Uni-Tübingen

P7 Stochastic Modeling Framework of Catchment-Scale Reactive Transport

P8 Conceptual Uncertainty, Model Legitimacy, and Optimization of Data Acquisition Strategies

People Involved

Principal Investigators

Prof. Dr.-Ing. Olaf A. Cirpka

University of Tübingen, Hydrogeology

Prof. Dr. Thilo Streck

University of Hohenheim, Biogeophysics

Prof. Dr.-Ing. Wolfgang Nowak

Researchers

Dr. Daniel Erdal

PostDoc, University of Tübingen (until 2020), Hydrogeology

Dr. Ana González-Nicolas

PostDoc, University of Stuttgart, Stochastic Simulation and Safety Research for Hydrosystems (until 2021)

Dr. Tobias Weber

PostDoc, University of Hohenheim, Biogeophysics

Dr. Aline Schäfer Rodrigues Silva, MSc

PhD candidate, University of Stuttgart, Stochastic Simulation and Safety Research for Hydrosystems (graduated 2022)

Han-Fang Hsueh, MSc (associated)

PhD candidate, University of Stuttgart, RTG Hydrosystem Modelling

Dr. Philipp Selzer, MSc (associated)

PhD candidate, University of Tübingen, RTG Hydrosystem Modelling (graduated 2022)

Dr. Anna Störiko, MSc (associated)

PhD candidate, University of Tübingen, RTG Hydrosystem Modelling (graduated 2022)

Michelle Viswanathan, MSc (associated)

PhD candidate, University of Hohenheim, RTG Hydrosystem Modelling

Objectives and General Approaches

Both projects P7 and P8 are concerned with stochastic modeling of hydrosystems, albeit under slightly different perspectives. Because these projects will be merged in phase II of CAMPOS, we present them here jointly.

Objective of P7

Process-based numerical models of flow, transport, and reactive turnover are necessary tools to understand major influences on water quality, nutrient cycling, and fate of pollutants. These models require spatially distributed parameters which, unfortunately, are often quite uncertain.

The main objective of project P7 is to develop a modeling framework for flow and reactive transport on the catchment scale that is

General Approach of P7

The model framework is based on simulating the strong feedbacks between soils and vegetation at the land-surface with fully coupled 1-D, vertical soil-crop models (Expert-N) that are weekly coupled to an underlying 3-D flow model (HydroGeoSphere) of the deeper subsurface and streamline-based models of reactive transport therein.

While the individual model components honor existing information about soil types, land use, topography/bathymetry, and geology, we address the uncertainty of parameters and geometries by treating all coefficients as random distributions, requiring ensemble calculations of the full system.

Research Questions of P8

For predicting flow and reactive transport in the environment, many scientific models concepts compete with each other. This adds an additional level of uncertainty to system understanding and prediction. Among the competing models, more accurate models are typically more complex. Unless they are fed with more data for calibration, their predictions are affected by more uncertainty.

To address this problem, we investigate:

  • What are the key uncertainties in model choice for CAMPOS-related applications?
  • What model concepts (lower versus higher complexity) are legitimate to use, given the limited amount of data that is affordable to collect?
  • How can we identify optimal instrumentation strategies that minimize dominant uncertainties (both parametric and model choice)?
  • With these insights, can we provide feedback to the overall CAMPOS research strategy?

General Approach of P8

Project P8 works with multi-model ensembles to address the uncertainty of predictions, including uncertainty in parameters, forcings, and model formulations. Then, we develop methods and apply them to perform

  • Bayesian multi-model analysis and cross-validation techniques to assess the legitimacy and predictive skills of our (multi-)models,
  • Optimal design of experiments, maximizing the confidence in model choice and model predictions.

We do this in close collaboration with projects that generate data and develop models, and provide our expertise in Bayesian analysis, computational statistics and mathematical optimization.

Achievements

Compiling a Dataset for the Conditioning of Agro-Ecosystem Models
Soil Hydraulic Properties Model over the Full Moisture Range
Probabilistic Land-Surface Model
Catchment-Scale Stochastic Modeling of Subsurface Flow
Machine-Learning Support in Model Preselection
Global Sensitivity Analysis of Catchment-Scale Flow Models
Development of Particle Tracking for Finite-Element Flow Fields
Travel-/Exposure-Time Based Reactive Transport
Reaction Models Informed by Molecular-Biological Data
Assessment of Model Legitimacy in Reactive Transport
Time-Windowed Bayesian Analysis of Model Errors
Optimal Experiments for Maximally-Confident Model Selection: Theory
Optimal Placement of Piezometers to Detect a Groundwater Divide
Joint Optimization of Observations and Model Choice
Optimal River-Sampling Campaigns for Characterizing Hysteretic Catchments