bluuebunny/crossref_metadata_2025 huggingface dataset:
In-Situ, Near Real Time and Low Cost Image Velocimetry for Debris Flows and Flash Flood Monitoring in the Chilean Andes
Authors: Dussaillant Alejandro , Sepúlveda Nelson , Aguilar Felipe , Valencia Johnny , Ancan Joel , Cotroneo Jaime , Herrera Rodrigo , Leiva Nikky , Peña Carolina , Alfaro Alejandro , Fernández Javier , Muñoz Antonio
Published: March 2022

Debris flows and flash floods occur frequently in Chile due to geology, geomorphology and weather, costing human lives and impacting settlements, infrastructure and economic activities. One of the problems relates to the lack of adequate monitoring technology in remote areas with limited connectivity. We have developed a low cost system that processes acquired lidar and image data in-situ with a Raspberry Pi obtaining flow level and velocity and transmits near real time via satellite (or cellular network if available).  The low implementation cost allows to replicate the system in the many hazardous sites, as well as advance towards early warning systems in locations with limited communication networks. The velocimetry method consists of two steps: first obtaining the images, and then a brightness filter and normalized cross correlation. To eliminate outliers a flow direction filter is used, and velocities are obtained by tracking of flow surface elements.  Also the flow level is measured with a lidar also connected to the R-Pi. We will present both laboratory and field test results.
Numerical analysis of the seismic hazard in Sichuan-Yunnan region
Authors: Yin Di , Dong PeiYu , Shi YaoLin
Published: March 2022

    The Sichuan-Yunnan region is located in the southern part of Chinese north-south seismic belt and has strong seismic activity. The prediction of future strong earthquake activity in this region has always been a research hotspot. In this study, firstly, we established a quasi-three-dimensional finite element elastic model, combined with the regional geological background and GPS observation data. Then, based on the information of 30 M>6.7 historical earthquakes that occurred in the region over the past 100 years, and constrained by the Coulomb-Mohr rupture criterion, we inverted a possible reasonable initial stress field at a specific time. Secondly, we simulated the development process of each historical earthquake and reproduced the 30 events orderly, by comprehensively considering the tectonic stress loading in the seismogenic stage and the stress change in the co-seismic adjustment stage. However, it is worth noting that there were some uncertainties in the numerical simulation process. We used Monte Carlo random experiments to obtain 5000 kinds of different possible initial values, which all can reproduce the development process of historical events. Then we got different current reginal stress values and calculated earthquake risk coefficient. Finally, we used mathematical methods to investigate the current seismic hazard of the different models, and assembled them into a probability distribution map of possible seismic risk in the region. The preliminary result shows that the seismic risk in the rupture zone of historical earthquakes is greatly reduced, which means relatively safe. Mainly due to the stress change caused by the 2008 Wenchuan Ms8.0 earthquake, the seismic probability in the northeastern segment of the Longmenshan fault is as high as 30%. At the junction of the southwestern section of the Longmenshan fault and the Xianshuihe fault zone, the seismic probability is about 15-20%. In addition, near the Longling Ruili fault and the Lancangjiang fault in southwestern Yunnan, the value is about 10-15%. In recent years, small earthquakes have occurred frequently in southwestern Yunnan, and the seismic risk in this area is also worth noting.
Sediment production and transport processes in an arctic watershed undergoing climate change  
Authors: Palucis Marisa , Marshall Jill , Strauss Justin
Published: March 2022

Arctic landscapes are among the most vulnerable on Earth to climate change, largely due to the degradation and thawing of permafrost. In steeper bedrock-dominated terrains, slope instability from warming permafrost leads to larger and more frequent rockfall and frost cracking events, which in turn increases the production and delivery of sediment to hillslopes and channel networks by debris flow and fluvial processes. However, there is a fundamental lack of data on past and current rates of sediment production and transport in Arctic watersheds. Without an understanding of these phenomena, it is impossible to predict the transient responses, rates, and directions of periglacial processes in response to future climate change. To begin to address this knowledge gap, we conducted a field-based study of the Black Mountain catchment in the Aklavik Range (Northwest Territories, Canada). This site was chosen due to its position within a zone of continuous permafrost and the presence of an alluvial fan at the base of the catchment, providing a closed system.

