest areas that were likely partially disturbed (i.e., with low height and high cover). By contrast, the PalSAR-2 RFDI showed a significant relationship with forest height (detecting low height in degraded forests), although its predictive ability was limited due to high variability and signal saturation. Our study supports the conclusion that radar-based observation can detect forest degradation that the time series observation failed to detect. Given the limited correspondence between radar and spectral algorithms, we suggest that integrations of spectral and radar data may be beneficial for mapping forest degradation.

The usefulness of extended records of streamflow from tree-ring based hydrologic reconstructions seems obvious- a longer record provides a broader range of the variability of extremes and allows recent and/or ongoing events to be evaluated in a long-term context. The information from these centuries-long records may have clear implications for water resource management, but it is often unclear exactly how this information can be applied to management. In this presentation, I will discuss some of the challenges I have observed that are involved in using streamflow reconstructions in management decisions. These range from issues related to an agency’s capacity to use new types of data to mismatches between what is needed (e.g., daily resolution, a network of gage inputs) and what reconstruction data provide. The skillfulness of a streamflow reconstruction also has a bearing on its perceived credibility in terms of useable data. In spite of these challenges, there is a variety of ways that these data have been used by water resource managers in the western US. The uses are often not immediately evident, but can take the form of, for example, sensitively assessment, awareness raising, and shifts in prior assumptions. Relationship building between researchers and resource managers can yield mutual respect and understanding that lead to both interesting research questions and relevant and valuable information, even if the application to management is not tangible or immediate.

The end-Permian mass extinction event resulted in the loss of approximately 80% to 90% of marine animal species due to drastic changes in climate. Because warming was a major factor in the extinction, it has been theorized the organisms that did survive were able to do so because they moved to higher latitudes and this hypothesis is consistent with tetrapod data. We hypothesized that this relationship holds true for marine mollusks and arthropods as well. Using Changhsingian (Late Permian) and Induan (Early Triassic) data from the Paleobiology Database, we extracted occurrences of classes Bivalvia, Cephalopoda, Gastropoda, and Ostracoda, which had 2433, 395, 379, and 1717 genus occurrences, respectively. Then, we used the paleolatitude data for each genus occurrence to characterize the latitude distribution of each class before and after the Permian/Triassic transition. We compared the paleolatitude medians before and after the mass extinction for each class to quantify the latitude shift for each class: 23.18° for Bivalvia, 37.45° for Cephalopoda, 29.82° for Gastropoda, and 6.29° for Ostracoda. This finding indicates that each individual class had a different latitudinal shift, with all classes exhibiting a poleward shift north. We also conducted Welch t-tests to compare the differences in latitudinal ranges and found that they were significant (Bivalvia: p < 2.2e-16, Cephalopoda: p = 3.83e-6, Gastropoda: p < 2.2e-16, Ostracoda: p = 0.0030). In addition, we ran multiple randomized models to compare them with our original results and found a significant difference between them via the Kolmogorov-Smirnov test, which means that the northward migration could be a biological response. Moreover, the results of our study show that the overall latitudinal range of most classes contracted after the extinction event, with the exception of the Cephalopoda class.

There is no doubt anymore that Earth Observation (EO) is contributing toward meeting the Sustainable Development Goals and addressing environmental challenges. Digital Earth Africa’s objective is to make freely available an EO data cube for all of Africa that democratizes the capacity to process and analyse satellite data. It allows to track changes across Africa in unprecedented detail and will provide data on a vast number of issues, including soil and coastal erosion, agriculture, forest and desert development, water quality, and changes to human settlements. To realise full benefits of an advanced Platform like Digital Earth Africa, Digital Earth Africa has co-designed and co-developed with five institutions namely the Regional Centre For Mapping Of Resources For Development (RCMRD, Kenya), Centre de Suivi Écologique (Senegal), l’observatoire du Sahara et du Sahel (Tunisia), AFRIGIST (Nigeria) and AGRHYMET (Niger). This was meant to ensure it meets end-users needs, this program has been developed by the future deliverers of the program. From the trainers’ perspective, the program is built to consider the recent changes in teaching approaches and methodologies including pedagogy that emerged from a Covid-19, and post Covid-19, pandemic world. On the end-user side, the curriculum covered a wide spectrum of topics, from understanding satellite images, python scripting in the JupyterLab environment to identifying solutions to SDGs challenges through use cases, available in English and French. Digital Earth Africa’s Gender Equity, Diversity and Social Inclusion principles strategy (GEDSI) is imprinted as a watermark across the whole program. It prioritises gender equality, diversity, and social inclusion so that women, people with disabilities and marginalised individuals and communities have the same opportunities to benefit from EO data. In addition, Digital Earth Africa started live virtual sessions, to stay connected with end users, who have developed impactive stories in their communities. Digital Earth Africa seeks to support the capacity development of individuals, academic and governmental institutions, and private sector organisations to empower present and next generation of decision makers to drive toward a sustainable future, leaving on one and place behind.

