Falk Heße - Academia.edu (original) (raw)

Papers by Falk Heße

Journal of Hydrology, 2021

Advances in Water Resources, 2021

Abstract Travel time distributions (TTDs) provide an effective way to describe the transport and ... more Abstract Travel time distributions (TTDs) provide an effective way to describe the transport and mixing processes of water parcels in a subsurface hydrological system. A major challenge in characterizing catchment TTD is quantifying the travel times in deep groundwater and its contribution to the streamflow TTD. Here, we develop and test a novel modeling framework for an integrated assessment of catchment scale TTDs through explicit representation of 3D-groundwater dynamics. The proposed framework is based on the linkage between a flux tracking scheme with the surface hydrologic model (mHM) for the soil-water compartment and a particle tracking scheme with the 3D-groundwater model OpenGeoSys (OGS) for the groundwater compartment. This linkage provides us with the ability to simulate the spatial and temporal dynamics of TTDs in these different hydrological compartments from grid scale to regional scale. We apply this framework in the Nagelstedt catchment in central Germany. Simulation results reveal that both shape and scale of grid-scale groundwater TTDs are spatially heterogeneous, which are strongly dependent on the topography and aquifer structure. The component-wise analysis of catchment TTD shows a time-dependent sensitivity of transport processes in soil zone and groundwater to driving meteorological forcing. Catchment TTD exhibits a power-law shape and fractal behavior. The predictive uncertainty in catchment mean travel time is dominated by the uncertainty in the deep groundwater rather than that in the soil zone. Catchment mean travel time is severely biased by a marginal error in groundwater characterization. Accordingly, we recommend to use multiple summary statistics to minimize the predictive uncertainty introduced by the tailing behavior of catchment TTD.

Advances in Water Resources, 2017

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  Coupling pore-scale flux simulation with geostatistical generation of porous media  Simulated seepage velocity is primarily affected by short distances autocorrelation  Low resolution leads to underestimation of seepage velocity for short correlation lengths  Uncertainty increases for lengths of autocorrelation approaching the domain size

Geoscientific Model Development Discussions, 2017

Most of the current large scale hydrological models do not contain a physically-based groundwater... more Most of the current large scale hydrological models do not contain a physically-based groundwater flow component. The main difficulties in large-scale groundwater modeling include the efficient representation of unsaturated zone flow, the characterization of dynamic groundwater-surface water interaction and the numerical stability while preserving complex physical processes and high resolution. To address these problems, we propose a highly-scalable coupled hydrologic and groundwater model (mHM#OGS) based on the integration of two open-source modeling codes: the mesoscale hydrologic Model (mHM) and the finite element simulator OpenGeoSys (OGS). mHM#OGS is coupled using a boundary condition-based coupling scheme that dynamically links the surface and subsurface parts. Nested time stepping allows smaller time steps for typically faster surface runoff routing in mHM and larger time steps for slower subsurface flow in OGS. mHM#OGS features the coupling interface which can transfer the g...

Frontiers in Water

The fluxes of water and solutes in the subsurface compartment of the Critical Zone are temporally... more The fluxes of water and solutes in the subsurface compartment of the Critical Zone are temporally dynamic and it is unclear how this impacts microbial mediated nutrient cycling in the spatially heterogeneous subsurface. To investigate this, we undertook numerical modeling, simulating the transport in a wide range of spatially heterogeneous domains, and the biogeochemical transformation of organic carbon and nitrogen compounds using a complex microbial community with four (4) distinct functional groups, in water saturated subsurface compartments. We performed a comprehensive uncertainty analysis accounting for varying residence times and spatial heterogeneity. While the aggregated removal of chemical species in the domains over the entire simulation period was approximately the same as that in steady state conditions, the sub-scale temporal variation of microbial biomass and chemical discharge from a domain depended strongly on the interplay of spatial heterogeneity and temporal dyna...

EGU General Assembly Conference Abstracts, Apr 1, 2019

EGU General Assembly Conference Abstracts, Apr 1, 2016

This is the first release of the geostatDB package. geostatDB is an R package that provides acces... more This is the first release of the geostatDB package. geostatDB is an R package that provides access to the World Wide Hydrological Parameters DAtabase (WWHYPDA).

This is the first release of exPrior, an R package for the derivation of informative prior distri... more This is the first release of exPrior, an R package for the derivation of informative prior distributions using external, i.e., ex-situ data. The package can also be found on CRAN

