bluuebunny/crossref_metadata_2025 huggingface dataset:
Categorization of landslide-triggering rainfall focusing on the antecedent rainfall and its implication for landslide prediction
Authors: Lee Won Young , Byun Jongmin
Published: February 2023

In mountainous areas in the Republic of Korea, landslides occur primarily due to heavy rainfall in the summer monsoon season. While these landslides tend to occur seasonally in summer, the rainfalls that have triggered landslides are complex and varied. Temporal prediction of landslides based on the variables of rainfall events mainly uses two variables: intensity-duration or cumulative event rainfall-duration. However, it does not consider the antecedent rainfall, another critical factor that could characterize various complex rainfalls regarding landslide occurrences. Here, we first attempted to determine critical rainfall variables and their threshold values for landslide occurring using the decision tree method necessary to consider multiple rainfall variables simultaneously. We then classified landslide-triggering rainfall based on the identified critical variables using the K-means clustering method. We chose as the study area Chuncheon in the middle of the Korean Peninsula, an eroded granite basin surrounded by schist and gneiss mountains, since it has not been affected hardly by earthquakes and thus is suitable for studying rainfall-induced landslides. According to the decision tree analysis, cumulative rainfall and 5-day antecedent rainfalls were determined as critical variables, implying that considering antecedent and cumulative rainfall simultaneously is significant for landslide prediction. The K-means clustering analysis classified landslide-triggering rainfalls into four types: 1) low cumulative rainfall (198.6 ± 90.9 mm) with high antecedent rainfall for seven days prior to the landslide, 2) medium cumulative rainfall (308.3 ± 81.1 mm) with a gradual increase in antecedent rainfall for four weeks, 3) high cumulative rainfall (534.5 ± 85.7 mm) with low antecedent rainfall for four weeks, and 4) high cumulative rainfall (538.4 ± 59.8 mm) with a gradual decrease in antecedent rainfall for four weeks. In particular, the high cumulative rainfall after gradually decreased antecedent rainfall caused the most frequent landslides. Our results suggest that the threshold of cumulative rainfall varies with the antecedent rainfall pattern and that antecedent rainfall data of at least four weeks have meaningful information in forecasting and preparedness for landslide occurrence.
Environmental and educational challenges of a conceptual model proposal for transboundary groundwater flow systems: Southern Mexican Borders.
Authors: Abud Russell Yussef , Ouysse Samira , Hatch Kuri Gonzalo , Carrillo Rivera Joel
Published: February 2023

Evidence on groundwater management policy in the Yucatan Peninsula indicates that Mexico, Guatemala, and Belize lack common scientific concepts to study shared groundwater flows. Their current international border treaties require an integrative systemic study to protect transboundary groundwater; it is required acknowledging the flows of different hierarchies and their respective recharge, transit, and discharge zones within their geological frame. UNESCO and IGRAC have identified transboundary aquifer systems shared between these countries as part of a regional strategy to manage shared natural elements. Thus, the objective of this paper deals with the study of political implications and environmental challenges under current national and international management policies on shared groundwater flows. This paper presents an interdisciplinary critical approach to the theoretical conceptualization of borders and the social nature of water within Geography. Also, the Gravity Driven Groundwater Flow Systems methodology is applied to elaborate a conceptual hydrogeological model to analyze regional groundwater flow boundaries, the flow conditions, and related environmental concerns. It is concluded that the UNESCO-IGRAC proposal requires understanding boundaries and flow conditions concerning regional groundwater flow systems and their potential environmental, social, and political implications. Current economic development in the Yucatan peninsula requires that regional land planning be designed based on the overall groundwater functioning to implement measures to prevent harmful environmental impacts and social conflicts at the local and regional scales. Regional groundwater flow systems education is to reconcile the political agenda of the countries involved to propose future and present sustainable development actions.
Keywords: Transboundary Groundwater; Recharge-discharge Zones; Groundwater Flows, Sustainable Politics
Effect of Ionic Liquids with Different Concentrations on Molecular Structure and Combustion Performance of Coal
Authors: Hao Ruirui , Zeng Qiang
Published: February 2023