NASA’s Planetary Data System (PDS)* contains data collected by missions to explore our solar system. This includes Lunar Reconnaissance Orbiter (LRO), which has collected as much data as all other planetary missions combined. Currently, PDS offers no way to search lunar images based on content. Working with the PDS Cartography and Imaging Sciences Node (IMG), we develop LROCNet, a deep learning (DL) classifier for imagery from LRO’s Narrow Angle Cameras (NACs). Data we get from NACs are 5km swaths, at nominal orbit, so we perform a saliency detection step to find surface features of interest. A detector developed for Mars HiRISE (Wagstaff et al, 2021) worked well for our purposes, after updating based on LROC image resolution. We use this detector to create a set of image chipouts (small cutouts) from the larger image, sampling the lunar globe. The chipouts are used to train LROCNet. We select classes of interest based on what is visible at the NAC resolution, consulting with scientists and performing a literature review. Initially, we had 7 classes: fresh crater, old crater, overlapping craters, irregular mare patches, rockfalls and landfalls, of scientific interest, and none. Using the Zooniverse platform, we set up a labeling tool and labeled 5,000 images. We found that fresh crater made up 11% of the data, old crater 18%, with the vast majority none. Due to limited examples of the other classes, we reduced our initial class set to: fresh crater (with impact ejecta), old crater, and none. We divided the images into train/validation/test set making sure no image swaths span multiple sets and fine tuned pre-trained DL models. VGG-11, a standard DL model, gives the best performance on the validation set, with an overall accuracy of 82% on the test set. We had 83% label agreement in our human label study; labeling was difficult as there is no clear class boundary. Our DL model accuracy is similar to human labelers. 64% of fresh craters, 80% old craters, and 86% of the none class are classified correctly. Predictions from this model will be integrated with IMG’s Atlas, allowing users to interactively search classes of interest. *https://pds-imaging.jpl.nasa.gov Copyright © 2022, California Institute of Technology. U.S. Government sponsorship acknowledged.

Graphene or graphene-based nanomaterials have emerged as novel scaffolds for developing robust bio-catalytic systems and a fast-developing promising contender for bioremediation. The interaction of bacteria and graphene is such an elusive issue that its implication in environmental biotechnology is unclear. The complexity and recalcitrant nature of the dyes make the conventional techniques inadequate and remain a challenge for industrial effluent treatment. Many scientists have developed hybrid processes and hybrid materials to enhance the treatment processes to satisfy increasingly stringent laws and criteria related to effluent discharge. The current study explicitly focuses on immobilization and growth of dye-degrading marine bacterial isolates on graphene oxide and their application in methylene blue dye degradation. The synergistic effects of adsorption and biodegradation achieved a unique clean-up performance that the counterpart-free bacteria could not fulfill. Further, toxicity analysis of intermediates also confirmed the non-toxic nature of the intermediates formed after synergistic treatment. This work has the potential to lead to zero effluent treatment processes.

