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Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can provide faster flood predictions if trained offline. While most studies have focused on predicting maximum water depth, in this study, we predict pixel-wise water depth maps for entire catchments at a lead time of 2 h. To that end, we propose a deep learning approach that uses a sequence encoding network with temporal self-attention. We also adapt the popular hydrological performance metric Nash–Sutcliffe efficiency (NSE) as our loss function. We test the effectiveness and generalizability of our method using a new dataset called SwissFlood, which consists of 100 catchments and 1500 rainfall events extracted from real observations in Switzerland. Our method produces 2 m spatial resolution flood maps with absolute error as low as 27 cm for water depth exceeding 1 m. [ABSTRACT FROM AUTHOR]

The spatial and temporal distribution of Alpine snow is a sensitive gauge of environmental change. While understanding past snow dynamics is essential for reconstructing past climate and forecasting future trends, reliable snowpack data prior to the instrumental record are scarce. We present a novel pairing of cosmogenic paleothermometry and luminescence photochronometry which constrain the temperature and insolation history of bedrock outcrops at the Gotthard Pass, Switzerland, over the last ∼15,000 years. By coupling these results with cosmogenic 14C‐10Be chronology and modern in situ rock thermometry, we infer a ∼70‐day reduction of snowpack at the topographic mid‐slope. Our data indicate stable environmental conditions throughout the Holocene, followed by a 6.6 ± 2.9°C increase of ground surface temperature, coeval with an order‐of‐magnitude or more increase in ground surface insolation. Bracketing the onset of these changes between 1504 and 1807 CE, our findings tie the snowpack decline with the onset of human industrialization. Plain Language Summary: The extent of snow cover is shrinking in high elevation mountain environments due to climate change. However, it is challenging to determine when snow cover began to change because humans have only been monitoring snow cover for several decades, and snow cover has appeared to shrink over this entire time. We used two new geologic records of ground temperature and light exposure—both of which are impacted by the duration and amount of snow cover—to assess when snow cover began to change in the Gotthard Pass in Switzerland. We found that the middle slopes of the pass began experiencing snow cover loss three centuries ago, well before historical monitoring of snow cover in this region. This record of prolonged snow cover loss matters for the informed management of mountain water sources, rock and snow avalanche risk, and ecosystem change. Key Points: We present a novel data set of cosmogenic paleothermometry and luminescence photochronometry at the Gotthard Pass, SwitzerlandStable Holocene environmental conditions are followed by increases in ground temperature and insolation caused by snow cover declineThese environmental changes began between 1504 and 1807 CE, tying the observed snowpack decline with the onset of human industrialization [ABSTRACT FROM AUTHOR]

We propose a probabilistic deep learning approach for the prediction of maximum water depth hazard maps at high spatial resolutions, which assigns well-calibrated uncertainty estimates to every predicted water depth. Efficient, accurate, and trustworthy methods for urban flood management have become increasingly important due to higher rainfall intensity caused by climate change, the expansion of cities, and changes in land use. While physically based flood models can provide reliable forecasts for water depth at every location of a catchment, their high computational burden is hindering their application to large urban areas at high spatial resolution. While deep learning models have been used to address this issue, a disadvantage is that they are often perceived as "black-box" models and are overconfident about their predictions, therefore decreasing their reliability. Our deep learning model learns the underlying phenomena a priori from simulated hydrodynamic data, obviating the need for manual parameter setting for every new rainfall event at test time. The only inputs needed at the test time are a rainfall forecast and parameters of the terrain such as a digital elevation model to predict the maximum water depth with uncertainty estimates for complete rainfall events. We validate the accuracy and generalisation capabilities of our approach through experiments on a dataset consisting of catchments within Switzerland and Portugal and 18 rainfall patterns. Our method produces flood hazard maps at 1 m resolution and achieves mean absolute errors as low as 21 cm for extreme flood cases with water above 1 m. Most importantly, we demonstrate that our approach is able to provide an uncertainty estimate for every water depth within the predicted hazard map, thus increasing the model's trustworthiness during flooding events. [ABSTRACT FROM AUTHOR]

Grain size analysis is the key to understand the sediment dynamics of river systems. We propose GRAINet , a data-driven approach to analyze grain size distributions of entire gravel bars based on georeferenced UAV images. A convolutional neural network is trained to regress grain size distributions as well as the characteristic mean diameter from raw images. GRAINet allows for the holistic analysis of entire gravel bars, resulting in (i) high-resolution estimates and maps of the spatial grain size distribution at large scale and (ii) robust grading curves for entire gravel bars. To collect an extensive training dataset of 1491 samples, we introduce digital line sampling as a new annotation strategy. Our evaluation on 25 gravel bars along six different rivers in Switzerland yields high accuracy: the resulting maps of mean diameters have a mean absolute error (MAE) of 1.1 cm, with no bias. Robust grading curves for entire gravel bars can be extracted if representative training data are available. At the gravel bar level the MAE of the predicted mean diameter is even reduced to 0.3 cm, for bars with mean diameters ranging from 1.3 to 29.3 cm. Extensive experiments were carried out to study the quality of the digital line samples, the generalization capability of GRAINet to new locations, the model performance with respect to human labeling noise, the limitations of the current model, and the potential of GRAINet to analyze images with low resolutions. [ABSTRACT FROM AUTHOR]

Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon and Switzerland. A deep convolutional neural network (CNN) was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height. In Gabon, reference heights for training and validation were derived from airborne LiDAR measurements. In Switzerland, reference heights were taken from an existing canopy height model derived via photogrammetric surface reconstruction. The resulting maps have a mean absolute error (MAE) of 1.7 m in Switzerland and 4.3 m in Gabon (a root mean square error (RMSE) of 3.4 m and 5.6 m, respectively), and correctly estimate vegetation heights up to >50 m. They also show good qualitative agreement with existing vegetation height maps. Our work demonstrates that, given a moderate amount of reference data (i.e., 2000 km2 in Gabon and ≈5800 km2 in Switzerland), high-resolution vegetation height maps with 10 m ground sampling distance (GSD) can be derived at country scale from Sentinel-2 imagery. Unlabelled Image • Vegetation height at 10 m ground sampling distance is regressed from Sentinel-2 • Country-wide maps are computed for Switzerland and Gabon • Mean absolute error (MAE) of 1.7 m in Switzerland and 4.3 m in Gabon • The deep convolutional neural network correctly predicts vegetation heights up to 50 m [ABSTRACT FROM AUTHOR]