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.
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
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.
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?
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.
Gonzáles-Nicolás, A., Schwientek, M., Sinsbeck, M., Nowak, W. (2020): Characterization of catchment regimes in discharge-concentration plots via an advanced time-series model and event-based sampling. Water Resour. Res. (submitted)
Palosu, T., et al., …, Weber, T.K.D., … (2020): Multi-model evaluation of phenology prediction for wheat in Australia, Agricult. Forest Meteor. (submitted).
Tóth, B., Weynants, M., Weber, T.K.D. (2020): Updated European Hydraulic Pedotransfer Functions with Communicated Uncertainties in the Predicted Variables (euptfv2), Geosci. Model Dev. (submitted).
Mequanint, F., Gayler, S., Weber, T.K.D., Tesfaye, K, Streck, T. (2020): Climate change impact on crop growth in Ethiopia: A multi-model uncertainty analysis, submitted to Agricult. Forest Meteor. (submitted).
Wallach, D., et al., …, Weber, T.K.D., … (2020): Crop model prediction of wheat phenology with emphasis on calibration, Eur. J. Agronomy (submitted).
Allgeier, J. , Gonzáles-Nicolás, A., Nowak, W., Cirpka, O.A. (2020): A stochastic framework to optimize monitoring strategies for delineating groundwater divides. Frontiers Earth Sci. (submitted).
Schäfer Rodrigues Silva, A., Guthke, A., Höge, M., Cirpka, O.A., Nowak,W. (2020): Strategies for simplifying reactive transport models - a Bayesian model comparison. Water Resour. Res. (submitted).
Erdal, D., Xiao, S., Nowak, W., Cirpka, O.A. (2020): Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian Process Emulation and Active Subspaces. Stoch. Environ. Res. Risk Ass., in press, doi: 10.1007/s00477-020-01867-0..
D. Erdal, O.A. Cirpka (2020): Technical Note: Improved sampling of behavioral subsurface flow model parameters using active subspaces. Hydrol. Earth Sys. Sci., 24: 4567–4574, doi: 10.5194/hess-24-4567-2020.
D'Affonseca, F.M., Finkel, M., Cirpka, O.A. (2020): Combining implicit geological modeling, field surveys, and hydrogeological modeling to describe groundwater flow in a karst aquifer. Hydrogeol. J. doi: 10.1007/s10040-020-02220-z.
Weber, T.K.D., Finkel, M., da Conceicáo Goncalves, M., Vereecken, H., Diamantopoulos, E. (2020) Pedotransfer function for the Brunswick soil hydraulic property model and comparison to the von Genuchten-Mualem model. Water Resour. Res. 56: e2019WR026820, doi: 10.1029/2019WR026820.
Riedel, T., Weber, T.K.D. (2020). Review: The influence of natural and anthropogenic changes in Europe´s water cycle on groundwater recharge, Hydrogeol. J. doi: 10.1007/s10040-020-02165-3.
Groh, J, Diamantopoulos, E., Duan, X, … Weber, TKD, …, and HH Gerke (2020) Crop growth and soil water fluxes at erosion-affected arable sites: Using weighing lysimeter data for model inter-comparison, Vadose Zone J. 19:e20058, doi: 10.1002/vzj2.20058
Sanchez-León, E., Erdal, D., Leven, C., Cirpka, O.A. (2020): Comparison of two Ensemble-Kalman based methods for estimating aquifer parameters from virtual 2-D hydraulic and tracer tomographic tests. Geosci. 10: 276, 2020, doi: 10.3390/geosciences10070276.
Streck, T., Weber, T.K.D. (2020): Analytical expressions for non-capillary soil water retention based on popular soil hydraulic functions, Vadose Zone J., 19:e20042, doi: 10.1002/vzj2.20042.
Höge, M., Guthke, A. Nowak, W. (2020): Bayesian model weighting: The many faces of model averaging. Water 12(2): 309, doi: 10.3390/w12020309.
Maier, R., Gonzalez-Nicolas, A., Leven, C., Nowak, W., Cirpka, O.A. (2020): Joint optimization of measurement and modeling strategies with application to radial flow in stratified aquifers. Water Resour. Res. 56(7): e2019WR026872, doi: 10.1029/2019WR026872.
