bluuebunny/crossref_metadata_2025 huggingface dataset:
Iodic acid formation and yield from iodine photolysis at the CERN CLOUD chamber
Authors: Finkenzeller Henning , Iyer Siddharth , Koenig Theodore K. , He Xu-Cheng , Simon Mario , Curtius Joachim , Kirkby Jasper , Kulmala Markku , Sipilä Mikko , Rissanen Matti , Kurten Theo , Volkamer Rainer
Published: March 2021

Iodine oxoacids are key species involved in the cycling of iodine between the gas- and aerosol phases. Iodic acid (HIO3) nucleates particles more efficiently than sulfuric acid and ammonia at comparable concentrations, and grows them at comparable rates, but the formation mechanism of HIO3 is essentially unknown. As a result, atmospheric models of iodine chemistry are currently incomplete. Proposed precursors for iodine oxoacids include iodine atoms and higher iodine oxides (e.g., I2O2, I2O3, I2O4), but theoretical predictions have not currently been assessed under experimental conditions that approximate the open ocean marine atmosphere. We present results from laboratory experiments at the CLOUD chamber that observe rapid oxoacid formation from photolysis of iodine (I2) at green wavelengths, in the presence of ozone and variable relative humidity (0-80%). Under these (soft) experimental conditions iodine oxide (IO) radical concentrations closely approximate those found in the remote marine boundary layer. A chemical box model is constrained by measurements of I2, ozone, RH, photolysis frequencies (i.e., I2, IO, OIO, HOI, IxOy) and known losses of gases to particles and the chamber walls, and evaluated using time resolved measurements of IO, OIO, and IxOy species in the chamber. Hypothesized mechanisms for HIO3 formation - either proposed in the literature or motivated from our observations - are then discussed in terms of their ability to explain the observed amounts (yield), and the temporal evolution of HIO3. Finally, the atmospheric relevance of the laboratory findings is assessed in context of unique field measurements at the Maido Observatory, La Reunion, during spring 2018, where IO radicals and HIO3 were measured simultaneously in the remote free troposphere.
3He rich periods measured by the Suprathermal Ion Telescope (SIT) on STEREO-A during solar cycle 24
Authors: Köberle Marlon , Bucik Radoslav , Dresing Nina , Heber Bernd , Klassen Andreas , Wang Linghua
Published: March 2021

3He-rich solar energetic particle (SEP) events are characterized by a peculiar elemental composition with rare species like 3He or ultra-heavy ions tremendously enhanced over the solar system abundances.
We report on 3He rich SEP periods measured by the Suprathermal Ion Telescope (SIT) onboard STEREO-A beginning in 2007 until 2020, covering the whole solar cycle 24.
The mass resolution capabilities of SIT do not allow to easily distinguish between 3He and 4He especially in cases of a low 3He to 4He ratio.
We therefore developed a semi-automatic detection algorithm to find time periods during which a 3He enhancement can be statistically determined.
Using this method we found 112 3He rich periods.
These periods were further examined in regards of their 3He/4He and Fe/O ratio. 
Previously about ten 3He-rich SEP periods measured by SIT on STEREO-A have been reported.
An association with in-situ electron measurements by STEREO-SEPT and STEREO-STE showed that ~60% of the 112 periods are accompanied with electron events.
The here presented catalogue of 3He rich periods is intended to serve as a reference for the community.
Towards disentangling heterogeneous soil moisture patterns in Cosmic-Ray Neutron Sensor footprints
Authors: Rasche Daniel , Köhli Markus , Schrön Martin , Blume Theresa , Güntner Andreas
Published: March 2021

Cosmic-Ray Neutron Sensing (CRNS) has constantly advanced during the last decade as a modern technique for non-invasive soil moisture estimation at the field scale. Latest studies led to an improved understanding of the CRNS integration volume, weighting functions for reference soil moisture measurements and correction procedures of raw neutron counts.

It is common knowledge that soil moisture is highly variable in space. Nevertheless, the CRNS processing techniques currently assume that there is little structure to this variability within the 10 ha measurement footprint, i.e. no distinct difference in average moisture content between near and far field. In particular, with a single CRNS probe and the current knowledge it is not possible to separate different soil moisture conditions within the footprint.

Against this background, we investigated the effect of soil moisture patterns on the size of the measurement footprint and on the response of thermal and epithermal neutron intensities at a CRNS observation site in north-eastern Germany. The site exhibits pronounced differences in soil water content (and dynamics) in the near (0-60 m) and far field (> 60 m) of the neutron detector as the near field is dominated by mineral and the far field by organic peatland soils.

Neutron transport simulations with URANOS revealed that thermal neutrons have a smaller measurement footprint compared to epithermal neutrons. We show that thermal neutrons mainly originated from the mineral soils in the near field, while the larger epithermal footprint area also includes the peatland soils. However, the simulated thermal neutrons still seem to be influenced by peatland soil water variations.

