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Institute
- Institut für Geowissenschaften (143) (remove)
En el presente trabajo se realizó una investigación multidisciplinaria combinando métodos de geomorfología tectónica con estudios geofisicos y estructurales, focalizados principalmente en la caracterización neotectónica de ambos faldeos de la sierra de La Candelaria y del extremo sur de la cuenca de Metán. La zona de estudio se encuentra ubicada en la región limítrofe entre las provincias de Salta y Tucumán y pertenece a la provincia geológica del Sistema Santa Bárbara.
El principal objetivo consistió en contextualizar las evidencias de actividad tectónica cuaternaria de la región mediante la propuesta de un modelo estructural novedoso, con el propósito de incrementar la información disponible sobre estructuras neotectónicas y su potencial sismogénico. Con este fin, se aplicaron e integraron diversas técnicas tales como la interpretación de líneas sísmicas de reflexión, construcción de secciones estructurales balanceadas, y métodos geofísicos someros, para constatar el comportamiento en profundidad tanto de las estructuras geológicas identificadas en superficie como de las posibles fallas ciegas corticales involucradas.
En primer lugar, se realizó un relevamiento regional del área de estudio empleando imágenes satelitales multiespectrales LANDSAT y SENTINEL 2, que permitieron reconocer diferentes niveles de abanicos aluviales y terrazas fluviales cuaternarios. Mediante la determinación de diferentes indicadores morfométricos en modelos de elevación digital (MED), junto con observaciones de campo, fue posible identificar evidencias de deformación sobre dichos niveles cuaternarios que han sido relacionadas genéticamente con cuatro fallas neotectónicas. Tres de ellas (fallas Arias, El Quemado y Copo Quile) fueron seleccionadas para efectuar estudios de mayor detalle por medio de la aplicación de métodos de geofísica somera (tomografía eléctrica resistiva (ERT) y tomografía sísmica de refracción Sísmica (SRT)), que permitieron corroborar su existencia en profundidad, realizar inferencias geométricas y cinemáticas, y estimar la magnitud de la deformación reciente. Las fallas Arias y El Quemado fueron interpretadas como fallas inversas relacionadas con deslizamiento flexural interstratal, mientras que la falla Copo Quile se interpretó como una falla inversa ciega de bajo ángulo.También se realizó una interpretación conjunta de líneas sísmicas de reflexión y pozos exploratorios pertenecientes a áreas hidrocarburíferas de las cuencas de Choromoro y Metán con el fin de contextualizar las principales estructuras reconocidas en el marco estratigráfico y tectónico regional. Toda la información fue integrada en una sección estructural balanceada mediante técnicas de modelado cinemático. Dicho modelo permite inferir que la deformación cuaternaria reconocida está relacionada al desplazamiento del basamento a lo largo de un corrimiento ciego, responsable del levantamiento de la sierra de La Candelaria y el cerr Cantero. Asimismo, el modelo cinemático permite interpretar la ubicación aproximada de los principales niveles de despegue que controlan el estilo de deformación. El nivel de despegue más somero, que controla la deformación de la cobertura sedimentaria se encuentra a 4 km de profundidad, a 21 km se estima la presencia de otra zona de cizalla subhorizontal dentro del basamento.
Finalmente, a partir de la integración de todos los resultados obtenidos, se evaluó el potencial sismogénico de las fallas en la zona de estudio. Las fallas de primer orden que controlan la deformación en la zona son las responsables de los grandes terremotos. Mientras, las fallas Cuaternarias flexodeslizantes e inversas afectan solamente a la cobertura sedimentaria y serían estructuras de segundo orden que acomodan la deformación y fueron activadas durante el cuaternario con movimientos asísmicos y/o sísmicos de muy baja magnitud.
Estos resultados permiten inferir que el corrimiento La Candelaria constituye una fuente sismogénica potencial de importancia para la región, donde se ubican numerosas poblaciones y obras civiles de envergadura. Por otra parte, la sección estructural balanceada implica la presencia de otras fallas ciegas de distinto orden de magnitud que podrían ser posibles fuentes sismogénicas profundas adicionales, marcando la necesidad de continuar con el desarrollo de este tipo de estudios en esta región tectónicamente activa.