As a new green chemical inhibitor, ionic liquids can inhibit spontaneous combustion of coal by dissolving and destroying the active structure in coal. In order to investigate the influence of ionic liquids with different concentrations on the molecular structure and the characteristics of low temperature oxidation kinetics of coal at oxygen-poor environment, taking Wucaiwan coal sample in Zhundong mining area as the research object, the molecular structure model of Wucaiwan coal in Zhundong mining area is constructed, and 1-(2-hydroxyethyl)-3-methylimidazole tetrafluoroborate [HOEtMIm] [BF4] ionic liquid is selected. The effect of ionic liquid on coal is investigated from macroscopic and microscopic levels by means of NMR carbon spectroscopy, X-ray photoelectron energy, infrared spectroscopy, thermogravimetry and X-ray diffraction. The results show that different concentrations of [HOEtMIm] [BF4] ionic liquids increase the ignition temperature point and the maximum weight loss rate temperature point of coal samples. The percentage of weight loss in the rapid oxidation stage and the whole combustion process of spontaneous combustion decreases with the increase of ionic liquid concentration. Compared with coal samples treated with other concentrations, the coal samples treated with 15% ionic liquids show good stability, and the fluctuation range of combustion characteristics parameters is small, which shows that high concentration ionic liquids can effectively reduce the influence of temperature on coal samples. Different stages of coal spontaneous combustion oxidation follow different reaction mechanism. The activation energy of the coal samples treated with different concentrations of [HOEtMIm] [BF4] ionic liquids did not change obviously in the evaporation and desorption stages of water, and the flame retardant effect was mainly shown in the oxygen absorption and weight gain stages and thermal decomposition stages. Higher concentration of ionic liquids can make aliphatic, oxygen-containing functional groups and side chains in coal structure fall off , so that the macromolecules of coal are arranged more closely, and the arrangement of organic carbon atoms tends to be oriented and regular gradually. The concentration of ionic liquids changed the polymerization degree of coal macromolecules, and the concentration of ionic liquids was proportional to the polymerization degree of coal macromolecules.
A multivariate bias correction algorithm for climate model predictions and projections
Authors: Lima Carlos , Kwon Hyun-Han
Published: February 2023

Model output statistics (MOS) are generally tailored for single variables, despite their application to multivariate time series being constantly required in many problems. For instance, distributed, physical-based hydrological models often require as input meteorological variables (e.g. precipitation, land temperature, evapotranspiration, etc) that are strongly correlated. Preserving the spatio-temporal variability of single variables as well as the inter-variables dependence structure is thus of fundamental importance in climate model outputs to enhance, for instance, reliable hydrological predictions. In this context, we extend the multivariate bias correction algorithm (MBCn) proposed by Cannon (2018) through pre-filtering the input data and improving the orthogonal rotation matrix. We finally evaluate different bias correction algorithms. Our proposed approach is grounded on the multivariate techniques of principal component analysis (PCA) and sparse principal component analysis (SPCA). It seeks to promote bias correction while preserving spatial and inter-variable dependencies. We apply and test our algorithm using S2S predictions provided by the C3S multi-system seasonal forecast service, which includes climate models such as ECMWF, NCEP and canCM4i. The ERA 5 reanalysis data are used as reference meteorological data. We particularly explore the application of the proposed methodology to daily S2S forecasts of precipitation, temperature, wind field and surface solar radiation, which are notably valuable as input to hydrological models and to estimate evapotranspiration, droughts indices and renewable energy yields. AcknowledgmentThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2019R1A2C2087944).
The role that radar forecasting and hydrodynamic modelling may play in predicting flooding in coastal areas
Authors: Xing Yun , Liang Qiaoqian , Yu Xin , Sun Yu
Published: February 2023

Flooding is one of the most common and destructive natural disasters in coastal areas, involving considerable deaths, losses, and other severe consequences in many low-lying regions across the globe. Because of their highly developed economies and dense populations, coastal cities are especially vulnerable to flooding. In recent years and beyond, climate change is anticipated to considerably increase the flood risk in coastal areas. Consequently, it is of ultimate importance to develop efficient flood risk mitigating measures based on numerical models that properly capture the dynamic processes of coastal flooding based on reliable rainfall measurements and forecasts.Radar rainfall measurements have been viewed as potential model input for flood modelling as it is able to provide better representative of rainfall patterns with their variations at high spatial and temporal resolution. Radar forecasts at short lead times are also found to be the most skilled method for producing QPE (Quantitative Precipitation Estimation) and QPF (Quantitative Precipitation Forecast) at high spatiotemporal resolutions. For urbanized catchments in coastal areas, hydrodynamic models driven by high resolution rainfall data provided by radar observation and forecasting may present efficient tool for simulating and predicting the rainfall–runoff responses.This study, therefore, aims to develop a robust way to assess the impact of spatial and temporal variability of floods using radar rainfall data against rain gauge ones over Guangzhou, a typical city in the Pearl River Delta of the Greater Bay Area in the southern China, which is frequently suffered from pluvial flooding. A 2D High-Performance Integrated hydrodynamic Modelling System (HiPIMS) was employed to support the flood modelling, especially for simulating highly transient flooding process in intensely urbanized part of Guangzhou. With the objective of exploring the spatial and temporal heterogeneity of flooding in coastal areas induced by complex rainfall patterns, comparisons were made between the simulations driven by rain gauge measurements and radar QPE and QPF, in terms of maximum inundation conditions and spatiotemporal evolutions. Results of this study may potentially help improve the accuracy of coastal flood forecasts and thus provide information for developing more reliable flood mitigation measures.
Protocol Development for Quantitative Estimation of Soil Ecosystem Services
Authors: Kim Sung Chul , Kim Jin Wook , Hong Young Kyu , Yoon Jung Hwan , Kim Hyuck Soo , Kim Hee Jung , Yang Jae E.
Published: February 2023

