bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of rainfall-induced shallow landslides through integration of hydrological model with a slope stability model
Authors: Thomas Juby , Gupta Manika
Published: March 2022

Landslides are one of the most widespread natural hazards on earth and has been a major problem in many countries, especially in developing countries. Rainfall induced shallow landslides are ubiquitous on steep terrains of Himalayas, India and are accountable for substantial damage to properties, loss of human lives and livestock. They are densely distributed across territories, very frequent in time and space, and occur without any significant premonitory signals. Due to the surge in occurrence of extreme precipitation events as a result of climate change, rainfall induced landslides have become more frequent in the Himalayas. Since the mountains are becoming increasingly inhabited because of the population expansion, the geohazards like landslides have become more destructible. The Himalayas is one of the most vulnerable areas in the world and is a region of crucial interests.  The Himalayas has been receiving surplus amount of rainfall and which is a trigger for devastating landslides along the steep terrains.  Prediction of rainfall induced landslides can help the policy makers and local administration to propose appropriate mitigation strategies for unstable and vulnerable terrains.

In the present study, a hydrological model is integrated with a dynamic physically based slope stability model for the grid-wise forecasting of the stability of the terrain in the central Himalayas. The model has been optimised and calibrated based on remotely sensed data and multi-temporal landslide inventory corresponding to various landslide inducing precipitation events. HYDRUS 1D platform is used for the hydrological modelling which includes the derivation of SHPs and subsurface soil moisture. The hydrological model with finer resolution SHPs and subsurface soil moisture is later integrated with Transient Rainfall Infiltration and Grid-based Regional Slope-stability (TRIGRS) model to compute the factor of safety of the terrain. The integrated model is validated for the study area with the previous occurrence of the rainfall induced landslides. The integrated model shows higher positive rate for landslide prediction as compared with the utilization of simple slope stability model.

Keywords: Himalayas, landslides, HYDRUS 1D, TRIGRS
Interactions of dissolved soil organic matter and manganese oxides
Authors: Brüggenwirth Lena , Lechtenfeld Oliver , Behrens Ricarda , Kaiser Klaus , Mikutta Robert , Mikutta Christian
Published: March 2022

Secondary minerals such as iron and aluminum (oxyhydr)oxides are a well-known key factor determining the accumulation and persistence of organic carbon (OC) in soil. Manganese (Mn) oxides, although being less abundant in soil than other oxide minerals, may also bind and stabilize organic matter. In addition, they exhibit a high redox activity that may promote oxidation of refractory organic compounds into substrates easily available to microorganisms. However, little is known about the adsorption and oxidation of dissolved OC (DOC) by Mn oxides. Therefore, we investigated the adsorption of dissolved organic matter (DOM) to vernadite, acid birnessite, and cryptomelane, by varying DOM type (beech and pine-derived), pH (4 and 7), and background electrolyte composition (no salt addition, 0.01 M NaCl or CaCl2). Preliminary results show that the extent and kinetics of DOM adsorption as well as oxidative DOM transformation strongly differed with Mn oxides and sorption conditions. Overall, DOM adsorption was higher at pH 4 than at pH 7. Vernadite was most sorptive, retaining 68% to 85% of added DOC at pH 4. At pH 7, on average 30% less DOC was adsorbed by Mn oxides. After reaction, reduced specific ultraviolet absorbance at 280 nm of DOM indicates preferential adsorption of aromatic moieties. Contact of DOM with Mn oxides also resulted in high concentrations of dissolved low-molecular-weight (LMW) organic acids, consisting mainly of formic, acetic, oxalic, and citric acid. In addition, we will present results from liquid chromatography/Fourier transform ion cyclotron resonance mass spectrometry and X-ray diffraction on the molecular transformation of reacted DOM and reductive changes of reacted Mn oxides, respectively. Consequently, interactions of DOM and Mn oxides may promote selective sorptive stabilization of organic matter as well as support microbial growth due to oxidative production of easily available organic compounds.
Assessment of the Water Cycle Acceleration in the Czech Republic
Authors: Vargas Godoy Mijael Rodrigo , Bešťáková Zuzana , Pradhan Rajani K. , Součková Markéta , Hanel Martin , Juras Roman , Kyselý Jan , Markonis Yannis
Published: March 2022