With the support of the computer simulations, we were able to better interpret and identify patterns in the observed neutron count rates that represent different features of the heterogeneous field site. The study presents a new application for thermal detectors in concert with the standard epithermal detectors, and revealed opportunities for improving the calibration against soil moisture reference measurements in the near field. We illustrate the potential of CRNS for estimating soil moisture time series at heterogeneous study sites and for disentangling different soil moisture conditions within the measurement footprint.
A test of an alternative approach for uncertainty representation in weather forecasting
Authors: Janjic Tijana , Lukacova Maria , Ruckstuhl Yvonne , Spichtinger Peter , Wiebe Bettina
Published: March 2021

Quantification of evolving uncertainties is required for both probabilistic forecasting and data assimilation in weather prediction. In current practice, the ensemble of model simulations is often used as primary tool to describe the required uncertainties. In this work, we explore an alternative approach, so called stochastic Galerkin method which integrates uncertainties forward in time using a spectral approximation in the stochastic space. 

In an idealized two-dimensional model that couples compressible non-hydrostatic Navier-Stokes equations to cloud dynamics, we investigate the propagation of initial uncertainty. The propagation of initial perturbations is followed through time for all model variables during two types of forecasts: the ensemble forecast and stochastic Galerkin forecast. Since model simulations are very expensive in weather forecasting, our hypothesis is that the stochastic Galerkin would provide more accurate and cheaper forecast statistics than the ensemble simulations. Results indicate that uncertainty as represented with mean, standard deviation and evolution of trace through time provides almost identical results if a 10000-member ensemble is used and truncation of stochastic Galerkin is made at ten spectral modes.  However, for coarser approximations,  for example if 50 ensemble members are used or the stochastic Galerkin is truncated at two modes, differences in standard deviations become significant in both approaches.  A series of experiments indicates that differences in performance of the two methods depend on the system state. For example, for stable flows, the stochastic Galerkin outperforms the ensemble of simulations for every truncation and every variable. In very unstable,  turbulent flows the estimate of the mean between the two methods still remains similar. However,  the ensemble of simulations needs more than 100 members (depending on the model variable) and the stochastic Galerkin a truncation with more than five spectral modes, to produce accurate results.
CAMELS-FR: A large sample, hydroclimatic dataset for France, to support model testing and evaluation
Authors: Andréassian Vazken , Delaigue Olivier , Perrin Charles , Janet Bruno , Addor Nans
Published: March 2021

Over the last decades, the development of large sample hydrology has allowed a generalization of sound model evaluation and testing practices (Andréassian et al., 2006; Gupta et al. 2014), based on various types of split-sample tests. This presentation aims at illustrating some of these tests, while introducing at the same time a French dataset that we have been working with for many years. This dataset has been assembled at INRAE (HYCAR research unit), based on an automatized assembling of national data products (Delaigue et al. 2020). CAMELS-FR will provide daily hydro-meteorological time series (streamflow, solid and liquid precipitation, potential evapotranspiration, temperature, etc.) covering the 1958-2020 period. Catchment characteristics such as land cover, topography (i.e. elevation and slope distributions, drainage density, topographic index, etc.) will be provided, with information about possible regulations upstream, and with some a priori information on data quality. Graphical summary sheets for each catchment are already available.

This approach is part of the CAMELS international initiative (Addor et al., 2017), whose purpose is to facilitate reproducible hydrological research by the use of large sample catchment datasets, and the CAMELS-FR dataset will be made available to scientific users in partnership with data owners.

References

Addor, N., Newman, A. J., Mizukami, N., Clark, M. P. (2017). The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017

Andréassian, V., Hall, A., Chahinian, N., Schaake, J. (2006). Introduction and Synthesis: Why should hydrologists work on a large number of basin data sets? IAHS Publication, 307, 1-5, https://hal.inrae.fr/hal-02588687.

Delaigue, O., Génot, B., Lebecherel, L., Brigode, P., Bourgin, P.Y. (2020). Database of watershed-scale hydroclimatic observations in France. Université Paris-Saclay, INRAE, HYCAR Research Unit, Hydrology group, Antony, https://webgr.inrae.fr/base-de-donnees.

Gupta, H.V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M., Andréassian, V. (2014). Large-sample hydrology: A need to balance depth with breadth. Hydrology and Earth System Sciences, 18(2), 463–477, doi: https://doi.org/10.5194/hess-18-463-2014.
The Inclusion of chemistry modules into the NOAA UFS Weather Model with the Common Community Physics Package (CCPP)
Authors: Li Haiqin , Grell Georg , Zhang Li , Ahmadov Ravan , Mckeen Stuart , Henderson Judy , Trahan Samuel , Barnes Hannah , Sun Shan , Schnell Jordan , Heinzeller Dominikus
Published: March 2021