El flanco oriental de los Andes Centrales en el noroeste argentino es una zona caracterizada por serranías limitadas por fallas inversas que conforman un orógeno de piel gruesa activo con un patrón espacio-temporal no sistemático de deformación contraccional. Este patrón queda representado tanto por la dispersión de la actividad sísmica cortical como de la localización de las estructuras cuaternarias a través de la Cordillera Oriental y el Sistema de Santa Bárbara, configurando un frente orogénico difuso de más de 200 km de extensión. El estudio de la actividad neotectónica en esta región ha tomado más relevancia en los últimos años, mediante la aplicación de herramientas variadas, incluyendo técnicas de geomorfología tectónica, herramientas de teledetección, geodesia y estudios de campo convencionales. Los depósitos lacustres han demostrado ser, en numerosos ejemplos, excelentes marcadores de la actividad tectónica, dadas la horizontalidad original de sus capas y la susceptibilidad a los cambios del entorno. Es por ello que en este trabajo se analizaron los depósitos lacustres que afloran en el sector central de los valles Calchaquíes (región de Cafayate), para comprender cómo se acomoda la deformación cuaternaria en una de las cuencas intermontanas de la cuña orogénica activa.
El rumbo de las estructuras cuaternarias en el área de estudio es subparalelo al de las fallas que exhuman los cordones serranos circundantes. A partir del estudio estratigráfico, morfotectónico y estructural de los depósitos lacustres, se identificó un mínimo de cinco episodios de deformación afectando a la columna estratigráfica cuaternaria. Integrando perfiles estructurales balanceados con edades obtenidas en este trabajo y recopiladas de la bibliografía, se calcularon para el Pleistoceno mediotardío, tasas mínimas y máximas de acortamiento que varían entre 0,19-2,80 y 0,21-4,47 mm/a, respectivamente. Para comparar estos resultados con mediciones de la tectónica activa a escala regional se recopilaron datos de estaciones geodésicas del noroeste argentino, con los cuales se elaboró un perfil de velocidades horizontales. El perfil obtenido muestra un decrecimiento gradual de los vectores hacia el este, indicando actividad interna del orógeno en congruencia con los registros de actividad sísmica y compilación regional de las estructuras cuaternarias.
Además de la caracterización neotectónica de este sector de la Cordillera Oriental, el análisis estratigráfico de los depósitos lacustres ha permitido refinar la evolución geológica del sector central de los valles Calchaquíes durante el Cuaternario. De esta manera se han identificado al menos siete episodios de inundación lacustre relacionados con la desconexión del sistema fluvial con su nivel de base, dando lugar a sucesivos eventos de agradación y erosión. Las cotas máximas alcanzadas por los paleolagos, en conjunto con un modelo hidrológico previamente publicado para esta región, permitieron asimismo efectuar una comparación con el registro paleoclimático regional.
Los resultados de esta tesis representan un aporte significativo al conocimiento de la evolución tectónica y estratigráfica del sector central de los valles Calchaquíes durante el Cuaternario. Por otra parte, su integración a escala regional contribuye a comprender mejor la dinámica de la deformación en la cuña orogénica de piel gruesa del noroeste argentino.
Anthropogenic climate change alters the hydrological cycle. While certain areas experience more intense precipitation events, others will experience droughts and increased evaporation, affecting water storage in long-term reservoirs, groundwater, snow, and glaciers. High elevation environments are especially vulnerable to climate change, which will impact the water supply for people living downstream. The Himalaya has been identified as a particularly vulnerable system, with nearly one billion people depending on the runoff in this system as their main water resource. As such, a more refined understanding of spatial and temporal changes in the water cycle in high altitude systems is essential to assess variations in water budgets under different climate change scenarios.
However, not only anthropogenic influences have an impact on the hydrological cycle, but changes to the hydrological cycle can occur over geological timescales, which are connected to the interplay between orogenic uplift and climate change. However, their temporal evolution and causes are often difficult to constrain. Using proxies that reflect hydrological changes with an increase in elevation, we can unravel the history of orogenic uplift in mountain ranges and its effect on the climate.
In this thesis, stable isotope ratios (expressed as δ2H and δ18O values) of meteoric waters and organic material are combined as tracers of atmospheric and hydrologic processes with remote sensing products to better understand water sources in the Himalayas. In addition, the record of modern climatological conditions based on the compound specific stable isotopes of leaf waxes (δ2Hwax) and brGDGTs (branched Glycerol dialkyl glycerol tetraethers) in modern soils in four Himalayan river catchments was assessed as proxies of the paleoclimate and (paleo-) elevation. Ultimately, hydrological variations over geological timescales were examined using δ13C and δ18O values of soil carbonates and bulk organic matter originating from sedimentological sections from the pre-Siwalik and Siwalik groups to track the response of vegetation and monsoon intensity and seasonality on a timescale of 20 Myr.