There is general agreement about the water cycle acceleration in the community, although its strength over land has been debated lately. While some common behavior is observed under similar climatic conditions across the globe, at the regional scale the water cycle's response to global warming is specific to its location's unique characteristics. Herein, we quantify the water cycle and characterize its climatology over the Czech Republic, which constitutes an essential headwaters area of the European continent, and in hydrological terms, it can be called the “roof of Europe”. The country's location involves three drainage catchments: the Elbe, Oder, and Danube rivers, which lead to the North Sea, the Baltic Sea, and the Black Sea respectively. Our analysis includes various data sets at  different spatiotemporal scales like: The Twentieth Century Reanalysis (20CR), CPC Merged Analysis of Precipitation (CMAP), CPC Global Unified Gauge-Based Analysis of Daily Precipitation (CPC), Climatic Research Unit gridded Time Series (CRU TS), Global Historical Climatology Network monthly (GHCN), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), Global Precipitation Climatology Centre (GPCC), The Global Precipitation Measurement Integrated Multi-satellite Retrievals (GPM IMERG), Global Runoff Data Centre (GRDC), Global Runoff Reconstruction (GRUN), Moderate Resolution Imaging Spectroradiometer Terra Net Evapotranspiration (MOD16A2), National Centers for Environmental Prediction DOE Reanalysis 2 (NCEP DOE), National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP NCAR), NOAA's Precipitation Reconstruction over Land (PRECL), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TRMM 3B43), and University of Delaware Precipitation (UDEL). To exploit the availability of the various data sets for each component of the water cycle we merged them via simple weighted averages, a multi-source data integration method that has proven to be effective and with low computational requirements. Subsequently, we linked the computed components constraining them by the water budget equation. Thereafter, the time series were analyzed to quantify trends and their statistical significance, as well as their uncertainty derived by the multiple datasets. In addition to the time series analysis and the statistics involved so far, a spatial analysis explored the water cycle climatology and its variability over the whole Czech Republic and then its behavior in subdomains defined by the watersheds within the borders of the country.
Physical drivers of patterns in autumn—winter chlorophyll variability from Saildrone measurements in the Southern Ocean
Authors: Joy-Warren Hannah , Giddy Isabelle , Rosenthal Hanna , du Plessis Marcel , Swart Sebastiaan
Published: March 2022

The first autonomous surface vehicle (Saildrone) circumnavigation of Antarctica revealed spatio-temporally variable chlorophyll with occasionally high concentrations in late autumn/early winter. Low light availability at this time of year makes high chlorophyll concentrations unexpected and the patterns of variability hint at physical processes, such as submesoscale fronts, controlling chlorophyll distributions. Here, we assess the physical drivers of spatio-temporal chlorophyll distribution measured by the Saildrone. Together with large-scale variability in surface heat and light, we identify submesoscale (0.1-10 km) frontal activity and regions of high eddy kinetic energy to characterize possible physical drivers of the observed variability in chlorophyll. Autonomous platforms measuring oceanic variables at fine spatial and temporal resolution are enabling new discoveries, such as this one, and open the door to understand the impact of submesoscale flows on the local ecosystem.
The effects of stream water velocity, streambed celerity, and particle properties on microplastic deposition in streams
Authors: Levy Sturm Verena , Gobrecht Silvia , Bernstein Roy , Arnon Shai
Published: March 2022

Microplastic (MP) is ubiquitously found in aquatic environments and poses a risk to organisms and potentially also to human health. While MP in oceans was studied extensively in recent years there is still a lack of knowledge especially on how MP is being transported and deposited in streams. We conducted flume experiments to study how plastic properties and bedform movement influence the deposition dynamics of MP. We used a recirculating stainless-steel laboratory flume (650 cm x 20 cm), that was packed with homogeneous sand (D50=0.65 mm). We compared the deposition dynamics of MP under stationary bed and under fast moving bedforms. We used aged MP fibers made from PET, PP, PA at various lengths (25, 100, 200 μm). The deposition rates were quantified by adding MP into the water and tracking their concentration in the stream water over time. In addition, streambed samples were taken to quantify and analyze the locations and concentrations of MPs within the sediment. It was found that the physical properties of MP including size, density, and type had a relatively minor influence on their deposition rate because the mechanism was dominated by the movement of the bedform and not due to their transport within the porous medium. Only a slight difference in deposition rate was observed for the different types of MP. The MP particles that we used are too large to be efficiently transported into the porous media and the resulting patterns were deposition close to the water-sediment interface in stationary bed, and below the moving fraction of the bed for moving bedforms. These experimental results represent unique observations of the transport mechanisms of MP in streams with moving bedforms. They are important for the understanding of the transfer of MP from its source toward the oceans, for understanding the life cycle of plastics in the environment, to develop sampling strategies in streams, and to find long-term solutions for reducing their concentrations and the associated risks.
Nutrient limitation influences moss-associated biological nitrogen fixation in pristine ecosystems
Authors: Avila Clasen Lina , Tora Foged Permin Aya , Rousk Kathrin
Published: March 2022