Online atmosphere-chemistry coupled models have been rapidly developed in recent years. In online models, the atmospheric model can impact air quality and atmospheric composition, while the aerosol feedbacks also impact the atmosphere through direct, semi-direct and indirect effects. At NOAA GSL, in collaboration with scientists from the Chemical Science Laboratory (CSL) and Air Resource Laboratory (ARL), we developed an atmospheric composition suite (based on WRF-Chem) and coupled it online with FV3GFS through the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software. This modeling system has been operational since September 24th, 2020 as an ensemble member of the Global Ensemble Forecast System (named as GEFS-aerosols) for global aerosol predictions. When using the NUOPC coupler, there are two independent components for atmosphere and chemistry that communicate via the NUOPC coupler every time-step. Because of the interactive and strongly couple nature of chemistry and physics, it is natural to allow for some of the atmospheric composition modules to be called directly from inside the physics suite. This can be accomplished through the use of the Common Community Physics Package (CCPP). CCPP, designed to facilitate a host-model agnostic implementation of physics parameterizations, is a community development and will be used by many different organizations. All the physics parameterizations in the NOAA Unified Forecast System (UFS) Weather Model are CCPP-compliant. Here we broke up the chemistry suite used in GEFS-aerosols, and all the chemical modules were embedded into UFS Weather Model using CCPP as subroutines of physics. This newly developed model with CCPP has been running in real-time starting in the middle of November, 2020. Because of this development we were able to include the CCPP-compliant modules of sea salt, dust, and wild-fire emissions into the NWP model to provide input for the double moment Thompson microphysics parameterization. The inclusion of smoke and aerosol emission modules into the Rapid Refresh Forecast System (RRFS) with CCPP is also ongoing. We will show results from real-time experiments for medium range weather forecasting and compare results with runs that do not include aerosol impacts.
Correlations of magnetic pore fabrics with pore fabrics derived from high-resolution X-ray computed tomography and with permeability anisotropy in sedimentary rocks and synthetic samples
Authors: Zhou Yi , Pugnetti Michele , Foubert Anneleen , Lanari Pierre , Neururer Christoph , Biedermann Andrea
Published: March 2021

Magnetic pore fabrics (MPF) are an indirect measure of the 3D pore structure. They are defined by measuring anisotropy of magnetic susceptibility after samples have been impregnated with ferrofluid. Previous studies proposed that MPFs target pores down to 10 nm. Therefore, the method complements X-ray computed tomography (XRCT) datasets, with resolution on the order of 1-10 µm. Empirical relationships exist between MPF and pore fabric, and between MPF and permeability anisotropy. This study investigates quantitative correlations between these three properties, and between measured quantities and digital-rock-model-simulations of permeability anisotropy and MPF. Samples used for this study include natural sedimentary rocks and synthetic samples. Sediments are Plio-Pleistocene calcarenite (Apulia, Italy) with ~50% porosity and complex pore structure, and Upper Marine Molasse sandstone (Belpberg, Switzerland) with 10-20% porosity and relatively homogeneous pore space properties. Synthetic samples were made from quartz sand and calcite powder in different proportions, to simulate sandstone and carbonate rocks. Samples were characterized by pycnometry, XRCT scans, MPF determination and directional permeability measurements to obtain porosity, digital rock models, MPFs and permeability anisotropy. Porosity, permeability anisotropy, and MPFs were also computed based on digital rock models derived from XRCT data, and compared to direct measurements. Permeability anisotropy and MPF are both second-order tensors, representing the average property of the entire sample. To directly relate the XRCT-derived individual pore properties to these second-order tensor quantities, a total shape ellipsoid was computed by adding the second-order tensors reflecting the best-fit ellipsoids of single pores. Once all properties were described by second-order tensors, they were correlated in terms of fabric orientation, degree and shape of anisotropy. The MPF and total shape ellipsoids are coaxial when the samples have sufficiently large pores to be resolved, and good impregnation efficiency, and as expected, total shape ellipsoids have larger anisotropy degree. Preliminary results further indicate that the permeability anisotropy is partly consistent with total shape ellipsoids and MPFs. The defined quantitative relationships facilitate the interpretation of MPF data, thus making the method more applicable to geological and fluid migration studies.
Agricultural spatial expansion in Ecuador through Intensity Analysis
Authors: Urgilez-Clavijo Andrea , Tarquis Ana M.
Published: March 2021

The expansion of the agricultural frontier is a process that has been affecting natural ecosystems, driving landscape fragmentation, and promoting habitat loss from 1990 in the Ecuadorian Amazon. Characterizing spatial patterns of the expansion of agricultural frontier can provide valuable data to take forward trade-offs in areas with exacerbated expansion rates and habitat loss (Urgilez-Clavijo et al., 2020). The aim of this work is to identify and characterize the spatial patterns of the expansion of the agricultural frontier in Ecuador and provide an alternative to setting the priority areas.