I find that Rayleigh distillation, with an ISM moisture source, mainly controls the isotopic composition of surface waters in the studied Himalayan catchments. An increase in d-excess in the spring, verified by remote sensing data products, shows the significant impact of runoff from snow-covered and glaciated areas on the surface water isotopic values in the timeseries.
In addition, I show that biomarker records such as brGDGTs and δ2Hwax have the potential to record (paleo-) elevation by yielding a significant correlation with the temperature and surface water δ2H values, respectively, as well as with elevation. Comparing the elevation inferred from both brGDGT and δ2Hwax, large differences were found in arid sections of the elevation transects due to an additional effect of evapotranspiration on δ2Hwax. A combined study of these proxies can improve paleoelevation estimates and provide recommendations based on the results found in this study.
Ultimately, I infer that the expansion of C4 vegetation between 20 and 1 Myr was not solely dependent on atmospheric pCO2, but also on regional changes in aridity and seasonality from to the stable isotopic signature of the two sedimentary sections in the Himalaya (east and west).
This thesis shows that the stable isotope chemistry of surface waters can be applied as a tool to monitor the changing Himalayan water budget under projected increasing temperatures. Minimizing the uncertainties associated with the paleo-elevation reconstructions were assessed by the combination of organic proxies (δ2Hwax and brGDGTs) in Himalayan soil. Stable isotope ratios in bulk soil and soil carbonates showed the evolution of vegetation influenced by the monsoon during the late Miocene, proving that these proxies can be used to record monsoon intensity, seasonality, and the response of vegetation. In conclusion, the use of organic proxies and stable isotope chemistry in the Himalayas has proven to successfully record changes in climate with increasing elevation. The combination of δ2Hwax and brGDGTs as a new proxy provides a more refined understanding of (paleo-)elevation and the influence of climate.
"In spite of ever-increasing research into natural hazards, the reported damage from natural disasters continues to rise, increasingly disrupting human activities. We, as scientists who study the way in which the part of Earth most relevant to society- the surface-behaves, are disturbed and frustrated by this trend. It appears that the large amounts of funding devoted each year to research into reducing the impacts of natural disasters could be much more effective in producing useful results. At the same time we are aware that society, as represented by its decision makers, while increasingly concerned at the impacts of natural disasters on lives and economies, is reluctant to acknowledge the intrinsic activity of Earth's surface and to take steps to adapt societal behaviour to minimise the impacts of natural disasters. Understanding and managing natural hazards and disasters are beyond matters of applied earth science, and also involve considering human societal, economic and political decisions"
Centroid moment tensor inversion can provide insight into ongoing tectonic processes and active faults. In the Alpine mountains (central Europe), challenges result from low signal-to-noise ratios of earthquakes with small to moderate magnitudes and complex wave propagation effects through the heterogeneous crustal structure of the mountain belt. In this thesis, I make use of the temporary installation of the dense AlpArray seismic network (AASN) to establish a work flow to study seismic source processes and enhance the knowledge of the Alpine seismicity. The cumulative thesis comprises four publications on the topics of large seismic networks, seismic source processes in the Alps, their link to tectonics and stress field, and the inclusion of small magnitude earthquakes into studies of active faults.
Dealing with hundreds of stations of the dense AASN requires the automated assessment of data and metadata quality. I developed the open source toolbox AutoStatsQ to perform an automated data quality control. Its first application to the AlpArray seismic network has revealed significant errors of amplitude gains and sensor orientations. A second application of the orientation test to the Turkish KOERI network, based on Rayleigh wave polarization, further illustrated the potential in comparison to a P wave polarization method. Taking advantage of the gain and orientation results of the AASN, I tested different inversion settings and input data types to approach the specific challenges of centroid moment tensor (CMT) inversions in the Alps. A comparative study was carried out to define the best fitting procedures.