Quantitative and qualitative estimation of soil ecosystem has been studied for understanding importance of soil ecosystem. However, estimation of soil ecosystem is a complex process and requires numerous soil properties. The main objectives of this research was i) to select minimum data set of soil properties, ii) to develop quantitative estimation protocol for estimating soil ecosystem and iii) apply the developed protocol for quantification of soil ecosystem in water supply conservation area in Korea. In order to select minimum data set of soil indicators, national soil database provided by Rural Developed Administration (RDA) was used as a raw data set and principal component analysis was used for choosing the minimum data set. Total of 11 soil properties including 4 physical properties (bulk density, sand and clay contents, available water capacity), 5 chemical properties (pH, electric conductivity, soil organic matter, available phosphorus, cation exchange capacity), and 2 biological properties (soil respiration, soil enzyme concentration) were selected for soil indicators. Three representative models (more is better, optimum, and less is better) were obtained for each soil indicators and scaled from 0 to 1 for quantification. Developed quantification protocol was applied in 250 locations from water supply conservation area. The average score for provisioning, regulating, and supplying ecosystem service was 0.50, 0.68, and 0.82 respectively. Based on quantitative estimation of soil ecosystem service in Korea, provisioning ecosystem service including biomass production and water storage needs to be improved.
EMIC wave induced proton precipitation during the 27-28 May 2017 storm:Comparison of BATSRUS+RAM-SCB simulations with ground/space based observations
Authors: Porunakatu Radhakrishna Shreedevi , Yu Yiqun , Miyoshi Yoshizumi , Tian Xingbin , Zhu Minghui , Kumar Sandeep , Nakamura Satoko , Jun Chae-Woo , Shoji Masafumi , Shiokawa Kazuo , Jordanova Vania , Hori Tomoaki , Asamura Kazushi , Shinohara Iku , Yokota Shoichiro , Kasahara Satoshi , Keika Kunihiro , Matsuoka Ayako , Connors Martin , Kadokura Akira
Published: February 2023

Recent studies have shown that the ion precipitation induced by EMIC waves can contribute significantly to the total energy flux deposited into the ionosphere and severely affect the magnetosphere-ionosphere coupling. During the geomagnetic storm of 27-28 May 2017, the ARASE and the RBSPa satellites observed typical signatures of EMIC waves in the inner magnetosphere. The DMSP and MetOp satellites observed enhanced proton precipitation during the main phase of the storm. In order to understand the evolution of proton precipitation into the ionosphere, its correspondence to the time and location of excitation of the EMIC waves and its relation to the source and distribution of proton temperature anisotropy, we conducted two simulations of the BATSRUS+RAMSCBE model with and without EMIC waves included. Simulation results suggest that the H- and He-band EMIC waves are excited within regions of strong temperature anisotropy of protons in the vicinity of the plasmapause. In regions where the Arase/RBSPa satellite measurements recorded EMIC wave activity, an increase in the simulated growth rates of H- and He-band EMIC waves is observed indicating that the model is able to capture the EMIC wave activity. The RAM-SCBE simulation with EMIC waves reproduces the precipitating fluxes in the premidnight sector fairly well, and is found to be in good agreement with the DMSP and MetOp satellite observations. The results suggest that the EMIC wave scattering of ring current ions gives rise to the proton precipitation in the premidnight sector at subauroral latitudes during the main phase of the 27 May 2017 storm.
Soil Water Content Estimation Using Drone-Based Ground Penetrating Radar and Machine Learning Techniques
Authors: shafian sanaz , Vahidi Milad
Published: February 2023

Soil water content (SWC) is an essential climatic variable that plays a leading role in different soil-related applications in the ecosystem. Especially in agriculture, SWC controls the plants' growth and soil productivity, so its accurate estimation could improve the crops’ yields and provide necessary information to optimize water consumption in precision farming.Optical sensors cannot accurately measure SWC in agricultural parcels, because agricultural fields are predominantly vegetated and covered with plants with high biomass. A Ground Penetrating Radar (GPR) sensor that can penetrate the ground and record pertinent information on soil roughness and its dielectric constant could be a solution for SWC monitoring in agricultural fields.In this study, we investigate the potential of a GPR sensor mounted on a drone for monitoring SWC on a profile scale from the surface to a specified depth. The results demonstrated that both the GPR reflection and derivative variables associated with the amplitude could be utilized as informative input variables in an Artificial Neural Network model for the estimation of the SWC.
Sensitivity analysis of green roof design parameters in SWMM for its improved understanding of hydrological performance
Authors: Tansar Husnain , Duan Huan-Feng , Mark Ole
Published: February 2023

