Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-1951 Peer reviewed articles 2021 Daniel Farinotti, Douglas J. Brinkerhoff, Johannes J. Fürst, Prateek Gantayat, Fabien Gillet-Chaulet, Matthias Huss, Paul W. Leclercq, Hansruedi Maurer, Mathieu Morlighem, Ankur Pandit, Antoine Rabatel, RAAJ Ramsankaran, Thomas J. Reerink, Ellen Robo, Emmanuel Rouges, Erik Tamre, Ward J. J. van Pelt, Mauro A. Werder, Mohod Farooq Azam, Huilin Li, Liss M. Andreassen Results from the Ice Thickness ModelsIntercomparison eXperiment Phase 2(ITMIX2) Frontiers in Earth Science - Cryospheric Sciences 10.3389/feart.2020.571923 Uncategorized Uncategorized Knowing the ice thickness distribution of a glacier is of fundamental importance for a number of applications, ranging from the planning of glaciological fieldwork to the assessments of future sea-level change. Across spatial scales, however, this knowledge is limited by the paucity and discrete character of available thickness observations. To obtain a spatially coherent distribution of the glacier ice thickness, interpolation or numerical models have to be used. Whilst the first phase of the Ice Thickness Models Intercomparison eXperiment (ITMIX) focused on approaches that estimate such spatial information from characteristics of the glacier surface alone, ITMIX2 sought insights for the capability of the models to extract information from a limited number of thickness observations. The analyses were designed around 23 test cases comprising both real-world and synthetic glaciers, with each test case comprising a set of 16 different experiments mimicking possible scenarios of data availability. A total of 13 models participated in the experiments. The results show that the inter-model variability in the calculated local thickness is high, and that for unmeasured locations, deviations of 16% of the mean glacier thickness are typical (median estimate, three-quarters of the deviations within 37% of the mean glacier thickness). This notwithstanding, limited sets of ice thickness observations are shown to be effective in constraining the mean glacier thickness, demonstrating the value of even partial surveys. Whilst the results are only weakly affected by the spatial distribution of the observations, surveys that preferentially sample the lowest glacier elevations are found to cause a systematic underestimation of the thickness in several models. Conversely, a preferential sampling of the thickest glacier parts proves effective in reducing the deviations. The response to the availability of ice thickness observations is characteristic to each approach and varies across models. On average across models, the deviation between modeled and observed thickness increase by 8.5% of the mean ice thickness every time the distance to the closest observation increases by a factor of 10. No single best model emerges from the analyses, confirming the added value of using model ensembles. 948290
publications-1952 Peer reviewed articles 2023 Afroditi Kita, Ioannis Manakos, Sofia Papadopoulou, Ioannis Lioumbas, Leonidas Alagialoglou, Matina Katsiapi and Aikaterini Christodoulou Land–Water Transition Zone Monitoring in Support of Drinking Water Production Water MDPI 10.3390/w15142596 IoT & Sensors Irrigation Systems Water utilities often use extended open surface water reservoirs to produce drinking water. Biotic and abiotic factors influence the water level, leading to alterations in the concentration of the dissolved substances (in cases of flood or drought), entry of new pollutants (in case of flooding) or reduction in the availability and inflow speed of water to the treatment plant (in case of drought). Spaceborne image analysis is considered a significant surrogate for establishing a dense network of sensors to monitor changes. In this study, renowned inundation mapping techniques are examined for their adaptability to the inland water reservoirs’ conditions. The results, from the Polyphytos open surface water reservoir in northern Greece, showcase the transferability of the workflows with overall accuracies exceeding—in cases—98%. Hydroperiod maps generated for the area of interest, along with variations in the water surface extent over a four-year period, provide valuable insights into the reservoir’s hydrological patterns. Comparison among different inundation mapping techniques for the surface water extent and water level reveal challenges and limitations, which are related to the spatial resolution, the data take frequency and the influence of the landscape synthesis beyond the water reservoir boundaries. 101004157
