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 Analyzing and characterizing spaceborne observation of water storage variation : past, present, future(2024) Saemian, Peyman; Sneeuw, Nico (Prof. Dr.-Ing.)Water storage is an indispensable constituent of the intricate water cycle, as it governs the availability and distribution of this precious resource. Any alteration in the water storage can trigger a cascade of consequences, affecting not only our agricultural practices but also the well-being of various ecosystems and the occurrence of natural hazards. Therefore, it is essential to monitor and manage the water storage levels prudently to ensure a sustainable future for our planet. Despite significant advancements in ground-based measurements and modeling techniques, accurately measuring water storage variation remained a major challenge for a long time. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) satellites have revolutionized our understanding of the Earth's water cycle. By detecting variations in the Earth's gravity field caused by changes in water distribution, these satellites can precisely measure changes in total water storage (TWS) across the entire globe, providing a truly comprehensive view of the world's water resources. This information has proved invaluable for understanding how water resources are changing over time, and for developing strategies to manage these resources sustainably. However, GRACE and GRACE-FO are subject to various challenges that must be addressed in order to enhance the efficacy of our exploitation of GRACE observations for scientific and practical purposes. This thesis aims to address some of the challenges faced by GRACE and GRACE-FO. Since the inception of the GRACE mission, scholars have commonly extracted mass changes from observations by approximating the Earth's gravity field utilizing mathematical functions termed spherical harmonics. Various institutions have already processed GRACE(-FO) data, known as level-2 data in the GRACE community, considering the constraints, approaches, and models that have been utilized. However, this processed data necessitates post-processing to be used for several applications, such as hydrology and climate research. In this thesis, we evaluate various methods of processing GRACE(-FO) level-2 data and assess the spatio-temporal effect of the post-processing steps. Furthermore, we aim to compare the consistency between GRACE and its successor mission, GRACE-FO, in terms of data quality and measurement accuracy. By analyzing and comparing the data from these two missions, we can identify any potential discrepancies or differences and establish the level of confidence in the accuracy and reliability of the GRACE-FO measurements. Finally, we will compare the processed level-3 products with the level-3 products that are presently accessible online. The relatively short record of the GRACE measurements, compared to other satellite missions and observational records, can limit some studies that require long-term data. This short record makes it challenging to separate long-term signals from short-term variability and validate the data with ground-based measurements or other satellite missions. To address this limitation, this thesis expands the temporal coverage of GRACE(-FO) observations using global hydrological, atmospheric, and reanalysis models. First, we assess these models in estimating the TWS variation at a global scale. We compare the performance of various methods including data-driven and machine learning approaches in incorporating models and reconstruct GRACE TWS change. The results are also validated against Satellite Laser Ranging (SLR) observations over the pre-GRACE period. This thesis develops a hindcasted GRACE, which provides a better understanding of the changes in the Earth's water storage on a longer time scale. The GRACE satellite mission detects changes in the overall water storage in a specific region but cannot distinguish between the different compartments of TWS, such as surface water, groundwater, and soil moisture. Understanding these individual components is crucial for managing water resources and addressing the effects of droughts and floods. This study aims to integrate various data sources to improve our understanding of water storage variations at the continental to basin scale, including water fluxes, lake water level, and lake storage change data. Additionally, the study demonstrates the importance of combining GRACE(-FO) observations with other measurements, such as piezometric wells and rain-gauges, to understand the water scarcity predicament in Iran and other regions facing similar challenges. The GRACE satellite mission provides valuable insights into the Earth's system. However, the GRACE product has a level of uncertainty due to several error sources. While the mission has taken measures to minimize these uncertainties, researchers need to account for them when analyzing the data and communicate them when reporting findings. This thesis proposes a probabilistic approach to incorporate the Total Water Storage Anomaly (TWSA) data from GRACE(-FO). By accounting for the uncertainty in the TWSA data, this approach can provide a more comprehensive understanding of drought conditions, which is essential for decision makers managing water resources and responding to drought events.Item Open Access Water level monitoring at SAPOS stations through GNSS-IR : a case study at the station Iffezheim(2023) Wagner, Sven B.The German SAPOS-Network comprises approximately 270 permanent GNSS receivers, capturing signals from Global Navigation Satellite Systems such as GPS, GLONASS, Galileo, and BeiDou. Primarily employed for generating