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 A Kalman filter approach for estimating daily discharge using space‐based discharge estimates(2025) Ke, Siqi; Tourian, Mohammad J.; Sneeuw, Nico; Frasson, Renato Prata de Moraes; Paiva, Rodrigo C. D.; Durand, Michael; Gleason, Colin; Elmi, Omid; Malaterre, Pierre‐Olivier; David, CédricThe SWOT satellite mission is the first to conduct a global survey of the Earth's surface waters, measuring water surface height, river width, and water surface slope, based on which river discharge is estimated. At mid‐latitudes, the repeat orbit design of SWOT only allows a sampling of twice per repeat cycle, which is considered too low for most hydrological applications. To address the spatiotemporal limitations of SWOT, we develop a method that assimilates SWOT observations across continuous reaches within a single‐branch river network to obtain daily discharge estimates. Our model‐free assimilation method provides a linear dynamic system that includes a process model based on a physically based spatiotemporal discharge correlation model and observation equations utilizing SWOT products. We solve this dynamic system using a simple Kalman filter in the time domain, assimilating SWOT observations and incorporating the physically based prior to estimate daily discharge. Since SWOT discharge products were not yet available during the period of this research, we used synthetic SWOT data sets, introducing random and systematic errors through Monte Carlo simulation. The validation of the estimated discharge against true discharge over all test cases leads to a median correlation as high as 0.95, a median NSE for residuals (mean‐removed discharge) as high as 0.81, and a median relative bias as low as 5%, respectively. These promising results suggest that daily discharge for continuous reaches in a river network can be obtained through our data assimilation framework.