06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access A method for evaluating population and infrastructure exposed to natural hazards : tests and results for two recent Tonga tsunamis(2023) Thomas, Bruce Enki Oscar; Roger, Jean; Gunnell, Yanni; Ashraf, SalmanBackground: Coastal communities are highly exposed to ocean- and -related hazards but often lack an accurate population and infrastructure database. On January 15, 2022 and for many days thereafter, the Kingdom of Tonga was cut off from the rest of the world by a destructive tsunami associated with the Hunga Tonga Hunga Ha’apai volcanic eruption. This situation was made worse by COVID-19-related lockdowns and no precise idea of the magnitude and pattern of destruction incurred, confirming Tonga’s position as second out of 172 countries ranked by the World Risk Index 2018. The occurrence of such events in remote island communities highlights the need for (1) precisely knowing the distribution of buildings, and (2) evaluating what proportion of those would be vulnerable to a tsunami.
Methods and Results: A GIS-based dasymetric mapping method, previously tested in New Caledonia for assessing and calibrating population distribution at high resolution, is improved and implemented in less than a day to jointly map population clusters and critical elevation contours based on runup scenarios, and is tested against destruction patterns independently recorded in Tonga after the two recent tsunamis of 2009 and 2022. Results show that ~ 62% of the population of Tonga lives in well-defined clusters between sea level and the 15 m elevation contour. The patterns of vulnerability thus obtained for each island of the archipelago allow exposure and potential for cumulative damage to be ranked as a function of tsunami magnitude and source area.
Conclusions: By relying on low-cost tools and incomplete datasets for rapid implementation in the context of natural disasters, this approach works for all types of natural hazards, is easily transferable to other insular settings, can assist in guiding emergency rescue targets, and can help to elaborate future land-use planning priorities for disaster risk reduction purposes.Item Open Access Analysis of water volume change of the lakes and reservoirs in the Mississippi River basin using Landsat imagery and satellite altimetry(2021) Wang, LingkeIn recent years, the demand for freshwater has been steadily increasing owing to population growth and economic expansion. Surface waters such as lakes and reservoirs function as a dominant factor in mankind's freshwater provision. Analysis of changes in their water storage is consequently vital for understanding of the global water cycle and water resources. However, the water volume changes in lakes or reservoirs cannot be measured directly from space, but can be inferred from lake areas and lake water levels. Lake area can be measured globally from space but lake water level is not easy to be obtained globally. Because the number of in situ stations is few, and in situ data are only accessible for some lakes with few measurement epochs, despite in situ stations can measure lake water level and provide high accuracy observations. Although the altimetry technique can generate the time series of the water level for the majority of lakes, they are not global coverage due to the distance between satellite tracks and the gap between different missions. Therefore, in situ data and satellite altimetry measurements of water levels of lakes and reservoirs are not always available. For example, there are only 22 lakes or reservoirs in this study covered by satellite altimetry or in situ stations out of 90 research cases in Mississippi River Basin. Then, in case of unavailable in situ data or altimetry measurements, this research proposes an alternative method to estimate the water level through Digital Elevation Model (DEM). Because satellite imagery offers global coverage and DEM is the global digital representation of the land surface elevation with respect to any reference datum, this study allows for the evaluation of global water volume changes by acquiring lake area data from space and lake height data from DEM. Therefore, the objective of this study is that changes in water volume in lakes or reservoirs can be successfully monitored even when in situ data and satellite altimetry measurements are not available for lakes or reservoirs. Hereby, we investigate 90 lakes and reservoirs in the Mississippi River Basin and develop an alternative remote sensing technique to monitor the water volume changes by combining the improved water mask with DEM. Meanwhile, we propose practical methods to detect the shoreline pixels of the water body from improved water mask. Given the assumption that all pixels in the shoreline should have the same height, four water level estimation models are developed, including water level estimation model based on statistical analysis, frequency maps, change pixels and pixel pair analysis. To this end, the study estimates the time series of