Clemens Simmer, Meteorological Institute, University Bonn, Auf dem Hügel 20, D-. 36 ...... Schomburg, A., V. Venema, R. Lindau, F. Ament, and C. Simmer, 2012:.
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Monitoring and Modeling the Terrestrial System from Pores to Catchments – the
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Transregional Collaborative Research Center on Patterns in the Soil-Vegetation-
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Atmosphere System
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by
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Clemens Simmer, Matthieu Masbou, Insa Thiele-Eich, Wulf Amelung, Bernhard
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Blümich, Georg Bareth, Heye Bogena, Andreas Bott, Carsten Burstedde, Christoph
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Clauser, Susanne Crewell, Bernd Diekkrüger, Hendrik Elbern, Frank Ewert, Peter
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Fiener, Harrie-Jan Hendricks Franssen, Petra Friederichs, Alexander Graf, Michael
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Griebel, Michael Herbst, Alexander J. Huisman, Andreas Kemna, Norbert Klitzsch,
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Stefan Kollet, Manfred Krafczyk, Ulrich Lang, Matthias Langensiepen, Ulrich Löhnert,
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Andreas Lücke, Oliver Mohnke, Andreas Pohlmeier, A. S. M. Mostaquimur Rahman,
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Uwe Rascher, Karl Schneider, Jan Schween, Yaping Shao, Prabhakar Shrestha,
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Maik Stiebler, Mauro Sulis, Jan Vanderborght, Harry Vereecken, Jan van der Kruk,
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Björn Waske, Lutz Weihermüller, Mark Van Wijk, Andreas Wahner, Gerd Welp, and
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Tanja Zerenner
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Capsule Summary:
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Observing and modeling the water and energy flow from soil pores to clouds and
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from the groundwater to the atmosphere via a strong interdisciplinary effort
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Affiliations:
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Amelung, Bott, Burstedde, Diekkrüger, Ewert, Friederichs, Griebel, Kemna,
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Langensiepen, Masbou, Rahman, Shrestha, Sulis, Simmer, Waske, Welp, Van Wijk,
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Zerenner - University Bonn; Bareth, Crewell, Elbern, Fiener, Lang, Löhnert,
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Schneider, Schween, Shao - University Cologne; Blümich, Clauser, Klitzsch - RWTH 1
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Aachen University; Bogena, Hendricks Franssen, Graf, Herbst, Huisman, Kollet,
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Lücke, Pohlmeier, Rascher, Vanderborght, Van der Kruk, Van Wijk, Vereecken,
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Weihermüller, Wahner – Forschungszentrum Jülich GmbH; Hendricks Franssen,
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Kollet - Centre for High-Performance Scientific Computing in Terrestrial Systems,
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Masbou – now at Deutscher Wetterdienst, Offenbach; HPSC TerrSys, Krafczyk,
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Stiebler – Technical University Braunschweig, Mohnke – now Baker Huges, Celle,
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Germany
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Corresponding author:
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Clemens Simmer, Meteorological Institute, University Bonn, Auf dem Hügel 20, D-
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53121 Bonn, Germany
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2
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Abstract:
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By far the most activities of mankind take place in the transition zone connecting
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groundwater, soil, vegetation and atmosphere. Mass, momentum, and heat energy
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fluxes within and between the neighboring compartments drive their mutual state
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evolution. Improved understanding of the processes that drive these fluxes is
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important for climate projections and weather prediction, flood forecasting, water and
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soil resources management, agriculture, and water quality control. The vastly
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different flow behavior within the different compartments leads to complex patterns
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on different time and spatial scales, which make quantitative predictions of the
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terrestrial system behavior a major challenge to both scientists and policymakers. In
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2007 the Transregional Collaborative Research Centre No. 32 (TR32) set out to
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investigate the groundwater-soil-vegetation-atmosphere continuum by integrating
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monitoring of system parameters, states and fluxes with modeling and data
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assimilation in order to reach a holistic view of the terrestrial system. The TR32
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belongs to a class of long-term research programs that are funded by the German
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national science foundation (Deutsche Forschungsgemeinschaft, DFG) in order to
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concentrate and integrate research activities of several universities on an emerging
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scientific topic of high societal relevance. With the aim to bridge the gap between
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micro-scale soil pores and catchment-scale atmospheric variables, the TR32 unites
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research groups from within the Geo Alliance (Geoverbund) ABC/J, namely the
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universities RWTH Aachen, Bonn, and Cologne, as well as the environmental and
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geoscience departments of the Forschungszentrum Jülich. Here we report about
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recent achievements in monitoring and modeling the terrestrial system including the
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development of new observation techniques for the subsurface, the establishment of
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cross-scale, multi-compartment modeling platforms from the pore to the catchment
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scale, and the use of these new methods and platforms to investigate the
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propagation of subsurface patterns to the atmospheric boundary layer.
