bluuebunny/crossref_metadata_2025 huggingface dataset:
Anatomy of a volcanic island inferred from a multiphysics approach
Authors: Gresse Marceau , Uyeshima Makoto , Koyama Takao , Hase Hideaki , Aizawa Koki , Yamaya Yusuke , Morita Yuichi , Weller Derek , Rung-Arunwan Tawat , Kaneko Takayuki , Sasai Yoichi , Zlotnicki Jacques , Ishido Tsuneo , Ueda Hideki , Hata Maki
Published: March 2021

Phreatic and phreatomagmatic eruptions are difficult to predict with accuracy on volcanoes due to complex interactions at depth between heat, water, and magmatic fluids. To better understand such multifaceted interactions, we present here a multidisciplinary geophysical approach performed on Miyakejima, a 10-km wide stratovolcano in the Izu Bonin arc. Its plumbing system was highlighted by combining four geophysical methods: magnetotellurics, seismicity (hypocenters), self-potential, and thermal image (remote sensing). We thus propose the first large-scale interpretation of the volcanic structure in terms of rock properties, temperature, fluid content, and fluid flow. Our findings indicate that hot volatiles released from a deep magmatic reservoir (> 350°C, 2.5–4.5 km depth) rise through a narrow permeable path, interact with the conductive hydrothermal system beneath the 2000 A.D. caldera (<250°C, 0–2 km depth). This mixture of fluid is finally released in the fumarolic area in the southern part of the caldera at 181°C. This combined approach allow us to: 1) delineate the water table of the volcano (300–700 m depth), 2) determine the general fluid flow circulation beneath the island, 3) characterize seismic signatures of long-period and volcano-tectonic events, and 4) elucidate the origin of the high water content of fumaroles developed since the last eruption in A.D. 2000.
Innovative utilization of internet search volume data to understand public awareness and perception of air quality
Authors: Ryu Young-Hee , Min Seung-Ki
Published: March 2021

Severe air pollution is hazardous to human health and long-term exposure to air pollution degrades not only human health but also the quality of life. In the recent years, public concern and awareness of air quality have been greatly raised in South Korea, and this is somewhat contradictory to the level of particulate matter with diameters less than 10 μm (PM10). The observed PM10 levels cannot explain the elevated levels of public concern specifically after 2013–2014 because the average PM10 was much higher in the past (prior to 2013) and shows a decreasing tendency during the recent decades over South Korea. This study utilizes big data from internet search engines (internet search volume data from Google and NAVER) to understand how people perceive air quality differently from the level of observed PM10 and what influences public perception of air quality. An index, air quality perception index (AQPI), is newly proposed in this study and it is assumed that the internet search volume data with a keyword of “air quality” are representative of this index. An empirical model that simulates AQPI is developed by employing the decay theory of forgetting and is trained by PM10, visibility, and internet search volume data. The results show that the memory decay exponent and the accumulation of past memory traces, which represent the weighted sum of past perceived air quality, play key roles in explaining the public's perception of air quality. A severe haze event with an extremely long duration that occurred in the year 2013–2014 is found to trigger the increase in public awareness of air quality, acting as a turning point. Before the turning point, AQPI is more influenced by sensory information (visibility) due to the low awareness level, but after the turning point it is more influenced by PM10 and people slowly forget about air quality. The retrospective AQPI analysis assuming a low level of awareness confirms that perceived air quality is indeed worst in the year 2013–2014. In other words, the high level of awareness after experiencing the record-long severe haze event in 2013–2014 makes people remember longer and more sensitive to the level of pollutants, thus explaining the increased public concerns in the recent years. Our results suggest the promising potential of social data for a better understanding of public perception and awareness of other natural and/or man-made hazards.
Analysis on impacts of the Three Gorges Reservoir on water temperature in the middle reach of the Yangtze River
Authors: Tao Yuwei , Wang Yuankun , Wang Dong
Published: March 2021

Water temperature, a crucial environmental factor, has a direct impact on almost all ecological and biogeochemical processes. The hydrological and thermal regimes in the Yangtze River have changed greatly due to the constructions of the Three Gorges Reservoir (TGR). To quantify the impact of TGR on the water temperature regime, we present a regression-modeling framework to reconstruct the temporal pattern of flow and temperature variation along the middle reach of the river in the absence of the TGR. By comparing reconstructed water temperatures to observed water temperature for the post-impounded period, the influence of impoundment on water temperature was estimated. Results show that TGR has had a greater impact on water temperature than natural changes in air temperature and discharge. The reservoir acts as a source of cold water in spring, summer and autumn and a warm source in winter. The results of this study illustrate the pronounced effect of the TGR on the temperature regime of the Yangtze River. We hope this study could provide a scientific reference for ecological operation of TGR facing biological conservation.

