Academy

Your training network to bridge Earth System and Data Science

The second cohort

The first cohort

Maria ISabel Arango @ UNIVERSITY OF POtSdam
#LANDSLIDES #FLOODS #NATURAL HAZARDS #MULTI-HAZARDS

Multi-hazard events in tropical regions
In topical and mountainous regions, intense and convective rainstorms often trigger a set of coupled or cascading hazards in the basin scale that includes clusters of landslides, hillslope debris flows, flash floods, and channelized debris flows. The final stage of such events is marked on fans or low-land areas that are often populated, causing extensive disasters. These events have become more recurrent and intense over time, due to the increase of extreme rainstorms, and the urbanization of susceptible areas.

The hazard assessment for such phenomena faces big challenges. One of them is the general lack of consensus and recognition of the multi-hazard phenomena as a whole, which translates in a lack of analysis of world-scale patterns of such events. Another big challenge in the hazard assessment are associated to their modelling. Several approaches have been developed to model their sub-processes independently (hydrology, flooding, slope stability, and debris flow runout). Even though individual models can reach high modelling accuracy, the interaction with other processes is neglected and does not give precise results as a multi-hazard assessment. On the other hand, some integrated multi-hazard modelling approaches have emerged, which account for the interactions within processes. Nonetheless, these models are heavily reliant on data and computational resources and propagate several uncertainties, making it difficult to trace the influence of single variables in the final results. The use of Artificial Intelligence (AI) algorithms is a growing field, and many applications have been developed to increase the accuracy of susceptibility, hazard and risk assessment using AI, taking advantage of big data availability. Nonetheless, the interpretability of very complex AI algorithms is sometimes very challenging from a physical perspective, undermining its applicability in different environments and credibility.

My research will focus in addressing these challenges. To start, I want to collect information about multi-hazard events in tropical areas from disaster databases and carry out a multi-variate analysis with all the information I can gather about them. Using remote sensing data, I want to understand the patterns and differences within multi-hazard interactions in different environments and terrains. Once I have a clearer idea about their nature, I want to work with detection models that allow to map and locate multi-hazard events in the world using remote sensing data and IA algorithms. Finally, I will combine physical and AI models, using AI to improve the quality or quantity of input data for physical models, and using physical models to improve the interpretability of AI models that are used to models multi-hazard phenomena.

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Important publications
Arango, M. I., Aristizábal, E., & Gómez, F. (2020). Morphometrical analysis of torrential flows- prone catchments in tropical and mountainous terrain of the Colombian Andes by machine learning techniques. Natural Hazards 130.

Aristizábal, E., Arango, M. I., & López, I. K. G. (2020). Definition and Classification of Torrential floods and Their Impact in the Colombian Andes. Cuadernos de Geografía: Revista Colombiana de Geografía, 29(1), 242-258.

Aristizábal, E., Arango, M.I., Gómez, F. J., Castro, S. M. L., Severiche, A. D. V., & Quintanilla, A. F. R. (2020). Hazard Analysis of Hydrometeorological Concatenated Processes in the Colombian Andes. Advances in Natural Hazards and Hydrological Risks: Meeting the Challenge 7-10.


Contact
arangocarmona[at]uni-potsdam.de

ESS methods
Remote sensing, flooding and slope stability physical models

DS methods
Deep Learning, Machine Learning, Bayesian Statistics

Software & tools
GIS, Python, R, Google Earth Engine

Libraries
GDAL/OGR, Geopandas, Geemap, Scikit

ESther BOSCH @ ​German Aerospace Center (DLR)
#affective maps #emotion-aware assistance systems #multimodal transport

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Yannick BURchart @ UNIVERSITY OF COLOGNE
#DATA SCIENCE #NUMERICAL MODELING #BIG DATA #OPEN SCIENCE #MACHINE LEARNING #LARGE-EDDY SIMULATIONS #ATMOSPHERIC CONVECTION #PHOTOGRAMMETRY #RENDERING

Stereo Observations of Clouds for LES Validation and Sub-scale Cloud Parameterizations (SOCLES)
Clouds play a crucial role in the Earth's energy and water cycles, and their accurate representation in numerical models is crucial for accurate weather prediction and climate projection. However, shallow cumulus clouds are highly heterogeneous and quickly evolving, making it difficult to represent their impact on the larger-scale flow and energy budget.  
Our project aims to address this challenge by combining high-frequency observations from multiple stereo cameras with Large-Eddy Simulation (LES) at a mid-latitude meteorological supersite. The detailed data provided by the stereo cameras will be used to validate LES simulations and improve cumulus parameterizations in weather and climate models. By utilizing open boundaries, multiple nesting, and detailed topography in the LES, we hope to achieve a higher level of realism in simulated clouds.  
We are also proposing an algorithm that uses ray-tracing techniques to create camera images from simulated clouds. These images will allow for the comparison of high-resolution meteorological models with camera measurements, providing a new way to evaluate the accuracy of the models.  
In summary, our project aims to improve our understanding of the fine-scale structures of shallow cumulus clouds and their impact on the larger-scale flow and energy budget. By capturing clouds in greater detail and utilizing advanced simulation techniques, we hope to improve the representation of these important features in numerical models, ultimately leading to more accurate weather prediction and climate projections.


ESS methods
ICOsahedral Nonhydrostatic Large Eddy Model (ICON-LEM), Dutch Atmospheric Large-Eddy Simulation Model (DALES), Photogrammetry, Remote Sensing

DS methods
Cluster Analysis (DBSCAN, KMeans, Mapper), Support Vector Machines, Gradient Tree Boosting, Principal Component Analysis (PCA), Neural Networks, Machine Learning Neural networks, ML, etc.