Lebanon’s natural water resources are facing serious problems and approaches exhaustion. One of these issues is deteriorating performance, which is linked to unregulated resource planning and rising demand. There are many different types of consumption, such as residential, industrial, and irrigation. Surface and groundwater are both referred to designate water resources. However, due to the obvious accessibility of exploitation, surface water resources such as rivers, lakes, and basins are primarily used. The Ras El-Ain basin is 6 km far south of Tyr, Lebanon. The Lebanese state dedicated it, along with other reservoirs, to supply potable water for Tyr and the surrounding villages. Today, these basins’ water quality has deteriorated significantly because of unrestricted liquid and soil waste dumping. As a result, contaminants develop in the basin water. Aside from laboratory testing for water quality, contamination can be seen through direct observations, odors, watercolors, and patterns. The purpose of this study is to assess the level of pollution in the Ras El-Ain basin. This basin has been progressively subjected to a variety of quality degradation characteristics. This includes the most important physiochemical properties. As a result, the physicochemical and microbiological water characteristics of five selected samples from each basin were tested. These tests were performed in accordance with European Standard Methods and World Health Organization guidelines (WHO). The effect of pollutant disposal in the Ras El-Ain basin was studied using multivariate approaches. The obtained results were used to evaluate the pollution degree in various regions of the basin.

Poem to imagine the “essence” of water as it circulates through the Earth universe, synergistically supporting all environments and living ecosystems, forming, and shaping land and life. The poem links key elements of the interactive global water cycle and international programs to sustainably manage natural, and socioeconomic resources, given the challenge of climate change. It is in awareness of: –Essential Water Variables (EWVs) of the Group on Earth Observations (GEO) Global Water Sustainability (GEOGLOWS) initiative; Earth Observations (EO) for the Water-Energy-Food Nexus (EO4WEF) community activity; UN Sustainable Development Goals (UN SDGs), UNFCCC–Climate Change. The poem hopes to bring water to the forefront of consciousness. Readers are invited to comment on the intangible “feelings” evoked by the poem.

The various extreme weather events that occurred globally in 2021, from Europe to China to North America, served as yet another reminder that robust strategies for climate adaptation are crucial at a time of rapid global warming. Building resilient communities and lessening the impact that natural disasters have on vulnerable infrastructure can be aided by automated systems driven by machine learning algorithms trained on Earth observation data. When deployed, computer vision models can analyze satellite imagery in real time and inform decision makers and nongovernmental organizations about the timely and targeted allocation of resources and humanitarian aid personnel to affected areas. Here, we overview several specific 2021 extreme events and the factors that caused the loss of life, damage to infrastructure, and economic loss. The events surveyed include flooding in Germany, wildfires in Greece, and Hurricane Ida in the Eastern United States. Taking this information into account, we further discuss barriers to the large-scale deployment of current machine learning technologies, especially models trained on Earth observation data. We examine the limitations of satellite imagery and big data applications in detecting damage and building collapse and how Interferometric Synthetic Aperture Radar (InSAR) can be a tool to resolve existing issues. The aim of this work is to understand why many state-of-the-art models being developed have not yet been successfully and extensively deployed in the real world and to foster discussion about optimizing the use of deep learning technology to save lives and lead effective disaster management efforts.

In this study we introduce a pair of idealized tracers to quantify how changes in physical advection and mixing under climate change affect the nutrient supply, new production, and particulate export rates. The low cost and simplicity of these tracers allows us to explore the sensitivity of the model biogeochemistry, and in particular its response to a changing physical environment, to the choice of model parameters. Using CESM2.1 with active ocean and ice only, at nominal one-degree resolution, under initial conditions and forcing representative of 2000 and 2100, our idealized nutrient and particulate are within the spread of nitrate and export from CMIP5 models. The simple form of the tracers allows us to identify the physical controls on the changing rates of supply, production, and export throughout the year, which together form the different seasonal cycles. We find that the ocean basins with the largest changes in the seasonal cycle over the 21st century are the North Atlantic, the Arctic, and the eastern tropical Pacific. We present results comparing the controls across basins, focusing on shifts in the timing of deepening mixed layers and maximum production rate in the northern North Atlantic through the Arctic, and changes in the spatial and temporal patterns of vertical advective exchange in the tropics and subtropics of the Pacific and Indian Oceans. In both cases we discuss how much these changes depend on the biogeochemical model parameter values.