With this purpose, an image analysis approach was applied to identify process patterns using classified images from 1990 to 2020. A statistical analysis of the agricultural expansion dynamics is performed in the Amazon region accumulating the land use information. Complementary to this, we used a soil map to detect a correlation of the process to soil types. Then the Intensity Analysis (IA) was implemented to characterize and visualize the spatio-temporal rates of the expansion process. This method allows identifying areas in which the process is faster and active.

The results show distinct patterns of agricultural expansion in the Amazon region, especially from Andean hill slopes to the primary forest. These processes are in part explained by soil type suitability, transportation network development, and urban expansion. The spatial priorities of the expansion of the agricultural frontier are identified from two sources, i) from intensity analysis graphs and ii) from regional maps. The spatial characteristics and identification of spatial priorities of the expansion of the agricultural frontier will bring valuable information to policymakers to achieve SDG 15th of the 2030 Agenda in Ecuador.

Keywords: expansion of agricultural frontier, Intensity Analysis, priority areas, image analysis, patterns

Reference

Urgilez-Clavijo, A., J. de la Riva, D. Rivas-Tabares and A.M. Tarquis. Linking deforestation patterns to soil types: A multifractal approach. European Journal of Soil Science, https://doi.org/10.1111/ejss.13032

Acknowledgements

The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330
Preliminary analysis and main problems of instrumental measurement complex at the Vernadsky Antarctic Station
Authors: Pishniak Denys , Krakovska Svitlana , Chyhareva Anastasia , Razumnyi Sergii
Published: March 2021

Measurements of precipitation has always had well known difficulties that caused inaccuracies. This is especially acute in Polar regions where prevailing solid precipitation is accomplished with strong winds. Alternatively some indirect methods of precipitation measurements still in development and numerous meteorological instruments have been created on their basis.

The Akademik Vernadsky station is located in the Antarctic Peninsula region with a large amount of precipitation and  the problem of its measuring has always been relevant here. Although the data of monthly precipitation have been found for Vernadsky (Faraday) station since 1964, the first standard Tretyakov precipitation gauge was set up there only in 1997. But in recent years, several new instruments for indirect precipitation measurement have been installed at the meteorological site. The consistency of their data are the subject for this study. 

Direct comparison of all measurement devices as well as investigation of their estimations dependencies from other meteorological parameters are analysed and will be presented for the period 2019-2020. Originally various instruments showed huge differences in precipitation estimates. Deep analysis and correction of the measurement results according to weather conditions is obviously needed for bias reduction. But the local features of the extremely heterogeneous underlying surface of the region affect the vertical component of the wind, and can cause the natural small scale precipitation variability. 

The advantages of indirect methods for precipitation measuring is a high sensitivity to registering even individual falling precipitation particles and, hence, the really high temporal resolution of the data. Therefore, it can be used for investigation of physical atmospheric processes. As an example, the case study of a cyclone with precipitation phase transition over Vernadsky station on December 5-6, 2020 is investigated and will be presented. A comparison of the measurement data of various devices (Tretyakov Precipitation Gauge, Snow Stick, Vaisala PWD22, Lufft WS100, METEK MRR-PRO) and the ERA-5 reanalysis was carried out. A vertical radar MRR-PRO is of special interest as a measuring instrument for polar regions because it can ignore surface snow transport and has proved reliability in the Antarctic environment recently. In Marine Antarctica this device can identify the height of precipitation melting and also show a number of other useful parameters. This complex of precipitation measurement instruments is planned to be used in the frames of the forthcoming YOPP-SH field campayne.
Advanced Image Preprocessing and Feature Tracking for Remote CME Characterization
Authors: Stepanyuk Oleg , Kozarev Kamen , Nedal Mohamed
Published: March 2021

Coronal Mass Ejections (CMEs) influence the interplanetary environment over vast distances in the solar system by injecting huge clouds of fast solar plasma and energetic particles (SEPs). A number of fundamental questions remain about how SEPs are produced, but current understanding points to CME-driven shocks and compressions in the solar corona. At the same time, unprecedented remote (AIA, LOFAR, MWA) and in situ (Parker Solar Probe, Solar Orbiter) solar observations are becoming available to constrain existing theories. As part of the MOSAIICS project under the VIHREN programme, we are developing a suite of Python tools to reliably analyze radio and EUV remote imaging observations of CMEs and  shock. We present the method for smart characterization and tracking of solar eruptive features, based on the A-Trous wavelet decomposition technique, intensity rankings and a set of filtering techniques. We showcase its performance on a small set of CME-related phenomena observed with the SDO/AIA telescope. With the data represented hierarchically on different decomposition and intensity levels our method allows to extract certain objects and their masks from the series of initial images, in order to track their evolution in time. The method presented here is general and applicable to detecting and tracking various solar and heliospheric phenomena in imaging observations.
Utilizing an interactive AI-empowered web portal for landslide labeling for establishing a landslide database in Washington state, USA
Authors: Pei Te , Nagendra Savinay , Banagere Manjunatha Srikanth , He Guanlin , Kifer Daniel , Qiu Tong , Shen Chaopeng
Published: March 2021