Note: This study has been published in Journal of Hydrology (Tao, Y., Wang, Y., Rhoads, B., Wang, D., Ni, L. and Wu, J., 2020. Quantifying the impacts of the Three Gorges Reservoir on water temperature in the middle reach of the Yangtze River. Journal of Hydrology. 582.).
Emergence of critical climate states during the Pleistocene
Authors: Golledge Nicholas
Published: March 2021

During the Pleistocene (approximately 2.6 Ma to present) glacial to interglacial climate variability evolved from dominantly 40 kyr cyclicity (Early Pleistocene) to 100 kyr cyclicity (Late Pleistocene to present). Three aspects of this period remain poorly understood: Why did the dominant frequency of climate oscillation change, given that no major changes in orbital forcing occurred? Why are the longer glacial cycles of the Late Pleistocene characterised by a more asymmetric form with abrupt terminations? And how can the Late Pleistocene climate be controlled by 100 kyr cyclicity when astronomical forcings of this frequency are so much weaker than those operating on shorter periods? Here we show that the decreasing frequency and increasing asymmetry that characterise Late Pleistocene ice age cycles both emerge naturally in dynamical systems in response to increasing system complexity, with collapse events (terminations) occuring only once a critical state has been reached. Using insights from network theory we propose that evolution to a state of criticality involves progressive coupling between climate system 'nodes', which ultimately allows any component of the climate system to trigger a globally synchronous termination. We propose that the climate state is synchronised at the 100 kyr frequency, rather than at shorter periods, because eccentricity-driven insolation variability controls mean temperature change globally, whereas shorter-period astronomical forcings only affect the spatial pattern of thermal forcing and thus do not favour global synchronisation. This dynamical systems framework extends and complements existing theories by accomodating the differing mechanistic interpretations of previous studies without conflict.
Estimation of in-situ hydro-geomechanical properties using the groundwater responses to natural cyclical forcing
Authors: McMillan Timothy , Rau Gabriel , Timms Wendy , Andersen Martin
Published: March 2021

Earth and atmospheric tides are prevalent across the land-surface and provide natural forcing to characterise the hydro-geomechanical confined subsurface by using their groundwater response. Since tides are harmonic, their individual influences on the pressure head can be separated into complex components containing level or pressure magnitudes and phases. The approximated planar strain from Earth tides, and the uniaxial loading from atmospheric tides, allow the estimation of a wide range of values based on hydraulic and poroelastic relationships. With recent research advances, tidal analysis can be used to estimate hydro-geomechanical properties including specific storage, hydraulic conductivity, porosity, shear, Young’s and Bulk moduli, Skempton’s and Biot-Willis coefficients and undrained/drained Poisson’s ratios. This approach does not require any assumption on mineral grain compressibility for unconsolidated systems. However, consolidated materials currently require an a priori estimate of grain compressibility. We applied this method to pressure measurements from different geological settings. The estimated hydro-geomechanical properties comply with theoretically expected values except for Poisson’s ratio, which differs from laboratory values due to differing confining pressures, and comparatively low frequencies of the Earth and Atmospheric tide signals. However, these estimated values from in-situ data are likely more realistic of the natural hydrogeological response. We anticipate that, by developing methods that routinely can derive engineering geotechnical values through the monitoring of hydraulic head variations, the collection of groundwater pressures will become a priority for large civic excavations or construction, such as mining, in addition to environmental studies and regulatory compliance.
Evaluate recession characteristics from baseflow and separate baseflow via the knowledge guided machine learning
Authors: Li Xiang
Published: March 2021

Baseflow, referred to as the groundwater discharge, is essential to investigate the groundwater system. A common and classic approach to study baseflow is recession analysis method, but current methods confuse the concept of streamflow recessions and baseflow recessions. This confusion leads to a mixing effect of the fluxes from different storage components and theoretically inconsistent recession analysis results accordingly. Therefore, it motivates an improvement and enhanced scientific understanding of the empirically derived baseflow recession characteristics.  In addition, quantifying baseflow from streamflow is defined as the baseflow separation problem. The state-of-the-art baseflow separation tools are in lack of physical rules and have either structural limitations or are inapplicable in regions with insufficient data, which confines the generalization performance. To overcome these issues, we applied a knowledge guided machine learning (KGML) approach to separate baseflow, which embeds physically derived baseflow recession characteristics in the traditional machine learning framework.

Recession parameter, which is derived from empirical recession analysis, has been observed to exceed its theoretical range on a recession event scale. Besides many potential environmental factors, we hypothesize that this well recognized inconsistency is because the quick flow from surficial water bodies has not been successfully excluded based on the recession selection criterion. We conduct recession analysis using both streamflow and baseflow over 1,000 gages across the continental United States. The baseflow was estimated from Eckhardt two-parameter digital filter and was calibrated against the in-stream field data. It was found that for gages whose calibration performance is satisfactory, the baseflow derived recession parameter agrees more consistently with the recession characteristics, which are indicated by the Boussinesq solutions.