Recent research in real-time tsunami early warning can be broadly classified into two approaches. The first involves the use of seismic and regional geodetic data to calculate the tsunami wavefield indirectly through the estimation of earthquake source parameters. The second directly reconstructs the tsunami wavefield using data assimilation of ocean-bottom pressure sensor data such as those from DONET and S-NET (Maeda et al. 2015, Gusman et al. 2016). Data assimilation interpolates between the numerical solution and the observations to make the forecast more consistent with real data. Currently, the most popular method for forecasting the waveform is optimal interpolation, which uses a Kalman filter (KF) like approach, but holds the Kalman gain matrix fixed to reduce the runtime. This approach, coupled with tsunami Green’s functions, is very efficient and generates useful predictions. Here, we demonstrate that more accurate and stable forecasts can be obtained using the ensemble KF (enKF), a more computationally efficient variant of KF, in which the gain matrix is updated according to the physical model and the evolution of the error covariance matrix. The ensemble representation is a form of dimensionality reduction, in that only a small ensemble is propagated, instead of the joint distribution including the full covariance matrix. This method also provides a means to obtain the probability distribution of the forecast at each grid point location. We use a scenario tsunami in the Cascadia subduction zone, generated from a 2D fully-coupled dynamic rupture simulation (Lotto et al., submitted 2018). Randomly perturbed tsunami wave height data is used in the assimilation process, as we propagate the wave using a 1D linear shallow water code on a staggered grid. Better waveform agreement is achieved even in the early stages of assimilation, with much less fluctuation compared to optimal interpolation. We also explore spatial and temporal aliasing effects, in terms of the relation between observation station spacing and wavelength, as well as between assimilation and forecast time intervals. Although enKF is computationally more expensive, we are working on a fast, parallelized GPU implementation, which will significantly reduce the runtime, taking us a step closer to reliable real-time tsunami early warning.

Environmental justice and equity should include access to clean water for all. It is expensive to drill borehole wells, typically over $10,000 US dollars, and so organizations working to provide wells in developing countries have typically installed community wells at some common gathering place. This requires that many users must walk long distances to access these water sources. This limits the quantity of water available to a family, and also creates vulnerabilities for the family member, usually a woman or child, sent for the water since the journey is often made early in the morning or at night in the dark. I have been drilling wells with a Kenyan team since 2010 using a simple, manual percussion hydraulic method developed by WaterForAllinternational.org whereby we can install a well generally for less than $200 US dollars excluding labor. Through their own participation in the drilling process, this low-cost enables families to pay for and drill their own well. In this way, they gain access to a much larger supply of water at or close to home, and eliminate the need and vulnerability associated with walking long distances to procure water for their family. Both the drilling apparatus and the cased well, including the pump, is constructed from materials available off-the-shelf at local hardware stores. Over the years I have made several modifications to the pump design, other infrastructure, and manufacturing process to improve the longevity, simplicity, and interchangeability of the final product. The drilling method is primarily applicable to aquifers lying above bedrock and it is feasible to drill wells to a depth of several hundred feet. The greatest challenge in the endeavor is earning the trust and cultivating the participation of the local community. This presentation will address the drilling process, the well infrastructure, and some socio-cultural aspects of the project.

Minirhizotron technique (MR), an image-based root phenotyping technology, has expanded our understanding of in situ root responses to changing environmental conditions. However, the conventional manual approaches to capturing and analyzing images are time-consuming, thus constraining the size and frequency of sampling and interpretation. To address this bottleneck, an automated MR system was developed, which worked in a fully automatic manner according to the pre-set schedule. Users can remotely control the system and download images. This system was tested in a net house by imaging bell pepper roots on a daily basis, which shows superior performance over commercial manual MR systems in terms of image resolution and sampling frequency. Besides, an image analysis model was built on convolutional neural networks to estimate root length from MR images directly without segmentation in the training process. This model was trained on a dataset of ~18,000 tomato root images taken by a manual MR camera and was used for estimating root length on 832 bell pepper root images taken by the automated MR camera. The high correlation coefficient (R 2 = 0.854) between the model estimation and manual measurement proved that this model generalizes well over different crop roots and camera types. Therefore, high-frequency in situ root phenotyping can be achieved by the automated Minirhizotron image acquisition and analysis tools proposed here.