Software & tools
Python, C++, Fortran, Blender, Shell Script, Github, Jupyter, Anaconda, Pip, Linux, Windows, Microsoft Office

Libraries
Numpy, Matplotlib, scikit-learn, SciPy, TensorFlow, Keras, PyTorch, scikit-tda, Pandas, OpenCV, Numba, netCDF4, moviepy

NIls ChudallA @ RWTH AAchen

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Anne-Karin Cooke @ Bundesanstalt für Geowissenschaften und Rohstoffe

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Katrin Dohmen @ Technische Universität BERLIN
#LANDSLIDES #TSUNAMIS #ENGINEERING GEOLOGY

Identification and investigation of mass movements on tsunamigenic coastal landforms
Landslide induced tsunamis have come into focus of scientific literature since 2018, when two catastrophic aseismic tsunamis hit the Indonesian coastline in Palu and the Sunda Strait, resulting in thousands of deaths. Waves induced by landslides are very local events but they can reach extreme heights compared to seismically induced tsunamis. Early warning is usually not possible for those events because the time that passes between wave generation and their arrival at the adjacent coastlines is often only minutes. The objective of this project is to investigate the characteristics of landslide tsunamis, to identify areas that might be endangered by future events and to model landslide tsunami scenarios in those areas. The focus of the study is the Indonesian coastline as it has been especially affected in the past.
In a first step, a literature review on historical landslide tsunami events was conducted. Parameters describing the landslide, the generated tsunami wave and landslide triggering events were extracted from scientific literature and tsunami databases. Further parameters describing the waterbody geometry and sea floor morphology were extracted with geoinformation systems from spatial data. All case studies and their related parameters were collected in a database. This database contains information on more than one hundred case studies and can help finding similarities between the case studies and identifying areas that are especially prone to that hazard.
The next step focuses on coastal landslide susceptibility analysis. Parameters that have been widely employed for landslide susceptibility investigations, combined with the knowledge gained from the database generated in the first step, will be used for the analysis. For highly endangered areas, the stability of slopes will be modelled numerically with particle flow codes. With the help of those models, parameters can be estimated, which are critical for the generation of tsunami waves, such as landslide volume, landslide velocity or sliding material.


Contact
k.dohmen[at]tu-berlin.de | Department Website | ResearchGate

ESS methods
Remote Sensing, Susceptibility Analysis, Slope Stability

DS methods
Artificial Neural Networks, Decision Trees, Bivariate Analysis, Interactive Data Visualization

Software & tools
R, QGIS, ArcGIS, SPSS Modeler

Libraries
Shiny, leaflet, sf

Ann-kathrin M. Edrich @ RWTH AAchen 

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Fernand B. Eloundou @ FZ Jülich, @CRC-DETECT, @RWTH Aachen
#Land Surface Modelling #Data Assimilation #Ecosystem Reanalysis

Towards Ecosystem Reanalysis by Coupling Water and Carbon Cycles
This Ph.D. topic is a part of the Collaborative Research Centre (CRC), cluster C, project C03 and undertaken at the research group ‘Stochastic Analysis of Terrestrial Systems’ at the Institute of Bio- and Geosciences (Agrosphere), Research Centre Jülich. 
 
The primary aim of this Ph.D. research is to investigate the impact of uncertain ecosystem parameters on the coupled water, energy, and biochemical cycles across Europe. The emphasis is on the variability of water and carbon fluxes in space and time, which is significant for simulating the impact of land use and land-cover change on these cycles. We hypothesize that continental-scale net ecosystem exchange (NEE) from a reanalysis with improved ecosystem parameters and improved representation of soil respiration (SR) will better correlate to observed total water storage (TWS) variability.  

To attain our research goals, we will implement the iterative ensemble smoother (IEnS) coupled with Community Land Model, version 5 (CLM5) (Lawrence et al., 2019) to estimate ecosystem parameters (i.e., soil and vegetation) at well-equipped measurements sites from the Long-Term Ecological Research (LTER) and FLUXNET networks by assimilating long-term time series of soil moisture, leaf area indices, and NEE. Using this approach, we will establish a prototype for ecosystem reanalysis for a period of 30 years (1991-2020) over Europe by combining the acquired knowledge of ecosystem parameters and soil respiration.

Contact
f.eloundou[at]fz-juelich.de

ESS methods
Iterative Ensemble Smoother (IEnS) , Ensemble simulations Probabilistic statistics, Time series analysis, Model validation from Eddy covariance and Meteorological measurements

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Python, Fortran, Linux, Gitlab, CLM5, Probabilistic data association filter (PDAF), JSC Supercomputer resources

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David ERNST @ CONSTRUCTOR University Bremen
#GEOCHEMISTRY #CRITICAL METALS #GALLIUM-ALUMINIUM #REY #EARLY EARTH

Quality assessment of analytical data in resource and environmental research (QuARUm)
Increasing amounts of analytical data are produced and published in geochemistry, like in all other scientific fields. The number of geochemical databases and the amount of entries are growing. However, quality assessment of literature data is often complicated or simply impossible due to missing background information (certified reference materials, blanks, interference correction, machine drift, daily performance, etc.). Therefore, we aim to develop a tool that helps researchers to evaluate the quality of their geochemical analytical data.  

The research project is a collaboration between the geochemistry research group at the Constructor University Bremen and the software engineering research group from TU Dortmund. We will develop a low-code environment that enables geochemists to objectively assess the quality of geochemical data based on fully modifiable sample-specific criteria. The quality assessment will be applicable directly to self-produced analytical data, literature data in publications and large-scale geochemical databases. Furthermore, the software tool will allow processing of geochemical raw data to obtain, for example, absolute concentration data from an ICP-MS measurement. The raw data processing will also be modifiable, based on the respective applications and user needs, and fully visible, unlike existing commercial software solutions that operate mainly as black boxes.  

I am a geochemist researching the behaviour of critical metals in Precambrian and modern natural systems. During the QuARUm project, I will compile and develop geochemical and statistical criteria for the data quality assessment. We will implement sufficient criteria for a broad spectrum of different sample types that can be used easily by the geoscience community. Here I can use the experience I have gathered in the last years in analysing (ultra)trace elements in various sample types.  Furthermore, I will work on an automated data processing protocol to compute final concentration data from ICP-MS and ICP-OES raw data. In addition, I will act as an interface between Data Science and my geochemist colleagues so that we can develop a software tool that is easy and intuitive to use for the broad geochemistry/geoscience community.


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Important publications
Ernst, D.M., Bau, M. (2021) Banded iron formation from Antarctica: The 2.5 Ga old Mt. Ruker BIF and the antiquity of lanthanide tetrad effect and super-chondritic Y/Ho ratio in seawater. Gondwana Research 91, 97–111.