The application to 4 years of seismicity in the Alps (2016-2019) substantially enhanced the amount of moment tensor solutions in the region. We provide a list of moment tensors solutions down to magnitude Mw 3.1. Spatial patterns of typical focal mechanisms were analyzed in the seismotectonic context, by comparing them to long-term seismicity, historical earthquakes and observations of strain rates. Additionally, we use our MT solutions to investigate stress regimes and orientations along the Alpine chain. Finally, I addressed the challenge of including smaller magnitude events into the study of active faults and source processes. The open-source toolbox Clusty was developed for the clustering of earthquakes based on waveforms recorded across a network of seismic stations. The similarity of waveforms reflects both, the location and the similarity of source mechanisms. Therefore the clustering bears the opportunity to identify earthquakes of similar faulting styles, even when centroid moment tensor inversion is not possible due to low signal-to-noise ratios of surface waves or oversimplified velocity models. The toolbox is described through an application to the Zakynthos 2018 aftershock sequence and I subsequently discuss its potential application to weak earthquakes (Mw<3.1) in the Alps.
DeepGeoMap
(2021)
In recent years, deep learning improved the way remote sensing data is processed. The classification of hyperspectral data is no exception. 2D or 3D convolutional neural networks have outperformed classical algorithms on hyperspectral image classification in many cases. However, geological hyperspectral image classification includes several challenges, often including spatially more complex objects than found in other disciplines of hyperspectral imaging that have more spatially similar objects (e.g., as in industrial applications, aerial urban- or farming land cover types). In geological hyperspectral image classification, classical algorithms that focus on the spectral domain still often show higher accuracy, more sensible results, or flexibility due to spatial information independence. In the framework of this thesis, inspired by classical machine learning algorithms that focus on the spectral domain like the binary feature fitting- (BFF) and the EnGeoMap algorithm, the author of this thesis proposes, develops, tests, and discusses a novel, spectrally focused, spatial information independent, deep multi-layer convolutional neural network, named 'DeepGeoMap’, for hyperspectral geological data classification. More specifically, the architecture of DeepGeoMap uses a sequential series of different 1D convolutional neural networks layers and fully connected dense layers and utilizes rectified linear unit and softmax activation, 1D max and 1D global average pooling layers, additional dropout to prevent overfitting, and a categorical cross-entropy loss function with Adam gradient descent optimization. DeepGeoMap was realized using Python 3.7 and the machine and deep learning interface TensorFlow with graphical processing unit (GPU) acceleration. This 1D spectrally focused architecture allows DeepGeoMap models to be trained with hyperspectral laboratory image data of geochemically validated samples (e.g., ground truth samples for aerial or mine face images) and then use this laboratory trained model to classify other or larger scenes, similar to classical algorithms that use a spectral library of validated samples for image classification. The classification capabilities of DeepGeoMap have been tested using two geological hyperspectral image data sets. Both are geochemically validated hyperspectral data sets one based on iron ore and the other based on copper ore samples. The copper ore laboratory data set was used to train a DeepGeoMap model for the classification and analysis of a larger mine face scene within the Republic of Cyprus, where the samples originated from. Additionally, a benchmark satellite-based dataset, the Indian Pines data set, was used for training and testing. The classification accuracy of DeepGeoMap was compared to classical algorithms and other convolutional neural networks. It was shown that DeepGeoMap could achieve higher accuracies and outperform these classical algorithms and other neural networks in the geological hyperspectral image classification test cases. The spectral focus of DeepGeoMap was found to be the most considerable advantage compared to spectral-spatial classifiers like 2D or 3D neural networks. This enables DeepGeoMap models to train data independently of different spatial entities, shapes, and/or resolutions.
Permafrost is warming globally, which leads to widespread permafrost thaw and impacts the surrounding landscapes, ecosystems and infrastructure. Especially ice-rich permafrost is vulnerable to rapid and abrupt thaw, resulting from the melting of excess ground ice. Local remote sensing studies have detected increasing rates of abrupt permafrost disturbances, such as thermokarst lake change and drainage, coastal erosion and RTS in the last two decades. All of which indicate an acceleration of permafrost degradation.
In particular retrogressive thaw slumps (RTS) are abrupt disturbances that expand by up to several meters each year and impact local and regional topographic gradients, hydrological pathways, sediment and nutrient mobilisation into aquatic systems, and increased permafrost carbon mobilisation. The feedback between abrupt permafrost thaw and the carbon cycle is a crucial component of the Earth system and a relevant driver in global climate models. However, an assessment of RTS at high temporal resolution to determine the dynamic thaw processes and identify the main thaw drivers as well as a continental-scale assessment across diverse permafrost regions are still lacking.