Improved understanding of dynamic hydrological performance of green roof (GR) design parameters towards different model responses is important for maximizing its target design goals at the unit-scale. Replication of an optimally designed GR unit at the catchment-scale significantly contributes to achieving its target design goals (i.e., surface runoff reduction, urban flood reduction, peak flow control, etc.). Moreover, adequate efforts are required to explore and provide appropriate knowledge about the categorization of influential and non-influential design parameters with their suitable design spaces to guide researchers, drainage engineers, and stormwater management practitioners for effective and efficient planning, designing and optimization of GR at catchment-scales.This study employs a robust and comprehensive global sensitivity analysis (GSA) method known as the variogram analysis of response surfaces (VARS) for sensitivity analysis of GR design parameters. Firstly, a total of 13,999 sample points for 14 GR parameters of three layers (i.e., surface, soil and drainage mat) are generated by using the latin hypercube sampling technique and their factor spaces are decided based on design guidelines in current SWMM manuals. Following that, the PySWMM is used to simulate these design samples in a Monte-Carlo-type setting on a conceptual catchment of 0.01km2 (100m2 × 100m2) with 50% treatment area of GR, and the model responses (e.g., surface infiltration, surface outflow, storage volume, and peak flow) are estimated and applied for sensitivity analysis. Finally, VARS evaluates different sensitivity analysis metrics by using different model responses corresponding to their designed samples.Overall, the senstivity analysis results demonstrate that 8 out of 14 design parameters are highly influential on different model responses, however, the parameters’ sensitivity varies towards different model responses under different perturbation scales and rainfall conditions. Moreover, the selection of an effective range of design space of design parameters is necessary as it has a higher influence on model responses, while the parameters’ rankings and contributions to total sensitivity indices change with the range of design spaces. Furthermore, this research also provides an opportunity through VARS directional variogram index (an integrated sensitivity index) to study and understand the underlying mechanisms of design parameters under different perturbation scales with no extra computational burden. Senstivity analysis results will be presented with insights and recommendations for other regions, which will be helpful for decision-makers for effective planning, designing and implementation of GR. The findings of this parametric study would be helpful for the calibration and optimization of design parameters of GR for different case studies. 
On the use of resolution test in calculating strain rate using GNSS velocity
Authors: Zhang Zhengfeng , Zhang Huai , Shi Yaolin
Published: February 2023

We proposed a method to simultaneously calculate the strain rate from GNSS (Global Navigation Satellite System) velocity data and present a set of inspection standards to assess the validity and resolution of this kind of method calculating strain rate using GNSS velocity in this study. We first explain the mathematical principle of the spherical spline method. And then, we introduce the spherical spline method to fit artificial GNSS velocity data of mainland China to illustrate inspection standards. In realization, we first calculate the artificial linear velocity value of the station with a rigid rotation model, then obtain the strain rate of the Chinese mainland by the spherical spline method. In this case, the theoretical rotational strain should be zero to illustrate the generality of the spherical coordinate method. Furthermore, we construct a spherical harmony model for the resolution test. By the test criteria, the spherical spline method can reproduce the velocity and strain rate field at quite a high level, suggesting that our method has high applicability and resolution in estimating strain rate. Finally, we used measured GNSS velocity data to calculate the strain rate field in mainland China using the spherical spline method. We also analyze the correlation between the seismic mechanism and the strain rate field of earthquakes since 1960 and consider the seismic rate of mainland China.
Incorporating Training Data Uncertainty in Machine Learning Models for Satellite Imagery
Authors: Alemohammad Hamed
Published: February 2023

Supervised machine learning (ML) models rely on labels in the training data to learn the patterns of interest. In Earth science applications, these labels are usually collected by humans either as labels annotated on imagery (such as land cover class) or as in situ measurements (such as soil moisture). Both annotations and in situ measurements contain uncertainties resulting from factors such as class misinterpretation and device error. These training data uncertainties propagate through the ML model training and result in uncertainties in the model outputs. Therefore, it is essential to quantify these uncertainties and incorporate them in the model [1].In this research, we will present results of inputting semantic segmentation label uncertainties into the model training and show that it improves model performance. The experiment is run using the LandCoverNet training dataset which contains global land cover labels based on time-series of Sentinel-2 multispectral imagery [2]. These labels are human annotations derived using a consensus algorithm based on the input labels from three independent annotators. The training dataset contains the consensus label and consensus score, and we treat the latter as a measure of uncertainty for each labeled pixel in the data. Our model architecture is a Convolutional Neural Network (CNN) trained on a subset of LandCoverNet with the rest of the dataset used for validation. We compare the results of this experiment with the same model trained on the dataset without the uncertainty information and show the improvement in the accuracy of the model. [1] Elmes, A., Alemohammad, H., Avery, R., Caylor, K., Eastman, J., Fishgold, L., Friedl, M., Jain, M., Kohli, D., Laso Bayas, J., Lunga, D., McCarty, J., Pontius, R., Reinmann, A., Rogan, J., Song, L., Stoynova, H., Ye, S., Yi, Z.-F., Estes, L. (2020). Accounting for Training Data Error in Machine Learning Applied to Earth Observations. Remote Sensing, 12(6), 1034. https://doi.org/10.3390/rs12061034[2] Alemohammad, H., Booth, K. (2020). LandCoverNet: A global benchmark land cover classification training dataset. NeurIPS 2020 Workshop on AI for Earth Sciences. http://arxiv.org/abs/2012.03
Origins of the Barents-Kara sea-ice interannual variability: Atlantic pathway of the El Niño–Southern Oscillation modulation
Authors: Luo Binhe
Published: February 2023