Landslides are common natural disasters around the globe. Understanding the accurate spatial distribution of landslides is essential for landslide analysis, prediction, and hazard mitigation. So far, many techniques have been used for landslide mapping to establish landslide inventories. However, these techniques either have a low automation level (e.g., visual interpretation-based methods) or a low generalization ability (e.g., pixel-based or object-based approaches); and improvements are required for landslide mapping. Therefore, we have developed an interactive, user-friendly web portal for landslide labeling. The web portal takes multi-temporal satellite images as inputs. A deep learning model will first detect landslide-suspicious areas in the image and present results to users for validation. Users can then review and annotate these machine-labeled landslides through a user-friendly interface. Users’ editions on landslide annotation will further improve the accuracy of the deep learning model. Two landslide-affected regions in Washington were selected to test the capability of our web portal for landslide mapping. The detected landslides were validated by expert labelers. The results indicated that our annotation tool was able to produce landslide maps with high precision, a high rate of annotation, and reduced human efforts.
Unusual Location of the Geotail Magnetopause Near Lunar Orbit: A Case Study
Authors: Shang Wensai , Tang Binbin , Shi Quanqi , al Et
Published: March 2021

The Earth's magnetopause is highly variable in location and shape and is modulated by solar wind conditions. On 8 March 2012, the ARTEMIS probes were located near the tail current sheet when an interplanetary shock arrived under northward interplanetary magnetic field conditions and recorded an abrupt tail compression at ∼(-60, 0, -5) Re in Geocentric Solar Ecliptic coordinate in the deep magnetotail. ~ 10 minutes later, the probes crossed the magnetopause many times within an hour after the oblique interplanetary shock passed by. The solar wind velocity vector downstream from the shock was not directed along the Sun-Earth line but had a significant Y component. We propose that the compressed tail was pushed aside by the appreciable solar wind flow in the Y direction. Using a virtual spacecraft in a global magnetohydrodynamic (MHD) simulation, we reproduce the sequence of magnetopause crossings in the X-Y plane observed by ARTEMIS under oblique shock conditions, demonstrating that the compressed magnetopause is sharply deflected at lunar distances in response to the shock and solar wind Vy effects. The results from two global MHD simulations show that the shocked magnetotail at lunar distances is mainly controlled by the solar wind direction with a timescale of about a quarter hour, which appears to be consistent with the windsock effect. The results also provide some references for investigating interactions between the solar wind/magnetosheath and lunar nearside surface during full moon time intervals, which should not happen in general.
Phosphorus recycling and retention in Lake of the Woods: Reactive-transport diagenetic modeling across spatial and temporal Scales
Authors: Alam Md Samrat , Zastepa Arthur , Dittrich Maria
Published: March 2021

The dynamics of sediment phosphorus (P) remobilization and recycling in many polymictic systems due to distinct external and internal loading conditions are poorly understood. Here we used a multifaceted approach of quantifying sediment P binding forms and corresponding metal contents in sediment cores down to 30 cm from 8 different locations at Lake of Woods (LOW) in different seasons. We also measured pH, redox potential and dissolved oxygen uptake across the sediment-water interface and the concentration of nutrient and metals in pore water at different depths. Additionally, we applied a reaction-transport diagenetic model to construct the spatial and temporal trend of internal P loading in response to environmental variations. The summer diffusive fluxes of P ranged between 3 and 83 µmol m-2 d-1 whereas the winter fluxes were lower ranged from 0.1 to 0.35 µmol m-2 d-1. P recycling efficiency were 13% to 77%. P bound to redox sensitive iron (Fe)-P binding forms in sediments were the major source of P release in all stations, while P immobilization is controlled by redox-insensitive calcium (Ca)-P phases. The modeling results supported the notion that P release was mostly driven by the diagenetic recycling of redox sensitive and organic bound P.
Operational Daily Streamflow Forecasts by coupling Variational Ensemble Forecasting and Machine Learning (VEF-ML) approaches
Authors: Valdés-Pineda Rodrigo , Valdés Juan B. , Wi Sungwook , Serrat-Capdevila Aleix , Tirthankar Roy , Demaria Eleonora M.C. , Durcik Matej
Published: March 2021

