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Nelemans, Peter (author)
Fully distributed hydrological models take into account the spatial variability of a catchment, and allow for assessing its hydrological response at virtually any location. However, these models can be time-consuming when it comes to model runtime and calibration, especially for large-scale catchments. Meanwhile, deep learning models have shown...
master thesis 2024
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DENG, Jing (author)
Under future warmer climates, drought events are projected to occur more frequently with increasing impacts in many regions and river basins. This study focuses on exploring the potential of the LSTM deep learning (DL) approach for operational streamflow drought forecasting for the Rhine River at Lobith with a lead time (LT) of up to 46 days. ...
master thesis 2023
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Mao, Kangmin (author)
Modeling the relationship between rainfall and runoff is a longstanding challenge in hydrology and is crucial for informed water management decisions. Recently, Deep Learning models, particularly Long short-term memory (LSTM), have shown promising results in simulating this relationship. The Transformer, a newly proposed deep learning...
master thesis 2023
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Wilbrand, Katharina (author)
Rainfall-runoff modelling is essential for short- and long-term decision-making in the water management sector. The accuracy of streamflow predictions of hydrologic models increases with the availability of and the access to streamflow observations. Therefore, one of the key challenges in the field of hydrology is to produce Predictions in...
master thesis 2021
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Teruel Cano, David (author)
Due to global warming, temperatures are expected to rise and, with it, the increase in moisture holding capacity of the atmosphere. Changes in precipitation extremes with temperature are governed by the Clausius-Clapeyron relationship (CC), which states that precipitation increases 7% per degree of warming. While global models and observations...
master thesis 2020
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