bluuebunny/crossref_metadata_2025 huggingface dataset:
Fusion applied to phase altimetry in a GNSS buoy system
Authors: Kouassi Williams , Stienne Georges , Reboul Serge
Published: March 2020

It has been shown that the earth surface can be observed using Global Navigation Satellite System (GNSS) signals as signals of opportunity. An important advantage of GNSS in this regard is that it provides a global coverage of the earth thanks to dozens of satellites, projected to be 120 by 2030, distributed in various constellations.

GNSS signals parameters such as the carrier phase and the amplitude can be used for example for soil moisture estimation, sea ice detection or sea surface altimetry, which is an important indicator for studying climate evolution. As the sea level varies in centimeters, sea surface altimeters have to be very precise. This accuracy can be achieved using satellite altimeters, provided the availability of precise validation and calibration techniques and in-situ experiments. The objective of this study is the definition of an original GNSS buoy system for satellite altimeters calibration.

GNSS buoy systems are a cheap and light alternative solution to mareographs and can provide high rate measurements. In our approach using this system, we consider, as observable, the phase difference between incoming GPS-L1 signals at a reference, fixed antenna at ~10m height on the ground and at a buoy antenna on the sea. In an analogy with a GNSS reflectometry system, the buoy can be compared to a specular reflection point, but presents the advantage of collecting the data from all visible satellites at the same location. The signals sensed by both antennas are digitized before processing.

Assuming that the horizontal two-dimensions position of the buoy is accurately known by GNSS positioning (which is more efficient in these dimensions than for estimating the height of the buoy), a new phase observable evolving linearly in the [-π , π] interval as a function of the sine of the satellite elevation can be defined. The slope of this linear evolution is proportional to the height between the two antennas, which is the parameter to estimate. For accuracy and robustness purpose, the estimation of this slope is realized using a circular-linear regression technique that includes the fusion of the data from all visible satellites signals. Indeed, we can show that, using the full span of the sines of visible satellites elevations, centimeter accuracy can be reached for integration times as short as a few milliseconds. The GNSS buoy technique described in this work is evaluated on synthetic and real data.
Bayesian inference of dynamics from partial and noisy observations using data assimilation and machine learning
Authors: Bocquet Marc , Brajard Julien , Carrassi Alberto , Bertino Laurent
Published: March 2020

The reconstruction from observations of the dynamics of high-dimensional chaotic models such as geophysical fluids is hampered by (i) the inevitably partial and noisy observations that can realistically be obtained, (ii) the need and difficulty to learn from long time series of data, and (iii) the unstable nature of the dynamics. To achieve such inference from the observations over long time series, it has recently been suggested to combine data assimilation and machine learning in several ways. We first rigorously show how to unify these approaches from a Bayesian perspective, yielding a non-trivial loss function.

Existing techniques to optimize the loss function (or simplified variants thereof) are re-interpreted here as coordinate descent schemes. The expectation-maximization (EM) method is used to estimate jointly the most likely model and model error statistics. The main algorithm alternates two steps: first, a posterior ensemble is derived using a traditional data assimilation step using an ensemble Kalman smoother (EnKS); second, both the surrogate model and the model error are updated using machine learning tools, a quasi-Newton optimizer, and analytical formula. In our case, the spatially extended surrogate model is formalized as a neural network with convolutional layers leveraging on the locality of the dynamics.

This scheme has been successfully tested on two low-order chaotic models with distinct identifiability, namely the 40-variable and the two-scale Lorenz models. Additionally, an approximate algorithm is tested to mitigate the numerical cost, yielding similar performances. Using indicators that probe short-term and asymptotic properties of the surrogate model, we investigate the sensitivity of the inference to the length of the training window, to the observation error magnitude, to the density of the monitoring network, and to the lag of the EnKS. In these iterative schemes, model error statistics are automatically adjusted to the improvement of the surrogate model dynamics. The outcome of the minimization is not only a deterministic surrogate model but also its associated stochastic correction, representative of the uncertainty attached to the deterministic part and which accounts for residual model errors.
Effect of ammonium salts on the photochemical degradation of iron containing organic aerosol
Authors: Krieger Ulrich , Bluvshtein Nir , Dou Jing
Published: March 2020

Formation of organic aerosol by oxidation of gas phase compounds has been intensely studied, and is much better understood than the aerosol ageing transformations during the lifetime of organic aerosol. Aerosol ageing influences how those aerosol particles affect climate and human health and is still not well constrained in current models.

Photochemistry in the condensed phase is an important mechanism responsible for ageing of organic aerosol. In the lower troposphere, where UV light intensity with sufficiently low wavelength to directly photolyze aerosol components is low, indirect photochemistry (catalyzing redox processes of non-absorbing molecules) is especially relevant. Recently we studied transition metal complex photochemistry in single particles levitated in an electrodynamic balance. In particular, we investigated the aqueous iron(III)-citrate/citric acid system and found that irradiation at 473 nm led to rapid and significant degradation of the citric acid. Up to 80% of the initial particle mass was partitioned to the gas phase with the degradation rate depending on kinetic transport limitations of oxygen. These kinetic limitations arise are influenced strongly by the relative humidity dependence of particle viscosity where water acts as a plasticizer.