Moss-associated cyanobacteria have been show to contribute greatly to the nitrogen (N) pools of pristine ecosystems. Moisture and temperature are the most important abiotic factors directly influencing the rates of biological nitrogen fixation (BNF) in mosses. However, the availability of some macro and micronutrients can also be correlated to changes in those rates, and this is linked to the functional role of nutrients involved in the nitrogenase enzyme complex. To identify the effects of nutrient availability on BNF we conducted two experiments with mosses from contrasting ecosystems, subarctic and tropical, in which we applied N and phosphorous (P) along an elevation gradient in the subarctic (Sweden), as well as in a laboratory experiment using three different moss species from a tropical cloud forest (Peru). Nitrogenase activity in mosses was assessed via the acetylene reduction assay (ARA) at different time points after the additions. Our results show that N addition rapidly reduce BNF rates associated with mosses from both ecosystems. But, P additions lead to variable results, especially in tropical mosses, where we detected species-specific responses towards the nutrients additions. Further, the effects of nutrient additions depended on the positioning along the elevation gradient in the subarctic, reflecting differences in nutrient availability and thereby, limitation. Our findings show how the availability of essential macronutrients (i.e. N and P) can play a fundamental role in promoting or inhibiting BNF in mosses, and that time frame and species identity can be significant in modulating the response of BNF to nutrient availability.
The PISSARO project: subseasonal impact-based forecasts of the cyclonic activity in the South West Indian Ocean basin
Authors: Vérèmes Hélène , Malardel Sylvie , Bonnardot François , Labbé Laurent , Langlade Sébastien , Peyrillé Philippe , Charpigny Simon , Lefort Thierry , Mékiès Dominique
Published: March 2022

The PISSARO project focuses on atmospheric and oceanic forecasting at the subseasonal scale for applications over the South West Indian Ocean basin (SWIO). It is a collaborative academic research project, developed and conducted in partnership with stakeholders from Reunion and Seychelles and a panel of scientific experts in subseasonal forecasting. The aim of this project is to evaluate, improve and valorize subseasonal forecasting data. For this purpose, we mainly use the data archived into the the S2S (Subseasonal-to-Seasonal prediction project) data base in order to 1) evaluate the quality of subseasonal forecasts for tropical cyclones and weather patterns, and 2) develop forecast products suitable for potential users. This project focuses on the SWIO, which has been little studied by the S2S community until now. The different territories of the SWIO are subject to extreme events and a significant cyclonic activity. It is important to take into account the specificities of this region in order to improve their warning systems.

The ambition to deploy early warning tools cannot be achieved without discussions between potential users and S2S experts. The users specify the characteristics of the products to be developed so that they offer an asset for decision-making, and the experts assess the feasibility of these products. In the presentation, we will first discuss the importance of collaboration between the users and the experts within the project using two concrete actions: the animation of a monthly experimental forecasting briefing with operational forecasters and the participation in conferences in the humanitarian field. Then, we will present the subseasonal forecasting products which are under development for the anticipation of cyclonic risk at monthly scale in the SWIO basin. To address the urgency of the need of the disaster risk reduction, we first made a basic adaptation of already existing tropical cyclone occurrence probability and rainfall forecasting products into products interpretable by non-meteorological users.

We consider that a crucial information from the S2S data base to provide to users is the level of uncertainty. However, estimating the quality of S2S forecasts is not straighforward. It is actually difficult to match a forecasted cyclone to an actual observed cyclone, let alone detect a false alarm. To this end, we are working on the classification of S2S tropical cyclone trajectories with clustering methods and we will show the first results. We aim to exploit the ensemble character of the subseasonal forecast for the development of future S2S-derived forecast products that would provide probabilities of scenarios of potential trajectories (based on these clusters).
Ecosystem engineering by desert macro-detritivores facilitates microbial litter decomposition
Authors: Sagi Nevo , Zaguri Moshe , Hawlena Dror
Published: March 2022

Litter decomposition in most terrestrial ecosystems is regulated by moisture-dependent microorganism activity, among other things. Decomposition models typically underestimate rates of plant litter decomposition in drylands, suggesting the existence of additional drivers of decomposition. Attempts to reveal these drivers have predominantly focused on abiotic degradation agents, alternative moisture sources, and soil–litter mixing. The role of burrowing animals in promoting decomposition has received less attention despite greatly contributing to plant litter transfer from the harsh desert surface to the moister and nutrient-rich environment belowground. In a previous study in the Negev Desert, we found that macrofauna account for 89% of the litter cleared from the desert surface, and detected elevated nutrient levels near desert isopod (Hemilepistus reaumuri) burrows. Here, our goal was to explore how macro-detritivore burrows affect plant litter mineralization dynamics. We introduced 13C-labeled litter belowground into (1) isopod burrows and (2) artificial burrows, and aboveground on top of (3) isopod fecal pellet mounds and (4) bare soil crust. We compared the litter mass loss between the four treatments and used cavity ring-down spectroscopy to reveal the in-situ mineralization dynamics. No litter mineralization was evident during the dry summer months both above- and belowground. Following rain events, mineralization rates spiked in all four micro-environments, quickly diminishing aboveground while slowly waning belowground. Total litter mass loss was twofold higher below than aboveground and was significantly higher in isopod burrows compared to artificial burrows. Our findings demonstrate that burrowing macro-detritivores promote litter decomposition in deserts by transferring organic matter to their burrows where favorable climatic conditions and a nutrient-enriched environment foster microbial activity. Thus, attempts to resolve the dryland decomposition conundrum should not be limited to exploring factors that allow decomposition under harsh desert surface climatic conditions, but focus on the role that animals play in facilitating decomposer-friendly environments to which they translocate plant litter.
Novel Coccolithophores from the Lower Deep Photic Zone Off Bermuda
Authors: Millan Josue , Winter Amos , Jordan Richard W , Blanco-Bercial Leocadio
Published: March 2022