In the summer of 2019, after a summer storm event, we observed several debris flows that initiated from ice-filled gullies, as well as fluvial sediment transport from snowmelt. We documented flow and sediment transport conditions on the fan, yielding modern-day fluvial transport rates of 0.2–2 m3/hr for water runoff rates of 0.01–0.2 mm/hr. However, less-frequent mass flow events can rapidly deposit large amounts of sediment. For example, we estimate that a mass flow event that occurred in 2016 delivered ~1.5*105 m3 of sediment to the fan—equivalent to ~8–85 years of continuous fluvial sediment transport. Based on our surficial and sedimentological mapping, the fan has likely been forming under a periglacial climate over the last ~13,000 years from a combination of mass flow and fluvial processes. Most of the fan (~67%) was deposited fluvially, but the upper, steeper portion of the fan was deposited by coarse granular debris flows. We hypothesize that accelerated warming has increased sediment supply due to frost cracking, leading to aggradation, increased debris flow activity, and upper fan steepening.
Extreme subtropical precipitation in Australia: reasons for decline  
Authors: Rudeva Irina , Holgate Chiara , Pepler Acacia , McKay Roseanna , Hope Pandora
Published: March 2022

A long-term reduction in southern Australian rainfall has been a focus of many studies. In south-eastern Australia it has been shown that after the Millennium Drought (1997 – 2009), the average precipitation has not recovered to the pre-drought values. Our analysis reveals a decline not only in the average precipitation but also in daily extreme rainfall amounts in the cold season. This study explores the physical processes leading to changes in extreme rainfall.  

High rainfall extremes are related, on the one hand, to a certain combination of weather systems at various height levels through the troposphere and, on the other hand, to moisture availability. We first identify which synoptic conditions lead to extreme rainfall events in south-eastern Australia and backtrack their development for a few days. Australia is believed to be affected by Rossby waves (RWs) propagating from the tropics. However, we show that extreme events in the southern part of the country are associated with breaking synoptic RWs propagating from the extratropical Indian Ocean. Interestingly, we find that the frequency of cut-off lows, that form following the breaking of RWs, have not declined over the recent decades.  This fact highlights that not all cut-off lows necessarily lead to extreme rainfall. We find that the strongest events occur in the presence of a Tasman High pressure system at the surface and a vertically developed low-pressure system to the west of it. We show that, despite little change in the frequency of cut-off lows in the upper troposphere, vertically developed lows have become less frequent after 1997 and when they occur, a larger moisture influxis required to produce an intense rainfall event. 
Evaluating The Impact of Agriculture Drought by Remote Sensing Drought Indices in 2011–2021: A Case Study of Southwest Taiwan
Authors: Chiu Sheng-Wen , Tseng Kuo-Hsin
Published: March 2022

Agricultural drought is attributed to the deficit of soil moisture in the agricultural area for a period, and might cause the crops failure during the specific growth period. This issue has drawn increasing attention in the contemporary climate change episode. In the first half of 2021, the delayed and reduced spring rains were anomalously insufficient and had induced severe impacts on the agricultural production of southwest Taiwan. The aim of this study is thus to understand the dynamic change of drought and its associated impact during the two main harvest periods in the main agricultural region of southwestern Taiwan. We analyze a time series of indices such as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Temperature Vegetation Dryness Index (TVDI), Vegetation Supply Water Index (VSWI), and Normalized Vegetation Supply Water Index (NVSWI). These indices are derived from MODIS datasets, including 8-day MOD11 LST and MOD13 NDVI products at 1 km resolution, in 2011–2021. Next, a supplemental dataset derived from optical satellite images is used for land-use classification, which could allow us to characterize actual agricultural zones in the region of interest. We also collect the statistics of paddy rice yield surveyed by the Agriculture and Food Agency of Taiwan for calibration between yield and drought indices.

The preliminary results show that the TVDI, TCI, VCI, VHI, VSWI have higher correlation in the 1st paddy rice harvest stage (Late June) of dry season than the 2nd paddy rice harvest stage (Mid-November) among the time series. The correlation coefficient is -0.88, 0.66, -0.85, 0.50, and 0.66, respectively. These indicated that the 1st paddy rice harvest stage could be more sensitive to the high temperature and deficit precipitation during the growing season.

Keywords：Agricultural drought monitoring, MODIS, PRISMA, Drought indices
Seismic Tomography of Peninsular Malaysia Inferred from Teleseismic Earthquake
Authors: Abdul Latiff Abdul Halim
Published: March 2022