Ernst, D.M., Schier, K., Garbe-Schönberg, D., Bau, M. (2022) Fractionation of germanium and silicon during scavenging from seawater by marine Fe (oxy)hydroxides: Evidence from hydrogenetic ferromanganese crusts and nodules. Chemical Geology 595.

Schier, K., Ernst, D.M., de Sousa, I.M.C., Garbe-Schönberg, D., Kuhn, T., Hein, J.R., Bau, M. (2021) Gallium-aluminum systematics of marine hydrogenetic ferromanganese crusts: Inter-oceanic differences and fractionation during scavenging. Geochimica et Cosmochimica Acta 310, 187–204.


Contact
dernst[at]constructor.university

ESS methods
ICP-MS (also SF-ICP-MS and QQQ-ICP-MS); laser-ablation ICP-MS; ICP-OES

DS methods
data visualisation, linear regression

Software & tools
Python

Libraries
pyrolite, SciPy

Till FOHRMANN @ UNIVERSITY OF Bonn 
#CLIMATOLOGY #EXTREME EVENTS #WATER CYCLE #CLIMATE CHANGE

Detection and attribution of anthropogenic drivers in extreme events
“Humans – through decades of land-use change and intensified water use and management – have caused persistent modifications in the coupled water and energy cycles of land and atmosphere.” This is the central hypothesis of the Collaborative Research Center 1502. To investigate this claim, an integrated modeling system coupling earth, atmosphere and the sub-surface is created and analyzed by the various subprojects of the CRC.

Our subproject is concerned with analyzing meteorological extreme events like heat waves and droughts. We investigate potential changes in the characteristics of such extremes and the drivers of such changes.



Contact
tfohrmann[at]uni-bonn.de | Colloaborative Research Centre DETECT | Working Group on Climate Dynamics | github.com/tfohrmann

ESS methods
Numerical Weather Prediction and Reanalysis Models, Meteorological Remote Sensing

DS methods
Bayesian Statistics, Extreme Value Statistics, Bayesian Hierarchical Modeling,  Machine Learning, Information Compression, Statistical Inference

Software & tools
Python, C, High Performance Computing

Libraries
SciPy, Pandas, Sklearn, Keras, Tensorflow, Xarray, Statsmodels

JULIAN ALBERTO GILES @ UNIVERSITY OF Bonn 
#Weather Radar #Polarimetry #Remote Sensing #Precipitation #Model

Precipitation processes
Atmospheric models still do not adequately represent precipitation generating processes, which is partly responsible for their deficiency in reproducing observed regional trends in total water storage (TWS). We will quantify these deficiencies in the Integrated Monitoring System (IMS) by exploiting especially polarimetric radar observations with inherent information on precipitation generating processes aloft. The use of polarimetric microphysical retrievals and the evaluation of climate model runs in radar observation space will enable us to compare the observed and simulated impact of greenhouse gas forcing and regional anthropogenic interventions on precipitation generation. The project focuses on Europe, with radar data from Germany and Turkey. This project is part of the Collaborative Research Centre 1502 – DETECT.


Contact
jgiles[at]uni-bonn.de | ResearchGate | DETECT Cluster A04github.com/JulianGiles

ESS methods
Numerical modelling analysis, weather radar data processing, remote sensing

DS methods
Statistical analysis, trend analysis, georeferenced visualization

Software & tools
Python, Git, Linux/UNIX

Libraries
xarray, wradlib, cartopy, scipy, numpy, matplotlib

Lena R. Happ @ Alfred-Wegener-INstitut (AWI)

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Lukas JOnkers @ MARUM BREMEN

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Bahareh Kamali @ UNIVERSITY OF Bonn

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Melina Knoke @ UNIVERSITY OF OLDENBURG 
#SEDIMENT#POREWATER#ORGANIC GEOCHEMISTRY#DOM#HYDROTHERMAL

The biogeochemistry of dissolved organic matter in hydrothermal Guaymas Basin sediments
I am a doctoral candidate working on the biogeochemistry of dissolved organic matter (DOM) in hydrothermal Guaymas Basin sediments. The Guaymas Basin is a young, active spreading ridge in the Gulf of California. During my Ph.D., I will characterize the DOM composition in the sediment and porewater using ultrahigh-resolution mass spectrometry and link the DOM composition with deep biosphere-related microbial metabolism along temperature and redox gradients. I want to identify whether carbon-rich but low-temperature hydrothermal systems have the potential to function as an organic carbon storage cell or whether these ecosystems are a source of organic carbon to the deep ocean.


Contact
Melina.knoke[at]uol.de | @Melina_Knk| https://uol.de/en/icbm/marine-geochemistry/staff/melina-knoke

ESS methods
ultra-high resolution mass spectrometry (FT-ICR-MS), (compound-specific) isotope analysis, 16S rRNA

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Timothy Kodikara @ German Aerospace Center (DLR)
#Space Weather #Aeronomy #Ionosphere #Thermosphere #Data Assimilation

Data Assimilation and Machine Learning in the Upper Atmosphere
This research is concerned with data assimilation and machine learning in the upper atmosphere - the ionosphere and thermosphere (IT) system. The specification and prediction of the IT system is critical for space-based services and technologies such as communications, navigation, agriculture, security, banking and healthcare. Although there are well-established data assimilation techniques in tropospheric numerical weather prediction, it is difficult to apply these techniques to IT data assimilation due, among others, to the physical, chemical and dynamical differences between the two systems. In other words, in tropospheric weather forecasting, data assimilation solves the initial value problem by giving the model the best estimate of the current state of the atmosphere, which is then used to make forecasts. Unlike the troposphere, the IT is strongly influenced by solar activity, geomagnetic activity and forcing from the lower atmosphere. Since IT numerical models typically do not resolve the external forcing in a self-consistent manner, data assimilation in the IT requires additional consideration of model boundary conditions. Combined data assimilation and machine learning techniques could help to accurately specify both initial and boundary conditions. The application of data assimilation and machine learning in IT is relatively new, and the techniques remain experimental for operational space weather applications. As a result, the inherent predictability of the IT system remains largely unknown. The extent to which IT can be accurately predicted is therefore an open scientific question.
Through this research, we aim to contribute to existing open source initiatives with new or improved tools and methodologies, explore machine learning-based techniques for predicting solar and geomagnetic forcing inputs to the IT models, perform data assimilation experiments with different kinds of observations, and assess the predictability of the IT system.