Here we will report on photochemical degradation experiments adding various salts in different (ammonium sulfate, ammonium bisulfate, etc.) to the reference system iron(III)-citrate/citric acid. Preliminary experiments suggest that pH of the aerosol particle influences the degradation rate in this system significantly.
Analysis of heavy-rainfall-induced fast soil erosion: examples the NE Abruzzo clayey hills (Central Italy)
Authors: Marsala Vincenzo , Piacentini Tommaso , Galli Alberto
Published: March 2020

Soil erosion induced by heavy rainfall deeply affects landscape changes and human activities. It depends on rainfall distribution (e.g., intensity, duration, cumulative) and is controlled by the interaction among several factors including lithology, orography, hydrography, land-use, and vegetation. The Abruzzo piedmont-coastal area features a clayey hilly landscape that is historically affected by heavy rainfalls. In the last decades, it was affected by several heavy rainfall events in close sequence. In this work, we investigated some~1-day heavy rainfall (>35 mm/h and 100-220 mm/d) events occurred in 2007, 2011, and 2012 that affected the clayey hilly-coastal NE Abruzzo area. We analyzed cumulative rainfall, intensity and duration, mapping triggered geomorphological effects (soil erosion and accumulation) and evaluating average erosion.

The analysis provides contributions to the soil erosion assessment on clayey landscapes that characterizes the Adriatic hilly area, to the estimation of rainfall triggering thresholds for heavy soil erosion, and to a comparison of erosion in single events with rates known in the Mediterranean area. Comparing the different areas and cases investigated, the triggering threshold for heavy soil erosion shows an expected value ~100–110 mm. The estimated average soil erosion is from moderate to high (0.08–3.08 cm in ~1-day heavy rainfall events). The investigated relationships show a good correlation of sol erosion with cumulative rainfall, which results to be the most effective triggering factor, and a poor correlation with peak rainfall intensity. Finally, this work outlines the strong impact of soil erosion on the landscape changes in clayey hilly landscapes largely present in Mediterranean environments, such as in the Abruzzo and Adriatic hilly areas.
An Inter-Comparison of Arctic Synoptic Scale Storms between Four Global Reanalysis Datasets
Authors: Vessey Alexander , Hodges Kevin , Shaffrey Len , Day Jonathan
Published: March 2020

Arctic sea ice has reduced significantly over recent decades and is projected to reduce further over this century. This has made the Arctic more accessible and increased opportunities for the expansion of business and industrial activities.  As a result, the exposure and risk of humans and infrastructure to extreme storms will increase in the Arctic.

Our understanding of the current risk from storms comes from analysing the past, for example, by using storm tracking algorithms to detect storms in reanalysis datasets.  However, there are multiple reanalysis datasets available from different institutions and there are multiple storm tracking methods.  Previous studies have found that there can be differences between reanalysis datasets and between storm tracking methods in the climatology of storms, particularly in mid-latitude regions rather than the Arctic.  In this study, we aimed to improve the understanding of Arctic storms by assessing their characteristics in multiple global reanalyses, the ECMWF-Interim Reanalysis (ERA-Interim), the 55-Year Japanese Reanalysis (JRA-55), the NASA-Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and the NCEP-Climate Forecast System Reanalysis (NCEP-CFSR), using the same storm tracking method based on 850 hPa relative vorticity and mean sea level pressure.

The results from this study show that there are no significant trends in Arctic storm characteristics between 1980-2017, even though the Arctic has undergone rapid change.  Although some similar Arctic storm characteristics are found between the reanalysis datasets, there are generally higher differences between the reanalyses in winter (DJF) than in summer (JJA).  In addition, substantial differences can arise between using the same storm tracking method based on 850 hPa relative vorticity or mean sea level pressure, which adds to the uncertainty associated with current Arctic storm characteristics.
Towards non-linear inverse problem for atmospheric source term determination
Authors: Tichý Ondřej , Šmídl Václav
Published: March 2020

The basic linear inverse problem of atmospheric release can be formulated as y = M x + e , where y is the measurement vector which is typically in the form of gamma dose rates or concentrations, M is the source-receptor-sensitivity (SRS) matrix, x is the unknown source term to be estimated, and e is the model residue. The SRS matrix M is computed using an atmospheric transport model coupled with meteorological reanalyses. The inverse problem is typically ill-conditioned due to number of uncertainties, hence, the estimation of the source term is not straightforward and additional information, e.g. in the form of regularization or the prior source term, is often needed. Besides, traditional techniques rely on assumption that the SRS matrix is correct which is not realistic due to the number of approximations made during its computation. Therefore, we propose relaxation of the inverse model using introduction of the term ΔM such as y = ( M+ ΔM ) x + e leading to non-linear inverse problem formulation, where ΔM can be, as an example, parametric perturbation of the SRS matrix M in the spatial or temporal domain. We estimate parameters of this perturbation together with solving the inverse problem using variational Bayes procedure. The method will be validated on synthetic dataset as well as demonstrated on real case scenario such as the controlled tracer experiment ETEX or episode of ruthenium-106 release over the Europe on the fall of 2017.
Cloud amount uncertainty in merged CloudSat-CALIPSO radar-lidar observations
Authors: Kotarba Andrzej , Solecki Mateusz
Published: March 2020