While there are several geological characterizations of Peninsular Malaysia based on the surface geological study, subsurface evaluation based on the seismic data is still lacking. In this work, tomography of the studied region is being investigated through teleseismic earthquake recorded by several seismic stations located along the peninsula. Throughout the tomography analysis, the 1D ak135 global velocity model is used for computing the travel times from the earthquake source to the edge of the 3-D model. In addition, a similar 1-D ak135 model also being used as the starting model for iterative tomography inversion within the 10.5°N to 0.5°S and 96.5°E to 108.0°E boundary region. The seismological data used for this tomography analysis was acquired from 11 stations that shared with International Seismological Centre (ISC) database and Malaysia Meteorological Department (MMD) respiratory. In total, there were 1598 teleseismic earthquakes events recorded in between 2005 to 2016 which satisfy the criteria of 6.0  or larger. Prior to the iterative travel-time computation, the model’s sensitivity and reliability towards the external changes in the data noise and initial conditions were evaluated through the checkerboard resolution test. The synthetic reconstruction images show that the pattern of the checkerboard anomaly is properly recovered at depth of 30 km, 60 km and 90 km with corresponding high and low wave speed have been recovered as per input model. From the 3580 P-wave arrival time, tomography output is generated at 30 km depth interval, from within the crustal layer of 30 km depth, till the uppermost mantle structure of 300 km depth. In addition, the North-South and East-West sections of the peninsula are produced for a better interpretation of the crustal and uppermost mantle layers in the region. In general, the variation from fast to slow wave speed is noticeable in the Northwards trend, apart from KGM station in the Southern Peninsular Malaysia where a slower velocity recorded compared to its surrounding. The Earth’s structure beneath the SRIT, SKLT, SURA, IPM and KUM stations are experiencing a relative negative wave speed perturbation, while the positive wave speed perturbation is recorded beneath JRM, KOM and BTDF stations. The slower wave speed is recorded in Southern Thailand region and continue southward to the North-West part of Peninsular Malaysia, indicated the sedimentation of Semanggol formation that consists of Carboniferous marine shales. It also concluded that Western-Eastern belt separation of the Malay Peninsula is clearly evident from the velocity contrast. In summary, the latest tomography analysis retrieve from teleseismic earthquake provides a new dimension of the subsurface analysis within the Malay Peninsula region.
Scaling of spacing between surface streaming on non-breaking and breaking wind waves
Authors: Tsai Wu-ting , Lu Guan-hung
Published: March 2022

The high-speed, wind-aligned streaks on the wind waves are geometrically similar to the low-speed streaks observed in the turbulent wall layer. It is generally accepted that the spanwise spacing between the low-speed streaks in wall-bounded turbulent flow, when scaled by the viscous length, exhibit probability distribution conforming to lognormal behavior with a universal mean value of 100 independent on the wall friction velocity. Analyses of thermal images from wind-wave flume experiments, however, reveal that the scaling between the mean streak spacing and the surface friction velocity is different from that of wall-bounded flow. For non-breaking waves, the scaled mean streak spacing becomes notably narrower than that between low-speed streaks next to the solid wall. Comparative numerical simulations reveal that the presence of surface waves intensifies the generation of quasi-streamwise vortices that form the elongated streaks, and reduces the streak spacings. For breaking wind waves, analyses of the consecutive image sequences reveal that the breakers wipe out the existing surface streaks. After the passage of the breakers, the wind-aligned streaks reform immediately, which are then destructed again by the next breaking waves. In contrast to the streaks on the non-breaking waves, the scaled mean streak spacing in the wake of breakers is close to the canonical value of 100, which approximately follows the wall-flow scaling.
Random Forests to classify salt-affected soils from soluble salt ions
Authors: Andrade Foronda Demis
Published: March 2022

Salt-affected soils are characterized by an excess of soluble salts and/or sodium. The widely used US Salinity Lab (USSL) classification considers the exchangeable sodium percentage (ESP), electrical conductivity (EC) and pH. Breiman and Cutler's random forests (RF) algorithm chooses the most voted class over all the trees at training time. The aim was to model and predict the USSL salt-term categories from soluble ions by applying RF model classification. Topsoil samples (110) were collected from the High Valley of Cochabamba (Bolivia) and were analyzed to measure the soluble cations (Na+, K+, Ca2+, Mg2+) and anions (HCO3–, Cl–, CO32–, SO42–), in addition to the required salinity parameters to classify the samples according to the thresholds: ESP of 5%, ECe of 4 dSm-1 and pH of 8.2. No samples matched in the saline category. The overall out-of-bag error was 17.4% and according to the confusion matrix, the class errors for normal, saline-sodic and sodic soil were 0.12, 0.00 and 0.36, respectively. The variables with higher estimated importance and also selected by RF backward elimination were: Na+, Cl–, Ca2+ and HCO3–. Additional sampling might be useful in order to reduce the errors and misclassifications, as well as to improve the selection of variables.
A Natural Language Processing-based Metadata Recommendation Tool for Earth Science Data
Authors: Mehrabian Armin , Gerasimov Irina , Khayat Mohammad
Published: March 2022

As one of NASA's Science Mission Directorate data centers, the Goddard Earth Sciences Data and Information Services Center (GES-DISC) provides Earth science data, information, and services to the public. One of the objectives of our mission is to facilitate data discovery for users and systems that utilize our data. Metadata plays a very important role in data discovery. As a result, if a dataset is to be used efficiently, it needs to be enhanced with rich and comprehensive metadata. For example, most search engines rely on matching the search query with the indexed metadata in order to find relevant results. Here we present a tool that supports data custodians in the process of creating metadata by utilizing natural language processing (NLP).