lake height from water level estimation model and obtains the time series of lake surface area from HydroSat. Subsequently, this study builds the unique function between the lake water level and the lake surface area and then develops the function between the lake water volume change and the lake surface area. Finally, this study analyses the water volume changes of lakes and reservoirs in the Mississippi River Basin using this alternative remote sensing method. Four water level estimation models are proposed and evaluated. They are respectively based on statistical analysis, frequency maps, change pixels and pixel pair analysis. As a result of their actions, the first model based on statistical analysis, with an average correlation of 0.62 and an average RMSE of 0.91 meters, functions in the majority of situations and demonstrates excessive outlier removal in some cases. The second model based on frequency maps is more general than the first, with an average correlation of 0.66 and an average RMSE of 1.11 meters. The average correlation for the third model based on change pixels is 0.71, and the average RMSE is 0.99 meters. The resulting model based on pixel pair analysis obtains a mean correlation of 0.67 and a mean RMSE of 1.00 meters. Finally, these models behave differently in different seasons, so they exhibit distinct monthly behaviour. To conclude, the above validation results show that this alternative method can be used in different lakes and reservoirs in case of absence of water level observation data, and achieve to monitor the water volume changes during a long period.Item Open Access The GRACE event calendar(2012) Vishwakarma, Bramha DuttGRACE mission is a joint venture of NASA and GFZ. This mission was launched to provide with unprecedented accuracy, estimates of the global high resolution models of the Earth’s gravity field. The study of time-variability of Earth’s gravity field is very helpful in climate sciences and earth’s sciences studies. People have done a lot of work to demonstrate the effect of many natural phenomenon on gravity. Gravity estimates from GRACE are used for estimating mass redistribution at continental scale. So, we can observe hydrology, seismology and glaciology potential areas where GRACE can be useful. This research work focuses on identifying the hydrological events such as floods and drought, seismic events such as earthquakes and volcanic activity and also the glacier melting in the GRACE time-series. The work includes the development of strategy for the analysis of these events keeping in mind their behaviour and GRACE limitations of spatial resolution and sensitivity. Further in this work we would produce a event calendar for such events stating whether gravity changes caused by such events are visible to GRACE. Calendars are generated for hydrological events, floods and droughts separately and also for earthquake events. For rest of the phenomenon we have not generated calendars since these events are very few in numbers. This work is a qualitative analysis, so we could observe whether GRACE signal is able to observe these events or not. Hydrological events are observed by searching outliers in the grace observed time-series. The large floods such as 2009 Amazon floods can be seen when we take whole catchment, but the small floods affecting smaller region such as Sao Paulo flood is not visible in catchment time-series, so we have to go for selected area time-series generation. The factors such as time period for floods and droughts are very important factors when we want to observe them by GRACE. Earthquakes visibility depends on range rate amplitude, and also the quality of ΔC20, we have discussed these aspects while analysing earthquakes occurred in last decade from GRACE. We have given the possible explanation for the events not visible, and those visible have helped in the development of a methodology for analysis of a particular event. The volcanic activity in Caldera and Bolivia are pushing earth upward so we can expect some signal, but the spatial extent of these areas is small with caldera area greater than that of Bolivia, only caldera showed a trend. We also did trend analysis for 2 Asian glaciers and a part of Greenland for observing the melting of these ice masses. The work finally produces a series of events which we were able to observe by GRACE and we also get the methodology suitable for analysis of an event.Item Open Access Variance-covariance matrix estimation with LSQR in a parallel programming environment(2008) Guo, RonggangKnowledge about the gravity field allows an insight into the structure and dynamics of the earth. It provides the geoid as the most important physical reference surface in geodesy and oceanography. Since 2000, the CHAMP (CHAllenging Mini-satellite Payload) mission detects the structure of the global gravity field, followed by the launch of GRACE (Gravity Recovery And Climate Experiment) in 2002. In 2008, finally, the GOCE (Gravity field and steady-state Ocean Circulation Explorer) satellite is supposed