The operational implementation of a Hydrologic Forecasting System (HFS) is limited in many catchments of the world by the lack of historical in-situ hydrologic data, i.e., long temporal records of rainfall or streamflow. By combining high-resolution Satellite Precipitation Products (SPPs), or Regional Climatological Models (RCMs), with Hydrologic Models, baselines can be established for the quantification and reduction of total hydrologic uncertainty in ungauged basins. We have studied how Variational Ensemble Forecasting (VEF) can be combined with Machine Learning (ML) techniques to improve a hydrologic system representation – i.e., raw data processing, model training, model evaluation, model selection, forecasts post-processing, etc. The VEF-ML method is applied and assessed with three general Hydrologic Processing Hypotheses (HPH): (1) Hydrologic Pre-processing (HPR), (2) Hydrologic Processing (HP), and (3) Hydrologic Post-processing (HPP). The operational implementation of VEF-ML was evaluated in the Upper Zambezi River Basin (UZRB) and its sub-basins, by using multiple precipitation products, multiple hydrologic models, and multiple optimal parameter sets. This extended VEF configuration and its coupling with ML techniques (VEF-ML) allows increasing the number of hydrologic ensembles available for the generation of operational streamflow forecasts products. The performance of VEF-ML is evaluated by comparing two hydrologic learning strategies (HLS) i.e. inference- and pattern-based approaches, which are used to improve hydrologic post-processing hypotheses (i.e. reduce total hydrologic uncertainty) in the poorly gauged UZRB.
Extreme variability of Tibetan thermal lithosphere 
Authors: Xia Bing , Artemieva Irina , Thybo Hans
Published: March 2021

We present a thermal model for the lithosphere in Tibet and adjacent regions based on the new thermal isostasy method and our compilation of the Moho depth based on published seismic models. The predicted surface heat flow is in agreement with the few available, reliable borehole measurements. Cratonic-type cold and thick lithosphere (200-240 km) with a surface heat flow of 40-50 mW/m2 typifies the Tarim craton, the north-western Yangtze craton, and most of the Lhasa Block that is possibly refrigerated by underthrusting Indian lithosphere. The thick lithosphere of the Lhasa block extends further north in its western and eastern segments than in its central section. We identify a North Tibet anomaly with a thin (<80 km) lithosphere and high surface heat flow (>80-100 mW/m2), possibly associated with the removal of lithospheric mantle and asthenospheric upwelling. Other parts of Tibet have an intermediate lithosphere thickness of 120-160 km and a surface heat flow of 45-60 mW/m2, with a patchy style in eastern Tibet. In the Qaidam deep sedimentary basin the lithosphere is about 100-120 km thick. The heterogeneous thermal lithosphere beneath Tibet suggests an interplay of several mechanisms as the driver of the observed uplift.
Automated detection of urban heat islands based on satellite imagery, digital surface models, and a low-cost sensor network                                     
Authors: Schlögl Sebastian , Bader Nico , Anet Julien Gérard , Frey Martin , Spirig Curdin , Renold Manuel , Gutbrod Karl
Published: March 2021

Today, more than half of the world’s population lives in urban areas and the proportion is projected to increase further in the near future. The increased number of heatwaves worldwide caused by the anthropogenic climate change may lead to heat stress and significant economic and ecological damages. Therefore, the growth of urban areas in combination with climate change can increase future mortality rates in cities, given that cities are more vulnerable to heatwaves due to the greater heat storage capacity of artificial surfaces towards higher longwave radiation fluxes.

To detect urban heat islands and resolve the micro-scale air temperature field in an urban environment, a low-cost air temperature network, including 450 sensors, was installed in the Swiss cities of Zurich and Basel in 2019 and 2020. These air temperature data, complemented with further official measurement stations, force a statistical air temperature downscaling model for urban environments, which is used operationally to calculate hourly micro-scale air temperatures in 10 m horizontal resolution. In addition to air temperature measurements from the low-cost sensor network, the model is further forced by albedo, NDVI, and NDBI values generated from the polar-orbiting satellite Sentinel-2, land surface temperatures estimated from Landsat-8, and high-resolution digital surface and elevation models.

Urban heat islands (UHI) are processed averaging hourly air temperatures over an entire year for each grid point, and comparing this average to the overall average in rural areas. UHI effects can then be correlated to high-resolution local climate zone maps and other local factors.

Between 60-80 % of the urban area is modeled with an accuracy below 1 K for an hourly time step indicating that the approach may work well in different cities. However, the outcome may depend on the complexity of the cities. The model error decreases rapidly by increasing the number of spatially distributed sensor data used to train the model, from 0 to 70 sensors, and then plateaus with further increases. An accuracy below 1 K can be expected for more than 50 air temperature measurements within the investigated cities and the surrounding rural areas. 

A strong statistical air temperature model coupled with atmospheric boundary layer models (e.g. PALM-4U, MUKLIMO, FITNAH) will aid to generate highly resolved urban heat island prediction maps that help decision-makers to identify local heat islands easier. This will ensure that financial resources will be invested as efficiently as possible in mitigation actions.
The Risk of Coal Fires And Land Subsidence in Jharia Coalfields, India, Analysed Using Remote Sensing Techniques
Authors: Karanam Vamshi , Garg Shagun , Motagh Mahdi , Jain Kamal
Published: March 2021

Coal fires, land subsidence, roof collapse, and other life-threatening risks are a predictable phenomenon for the mineworkers and the neighbourhood population in coalfields. Jharia Coalfields in India are suffered heavily from land subsidence and coal fires for over a century. In addition to the loss of precious coal reserves, this has led to severe damage to the environment, livelihood, transportation, and precious lives.