kinematic, mathematical, and physical models within their respective regions, these receivers hold untapped potential for alternative applications. GNSS receivers capture multipath errors, typically considered unwanted interferences resulting from signal reflections off surfaces beneath the antenna. Despite their potential to adversely affect data precision, these interferences contain valuable information about the reflecting surface. As satellites pass through the receivers’ field of view at specific elevation angles, the interference between the direct and reflected signals leads to constructive and destructive patterns. This phenomenon occurs due to variations in signal phase between the direct and reflected signal, enhancing or dampening the signal strength. These variations in signal strength are captured in the satellites Signal-to-Noise Ratio (SNR) data. Spectral analysis of the SNR data can be used to determine the frequency of the interference pattern. Combining this frequency with the corresponding signal wavelength and satellite elevation angles allows the calculation of the vertical distance between the antenna phase centre and the reflecting surface on Earth. This method, known as GNSS Interferometric Reflectometry (GNSS-IR), provides a valuable means of monitoring surface information, including soil moisture, snow depth, and water levels. At SAPOS stations near rivers and water bodies, GNSS-IR offers a cost-effective, accessible, and innovative opportunity to gather water level information using the already existing infrastructure. This research explores the potential of GNSSIR for water level monitoring at SAPOS stations focusing on the Iffezheim station along the Rhine River near the City of Karlsruhe in southern Germany.Item Open Access Exploring the performances of SAR altimetry and improvements offered by fully focused SAR(2021) Wu, YuweiWith the development of the altimetry techniques, the measurement principle has been changed from the conventional pulse-limited principle to the delay-Doppler principle since CryoSat-2. The delay-Doppler altimetry presents scientists with the chance to develop new processing schemes and improve products that maximize the benefits of the measurements. Nevertheless, one of the challenges for delay-Doppler Altimetry lies in the complexity of the post-processing, especially the Delay-Doppler processing. The focus of this thesis is to better understand delay-Doppler and fully focused SAR altimetry. This thesis compares the retrieved waveforms and resultant water level time series with different altimetry principles, processing options and retracking methods. By using platform SARvatore for delay-Doppler altimetry and SMAP for fully focused SAR altimetry, different processing options (data posting rate, Hamming window and zero padding) and different retrackers (SAMOSA family for SARvatore, PTR for SMAP) can be applied and compared. Our results reveal that the waveforms generated by different configurations have different peaks for SARvatore. For SMAP, with or without zero padding or Hamming window had very little impact, with more differences mainly coming from the different retracking methods. Our results also show that fully focused SAR does not bring a significant improvement when applied to Sentinel-3 data. In summary, different configurations and retracking methods can significantly affect the shape of waveforms and their derived ranges. According to this thesis's experiments, the configuration with 80 Hz data posting rate, Hamming window, zero padding, extended receiving window and retracker SAMOSA++ offers the best performance.Item Open Access Characterizing storage-based drought using satellite gravimetry(2021) Saemian, PeymanDrought is a complex phenomenon leading to a wide range of socio-economic, environmental, and political problems. The storage-based drought which represents the persistent lack of water in different levels of the Total Water Storage (TWS) from deep groundwater to surface water plays a vital role in proactive drought management. Despite its necessity, TWS could not be monitored due to the lack of consistent measurements from regional to continental scale. Since its launch in 2002, the Gravity Recovery and Climate Experiment (grace) mission and its successor GRACE Follow-On have provided unique observations of the TWS change at the global scale. In this study, we have investigated characterizing the storage-based drought at the global scale using GRACE measurements. To this end, the Equivalent Water Height (EWH) has been retrieved from GRACE level 02 solutions. We have addressed the short record of GRACE observations in capturing the full hydroclimate variations. Based on our analysis, regions with a considerable direct human intervention like overexploitation of groundwater in the Middle East, regions that were affected by climate change like ice-melting over the Mackenzie river basin in Canada, or extreme precipitation events over the Ob river basin in the boreal regions are more sensitive to the length of ewh time series. Due to the crucial need for a long (at least 30 years) record of EWH, we have extended GRACE observations back to 1980 using an ensemble of models. The extended dataset has been developed using a pixel-wise selection of best-performed models among global hydrological models, land surface models, and atmospheric reanalysis models. The extended dataset has been used in the study for drought characterization over the grac period. The proposed Storage-based Drought Index (SDI) successfully captured the documented drought events globally in terms of intensity and spatio-temporal distribution. Moreover, the analysis of SDI over the five classes of drought