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 probabilistic approach to characterizing drought using satellite gravimetry(2024) Saemian, Peyman; Tourian, Mohammad J.; Elmi, Omid; Sneeuw, Nico; AghaKouchak, AmirIn the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.Item Open Access Forecasting next year's global land water storage using GRACE data(2024) Li, Fupeng; Kusche, Jürgen; Sneeuw, Nico; Siebert, Stefan; Gerdener, Helena; Wang, Zhengtao; Chao, Nengfang; Chen, Gang; Tian, KunjunExisting approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real‐time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision‐makers to improve current drought and water resource management.Item Open Access Downscaling GRACE total water storage change using partial least squares regression(2021) Vishwakarma, Bramha Dutt; Zhang, Jinwei; Sneeuw, NicoThe Gravity Recovery And Climate Experiment (GRACE) satellite mission recorded temporal variations in the Earth’s gravity field, which are then converted to Total Water Storage Change (TWSC) fields representing an anomaly in the water mass stored in all three physical states, on and below the surface of the Earth. GRACE provided a first global observational record of water mass redistribution at spatial scales greater than 63000 km2. This limits their usability in regional hydrological applications. In this study, we implement a statistical downscaling approach that assimilates 0.5° × 0.5° water storage fields from the WaterGAP hydrology model (WGHM), precipitation fields from 3 models, evapotranspiration and runoff from 2 models, with GRACE data to obtain TWSC at a 0.5° × 0.5° grid. The downscaled product exploits dominant common statistical modes between all the hydrological datasets to improve the spatial resolution of GRACE. We also provide open access to scripts that researchers can use to produce downscaled TWSC fields with input observations and models of their own choice.Item Open Access Current availability and distribution of Congo Basin’s freshwater resources(2023) Tourian, Mohammad J.; Papa, Fabrice; Elmi, Omid; Sneeuw, Nico; Kitambo, Benjamin; Tshimanga, Raphael M.; Paris, Adrien; Calmant, StéphaneThe Congo Basin is of global significance for biodiversity and the water and carbon cycles. However, its freshwater availability and distribution remain relatively unknown. Using satellite data, here we show that currently the Congo Basin’s Total Drainable Water Storage lies within a range of 476 km 3 to 502 km 3 , unevenly distributed throughout the region, with 63% being stored in the southernmost sub-basins, Kasaï (220-228 km 3 ) and Lualaba (109-169 km 3 ), while the northern sub-basins contribute only 173 ± 8 km 3 . We further estimate the hydraulic time constant for draining its entire water storage to be 4.3 ± 0.1 months, but, regionally, permanent wetlands and large lakes act as resistors resulting in greater time constants of up to 105 ± 3 months. Our estimate provides a robust basis to address the challenges of water demand for 120 million inhabitants, a population expected to double in a few decades.Item Open Access High-dimensional experiments for the downward continuation using the LRFMP algorithm(2024) Schneider, Naomi; Michel, Volker; Sneeuw, NicoTime-dependent gravity data from satellite missions like GRACE-FO reveal mass redistribution in the system Earth at various time scales: long-term climate change signals, inter-annual phenomena like El Niño, seasonal mass transports and transients, e. g. due to earthquakes. For this contemporary issue, a classical inverse problem has to be considered: the gravitational potential has to be modelled on the Earth’s surface from measurements in space. This is also known as the downward continuation problem. Thus, it is important to further develop current mathematical methods for such inverse problems. For this, the (Learning) Inverse Problem Matching Pursuits ((L)IPMPs) have been developed within the last decade. Their unique feature is the combination of local as well as global trial functions in the approximative solution of an inverse problem such as the downward continuation of the gravitational potential. In this way, they harmonize the ideas of a traditional spherical harmonic ansatz and the radial basis function approach. Previous publications on these methods showed proofs of concept. In this paper, we report on the progress of our developments towards more realistic scenarios. In particular, we consider the methods for high-dimensional experiment settings with more than 500 000 grid points which yields a resolution of 20 km at best on a realistic satellite geometry. We also explain the changes in the methods that had to be done to work with such a large amount of data.Item Open Access Mass variation observing system by high low inter-satellite links (MOBILE) : a new concept for sustained observation of mass transport from space(2019) Pail, R.; Bamber, J.; Biancale, R.; Bingham, R.; Braitenberg, C.; Eicker, A.; Flechtner, F.; Gruber, T.; Güntner, A.; Heinzel, G.; Horwath, M.; Longuevergne, L.; Müller, J.; Panet, I.; Savenije, H.; Seneviratne, S.; Sneeuw, Nico; Dam, T. van; Wouters, B.As changes in gravity are directly related to mass variability, satellite missions observing the Earth’s time varying gravity field are a unique tool for observing mass transport processes in the Earth system, such as the water cycle, rapid changes in the cryosphere, oceans, and solid Earth processes, on a global scale. The observation of Earth’s gravity field was successfully performed by the GRACE and GOCE satellite missions, and will be continued by the GRACE Follow-On mission. A comprehensive team of European scientists proposed the next-generation gravity field mission MOBILE in response to the European Space Agency (ESA) call for a Core Mission in the frame of Earth Explorer 10 (EE10). MOBILE is based on the innovative observational concept of a high-low tracking formation with micrometer ranging accuracy, complemented by new instrument concepts. Since a high-low tracking mission primarily observes the radial component of gravity-induced orbit perturbations, the error structure is close to isotropic. This geometry significantly reduces artefacts of previous along-track ranging low-low formations (GRACE, GRACE-Follow-On) such as the typical striping patterns. The minimum configuration consists of at least two medium-Earth orbiters (MEOs) at 10000 km altitude or higher, and one low-Earth orbiter (LEO) at 350-400 km. The main instrument is a laser-based distance or distance change measurement system, which is placed at the LEO. The MEOs are equipped either with passive reflectors or transponders. In a numerical closed-loop simulation, it was demonstrated that this minimum configuration is in agreement with the threshold science requirements of 5 mm equivalent water height (EWH) accuracy at 400 km wavelength, and 10 cm EWH at 200 km. MOBILE provides promising potential future perspectives by linking the concept to existing space infrastructure such as Galileo next-generation, as future element of the Copernicus/Sentinel programme, and holds the potential of miniaturization even up to swarm configurations. As such MOBILE can be considered as a precursor and role model for a sustained mass transport observing system from space.