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Important publications
Kodikara, T., Zhang, K., Pedatella, N. M., & Borries, C. (2021). The impact of solar activity on forecasting the upper atmosphere via assimilation of electron density data. Space Weather, 19, e2020SW002660. https://doi.org/10.1029/2020SW002660

Forootan, E., Kosary, M., Farzaneh, S., Kodikara, T., et al. (2022). Forecasting global and multi-level thermospheric neutral density and ionospheric electron content by tuning models against satellite-based accelerometer measurements. Scientific Reports 12, 2095. https://doi.org/10.1038/s41598-022-05952-y

Fernandez-Gomez, I., Kodikara, T., Borries, C., et al. (2022). Improving estimates of the ionosphere during geomagnetic storm conditions through assimilation of thermospheric mass density. Earth Planets Space 74, 12. https://doi.org/10.1186/s40623-022-01678-3

Kodikara, T. (2019). Physical Understanding and Forecasting of the Thermospheric Structure and Dynamics (Doctoral dissertation). RMIT University, Melbourne, Australia. https://researchrepository.rmit.edu.au/esploro/outputs/9921863942601341


Contact
timothy.kodikara[at]dlr.de | https://orcid.org/0000-0003-4099-9966

ESS methods
global circulation modelling, forecasting, uncertainty quantification, model validation

DS methods
data assimilation, machine learning, time series analysis

Software & tools
Fortran, Python, Shell, Matlab, Github, Supercomputers, LaTeX

Libraries
numpy, scipy, astropy, pandas

Anya Makushkina @ German Research Centre for Geosciences (GFZ)

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Verena Maleska @ ​Leibniz Institute of Ecological Urban and Regional Development


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PAUL MICHAELIS @ Centre for Environmental Research (UFZ)

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FABIAN MOCKERT @ ​Karlsruhe Institute of Technology , Institute of Meteorology and Climate Research, Department Troposphere Research (IMK-TRO)
#WEATHER REGIMES #FORECASTING #MACHINE LEARNING #POST-PROCESSING

Probabilistic weather regime prediction: Combining physical models and generative machine learning
Accurate weather forecasts with a wide range of forecast lead times are of great importance for economic and societal decisions. Physical numerical weather prediction (NWP) models are constantly being improved and are currently skilful on the medium-range time scale, with a forecast horizon of 5-10 days.  In order to quantify uncertainty and to provide probabilistic predictions, NWP models are run several times with slightly varying initial conditions and  model physics. The process of running multiple NWP models is called ensemble forecasting, which is inherently subject to systematic errors that must be corrected by statistical post-processing methods.  

The energy sector, where the share of renewable energy generation is increasing, has a great interest in accurate and reliable probabilistic forecasts with lead times beyond the medium-range. Forecasts with a lead time of 10 to 60 days are called subseasonal to seasonal (S2S) forecasts. This time range is currently still considered a “predictability desert” and is therefore subject of ongoing research. The dominant feature for medium range forecasts are the initial conditions, on seasonal predictions the boundary conditions are most relevant. S2S forecasts, with lead times between medium range forecasts and seasonal predictions, are inherently more difficult since both initial conditions and boundary conditions are relevant. In the S2S domain, the aim is to describe the general characteristics of weather probabilistically based on the large-scale atmospheric circulation. For this description, a concept called weather regimes can be used. Weather regimes describe continent-size, quasi-stationary, persistent large-scale flow patterns and state of the art NWP models are able to predict weather regimes with a lead time of up to two weeks. Studies suggest that with knowledge of slowly varying components of the climate systems such as the Madden-Julian Oscillation, the state of the stratosphere, or sea-ice distribution, it should be possible to predict weather regimes with even longer lead times than currently possible.  

These promising suggestions from previous studies indicate that the forecast horizon of weather regimes could be extended using knowledge about teleconnections emerging from these slowly varying components. In my PhD project I will develop hybrid models that combine physical model predictions and teleconnection pathways with data-driven statistical and machine learning methods to improve the prediction of weather regimes with lead times of up to 6 weeks.

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Important publications
Mockert, F, Grams, C M, Brown, T, Neumann, F (2023) Meteorological conditions during Dunkelflauten in Germany: Characteristics, the role of weather regimes and impacts on demand. ArXiv: https://doi.org/10.48550/arXiv.2212.04870 [Preprint, in Review at Meteorological Applications]


Contact
fabian.mockert[at]kit.edu | @FMockert | @KITKarlsruhe | https://www.imk-tro.kit.edu/14_11644.php

ESS methods
weather regimes, forecasting, large-scale atmospheric circulation

DS methods
Neural networks, machine learning, post-processing

Software & tools
Python, Shell, LaTeX

Libraries
Tensorflow (Keras), pandas, xarray, netCDF4, scikit-learn

Sweety Mohanty @ GEOMAR KIEL

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Amirpasha Mozaffari @ FZ Jülich

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MALTE MUES @ TU DOrtmund 
#DATA ANALYSIS #DOMAIN SPECIFIC LANGUAGES #SOFTWARE ENGINEERING

Project title Quality assessment of analytical data in resource and environmental research (QuARUm)
Increasing amounts of analytical data are produced and published in geochemistry, like in all other scientific fields. The number of geochemical databases and the amount of entries are growing. However, quality assessment of literature data is often complicated or simply impossible due to missing background information (certified reference materials, blanks, interference correction, machine drift, daily performance, etc.). Therefore, our aim is to develop a tool that helps researchers to evaluate the quality of their geochemical analytical data.

The research project is a collaboration between the geochemistry research group at the Constructor University Bremen and the software engineering research group from TU Dortmund. We will develop a low-code environment that enables geochemists to objectively assess the quality of geochemical data based on fully modifiable sample-specific criteria. The quality assessment will be applicable directly to self-produced analytical data, literature data in publications and large-scale geochemical databases. Furthermore, the resulting tool will allow processing of geochemical raw data to obtain, for example, absolute concentration data from an ICP-MS measurement. The raw data processing will also be modifiable, based on the respective applications and user needs, and fully visible, unlike existing commercial software solutions that operate mainly as black boxes.  

