High-Resolution Virtual Catchment Simulations of the Subsurface-Land Surface-Atmosphere System

Abstract

Abstract. Combining numerical models, which simulate water and energy fluxes in the subsurface-land surface-atmosphere system in a physically consistent way, becomes increasingly important to understand and study fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models, when run at highest possible resolutions while incorporating as many processes as attainable, may be regarded as a proxy of the real world – a virtual reality – which can be used to test hypotheses on functioning of the coupled terrestrial system and may serve as source for virtual measurements to develop data-assimilation methods. Such simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in the compartments of the terrestrial system. The present study is motivated by the development of cross-compartmental data-assimilation methods, which face the difficulty of data scarcity in the subsurface when applied to real data. With appropriate and realistic measurement operators, the virtual reality not only allows taking virtual observations in any part of the terrestrial system at any density, thus overcoming data-scarcity problems of real-world applications, but also provides full information about true states and parameters aimed to be reconstructed from the measurements by data assimilation. In the present study, we have used the Terrestrial Systems Modeling Platform TerrSysMP, which couples the meteorological model COSMO, the land-surface model CLM, and the subsurface model ParFlow, to set up the virtual reality for a regional terrestrial system roughly oriented at the Neckar catchment in southwest Germany. We find that the virtual reality is in many aspects quite close to real observations of the catchment concerning, e.g., atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent both in the ability of such models to correctly simulate some processes – which still need improvement – and the realism of the results of some observation operators like the SMOS and SMAP sensors, when faced with model states. In a succeeding step, we will use the virtual reality to generate observations in all compartments of the system for coupled data assimilation. The data assimilation will rely on a coarsened and simplified version of the model system.

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

“…A broader challenge is to simulate the myriad controls on catchment evolution, e.g., to predict how energy gradients dictate landscape evolution, how natural selection favors plants that make optimal use of available resources, and how the dynamic interactions between humans and the environment shapes the storage and transmission of water across the landscape (Rodríguez-Iturbe et al, 1992;Eagleson, 2002;Schymanski et al, 2009Schymanski et al, , 2010Sivapalan et al, 2012;Harman and Troch, 2014;Zehe et al, 2014;Clark et al, 2016;Grant and Dietrich, 2017). Addressing this challenge requires shifting focus from traditional approaches at short timescales where "properties define processes" towards approaches on longer timescales that focus on predicting how "processes define properties" (Rodríguez-Iturbe et al, 1992;Eagleson, 2002;Harman and Troch, 2014).…”

“…Once these fields are estimated at high resolution, MPR could be used to estimate effective hydraulic conductivity values to characterize the required subsurface parameters. Also, stochastic methods need to be extended to capture the large structural variability in the formations and layers that dominate continental domains (Baroni et al, 2017;Schalge et al, 2016).…”

“…We need to substantially advance the development of new modeling approaches that simulate the temporal dynamics of environmental change. Key challenges include predicting how energy gradients dictate landscape evolution, how natural selection favors plants that make optimal use of the available resources, and how the dynamic interactions between humans and the environment shapes the storage and transmission of water across the landscape (Rodríguez-Iturbe et al, 1992;Eagleson, 2002;Schymanski et al, 2009Schymanski et al, , 2010Sivapalan et al, 2012;Harman and Troch, 2014;Zehe et al, 2014;Clark et al, 2016;Grant and Dietrich, 2017).…”

An Octave/MATLAB® Interface for Rapid Processing of SMOS L1C Full Polarization Brightness Temperature

“…This region contains the Neckar river catchment and is used for data assimilation studies by [12,14] using of the "Process_SMOSxL1C" mexfunction tool.…”

“…The "Process_SMOSxL1C" mex-function is being used for calibration and comparisons with synthetic observations obtained by satellite simulators alike SMOS or SMAP by [12]. The calibrated synthetic brightness temperatures are then utilized as state variables for data assimilation experiments applied to land-surface-atmosphere coupled models as shown by [14]. For instance, Figure 5 depicts the agreement of simulations by the Community Microwave Emission Model (CMEM) [3] after being calibrated against SMOS multi-incidence angle brightness temperatures processed by "Process_SMOSxL1C" mex-function.…”