Winter Arctic sea-ice concentration (SIC) decline plays an important role in Arctic amplification which, in turn, influences Arctic ecosystems, midlatitude weather and climate. SIC over the Barents-Kara Seas (BKS) shows large interannual variations, whose origin is still unclear. Here we find that interannual variations in winter BKS SIC have significantly strengthened in recent decades likely due to increased amplitudes of the El Niño-Southern Oscillation (ENSO) in a warming climate. La Niña leads to enhanced Atlantic Hadley cell and a positive phase North Atlantic Oscillation-like anomaly pattern, together with concurring Ural blocking, that transports Atlantic ocean heat and atmospheric moisture toward the BKS and promotes sea-ice melting via intensified surface warming. The reverse is seen during El Niño which leads to weakened Atlantic poleward transport and an increase in the BKS SIC. Thus, interannual variability of the BKS SIC partly originates from ENSO via the Atlantic pathway.
Outer radiation belt electron flux and phase space density changes during sheath regions of coronal mass ejections from Van Allen Probes and GPS data
Authors: Kalliokoski Milla , Henderson Michael , Morley Steven , Kilpua Emilia , Osmane Adnane , Olifer Leonid , Turner Drew , Jaynes Allison , George Harriet , Hoilijoki Sanni , Turc Lucile , Palmroth Minna
Published: February 2023

Turbulent and compressed sheath regions ahead of interplanetary coronal mass ejections are key drivers of dramatic changes in the electron fluxes in the Earth’s outer radiation belt. They are also associated with elevated wave activity in the inner magnetosphere. These changes in electron fluxes can occur on timescales of tens of minutes that are not readily captured by a two-satellite mission such as the Van Allen Probes due to long revisit times. The recently released Global Positioning System (GPS) data set, on the other hand, provides a larger number of measurements at a given location within a given amount of time, owing to the many satellites in the constellation. In our statistical study on the impact of sheath regions on the outer radiation belt, we investigated events in 2012-2018 at timescales of 6 hours (Van Allen Probes data) and 30 minutes (GPS data). The study showed that the flux response to sheaths as reported from Van Allen Probes observations is reproduced by GPS data.  We highlight that the shorter timescale allowed by GPS data further confirms that the energy and L-shell dependent flux changes are associated with the sheaths rather than the following ejecta. Additionally, we studied the electron phase space density, which is a key quantity for identifying non-adiabatic electron dynamics. This showed that electrons are effectively accelerated only during geoeffective sheaths (SYM-H < -30 nT). Outer belt losses are common for all sheaths, and the lost electrons are replenished during the early ejecta.
Carbon stocks in cool temperate forests with different stand structure due to deer overgrazing
Authors: Abe Hayato , Kume Tomonori , Katayama Ayumi
Published: February 2023

Recently, an overpopulation of large herbivorous mammals has been observed in the Northern Hemisphere. Their overgrazing can degrade eatable understory vegetation, enhance establishment of unpalatable species, and increase overstory trees mortality. It is still unknown how these vegetation changes affect carbon (C) stock capacity in forest ecosystems. We aimed to evaluate the effects of forest vegetation changes on C stocks due to over 20-years grazing of Japanese sika deer in southern Kyushu, Japan. The study area was divided into less grazed forests (LG) and heavily grazed forests (HG). The HG was further divided into three treatments: forests with no understory vegetation (HG-nu), forests with dominance of unpalatable shrubs (HG-ud), and forests with gap areas created by overstory trees mortality (HG-gap). Four 100-400 m2 survey plots were established for each treatment. We evaluated differences in vegetation structure (e.g., stem density) and C stocks, including overstory trees (height <2 m), understory vegetation, leaf litter, fine woody debris (FWD), and coarse woody debris (CWD) between the LG and HG treatments. We also separated overstory trees into eatable and unpalatable trees for sika deer diet. Stem density in LG, HG-nu, HG-ud, and HG-gap were 2548 ± 1813, 1544 ± 1145, 15619 ± 5326, and 63 ± 75 stems ha-1, respectively. Lower stem density was found in HG-nu compared to that in LG resulting from low density of small-diameter eatable trees (i.e., individuals with the diameter of < 10 cm). Instead, higher stem density was found in HG-ud compared to that in LG resulting from high density of small-diameter unpalatable trees. C stocks of overstory trees in HG-nu (12526.1 ± 5367.0 g C m-2) was comparable to that in LG (10771.4 ± 3351.3 g C m-2). C stocks of overstory trees in HG-ud (5118.2 ± 5656.3 g C m-2) and HG-gap (2028.9 ± 2343.4 g C m-2) were 50% and 81% lower compared to that in LG, respectively. C stocks of overstory trees in these treatments was dominated by few large-diameter trees, and differences in the stem density driven by small-diameter trees of each treatment did not contribute to the difference of C stocks. Understory vegetation C stocks in three HG treatments (i.e., HG-nu, HG-ud, and HG-gap) were >96% lower compared to LG. C stocks of leaf litter and FWD in HGs was also 36-68% lower compared to LG. The understory vegetation biomass was positively correlated with the total amount of leaf litter and FWD, suggesting that understory contributes to litter production and holdings. C stocks of CWD in HG-nu and HG-ud were comparable to LG whereas HG-gap showed 6.8-fold higher C stocks of CWD than that of LG due to the death of large-diameter trees that occurred gap formation. Our results highlight that deer-induced vegetation changes decrease in C stocks due to the mortality of large diameter trees and the loss of understory vegetation, and changes contribution of their components.
Development of the Concept of Operations for depth-data collection on the Lunar Surface
Authors: Paige Cody , Haddad Don Derek , Ward Ferrous , Deutsch Ariel , Jha Vandana , Bickel Valentin , Colaprete Anthony , Cook Amanda , Heldmann Jennifer , Newman Dava
Published: February 2023