publications-1953 Peer reviewed articles 2024 Johannes J. Fürst, David Farías-Barahona, Norbert Blindow, Gino Casassa, Guisella Gacitúa, Michèle Koppes, Emanuele Lodolo, Romain Millan, Masahiro Minowa, Jérémie Mouginot, Michał Pȩtlicki, Eric Rignot, Andres Rivera, Pedro Skvarca, Martin Stuefer, Shin Sugiyama, José Uribe, Rodrigo Zamora, Matthias H. Braun, Fabien Gillet-Chaulet, Philipp Malz, Wolfgang J.-H. Meier & Marius Schaefer The foundations of the Patagonian icefields Communications Earth & Environment 10.1038/s43247-023-01193-7 Data Management & Analytics Natural Water Bodies AbstractThe two vast Patagonian icefields are a global hotspot for ice-loss. However, not much is known about the total ice volume they store - let alone its spatial distribution. One reason is that the abundant record of direct thickness measurements has never been systematically exploited. Here, this record is combined with remotely-sensed information on past ice thickness mapped from glacier retreat. Both datasets are incorporated in a state-of-the-art, mass-conservation approach to produce a well-informed map of the basal topography beneath the icefields. Its major asset is the reliability increase of thicknesses values along the many marine- and lake-terminating glaciers. For these, frontal ice-discharge is notably lower than previously reported. This finding implies that direct climatic control was more influential for past ice loss. We redact a total volume for both icefields in 2000 of 5351 km3. Despite the wealth of observations used in this assessment, relative volume uncertainties remain elevated. 948290
publications-1954 Peer reviewed articles 2023 Samuel Cook; Fabien Gillet-Chaulet; Johannes Fürst Robust reconstruction of glacier beds using transient 2D assimilation with Stokes Journal of Glaciology 10.1017/jog.2023.26 Data Management & Analytics Wastewater Treatment Plants AbstractInitialising model glaciers such that they match well with their real counterparts and are thus able to make more accurate predictions is an ongoing challenge in glacier modelling. We set out a data-assimilation approach using an ensemble Kalman filter in a 2D flowline example that provides one possible solution to this problem. We show that our approach is valid across a range of parameters and scenarios, including deliberately data-deficient or inaccurate ones, and leads to robust retrieval of the glacier bed. We also provide some suggestions for how best to use data assimilation within a mountain-glacier context. 948290
publications-1955 Peer reviewed articles 2023 Sommer Christian, Fürst Johannes J., Huss Matthias, Braun Matthias H. Constraining regional glacier reconstructions using past ice thickness of deglaciating areas – a case study in the European Alps The Cryosphere 10.5194/tc-17-2285-2023 Simulation & Modeling Uncategorized Abstract. In order to assess future glacier evolution and meltwater runoff, accurate knowledge on the volume and the ice thickness distribution of glaciers is crucial. However, in situ observations of glacier thickness are sparse in many regions worldwide due to the difficulty of undertaking field surveys. This lack of in situ measurements can be partially overcome by remote-sensing information. Multi-temporal and contemporaneous data on glacier extent and surface elevation provide past information on ice thickness for retreating glaciers in the newly deglacierized regions. However, these observations are concentrated near the glacier snouts, which is disadvantageous because it is known to introduce biases in ice thickness reconstruction approaches. Here, we show a strategy to overcome this generic limitation of so-called retreat thickness observations by applying an empirical relationship between the ice viscosity at locations with in situ observations and observations from digital elevation model (DEM) differencing at the glacier margins. Various datasets from the European Alps are combined to model the ice thickness distribution of Alpine glaciers for two time steps (1970 and 2003) based on the observed thickness in regions uncovered from ice during the study period. Our results show that the average ice thickness would be substantially underestimated (∼ 40 %) when relying solely on thickness observations from previously glacierized areas. Thus, a transferable topography-based viscosity scaling is developed to correct the modelled ice thickness distribution. It is shown that the presented approach is able to reproduce region-wide glacier volumes, although larger uncertainties remain at a local scale, and thus might represent a powerful tool for application in regions with sparse observations. 948290