I work on developing the low-code platform using the software engineering experience at TU Dortmund that allows the easy configuration of an analysis by the geochemist. Cleaning data and separating high quality samples from those with a potential pollution is in our experience an important step for developing future higher-level analyses using this data entries. Apart from developing the low-code platform itself, the project also aims on training geochemistry students in adapting the platform to their analysis requirements. By conducting the analysis automatically, it is expected to be more replicable and reliable than manual quality assessments. Additionally, we expect time savings as a result so that the geochemistry researchers have more time for theory building rather than data cleaning tasks.



Contact
malte.mues[at]tu-dortmund.de   | Personal homepage | github.com/mmuesly

ESS methods
tba

DS methods
SMT Solving, Linear Regression, Anomaly Detection, Clustering Analysen

Software & tools
Bash, Java, C, C++, Python, JavaScript

Libraries
Weka, SciPy

BUSE ONAY @ FZ Jülich

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Anna PEREPONOVA @ ​​​​​​​​​​​​Leibniz Centre for Agricultural Landscape Research (ZALF)

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OMAR Seleem @ UNIVERSITY OF POTSDAM

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Meghna Sengupta @ ​ Leibniz Centre for Tropical Marine Research (ZMT), Bremen
#coral reef islands #coastal geomorphology #shoreline dynamics #climate change

Evolution and dynamics of Indo-Pacific coral reef islands in the light of climate change
Coral reef islands are low-lying sedimentary landforms that are considered extremely vulnerable to the impacts of anthropogenic climate change. Studies suggest sea-level rise is a key threat and islands may become physically unstable and consequently uninhabitable within the coming decades. While a growing number of studies in the past decade have documented island change across the Pacific and Indian Oceans, investigations to statistically link processes and island response are limited. Moreover, due to most studies relying solely on remotely sensed datasets, critically important factors such as sediment composition, island formation history as well as reef morphology remain typically unexplored. The primary aim of this study is to investigate the morphological evolution of reef islands using a combination of remotely sensed datasets, sedimentological analysis, geomorphological observations, and statistical evaluation. The project aims to investigate the islands of the Spermonde Archipelago in Indonesia, a region that has a complex climatic and hydrodynamic regime and is considerably understudied despite being deemed as a climate change hotspot. The study aims to first, take a multi-proxy approach to document island change over multi-decadal to seasonal timescales using remote sensing. Second, use surface and sub-surface sediment samples from these islands to analyse island chronology, composition, and facies distribution. Finally, develop a set of machine learning models that are computationally robust and novel within reef island research to enable the analysis of variability in sediment composition across the archipelago, as well as a comprehensive investigation of linkages between a range of processes and recorded island change. With projections of accelerating sea-level rise rates and changes in wave regime, results from this study will provide a robust knowledge base of reef island dynamics and attribution, which would be critical in informing planning and adaptation for the coastal communities within the Spermonde Archipelago over the coming decades and the prospect of applying similar approaches elsewhere.


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Important publications
Sengupta M, Ford M-R & Kench P-S (2021) “Multi-decadal planform changes on coral reef islands from atolls and mid-ocean reef platforms of the equatorial Pacific Ocean: Gilbert Islands, Republic of Kiribati”. Geomorphology, 389, 107831. https://doi.org/10.1016/j.geomorph.2021.107831

Sengupta M, Ford M-R & Kench P-S (2021) “Shoreline changes in coral reef islands of the Federated States of Micronesia since the mid-20th century”. Geomorphology, 377, 107584. https://doi.org/10.1016/j.geomorph.2020.107584



Contact
msen548[at]ucklanduni.ac.nz | ResearchGate | LinkedIn

ESS methods
tba

DS methods
Remote sensing, ML models – random forest, CART, cluster analysis

Software & tools
ArcGIS, DSAS, R

Libraries
cart, rpart, randomForest, vip, ggplot2

Mohamad HAKAM Shams Eddin @ UNIVERSITY OF Bonn
#COMPUTER VISION #ANOMALY DETECTION #REMOTE SENSING

Deep generative networks for detecting anomalous events in the water cycle
Although there is a general expectation that extreme events in the water cycle are occurring more frequently and become stronger due to climate change, it remains a challenge to identify them in large simulation data sets. While extreme events can be defined based on impact indicators like agricultural droughts, these indicators do not cover all extreme events. We therefore aim to identify extreme events in simulated water cycle components by developing novel deep networks that detect anomalous events in simulated data.

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Important publications
Shams Eddin M H, Roscher R & Gall J (2022) Location-aware Adaptive Denormalization: A Deep Learning Approach for Wildfire Danger Forecasting. arXiv preprint arXiv:2212.08208.33

Laupheimer D, Shams Eddin M H & Haala N (2020). On The Association Of Lidar Point Clouds And Textured Meshes For Multi-ModaL Semantic Segmentation. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 509–516.

Laupheimer D, Shams Eddin M H & Haala N (2020). The Importance of Radiometric Feature Quality for Semantic Mesh Segmentation. DGPF 40. Wissenschaftlich-Technische Jahrestagung der DGPF in Stuttgart – Publikationen der DGPF, Band 29, 2020, pp. 205-218.



Contact
shams[at]iai.uni-bonn.de | Personal homepage | github.com/HakamShams

ESS methods
tba

DS methods
Transformers, CNN, GAN

Software & tools
tba

Libraries
PyTorch, Xarray, OpenCV, Open3D

Amalie Skalevag @ UNIVERSITY OF POTSDAM

Project title
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Contact
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ESS methods
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DS methods
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Software & tools
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Libraries
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Taylor Smith @ UNIVERSITY OF POTSDAM, Institute of Geosciences
#WATER #SNOW #RIVERS #CLIMATECHANGE

Quantifying the Influence of SnowmelT on RIVEr Hydrology in High Mountain Asia (STRIVE)
A significant amount of the moisture received throughout HMA is stored transiently in snowpack; along the steep elevation gradients found in much of High Mountain Asia, the timing of the onset and cessation of snowmelt will vary significantly over small distances due to the impacts of local-scale topography and insolation variability. Snowmelt, which is distributed to lower elevations by rivers, will leave distinct, high-frequency, traces in both water volume and water temperature that can be used to measure the timing and duration of snowmelt. In-situ river temperature and river height data collected at the confluences of several sub-catchments along an elevation gradient can thus be used to disentangle where and when snowmelt enters the wider river system and constrain which climatic factors are responsible for snowmelt timing. Better constraints on how quickly, where, and when snowmelt enters river systems will be of critical importance for urban and agricultural water use planning, hydropower provisioning, and natural hazard risk assessment as regional precipitation and temperature patterns continue to shift.