Traditional baseflow separation tools partition streamflow into quick flow and base flow. Those tools have data scarcity issues and structural limitations without involving physical perspectives. To introduce physical rules into baseflow separation and overcome data scarcity issues, we apply a recession-based loss function to train the machine learning model such that the recession characteristics of separated baseflow agree with their theoretical behaviors. Guided by the recession knowledge of baseflow on a catchment scale, progress is being made to finalize this KGML implementation and to improve the baseflow separation approach.
The microphysical characters of wintertime mixed-phase clouds in North China.
Authors: Wu Xuexu , Wang Minghuai , Rosenfeld Daniel , Zhao Delong , Ding Deping
Published: March 2021

We use aircraft observation data to investigate the microphysical characters of wintertime mixed-phase clouds in North China, including the cloud particle number concentration (Nc), the liquid water content (LWC), the ice particle number concentration (Ni), the ice water content (IWC), the particle spectrum distributions (PSDs) and the effective diameter (De). For wintertime mixed-phase clouds, the average Nc and Ni were 170±154 cm-3 and 26±39 L-1, respectively; the average LWC and IWC were 0.05±0.06 and 0.07±0.09g/m3, respectively; the De for cloud particles was 10±4 μm. When compared to the results from other regions, including East Europe, North America, Southern Ocean and Tibetan Plateau, we found that the wintertime mixed-phase cloud in North China has larger Nc, smaller LWC, IWC and De, and narrower PSDs. The main reason might be the larger aerosol loading and smaller water content in the atmosphere in winter in North China. With increasing temperature, Nc and LWC increased, but Ni and De decreased. The dominate physical processes in wintertime mixed-phase cloud were aggregation process and riming process. As the temperature increased, the peak concentration of ice PSD decreased, but Ni increased and the ice PSD became wider, indicating more ice crystals and the ice crystals became larger at higher temperature. With temperature increasing, the ice habit also changed, and the amount of plates, irregular crystals and their aggregates increased. What’s more, with the existence of larger LWC at higher temperature, the ice crystals gradually tightened and their surface became more complicated as well. Therefore, both aggregation process and riming process were more active at higher temperature, but riming process changed much more. This work fills the gap in the observation of wintertime mixed-phase clouds in north China, and the results suggest that the wintertime mixed-phase clouds have some unique microphysical characters.

 
Regional Scale Distribution of Gully in Loess Plateau Based on High Resolution Google Earth Satellite Imagery
Authors: Su Jianhua , Wang Chunmei , Pang Guowei , Yang Qinke , Liu Xin , Guo Zitian , Zhong Yuan
Published: March 2021

Gully erosion is one of the most important erosion gully types in the Loess Plateau. Its generation and development seriously affect and destroy the ecological environment. Therefore, it is the premise of scientific management to make clear the spatial distribution of gully region scale. At present, scholars at home and abroad mainly focus on the spatial distribution of gully in a specific small watershed, and there are few reports on the regional scale. In view of this, this study, based on Google Earth sub meter image combined with GIS method, uses the means of manual visual interpretation to conduct sampling survey of gully in the Loess Plateau. A total of 137 sampling units were set up, and the area of each sampling unit was about 0.2 square kilometers. The results showed that: (1) there were 75 gullies in 54.7% of the survey units, with a total of 712 gullies. The sampling units with gullies were mainly located in the northeast of the Loess Plateau (yan'an-dongsheng area on both sides of the Shanxi Shaanxi Yellow River) and the middle of the Loess Plateau (the border area of Guyuan, Ningxia and Huining, Dingxi, Gansu), with an average gulley density of 3.32km/km2 and a maximum of 19.94km/km2; (2) the grassland was the most gullied area The main land use types of gully development accounted for 40.03%, followed by cultivated land and forest land, accounting for 30.06% and 20.08% respectively, and 9.83% of gully development was in bare land, orchard and residential land, collectively referred to as other land; (3) the average values of gully width, length and distance from watershed were 7.76m, 69.81m and 79.19m, respectively, and the gully width was mainly distributed in 3-5M, accounting for 50% 39.6％。 The length of gully was mainly distributed in 30-70m, accounting for 46.3%. The distance between  and watershed was mainly 25-50m, accounting for 41.6%; (4)  existed most in sub region I of Loess Plateau (22.7%), followed by sub region III of Loess Plateau (20.0%), sub region V of Loess Plateau (12.0%) and sub region II of Loess Plateau (9.3%); (5) according to the existing erosion classification standards,  erosion intensity of Loess Plateau reached strong level No. The gully density and gully length show obvious spatial differentiation characteristics. The highest density area is in yan'an-dongsheng area on both sides of Shanxi Shaanxi Yellow River, and the gully length in this area is about 100m. This study will help to better understand the spatial distribution characteristics of  in the Loess Plateau, support  management in the Loess Plateau, and promote scientific decision-making of  control in the Loess Plateau.
Visibility Nowcasting For UAS Operations using Deep Learning 
Authors: Majdi Marwa , Delene David
Published: March 2021

Unmanned Aircraft System (UAS) operations have spread rapidly worldwide performing a variety of military and civilian applications. The ability and performance of UAS to carry out these applications are strongly affected by poor weather conditions. Fog is one of the critical issues that threaten the safety of UAS missions by altering visibility. Therefore, the mission planning based on accurate visibility nowcasts prior to Beyond Visual Line Of Sight (BVLOS) UAS missions will be mandatory to ensure safer UAS operations.

Two types of models are generally considered for visibility nowcasting: physics-based or data-driven models. However, physics-based visibility forecasts remain expensive and difficult to use operationally. Recently, with the increase of the number of available historical data, data-driven models, especially those using deep learning approaches in particular, have attracted increasing attention in weather forecasting and have proven themselves as a powerful prediction tool.