80 years after aerial photography revealed thousands of aligned oval depressions on the USA’s Atlantic Coastal Plain, the geomorphology of the “Carolina bays” remains enigmatic. Geologists and astronomers alike hold that invoking a cosmic impact for their genesis is indefensible. Rather, the bays are commonly attributed to gradualistic fluvial, marine and/or aeolian processes operating during the Pleistocene era. The major axis orientations of Carolina bays are noted for varying statistically by latitude, suggesting that, should there be any merit to a cosmic hypothesis, a highly accurate triangulation network and suborbital analysis would yield a locus and allow for identification of a putative impact site. Digital elevation maps using LiDAR technology offer the precision necessary to measure their exquisitely-carved circumferential rims and orientations reliably. To support a comprehensive geospatial survey of Carolina bay landforms (Survey) we generated about a million km2 of false-color hsv-shaded bare-earth topographic maps as KML-JPEG tile sets for visualization on virtual globes. Considering the evidence contained in the Survey, we maintain that interdisciplinary research into a possible cosmic origin should be encouraged. Consensus opinion does hold a cosmic impact accountable for an enigmatic Pleistocene event - the Australasian tektite strewn field - despite the failure of a 60-year search to locate the causal astroblem. Ironically, a cosmic link to the Carolina bays is considered soundly falsified by the identical lack of a causal impact structure. Our conjecture suggests both these events are coeval with a cosmic impact into the Great Lakes area during the Mid-Pleistocene Transition, at 786 ka ± 5 k. All Survey data and imagery produced for the Survey are available on the Internet to support independent research. A table of metrics for 50,000 bays examined for the Survey is available from an on-line Google Fusion Table: https://goo.gl/XTHKC4 . Each bay is also geospatially referenceable through a map containing clickable placemarks that provide information windows displaying that bay’s measurements as well as further links which allows visualization of the associated LiDAR imagery and the bay’s planform measurement overlay within the Google Earth virtual globe: https://goo.gl/EHR4Lf .

The Inter-Tropical Convergence Zone (ITCZ) is a persistent band of organized convection in the tropics that arises due to the surface convergence of the Hadley cells. The location and intensity of the ITCZ is heavily influenced by sea surface temperature and low-level latent heat transport. The ITCZ undergoes an annual march across the equator, and during the summer moves north over India and the Bay of Bengal, affecting the Indian summer monsoon. Occasionally a second parallel band of convection forms to the south, referred to as a double-ITCZ. Double-ITCZs in the tropical east Pacific have been heavily studied, and their development is understood to be linked to seasonal changes in sea-surface temperature. The existence of double ITCZs over the tropical Indian Ocean is well documented, but the underlying mechanism is poorly understood. We develop an algorithm to identify this phenomenon in NOAA outgoing longwave radiation data, and create a thirty-year record of double-ITCZ occurrence. We then use this record to investigate linkages between summer-time double-ITCZ occurrence and intra-seasonal variability in the Indian summer monsoon, and discuss possible physical mechanisms.

Holistic approaches are needed to investigate the capacity of current water resource operations and infrastructure to sustain water supply and critical ecosystem health under projected drought conditions. Drought vulnerability is complex, dynamic, and challenging to assess, requiring simultaneous consideration of changing water demand, use and management, hydrologic system response, and water quality. We are bringing together a community of scientists from the U.S. Geological Survey, National Center for Atmospheric Research, Department of Energy, and Cornell University to create an integrated human-hydro-terrestrial modeling framework, linking pre-existing models, that can explore and synthesize system response and vulnerability to drought in the Delaware River Basin (DRB). The DRB provides drinking water to over 15 million people in New York, New Jersey, Pennsylvania, and Delaware. Critical water management decisions within the system are coordinated through the Delaware River Basin Commission and must meet requirements set by prior litigation. New York City has rights to divert water from the upper basin for water supply but must manage reservoir releases to meet downstream flow and temperature targets. The Office of the Delaware River Master administers provisions of the Flexible Flow Management Program designed to manage reservoir releases to meet water supply demands, habitat, and specified downstream minimum flows to repel upstream movement of saltwater in the estuary that threatens Philadelphia public water supply and other infrastructure. The DRB weathered a major drought in the 1960s, but water resource managers do not know if current operations and water demands can be sustained during a future drought of comparable magnitude. The integrated human-hydro-terrestrial modeling framework will be used to identify water supply and ecosystem vulnerabilities to drought and will characterize system function and evolution during and after periods of drought stress. Models will be forced with consistent input data sets representing scenarios of past, present, and future conditions. The approaches used to unify and harmonize diverse data sets and open-source models will provide a roadmap for the broader community to replicate and extend to other water resource issues and regions.