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Important publications
T Smith, D Traxl, and N Boers. “Empirical evidence for recent global shifts in vegetation resilience.” Nature Climate Change (2022). https://doi.org/10.1038/s41558-022-01352-2

T Smith, A Rheinwalt, and B Bookhagen. “Topography and Climate in the Upper Indus Basin: Mapping Elevation-Snow Cover Relationships.” Science of The Total Environment, 2021, 147363, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2021.147363

T Smith and B Bookhagen. “Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data.” Front. Earth Sci. (2020) 8:559175. https://doi.org/10.3389/feart.2020.559175

T Smith and B Bookhagen. “Changes in seasonal snow water equivalent distribution in High Mountain Asia (1987 to 2009)’’, Science Advances 4 (2018): 1, https://doi.org/10.1126/sciadv.1701550

T Smith, B Bookhagen, and A Rheinwalt. “Spatio-temporal Patterns of High Mountain Asia’s Snowmelt Season Identified with an Automated Snowmelt Detection Algorithm, 1987-2016’’, The Cryosphere 11 (2017): 2329-2343, https://doi.org/10.5194/tc-11-2329-2017


Contact
tasmith[at]uni-potsdam.de | Personal homepage| github.com/tasmi

ESS methods
tba

DS methods
tba

Software & tools
tba

Libraries
tba

VINCENT SObottke @ FU BERLIN

Project title
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Important publications
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Contact
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ESS methods
tba

DS methods
tba

Software & tools
tba

Libraries
tba

JAN SODOGE @ ​ Helmholtz-Centre for Environmental research and University of Potsdam
#NATURAL HAZARDS #NATURAL LANGUAGE PROCESSING #MACHINE LEARNING

Current drought impacts, stakeholders and future scenarios for drought-resilient transformation: development of a mixed-methods approach
Drought impacts on social-ecological systems cause an estimated 9 billion USD per year in the European Union. In Germany, long-term climate predictions describe trends towards longer-lasting droughts. I investigate the socio-economic impacts of droughts and how they become relevant. I use a mixed-methods approach that combines both data-driven and participatory research designs. To assess drought impacts, I developed a text-mining approach that leverages machine learning to automatically detect drought impacts from newspaper articles. Currently, I am investigating the patterns and mechanisms underlying the occurrence of these impacts using machine learning methods such as unsupervised clustering or information theory. Complementing, I work on a case-study in the German state of Thuringia with stakeholders to understand these mechanisms and patterns from a qualitative perspective.


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Important publications
Boelaert, J., Ollion, E., Sodoge, J., Megdoud, M., Naji, O., Kote, A. L., ... & Boelaert, M. J. (2022). Package ‘aweSOM’.  

Sodoge, J., Kuhlicke, C., & de Brito, M. M. AutomatizedSpatio-Temporal Detection of Drought Impacts from Newspaper Articles UsingNatural Language Processing and Machine Learning. Available at SSRN 4178096. (under review)


Contact
Jan.sodoge[at]ufz.de | @jsodoge| jsodoge.eu | github.com/jansodoge

ESS methods
tba

DS methods
Natural language processing, unsupervised learning, information theory, random forest.

Software & tools
R, Python, SQL

Libraries
aweSOM, tidyverse, tidymodels, tidytext

Melanie Stammler @ University of Bonn
#GEOMORPHOLOGY #PERMAFROST #SURFACE CHANGE

Interannual and seasonal surface change in a glacial-periglacial (de)coupled landscape and its implications on local hydrology in the semi-arid catchment of the Agua Negra river, Argentina
Glacial and periglacial landforms in the semi-arid Andes represent an essential water storage as this water feeds river runoff, thus, all water use. Glacial and periglacial systems are undergoing change; with the relative hydrological significance of periglacially stored waters increasing due to the rapid melt of glaciers. Changes of possible interaction and seasonality need to be understood to be able to assess a rock glacier’s future input to the hydrological system. Surface changes are often indicators of thawing and freezing processes and/or permafrost degradation. The analysis of surficial changes provides local patterns of surface changes and delivers insight on meltwater contribution to runoff.  

In my PhD project I decipher the spatiotemporal variability of interannual and seasonal surface changes in the permafrost environment of the Agua Negra river catchment (San Juan Province, Argentina). I support and validate remotely sensed data with fieldwork, relying on tristereoscopic Pléiades data, drone flights, DGPS measurements and installed temperature loggers. I focus on the generation of digital elevation models (Agisoft Metashape / AMES stereo pipeline) and consequent DEMs of Difference while exploring M3C2 application (Lague et al., 2013). 
I am generally motivated by combining remotely sensed, data-driven analysis with fieldwork and co-run a project where object-based image detection is applied to map aeolian dunes in Northern Scandinavia (ArcDune).


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Important publications
Stammler, M, Bell, R, Bodin, X, Blöthe, J, and Schrott, L (2023) Rock Glacier Surface Change Detection Based on UAV- and Tristereo Pléiades Data (Agua Negra, Argentina), EGU General Assembly 2023, Vienna, Austria.

Stammler, M, Stevens, T, Hölbling, D (2023) Geographic object-based image analysis (GEOBIA) of the distribution and characteristics of aeolian sand dunes in Arctic Sweden. Permafrost and Periglac Process. 34(1): 22- 36.


Contact
Melanie.Stammler[at]uni-bonn.com | @mel_stam| https://www.geographie.uni-bonn.de/de/forschung/arbeitsgruppen/ag-schrott/team/melanie-stammler

ESS methods
geomorphological mapping, cartography, system theory, structure from motion photogrammetry, remote sensing

DS methods
tba

Software & tools
GIS, Agisoft Metashape, R, Matlab, drones (Phantom 4 RTK, Mavic 2 Enterprise), DGPS

Libraries
sp, raster, tiff, ggplot2, reshape2, rgdal, caTools, rmarkdown

JOSEFINE UMLAUFT @ ​ ScaDS.AI / Leipzig University
#SEISMOLOGY #GLACIOLOGY #MACHINE LEARNING

Mapping Glacier-Wide Basal Sliding with Artificial Intelligence and Distributed Acoustic Sensing
A profound understanding and the formulation of sliding laws for glacier basal motion are still a big challenge for the scientific community but essentially needed for hazard assessment and the generation of prediction models. Especially for temperate glaciers in Alpine regions, sliding is difficult to monitor with conventional geophysical approaches. On-ice seismological records prove to be a very rich archive of glacial activity, but due to glacial noise from other cryoseismic sources, stick-slip events and tremors from the glacier bed are often masked and remain unnoticed. New approaches are needed which involve on-ice seismological measurements densely sampled in space and time, as well as modern tools that efficiently analyze such large datasets and reveal previously hidden signals.  

For the first time, the passive seismic instrumentation of an entire glacier close to the flow line was realized by researchers from ETH Zurich (Prof. Dr. A. Fichtner & Dr. F. Walter, SNF Spark grant): State of the art DAS (distributed acoustic sensing) technology paved the ground to acquire seismic data with a fiber-optic cable over an extent of 9 km following a zig-zag pattern and covering Rhônegletscher (Swiss Alps) from its accumulation to its ablation zone. The main goal of my current research project is to detect glacier microseismic stick-slip events and in particular tremors along the entire length of Rhônegletscher. This will elucidate the role of frictional sliding in different surface melt and ice-thicknesses regimes.  

This project will be carried out in strong collaboration with ETH Zurich, WSL Zurich, the Los Alamos National Laboratory and Colorado School of Mines. We will combine our core competencies (Cryo-/Seismology, Wave Physics and Data Science) to address the following research questions:  

1. Is subglacial stick-slip sliding a local phenomenon or does it affect the entire extent of a glacier with different surface melt and ice thickness regimes?
2. How do subglacial events respond to changing meteorological conditions, in particular melt-induced surges? 3. Does the distribution of stick-slip activity and changes thereof under different hydraulic conditions allow to predict the stability and failure of steep ice tongues?  

The DAS system acquired about 18 TB of seismic data during one month in spring 2020 along Rhônegletscher. We will establish a pipeline to efficiently preprocess the DAS measurements including de-trending, tapering, integration from strain to strain rate, normalization and the application of different bandpass filters. We will further use the DAS channels with the most possible available data quality and high signal-to-noise ratio for the feature computation. For a moving time window, we will compute statistical features from the continuous seismic records  (e.g., variance, kurtosis, interquantile ranges, mean, skewness) and compile frequency- dependent beamforming catalogues (seismic source locations) which we quantify for a voxeled ice body. Based on these seismic features and additional meteorological parameters from an on-site weather station, we will train a supervised machine learning model (gradient tree boosting, XGBoost implementation) to estimate surface displacement on Rhônegletscher and relate it to stick-slip basal motion. The approach proved to be suitable to observe frictional processes in the laboratory and tectonic environments and might enable us to uncover signals related to sliding that are not traceable by the human eye as they are covered by glacial noise. This would allow us to measure and quantify glacier sliding behavior directly from the surface and thus open completely new perspectives for ice dynamic monitoring. If successful, the factors that drive glacier basal motion could further be revealed through the feature importance.


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Important publications
Umlauft J, Linder F., Roux P, Mikesell DT, Haney MM, Korn M & Walter, F (2021) Stick-Slip Tremor beneath an Alpine Glacier. Geophysical Research Letters. 48: 1-10.

Umlauft J & Korn M (2019) 3D fluid channel location from noise tremors using matched field processing. Geophysical Journal International. 219:1550-1563.



Contact
josefine.umlauft[at]uni-leipzig.de | @JosefineUmlauft

ESS methods
seismic data analysis, beamforming / matched field processing, cross-correlations, finite difference modelling of elastic wavefields

DS methods
signal processing / time series analysis, machine learning (e.g. gradient boosted trees, neural networks / autoencoders)

Software & tools
Python, Matlab, C++, GIS

Libraries
numpy, scipy, pandas, scikit-learn, tensorflow, tensorboard, pytorch, ml flow, obspy

STENKA Vulova @ TU BERLIN

Project title
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Important publications
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Contact
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ESS methods
tba

DS methods
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Software & tools
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Libraries
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NIls weitzel @ UNIVERSITY OF Tübingen
#PALEOCLIMATOLOGY #CLIMATE VARIABILITY #CLIMATE RECONSTRUCTIONS

Evaluation of Earth system model simulations from the Last Glacial Maximum to present-day
The last deglaciation is the transition from the Last Glacial Maximum (~21,000 years before present) to the current warm period, the Holocene. During this transition, carbon dioxide concentrations increased from ~185 ppm to ~280 ppm and large ice sheets over North America and Eurasia retreated entirely. This makes the last deglaciation a valuable period for evaluating if Earth system models (ESMs) are able to reproduce past climate transitions. However, so far no established methodologies exist to compare transient simulations of the last deglaciation with indirect observations of past climate extracted from natural climate archives such as ice and sediment cores, so-called proxy data. Challenges arise from the complex and uncertain relationship between measured proxy and underlying climate evolution, chronological uncertainties of proxy records, and the need to compare typically coarse spatial resolutions of simulations with proxy records reflecting local environmental changes. This project develops workflows for comparing simulated climate changes on different timescales against marine and terrestrial climate proxies building on two prototypes that employ global databases of proxy records.
The first prototype employs paleoclimate network techniques to compare the spatio-temporal characteristics of simulated forest cover with arboreal pollen records (Adam et al. 2021). To better separate biases in the simulated climate from deficiencies in the employed vegetation model, we use statistical emulators of the simulated vegetation response to climate and carbon dioxide changes. The second prototype (Weitzel et al., in prep.) assesses the similarity of timescale-dependent sea surface temperature variations as simulated by ESMs and reconstructed from biological and geochemical proxies. We use a Monte Carlo approach and probabilistic score functions to account for reconstruction uncertainties. Proxy forward models are employed to imitate non-climatic processes influencing the proxies. In my project, I aim at (i) optimizing the design and computational efficiency of the prototypes, (ii) combining the two prototypes for a joint assessment of marine and terrestrial environmental changes, and (iii) creating a standardized workflow following FAIR principles.