Three dimensional structure of cloud cover is one of the Essential Climate Variable required for accurate monitoring of the state and change of global climate. Joint CloudSat-CALIPSO space mission have provided the most reliable and comprehensive 3D information on cloud distribution worldwide to date. However, the data resulted from observations collected every 16 days – sampling interval which can be considered infrequent for most of climate-oriented applications. The reliability of the data also depends on cloud regime, and area (grid cell size) over which the data are aggregated, further complicating the uncertainty aspect of lidar-radar profiling missions. The important question related to the CloudSat-CALIPSO dataset is whether 16-day revisit period for CloudSat-CALIPSO mission is sufficient to provide a climate characteristics at high statistical significance? We address that problem evaluating the full CloudSat-CALIPSO record (2006-2011), available to the scientific community as 2B-GEOPROF-LIDAR product. The analysis focuses on two aspects. First, we perform a point estimation to determine the minimum significance level at which the lidar-radar data (mean value) is statistically significant. Second, using a bootstrap approach we calculate confidence intervals for the mean value at fixed .95 and .99 thresholds. Therefore we reveal how wide is the actual uncertainty range at 16-day revisit. The analysis accounts for grid box size over which individual lidar-laser profiles were aggregated. The study was founded by National Science of Poland under the contract no. UMO-2017/25/B/ST10/01787.
Which hydro-meteorological variables control large-scale photosynthesis?
Authors: Li Wantong , Migliavacca Mirco , Luo Yunpeng , Orth René
Published: March 2020

Vegetation dynamics are determined by a multitude of hydro-meteorological variables, and this interplay changes in space and time. Due to its complexity, it is still not fully understood at large spatial scales. This knowledge gap contributes to increased uncertainties in future climate projections because large-scale photosynthesis is influencing the exchange of energy and water between the land surface and the atmosphere, thereby potentially impacting near-surface weather. In this study, we explore the relative importance of several hydro-meteorological variables for vegetation dynamics. For this purpose, we infer the correlations of anomalies in temperature, precipitation, soil moisture, VPD, surface net radiation and surface downward solar radiation with respective anomalies of photosynthetic activity as inferred from Sun-Induced chlorophyll Fluorescence (SIF). To detect changing hydro-meteorological controls across different climate conditions, this global analysis distinguishes between climate regimes as determined by long-term mean aridity and temperature. The results show that soil moisture was the most critical driver with SIF in the simultaneous correlation with dry and warm conditions, while temperature and VPD was both influential on cold and wet regimes during the study period 2007-2018. We repeat our analysis by replacing the SIF data with NDVI, as a proxy for vegetation greenness, and find overall similar results, except for surface net radiation expanding controlled regions on cold and wet regimes. As the considered hydro-meteorological variables are inter-related, spurious correlations can occur. We test different approaches to investigate and account for this phenomenon. The results can provide new insight into mechanisms of vegetation-water-energy interactions and contribute to improve dynamic global vegetation models.
Effect of forest canopy structure on wintertime Land Surface Albedo: Comparing CLM5 simulations to in-situ observations
Authors: Malle Johanna , Rutter Nick , Webster Clare , Mazzotti Giulia , Wake Leanne , Jonas Tobias
Published: March 2020

Seasonal snow massively impacts the surface energy budget through its high reflectivity and is therefore an important component of land-atmosphere models. It affects climate through Snow Albedo Feedback (SAF), a positive feedback mechanism between a reduced snow cover extent due to climate warming and the corresponding increase of shortwave absorption, which provokes a further reduction in snow cover extent. SAF has been shown to be the largest climate feedback over the extratropical Northern Hemisphere (NH) during the snow melt period. Yet, large biases in SAF projections are linked to snow-vegetation interactions.

This study aims at investigating uncertainties associated with the representation of wintertime Land Surface Albedo (LSA) of forested environments in global climate models, which is an essential aspect when studying SAF. UAV-based observations of LSA were used to assess corresponding LSA simulations in CLM5, the land component of the NCAR Community Earth System Model. Our measurements capture a wide range of forest structure and species found in seasonally snow covered environments, spanning from Swiss sub-alpine to Finnish boreal forests, and show a strong dependency of LSA on solar angle and canopy density. CLM5 simulations failed to capture a realistic range in LSA and shortcomings were identified particularly with regards to simulations at sparsely forested sites. In these environments, Leaf Area Index as the main descriptor of canopy structure was unable to explain observed LSA differences in space and time. This study emphasizes the need to improve the representation of canopy structure in land surface models with critical implications for simulations of Snow Albedo Feedback strength over the NH extratropics.
Temporal variations of CH4/CO2/CO fluxes in the central Amazon rainforest
Authors: Komiya Shujiro , Lavric Jost , Walter David , Botia Santiago , Araujo Alessandro , Sá Marta , Sörgel Matthias , Wolff Stefan , Asperen Hella , Kondo Fumiyoshi , Trumbore Susan
Published: March 2020