Selzer, P., Cirpka, O.A. (2020). Postprocessing of standard Finite-Element velocity fields for accurate particle tracking applied to groundwater flow. Computat. Geosci., 24(4), doi: 10.1007/s10596-020-09969-y.
Chow, R., Bennett, J., Dugge, J., Wöhling, T., Nowak, W. (2019): Evaluating subsurface parameterization to simulate hyporheic exchange: The Steinlach River Test Site. Groundwater 58(1): 93-109, doi: 10.1111/gwat.12884.
Motavita, D.F., Chow, R., Guthke, A., Nowak, W. (2019): The Comprehensive Differential Split-Sample Test: A stress-test for hydrological model robustness under climate variability. J. Hydrol. 573: 501-515, doi: 10.1016/j.jhydrol.2019.03.054.
Höge, M., Guthke, A., Nowak, W. (2019): The hydrologist's guide to Bayesian model selection, averaging and combination. J. Hydrol. 572: 96-107, doi: 10.1016/j.jhydrol.2019.01.072.
Erdal, D., Cirpka, O.A. (2019): Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces. Hydrol. Earth Sys. Sci. 23: 3787–3805, doi: 10.5194/hess-23-3787-2019.
Erdal, D., Baroni, G., Cirpka, O.A. (2019): The value of simplified models for spin up of complex models with an application to subsurface hydrology. Computers and Geosciences 126: 62-72, doi: 10.1016/j.cageo.2019.01.014.
Eshonkulov, R., Poyda, A., Ingwersen, J., Wizemann, H.-D., Weber, T.K.D., Kremer, P., Högy, P., Pulatov, A., Streck, T. (2019): Evaluating multi-year, multi-site data on the energy balance closure of eddy-covariance flux measurements at cropland sites in southwest Germany, bg-2018-422, Biogeosci., 16, 521-540, doi: 10.5194/bg-16-521-2019.
Weber, T.K.D., Durner, W., Streck, T., Diamantopoulos, E. (2019): A modular framework for modeling unsaturated soil hydraulic properties over the full moisture range. Water Resour. Res., 55(6): 4994-5011, doi: 10.1029/2018WR024584.
M. Loschko, T. Wöhling, D.L. Rudolph, O.A. Cirpka (2019): An electron-balance based approach to predict the decreasing denitrification potential of an aquifer. Groundwater 57(6): 925-939, doi: 10.1111/gwat.12876.
Höge, M., Wöhling, T., Nowak, W. (2018): A primer for model selection: The decisive role of model complexity. Water Resour. Res., 54(3), 1688-1715, doi: 10.1002/2017WR021902.
Loschko, M., Wöhling, T., Rudolph, D.L., Cirpka, O.A. (2018): Accounting for the decreasing reaction potential of heterogeneous aquifers in a stochastic framework of aquifer-scale reactive transport. Water Resour. Res. 54(1): 442–463, doi: 10.1002/2017WR021645.
Wallach, D., Martre, P., Liu, B., …, Streck, T., …, et al. (2018): Multi-model ensembles improve predictions of crop-environment-management interactions. Glob. Change Biol. 24:5072–5083.
Alexander Schade (2020): Comparison of evapotranspiration models in Expert-N5 and evaluation with field data from Kraichgau and Swabian Alb.
Nigar Goyayeva (2020): Modelled effects of future climate change on crop yields, water cycle, and nitrate leaching in the Ammer catchment, SW Germany, using the model XN5-GECROS.
Aytaj Mammadly (2020): Assessing the impact of future climate change on grassland productivity, water cycle, and nitrate leaching in the Ammer catchment, SW Germany, using the model XN 5- HPM.
Jorge Olivares Rivas (2019): Evaluating groundwater flow in the karstic Muschelkalk aquifer between Nagold Valley and Ammer Spring using 2-D hydrogeological models.
Carolyn Elizabeth Duffy ( 2019): Karstification screening tool: A GIS approach for the assessment of karstified regions, Baden-Württemberg, Germany.
Samuel Kelsey (2019): Model-based quantification of run-off generation processes at the hillslope scale.
Stefania Scheurer (2019): Quantifying similarities between computational expensive reactive transport models.
Timo Seitz (2018): Identifying similarities between reactive transport models based on Bayesian model selection.