publications-1956 Peer reviewed articles 2023 Oskar Herrmann, Nora Gourmelon, Thorsten Seehaus, Andreas Maier, Johannes J. Fürst, Matthias H. Braun, Vincent Christlein Out-of-the-box calving-front detection method using deep learning The Cryosphere 10.5194/tc-17-4957-2023 Data Management & Analytics Uncategorized Abstract. Glaciers across the globe react to the changing climate. Monitoring the transformation of glaciers is essential for projecting their contribution to global mean sea level rise. The delineation of glacier-calving fronts is an important part of the satellite-based monitoring process. This work presents a calving-front extraction method based on the deep learning framework nnU-Net, which stands for no new U-Net. The framework automates the training of a popular neural network, called U-Net, designed for segmentation tasks. Our presented method marks the calving front in synthetic aperture radar (SAR) images of glaciers. The images are taken by six different sensor systems. A benchmark dataset for calving-front extraction is used for training and evaluation. The dataset contains two labels for each image. One label denotes a classic image segmentation into different zones (glacier, ocean, rock, and no information available). The other label marks the edge between the glacier and the ocean, i.e., the calving front. In this work, the nnU-Net is modified to predict both labels simultaneously. In the field of machine learning, the prediction of multiple labels is referred to as multi-task learning (MTL). The resulting predictions of both labels benefit from simultaneous optimization. For further testing of the capabilities of MTL, two different network architectures are compared, and an additional task, the segmentation of the glacier outline, is added to the training. In the end, we show that fusing the label of the calving front and the zone label is the most efficient way to optimize both tasks with no significant accuracy reduction compared to the MTL neural-network architectures. The automatic detection of the calving front with an nnU-Net trained on fused labels improves from the baseline mean distance error (MDE) of 753±76 to 541±84 m. The scripts for our experiments are published on GitHub (https://github.com/ho11laqe/nnUNet_calvingfront_detection, last access: 20 November 2023). An easy-access version is published on Hugging Face (https://huggingface.co/spaces/ho11laqe/nnUNet_calvingfront_detection, last access: 20 November 2023). 948290
publications-1957 Peer reviewed articles 2023 Franziska Temme; David Farías-Barahona; Thorsten Seehaus; Ricardo Jaña; Jorge Arigony-Neto; Inti Gonzalez; Anselm Arndt; Tobias Sauter; Christoph Schneider; Johannes J. Fürst Strategies for regional modeling of surface mass balance at the Monte Sarmiento Massif, Tierra del Fuego The Cryosphere 10.5194/tc-17-2343-2023 Simulation & Modeling Water Distribution Networks Abstract. This study investigates strategies for calibration of surface mass balance (SMB) models in the Monte Sarmiento Massif (MSM), Tierra del Fuego, with the goal of achieving realistic simulations of the regional SMB. Applied calibration strategies range from a local single-glacier calibration to a regional calibration with the inclusion of a snowdrift parameterization. We apply four SMB models of different complexity. In this way, we examine the model transferability in space, the benefit of regional mass change observations and the advantage of increasing the complexity level regarding included processes. Measurements include ablation and ice thickness observations at Schiaparelli Glacier as well as elevation changes and flow velocity from satellite data for the entire study site. Performance of simulated SMB is validated against geodetic mass changes and stake observations of surface melting. Results show that transferring SMB models in space is a challenge, and common practices can produce distinctly biased estimates. Model performance can be significantly improved by the use of remotely sensed regional observations. Furthermore, we have shown that snowdrift does play an important role in the SMB in the Cordillera Darwin, where strong and consistent winds prevail. The massif-wide average annual SMB between 2000 and 2022 falls between −0.28 and −0.07 m w.e. yr−1, depending on the applied model. The SMB is mainly controlled by surface melting and snowfall. The model intercomparison does not indicate one obviously best-suited model for SMB simulations in the MSM. 948290
