Johannes Marian Landmann


Last Name

Landmann

First Name

Johannes Marian

ORCID

0000-0003-0514-3521

Organisational unit

Filters

2019 - 201922020 - 20235
Reset filters

Search Results

Publications 1 - 7 of 7
  • Assimilating near-real-time mass balance stake readings into a model ensemble using a particle filter
    Item type: Journal Article
    Landmann, Johannes Marian; Künsch, Hans Rudolf; Huss, Matthias; et al. (2021)
    The Cryosphere
    Short-term glacier variations can be important for water supplies or hydropower production, and glaciers are important indicators of climate change. This is why the interest in near-real-time mass balance nowcasting is considerable. Here, we address this interest and provide an evaluation of continuous observations of point mass balance based on online cameras transmitting images every 20 min. The cameras were installed on three Swiss glaciers during summer 2019, provided 352 near-real-time point mass balances in total, and revealed melt rates of up to 0.12 m water equivalent per day (mw.e.d−1) and of more than 5 mw.e. in 81 d. By means of a particle filter, these observations are assimilated into an ensemble of three TI (temperature index) and one simplified energy-balance mass balance models. State augmentation with model parameters is used to assign temporally varying weights to individual models. We analyze model performance over the observation period and find that the probability for a given model to be preferred by our procedure is 39 % for an enhanced TI model, 24 % for a simple TI model, 23 %, for a simplified energy balance model, and 14 % for a model employing both air temperature and potential solar irradiation. When compared to reference forecasts produced with both mean model parameters and parameters tuned on single mass balance observations, the particle filter performs about equally well on the daily scale but outperforms predictions of cumulative mass balance by 95 %–96 %. A leave-one-out cross-validation on the individual glaciers shows that the particle filter is also able to reproduce point observations at locations not used for model calibration. Indeed, the predicted mass balances is always within 9 % of the observations. A comparison with glacier-wide annual mass balances involving additional measurements distributed over the entire glacier mostly shows very good agreement, with deviations of 0.02, 0.07, and 0.24 mw.e.
  • European heat waves 2022: contribution to extreme glacier melt in Switzerland inferred from automated ablation readings
    Item type: Journal Article
    Cremona, Aaron; Huss, Matthias; Landmann, Johannes Marian; et al. (2023)
    The Cryosphere
    Accelerating glacier melt rates were observed during the last decades. Substantial ice loss occurs particularly during heat waves that are expected to intensify in the future. Because measuring and modelling glacier mass balance on a daily scale remains challenging, short-term mass balance variations, including extreme melt events, are poorly captured. Here, we present a novel approach based on computer-vision techniques for automatically determining daily mass balance variations at the local scale. The approach is based on the automated recognition of colour-taped ablation stakes from camera images and is tested and validated at six stations installed on three Alpine glaciers during the summers of 2019-2022. Our approach produces daily mass balance with an uncertainty of ±0.81 cm w.e. d-1, which is about half of the accuracy obtained from visual readouts. The automatically retrieved daily mass balances at the six sites were compared to average daily mass balances over the last decade derived from seasonal in situ observations to detect and assess extreme melt events. This allows analysing the impact that the summer heat waves which occurred in 2022 had on glacier melt. Our results indicate 23 d with extreme melt, showing a strong correspondence between the heat wave periods and extreme melt events. The combination of below-average winter snowfall and a suite of summer heat waves led to unprecedented glacier mass loss. The Switzerland-wide glacier storage change during the 25 d of heat waves in 2022 is estimated as 1.27 ± 0.10 km3 of water, corresponding to 35 % of the overall glacier mass loss during that summer. The same 25 d of heat waves caused a glacier mass loss that corresponds to 56 % of the average mass loss experienced over the entire melt season during the summers 2010-2020, demonstrating the relevance of heat waves for seasonal melt.
  • Near-Real-Time Monitoring, Modelling, and Data Assimilation of Glacier Mass Balance
    Item type: Doctoral Thesis
    Landmann, Johannes Marian (2022)