 

Our approach involves combining several text corpora and training a semantic embedding. An embedding is a numerical representation of linguistic features that is aware of the semantics and context. The text corpora we use to train our embedding model contains publication abstracts, our data collections metadata, and ontologies. Our recommendations are based on keywords selected from the Global Change Master Directory (GCMD) and a collection of ontologies including SWEET and ENVO. GCMD offers a comprehensive collection of Earth Science vocabulary terms. This data lexicon enables data curators to easily search metadata and retrieve the data, services, and variables associated with each term. When a query is matched against various keywords in the GCMD branch, the probability of the query matching these keywords is calculated. A similarity score is then assigned to each of the branches of the GCMD, and each branch is sorted according to this similarity metric. In addition to unsupervised training, our approach has the advantage of being able to search for keyword recommendations of different sizes, ranging from sub-words to sentences and longer texts.
Effect of high spatial and temporal resolution of the wind field on the evolution of the wave field in a hurricane
Authors: Osuna Pedro , Perez-Sampablo Laura Marcela , Ocampo-Torres Francisco J. , Esquivel-Trava Bernardo , Rascle Nicolas
Published: March 2022

It is generally accepted that the typical average wind field in a hurricane is well described by a parametric Holland model. The spatial distribution of winds can be determined from data provided by the National Hurricane Center with a 6-hour regularity. The wave field can be calculated by a numerical model from the temporal interpolation of these wind fields. However, a characteristic of wind fields calculated by parametric models is that their spatial and temporal variability are relatively smooth compared to wind fields computed by a high-resolution dynamic model, such as the HWRF. This study analyses the effect of the high temporal and spatial variability of the wind field in a hurricane and its effect on the variability of the wave field calculated by the third-generation wave model Wavewatch III. The wind fields obtained from the use of the HWRF model correspond to the simulation of Hurricane Isaac while crossing through the Gulf of Mexico between 27 and 29 August 2012. The wind fields have a spatial resolution of approximately 2 km and a temporal resolution of 15 minutes. This information is used by an implementation of the Wavewatch III model in the Gulf of Mexico to obtain wave fields with a spatial resolution of 2 km and a temporal resolution of 15 minutes. We find that, despite the high temporal and spatial variability, the mean wind field has a spatial pattern similar to that described by the parametric models. In contrast, the wave field computed by the wave model is much more stable and has a typical structure already described in other studies: large values of significant height in the region of the first quadrant of the hurricane and at the front of its path, with maximum values at distances between one and two times the radius of maximum winds. Smaller values of significant height are found behind the hurricane's track, especially in the third quadrant. The structure of the wave field is largely determined by the translation speed of the storm and the extended fetch effect in the quadrants to the right of the hurricane's path.
Flood prediction in ungauged basins with machine learning and satellite precipitation data
Authors: Rasheed Zimeena , Aravamudan Akshay , Anagnostopoulos Georgios , Nikolopoulos Efthymios
Published: March 2022

Global hydrologic climate assessments posit increasing flood risk. Hydrologic forecasting is critical in both gauged and ungauged basins having implications not only for hazard assessments and the development of mitigation strategies but also for informing the design and operation of critical infrastructure. The hydrology community grapples with the need to predict floods particularly in ungauged basins where the absence of continuous and spatially representative precipitation and streamflow data are enunciated. 

Global precipitation observations from satellite constellations combined with recent advancements of hydrologic forecasting with machine-learning (ML) models, offer an attractive solution for addressing flood prediction in ungauged regions. Towards that end, in this work, we investigate a) the performance of ML flood prediction models integrated with satellite precipitation estimates and b) the transferability/applicability of ML models trained in data rich regions for flood prediction in ungauged regions. We use NASA IMERG precipitation dataset for ML-based predictions and we train the ML models for ~600 catchments from different hydroclimatic zones in Contiguous US. The performance of the ML-IMERG predictions are then evaluated for a large number of catchments (~1000) in the UK, Brazil, Chile and Australia. Predictive performance is evaluated with respect to climate and catchment characteristics in each region. Results suggest that despite the variability in the performance across regions, there is great promise on the integration of global satellite precipitation estimates with ML models for flood prediction in ungauged basins.  
Improvement of river flow estimation accuracy using ensemble learning stacking
Authors: Sakaguchi Daiju , Ishida Kei , Nagasato Takeyoshi , Amagasaki Motoki , Kiyama Masato
Published: March 2022

In recent years, disasters are more frequent and enormous due to global warming. In the field of hydrology, high-precision rainfall-runoff modeling is required. Recently, deep learning has been applied to rainfall-runoff modeling and shows high accuracy. It is also expected that the accuracy will be improved by using ensemble learning for deep learning. This study tried to improve the accuracy of river flow estimation by performing ensemble learning for deep learning. Stacking was used as the ensemble learning method. For deep learning, LSTM, CNN, and MLP was used and compared. XGBoost was used as the learning device used for ensemble learning. The target area was the Tedori River basin in Ishikawa Prefecture, Japan. In deep learning, the input data were daily average precipitation and temperature. In deep learning and ensemble learning, the target data was the daily average river flow. RMSE was used as the evaluation index. As a result, the accuracy was the highest after ensemble learning when using LSTM. It shows that the selection of the learning device is important for ensemble learning.
Stationary Wave Biases and Their Effect on Upward Troposphere - Stratosphere Coupling in Sub-seasonal Prediction Models
Authors: Schwartz Chen , Garfinkel Chaim , Domeisen Daniela , Yadav Priyanka , Chen Wen
Published: March 2022