This study aims at developing a Visibility Nowcasting System (VNS) that improves the performance and the capability of nowcasting the visibility using deep learning over the U.S.. To that end,  a deep neural network, called an encoder-decoder convolutional neural network (CNN), is used to demonstrate specifically how basic NWP fields such as temperature, wind speed, relative humidity, etc. and visibility from surface observations can provide accurate visibility nowcasts. The VNS will be then tested in different geographical environments where UAS flights are deployed (for example, over North Dakota) since it can learn the time and space correlation according to the historical data.

To train the network, we created a labeled data set from available METAR reports and hourly reanalysis data from the High-Resolution Rapid Refresh (HRRR) model. This dataset will be also used to test the CNN and evaluate their nowcasting performance. The model will be then evaluated in operational use cases and compared to other available visibility observations during fog events.

 
Deep learning facilitated local deformation monitoring with large-scale SAR interferometry
Authors: Wang Teng , Luo Heng , Wu Zhipeng , Fu Lv , Zhang Qi
Published: March 2021

SAR interferometry has stepped in the big-data era, particularly with the acquisition capability and open-data policy of ESA’s Sentinel-1 SAR mission. Large amount of Sentinel-1 SAR images has been acquired and archived, allowing for generating thousands of interferograms, covering millions of square kilometers. In such a large-scale interferometry scenario, many applications still focus on monitoring kilometer-scale local deformation, sparsely distributed in a large area. It is thus not effective to apply the time-series InSAR analysis to the whole image stack, but to focus on areas with deformation. Aiming at this target, we present our recent work built upon deep neural networks to firstly detect localized deformation and then carry on the time-series analysis on small interferogram patches with deformation signals.

Here, we first introduce our burst-based Sentinel-1 processor, which has been fully paralleled for large-scale InSAR processing. From these interferograms, we adapt and train several deep neural networks for masking decorrelation areas, detecting local deformation, and unwrapping high-gradient phases. We apply our networks for mining subsidence and landslides monitoring. Comparing with traditional time-series InSAR analysis, the presented strategy not only reduces the computation time, but also avoids the influence of large-scale tropospheric delays and the propagation of possible unwrapping errors.

The presented methods introduce artificial intelligence to the time-series InSAR processing chain and make the mission of regularly monitoring localized deformation sparsely distributed in large scale feasible and more efficient. As future work, we can further improve the temporal resolution of InSAR based local deformation monitoring by training networks combining interferograms from C-band and L-band SAR images, which will be available soon from future SAR missions such as NiSAR and LuTan-1.
Monitoring Volcanic Activity of Baekdu Mountain based on Ice Area Changes During the Winters of 2015-2020
Authors: Nur Arip Syaripudin , Park Sungjae , Lee Seulki , Lee Chang-Wook
Published: March 2021

Baekdu Mountain is a 2,744 m high stratovolcano, located on the border of China and North Korea. The mountain has a caldera lake, Lake Cheonji, as a result of past volcanic activity. The ice area changes during winter in Lake Cheonji could act as a proxy for volcanic activity monitoring in Baekdu. As Baekdu laid on a political border, remote sensing allows us to quantify attributes of otherwise inaccessible or dangerous places. We assessed changes in winter (October–April) ice area in a high-altitude groundwater-fed caldera lake using Sentinel-1 synthetic aperture radar (SAR) data acquired from 2015 to 2020. To calculate the ice-covered area, 10 gray level co-occurrence matrix (GLCM) texture features were computed from SAR images obtained with VH (vertical transmission and horizontal reception) and VV (vertical transmission and vertical reception) polarizations. A support vector machine (SVM) algorithm was used to classify ice and water pixels from the GLCM layers, and the results from VH and VV imagery were combined to calculate the total area covered by ice. We examined the relationship between ice area and air temperature from the closest weather station, Samjiyeon using fixed period regression. The ice area was inversely proportional to 30-day averaged air temperature and these variables were highly correlated (-0.86). Our results show that there were no significant ice changes during the period, which indicates that there was no significant volcanic activity in Baekdu Mountain during the winters of 2015–2020. This study is expected to be useful for a better understanding of whether and how ice area changes in volcano lakes aid in eruption forecasting.
Mapping Land Subsidence in Pekalongan, Indonesia using Time Series Interferometry and Optimized Hot Spot Analysis with Sentinel-1 SAR Data 
Authors: Hakim Wahyu Luqmanul , Lee Seul Ki , Lee Chang-Wook
Published: March 2021