Purpose ogs5py is A python-API for the OpenGeoSys 5 scientific model... more Purpose ogs5py is A python-API for the OpenGeoSys 5 scientific modeling package. Installation You can install the latest version with the following command: <pre><code>pip install ogs5py </code></pre> Documentation for ogs5py You can find the documentation under geostat-framework.readthedocs.io. Further Information General homepage: https://www.opengeosys.org/ogs-5 OGS5 Repository: https://github.com/ufz/ogs5 Keyword documentation: https://ogs5-keywords.netlify.com OGS5 Benchmarks: https://github.com/ufz/ogs5-benchmarks ogs5py Benchmarks: https://github.com/GeoStat-Framework/ogs5py_benchmarks Release Notes Bugfixes <code>GLI.add_polyline</code> now allows integer coordinates for points: bf5d684 <code>MSH.centroids</code> are now calculated as center of mass instead of center of element nodes: b0708a6 <code>MSH.show</code> was not working: 6a0489b <code>OGS.run_model</code> has now a better check for OGS success: 143d0ab <code>GMSH</code> interface was updated to new meshio-API: d3e0594 <code>RFR</code> file was not written: 41e55f3 <code>BC</code> new sub-key TIME_INTERVAL was missing: 94ec5c5 Additions <code>download_ogs</code> downloads a system dependent OGS5 executable: ede32e4 <code>add_exe</code> add a self compiled OGS5 executable: ede32e4 <code>MSH.import_mesh</code> now allows the import of material_id and element_id if given as cell_data in the external mesh: 00a77fa <code>MSH.export_mesh</code> now automatically exports material_id (already the case before) and element_id. Also you can now export additional <code>point_data</code> and <code>field_data</code>: 00a77fa New method <code>MSH.set_material_id</code> to set the material IDs for specific elements: 4b11c6a <code>MSH.show</code> now can show additional cell_data: ffd7604 New routine <code>show_vtk</code> to show vtk output with mayavi: f640c19 New method <code>OGS.output_files</code> to get a list of output files: 2f5f102 New attribute <code>file_name</code> for files: 632c2 [...]

Travel time distributions (TTDs) are fundamental descriptors to characterize the functioning of s... more Travel time distributions (TTDs) are fundamental descriptors to characterize the functioning of storage, mixing and release of water and solutes in a river basin. Identifying the relative importance (and controls) of climate and landscape attributes on TDDs is fundamental to improve our understanding of the underlying mechanism controlling the spatial heterogeneity of TTDs, and their moments (e.g., mean TT). Studies aimed at elucidating such controls have focused on either theoretical developments to gain (physical) insights using mostly synthetic datasets or empirical relationships using limited datasets from experimental sites. A study painting a general picture of emerging controls at a continental scale is still lacking.

A framework for interpreting transient pumping tests in heterogeneous transmissivity fields is de... more A framework for interpreting transient pumping tests in heterogeneous transmissivity fields is developed to infer the overall geostatistical parameters of the medium without reconstructing the specific heterogeneous structure point wise. This method is applied to data of the field site “Horkheimer Insel” [1] (South-West Germany) as well as the aquifer analogon “Herten” [2] to estimate the parameters of heterogeneity from pumping test data of each site. The methodology is based on the upscaling approach Radial Coarse Graining [3] which is applied to deduce an effective radial description of multi-Gaussian transmissivity. It was used to derive an Effective Well Flow Solution [4] for transient flow conditions including not only the storativity, but also the geometric mean, the variance, and the correlation length of log-transmissivity. This solution is shown to be appropriate to characterize the pumping test drawdown behavior in heterogeneous transmissivity fields making use of ensembl...

In a changing climate scenario, we expect weather event patterns to change, both in frequency and... more In a changing climate scenario, we expect weather event patterns to change, both in frequency and in intensity. The subsequent impacts of these changing patterns on ecosystem functions are of great interest. Water quality particularly is critical due to public health concerns. Already, seasonal variation of water quality has been attributed to varying microbial community assemblages and nutrient loading in the corresponding water body but the contribution of the variations in the quantity of groundwater recharge is a missing link. It is thus beneficial to establish links between external forcing such as changing infiltration rate or recharge on nutrient cycling in the subsurface. We undertake this study to investigate the impact of temporal variation in external forcing on the biogeochemical potential of spatially heterogeneous subsurface systems using a numerical modeling approach. We used geostatistical tools to generate spatial random fields by considering difference combinations...

Travel-time distributions are a comprehensive tool for the characterization of hydrological syste... more Travel-time distributions are a comprehensive tool for the characterization of hydrological system dynamics. Unlike stream ow hydrographs, they describe the movement and storage of water inside and through the hydrological system. Until recently, studies using such travel-time distributions have generally either been applied to simple (arti cial toy) models or to real-world catchments using available time series, e.g. ✿ , stable isotopes. Whereas the former are limited in their realism, 5 the latter are limited in their use of available data sets. In our study, we employ a middle ground by using the mesoscale Hydrological Model (mHM) and apply it to a catchment in Central Germany. Being able to draw on multiple large data sets for calibration and veri cation, we generate a large array of spatially distributed states and uxes. These hydrological outputs are then used to compute the travel-time distributions for every grid cell in the modeling domain. A statistical analysis shows the ...

Goldschmidt2021 abstracts, 2021

Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in ma... more Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of e.g. Earth Sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields, it can perform kriging and variogram estimation and much more. We demonstrate its abilities by virtue of a series of example application detailing their use.

The R Journal, 2021

The exPrior package implements a procedure for formulating informative priors of geostatistical p... more The exPrior package implements a procedure for formulating informative priors of geostatistical properties for a target field site, called ex-situ priors and introduced in Cucchi et al. (2019). The procedure uses a Bayesian hierarchical model to assimilate multiple types of data coming from multiple sites considered as similar to the target site. This prior summarizes the information contained in the data in the form of a probability density function that can be used to better inform further geostatistical investigations at the site. The formulation of the prior uses ex-situ data, where the data set can either be gathered by the user or come in the form of a structured database. The package is designed to be flexible in that regard. For illustration purposes and for easiness of use, the package is ready to be used with the worldwide hydrogeological parameter database (WWHYPDA) Comunian and Renard (2009).