Coccolithophores are a ubiquitous oceanic phytoplankton group. Their unique ability to acquire carbon from different environmental sources and to produce calcareous body scales (coccoliths) make them an integral functional group in the biogeochemical cycling of carbon. Despite this, their vertical distribution, particularly in the lower photic zone (LPZ), species composition, and life cycles, are still poorly understood. Discrete water samples were examined from the LPZ during the 2020 fall overturn event occurring from October to November at hydrostation S of the Bermuda Atlantic Time-Series (BATS). This provided an opportunity to compare our results with a previous BATS survey of coccolithophore population dynamics taken 28 to 26 years earlier (1992-1994). This latter study demonstrated that coccolithophores exhibit seasonal changes in their vertical and horizontal distribution and showed that the coccolithophore population transition of the LPZ occurs primarily at overturn events. Here, we place particular emphasis on those LPZ coccolithophore species adapted to live between the deep chlorophyll maximum and the upper mesopelagic zone because of their potential for mixotrophic activity. We discovered numerous unidentified taxa in this region, which may be either new to science or alternate phases of already described species. Some of the holococcolithophores appear to be associated with the Papposphaeraceae, with similarities to the Turrisphaera-phase. In addition, we provide the first unquestionable evidence of Florisphaera profunda combination coccospheres, featuring both heterococcolith and holococcolith phases in the same sample.
Surface loading on GNSS stations in Africa
Authors: Usifoh Saturday Ehisemhen , Le T.Nhung , Männel Benjamin , Sakic Pierre , Joseph Dodo , Schuh Harald
Published: March 2022

Surface loading on GNSS stations in Africa

Usifoh Saturday E1,2,3, Nhung Le Thi1,2, Benjamin Männel1, Pierre Sakic1, Dodo Joseph3, Harald Schuh1,2
1GFZ German Research Centre for Geosciences, Potsdam, Germany, 2Institut für Geodäsie und Geoinformationstechnik Technische Universität, Berlin, Germany, 3Centre for Geodesy and Geodynamics, Toro, Bauchi State, Nigeria.

 Corresponding author: parker@gfz-potsdam.de

Abstract

The global navigation satellite systems (GNSS) have revolutionalized the ability to monitor the Earth’s system related to different types of natural processes. This includes tectonic and volcanic deformation, earthquake-related displacements, redistribution of oceanic and atmospheric mass, and changes in the continental water storage. As loading affects the GNSS cordinates, we investigated the effect and assessed the impact of applying dedicated corrections provided by the Earth System Modeling group of German Research Center for Geosciences (GFZ). However, loading caused by mass redistribution results in displacement, predominantly with seasonal periods. Significant temporal changes in mass redistribution (e.g caused by climate change) will result to further trends in the station coordinate time series.

In this contribution, we will compare the PPP coordinate time series with the loading-corrected PPP time series by looking at the amplitude and the correlation between the GNSS time series and the model corrections. Also we will compare the PPP coordinate time series with the loading time series by assessing the RMS reduction and change of amplitude.The result shows that loading-induced displacement varies considerably among GNSS stations and applying corrections to the derived time series has favourable impacts on the reduction in the non-linear motion in GNSS height time series of the African stations.
Sensitivity analysis of Radiative Transfer model towards leaf biophysical and biochemical parameter retrieval
Authors: Singh Prachi , Srivastava Prashant K. , Mall Rajesh K.
Published: March 2022

Coupling of leaf physiological models with leaf and canopy RT (Radiative Transfer) models allow evaluation and quantification of most influential variables using fluorescence signal emitted by the leaf and canopy. Using RTM output, one of the most successful method i.e. Sobol global sensitivity analysis was used to identify the most influential input variables through matrices such as first-order (Si) and total-Order effect (STi). The present study was conducted using the field-based and hyperspectral datasets in the agricultural site in Northern India. Ground bio-physical (Leaf water content, Leaf area Index) and bio-chemical (Chlorophyll) parameters were collected. RTM spectral outputs were generated for hyperspectral data within the spectral range of 350-2500 nm. To calculate the first order and total order sensitivity result of PROSPECT-4 (leaf) and PROSPECT+SAIL (canopy) RTMs were evaluated. Sobol results for PROSPECT-4 model reveal that the role of biochemical parameter chlorophyll content in the visible region and the influence of the other biophysical parameters such as Leaf structure and dry matter across the whole spectral range. Only Leaf water content reflectance was found around 1200 nm onwards. After coupling the leaf PROSPECT-4 model with SAIL (Scattering by Arbitrary Inclined Leaves) model, reference PROSAIL STi results showed that the LAI variable shows 50% of the total variability, especially in the SWIR region. The present study is not only useful to know wavelength-dependent influential and non-influential RTM input variables but also for driving input variables of fluxes such as photosynthesis of the canopy and for the estimation of FPAR (Fraction of photosynthetically active radiation) values.  
Ecological prediction of Baltic region basing on combination of static and dinamic modeling
Authors: Sapunov Valentin
Published: March 2022

The work is based on long term work of Helsinki commission of Baltic sea and own field and laboratory researches. High rate of knowledge is possibility of forecast. The highest rate is possibility of control. Baltic sea has giant importance in Europe ecology, economic and policy as sea dividing and uniting many states. The growth of instability of environmental-climatic processes, an increase in the number of natural disasters, has been observed since the beginning of the XX century and requires the improvement of methods of environmental-climatic forecasts. The economic situation makes relevant low-cost and progressive forecasting methods. Of particular importance is the logical-mathematical modeling, combining two types of models - dynamic and static. Dynamic based on the analysis of trends. Static models are cheaper and more intellect. They are based on an assessment of the state of the systems (primarily ecological) at the moment, taking into account the element of anticipatory reflection characteristic of living systems. Monitoring the behavior of animals can be the basis of predictions and the creation of static prognostic models. An important and little developed direction of environmental forecasting is the phenogenetic indication. It is based on the analysis of morphologic characteristics of biological populations, quantitative and qualitative variability, sex ratio, sexual dimorphism. The level of development of population and ecological genetics makes this method a promising direction of environmental forecasting. Modeling of environmental processes in the Baltic zone requires taking into account both global processes (increase in climatic instability and the number of extreme natural events, increase in anthropogenic pressure), as well as regional processes (melting ice in the Arctic due to the “pseudo-greenhouse effect” and an increase in the concentration of methane in the atmosphere, cyclical changes in salinity with a period of 55 - 50 years, etc.). Comprehensive assessment of remote and contact monitoring data can be the basis for integrated static and dynamic modeling and a promising direction for the development of environmental safety. The expected trend in the Baltic zone for 10 to 20 years is a slight decrease in temperature, a continuing increase in the number of extreme events and an increase in the biomass and biodiversity of both desirable and undesirable species.
Modeling polymer-specific exposure to micro- and macroplastics in freshwaters at high spatial resolution at the country-scale
Authors: Mennekes David , Nowack Bernd
Published: March 2022

Starting from plastic pollution in oceans as a widely recognized environmental problem, the research focus has also shifted towards rivers which were found to be a major inflow of plastics into the oceans. However, the source of plastic pollution itself is mostly upstream and for instance related to waste water treatment plants, littering of on-the-go consumer packages or agricultural films. For Switzerland a recent study modeled the release of the seven most used plastic polymers (LDPE, HDPE, PP, PS, EPS, PVC, PET) as micro- und macroplastic to the environment. Release maps of plastic emissions were obtained at high spatial resolution of 100 m by 100 m for soil grid cells and for each single river section in Switzerland. The aim of the current work was to couple this release model with its high spatial resolution to a model for the transport, accumulation and removal of the plastic polymers.

The model for the Swiss river and lake network allows to follow plastic pollution through every stream in Switzerland from the sources towards the outflows to the neighboring countries of Switzerland. We differentiate between the different plastic polymers and micro- and macroplastic. Furthermore, we consider physical properties (e. g. density, size) to establish parameters for accumulation and removal or cleaning rates of plastics in each river section and lakes based on parameters such as slope, urbanization or volume. In detail, we model the movement of plastic mass along the rivers based on average flow velocities while in lakes we consider sedimentation rates (accumulation) based on literature data. Input for the model is the yearly release of the seven polymers into about 2000 river sections. The transport model considers a network of over 600,000 river segments and 210 lakes. Our model provide contamination data on the scale of each river section which compared with so far available catchment scale model will provide an even closer look at rivers and local sources and sinks of plastics. However, many parameters regarding micro- or macroplastic transport in natural rivers at a large scale are still unknown or hydrological parameters on a country scale are not available. Therefore, a compromise between data availability and implementation of physical processes in the model had to be found. First results show the strength of our model to trace plastic particles regardless the size or polymer type. We are able to detect lakes as major sinks which substantially reduce plastic transport in rivers.

Our work can help to better understand the sources of the global plastic pollution but rises the need for experimental data on plastic transport in the environment. The large-scale understanding of plastic transport processes will provide policy makers with options were to tackle the spread of plastic pollution in the most efficient way.
Assessing the impact of sea-level rise on future compound flooding hazards in the Kapuas River delta
Authors: Sampurno Joko , Vallaeys Valentin , Ardianto Randy , Hanert Emmanuel
Published: March 2022

Compound flooding hazard in estuarine delta is increasing due to mean sea-level rise (SLR) as the impact of climate change. Decision-makers need future hazard analysis to mitigate the event and design adaptation strategies. However, to date, no future hazard analysis has been made for the Kapuas River delta, a low-lying area on the west coast of the island of Borneo, Indonesia. Therefore, this study aims to assess future compound flooding hazards under SLR over the delta, particularly in Pontianak (the densest urban area over the region). Here we consider three SLR scenarios due to climate change, i.e., low emission scenario (RCP2.6), medium emission scenario (RCP4.5), and high emission scenario (RCP8.5). We implement a machine-learning technique, i.e., the multiple linear regression (MLR) algorithm, to model the river water level dynamics within the city. We then predict future extreme river water levels due to interactions of river discharges, rainfalls, winds, and tides. Furthermore, we create flood maps with a likelihood of areas to be flooded in 100 years return period (1% annual exceedance probability) due to the expected sea-level rise. We find that the extreme 1% return water level for the study area in 2100 is increased from about 2.80 m (current flood frequency state) to 3.03 m (under the RCP2.6), to 3.13 m (under the RCP4.5), and 3.38 m (under the RCP8.5).
A 20-year satellite-reanalysis-based climatology of extreme precipitation characteristics over the Sinai Peninsula
Authors: Soltani Mohsen , Hamelers Bert , Mofidi Abbas , van der Hoeven Ties , Staal Arie , Dekker Stefan C. , Arnault Joel , Laux Patrick , Kunstmann Harald , Lanters Maarten
Published: March 2022

Abstract. Extreme precipitation events and associated flash floods caued by the synoptic cyclonic-systems have profound impacts on society and the environment particularly in dry regions. This study brings forward a satellite-reanalysis-based approach to quantify the extreme precipitation characteristics over the Sinai Desert in Egypt, from a statistical-synoptic perspective for the period of 2001–2020. Using the satellite remote-sensing precipitation and a set of climate indices, we characterize the spatiotemporal distribution of extreme rainfall climatologies across the Sinai region. Then, using the reanalysis dataset, the synoptic systems responsible for the occurrence of precipitation events along with the major tracks of cyclones during the wet and dry periods are explored. Our results indicate that the temporal changes and spatial patterns of the precipitation events do not show a homogenous tendency, rather lack of spatiotemporal coherence across the Sinai. Northern parts of the Sinai, unlike other areas, exhibit the highest anomalies (approx. ±45 mm/decade); and the annual rainfall trends indicate a drier-climate in the north at −0.03 mm/decade, while a wetter-climate is observed in the central and southern parts at 0.10 and 0.36 mm/decade, respectively. The Mediterranean cyclones accompanied by the Red Sea -and Persian Troughs are responsible for the majority of extreme rainfall events all-round the year. A remarkable spatial relationship between the Sinai’s rainfall and the atmospheric variables of sea level pressure, wind direction and vertical velocity is found. Furthermore, the cyclone-tracking analysis indicates that 125 and 31 cyclones (rainfall ≥ 10mm/day) either formed within -or transferred to the Mediterranean basin and precipitated over the Sinai during wet and dry periods, respectively; while some (~15 % with rainfall > 40mm/day) being capable of leading to flash flood in the wet period of the region. This study, therefore, sheds new light on the extreme rainfall characteristics and the dominant synoptic mechanisms over the Sinai region in the eastern Mediterranean basin.
A global, spherical finite-element model for post-seismic deformation using Abaqus
Authors: Nield Grace A. , King Matt A. , Steffen Rebekka , Blank Bas
Published: March 2022

Abstract. We present a finite-element model of post-seismic solid
Earth deformation built in the software package Abaqus (version 2018). The model
is global and spherical, includes self-gravitation and is built for the
purpose of calculating post-seismic deformation in the far field
(>∼300 km) of major earthquakes. An earthquake is
simulated by prescribing slip on a fault plane in the mesh and the model
relaxes under the resulting change in stress. Both linear Maxwell and
biviscous (Burgers) rheological models have been implemented and the model
can be easily adapted to include different rheological models and lateral
variations in Earth structure, a particular advantage over existing models.
We benchmark the model against an analytical coseismic solution and an
existing open-source post-seismic model code, demonstrating good agreement
for all fault geometries tested. Due to the inclusion of self-gravity, the
model has the potential for predicting deformation in response to multiple
sources of stress change, for example, changing ice thickness in
tectonically active regions.
GCAM-USA v5.3_water_dispatch: integrated modeling of subnational US energy, water, and land systems within a global framework
Authors: Binsted Matthew , Iyer Gokul , Patel Pralit , Graham Neal T. , Ou Yang , Khan Zarrar , Kholod Nazar , Narayan Kanishka , Hejazi Mohamad , Kim Son , Calvin Katherine , Wise Marshall
Published: March 2022