Floods in Pekalongan, Indonesia often occur due to the overflowing of river water during heavy monsoon rain. While the northern coast area of Pekalongan which located adjacent to the Java sea was affected by coastal floods due to sea-level rise. The flood conditions in every area were exacerbated by land subsidence and lead to coastal inundation. Monitoring land subsidence in Pekalongan becomes important to predict the further possible land subsidence occurrence area and mitigate the possible hazard caused by land subsidence. The analysis of land subsidence is much easier since the existence of radar satellites. This study used Synthetic Aperture Radar (SAR) datasets from the Sentinel-1 radar satellite between 2017 and 2020 in descending tracks. The data was processed through a time-series Interferometry SAR (InSAR) method based on the Stanford Methods for Persistent Scatterer (StaMPS) algorithm to provide accurate measurements over large areas by improving the selection of coherent pixels and reducing atmosphere noises. The result of persistent scatterer points then spatially clustered using Optimized Hot Spot Analysis (OHSA) to estimate significant points statistically and define them as the hot spot points. The results of time-series vertical deformation in Pekalongan were compared with the GPS station measurements. The comparison showed a good correlation in deformation patterns between time-series InSAR and GPS measurements. Our study revealed that the land subsidence in Pekalongan occurred mostly in settlement areas under the young alluvium soil which did not support the maximum compression from many buildings. Another cause of land subsidence in Pekalongan was the excessive groundwater extraction in the settlement areas could reduce the effective stress of pore pressure and lead to compaction in the aquifer areas. The time-series method that using the StaMPS algorithm and Optimized Hot Spot Analysis in this study can be applied for monitoring land subsidence in another area and from all-terrain.
Correlation Analysis among Meteorological Factors and PM10 Concentrations in the Atmosphere and Rainwater Quality using Multivariate Methods
Authors: Kim Taeyong , Yang Minjune
Published: March 2021

This study investigates the effect of meteorological factors on the concentration of PM10 (particulate matter 10) in the atmosphere and evaluates the variation of chemical quality in rainwater using correlation analysis at Daeyeon dong, Busan, South Korea. The real-time concentration of PM10 in the atmosphere was measured automatically during eleven rainfall events with a custom-built PM10 sensor node. The 183 rainfall samples were analyzed for chemical quality (pH and electrical conductivity (EC)). The values of meteorological factors (humidity, wind speed, wind direction, temperature, cumulative rainfall, and rainfall intensity) were obtained from an automatic weather system (AWS) in Nam-gu, Busan. Pearson correlation analysis and principal component analysis (PCA) were performed to identify relationships among PM10 concentrations, meteorological factors, and chemical quality in rainwater. Cumulative rainfall and rainfall intensity had negative correlations with the concentration of PM10 (r =  −0.52, and −0.35), and other meteorological factors were shown no correlation with the concentration of PM10. When the rainfall intensity was strong (> 5 mm/h), the concentration of PM10 showed a negative correlation with the cumulative rainfall (r = −0.55) and pH (r = −0.7). However, for the weak rainfall intensity (< 5 mm/h), there was no correlation between the PM10 concentration with cumulative rainfall and pH. The results of this study provide an understanding of the interaction between PM10 concentrations and hydro-meteorological factors and can be used as a decision tool to evaluate the relative magnitude of PM10 reduction resulting from various rainfall types.
The Use of Edible Plants for Rhizofiltration under Different Hydroponic Conditions
Authors: Lee juyeon , Yang minjune
Published: March 2021

This study conducted a rhizofiltration experiment for uranium-removal with the edible plants (Lactuca sativa, Brassica campestris L., Raphanus sativus L., and Oenanthe javanica) which generally consumed in South Korea. Various batch experiments were performed with different initial uranium concentrations, pH conditions, and genuine groundwater. The results showed the uranium accumulation and bioconcentration factor (BCF) of plant roots increase with an increase in initial uranium concentrations in the solution. Of the four plants, the amount of uranium accumulated in Raphanus sativus L. roots was 1215.8 μg/g DW with the maximum BCF value of 2692.7. The BCF value based on various pH conditions (pHs 3, 5, 7 and 9) of artificial solutions was highest at pH 3 for all four plants, and the BCF value of Brassica campestris L. was the maximum of 11580.3 at pH 3. As a result of rhizofiltration experiments with genuine groundwater contaminated with uranium, the BCF values of Raphanus sativus L. were 1684.7 and 1700.1, the highest among the four species, in Oesam-dong and Bugokdong groundwater samples with uranium concentration of 83 and 173 μg/L. From SEM/EDS analysis, it was confirmed that uranium in contaminated groundwater was adsorbed as a solid phase on the root surface. These results demonstrate that Raphanus sativus L. not only has a high tolerance to high concentrations of uranium and low pH conditions but also has a remarkable potential for uranium accumulation capacity.
Freshwater under the MOSAiC floe: implications of under-ice melt ponds for mass balance
Authors: Smith Madison , von Albedyll Luisa , Raphael Ian , Matero Ilkka , Lange Benjamin A.
Published: March 2021

During the melt season in the Arctic, freshwater ponds can accumulate under ice floes as a result of local snow and sea ice melt, far from terrestrial freshwater inputs. Under-ice freshwater ponds have been suggested to play a role in the summer sea ice mass balance both by isolating the sea ice from salty, warmer water below, and by driving formation of ice ‘false bottoms’ at the interface of the under-ice pond and the underlying ocean. 