Such incidents highlight the dire need for a well-defined methodology for risk analysis for the coalfield. In this study, we regenerated a Land Use Land Cover map prepared using Indian Remote Sensing satellite imagery and ground survey. Persistent Scatterer Interferometry analysis using Sentinel -1 images was carried out to study the land subsidence phenomenon between Nov 2018 and Apr 2019. For the same study period, coal fire zones were identified with Landsat – 8 thermal band imagery. Integration of coal fire maps, subsidence velocity maps, and land use maps was further implemented in a geographical information background environment to extract the high-risk zones. These high-risk areas include residential areas, railways, and mining sites, requiring immediate attention.

The results show that the coal mines are affected by subsidence of up to 20 cm/yr and a temperature anomaly of nearly 20oC is noticed. A high-risk zone of almost 18 sq. km. was demarcated with Kusunda, Gaslitand, and West Mudidih collieries being the most critically affected zones in the Coal mines. The study demonstrates the potential to combine data from multiple satellite sensors to build a safer ecosystem around the coal mines.  
Evaluation of the reliability of the high-resolution WRF fire danger forecasts in Poland
Authors: Gruszczynska Marta , Mandal Alan , Nykiel Grzegorz , Strzyzewski Tomasz , Wronska Weronika , Jaczewski Adam , Figurski Mariusz
Published: March 2021

Fires negatively affect the composition and structure of fauna and flora, as well as the quality of air, soils and water. They cause economic losses and pose a risk to human life. Poland is at the forefront of European countries in terms of forest fires. Therefore, Institute of Meteorology and Water Management - National Research Institute (IMWM-NIR) implemented fire danger forecast system based on high-resolution (2.5 km) Weather Research and Forecast (WRF) model. Forecasted meteorological data are used to calculate parameters of Canadian Forest Fire Weather Index (FWI) System: Fire Weather Index (FWI), Initial Spread Index (ISI), Buildup Index (BUI), Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), and Drought Code (DC). Each parameter is presented in one of the classes corresponding to the fire danger – from low to extreme. In this way, a daily 24- and 48-hour fire danger forecasts are generated for the whole area of Poland and presented on IMWM-NIR meteorological website (meteo.imgw.pl).

In this presentation we show analyses of reliability of implemented FWI system. For this purpose, data reprocessing from March to September 2019 were made. Also data on fires occurrence on forest lands: time of occurrence, characteristics and location, from the resources of the State Fire Service were collected. Finally, for the selected period, we obtained a dataset of about 8 thousand events for which we assigned values of FWI parameters. Generally, based on our analysis, correlation between number of fires and averaged value of FWI amounted over 0.8. We found out, the correlation coefficient calculated for regions differ. The correlation is higher in central and northern Poland compared to the eastern part of the country, which also correspond to the number of fires. This may be related to the different forest structure - there is a higher proportion of broadleaf forests in the east. The comparison of 24- and 48-hour forecasts showed that they have similar reliability.
Knowledge discovery using clustering analysis of rainfall timeseries
Authors: Vantas Konstantinos , Sidiropoulos Epaminondas
Published: March 2021

Rainfall time series analysis using clustering involves the identification of temporal patterns, with each data item representing an individual storm. This analysis results in clusters of data items that trend in a common way and can be utilized in stochastic simulation, water resources planning and the identification of future directions due to climate change. A comparative analysis is carried out of several methods that use intra versus inter-cluster distances, for the estimation of the relevant number of clusters using a big dataset of the described rainfall time series. Visualization using topographic maps that are produced via nonlinear projection techniques is applied, to validate the presence of both distance and density structures and to assist in the final determination of the numbers of clusters. This stands in contrast to empirical and not completely data-driven approaches of the literature, in which constrained clustering methods are employed with assumptions on the presence of four classes.
Real-time Rockfall Detection System with Automatic Road Closure and Reopening using Doppler Radar Technology at the Ruinon Landslide, Italy
Authors: Wahlen Susanne , Stähly Severin , Schmid Lino , Meier Lorenz , Carlà Tommaso , Casagli Nicola
Published: March 2021

Transportation corridors in mountain regions are often situated at the bottom of narrow valleys. Changing slope stability conditions can put these routes at critical risk. Slope stabilization works (e.g., rock scaling, blasting) or structural protection measures (e.g., rock sheds, reinforced embankments, tunnels) are not always feasible or may not be cost-effective due to low average daily traffic or the expected event size. Route SP29 is the main connection road to Santa Catarina, a popular tourist resort in the Frodolfo River Valley, Lombardy, Italy. The Ruinon landslide is a major slope instability involving approximately 30 million m3 of rock and debris and causes repeated rockfalls that can reach as far as the road. 