publications-1958 Peer reviewed articles 2021 Diener Theresa, Sasgen Ingo, Agosta Cécile, Fürst Johannes J., Braun Matthias H., Konrad Hannes, Fettweis Xavier Acceleration of Dynamic Ice Loss in Antarctica From Satellite Gravimetry Frontiers in Earth Science 10.3389/feart.2021.741789 Data Management & Analytics Water Distribution Networks The dynamic stability of the Antarctic Ice Sheet is one of the largest uncertainties in projections of future global sea-level rise. Essential for improving projections of the ice sheet evolution is the understanding of the ongoing trends and accelerations of mass loss in the context of ice dynamics. Here, we examine accelerations of mass change of the Antarctic Ice Sheet from 2002 to 2020 using data from the GRACE (Gravity Recovery and Climate Experiment; 2002–2017) and its follow-on GRACE-FO (2018-present) satellite missions. By subtracting estimates of net snow accumulation provided by re-analysis data and regional climate models from GRACE/GRACE-FO mass changes, we isolate variations in ice-dynamic discharge and compare them to direct measurements based on the remote sensing of the surface-ice velocity (2002–2017). We show that variations in the GRACE/GRACE-FO time series are modulated by variations in regional snow accumulation caused by large-scale atmospheric circulation. We show for the first time that, after removal of these surface effects, accelerations of ice-dynamic discharge from GRACE/GRACE-FO agree well with those independently derived from surface-ice velocities. For 2002–2020, we recover a discharge acceleration of -5.3 ± 2.2 Gt yr−2 for the entire ice sheet; these increasing losses originate mainly in the Amundsen and Bellingshausen Sea Embayment regions (68%), with additional significant contributions from Dronning Maud Land (18%) and the Filchner-Ronne Ice Shelf region (13%). Under the assumption that the recovered rates and accelerations of mass loss persisted independent of any external forcing, Antarctica would contribute 7.6 ± 2.9 cm to global mean sea-level rise by the year 2100, more than two times the amount of 2.9 ± 0.6 cm obtained by linear extrapolation of current GRACE/GRACE-FO mass loss trends. 948290
publications-1959 Peer reviewed articles 2023 Thomas Papadimos, Stelios Andreadis, Ilias Gialampoukidis , Stefanos Vrochidis and Ioannis Kompatsiaris Flood-Related Multimedia Benchmark Evaluation: Challenges, Results and a Novel GNN Approach Sensors MDPI 10.3390/s23073767 Simulation & Modeling Wastewater Treatment Plants This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event detection techniques have focused on either images or text, but combining the two can improve performance. The authors present lessons learned from the Flood-related multimedia task in MediaEval2020, provide a dataset for reproducibility, and propose a new multimodal fusion method that uses Graph Neural Networks to combine image, text, and time information. Their method outperforms state-of-the-art approaches and can handle low-sample labelled data. 101004157
publications-1960 Peer reviewed articles 2023 Christos Psychalas, Konstantinos Vlachos, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris Towards a Paradigm Shift on Mapping Muddy Waters with Sentinel-2 Using Machine Learning Sustainability 10.3390/su151813441 Data Management & Analytics Uncategorized The quality of drinking water is a critical factor for public health and the environment. Inland drinking water reservoirs are essential sources of freshwater supply for many communities around the world. However, these reservoirs are susceptible to various forms of contamination, including the presence of muddy water, which can pose significant challenges for water treatment facilities and lead to serious health risks for consumers. In addition, such reservoirs are also used for recreational purposes which supports the local economy. In this work, we show as a proof-of-concept that muddy water mapping can be accomplished with machine learning-based semantic segmentation constituting an extra source of sediment-laden water information. Among others, such an approach can solve issues including (i) the presence/absence, frequency and spatial extent of pollutants (ii) generalization and expansion to unknown reservoirs (assuming a curated global dataset) (iii) indications about the presence of other pollutants since it acts as their proxy. Our train/test approach is based on 13 Sentinel-2 (S-2) scenes from inland/coastal waters around Europe while treating the data as tabular. Atmospheric corrections are applied and compared based on spectral signatures. Muddy water and non-muddy water samples are taken according to expert knowledge, S-2 scene classification layer, and a combination of normalized difference indices (NDTI and MNDWI) and are evaluated based on their spectral signature statistics. Finally, a Random Forest model is trained, fine-tuned and evaluated using standard classification metrics. The experiments have shown that muddy water can be detected with high enough discrimination capacity, opening the door to more advanced image-based machine learning techniques. 101004157