The MOSAiC drift expedition in the Central Arctic observed the presence of under-ice ponds and false bottoms beginning early in the melt season (June - July) at primarily first-year ice locations on the floe. We examine the prevalence and drivers of these ponds and resulting false bottoms during this period. Additionally, we explore the impact for mass balance using observations from ablation stakes and a 1D model, where freshwater ponds can not only delay summer melt but also result in growth. We speculate that the unique history of the MOSAiC floe likely led to a relatively high occurrence of these features, but the results also suggest that freshwater under-ice ponds and false bottoms may be more common and more persistent in early summer in the Arctic than previously thought. Both have implications for the broader ice-ocean system, for example by reducing fluxes between the ice and the ocean, isolating the primary producers in ice from pelagic nutrient sources, and altering the optical properties.
Responses of Soil Microbes to Hydrological Perturbations in Tropical Forest Soils
Authors: Chacon Stephany S , Khurram Aizah , Bill Markus , Bechtel Hans , Voriskova Jana , Chen Liang , Dietterich Lee H. , Karaoz Ulas , Holman Hoi-Ying , Cusack Daniela F. , Bouskill Nicholas
Published: March 2021

Model projections predict that climate change impacts on the tropics will include an increased frequency of drought and precipitation cycles. Such environmental fluctuations at the soil pore-scale play an important role in shaping microbial adaptive capacity, and trait composition of a community, which feeds back on to the breakdown and formation of soil organic matter (SOM). Understanding the factors controlling the carbon balance of humid tropical forest soils remains a social imperative. Microbial feedback to SOM pools is critical. Herein, we examine the microbial response to drought perturbations across  3 different, but complementary scales. At the largest scale, we explored the impacts of drought across a 1 m precipitation gradient spanning four sites from the Caribbean coast to the interior of Panama. At each site 4, throughfall exclusion plots (10 x 10 m) were established to reduce precipitation by 50 %. In addition, 4 corresponding control plots were also constructed. At the meso-scale, we incubated intact soil cores from one of these sites (P12) under 3 different hydrological treatments (control, drought, rewetting-drying cycles) for over a 5-month period. For the field and meso-scale experiments, we evaluated changes imparted by hydrological perturbations using multi-omic approaches, and physico-chemical measurements.   In order to identify the traits involved in response to drought at the field and meso-scale, we isolated a range of bacteria to subject to stress at the scale of the single-cell and simple communities.  Cell extracts were subjected to osmotic or matric stress and the short-term physiological responses determined using non-destructive synchrotron radiation-based Fourier Transform-Infrared spectromicroscopy. Through this approach, we identified changes in metabolic allocation within different cells, in particular to the secondary metabolome of the different bacteria. Our contribution will discuss the outcomes of these multi-scale experiments.  Specifically focusing on how shifts in the microbial community and physiological changes may influence tropical soil carbon stability under future scenarios of altered drought and precipitation cycles.
Magnetic monitoring of PM filters from Air Monitoring Stations during the COVID-19 lockdown in Rome, Italy (March 10th - May 18th, 2020)
Authors: Winkler Aldo , Amoroso Antonio , Di Giosa Alessandro , Marchegiani Giada
Published: March 2021

An extensive survey of the magnetic properties of PM filters from selected air monitoring stations in Rome and other localities in Latium Region (Sacco Valley, Civitavecchia, Fiumicino) was conducted for outlining the impact of the lockdown measures on air quality.

The magnetic measurements highlighted a relevant content of magnetic minerals, mostly attributable to traffic related sources, on the filters from two stations in Rome and two stations from the urban areas of Civitavecchia and Fiumicino.

The PM filters from the Sacco Valley showed reduced concentrations of magnetic minerals, compared to Rome, however higher than the Castel Di Guido and Civitavecchia Sant'Agostino control stations.

The daily PM concentration data did not generally correlate with the mass susceptibility data, indicating that PM was often dominated by non-ferromagnetic contents, presumably due to wind-driven natural dusts, as stressed by the frequent anticorrelation between mass magnetic susceptibility and PM concentration.

In Magnagrecia air quality station, Rome, the average values ​​of the concentration depending magnetic parameters resulted about a half of those measured in 2005 on the filters from the same station.

From the Day plot, the filters with higher magnetic susceptibility values showed relatively coarse magnetite-like particles as the main magnetic minerals, ascribable to non-exhaust PM emissions from brakes.

This study confirmed that the interpretation of PM concentration during the lockdown is not straightforward and depends on many factors, such as natural inputs, resuspension and local conditions; anyway, magnetic analyses confirmed to be a valuable tool in PM source apportionment and concentration data interpretation.
Evaluation of FIO-ESM v1.0 Seasonal Prediction Skills Over the North Pacific
Authors: Song Yajuan , Yin Xunqiang
Published: March 2021