to be set in orbit. These missions demonstrate satellite-based gravity field recovery to be at the center of geo-scientific interest. Interpretation and evaluation of satellite observations are difficult, especially the determination of the unknown gravity field parameters from a huge amount of measurements. Because of the immense demand for memory and computing time, the occurring systems of equations pose a real numerical challenge. Therefore, High-Performance Computing (HPC) is commonly adopted to overcome computational problems. Basically, parallel programming with MPI and OpenMP routines allows to speed up the solution process considerably. In this thesis, firstly global gravity field modelling by means of satellite observations is reviewed. Secondly, the LSQR method (Least-Squares using QR factorization) is introduced in detail in order to solve the resulting least-squares problems. Because the LSQR method is an iterative solver, it basically can not provide the variance-covariance information of the parameter estimate. To investigate the approximate computation of the variance-covariance matrix, two methods are introduced. The first one is based on the generalized inverse of the design matrix. The second approach applies Monte-Carlo integration techniques. Because parallel programming is very helpful to implement such iterative methods, it is necessary to introduce some basic principles and concepts about HPC.Item Open Access Noise performance of the modernized GPS and GALILEO systems(2010) Gerst, SebastianWith the modernization of GPS and the development of the new European satellite system Galileo, the noise performance of the newly introduced signals is understood insufficiently. Today, these new signals are not used operationally, but they are implemented in two satellites in the case of GPS and two Galileo test satellites. Both systems are emitting the new and conventional signals, which are recorded by a Septentrio receiver. The main goal of this thesis is to split the portion from the received signal, which is dependent on the temporal trend. On the one hand the satellite-receiver geometry, on the other hand the ionospherical runtime error. The resulting signal can be interpreted as noise, of which the correlation behavior and the power spectrum should be discussed.Item Open Access GOCE data and gravity field model filter comparison(2008) Raizner, CarinaNew approaches with respect to space borne gravity observations are expected to significantly improve the overall knowledge of the Earth's gravity field and its geoid. The Gravity field and steady-state Ocean Circulation Explorer (GOCE) is the first Core Earth Explorer Mission of the ESA Living Planet Programme. This new satellite mission based on the concept of satellite gradiometry is designed to support applications in Earth physics, oceanography and geodesy with an accurate and detailed global model of the Earth's gravity field and its geoid. One of the main problems in the use of the GOCE data is that the retrieval algorithms need along-track filtering on one hand and/or the implementation of spherical filters on the other. The match between these along-track one-dimensional filters and the spherical two-dimensional ones is far from obvious. Thus, the objective of this study is to investigate the influences of these two filter types by analyzing the differences between simulated GOCE reference and filtered data. Apart from closed-loop tests in order to check the consistency and correctness of the data and software used, the testing procedure for along-track as well as spherical filtering is implemented as follows. First, a global reference model is used for data generation which yields a reference signal along the orbit. By applying a one-dimensional along-track filter to these synthetic satellite data, a filtered global model is retrieved. On the other hand, the synthetic satellite data can be also generated after applying spherical filters to the global reference model. The outcome is a filtered global model estimated from these synthetic satellite data. The influences of both filter types are assessed by comparing the reference and filtered signals along the orbit as well as by comparing the reference and filtered models on the ground. Additionally, the properties of both filter types can be varied. In order to examine the empirical relation between along-track and spherical filters, transfer functions of the filters are investigated in a second step of this study. The transfer function for the spherical filter in the model domain is the ratio between reference and filtered signal which represents a corresponding one dimensional along-track filter in the signal domain. On the other hand, computing the ratio between reference and filtered model estimated from the along-track filtered signal relates the one-dimensional filter in the signal domain to a two-dimensional spherical filter in the model domain. The outcome of the study will be very useful for explaining some of the differences between current global model