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Important publications
Dallmeyer A, Kleinen T, Claussen M, Weitzel N, Cao X & Herzschuh U (2022) The deglacial forest conundrum. Nat Commun 13: 6035.
Adam M, Weitzel N & Rehfeld K (2021) Identifying Global-Scale Patterns of Vegetation Change During the Last Deglaciation from Paleoclimate Networks. Paleoceanography and Paleoclimatology 36: e2021PA004265.
Weitzel N, Hense A & Ohlwein C (2019) Combining a pollen and macrofossil synthesis with climate simulations for spatial reconstructions of European climate using Bayesian filtering. Clim. Past 15: 1275-1301.

Contact
nils.weitzel[at]uni-tuebingen.de | uni-tuebingen.de/en/220707

ESS methods
Paleoclimate reconstructions, Proxy system modelling, Climate modelling

DS methods
Model-data comparison, Time series analysis, Statistical emulation

Software & tools
R, cdo

Libraries
tba

EVA WICKERT @ RWTH AACHEN

Project title
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Important publications
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Contact
tba

ESS methods
tba

DS methods
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Software & tools
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Libraries
tba

INGE WIEKENKAMP @ GERMan Research centre for Geosciences (GFZ) Potsdam
#MICROMETEOROLOGY #HYDROLOGY

Airborne Eddy Covariance
Currently, I am working on airborne eddy covariance measurements to understand the spatial variability in turbulent energy and GHG (greenhouse gas) fluxes. As a state-of-the-art, tower-based eddy covariance measurements are used to understand the turbulent exchange of heat, water and greenhouse gasses between the surface and the atmosphere. These measurements are, however, quite local and only representative for a relatively small area. Airborne measurements can be used to understand regional variability in turbulent fluxes. In my project, I am working with campaign-based eddy covariance measurements (mainly from peatlands in Germany and arctic landscapes), which I am trying to link to spatial surface properties (e.g. satellite, reanalysis data) via e.g. machine learning.

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Important publications
Wiekenkamp, I., Huisman, J.A. , Bogena, H.R. , Lin, H.S., Vereecken, H., 2016. Spatial and temporal occurrence of preferential flow in a forested headwater catchment. Journal of Hydrology, 534, pp 139-149.

Wiekenkamp, I, Huisman, J.A., Bogena, H.R., Graf, A., Lin, H.S., Drüe, C., Vereecken, H., 2016. Changes in measured spatiotemporal patterns of hydrological response after partial deforestation in a headwater catchment. Journal of Hydrology, 542, pp 648–661.

Wiekenkamp, I., Huisman, J.A., Bogena, H.R., Vereecken, H., 2020. Effects of Deforestation on Water Flow in the Vadose Zone. Water, 12(1):35.

Wiekenkamp, I., 2020. Measuring and modelling spatiotemporal changes in hydrological response after partial deforestation. PhD thesis, University of Stuttgart.

Araki, R., Branger, F., Wiekenkamp, I., McMillan, H., 2022. Signature-based approach to quantify soil moisture dynamics under contrasting land-uses. Hydrological Processes, Volume36, Issue4
.


Contact
Inge.wiekenkamp[at]gfz-potsdam.de | @ingewiekenkamp| ingewiekenkamp.weebly.com | LinkedIn | github.com/IngeWiekenkamp

ESS methods
Eddy covariance method, spatial geostatistics (e.g. kriging), hydrological discharge analyses, wavelet analysis, time series analysis, water and soil chemical analysis

DS methods
Regression analysis, spectral analysis, inverse modelling/ optimisation, numerical modelling (finite differences, finite elements), sensitivity analysis

Software & tools
R, Python, MATLAB, QGIS, ArcGIS, Git, docker

Libraries
Pandas, geopandas, rasterio, numpy, jupyter, ggplot2, dplyr, tidyverse ,roxigen2, markdown and eddy4R  

Johann MAximilian Zollner @ ​Technical University of Munich
#Data Mining #Pattern Recognition #Raster Data #Quantum Computing

SoilCarbonHack
The chair of Soil Science at the Technical University of Munich gained novel insights on the importance of the biogeochemical arrangement of soil microstructures for the turnover of organic carbon with a nano- scale secondary ion mass spectrometer (NanoSIMS). In cooperation with the Professorship of Big Geospatial Data Management, further development and adaption of spatial data mining methods, which are already widely applied in the field of remote sensing, will now enable data science in the field of soil science. The goal of SoilCarbonHack is to organize the already massively collected NanoSIMS data and make it usable to answer questions on the dynamics of carbon storage in soils with the analysis of spatial patterns. Thus, we aim to apply data-driven methods and models of Spatial Data Science to the reorganized NanoSIMS data to gain novel insights into the storage mechanisms of organic carbon soils.


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Important publications
Zollner J M (2022) Quantum Classifiers for Remote Sensing. Proceedings of the 30th International Conference in Advances in Geographic Information Systems. https://doi.org/10.1145/3557915.3565537.

Rußwurm M, Pelletier C, Zollner M, Lefèvre S & Körner M (2020) BreizhCrops: A Time Series Dataset for Crop Type Mapping. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS (2020). https://doi.org/10.48550/arXiv.1905.11893.



Contact
maximilian.zollner[at]tum.de | github.com/maxzoll | ORCID

ESS methods
Secondary ion mass spectrometry, Remote sensing

DS methods
Classification, Dimensionality reduction, Autoencoder, Image registration

Software & tools
Git, Jupyter, Docker, Linux

Libraries
Numpy, Matplotlib, Tensorflow, Scikit-learn, Scikit-image, Cirq, PennyLane

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