06 Fakultät Luft- und Raumfahrttechnik und Geodäsie

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7

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Publication Type: search.filters.UBSTyp.ZeitschriftenartikelAuthor: search.filters.author.Tourian, Mohammad J.Author: search.filters.author.AghaKouchak, Amir

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    A probabilistic approach to characterizing drought using satellite gravimetry
    (2024) Saemian, Peyman; Tourian, Mohammad J.; Elmi, Omid; Sneeuw, Nico; AghaKouchak, Amir
    In 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.
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    A machine learning approach for total water storage anomaly eXtension back to 1980 (ML-TWiX)
    (2026) Saemian, Peyman; Tourian, Mohammad J.; Douch, Karim; Foster, James; Gou, Junyang; Wiese, David; AghaKouchak, Amir; Sneeuw, Nico
    We present ML-TWiX, a global dataset of monthly total water storage anomalies (TWSA) reconstructed from 1980 to 2012, provided on a 0.5 ° × 0.5 ° global grid. While the GRACE and GRACE Follow-On satellite missions have provided valuable observations of global TWSA, their combined record spans just over two decades, limiting their utility for long-term climate and hydrological studies. ML-TWiX extends the GRACE-era record into the pre-GRACE period by learning from global hydrological and land surface model simulations using an ensemble of three machine learning models: Random Forest, XGBoost, and Gaussian Process Regression. The three machine learning models were independently used to reconstruct TWSA, and their outputs were subsequently combined through ensemble averaging to produce a unified product with spatially explicit uncertainty estimates. We validated ML-TWiX against multiple independent datasets, including satellite laser ranging, storage deduced from the water mass balance closure, and global mean sea level budget estimates. It provides a continuous reconstruction of global TWSA, enabling a wide range of applications in hydrology, climate science, and water resource assessment.