from D0 as abnormally dry to D4 as exceptional drought showed that most regions have suffered at least once from the storage-based drought over the GRACE period (2002–2016). Besides, the map of exceptional drought frequency highlights regions with significant groundwater extraction like California, the Middle East, and north of India and regions with exceptional shifts in the precipitation and temperature pattern and intensity like Amazon in South America and China. Finally, our comparison of SDI with three most widely used drought indices namely the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Palmer Drought Severity Index (PDSI) reveals that despite their high correlation over climate-driven regions, these indices failed to characterize anthropogenic drought events, especially over regions with considerable groundwater withdraws. The study allows for a more informative storage-based drought with a more robust climatology as the reference, thus enabling a more realistic risk assessment.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 Analysis of river surface slope using ICESat-2 satellite altimetry(2022) Bao, JingyiItem Open Access Spatio-temporal evaluation of GPM-IMERGV6.0 final run precipitation product in capturing extreme precipitation events across Iran(2022) Bakhtar, Aydin; Rahmati, Akbar; Shayeghi, Afshin; Teymoori, Javad; Ghajarnia, Navid; Saemian, PeymanExtreme precipitation events such as floods and droughts have occurred with higher frequency over the recent decades as a result of the climate change and anthropogenic activities. To understand and mitigate such events, it is crucial to investigate their spatio-temporal variations globally or regionally. Global precipitation products provide an alternative way to the in situ observations over such a region. In this study, we have evaluated the performance of the latest version of the Global Precipitation Measurement-Integrated Multi-satellitE Retrievals (GPM-IMERGV6.0 Final Run (GPM-IMERGF)). To this end, we have employed ten most common extreme precipitation indices, including maximum indices (Rx1day, Rx5day, CDD, and CWD), percentile indices (R95pTOT and R99pTOT), and absolute threshold indices (R10mm, R20mm, SDII, and PRCPTOT). Overall, the spatial distribution results for error metrics showed that the highest and lowest accuracy for GPM-IMERGF were reported for the absolute threshold indices and percentile indices, respectively. Considering the spatial distribution of the results, the highest accuracy of GPM-IMERGF in capturing extreme precipitations was observed over the western highlands, while the worst results were obtained along the Caspian Sea regions. Our analysis can significantly contribute to various hydro-metrological applications for the study region, including identifying drought and flood-prone areas and water resources planning.Item Open Access Item Open Access Crop water productivity mapping and benchmarking using remote sensing and Google Earth Engine cloud computing(2022) Ghorbanpour, Ali Karbalaye; Kisekka, Isaya; Afshar, Abbas; Hessels, Tim; Taraghi, Mahdi; Hessari, Behzad; Tourian, Mohammad J.; Duan, ZhengScarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49-0.55 (kg/m3) for irrigated wheat, 0.27-0.34 for rainfed wheat, 1.7-2.2 for apples, 1.2-1.7 for grapes, 5.5-6.2 for sugar beets, and 0.67-1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.Item Open Access Understanding the hydrological signature in gravity data(2023) Schollmeier, PhilippOver the past two decades, the subsequent advancements in Superconducting Gravimeters (SGs) have ushered in a level of precision that enables the measurement of the impact of ground water and soil water on gravity. Because of the challenging nature of monitoring the total water volume and the relatively subtle amplitude of the hydrological signal, a comprehensive understanding of the precise hydrological signature in continuous gravity data remains elusive. In this study, I use SG data in conjunction with hydrological measurements from a geoscientific observatory in Germany to find the signature of hydrological signals in gravity data. I scrutinize the various steps involved in extracting this signal, presenting new methodologies, including a technique to eliminate oscillations in gravity residuals that are likely attributed to remaining tidal signals due to an imperfect tidal model. A major contribution of this work involves constructing a data-driven model that incorporates precipitation and soil moisture measurements to elucidate gravity variations. I address critical questions such as the impact of utilizing soil moisture data on the model’s performance, determining the optimal model for achieving the closest fit with gravity measurements, and assessing the applicability of computed model parameters to new epochs. Furthermore, I provide recommendations for refining the model-building process in future investigations. Results show that a convolution of the different hydrological timeseries with one half of a Gaussian bell curve leads to a strong agreement with the gravity measurements. The use of soil moisture data significantly improves the fit, especially when the measurement stations are spatially well distributed. This fit becomes less strong when the computed parameters are applied to new events, but the approach showed promise for some of the events. Enhancing our comprehension of the hydrological influence on gravity measurements holds promising implications, potentially positioning SGs as instruments for monitoring soil and ground water in the future. Moreover, this improved understanding could elevate the pre cision of analyzing other subtle signals, such as the effects of Polar Motion.