retrieval philosophies and will also be applicable to other satellite missions and data types in the future.Item Open Access Understanding the limitations of Sentinel-3 inland altimetry through validation over the Rhine River(2022) Schneider, Nicholas M.Satellite altimetry is developing into one of the most powerful measurement techniques for long-term water body monitoring thanks to its high spatial resolution and its increasing level of precision. Although the principle of satellite altimetry is very straightforward, the retrieval of correct water levels remains rather difficult due to various factors. Waveform retracking is an approach to optimize the initially determined range between the satellite and the water body on Earth by exploiting the information within the power-signal of the returned radar pulse to the altimeter. Several so-called retrackers have been designed to this end, yet remain one of the most open study areas in satellite altimetry due to their crucial role they play in water level retrieval. Moreover, geophysical properties of the stratified atmosphere and the target on Earth have an effect on the travel time of the transmitted radar pulse and can amount to severalmeters in range. In this study we provide an overall analysis of the performances of the retrackers dedicated to the Sentinel-3 mission and the applied geophysical corrections. For this matter, we focus on nine different locations within the Rhine River basin where locally gauged data is available to validate the Sentinel-3 level-2 products. Furthermore, we present a reverse retracking approach in the sense that we use the given in-situ data to determine the offset to each altimetry-derived measurement of every epoch. Under the assumption that these offsets are legitimate, they can be seen as an a-posteriori correction which we project onto the range and thus on a waveform level. Further analyses consist in the investigation of the relationship these a-posteriori corrections have to the waveform properties of the same epoch. Later, the question whether the a-posteriori corrections to the initial retracking gates are appropriate for the retrieval of correct water levels, drives us to assign a probability to each and every bin of the waveform. Following this idea, we design stochastic-based retrackers which determine the retracking gate for water level retrieval from the bin with the highest probability assigned to it. To distribute the probabilities across all bins of the waveform, we consider three empirical approaches that take both the waveform itself and its first derivative into account: Addition, multiplication and maximum of both signals. For all three of the new retrackers, we generate the water level timeseries over the aforementioned sites and validate them against in-situ data and the retrackers dedicated to the Sentinel-3 mission.Item Open Access Analyzing global river discharge changes using remote sensing-based and in situ data(2025) Ren, YufanUnderstanding how river discharge changes across space and time is fundamental for hydrologic science and water management. Given the reduction in global in situ observations, remote sensing and reanalysis products have become key data supplements. In 2024, Feng and Gleason released the Global River Discharge Reanalysis (GRDR) dataset, concluding that there is now "more flow upstream and less flow downstream" in global rivers. Motivated by their research, this study evaluates the quality of the GRDR dataset and establishes a "Remote Sensing-based and In situ" (RSI) dataset -combining remote sensing estimates with gauge observations - to analyze spatiotemporal trends in global river discharge from 1984 to 2018. First, an independent validation of GRDR was conducted using a more extensive global network of ground observations. The assessment reveals that GRDR’s overall performance is lower than reported in the original study, particularly for stream orders 1, 6, 7, and ≥ 8. For stream orders ≥ 8, the median Nash-Sutcliffe Efficiency (NSE) in this study is approximately 1.4 lower than that in the original report. Comparisons of GRDR with remote sensing (RS) derived data reveal that RS data outperforms GRDR in stream orders 1, 7, and ≥8. GRDR exhibits a tendency to severely underestimate discharge in stream orders 1 and 2, with a significant fraction of its relative bias falling below −50%. Based on the RSI dataset, long-term trends, seasonality, and the Longitudinal Hydrographic Distribution Index (LHDI) were further analyzed. Results show an increasing trend in discharge for river orders 1 to 5 and 7 (with a significant increase of ∼0.19%/year in order 4 rivers), whereas discharge in orders 6 and ≥8 is decreasing. Seasonally, for all stream order groups, discharge typically increases from March to May, whereas it decreases from December to February. Furthermore, the LHDI confirms that the global discharge centroid is shifting upstream. These conclusions are generally consistent with those derived by Feng and Gleason using the GRDR dataset. In summary, while