Amazon rainforests and soils contain large amounts of carbon, which is under pressure from ongoing climate and land use change in the Amazon basin. It is estimated that methane (CH4), an important greenhouse gas, is largely released from the flooded wetlands of the Amazon, but the trends and balances of CH4 in the Amazon rainforest are not yet well understood. In addition, the change in atmospheric CH4 concentration is strongly associated with a change in carbon monoxide (CO) concentration, often caused by the human-induced combustion of biomass that usually peaks during dry season. Understanding the long-term fluctuations in the fluxes of greenhouse gases in the Amazon rainforest is essential for improving our understanding of the carbon balance of the Amazon rainforest.

Since March 2012, we have continuously measured atmospheric CO2/CH4/CO concentrations at five levels (79, 53, 38, 24, and 4 m a.g.l.) using two wavelength-scanned cavity ring-down spectroscopy analyzers (G1301 and G1302, Picarro Inc., USA), which are automatically calibrated on site every day. In addition, we measured the CO2 flux by the eddy covariance method at the same tower. We estimated the CO2/CH4/CO fluxes by combining the vertical profile of the CO2/CH4/CO concentrations with the flux gradient method. Our results generally show no major difference in CO2 flux between the wet and dry seasons except for year 2017, when an elevated CO2 uptake was documented during the dry season despite the lowest precipitation between 2014 and 2018. The CH4 flux showed the largest CH4 emission during the dry season in year 2016. Further results will be analyzed and discussed in the presentation.
Airborne Experiment for Soil Moisture Retrieval using GNSS Reflectometry
Authors: Issa Hamza , Stienne Georges , Reboul Serge , Semmling Maximilian , Raad Mohamad , Faour Ghaleb , Wickert Jens
Published: March 2020

Measurement of soil moisture content on a global scale have gained increased interest over the years, due to its essential role in agriculture and most importantly in predicting the occurrence of natural disasters. This paper is dedicated to a study on GNSS Reflectometry (GNSS-R) using a low-altitude airborne carrier for soil moisture estimation with 1 ms rate of carrier-to-noise ratio observations. The principle of GNSS-R is the exploitation of L-band navigation signals as sources of opportunity to characterize the earth surface, because the reflected signals are often affected by the nature of the reflective surface. To scan large regional surface areas and quickly reach the areas to monitor, a dynamic GNSS-R system is considered.
The GNSS-R setup used in this study consists of RHCP and LHCP antennas mounted on the nose of a gyrocopter and of a Syntony front-end GNSS receiver. In addition, the gyrocopter is equipped with a signal digitizer and mass storage devices for digitizing and storing the base-band GNSS direct and reflected signals along the flight. A drone sensors board is also attached to the gyrocopter, which records the gyrocopter’s attitude and position at 1000 Hz rate along its trajectory. To cope with the rapid displacement of the satellites’ footprints along the receiver trajectory, high rate (1 ms) of carrier-to-noise ratio observations are processed with the data collection of the base-band RHCP and LHCP signals.
In the context of the study, it is very important to localize the reflective surfaces (satellites’ footprints) from which each processed signal has reflected, and thus detect which areas were scanned during the flight. The link between the reflected signals and the satellites' footprints is based on the GPS time, attitude and position provided by the drone board and the GPS time extracted from the digitized GNSS signals. We show that these parameters allow to determine, at ms rate, the satellites’ footprints locations (i.e. the surface areas) from which each signal has reflected at a specific GPS Time. A Geographic Information System is developed based on this principle to map the measurements obtained from the GNSS-R airborne setup along a real receiver trajectory. The ultimate aim of this study is to link the obtained GNSS-R measurements with the scanned surfaces to provide a soil moisture mapping of the studied area.
The Alpine Drought Observatory
Authors: Greifeneder Felix , Crouzat Emilie , Fosatti Mario , Gregoric Gregor , Haslinger Klaus , Klemencic Stanka , Kozinc Zdravko , Mancin Paolo , Notarnicola Claudia , Schneiderbauer Stefan , Stahl Kerstin , Zappa Massimiliano , Zebisch Marc ,   ADO Team
Published: March 2020

Water scarcity and related conflicts are becoming a worrying topic in Alpine regions. Moreover, lowland regions far beyond the Alps suffer from missing water from the Alps. Thus, countries are urged to act on this topic with common strategies. To support this cause, the Interreg Alpine-Space project, Alpine Drought Observatory (ADO), aims to set up a virtual observatory for the monitoring of drought in the entire Alpine region and beyond this, to derive recommendations for improved risk preparedness and efficiency of drought management.

The ADO itself will be a transnational alpine-wide operational system with a web-interface (e.g. WebGIS, periodic reports) to access data and specific impact-oriented indices for monitoring droughts and their impacts. It will provide optimized observations and forecasts for mountainous areas, which could be integrated in existing EU-level monitoring systems (e.g. European Drought Observatory). Monitoring will be based on a fusion of existing approaches (e.g. meteorological drought indices, hydrological drought indices), and newly available information (e.g. remote sensing of snow and soil moisture), to provide an optimized set of drought indices and a common drought classification. One of the further project activities will be the collection and recording of specific drought impacts. This knowledge will help to relate meteo-hydrological indices to concrete, real world effects and thus significantly enhance their applicability for drought monitoring and management.

The ADO will be tested in six case studies in all alpine countries with local partners. The case studies represent different drought issues such as agricultural drought, hydrological drought or drought impact on ecosystems. Out of the case studies, guidelines for an improved drought risk management will be developed. Findings will be upscaled to recommendations for drought governance policies for the Alps. Main beneficiaries of project findings are institutions with decision-making capacities in the field of water management, energy production, and agriculture.
Climate extremes relevant for permafrost degradation
Authors: Georgievski Goran , Hagemann Stefan , Sein Dmitry , Drozdov Dmitry , Gravis Andrew , Romanovsky Vladimir , Nicolsky Dmitry , Onaca Alexandru , Ardelean Florina , Chețan Marinela , Dornik Andrei
Published: March 2020

During the past several decades, Arctic regions warmed almost twice as much as the global average temperature. Simultaneously in the high northern latitudes, observations indicate a decline in permafrost extend and landscape modifications due to permafrost degradation. Climate projections suggest an accelerated soil warming, and consequently deepening of the active layer thickness in the near future. Except air temperature, two other parameters i.e. precipitation and snow depth are the most important climatic parameters affecting the thermal state and extend of the permafrost. The key research question of this study is whether or not certain climatic conditions can be identified that can be considered as an extreme event relevant for permafrost degradation. Here we apply data mining techniques on meteorological re-analysis to develop a coherent framework for the identification of extreme climate conditions relevant for active soil layer deepening and a decline of permafrost extend.
Several key types of events have been classified based on various combinations of temperature, precipitation and snow depth statistics. Then, the respective events have been identified in ERA-Interim reanalysis and evaluated against in situ observations in West Siberia region. The evaluation proved that the developed algorithm could successfully detect relevant extreme climate conditions in meteorological re-analysis dataset. It also indicated possibilities to improve the algorithm by refining definitions of extreme events. Refinement of algorithm is currently work in progress as well as the evaluation against satellite observations and a hierarchy of numerical models. Nevertheless, the method is applicable for all kinds of gridded climatological datasets that contain air temperature, precipitation and snow depth.

Acknowledgement
This work is funded in the frame of ERA-Net plus Russia. TSU is supported by MOSC RF # 14.587.21.0048 (RFMEFI58718X0048), AWI and HZG are supported by BMBF (Grant no. 01DJ18016A and 01DJ18016B), and WUT by a grant of the Romanian National Authority for Scientific Research and Innovation, CCDI-UEFISCDI, project number ERANET-RUS-PLUS-SODEEP, within PNCD III
Selective Removal of selenium by phytoremediation from post mining coal wastes: practicality and implications
Authors: Monei Nthati , Veetil Sanoop Kumar Puthiya , Hitch Michael , Gao Jeffrey
Published: March 2020

Selenium (Se), a metalloid typically natural in origin, is also present in coal washery by-products such as fly-ash stockpiles. The removal of Se in coal washery by-products can be achieved through various bio-physico-chemical processes. In the attempt to find more environmentally friendly and cost effective methods phytoremediation has been selected as a remediation option. This study was a small scale, screening test that investigated the phytoremediation of Se from post coal process wastes using Brassica juncea species. Therefore, the aim of this study was to assess the concentration of Se, and target elements (As, Cd, Cu, Pb). The selected plant species was grown in coal process wastes enriched with a growth soil mix. The concentrations of the elements were determined by ICP-MS. 48% Se extraction was achieved. Low percentages of As, Cd, Cu, Pb were accumulated in the biomass, (in the order Cd>Cu>As>Pb). The results overall indicate that a minimal amount of Se can be accumulated within the plant biomass of Brassica juncea. Therefore, this study provides only as an initial step towards continued studies on phytoremediation of the coal washery by-products.

Keywords: phytoremediation; phytoextraction; selenium; Brassica juncea

 
A socio-hydrological model to explore the role of social inequality on human-flood interactions
Authors: Moulds Simon , Buytaert Wouter
Published: March 2020

In several developing cities flooding has become an annual occurrence which threatens lives, livelihoods and homes. However, the exposure of urban populations to climate risks such as flooding is highly heterogeneous, with the effects of climate variability felt disproportionately by the poor. Rapid urbanisation and population growth, combined with systemic factors such as complex land tenure arrangements, inadequate transportation and weak governance, has led to the proliferation of unplanned urban settlements which lack basic infrastructure and are frequently situated on marginal, flood-prone land. While the immediate effects of floods can cause physical, economic and social devestation, floods also have multiple long-lasting effects which may increase vulnerability and exacerbate inequalities. Here, we adapt an existing socio-hydrological model of human-flood interactions (Di Baldassarre et al. 2013; Viglione et al. 2014) to explicitly account for a stratified society consisting of the “haves”, who reside in planned settlements with some degree of flood protection, and the “have-nots”, who live in unplanned, informal settlements which are more vulnerable to flooding. Initially, we conduct a sensitivity analysis to identify the parameters in the original model which may be influenced by social inequality. We then introduce a new state variable to represent the transfer of wealth from the “haves” to the “have-nots”, and use this setup to investigate the effects of wealth redistribution on the co-evolution of the coupled system.

References:

Baldassarre, G. D. et al. (2013). Socio-hydrology: conceptualising human-flood interactions. Hydrology and Earth System Sciences, 17(8), 3295-3303.

Viglione, A. et al. (2014). Insights from socio-hydrology modelling on dealing with flood risk–roles of collective memory, risk-taking attitude and trust. Journal of Hydrology, 518, 71-82.
Contribution of continuous waveform processing to induced seismicity realtime monitoring during geothermal stimulation at Geldinganes, Iceland.
Authors: Grigoli Francesco , Heimann Sebastian , Milkereit Claus , Mikulla Stefan , Nooshiri Nima , Metz Malte , Petersen Gesa , Cesca Simone , Hjörleifsdóttir Vala , Magnússon Rögnvaldur , Ásgeirsdóttir Ragnheidur St. , Hofmann Hannes , Broccardo Marco , Karvounis Dimitrios , Mignan Arnaud , Augustsson Kristjan , Stefansson Stefan Audunn , Zimmermann Gunter , Dahm Torsten , Wiemer Stefan
Published: March 2020

At Geldinganes Island, Reykjavik, Iceland a hydraulic stimulation was recently conducted to enhance the productivity of an existing hydrothermal well. An experimental cyclic soft stimulation concept was applied. Seismic risk was assessed with an appropriate monitoring network which was set up and operated before, during, and for some time after the stimulation activities. An advanced traffic light system was developed and operated for the first time in this setup.

A crucial element in such traffic light systems is the real-time monitoring of background and induced seismicity. During the experiment, real-time seismograms from the monitoring network were streamed over the internet to three different institutions (ISOR, ETHZ and GFZ), where they were analysed independently, with different combinations and setups of automatic, semi-automatic and manual methods. Both, classic pick based approaches and modern full-waveform methods were applied. Locations, magnitudes, and centroid moment tensor solutions were determined.

Many things can go wrong in real-time or near-real-time processing of seismic data. Sensor failures, transmission failures, timing issues, processing hardware failures, computational limitations, software bugs and human error, just to name a few. In a temporary network the challenges are additionally salted by the need to validate sensor responses, orientations, gain factors and site conditions in a short time frame between station setup and beginning of the experiment. Furthermore, tuning of advanced analysis methods can be difficult without example events at hand.

In this contribution, we would like to share our lessons learned in near-real-time processing of data from a heterogeneous temporary seismic network. 
Comparison of peri-Antarctic sub-shelf melt rates in coupled and uncoupled ice-sheet model simulations
Authors: Zipf Lars , Pelletier Charles , Haubner Konstanze , Sun Sainan , Goosse Hugues , Pattyn Frank
Published: March 2020

Sub-shelf melting is the main driver of the mass loss of the Antarctic ice sheet. Various parametrizations exist to estimate basal melt rates within standalone ice sheet models, but they are not able to capture complex ocean circulation. Therefore, high resolution coupled ice sheet-ocean models are the ultimate approach to simulate observed sub-shelf melt rates on short time scales and thereby improve projections of future Antarctic sea level contribution.

Here, we present first results of a hindcast (last 30 years) of the new circumpolar coupled Southern Ocean – Antarctic ice sheet configuration, developed within the framework of the PARAMOUR project. The configuration, which captures whole Antarctica, is based on the ocean and sea ice model NEMO3.6-LIM3, providing the ice sheet model with monthly sub-shelf melt rates, and the Antarctic ice sheet model f.ETISh v1.5, providing the updated ice shelf cavity geometry to the ocean model. Different difficulties are tackled for the coupling: The initialisation of the ice sheet model is optimised for the chosen resolution of 8km, which is a tradeoff between capturing the main features for the peri-Antarctic setup and respecting the model purpose as fast ice sheet model. Framework conditions for the coupling, e.g. a constant ice-ocean mask, are tested and implemented. The optimal solution to estimate sub-shelf melt for small ice shelves that are not resolved in the ocean model due to the different resolution of the ice sheet and the ocean model, is investigated.
Sub-shelf melt rates of the coupled setup are compared to those modeled by the standalone ocean model and those of the standalone ice sheet model with different sub-shelf melt rate parametrizations (ISMIP6, plume, PICO, PICOP) and the sensitivity of the response of the ice sheet for the different basal melt rate patterns are investigated.
RED DGVM: simple approach to modelling vegetation with novel implications.
Authors: Argles Arthur , Moore Jonathan , Cox Peter
Published: March 2020

The modelled global vegetation for the end of the 21st century is currently is insufficiently constrained
by climate models. A significant proportion of that uncertainty has been attributed to the limitations
of current Dynamic Global Vegetation Models (DGVMs), and the misrepresentation of mortality, dis-
turbance and regrowth within forests. Improving the simulation of the underlying processes of de-
mographic change is of primary importance in the development of predictors of future climate.

Here we present the Robust Ecosystem Demography (RED), a new dynamical vegetation model which
simulates the size-structure of forests by partitioning the population of a Plant Functional Type (PFT)
into mass classes. Allometric scaling of mortality and growth across mass classes allows for a variety
of complex demographic processes to be captured, such as disturbances and regrowth. Competition
among PFTs is done purely through restricting the recruitment of new vegetation to unshaded space.
RED represents a reduction of complexity from more numerically unwieldy cohort DGVMs which
simulate both size and patch dimensions. The limited number of dimensions and simple competitive
regime allows the equilibrium state to be solved for analytically, providing two potential functions - (i)
Avoiding-spinning up by providing an equilibrium state for intilisation. (ii) Insights into the demog-
raphy of vegetated areas, arising from parameter tuning to fit observation, such as coverage or carbon
mass. When paired with a rate of mortality or carbon assimilate rate gives, respectfully, required as-
similate or mortality rates.
We demonstrate the model functionality using offline UKESM PFT carbon assimilate rates, paired
with observed vegetation cover from the ESA LC_CCI datasets for the 9 different PFTs. From this
dataset we calibrate a novel global equilibrium mortality map for each PFT and show the competitive
and successional behaviour of dynamical runs with convergence to the fitted equilibrium. Finally, we
explore underlying ecological questions that emerge from the equilibrium solutions.
Cooling effects on induced seismicity in supercritical geothermal systems
Authors: Parisio Francesco , Vilarrasa Victor , Wang Wenqing , Kolditz Olaf , Nagel Thomas
Published: March 2020

 Geothermal energy is a fundamental piece of the puzzle in the carbon-free energy transition necessary to mitigate adverse effects of climate change. In this context, so-called supercritical geothermal systems (where resident brine is above its critical point) can reach a power output of up to 50 MW per-well and are becoming a reality thanks to the research effort of the international community. Supercritical systems are inherently complex because of fluid and solid rheology and because of the non-linear couplings involved between pore pressure, temperature and deformation in the porous and fractured rock. In this contribution, we have performed finite element analyses of coupled thermo-hydro-mechanical (THM) conditions in a supercritical geothermal system. The formulation employed includes a porosity-dependent permeability relationship that is derived through mass balance equations of the solid skeleton. The equations of state of water are based on IAPWS standards and span the whole range of temperature and pressure of interest in supercritical geothermal systems. We have analyzed an injection-production doublet scenario at 5.5 km depth, where the two wells are spaced 500 m apart and a major fault lies between them. When injecting in isothermal conditions, because of water mobility of the supercritical phase is higher than the liquid phase, minor perturbations are observed in the major fault. Seismicity increases rapidly following a pressure-diffusion response and only micro-seismicity is expected as the stress in the major fault shows minor changes. On the contrary, cold fluid injection generates large thermal-induced stress changes that diffuse following advective heat transport laws. Micro-seismicity is quickly triggered and, within 10 years in the current setting, the perturbation reaches the 250 m distant fault, where larger magnitude events are possible. Our findings bear important consequences in terms of safety of supercritical geothermal systems and proved that seismic response in amplified by re-injection-induced cooling.
Calculating the hydrological response of a mountain catchment using conventional and unconventional (CML) rainfall observations: the case study of Mallero catchment
Authors: Cazzaniga Greta , De Michele Carlo , Deidda Cristina , D'Amico Michele , Ghezzi Antonio , Nebuloni Roberto , Sileo Angelo
Published: March 2020

Rainfall plays a critical role in the hydrological cycle, being the main downward forcing. It is well known that rainfall exhibits large variability in space and time due to the storm dynamics and its interaction with the topography. It is a difficult task to reconstruct the rainfall over an area accurately. Rainfall is usually collected through rain gauges, disdrometers, and weather radars. Rain gauges and disdrometers provide quite accurate measurements of rainfall on the ground, but at a single site, while weather radars provide an indication of rainfall field variability in space, even if their use is restricted to plain areas.

Recently, unconventional observations have been considered for the monitoring of rainfall. These consist in signal attenuation measurements induced by rain on a mesh of point-to-point commercial microwave links (CML). These data, integrated with the ones collected by a network of conventional rain gauges, can provide further information about rainfall dynamics leading to improvements in hydrological modelling, which requires accurate description of the rainfall field.

The work we are going to describe is part of MOPRAM (MOnitoring Precipitation through a Network of RAdio links at Microwaves), a scientific project funded by Fondazione Cariplo (see also the EGU abstract of Nebuloni et al., 2020). Here we use rainfall data, obtained both from a rain gauge network and from signal attenuation measurements, into a hydrological model in order to evaluate the improvement in the hydrological modelling due to a better description of the rainfall field. We consider a semi-distributed rainfall-runoff model and we apply it to the Mallero catchment (Western Rhaetian Alps, Northern Italy), with the outlet located in Sondrio. This catchment is equipped with 13 microwave links and a network of 13 rain gauges.