    Glaciers are among the most prominent indicators of climate change, since their behavior is directly linked to climatic variables such as temperature, precipitation, and solar radiation. Under the longterm trend of shrinkage due to recent and future climate warming, near-real-time glacier mass balance information and its prediction play a particular role: on scales of days to months, glaciers fulfill important functions concerning water supply, planning for hydroelectricity production, ecology, and mountain tourism. Due to this importance, near-real-time glacier mass balance information is also of interest to the broad media. However, supplying such near-real-time information, for example on glacier mass balance, is not simple. This is mainly for two reasons: first, acquiring up-to-date glacier observations comes with a high cost, because glaciers are often located in remote areas and a considerable amount of time and humanpower is required to access and observe them in situ. Second, there is the uncertainty that affects glacier mass balance models, which are often justified by physics, but still parametrized by statistical relations between mass change and meteorological variables. As a consequence of sparse and uncertain observations, model parameters are often not uniquely identifiable, or their spatial and temporal variability cannot be accounted for. A lack of observations thus associates the calculation of near-real-time glacier mass balance with high uncertainties. This thesis aims at treating the issue of uncertain observations and models by making use of available observations and their respective uncertainties. It does so by presenting Cryospheric Monitoring and Prediction Online (CRAMPON), a Bayesian framework that allows determining near-real-time glacier mass balances in an optimal fashion, i.e. by using all available direct and indirect mass balance information and by minimizing the uncertainties. Bayesian methods are widely used in fields like meteorology, hydrology, snow sciences, and oceanography, but applications in glaciology are sparse to date. CRAMPON builds upon a Sequential Importance Resampling (SIR) scheme, also known as Particle Filtering, which comprises three steps: first, a prior estimate of a glacier’s mass balance on a particular day is given through forward integration of a mass balance model ensemble. The forward integration is driven by gridded meteorological data, and accounts for the corresponding uncertainty. Second, this prior estimate is updated with observations. This is done by using various measurements, including daily point mass balance observations from cameras, as well as surface albedo and transient snow lines derived from optical satellites. The combination of observations ensures that both temporally frequent point observations and less frequent but spatially comprehensive observations complement each other. This second step results in a so-called posterior mass balance estimate. Third, a resampling technique is applied to ensure temporal stability of the particle filter. Here, CRAMPON focuses on (1) making the resampling technique compatible with an ensemble modeling approach, and (2) using the filter to estimate model parameter distributions. This statistical data assimilation approach ensures that, at any instance, the framework delivers an optimal estimate of the current mass balance of a glacier, given all observations and respecting all observation uncertainties. Special focus is put on handling variables in a probabilistic fashion. This allows calculating uncertainties for the near-real-time glacier mass balance estimates during model runtime. The daily estimates and their uncertainties are then used for predicting the glacier mass balances into the near future. This is achieved by using Consortium for Small-Scale Modelling (COSMO) numerical weather predictions with lead times of up to five days, and European Centre for Medium-Range Weather Forecasts (ECMWF) extended-range forecasts for lead times of up to one month. Analyses of the results show that (1) CRAMPON delivers up to 95% more accurate results than conventional approaches that model mass balance deterministically and with constant parameters, (2) the produced mass balances are in line with seasonal, glacier-wide mass balances obtained from interpolation of in situ observations, and (3) the combination of point mass balances and satellite information is helpful to reduce the uncertainty. The thesis also explores other options for improving near-real-time glacier mass balances. In particular, the possibility of (i) extrapolating glacier mass balance signals in space to otherwise unobserved glaciers, (ii) using short-term geodetic volume changes, (iii) predicting model parameters through machine learning, (iv) acquiring glacier melt observations with Unmanned Aerial Vehicles (UAVs), and (v) using Bayesian calibration for cases where model parameters cannot be uniquely identified, are tested and discussed. The daily, near-real-time estimates calculated by CRAMPON are provided at the resolution of individual glaciers, the results being summarized on an interactive web site.
  • Worldwide version-controlled database of glacier thickness observations
    Item type: Journal Article
    Welty, Ethan; Zemp, Michael; Navarro, Francisco; et al. (2020)
    Earth System Science Data
