Moritz Feigl
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View article: Distilling Hydrological and Land Surface Model Parameters from Physio-Geographical Properties Using Text-Generating AI
Distilling Hydrological and Land Surface Model Parameters from Physio-Geographical Properties Using Text-Generating AI Open
The estimation of parameters for distributed hydrological and land-surface models is inherently difficult, particularly in data-scarce regions. We present a novel approach that employs variational autoencoders (VAEs) that have been trained…
View article: From multiple meteorological forecasts to river runoff: Learning and adjusting real-time biases to enhance predictions
From multiple meteorological forecasts to river runoff: Learning and adjusting real-time biases to enhance predictions Open
Deep learning models such as the Long Short Term Memory Network (LSTM) are capable of representing rainfall-runoff relationships and outperform classical hydrological models in gauged and ungauged settings (Kratzert et al., 2018). Previous…
View article: Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity
Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity Open
Saturated hydraulic conductivity (Ksat) and other soil (hydraulic) properties are fundamental for applications that depend on modeling hydrological processes, such as the quantification of future groundwater recharge rates. Yet, for most a…
View article: RechAUT - Variability of Groundwater Recharge and its Implication for Sustainable Land Use in Austria
RechAUT - Variability of Groundwater Recharge and its Implication for Sustainable Land Use in Austria Open
Water in the vadose zone is an essential part of the global water cycle making up some of the largest freshwater resources on Earth. Climate as well as land use change are known to alter water fluxes in the vadose zone, and thus changes in…
View article: Automatic Regionalization of Model Parameters for Hydrological Models
Automatic Regionalization of Model Parameters for Hydrological Models Open
Parameter estimation is one of the most challenging tasks in large‐scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number o…
View article: Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity
Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity Open
Version 1.0 - This version is the final revised one. This is the dataset accompanying the paper: Zeitfogel et al., Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity,…
View article: Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity
Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity Open
Version 1.0 - This version is the final revised one. This is the dataset accompanying the paper: Zeitfogel et al., Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity,…
View article: Soil information on a regional scale: Two Machine Learning based approaches for predicting Ksat
Soil information on a regional scale: Two Machine Learning based approaches for predicting Ksat Open
Version Submission: Please be aware that this version of the dataset is NOT the final revised one! Soil property and Ksat maps for Austria. The digital soil maps were generated based on a Machine Learning and PTF-based approach (indirect a…
View article: Prediction of runoff characteristics in ungauged basins in Central Europe with machine learning – files
Prediction of runoff characteristics in ungauged basins in Central Europe with machine learning – files Open
English This are the shapefiles accompanying the paper: Klingler et al. (2022), Prediction of runoff characteristics in ungauged basins with machine learning, published in the journal Österreichische Wasser- und Abfallwirtschaft: https://d…
View article: Prediction of runoff characteristics in ungauged basins in Central Europe with machine learning – files
Prediction of runoff characteristics in ungauged basins in Central Europe with machine learning – files Open
English This are the shapefiles accompanying the paper: Klingler et al. (2022), Prediction of runoff characteristics in ungauged basins with machine learning, published in the journal Österreichische Wasser- und Abfallwirtschaft: https://d…
View article: Groundwater recharge modeling – the importance of distributed soil information in hydrological models 
Groundwater recharge modeling – the importance of distributed soil information in hydrological models  Open
<p>Spatially distributed soil information as input for hydrological models has the potential to improve the representation and physical realism of spatio-temporal hydrological processes. Since spatially distributed soil information i…
View article: Learning from mistakes—Assessing the performance and uncertainty in process‐based models
Learning from mistakes—Assessing the performance and uncertainty in process‐based models Open
Typical applications of process‐ or physically‐based models aim to gain a better process understanding or provide the basis for a decision‐making process. To adequately represent the physical system, models should include all essential pro…
View article: Automatic regionalization of model parameters for hydrological models
Automatic regionalization of model parameters for hydrological models Open
Parameter estimation is one of the most challenging tasks in large-scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number o…
View article: Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi river basin of East Africa
Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi river basin of East Africa Open
Study region: Sio Malaba Malakisi river basin, East Africa. Study focus: Poor rain-gauge density is a limitation to comprehensive hydrological studies in Sub-Saharan Africa. Consequently, Satellite precipitation products (SPPs) provide an …
View article: Learning from mistakes - Assessing the performance and uncertainty in process-based models
Learning from mistakes - Assessing the performance and uncertainty in process-based models Open
Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential pro…
View article: Data for "Learning from mistakes - Assessing the performance and uncertainty in process-based models"
Data for "Learning from mistakes - Assessing the performance and uncertainty in process-based models" Open
Data for the publication "Learning from mistakes - Assessing the performance and uncertainty in process-based models" The corresponding python code can be found at github.com/MoritzFeigl/Learning-from-mistakes. This dataset contains data o…
View article: Data for "Learning from mistakes - Assessing the performance and uncertainty in process-based models"
Data for "Learning from mistakes - Assessing the performance and uncertainty in process-based models" Open
Data for the publication "Learning from mistakes - Assessing the performance and uncertainty in process-based models" The corresponding python code can be found at github.com/MoritzFeigl/Learning-from-mistakes. This dataset contains data o…
View article: Machine-learning methods for stream water temperature prediction
Machine-learning methods for stream water temperature prediction Open
Water temperature in rivers is a crucial environmental factor with the ability to alter hydro-ecological as well as socio-economic conditions within a catchment. The development of modelling concepts for predicting river water temperature …
View article: Learning from mistakes - Assessing the performance and uncertainty in process-based models
Learning from mistakes - Assessing the performance and uncertainty in process-based models Open
<p>Typical applications of process- or physically-based models aim to gain a better process understanding of certain natural phenomena or to estimate the impact of changes in the examined system caused by anthropogenic influences, su…
View article: Catchment to model space mapping &#8211; learning transfer functions from data by symbolic regression
Catchment to model space mapping – learning transfer functions from data by symbolic regression Open
<p>The Function Space Optimization (FSO) method, recently developed by Feigl et al. (2020), automatically estimates the transfer function structure and coefficients to parameterize spatially distributed hydrological models. FSO is a …
View article: Variability across scales - exploring methods for predicting soil properties from multiple sources
Variability across scales - exploring methods for predicting soil properties from multiple sources Open
<p>To assess future groundwater recharge rates in Austria under climate change conditions, detailed spatial soil information is required. &#160;Different data sources such as global soil maps (SoilGrids), regional soil maps of ar…
View article: Automatic Estimation of Parameter Transfer Functions for Distributed Hydrological Models - Function Space Optimization Applied on the mHM Model
Automatic Estimation of Parameter Transfer Functions for Distributed Hydrological Models - Function Space Optimization Applied on the mHM Model Open
Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Automatic Estimation of Parameter Transfer Functions for Distributed Hydrological Models - Function Space Optimization Applied on the m…
View article: Machine learning methods for stream water temperature prediction
Machine learning methods for stream water temperature prediction Open
Water temperature in rivers is a crucial environmental factor with the ability to alter hydro-ecological as well as socio-economic conditions within a catchment. The development of modelling concepts for predicting river water temperature …
View article: Function Space Optimization: A Symbolic Regression Method for Estimating Parameter Transfer Functions for Hydrological Models
Function Space Optimization: A Symbolic Regression Method for Estimating Parameter Transfer Functions for Hydrological Models Open
Estimating parameters for distributed hydrological models is a challenging and long studied task. Parameter transfer functions, which define model parameters as functions of geophysical properties of a catchment, might improve the calibrat…
View article: On the Ability of LIDAR Snow Depth Measurements to Determine or Evaluate the HRU Discretization in a Land Surface Model
On the Ability of LIDAR Snow Depth Measurements to Determine or Evaluate the HRU Discretization in a Land Surface Model Open
To find the adequate spatial model discretization scheme, which balances the models capabilities and the demand for representing key features in reality, is a challenging task. It becomes even more challenging in high alpine catchments, wh…
View article: Genome-wide association study of germline copy number variations reveals an association with prostate cancer aggressiveness
Genome-wide association study of germline copy number variations reveals an association with prostate cancer aggressiveness Open
Prostate cancer is a major health burden, being the second most commonly diagnosed malignancy in men worldwide. Overtreatment represents a major problem in prostate cancer therapy, leading to significant long-term quality-of-life effects f…
View article: Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models
Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models Open
Estimating parameters for distributed hydrological models is a challenging and long studied task. Parameter transfer functions, which define model parameters as functions of geo-physical properties of a catchment, might improve the calibra…
View article: Efficient modelling of water temperature patterns in river systems &#8211; benchmarking a set of machine learning approaches
Efficient modelling of water temperature patterns in river systems – benchmarking a set of machine learning approaches Open
<p>Many approaches for modelling river water temperature are available, but not one exist that can be applied without restrictions. The applied method depends on data availability, dominant processes, scales and transferability. Proc…