Firstly, we implement and test the Rain field Reconstruction Algorithm (RRA), which retrieves the 2D rainfall field from CML data through a tomographic inversion technique, developed by D’Amico et al., 2016. By RRA we generate synthetic rainfall maps from attenuation data measured by 13 links located in the Mallero basin, for a few historical events in the period 2016-2019. To improve the accuracy of rainfall field reconstruction, we also integrate the reconstructed maps with on ground data from 13 rain gauges. These maps are used as input to the hydrological rainfall-runoff model. Finally, we compare the observed discharge with the calculated one using the hydrological model and different rainfall inputs.
What pesticide legislation forgot about: Pesticide transport through hydraulic shortcuts
Authors: Schönenberger Urs , Stamm Christian
Published: March 2020

Pesticides from agricultural origin may harm surface water quality and pose a risk for aquatic organisms. In Europe, the regulations on agricultural pesticide usage are currently focusing on “classical” pesticide transport pathways, such as surface runoff, spray drift into surface waters, or tile drainage flow. Recent studies have shown that in certain cases also so-called hydraulic shortcuts (e.g. road storm drains, or manholes of the tile drainage systems) can be of major importance for pesticide transport into surface waters. However, until now research has widely neglected this transport pathway.

In this study, we investigated the relevance of hydraulic shortcuts for the pesticide transport from arable land to surface waters in Switzerland. We selected twenty small catchments throughout the Swiss midlands as study areas by performing a weighted random selection on a nation-wide hydrological catchment stratification dataset. On average, they have an area of 3.5 km2 with a fraction of 44 % of arable land. In the agricultural areas of these catchments, we mapped hydraulic shortcuts using different data sources: Field surveys, high-resolution aerial images captured by a fixed-wing drone as well as plans of the road storm drains and the tile drainage systems. Subsequently, we modelled the hydrological connectivity of arable areas to surface waters using a digital elevation model and a D-infinity flow direction algorithm. Within this model, we distinguished between areas with a direct and indirect (i.e. via shortcuts) surface water connectivity.

Our model results show that major fractions of the arable areas with surface water connectivity are not connected directly, but via hydraulic shortcuts: The fraction of indirectly connected areas ranges between 18 % and 90 %, with a median of 52 % for the 20 catchments. In order to check the model robustness we performed sensitivity analyses for different model parameters, such as sink filling depth, maximal flow length, or parameters addressing the influence of roads, forests, and hedges. In certain cases, changes of those model parameters have a strong influence on the absolute extent of directly and indirectly connected areas. However, their fractions compared to the total connected area were insensitive to changes in the model parameters.

In addition, we will present the results of a model predicting the fraction of arable land connected to shortcuts within a catchment, depending on auxiliary quantities (e.g. length of roads of a certain type, land use, slope). Using this model, we can estimate the arable land fraction per catchment on a national scale.
Global Ionospheric Response to CIR/HSS Induced Geomagnetic Storms
Authors: Negrea Catalin , Munteanu Costel , Echim Marius
Published: March 2020

The solar wind is one of the main drivers for the thermosphere-ionosphere, affecting both long-term trends and short-term variability. In this study, we investigate the global ionospheric impact of high-speed solar wind streams/corotating interaction regions (HSS/CIR). Ten such events are identified between December 1st 2007 and April 16th 2008, based on solar wind speed, density and magnetic field measurements. Each event triggered a geomagnetic storm, highlighted by the temporal evolution of the SYM-H and AE geomagnetic indices. The ionospheric response to these storms is investigated using 28 globally distributed ionosonde stations, providing NmF2 and hmF2 measurements. Spectral peaks associated with 27-, 13- and 9-day periodicities are identified at most locations, highlighting the global nature of the ionospheric response. The amplitude of the ionospheric diurnal variability is also shown to vary, to a large extent correlated with the HSS/CIR induced geomagnetic storms.
Simulation of the observed climate extremes trends during 1901–2010 with INMCM5
Authors: Tarasevich Maria , Volodin Evgeny
Published: March 2020

Extreme climate and weather events have a great influence on society and natural systems. That’s why it is important to be able to precisely simulate these events with the climate models. To asses the quality of such simulations 27 climate extremes indices were defined by ETCCDI. In the present work these indices are calculated for the 1901–2010 in order to estimate their trends.
Climate extremes trends are studied on the basis of ten historical runs with the up-to-date INM RAS climate model (INMCM5) under the scenario proposed for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Developed by ECMWF ERA-20C and CERA-20C reanalyses are taken as observational data.
Trends obtained from the reanalysis data are compared with the simulation results of the INMCM5. The comparison shows that the simulated land-averaged climate extremes trends are in good agreement with the reanalysis data, but their spatial distributions differ significantly even between the reanalyses themselves.