Accurate prediction over the North Pacific, especially for the key parameter of sea
surface temperature (SST), remains a challenge for short-term climate prediction. In
this study, seasonal predicted skills of the First Institute of Oceanography Earth System
Model version 1.0 (FIO-ESM v1.0) over the North Pacific were assessed. Ensemble
adjustment Kalman filter (EAKF) and Projection Optimal Interpolation (Projection-OI) data
assimilation schemes were used to provide initial conditions for FIO-ESM v1.0 hindcasts
that were started from the first day of each month between 1993 and 2017. Evolution
and spacial distribution of SST anomalies over the North Pacific were reasonably
reproduced in EAKF and Projection-OI assimilation output. Two hindcast experiments
show that the skill of FIO-ESM v1.0 with the EAKF data assimilation scheme to predict
SST over the North Pacific is considerably higher than that with Projection-OI data
assimilation for all lead times of 1–6 months, especially in the central North Pacific where
the subsurface ocean temperature in the initial conditions is significantly improved by
EAKF data assimilation. For the Kuroshio–Oyashio extension (KOE) region, the errors
in the initial conditions have more rapid propagation resulting in large discrepancies
between simulated and observed values, which are reduced by inducing surface
waves into the climate model. Incorporation of realistic initial conditions and reasonable
physical processes into the coupled model is essential to improving seasonal prediction
skill. These results provide a solid basis for the development of operational seasonal
prediction systems for the North Pacific.
Steady radiating baroclinic vortices in vertically sheared flows
Authors: Sutyrin Georgi , Nycander Jonas , Radko Timour
Published: March 2021

Baroclinic vortices embedded in a large-scale vertical shear are examined. We describe a new class of steady propagating vortices that radiate Rossby waves but yet do not decay. This is possible since they can extract available potential energy (APE) from a large-scale vertically sheared flow, even though this flow is linearly stable. The vortices generate Rossby waves which induce a meridional vortex drift and an associated heat flux explained by an analysis of pseudomomentum and pseudoenergy. An analytical steady solution is considered for a marginally stable flow in a two-layer model on the beta-plane, where the beta-effect is compensated by the potential vorticity gradient (PVG) associated with the meridional slope of the density interface. The compensation occurs in the upper layer for an upper layer westward flow (an easterly shear) and in the lower layer for an upper layer eastward flow (the westerly shear). The theory is confirmed by numerical simulations indicating that for westward flows in subtropical oceans, the reduced PVG in the upper layer provides favorable conditions for eddy persistence and long-range propagation. The drifting and radiating vortex is an alternative mechanism besides baroclinic instability for converting background APE to mesoscale energy. 
A new numerical method to approximate root water uptake fluxes in a mixed-dimensional 1D-3D setting
Authors: Koch Timo , Wu Hanchuan , Mardal Kent-André , Helmig Rainer , Schneider Martin
Published: March 2021

1D-3D methods are used to describe root water and nutrient uptake in complex root networks. Root systems are described as networks of line segments embedded in a three-dimensional soil domain. Particularly for dry soils, local water pressure and nutrient concentration gradients can be become very large in the vicinity of roots. Commonly used discretization lengths (for example 1cm) in root-soil interaction models do not allow to capture these gradients accurately. We present a new numerical scheme for approximating root-soil interface fluxes. The scheme is formulated in the continuous PDE setting so that is it formally independent of the spatial discretization scheme (e.g. FVM, FD, FEM). The interface flux approximation is based on a reconstruction of interface quantities using local analytical solutions of the steady-rate Richards equation. The local mass exchange is numerically distributed in the vicinity of the root. The distribution results in a regularization of the soil pressure solution which is easier to approximate numerically. This technique allows for coarser grid resolutions while maintaining approximation accuracy. The new scheme is verified numerically against analytical solutions for simplified cases. We also explore limitations and possible errors in the flux approximation with numerical test cases. Finally, we present the results of a recently published benchmark case using this new method.
Activity of phosphatases in technosols afforested with N-fixing and non-N-fixing tree species
Authors: Chodak Marcin , Sroka Katarzyna , Pietrzykowski Marcin
Published: March 2021

Tree species capable of forming symbiosis with N-fixing bacteria planted on reclaimed wastelands may increase not only their N content but also increase availability of P. The aim of our study was to compare the effect of rhizobial and actinorhizal N-fixing tree species and non-N-fixing species on the activity of phosphatases in various technosols. Soil samples were taken under black locust (Robinia pseudoaccaccia), black alder (Alnus glutinosa), silver birch (Betula pendula) and Scots pine (Pinus sylvestris) from two depths (0-5 cm and 5 – 20 cm) of technosols developing from different parent materials (Quaternary sands, fly ashes after lignite combustion,  acid and alkaline Tertiary clays). The samples were measured for the activities of acid and alkaline phosphatase, inorganic pyrophosphatase, microbial biomass (Cmic), texture, as well as contents of organic C (Corg), total N (Nt) and total P (Pt). Activities of acid (Pho_Aci), alkaline (Pho_Alk), total phosphatase (Pho_Sum) and inorganic pyrophosphatase (Pyro_Pho) were expressed per soil dry mass and per unit of Cmic (specific enzyme activities - Pho_AciSP, Pho_AlkSP and Pho_SumSP for acid, alkaline and total phosphatase, respectively, Pyro_PhoSP for pyrophosphatase). The soils under black locust exhibited higher Pho_Aci activity and higher specific activities of all enzymes (Pho_AciSP, Pho_AlkSP,, Pho_SumSP and Pyro_PhoSP) than the soils under both non-N-fixing trees. For alder  Pho_Aci activity was significantly higher only when compared to pine. However, the values of Pho_AciSP and Pho_SumSP were higher under alder than under both non-N-fixing trees. There were no differences in the activities or specific activities of measured enzymes between the soils under pine and birch. Our results indicated that rhizobial black locust stimulated activity of soil enzymes involved in P cycling much stronger than non-N-fixing tree species. This effect of black locust was consistent in technosols developing from various parent materials. The effect of actinorhizal black alder was less pronounced, but also evident.  The results of our study indicated that both N-fixing trees stimulated activity of enzymes involved in P cycling stronger than the non-fixing trees. Thus, the N-fixing trees may alleviate P deficiency in technosols as they stimulate development of phosphatase releasing microorganisms and increase P availability.