    Although worldwide inventories of glacier area have been coordinated internationally for several decades, a similar effort for glacier ice thicknesses was only initiated in 2013. Here, we present the third version of the Glacier Thickness Database (GlaThiDa v3), which includes 3 854 279 thickness measurements distributed over roughly 3000 glaciers worldwide. Overall, 14 % of global glacier area is now within 1 km of a thickness measurement (located on the same glacier) – a significant improvement over GlaThiDa v2, which covered only 6 % of global glacier area and only 1100 glaciers. Improvements in measurement coverage increase the robustness of numerical interpolations and model extrapolations, resulting in better estimates of regional to global glacier volumes and their potential contributions to sea-level rise. In this paper, we summarize the sources and compilation of glacier thickness data and the spatial and temporal coverage of the resulting database. In addition, we detail our use of open-source metadata formats and software tools to describe the data, validate the data format and content against this metadata description, and track changes to the data following modern data management best practices. Archived versions of GlaThiDa are available from the World Glacier Monitoring Service (e.g., v3.1.0, from which this paper was generated: https://doi.org/10.5904/wgms-glathida-2020-10; GlaThiDa Consortium, 2020), while the development version is available on GitLab (https://gitlab.com/wgms/glathida, last access: 9 November 2020).
  • The Open Global Glacier Model (OGGM) v1.1
    Item type: Journal Article
    Maussion, Fabien; Butenko, Anton; Champollion, Nicolas; et al. (2019)
    Geoscientific Model Development
    Despite their importance for sea-level rise, seasonal water availability, and as a source of geohazards, mountain glaciers are one of the few remaining subsystems of the global climate system for which no globally applicable, open source, community-driven model exists. Here we present the Open Global Glacier Model (OGGM), developed to provide a modular and open-source numerical model framework for simulating past and future change of any glacier in the world. The modeling chain comprises data downloading tools (glacier outlines, topography, climate, validation data), a preprocessing module, a mass-balance model, a distributed ice thickness estimation model, and an ice-flow model. The monthly mass balance is obtained from gridded climate data and a temperature index melt model. To our knowledge, OGGM is the first global model to explicitly simulate glacier dynamics: the model relies on the shallow-ice approximation to compute the depth-integrated flux of ice along multiple connected flow lines. In this paper, we describe and illustrate each processing step by applying the model to a selection of glaciers before running global simulations under idealized climate forcings. Even without an in-depth calibration, the model shows very realistic behavior. We are able to reproduce earlier estimates of global glacier volume by varying the ice dynamical parameters within a range of plausible values. At the same time, the increased complexity of OGGM compared to other prevalent global glacier models comes at a reasonable computational cost: several dozen glaciers can be simulated on a personal computer, whereas global simulations realized in a supercomputing environment take up to a few hours per century. Thanks to the modular framework, modules of various complexity can be added to the code base, which allows for new kinds of model intercomparison studies in a controlled environment. Future developments will add new physical processes to the model as well as automated calibration tools. Extensions or alternative parameterizations can be easily added by the community thanks to comprehensive documentation. OGGM spans a wide range of applications, from ice–climate interaction studies at millennial timescales to estimates of the contribution of glaciers to past and future sea-level change. It has the potential to become a self-sustained community-driven model for global and regional glacier evolution.
  • A consensus estimate for the ice thickness distribution of all glaciers on Earth
    Item type: Journal Article
    Farinotti, Daniel; Huss, Matthias; Fürst, Johannes J.; et al. (2019)
    Nature Geoscience
    Knowledge of the ice thickness distribution of the world’s glaciers is a fundamental prerequisite for a range of studies. Projections of future glacier change, estimates of the available freshwater resources or assessments of potential sea-level rise all need glacier ice thickness to be accurately constrained. Previous estimates of global glacier volumes are mostly based on scaling relations between glacier area and volume, and only one study provides global-scale information on the ice thickness distribution of individual glaciers. Here we use an ensemble of up to five models to provide a consensus estimate for the ice thickness distribution of all the about 215,000 glaciers outside the Greenland and Antarctic ice sheets. The models use principles of ice flow dynamics to invert for ice thickness from surface characteristics. We find a total volume of 158 ± 41 × 10³ km³, which is equivalent to 0.32 ± 0.08 m of sea-level change when the fraction of ice located below present-day sea level (roughly 15%) is subtracted. Our results indicate that High Mountain Asia hosts about 27% less glacier ice than previously suggested, and imply that the timing by which the region is expected to lose half of its present-day glacier area has to be moved forward by about one decade.
  • Near-Real-Time Monitoring, Modelling, and Data Assimilation of Glacier Mass Balance
    Item type: Monograph
    Landmann, Johannes Marian (2022)