The simulated Northern Hemisphere stationary wave (SW) field is investigated in 11 subseasonal-to-seasonal (S2S) models. It is shown that while most models considered can well-simulate the stationary wavenumbers 1 and 2 during the first two weeks of integration, they diverge from observations following week 3. Those models with a poor resolution in the stratosphere struggle to simulate the waves, both in the troposphere and the stratosphere, even during the first two weeks, and biases extend from the troposphere all the way up to the stratosphere. Focusing on the tropospheric regions where SWs peak in amplitude reveals that the models generally do a better job in simulating the Northwest Pacific stationary trough, while certain models struggle to simulate the stationary ridges both in Western North America and the North Atlantic. In addition, a strong relationship is found between regional biases in the stationary height field and model errors in simulated upward propagation of planetary waves into the stratosphere. In the stratosphere, biases mostly are in wave-2 in those models with high stratospheric resolution, whereas in those models with low resolution in the stratosphere, a wave-1 bias is evident, which leads to a strong bias in the stratospheric mean zonal circulation due to the predominance of wave-1 there. Finally, biases in both amplitude and location of mean tropical convection and the subsequent subtropical downwelling, are identified as possible contributors to biases in the regional SW field in the troposphere.
Extreme droughts in Transilvania during Little Ice Age derived from documentary evidences
Authors: Badaluta Gheorghe , Badaluta Carmen-Andreea , Ionita Monica , Mindrescu Marcel
Published: March 2022

Drought represent one of the extreme aspects of the water cycle with impact on water resources, agriculture and socio-economic activities. Currently, droughts are used as an indicator of climate variability and change and are mainly driven by changes in the hydrological cycle and the large-scale atmospheric circulation. In order to have a long-term perspective on the drought variability and change, beyond the instrumental record, one needs to use different proxies and/or historical evidences. In this study we will present documentray evidences regarding the occurence of extreme droughts in Transilvania region (Romania) during the Little Ice Age (AD 1500-1800) and their socio-economic impacts. Between AD 1500-1800 we identify 126 drought events, frm which 33 are considered extreme droughts (e.g. with the character of a calamity). Of the 33 extreme droughts, 3 occurred between AD 1500-1600, 14 between AD 1600-1700 and 16 between AD1700-1800. These events have been driven by anomalous large-scale atmospheric and oceanic patterns in combination with strong variation in the solar and volcanic variability. In conclusion, our results will contribute to the knowledge of extreme events of Central Eastern Europe during the Little Ice Age and may be used as an indicator to predict their influences in the context of the climate changes.
Identifying anthropogenic seismic events generated in western Romania using statistical approaches and novel waveform processing techniques
Authors: Varzaru Lavinia-Cristina , Borleanu Felix
Published: March 2022

Seismicity in western Romania is the result of tectonic processes that continuously shaped the landscape generating a fractured crust, which showed significant movements as a result of overall tectonic stress in the area as well as secondary effects such as erosion or lateral density fluctuations. At the same time, this region has an important natural resource, being identified here various deposits that have been intensively explored lately. The exploitation of these resources, as well as the development of the infrastructure in the region, led to the generation of anthropogenic seismic events. Due to the recent improvement of the Romanian Seismic Network, the coverage with seismic stations increased and these events were detected and located as natural tectonic events, contaminating the Romanian earthquakes (ROMPLUS) catalog.

To eliminate anthropogenic event contamination in the ROMPLUS catalog, we ran a complex statistical approach on the catalog data. In addition, to build a robust discriminant, we further applied cross-correlation and spectral analysis algorithms on the seismic waveforms recorded between 2014 and 2021 by the Surduc (SURR) and Gura Zlata (GZR) stations, which are located in the proximity of the major clusters of seismic events.

Our results showed a good distinction between tectonic and anthropogenic events and revealed that most of the clustered events are located near the explorations sites. We also noted that most of the events occurred during working hours. At the same time, the high similarity among these events indicates the existence of repetitive seismic sources.
Computationally efficient hypothesis testing using an iterative ensemble smoother and reproducible workflows
Authors: Martínez Guillermo
Published: March 2022

The great environmental challenges we face as a society require the use numerical models to evaluate potential undesirable conditions using limited data and imperfect representation of natural and man-made systems. Iterative ensemble smoother (IES) methods provide not only a computationally efficient and robust approach to evaluate the uncertainty of numerical models and the worth of adding additional observation sources but a tool to test hypotheses about modeling assumptions and conditions in problems often too complex for traditional statistical methods. This work presents an approach to test hypotheses on undesirable outcomes in complex decision support models by taking advantage of the efficient and parallel capabilities of IES methods. The model used to illustrate the approach represents a groundwater system with more than 70,000 parameters used to describe hydraulic properties and boundary conditions and around 200 observation points. The version-controlled workflow used to implement the uncertainty analysis and hypothesis testing meticulously document each step of the analysis and takes advantage of open-source libraries to manipulate model files, implement the ensemble methods, and efficiently evaluate the fate of a contaminant of concern.  After a careful evaluation of conflicts between prior parameters and observations, the prior parameter distribution is updated using the ensemble-form of the Gauss-Levenberg-Marquardt algorithm (Chen and Oliver, 2013; White, 2018) to improve the match between simulated and observed data. A series of analysis were then performed to define reasonable metrics for hypothesis testing using spatial distance metrics. Results of the analysis illustrate the tradeoff between fitting the observations and the negative outcome under consideration as well as their impact on the realism of posterior parameter distributions. The computational performance and flexibility of this approach contributes to support robust, efficient, and reproducible decision-support modeling tools for water resources.
The contribution of soil, topographic, and vegetation traits to plant-water sensitivity
Authors: Konings Alexandra , Rao Krishna , Zhao Meng , Williams A. Park , Diffenbaugh Noah , Yebra Marta
Published: March 2022

Spatio-temporal patterns of plant water uptake, loss, and storage are a first-order control on photosynthesis, evapotranspiration dynamics, and thus, land-atmosphere interactions. These patterns depend on temporally variable hydrometeorological conditions but also on geographically varying characteristics. These include, but are not limited to, topographic and soil properties that influence rainfall infiltration and water distribution in the unsaturated zone and vegetation properties, such as rooting depth, stomatal and xylem properties, leaf area, and more. Understanding how these different factors interact to control the overall dynamics of plant water uptake is fundamental to understanding the response of vegetation to hydrologic variations, but has traditionally been hindered by data limitations. In situ measurements are too sparse to sufficiently span the range of possible variations across different geographic factors. Remote sensing estimates of plant water uptake either are not available or (in the case of ET estimates) are sufficiently indirect that they are unlikely to correctly account for all of the factors above. Here, we study the effects of different geographic factors on plant-water interactions by analyzing the dynamics of live fuel moisture content (LFMC, defined as the vegetation water content divided by dry biomass) determined from Sentinel-1 synthetic aperture radar and Landsat multispectral observations. LFMC directly reflects vegetation water content and therefore patterns of plant water uptake and evapotranspiration. We quantify the "plant-water sensitivity" by using an auto-regressive model comparing LFMC to climate and analyze the spatial patterns of plant water sensitivity at 4 km resolution across the Western United States. No individual factor explains a majority of the spatial patterns in plant-water sensitivity. With the exception of the maximum soil conductance, no soil, topographic, or vegetation traits exerts a dominant control on plant water sensitivity. However, when aggregated, soil characteristics explain about twice as much variability in plant water sensitivity as topographic or plant characteristics do, despite little previous recognition of the influence of soil hydraulic properties on plant-water interactions. 
Super recovery of the Hadley Cell edge to the CO2 removal
Authors: Choi Yeong-Ju , Kim Seo-Yeon , Son Seok-Woo , An Soon-il , Yeh Sang-Wook , Kug Jong-Seong , Min Seung-Ki , Shin Jongsoo
Published: March 2022

The poleward shift of the Hadley cell (HC) edge by global warming is widely documented. However, its reversibility to CO2 removal remains unknown. By conducting a climate model experiment where CO2 concentration is systematically increased and then decreased in time, this study shows that a poleward-shifted HC edge in warm climate returns equatorward as CO2 concentration decreases. It is also shown that the rate significantly differs between the two hemispheres. While the southern HC edge monotonically changes with CO2 concentration, the northern HC edge exhibits a super recovery, locating on the equatorward side of the present-climate HC edge when CO2 concentration returns to the present level. Such a super recovery is associated with the hysteresis of the North Atlantic sea surface temperature. Our findings suggest that the HC edge change may result in the super recovery of subtropical dryness in the northern hemisphere except California.
 Statisrical Probable Maximum Precipitation using RCP 4.5 and RCP 8.5 scenarios
Authors: Seo Miru , Kim Sunghun , Kwon jihye , Heo Junhaeng
Published: March 2022

Probable maximum precipitation (PMP) means the maximum precipitation that can occur under the most severe weather conditions at specific area and rainfall duration in watershed. Greenhouse gas emissions in the atmosphere have increased due to industrialization caused by economic development and population growth. As a result, natural disaster damage from climate change is rapidly increasing because of many abnormal climates and phenomena. Futhemore, PMP has been increased due to such climate change. There are several methods for estimating PMP; statistical method, hydrometeorlogical method, and encelope method. In this study, statistical PMP was calculated using observed data up to 2020, and future PMP was estimated using the RCP 4.5 and RCP 8.5 scenarios up to 2100. The Hershfield’s method was used to calculate the statistical PMP, World meteorological organization (WMO) introduced the statistical method suggested by Hershfield (1961) in which frequency factor was 15. However, the frequency factor of 15 was reported to be too large in the area with heavy rainfall and too small in a dry area. Therefore, Hershfield (1965) suggested the range of 5 ~ 20 as a frequency factor.  In this study, PMPs for observed(historical) data and simulated data from RCP 4.5 and RCP 8.5 scenarios were calculated. Then the frequency factors were compared with those suggested by Hershfield. Finally, the derived statistical PMPs were compared with those from hydrometeorlogical method.
Impacts of future changes in seasonal hydrological drought on hydropower potential in the Yangtze River Basin
Authors: Liu Xinyue , Yuan Xing
Published: March 2022

As an important renewable energy source, hydropower can meet China's needs for sustainable decarbonization. But it is very sensitive to climate change, and the occurrence of hydrological droughts will have a severe impact on hydropower. The significant decline in hydropower supply in dry years or seasons increases the demand for other power resources, especially fossil fuel, which will further increase greenhouse gas emissions. In the future, seasonal droughts are expected to change in the context of global warming, and their impact on hydropower generation needs to be studied, especially over the Yangtze River basin that has the largest hydropower resources and potential in China. In this study, the characteristics of seasonal hydrological drought events under historical and future climate conditions are analyzed in the Yangtze River Basin, and the PCR-GLOBWB hydrological model is further used to simulate the changes of water resources and hydropower generation under drought conditions. This study is beneficial to bring extreme events into the consideration of hydropower development and operation planning in China, and provides scientific basis for ensuring the safety of hydropower system.
Gas leakage in the NW Russian Barents Sea
Authors: Kishankov Aleksei , Mazzini Adriano , Akhmanov Grigorii , Solovyeva Marina , Vidishcheva Olesya , Piatilova Anna , Krylova Maria
Published: March 2022

The Barents Sea region is an area of extensive erosion that occurred during the Cenozoic due to a tectonic uplift followed by several Quaternary glaciations. Several hydrocarbon fields have been discovered in the region where gas leakage through the seafloor is widespread. One of the promising regions of hydrocarbon occurrence is the north-western sector of the Russian Barents Sea. This poorly studied region has been recently targeted for scientific studies by several expeditions conducted in the framework of the Training Through Research program (TTR). The obtained geophysical and geological data revealed the presence of numerous acoustic and bathymetry anomalies (e.g., gas chimneys, bright spots, pockmarks) that are associated with higher gas content in the sampled sediments.

Here we combine (i) a set of shallow seismic data acquired during recent TTR expeditions (sparker seismic and sub-bottom profiling sections) with (ii) conventional deep seismic sections and (iii) a database of geochemical surveys of cored sediments. These merged data are used to compile a comprehensive database for the north-western sector of the Russian Barents Sea providing information on:

the area of the potential Mesozoic reservoirs reaching the seafloor

distribution of the seismically interpreted fluid pathways reaching the surface

the position of the known or inferred seafloor seepage sites

One of the major goals is to correlate the geology of the outcropping strata with the variations of gas and water geochemistry, and ultimately to link the mapped/inferred fluid migration pathways to the Triassic-Jurassic reservoirs which are known to have high hydrocarbon potential in this region. Finally, the compiled database may represent a useful tool to geochemically characterize so far undiscovered hydrocarbon fields.
Observational and numerical study of a giant hailstorm in Attica, Greece, on October 4, 2019
Authors: Papavasileiou Georgios , Kotroni Vasiliki , Lagouvardos Konstantinos , Giannaros Theodore M.
Published: March 2022

On October 4, 2019, giant hailstones of 11 cm were reported in northern parts of Attica in southern Greece. During the same day, multiple large hail reports of hailstones larger than 3 cm as well as 5 tornadoes were reported in the European Severe Weather Database along the track of a long lived supercell thunderstorm that formed over northeastern Peloponnese and moved northeastwards to Attica and Euboea. In this study, we investigate the synoptic and mesoscale weather conditions that led to this rare event by using upper-air measurements from the Athens International Airport, satellite retrievals from METEOSAT, and reanalysis data from ERA5. 

Furthermore, the predictability of this rare event is studied through high-resolution simulations performed with BOLAM, MOLOCH and WRF-ARW models, which are used operationally by the METEO unit at the National Observatory of Athens. The models were able to reproduce the mesoscale environment associated with these severe weather events, showing a highly unstable environment in Saronic gulf with more than 3000 J kg-1 MLCAPE overlapped by more than 25 m s-1 0–6 km Bulk Shear. However, the models were not able to fairly reproduce the triggering, track and timing of the supercell formation highlighting the great uncertainties associated with the initiation of deep moist convection over areas with complex terrain. Here, we attempt to constrain these uncertainties by applying a diagnostic tool for predicting hail size using an ensemble of high resolution simulations and we discuss its operational usage. 
Hybrid wind power forecasting model (WRFv4.1.3 and Artificial Neural Network) considering roughness sublayer characteristics
Authors: Kim Jeongwon , Gim Kyeongmin , Hong Jinkyu
Published: March 2022

Wind power is attracting more attention as an alternative energy in accordance with efforts to reduce carbon emissions, but its stable operation is difficult due to the intermittency of wind. Since the 1990s, many previous studies have suggested forecasting models focusing on accuracy and speed using numerical weather prediction models, statistical approaches, and hybrid techniques. However, although their verification methods and periods are different, most showed high errors of about 15% or more. In this study, we developed and validated a hybrid forecasting model using the mesoscale model Weather Research and Forecasting (WRF) and artificial neural network (ANN) for a wind farm located in Yeongyang-gun, Gyeongsangbuk-do, South Korea, a complex mountainous terrain (36°36ʹ49ʺN, 129°13ʹ21ʺE). In order to simulate more accurate wind at hub-height of wind turbine (i.e., 80 m), roughness sublayer parameterization (Yonsei surface layer scheme; YSL) is used and daily spatio-temporal high resolution wind forecasts are performed for one year in 2020. The simulated wind speed and actual wind power generation at that time are used as training data set for ANN to construct a hybrid forecast model, and it is validated for January and February 2021. Our analysis shows that improved parameterization in the roughness sublayer can significantly contribute to wind power forecasting through more accurate wind speed forecasting.
The enigma of multidecadal to centennial global vs. local temperature variability in models and proxies
Authors: Laepple Thomas , Bothe Oliver , Chevalier Manuel , Ellerhoff Beatrice , Hébert Raphaël , Herbert Annika , Martrat Belen , Moreno Chamarro Eduardo , Rehfeld Kira , Schoch Patrizia , Weitzel Nils , Ziegler Elisa
Published: March 2022

Climate variability, resulting from natural radiative forcing and interactions within the climate system, is a major source of uncertainty for regional climate projections. Constraining the amplitude of these natural variations is fundamental to assess the range of plausible future scenarios. As the instrumental record is limited to the last two centuries, information about climate variations on multi-decadal to millennial timescales relies on the analysis of climate proxy records and climate model simulations. However, current results from systematic model-proxy comparisons of natural variability seem contradictory. Several studies suggest that simulated local temperature variability is consistently smaller than proxy-based reconstructions and conclude that climate models might have major deficiencies. Other studies find agreement in global temperature variability across timescales and argue that current models can faithfully simulate climate variability. 

Here, we review the evidence on the strength of natural temperature variability during recent millennia. We identify systematic biases in the reconstructions that may contribute to the model-proxy discrepancy but are likely not sufficient to reach consistency. Instead, we propose that the seemingly contradictory  findings on the (dis)agreement between proxies and simulations can be reconciled assuming that regional climate variations persist on longer time scales than currently simulated by climate models. The combined evidence argues for deficiencies in the simulation of internal variability but a faithful response of climate models to natural radiative forcing. We propose a strategy to test our hypothesis and discuss the implications for future climate projections.
Rectified multiyear warming in high latitudes by interannually varying biomass burning emissions in CESM2 Large Ensemble simulations
Authors: Kim Ji-Eun , Yamaguchi Ryohei , Rodgers Keith , Timmermann Axel , Lee Sun-Seon , Stein Karl , Danabasoglu Gokhan , Lamarque Jean-Francois , Fasullo John , Deser Clara , Simpson Isla , Rosenbloom Nan , Edwards Jim , Kay Jennifer , Steuker Malte
Published: March 2022

A merged biomass burning aerosol (BBA) emission dataset of satellite observations with fire proxies and fire models has been used in the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. Although this utilizes best estimates of fire emissions based on available observations, it results in inconsistency in interannual variability of BBA forcing in CMIP6 between the period of satellite-based fire emissions (1997-2014) and the periods before and after. Using the Community Earth System Model version 2 Large Ensemble (CESM2-LE) simulations, we identify rectified multiyear mean climate responses to interannually varying BBA emissions. The comparison of 50 ensemble members forced by high BBA variability with 50 members by low BBA variability over a limited time domain provides a unique opportunity to identify a forced climate response to interannual fluctuations of fire emissions with high fidelity. While mean aerosol emissions are nearly conserved between the two sets of ensembles, there is detectable warming in northern high latitudes with regionally distinct seasonal changes in response to variable emissions. We find that the multiyear warming occurs in concert with a net loss of soil ice and moisture in addition to a loss of Arctic sea ice. Our results suggest that the magnitude of interannual variability of aerosol emissions can act as climate forcing over multiple years through nonlinear interactions with the cryosphere and soil processes.