Space start-ups, industry partners, and the Commercial Lunar Payload Services are all contributing to making space more accessible. Using low-cost technologies in partnership with these services furthers this accessibility opening the scientific exploration of data collected in space to a broader range of researchers. As part of MIT’s work with the Resources for Exploration & Science of OUR Cosmic Environment (RESOURCE) project with NASA Ames and the Solar System Exploration Research Virtual Institute, we are working with NASA Ames to develop the concept of operations (ConOps) for a commercial off-the-shelf (COTS) LiDAR and RGB camera for lunar surface exploration in Virtual Reality (VR). This is a low-cost camera that has been evaluated for flight-compliant materials and modified to reduce mass. The camera was flight qualified to TRL 6 including thermal vacuum and random vibration testing. The camera is manifested on the Nova-C lander, which flies on the Intuitive Machines mission IM-2, landing at the lunar south pole for a mission duration of 14 days (one lunar daylight cycle).

Here we discuss the development of the ConOps for this mission including four data-capture methods: 1) single target capture, 2) traverse capture, 3) raster capture and 4) single-target circumference capture. Using a custom software developed for the mission we optimized image resolution while minimizing data and power requirements through a series of ground tests.  The results were used to provide data requirements for each of the data-capture methods as well as to refine the rover motions needed to achieve optimal image quality. Camera settings for each method were also defined. Final ConOps are presented for the 14-day lunar mission with flexibility for real-time target definition.

The data collected during this mission, as well as the lessons learned from the COTS camera modifications and ConOps development, will provide a low-cost opportunity for a broader range of scientists to access lunar surface exploration. Given the challenges associated with exploring craters and lava tubes, regions of interest for in-situ resource utilization, enabling exploration through VR could allow for safer methods for astronauts to both train for upcoming missions, such as the Artemis missions, and for robotic data collection to supplement astronaut exploration in challenging areas. These methods can also be expanded to sensitive environments on Earth providing a methodology to create a virtual environment to give access to areas like the Galapagos or the Antarctic to scientists around the world without increasing our environmental impact.
A Computationally Efficient Flood Evacuation Planning Tool to Assess the Impacts of Flooding on Transportation Networks.
Authors: Karanjit Rishav , Samadi Vidya , Murray-Tuite Pamela , Hughes Amanda , Stephens Keri
Published: February 2023

Floods are a serious natural hazard that may disrupt essential infrastructure, towns, and communities worldwide. As a result, hundreds of lives are lost every year, and floods cause massive economic damage to critical infrastructure (CI). Preparing in advance for possible evacuations can drastically reduce the likelihood of potential deaths and damage to the environment and CIs. However, the absence of a reliable cyber system to define evacuation routes creates considerable delays in the process of evacuation, which in turn slows down disaster preparation and response efforts. Recent advancements in data-driven and machine-learning approaches have given faster and more inventive ways to forecast flooding events, which serve as the primary causes and processes for a large number of issues associated with flood prediction. The purpose of this research is to devise a one-of-a-kind, computationally efficient surrogate model for defining evacuation routes in Low Country, South Carolina, USA. The tool incorporates the distinctive characteristics of machine learning (ML) modeling, transportation geospatial data, and hydraulics and drainage qualities to assess evacuation routes. The system was expertly designed to estimate flood stage levels using ML across USGS gaging stations, combine the findings with the results of Manning's equation, and traffic data, and then integrate all this information into a remote web application. The architecture of the tool is made up of several interchangeable components, such as modules for ML modeling, performance assessment, inundation mapping, and online visualization. The efficacy of  the Flood Evacuation Planning Tool, with a simple conceptual inundation model and a dynamic user interface, is shown by thorough testing and processing measurements. Enhancements to the system that are now being implemented and those that will be implemented in the near future include expanding coverage to areas that are more prone to flooding and boosting the capabilities and accuracy of the tool will be discussed.
Comparing the effects of large scale solar farms on climate and regional surface energy budget in different climate models
Authors: Samanta Arya , Adam Moritz , May Mathias M. , Rehfeld Kira
Published: February 2023