    VAW-Mitteilungen
    Glaciers are among the most prominent indicators of climate change, since their behavior is directly linked to climatic variables such as temperature, precipitation, and solar radiation. Under the long- term trend of shrinkage due to recent and future climate warming, near-real-time glacier mass balance information and its prediction play a particular role: on scales of days to months, glaciers fulfill important functions concerning water supply, planning for hydroelectricity production, ecology, and mountain tourism. Due to this importance, near-real-time glacier mass balance information is also of interest to the broad media. However, supplying such near-real-time information is not simple. This is mainly for two reasons: first, acquiring up-to-date glacier observations comes with a high cost, because glaciers are often located in remote areas and a considerable amount of time and humanpower is required to access and observe them in situ. Second, there is the uncertainty that affects glacier mass balance models, which are often justified by physics, but still parametrized through statistical relations between mass change and meteorological variables. As a consequence of sparse and uncertain observations, parameters of these models are often not uniquely identifiable, or their spatial and temporal variability cannot be accounted for. A lack of observations thus associates the calculation of near-real-time glacier mass balance with high uncertainties. This thesis aims at treating the issue of not easily computable near-real-time mass balance estimates by combining available observations and their respective uncertainties with uncertain modeling results in a beneficial way. It does so by presenting Cryospheric Monitoring and Prediction Online (CRAMPON), a Bayesian framework that allows determining near-real-time glacier mass balances in an optimal fashion, i.e. by using all available direct and indirect mass balance information and minimizing the uncertainty of the common information. Bayesian methods are widely used in fields like meteorology, hydrology, snow sciences, and oceanography, but applications in glaciology are sparse to date. CRAMPON builds upon a Sequential Importance Resampling ( SIR ) scheme, also known as Particle Filtering, which comprises three steps: first, a prior estimate of a glacier’s mass balance on a particular day is given through forward integration of a mass balance model ensemble. The forward integration is driven by gridded meteorological data, and accounts for the corresponding uncertainty. Second, this prior estimate is updated with observations. This is done by using various measurements, including daily point mass balance observations from cameras, as well as surface albedo and transient snow lines derived from optical satellites. The combination of observations ensures that both temporally frequent point observations and less frequent but spatially comprehensive observations complement each other. This second step results in a so-called posterior mass balance estimate. Third, a resampling technique is applied to ensure temporal stability of the particle filter. Here, CRAMPON focuses on (1) making the resampling technique compatible with an ensemble modeling approach, and (2) using the filter to estimate model parameter distributions. This statistical data assimilation approach ensures that, at any instance, the framework delivers an optimal estimate of the current mass balance of a glacier, given all observations and respecting all observation uncertainties. Special focus is put on handling variables in a probabilistic fashion. This allows calculating uncertainties for the near-real-time glacier mass balance estimates during model runtime. The daily estimates and their uncertainties are then used for predicting the glacier mass balances into the near future. This is achieved by using Consortium for Small-Scale Modelling ( COSMO ) numerical weather predictions with lead times of up to five days, and European Centre for Medium-Range Weather Forecasts (ECMWF) extended-range forecasts for lead times of up to one month. Analyses of the results show that (1) CRAMPON delivers up to 95% more accurate results than conventional approaches that model mass balance deterministically and with constant parameters, (2) the produced mass balances are in line with seasonal, glacier-wide mass balances obtained from v interpolation of in situ observations, and (3) the combination of point mass balances and satellite information is helpful to reduce the uncertainty. The thesis also explores other options for improving near-real-time glacier mass balances. In particular, the possibility of (i) extrapolating glacier mass balance signals in space to otherwise unobserved glaciers, (ii) using short-term geodetic volume changes, (iii) predicting model parameters through machine learning, (iv) acquiring glacier melt observations with Unmanned Aerial Vehicles (UAVs), and (v) using Bayesian calibration for cases where model parameters cannot be uniquely identified, are tested and discussed. The daily, near-real-time estimates calculated by CRAMPON are provided at the resolution of individual glaciers, the results being summarized on an interactive web site.
Publications 1 - 7 of 7