the limitations of the GRDR dataset are highlighted through independent validation, the findings of this study ultimately support the conclusion that global river systems are experiencing increased upstream flow and decreased downstream flow.Item Open Access The potential of EO data for enhanced flood monitoring and forecasting : a consortium assessment(2026) Tarpanelli, Angelica; Massari, Christian; Revilla-Romero, Beatriz; Tourian, Mohammad J.; Saemian, Peyman; Elmi, Omid; Scherer, Daniel; Pedinotti, Vanessa; Kittel, Cecile; Benveniste, Jérôme; Bauer-Gottwein, Peter; Ciabatta, Luca; Chewning, Connor; Barbetta, Silvia; Filippucci, Paolo; Cantoni, Èlia; Dettmering, Denise; Andersson, Jafet; Gal, Laetitia; Gustafsson, David; Hundecha, Yeshewatesfa; Larnicol, Gilles; Larnier, Kevin; Nielsen, Karina; Paris, Adrien; Sadki, Malak; Schwatke, Christian; Tamagnone, Paolo; Vrettou, Artemis; Douch, Karim; Volden, Espen; Schumann, GuyThe monitoring and modeling of riverine floods have been covered extensively in the scientific literature with a substantial number of scientific contributions related to calibration/validation of hydraulic and hydrological models and assimilation of Earth Observation (EO) data into them. These models, when used for flood forecasting purposes, rely heavily on ground-based hydrological networks along with numerical weather models which, particularly in data-scarce regions, are often challenged by data sparsity. In these situations, EO data offer a viable solution to enhance the skill of these flood forecasting systems by providing global-scale observations of key hydrological variables such as precipitation, soil moisture, river discharge, water levels, and flood extent. This manuscript reviews and discusses the capability of these EO data in enhancing flood forecasting systems, by analyzing their accuracy, lead time, and reliability, while at the same time highlighting key challenges such as data latency, spatial–temporal resolution trade-offs, and model assimilation constraints. By leveraging recent advancements in remote sensing, data assimilation techniques, and artificial intelligence, EO-based flood forecasting has the potential to bridge existing observational gaps, particularly in vulnerable regions. The paper also outlines future research directions and technological developments needed to maximize the impact of satellite data in operational flood forecasting systems.Item Open Access Use of the autocorrelation function in EOF analysis of GRACE data(2014) Goswami, SujataThe GRACE mission launched with a pair of satellites, orbiting in a near-polar orbit, at a distance of about 220 km from eachother, to map the time-variable earth’s gravity field, since 2002. The study of time-variable gravity field has been proved to be very helpful in climate science studies. The gravity variations in the GRACE observations are mass variations inside the earth, exchanges between glaciers and oceans, changes due to surface and deep currents in the ocean. Monthly maps are used to study these gravity variations. The raw gravity field data obtained, is too much noisy and the main source of this noise is north-south stripes which is due to polar orbit of satellites. Due to these noisy stripes, filtering of GRACE time-variable gravity field is required. In this thesis, Empirical Orthogonal Function (EOF) analysis is used to filter and analyse the GRACE gravity data. The method is used to extract the dominant variations by reducing the dimensionality of a dataset, among a group of time-series data. This dimensionality reduction and extraction of dominant variance is achieved by linear coordinate transformation to a new set of basis vectors via singular value decomposition. The decomposition gives spatial and temporal components along with variance values. Temporal components are analysed by the dominant variance rule, Kolmogorov-Smirnov rule and autocorrelation function respectively, in order to recover signal from noise. Dataset recovered by dominant variance rule, reduces the striping but it may remove the signals as well, especially the signals from ocean. The level of signal reduction is less in Kolmogorov-Smirnov rule, whereas autocorrelation performs well in comparison to both. Geophysical signal reduction is very less in using the autocorrelation function for filtered data analysis and the results are even much better if both Kolmogorov-Smirnov rule and autocorrelation results are combined together. Thus, autocorrelation can be a better approach to select the signal components from the noisy ones. EOF anlaysis is explained with its theoretical background and then its application on the GRACE data. The focus is on the use of autocorrelation function and its performance in the filtered data analysis. Here, the entire procedure is applied in spatial domain on the processed equivalent water height values. In future, autocorrelation function can be used for data analysis in spectral domain for more better results. Its performance can be evaluated on regional analysis basis.