Solar panels of utility scale are a rapidly growing contribution to the renewable energy supply with increasing efficiency and steeply reducing costs of photovoltaics (PV). Although there is consensus on long term reduction of greenhouse gas emissions and advantages of avoided emissions by using PV systems, there is need to understand the climatic effects of land surface modifications of such systems.We first review studies focusing on effects of utility-scale solar farms and compare their results in accordance to their characterization of a photovoltaic and the type of model used to evaluate effects. Then, we perform simulations of larger than current utility-scale solar farms but also comparatively small localized farms which are both characterized by modified land surface properties. These regions are either identified on the basis of solar insolation and desert-like criteria (low precipitation) or are proposed in existing studies for potential deployment of solar farms. The solar farm deployments are characterized by static or gradual changes in respective boundary conditions of albedo, soil roughness and outgoing longwave thermal properties. Separate experiments are conducted with non-dynamic and dynamic vegetation components to investigate potential feedbacks originating from the interplay of vegetation and precipitation. To this end, we use a comprehensive model (MPI-ESM-LR; Mauritsen et al., 2019), and the Earth System Model of Intermediate Complexity PlaSim (Fraedrich et al., 2005) to understand, and cross-validate, the effects in two models of different complexity model.We examine the results considering changes in radiative forces and surface energy budget and, therefore, the surface temperatures which can likely lead to atmospheric circulation patterns, precipitation and vegetation feedbacks. If lower complexity models provide results in an acceptable ballpark of comprehensive ESMs, then they could be further used for future quick compute simulations with various deployment scenarios and experimentation with variable technical aspects. Spatially explicit deployment of PV with focus on parameterized thermal properties dependent on the simulated climate would also allows us to look at the complementary effect of climate on panels and could help constrain the effect of different pathways on PV technology in the future.
Rotation and Confined Eruption of a Double Flux-Rope System
Authors: Zhang Xiaomeng , Guo Jinhan , Guo Yang , Ding Mingde , Keppens Rony
Published: February 2023

We perform a data-constrained simulation with the zero-β assumption to study the mechanisms of strong rotation and failed eruption of a filament in active region 11474 on 2012 May 5 observed by Solar Dynamics Observatory and Solar Terrestrial Relations Observatory. The initial magnetic field is provided by nonlinear force-free field extrapolation, which is reconstructed by the regularized Biot-Savart laws and magnetofrictional method. Our simulation reproduces most observational features very well, e.g., the filament large-angle rotation of about 130°, the confined eruption and the flare ribbons, allowing us to analyze the underlying physical processes behind observations. We discover two flux ropes in the sigmoid system, an upper flux rope (MFR1) and a lower flux rope (MFR2), which correspond to the filament and hot channel in observations, respectively. Both flux ropes undergo confined eruptions. MFR2 grows by tether-cutting reconnection during the eruption. The rotation of MFR1 is related to the shear-field component along the axis. Moreover, we find that the magnetic tension force is the cause of the confined eruption of MFR1. We also suggest that the mutual interaction between MFR1 and MFR2 contributes to the large-angle rotation and the eruption failure. In addition, we calculate the temporal evolution of the twist and writhe of MFR1, which may be a hint of probably existing reversal rotation.
High-Alitutde Aircraft Measurements of NO and NO2 in the Upper Troposphere and Lower Stratosphere over South-East Asia
Authors: Waxman Eleanor , Gao Ru-Shan , McLaughlin Richard , Thornberry Troy , Rollins Andrew
Published: February 2023

Nitric oxide (NO) and nitrogen dioxide (NO2) are important trace gases in the upper troposphere and lower stratosphere (UTLS).  In the upper troposphere, NO is primarily formed by lightning and can react with other radicals to form a suite of reactive nitrogen (NOy) species and produce ozone.  In the lower stratosphere, NOx can be responsible for catalytic ozone destruction and can form halogen reservoirs such as ClNO2 and ClONO2.  Accurate measurements of NOx species are therefore important for understanding and diagnosing UTLS chemical regimes.  Further, understanding NO2 concentrations in the stratosphere and upper troposphere is critical for accurate satellite retrievals of tropospheric NO2. In-situ measurements of NOx species above 12-15 km are infrequent and challenging.  Where there are measurements of NOx species up to about 12 km, NO mixing ratios tend to be reasonably well-reproduced using global models.  However, NO2 measurements frequently agree well with models and photostationary state calculations up to about 8 km, but at higher altitudes routinely are significantly higher than expected relative to the measured NO.  Proposed sources of this discrepancy include measurement artifacts affecting the in-situ measurements, inaccuracies in rate constants at low temperatures, and missing chemistry.  The net result is significant uncertainty in the NO2 concentrations in the UTLS.Here we present results from the recent high-altitude aircraft campaign Asian Summer Monsoon Chemistry and CLimate Impact Project (ACCLIP) over southeast Asia in summer 2022.  We show measurements from the recently-developed NOAA NO laser-induced fluorescence instrument up to altitudes of almost 20 km.  This instrument makes direct NO measurements with precision and accuracy sufficient for measurements below one ppt with one second of integration, making it ideal for aircraft campaigns.  In order to measure NO2 with this instrument, we have developed a photolysis inlet which converts NO2 to NO using an LED centered at 395 nm.  What is novel about this NO2 conversion is that it is done in an unpressurized pylon of the aircraft and thus the sample remains at the UTLS ambient temperatures until after photolysis.  This thus significantly reduces the potential interferences from NOy species that can thermally decompose to NO2 at room temperature, eliminating a potential source of major artifact.  Our data show good agreement with photostationary state calculations performed for these flights. 
Identifying the Lineament Structure Cooperatively Using the Airborne Gravimetric, Magnetic and Remote Sensing Data: A Case Study from the Pobei Area, NW China
Authors: Li Guoli , Liu Shuang
Published: February 2023