Abstract. This paper describes GCAM-USA v5.3_water_dispatch, an open-source model that represents key interactions across economic, energy, water, and land
systems in a consistent global framework with subnational detail in the United States. GCAM-USA divides the world into 31 geopolitical regions
outside the United States (US) and represents the US economy and energy systems in 51 state-level
regions (50 states plus the District of Columbia). The model also includes 235 water basins and 384 land use regions, and 23 of each fall at least
partially within the United States. GCAM-USA offers a level of process and temporal resolution rare for models of its class and scope, including
detailed subnational representation of US water demands and supplies and sub-annual operations (day and night for each month) in the US electric power
sector. GCAM-USA can be used to explore how changes in socioeconomic drivers, technological progress, or policy impact demands for (and production
of) energy, water, and crops at a subnational level in the United States while maintaining consistency with broader national and international
conditions. This paper describes GCAM-USA's structure, inputs, and outputs, with emphasis on new model features. Four illustrative scenarios
encompassing varying socioeconomic and energy system futures are used to explore subnational changes in energy, water, and land use outcomes. We
conclude with information about how public users can access the model.
Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics
Authors: Shehu Bora , Haberlandt Uwe
Published: March 2022

Abstract. The nowcast of rainfall storms at fine temporal and spatial resolutions is
quite challenging due to the unpredictable nature of rainfall at such
scales. Typically, rainfall storms are recognized by weather radar and extrapolated in the future by the Lagrangian persistence. However, storm
evolution is much more dynamic and complex than the Lagrangian persistence,
leading to short forecast horizons, especially for convective events. Thus, the aim of this paper is to investigate the improvement that past similar
storms can introduce to the object-oriented radar-based nowcast. Here we propose a nearest-neighbour approach that measures first the similarity
between the “to-be-nowcasted” storm and past observed storms and later uses the behaviour of the past most similar storms to issue either a single
nowcast (by averaging the 4 most similar storm responses) or an ensemble nowcast (by considering the 30 most similar storm responses). Three questions are tackled here. (i) What features should be used to describe storms in
order to check for similarity? (ii) How should similarity between past storms be measured? (iii) Is this similarity useful for object-oriented nowcast? For
this purpose, individual storms from 110 events in the period 2000–2018
recognized within the Hanover Radar Range (R∼115 km2), Germany, are used as a basis for investigation. A “leave-one-event-out”
cross-validation is employed to test the nearest-neighbour approach for the prediction of the area, mean intensity, the x and y velocity components, and
the total lifetime of the to-be-nowcasted storm for lead times from + 5 min up to + 3 h. Prior to the application, two importance analysis methods (Pearson correlation and partial information correlation) are
employed to identify the most important predictors. The results indicate
that most of the storms behave similarly, and the knowledge obtained from such similar past storms helps to capture better the storm dissipation and
improves the nowcast compared to the Lagrangian persistence, especially for convective events (storms shorter than 3 h) and longer lead times (from
1 to 3 h). The main advantage of the nearest-neighbour approach is seen when applied in a probabilistic way (with the 30 closest neighbours as
ensembles) rather than in a deterministic way (averaging the response from the four closest neighbours). The probabilistic approach seems promising, especially
for convective storms, and it can be further improved by either increasing the sample size, employing more suitable methods for the
predictor identification, or selecting physical predictors.
Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions
Authors: Yang Yuanyuan , Shen Zefang , Bissett Andrew , Viscarra Rossel Raphael A.
Published: March 2022

Abstract. Soil fungi play important roles in the functioning of ecosystems, but they are challenging to measure. Using a continental-scale dataset, we developed and evaluated a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. The method relies on the development of spectrotransfer functions with state-of-the-art machine learning and uses publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared (vis–NIR) wavelengths, to estimate the relative abundances of Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota and Mucoromycota and community diversity measured with the abundance-based coverage estimator (ACE) index. The algorithms tested were partial least squares regression (PLSR), random forest (RF), Cubist, support vector machines (SVM), Gaussian process regression (GPR), extreme gradient boosting (XGBoost) and one-dimensional convolutional neural networks (1D-CNNs). The spectrotransfer functions were validated with a 10-fold cross-validation (n=577). The 1D-CNNs outperformed the other algorithms and could explain between 45  % and 73  % of fungal relative abundance and diversity. The models were interpretable, and showed that soil nutrients, pH, bulk density, ecosystem water balance (a proxy for aridity) and net primary productivity were important predictors, as were specific vis–NIR wavelengths that correspond to organic functional groups, iron oxide and clay minerals. Estimates of the relative abundance for Mortierellomycota and Mucoromycota produced R2≥0.60, while estimates of the abundance of the Ascomycota and Basidiomycota produced R2 values of 0.5 and 0.58 respectively. The spectrotransfer functions for the Glomeromycota and diversity were the poorest with R2 values of 0.48 and 0.45 respectively. There is no doubt that the method provides estimates that are less accurate than more direct measurements with conventional molecular approaches. However, once the spectrotransfer functions are developed, they can be used with very little cost, and could serve to supplement the more expensive and laborious molecular approaches for a better understanding of soil fungal abundance and diversity under different agronomic and ecological settings.
Wake properties and power output of very large wind farms for different meteorological conditions and turbine spacings: a large-eddy simulation case study for the German Bight
Authors: Maas Oliver , Raasch Siegfried
Published: March 2022