The study was financed by The National Science Centre, Poland, grant No. 2018/31/B/ST10/01626.
Early Mercury’s magma ocean atmosphere
Authors: Jäggi Noah , Gamborino Diana , Bower Dan J. , Sossi Paolo , Wolf Aaron , Vorburger Audrey , Oza Apurva V. , Wurz Peter , Galli André
Published: March 2021

The large iron core of Mercury and the low iron contents on the surface inferred from MESSENGER data suggest the presence of a magma ocean after accretion. We modeled the lifetime of an early Hermean magma ocean as well as the structure and loss rates of an atmosphere that is sourced by degassing. We use a large range of initial conditions including several bulk compositions associated with varying degrees of differentiation, the inclusion of carbon and hydrogen degassing volatiles such as CO2 and H2O, as well as considering a larger proto-Mercury size. After obtaining the magma ocean lifetime and volatile vapor pressures, the result is passed on to further models to obtain metal oxide vapor pressures, a complete atmospheric photochemical speciation and ultimately the mass loss rate of the atmosphere.

We show that magma ocean cooling times are sensitive to the size of the planet and the efficiency of radiative heat transfer in the atmosphere. A volatile-free proto-Mercury radiating as a blackbody with its present-day size would cool down within 400 years from an assumed initial surface temperature of 2500 K to an early crust formation threshold of 1500 K. In contrast it takes 9000 years for a volatile rich proto-Mercury with a greenhouse atmosphere and a mantle size representing Mercury before the occurrence of a mantle stripping event. Volatile-rich cases reach massive atmosphere pressures, whereas volatile-free cases are dominated by Si, Na, K, Mg, and Fe species degassed from the magma ocean and end up at a maximum pressure of 0.1 bar at 2500 K. There is however only a small difference in the atmospheric extent, as the absence of volatile species in the thin metal oxide atmosphere causes it to become extended to a degree, where an upper atmosphere height comparable to the volatile cases is reached. In terms of mass loss we found that upper atmospheric loss due to photoionization is highly efficient in the environment of a young Sun, ionizing 100% of the particles reaching Mercury’s exosphere. This leads to loss rates of up to 106 kg/s, which are however diffusion limited by the supply from the homopause, reducing them by 2-3 orders of magnitude. In regards to Na and K loss, we found that a thin, volatile-free atmosphere is most efficient with its extended structure allowing for large loss rates as well as the high Na and K mixing ratio.
Worldwide water constraints on attainable irrigated production for major crops
Authors: Droppers Bram , Supit Iwan , van Vliet Michelle TH , Ludwig Fulco
Published: March 2021

Currently, irrigation withdrawals are resulting in groundwater exploitation and unmet ecosystem water requirements. However, to achieve worldwide food security, there is a need to focus on sustainable intensification of crop production. This requires a more sustainable use of water for irrigated croplands. Our presentation focuses on quantifying attainable wheat, maize, rice and soybean production on currently irrigated cropland under sustainable water use. Attainable production accounts for increases in nutrient application, while limiting irrigation withdrawals to renewable water availability and without compromising river ecosystem water requirements.

Attainable crop production was quantified using a newly developed two-way coupling between the VIC hydrological model (Droppers et al., 2020) and the WOFOST crop model (Wit et al., 2019). This VIC-WOFOST model framework comprehensively simulates biophysical processes related to water availability and crop growth under water and nutrient limitations. Our results indicate that worldwide crop nitrogen uptake should increase by 20%, to achieve production gap closure. However, worldwide irrigation withdrawals should decrease by more than a third in order to ensure sustainable water use. Under these constraints, decreases in attainable irrigated yields of 5% are expected (14% decrease due to water constraints, 9% increase due to increased nutrient availability). Moreover, achievable irrigated crop production in the extensively irrigated croplands of north-eastern China, Pakistan and north-western India would be reduced by up to a third.

In addition we explored the impact of atmospheric CO2 enrichment on worldwide attainable irrigated production using VIC-WOFOST. Increased atmospheric CO2 concentration increases crop assimilation and decreases crop transpiration. Initial results show that these effects may offset the unsustainable water withdrawals and increase attainable irrigated yields.

References:

Droppers, B., Franssen, W. H., Van Vliet, M. T., Nijssen, B., & Ludwig, F. (2020): Simulating human impacts on global water resources using VIC-5. Geoscientific Model Development, 13(10), 5029-5052, https://doi.org/10.5194/gmd-13-5029-2020

de Wit, A., Boogaard, H., Fumagalli, D., Janssen, S., Knapen, R., van Kraalingen, D., ... & van Diepen, K. (2019): 25 years of the WOFOST cropping systems model. Agricultural Systems, 168, 154-167, https://doi.org/10.1016/j.agsy.2018.06.018.