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 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, NicoWe 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.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.