In northern high latitudes optical remote sensing is restricted by environmental factors and frequent cloud coverage. This decreases image availability and thus constrains the application of automated algorithms for time series disturbance detection for large-scale abrupt permafrost disturbances at high temporal resolution. Since models and observations suggest that abrupt permafrost disturbances will intensify, we require disturbance products at continental-scale, which allow for meaningful integration into Earth system models.
The main aim of this dissertation therefore, is to enhance our knowledge on the spatial extent and temporal dynamics of abrupt permafrost disturbances in a large-scale assessment. To address this, three research objectives were posed:
1. Assess the comparability and compatibility of Landsat-8 and Sentinel-2 data for a combined use in multi-spectral analysis in northern high latitudes.
2. Adapt an image mosaicking method for Landsat and Sentinel-2 data to create combined mosaics of high quality as input for high temporal disturbance assessments in northern high latitudes.
3. Automatically map retrogressive thaw slumps on the landscape-scale and assess their high temporal thaw dynamics.
We assessed the comparability of Landsat-8 and Sentinel-2 imagery by spectral comparison of corresponding bands. Based on overlapping same-day acquisitions of Landsat-8 and Sentinel-2 we derived spectral bandpass adjustment coefficients for North Siberia to adjust Sentinel-2 reflectance values to resemble Landsat-8 and harmonise the two data sets. Furthermore, we adapted a workflow to combine Landsat and Sentinel-2 images to create homogeneous and gap-free annual mosaics. We determined the number of images and cloud-free pixels, the spatial coverage and the quality of the mosaic with spectral comparisons to demonstrate the relevance of the Landsat+Sentinel-2 mosaics. Lastly, we adapted the automatic disturbance detection algorithm LandTrendr for large-scale RTS identification and mapping at high temporal resolution. For this, we modified the temporal segmentation algorithm for annual gradual and abrupt disturbance detection to incorporate the annual Landsat+Sentinel-2 mosaics. We further parametrised the temporal segmentation and spectral filtering for optimised RTS detection, conducted further spatial masking and filtering, and implemented a binary object classification algorithm with machine-learning to derive RTS from the LandTrendr disturbance output. We applied the algorithm to North Siberia, covering an area of 8.1 x 106 km2.
The spectral band comparison between same-day Landsat-8 and Sentinel-2 acquisitions already showed an overall good fit between both satellite products. However, applying the acquired spectral bandpass coefficients for adjustment of Sentinel-2 reflectance values, resulted in a near-perfect alignment between the same-day images. It can therefore be concluded that the spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to those of Landsat-8 in North Siberia.
The number of available cloud-free images increased steadily between 1999 and 2019, especially intensified after 2016 with the addition of Sentinel-2 images. This signifies a highly improved input database for the mosaicking workflow. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas, while Landsat-only mosaics contained data-gaps for the same years. The spectral comparison of input images and Landsat+Sentinel-2 mosaic showed a high correlation between the input images and the mosaic bands, testifying mosaicking results of high quality. Our results show that especially the mosaic coverage for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining data from both Landsat and Sentinel-2 sensors we reliably created input mosaics at high spatial resolution for comprehensive time series analyses.
This research presents the first automatically derived assessment of RTS distribution and temporal dynamics at continental-scale. In total, we identified 50,895 RTS, primarily located in ice-rich permafrost regions, as well as a steady increase in RTS-affected areas between 2001 and 2019 across North Siberia. From 2016 onward the RTS area increased more abruptly, indicating heightened thaw slump dynamics in this period. Overall, the RTS-affected area increased by 331 % within the observation period. Contrary to this, five focus sites show spatiotemporal variability in their annual RTS dynamics, alternating between periods of increased and decreased RTS development. This suggests a close relationship to varying thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period. This highlights that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by newly initiated RTS.
Overall, this research showed the advantages of combining Landsat and Sentinel-2 data in northern high latitudes and the improvements in spatial and temporal coverage of combined annual mosaics. The mosaics build the database for automated disturbance detection to reliably map RTS and other abrupt permafrost disturbances at continental-scale. The assessment at high temporal resolution further testifies the increasing impact of abrupt permafrost disturbances and likewise emphasises the spatio-temporal variability of thaw dynamics across landscapes. Obtaining such consistent disturbance products is necessary to parametrise regional and global climate change models, for enabling an improved representation of the permafrost thaw feedback.
Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes.
Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes.