Thomas Wöhling

Catchments as reactors: a comprehensive approach for water fluxes and solute turnover

Sustainable water quality management requires a profound understanding of water fluxes (precipitation, run-off, recharge, etc.) and solute turnover such as retention, reaction, transformation, etc. at the catchment or landscape scale. The Water and Earth System Science competence cluster (WESS, http://www.wess.info/) aims at a holistic analysis of the water cycle coupled to reactive solute transport, including soil–plant–atmosphere and groundwater–surface water interactions. To facilitate exploring the impact of land-use and climate changes on water cycling and water quality, special emphasis is placed on feedbacks between the atmosphere, the land surface, and the subsurface. A major challenge lies in bridging the scales in monitoring and modeling of surface/subsurface versus atmospheric processes. The field work follows the approach of contrasting catchments, i.e. neighboring watersheds with different land use or similar watersheds with different climate. This paper introduces the featured catchments and explains methodologies of WESS by selected examples.

Incorporating dynamic root growth enhances the performance of Noah‐MP at two contrasting winter wheat field sites

Interactions between the soil, the vegetation, and the atmospheric boundary layer require close attention when predicting water fluxes in the hydrogeosystem, agricultural systems, weather, and climate. However, land-surface schemes used in large-scale models continue to show deficiencies in consistently simulating fluxes of water and energy from the subsurface through vegetation layers to the atmosphere. In this study, the multiphysics version of the Noah land-surface model (Noah-MP) was used to identify the processes, which are most crucial for a simultaneous simulation of water and heat fluxes between land surface and the lower atmosphere. Comprehensive field data sets of latent and sensible heat fluxes, ground heat flux, soil moisture, and leaf area index from two contrasting field sites in South-West Germany are used to assess the accuracy of simulations. It is shown that an adequate representation of vegetation-related processes is the most important control for a consistent simulation of energy and water fluxes in the soil-plant-atmosphere system. In particular, using a newly implemented submodule to simulate root growth dynamics has enhanced the performance of Noah-MP. We conclude that further advances in the representation of leaf area dynamics and root/soil moisture interactions are the most promising starting points for improving the simulation of feedbacks between the subsoil, land surface and atmosphere in fully coupled hydrological and atmospheric models.

Assessing the relevance of subsurface processes for the simulation of evapotranspiration and soil moisture dynamics with CLM3.5: comparison with field data and crop model simulations

Plant water uptake is a crucial process linking water fluxes in the soil–plant–atmosphere continuum. Soil water extraction by roots affects the dynamics and distribution of soil moisture. Water supply of plants controls transpiration, which makes up for an important fraction of the energy balance at the land surface, and influences soil–vegetation–atmosphere feedback processes. Therefore, efficient algorithms for an accurate estimation of root water uptake are essential in land-surface models that are coupled with climate models, in agricultural crop models that predict water budget and plant growth at the field and plot scale, and in hydrological models. Due to different purposes and demands on computational time, the degree of detail in representing belowground processes varies considerably between these model types. This study investigates the impact of the degree of detail in process descriptions of root growth and water uptake and of information about soil hydraulic properties on simulated seasonal patterns of evapotranspiration and soil moisture in a field study with winter wheat (Triticum aestivum L. cv. Cubus). Evapotranspiration was well simulated by CLM3.5 until the beginning of crop senescence, but it overestimates the water flux through plants in the last three weeks of the vegetation period and showed a lower performance in simulating soil moisture compared to crop models. The best simultaneous fit of soil moisture and latent heat flux was achieved by the crop model XN-SPASS, which consists of the most detailed representation of root growth dynamics. The results indicate the importance of implementing improved belowground process descriptions for advanced simulations with coupled hydrological and atmospheric models.

Bayesian model averaging to explore the worth of data for soil-plant model selection and prediction

A Bayesian model averaging (BMA) framework is presented to evaluate the worth of different observation types and experimental design options for (1) more confidence in model selection and (2) for increased predictive reliability. These two modeling tasks are handled separately because model selection aims at identifying the most appropriate model with respect to a given calibration data set, while predictive reliability aims at reducing uncertainty in model predictions through constraining the plausible range of both models and model parameters. For that purpose, we pursue an optimal design of measurement framework that is based on BMA and that considers uncertainty in parameters, measurements, and model structures. We apply this framework to select between four crop models (the vegetation components of CERES, SUCROS, GECROS, and SPASS), which are coupled to identical routines for simulating soil carbon and nitrogen turnover, soil heat and nitrogen transport, and soil water movement. An ensemble of parameter realizations was generated for each model using Monte-Carlo simulation. We assess each models plausibility by determining its posterior weight, which signifies the probability to have generated a given experimental data set. Several BMA analyses were conducted for different data packages with measurements of soil moisture, evapotranspiration (ETa), and leaf area index (LAI). The posterior weights resulting from the different BMA runs were compared to the weight distribution of a reference run with all data types to investigate the utility of different data packages and monitoring design options in identifying the most appropriate model in the ensemble. We found that different (combinations of) data types support different models and none of the four crop models outperforms all others under all data scenarios. The best model discrimination was observed for those data where the competing models disagree the most. The data worth for reducing prediction uncertainty depends on the prediction to be made. LAI data have the highest utility for predicting ETa, while soil moisture data are better for predicting soil water drainage. Our study illustrates, that BMA provides an objective framework for data worth analysis with respect to both model discrimination and model calibration for a wide range of applications.

Dual-tracer, non-equilibrium mixing cell modelling and uncertainty analysis for unsaturated bromide and chloride transport.

A model is presented for simulating one-dimensional advective dispersive solute transport in the vadose zone. The finite-volume, mixing-cell model uses drainage flux intervals as the index variable, which are calculated by a soil water balance model. The modelling approach considers solute transport from two different regions as well as a slow and a fast transport domain in each region as parallel transport processes. The model is applied to breakthrough curves of Cl(-) and Br(-) measured at different locations and different depths in the volcanic vadose zone of the Tutaeuaua subcatchment of Lake Taupo, New Zealand, following a dual tracer application. Estimates of transport parameter and model predictive uncertainty were derived using the differential evolution adaptive metropolis, DREAM(ZS) adaptive Markov chain Monte Carlo algorithm, a formal Bayesian likelihood function, observed leachate volumes, and Cl(-) breakthrough curves. The model was subsequently evaluated using Br(-) breakthrough curves from the dual tracer experiment and a previously conducted Br(-) tracer-only experiment. Uncertainty bounds derived by this MCMC method simultaneously capture the observed Br(-) and Cl(-) breakthrough curves and corresponding drainage volumes. Results suggest that the slow transport domain properties are relatively similar for different locations in the vadose zone and that the variability in contaminant fluxes is predominantly driven by structural variability of the vadose zone causing lateral flow.

Optimal Design of Multitype Groundwater Monitoring Networks Using Easily Accessible Tools.

Monitoring networks are expensive to establish and to maintain. In this paper, we extend an existing data-worth estimation method from the suite of PEST utilities with a global optimization method for optimal sensor placement (called optimal design) in groundwater monitoring networks. Design optimization can include multiple simultaneous sensor locations and multiple sensor types. Both location and sensor type are treated simultaneously as decision variables. Our method combines linear uncertainty quantification and a modified genetic algorithm for discrete multilocation, multitype search. The efficiency of the global optimization is enhanced by an archive of past samples and parallel computing. We demonstrate our methodology for a groundwater monitoring network at the Steinlach experimental site, south-western Germany, which has been established to monitor river-groundwater exchange processes. The target of optimization is the best possible exploration for minimum variance in predicting the mean travel time of the hyporheic exchange. Our results demonstrate that the information gain of monitoring network designs can be explored efficiently and with easily accessible tools prior to taking new field measurements or installing additional measurement points. The proposed methods proved to be efficient and can be applied for model-based optimal design of any type of monitoring network in approximately linear systems. Our key contributions are (1) the use of easy-to-implement tools for an otherwise complex task and (2) yet to consider data-worth interdependencies in simultaneous optimization of multiple sensor locations and sensor types.

Cumulative Relative Reactivity: A Concept for Modeling Aquifer‐Scale Reactive Transport

We simulate aquifer-scale reactive transport using an approach based on travel times and relative reactivity. The latter quantifies the intensity of the chemical reaction relative to a reference reaction rate with identical concentrations and can be interpreted as the strength of electron-donor (or electron-acceptor) release by the matrix, scaled by a reference release. In general, the relative reactivity is a spatially variable property reflecting the geology of the formation. In the proposed approach, we track the path of individual water parcels through the aquifer and evaluate the age of the water parcels and the relative reactivity integrated along their trajectories. By switching from spatial discretization to cumulative relative reactivity, advective-reactive transport can be simulated by solving a single system of ordinary differential equations for each combination of concentrations in the inflow. We test the validity of the approach in a two-dimensional test case of steady-state groundwater flow and reactive transport involving aerobic respiration and denitrification. Here we compare steady-state concentration distributions of the spatially explicit virtual truth, accounting for dispersive mixing, with the approximation based on cumulative relative reactivity and show that the errors introduced by neglecting dispersive mixing are minor if the target quantities are the mass fluxes crossing a control plane or being collected by a well. We further demonstrate the efficiency of the approach in a synthetic three-dimensional case study. The proposed approach is computationally so efficient, that ensemble runs to assess statistical distributions of concentration time series of reactive solutes become feasible, which is not practical with a spatially explicit model. This article is protected by copyright. All rights reserved.

A Primer for Model Selection: The Decisive Role of Model Complexity

Selecting a “best” model among several competing candidate models poses an often encountered problem in water resources modeling (and other disciplines which employ models). For a modeler, the best model fulfills a certain purpose best (e.g., flood prediction), which is typically assessed by comparing model simulations to data (e.g., stream flow). Model selection methods find the “best” trade-off between good fit with data and model complexity. In this context, the interpretations of model complexity implied by different model selection methods are crucial, because they represent different underlying goals of modeling. Over the last decades, numerous model selection criteria have been proposed, but modelers who primarily want to apply a model selection criterion often face a lack of guidance for choosing the right criterion that matches their goal. We propose a classification scheme for model selection criteria that helps to find the right criterion for a specific goal, i.e., which employs the correct complexity interpretation. We identify four model selection classes which seek to achieve high predictive density, low predictive error, high model probability, or shortest compression of data. These goals can be achieved by following either nonconsistent or consistent model selection and by either incorporating a Bayesian parameter prior or not. We allocate commonly used criteria to these four classes, analyze how they represent model complexity and what this means for the model selection task. Finally, we provide guidance on choosing the right type of criteria for specific model selection tasks. (A quick guide through all key points is given at the end of the introduction.)

Sensitivity of Simulated Hyporheic Exchange to River Bathymetry: The Steinlach River Test Site: R. Chow et al. Groundwater XX, no. XX: XX-XX

This study determines the aspects of river bathymetry that have the greatest influence on the predictive biases when simulating hyporheic exchange. To investigate this, we build a highly parameterized HydroGeoSphere model of the Steinlach River Test Site in southwest Germany as a reference. This model is then modified with simpler bathymetries, evaluating the changes to hyporheic exchange fluxes and transit time distributions. Results indicate that simulating hyporheic exchange with a high-resolution detailed bathymetry using a three-dimensional fully coupled model leads to nested multiscale hyporheic exchange systems. A poorly resolved bathymetry will underestimate the small-scale hyporheic exchange, biasing the simulated hyporheic exchange towards larger scales, thus leading to overestimates of hyporheic exchange residence times. This can lead to gross biases in the estimation of a catchments capacity to attenuate pollutants when extrapolated to account for all meanders along an entire river within a watershed. The detailed river slope alone is not enough to accurately simulate the locations and magnitudes of losing and gaining river reaches. Thus, local bedforms in terms of bathymetric highs and lows within the river are required. Bathymetry surveying campaigns can be more effective by prioritizing bathymetry measurements along the thalweg and gegenweg of a meandering channel. We define the gegenweg as the line that connects the shallowest points in successive cross-sections along a river opposite to the thalweg under average flow conditions. Incorporating local bedforms will likely capture the nested nature of hyporheic exchange, leading to more physically meaningful simulations of hyporheic exchange fluxes and transit times.