Abstract. Germany's expansion target for offshore wind power capacity of 40 GW by the year 2040 can only be reached if large portions of the Exclusive Economic Zone in the German Bight are equipped with wind farms. Because these wind farm clusters will be much larger than existing wind farms, it is unknown how they will affect the boundary layer flow and how much power they will produce.
The objective of this large-eddy simulation study is to investigate the wake properties and the power output of very large potential wind farms in the German Bight for different turbine spacings, stabilities and boundary layer heights.
The results show that very large wind farms cause flow effects that small wind farms do not. These effects include, but are not limited to, inversion layer displacement, counterclockwise flow deflection inside the boundary layer and clockwise flow deflection above the boundary layer. Wakes of very large wind farms are longer for shallower boundary layers and smaller turbine spacings, reaching values of more than 100 km. The wake in terms of turbulence intensity is approximately 20 km long, in which longer wakes occur for convective boundary layers and shorter wakes for stable boundary layers. Very large wind farms in a shallow, stable boundary layer can excite gravity waves in the overlying free atmosphere, resulting in significant flow blockage.
The power output of very large wind farms is higher for thicker boundary layers because thick boundary layers contain more kinetic energy than thin boundary layers. The power density of the energy input by the geostrophic pressure gradient limits the power output of very large wind farms. Because this power density is very low (approximately 2 W m−2), the installed power density of very large wind farms should be small to achieve a good wind farm efficiency.
Cooperación internacional e intergubernamental para abordar la mejora de la calidad del aire en el marco del cambio climático: el ozono troposférico y sus efectos en cultivos
Authors: Bermejo-Bermejo Victoria , Rabago Isaura , Ben Amor Afef , García-Gómez Héctor , Nagaz Kamel , Alonso Rocío , González-Fernández Ignacio
Published: March 2022

La Convención del Aire ONU-CEPE es el principal foro internacional de negociación para la limitar las emisiones de contaminantes atmosféricos con el fin de reducir sus efectos nocivos, y constituye un ejemplo de cooperación intergubernamental en defensa de la calidad del aire a escala global. Sus grupos técnicos de trabajo, como el ICP Vegetation, son también un ejemplo de colaboración internacional científico-técnica para la definición de indicadores ambientales de riesgo (niveles críticos); en concreto, los de riesgo por ozono para cultivos y ecosistemas.En su seno, se desarrollan grupos de trabajo para implementar estas metodologías para los agrosistemas mediterráneos, que sería interesante extender hacia los países del sur de la cuenca mediterránea; algo actualmente en exploración a través de la colaboración entre grupos de investigación de España y Túnez.
The Weaker Sex in War: Gender and Nationalism in Civil War Virginia
Authors: Brill Kristen
Published: March 2022

"The Weaker Sex in War" explores the relationship between middle- and planter-class white Southern women who supported the Confederacy and Confederate nationalism, and it argues that Confederate leaders used these women to advance the Southern cause. This is not to say that women were passive in this process: women were in control of their contributions to national devotion and were knowing and keen participants in shaping and circulating a gendered nationalist narrative. In this way, such conservative women inextricably tied themselves to the creation and circulation of Confederate nationalism as both actors for and symbols of the Confederate cause. These women were not concerned with expanding their individual rights as citizens, nor were they passive in political culture; they actively defined the terms of their engagement through performance of national devotion. Confederate male elites may have mobilized these women as archetypes to advance a Confederate agenda, but conservative women were complicit in this process. Seen in this way, the book contends that Confederate nationalism was more dependent on gender than has been previously argued.
A comparative assessment on gene expression classification methods of RNA-seq data generated using next-generation sequencing (NGS)
Authors: Pramana Setia , Hardiyanta I Komang Y. , Hidayat Farhan Y. , Mariyah Siti
Published: March 2022

Next-generation sequencing or massively parallel sequencing have revolutionized genomic research. RNA sequencing (RNA-Seq) can profile the gene-expression used for molecular diagnosis, disease classification and providing potential markers of diseases. For classification of gene expressions, several methods that have been proposed are based on microarray data which is a continuous scale or require a normal distribution assumption. As the RNA-Seq data do not meet those requirements, these methods cannot be applied directly. In this study, we compare several classifiers including Logistic Regression, Support Vector Machine, Classification and Regression Trees and Random Forest. A simulation study with different parameters such as over dispersion, differential expression rate is conducted and the results are compared with two mRNA experimental datasets. To measure predictive accuracy six performance indicators are used: Percentage Correctly Classified, Area Under Receiver Operating Characteristic (ROC) Curve, Kolmogorov Smirnov Statistics, Partial Gini Index, H-measure and Brier Score. The result shows that Random Forest outperforms the other classification algorithms.