Identification of lineament structure plays a vital role in determining the metallogenic area and distribution of the geologic structure. Edge detection methods are mostly used to recognize the lineaments and define the geologic boundaries. Cooperatively using edge detection results of the gravity, magnetic and remote sensing data to recognize lineaments would obtain more geologic information. In this paper, new edge detectors of potential field derivatives are proposed to determine the sources’ boundary, named second tilt derivative, tilt of vertical derivative, and normalized second vertical derivative, respectively. Presented approaches are characterized by producing zero amplitude over sources’ edges and equalizing anomalies from different depths. Compared with original edge detection techniques including other second derivative methods, synthetic examples reveal significant superiorities of suggested approaches in providing more accurate and sharper edges and are especially effective in distinguishing superimposed anomalies. The experiments also demonstrate that the normalization to the edge detectors will make images cleaner and geologic edges more easily captured. Applied to airborne gravimetric and magnetic data in the Pobei area (NW China), the proposed methods display more geologic details and lineaments. Canny, Sobel, and Prewitt operators are applied to extract boundaries of remote sensing image. Lineaments picked by the three different types of data are combined collectively to get a comprehensive lineaments structure interpretation.
Global climate teleconnections into and out of the North Atlantic Ocean
Authors: England Matthew H. , Orihuela-Pinto Bryam , Taschetto Andréa
Published: February 2023

The Atlantic Meridional Overturning Circulation (AMOC) has a profound impact on both global and regional climate, yet our understanding of the mechanisms controlling remote teleconnections remains limited. In addition, it is unclear how remote processes impact the North Atlantic and alter the strength of the AMOC.  In this presentation I will show how a slowdown in the AMOC can drive an acceleration of the Pacific trade winds and Walker circulation by leaving an excess of heat in the tropical South Atlantic. This tropical Atlantic warming drives anomalous atmospheric convection, resulting in enhanced subsidence over the east Pacific, and a strengthened Walker circulation and trade winds. Further teleconnections include a shift in the ITCZ, enhanced zonal SST gradients across the tropical Pacific, strengthened convection over the West Pacific Warm Pool, and a deepening of the Amundsen Sea Low off Antarctica.  Teleconnections back to the North Atlantic can in turn be triggered by Southern Hemisphere wind anomalies on a relatively rapid time-scale via propagating planetary waves in the ocean.  There is also evidence that tropical Pacific cooling can feedback and influence the strength of the AMOC.  These findings have implications for understanding both intrinsic decadal climate variability as well as longer-term climate change.
Exploring Pacific Island hydroclimatic extremes using the South Pacific Drought Atlas
Authors: Higgins Philippa , Palmer Jonathan , Johnson Fiona , Andersen Martin , Turney Chris
Published: February 2023

Droughts are a natural occurrence in many small Pacific Islands and can have severe impacts on local populations and environments. The El Niño-Southern Oscillation (ENSO) is a well-known driver of drought in the South Pacific, but our understanding of extreme ENSO events and their influence on island hydroclimate is limited by the short instrumental record and the infrequency of ENSO extremes. To address this gap, we present the South Pacific Drought Atlas (SPaDA), a multi-proxy, spatially resolved reconstruction of the November-April Standardised Precipitation Evapotranspiration Index for the southwest Pacific islands. The reconstruction integrates coral proxies, which provide local information on the South Pacific hydroclimate but are limited in number and length, with a network of continental tree-ring chronologies targeting Pacific climate variability through remote teleconnections. The reconstruction demonstrates the benefits of multi-proxy reconstructions incorporating tree rings, which allow for the alignment of other proxy records without chronological error.The SPaDA provides a 350-year, continuous dataset of climate information, which can be used to explore the occurrence of extreme events in the pre-instrumental period. The SPaDA closes the gap between existing paleo-reconstructions of point ENSO indices, and a spatially resolved drought atlas, allowing both the hydroclimate of individual islands and regional patterns of drought to be assessed. The benefit of a spatially resolved dataset to assess climate extremes in small Pacific islands is highlighted in the case of extreme El Niño events, which can have substantially different hydroclimatic impacts than more moderate events.We used an Isolation Forest, an unsupervised machine learning algorithm, to identify anomalous hydroclimatic states in the SPaDA that may indicate the occurrence of an extreme event. Extreme El Niño events characterised by very strong southwest Pacific drought anomalies and a zonal South Pacific Convergence Zone orientation are shown to have occurred semi-regularly throughout the reconstruction interval, providing a valuable baseline to compare to climate model projections. By identifying the spatial patterns of drought resulting from extreme events, we can better understand the impacts these events may have on individual Pacific Islands in the future.