We present a Doppler radar system for real-time rockfall detection and immediate road closure in case of an event. The rockfall radar permanently monitors the landslide area from the opposite side of the slope with a range of more than 1 km to the upper scarp. Radar technology works reliably regardless of visibility, i.e. in rain, fog or snowfall as well as at night. After an initial calibration period in summer 2020, we activated automatic road closure and reopening in case of a rockfall event; upon detection of rockfall in a defined region of interest, the radar system automatically switches the traffic lights to red. If the rock fall event reaches a defined zone near the road or the road itself, it remains closed and requires manual reset after site inspection with webcams at the radar site and the traffic lights. If the rockfall event remains above the road, then the radar system automatically releases the road again after 90 seconds. Automatic notifications about the status are sent to authorized user via email and SMS. In addition to the deployment of the alarm system using Doppler radar, the embankment along the endangered road section was reinforced and raised. These combined measures of protection structures and alarm system aim at maximising the opening hours of the street while providing the highest possible level of protection. Between July (installation) and December 2020, 60 rockfall events caused a road closure, with the road being automatically reopened by the system in approximately 85% of cases.
Holocene human-environment interactions and their link to erosion in the Eastern Alps, inferred from sedaDNA
Authors: Morlock Marina A. , Rodriguez-Martinez Saúl , Huang Doreen Yu-Tuan , Glaus Nicole , Vogel Hendrik , Anselmetti Flavio S. , Klaminder Jonatan
Published: March 2021

Mountain ecosystems are particularly sensitive to soil erosion and it is critical to better understand how climate and human activities can be linked to erosion in the alpine environment. To explore this link across time, we combine sedimentary ancient DNA (sedaDNA) analyses with erosion proxies to assess the timing and type of animal husbandry in the Eastern Alps and its impact on hill-slope processes and vegetation. With its clear and continuous erosion history, the sediment record contained in Lake Grosssee, Switzerland, is ideally suited to resolve key stages in ecosystem change and anthropogenic land use since the last glaciation. Initial results show that sedaDNA can successfully detect key species in today’s ecosystem and suggest significant changes in mammal and plant diversity across the Holocene. DNA of domestic animals such as cattle and sheep has been the dominant in the past 4,000 years BP, which coincides with frequent and intensive erosion events in this period. Our record promises a detailed history of anthropogenic activities and terrestrial ecosystem change across the Holocene, which will help to determine how erosion, land use, and climate shape alpine ecosystems through time.
A web based demonstration platform for flood warning in small catchments using ensemble hydrological forecasting
Authors: Grundmann Jens , Six Achim , Philipp Andy
Published: March 2021

Reliable warnings and forecasts of extreme precipitation and resulting floods are an important prerequisite for disaster response. Especially for small catchments, warning and forecasting systems are challenging due to the short response time of the catchments and the uncertainties of the meteorological forecasts. Thus, ensemble forecasts of precipitation are an option to portray these inherent uncertainties. By this contribution, we present our operational web-based demonstration platform for ensemble hydrological forecasting in small catchments of Saxony, Germany. We use the ICON/COSMO-D2-EPS product of the German Weather Service, which provides an ensemble of 20 members each three hours, for lead times up to 27 hours. Each member is evaluated regarding specific extreme precipitation thresholds for predefined hydrological warning regions. If these thresholds are exceeded in a specific region, rainfall-runoff models for the associated catchments are started to propagate the meteorological uncertainty into the resulting runoff, followed by statistical post processing and visualization. Different options for the visualization of the uncertainty information were discussed and evaluated by a series of (virtual) workshops with locally responsible civil protection forces and water authorities. This leads to the current design of the web-based demonstration platform in an iterative process, which is still ongoing. The web-based demonstration platform is established for three pilot regions with different hydrological settings in Saxony, Germany. Besides layout and technical issues, first experiences with the demonstration platform are presented as well as first results regarding forecast performance in the small pilot regions.
SIOS Data Management System: distributed data system for Earth System Science
Authors: Ignatiuk Dariusz , Godøy Øystein , Ferrighi Lara , Jennings Inger , Hübner Christiane , Jawak Shridhar , Lihavainen Heikki
Published: March 2021

Svalbard Integrated Arctic Earth Observing System (SIOS) is an international consortium to develop and maintain a regional observing system in Svalbard and the associated waters. SIOS brings together the existing infrastructure and data of its members into a multidisciplinary network dedicated to answering Earth System Science (ESS) questions related to global change. The Observing System is built around “SIOS core data” – long-term data series collected by SIOS partners. SIOS Data Management System (SDMS) is dedicated to harvesting information on historical and current datasets from collaborating thematic and institutional data centres and making them available to users. A central data access portal is linked to the data repositories maintained by SIOS partners, which manage and distribute data sets and their associated metadata. The integrity of the information and harmonisation of data is based on internationally accepted protocols assuring interoperability of data, standardised documentation of data through the use of metadata and standardised interfaces by data systems through the discovery of metadata. By these means, SDMS is working towards FAIR data compliance (making data findable, accessible, interoperable and reusable), among other initiatives through the H2020 funded ENVRI-FAIR project (http://envri.eu/envri-fair/).