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4
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1 Introduction
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State variables and parameters of the terrestrial system, which encompasses
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groundwater, soil, vegetation, and the atmosphere, exhibit complex patterns on a
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wide range of temporal and spatial scales that extends from seconds to years and
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from the soil pore scale to the global scale. These patterns are also reflected in the
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heterogeneous inter- and intra-compartmental fluxes of heat energy, water, carbon,
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nitrogen, and momentum. Most patterns of terrestrial state variables can be traced to
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the control of the relatively static and often extremely heterogeneous soil and sub-soil
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parameters, which are usually generated on geological time scales. The fast-moving
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and mixing, almost parameter-free atmosphere drives – and is driven by - these
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fluxes and further adds considerable heterogeneity via its own internal scales of
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motion ranging from turbulence over convection to synoptic systems. This again adds
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considerable variability to the driving radiative fluxes due to clouds and water vapor
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and to water fluxes via precipitation. Vegetation acts to all this as a living
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transmission medium, which is more than any other compartment subject to human
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interaction by agricultural and forest management and land use change but still
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inherits geomorphological patterns from the geological past (see e.g. Figure 2 (EMI
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results)). This prominent heterogeneous nature of terrestrial systems constitutes a
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major challenge to monitoring and predicting their state. Improving our understanding
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and prediction capabilities of the terrestrial system therefore requires measurement
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techniques that allow us to characterize and monitor the spatio-temporal evolution of
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system properties across scales, terrestrial system model platforms that include all
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relevant processes, and state variable assimilation and parameter estimation
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methods. The Collaborative Research Center (CRC) TR32 on “Patterns in the Soil-
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Vegetation-Atmosphere System – Monitoring, Modeling, and Data Assimilation” 5
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began working on these goals in 2007 including a graduate school for the PhD
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students working within the CRC (incorporating external PhD projects on related
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topics). A data management component (Curdt et al., 2013) stores and secures all
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observations, analyses, and documents for at least a decade.
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1.1 German Collaborative Research Centers
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CRCs are established at German universities and co-funded by the German national
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science foundation (Deutsche Forschungsgemeinschaft, DFG) for a period of up to
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12 years subdivided into three phases. Each CRC phase is granted based on
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applications that need to be positively reviewed by international review panels and
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involves a highly competitive evaluation procedure that assesses and compares
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proposed CRCs from all science disciplines including humanities. CRCs enable
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researchers to pursue an outstanding research program with a long-term perspective
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while crossing the boundaries of disciplines, departments, institutes, and faculties.
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The CRC program in turn contributes towards defining and sharpening the profiles of
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participating universities both with respect to research as well as teaching. CRCs
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may incorporate projects at neighboring universities or non-university research
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institutions and collaborations with industry and business. While CRCs are usually
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applied for by one university, the TR32 is a transregional CRC within which the three
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universities Bonn (leading), Cologne and RWTH Aachen and the Forschungszentrum
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Jülich GmbH of the Helmholtz Association join their efforts to better understand the
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origin and propagation of patterns in the terrestrial system and their value for model-
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based predictions.
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1.2 Patterns in System Parameters and State Variables 6
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The TR32 focuses on the role of patterns in the terrestrial system state variables and
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parameters for observations and modeling. In terrestrial system models - as
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developed and applied in the TR32 - system parameters like the hydraulic
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conductivity of the soil or the turbulent diffusion coefficients of the atmospheric
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boundary layer (ABL) control the flow of energy, matter and momentum within and
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between compartments on timescales for which predictability of the system variables
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is aspired. On these time scales, system parameters of the soil are usually assumed
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to be constant, or in case of the ABL, predictable from the state variables. The
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distinction between system parameters and state variables is, however, a concept
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born out of the model perspective: parameters refer to parameterizations of
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processes, which evade direct numerical simulation based on the primitive equations
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for the conservation of mass, energy and momentum due to the required but mostly
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computationally unattainable resolution. Both in the soil and in the atmosphere,
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homogeneity
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parameterization concepts such as e.g. the use of hydraulic conductivity in the
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Richards equation or the Monin-Obukhov similarity of the ABL in Reynolds-Averaged
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Numerical Simulation (RANS) models. All parameters do actually vary - not only with
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the grid spacing of the models due to e.g. the natural variability of soils and the non-
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linearity of the processes, but also with time because they depend on the system
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state variables, their subgrid variability and possibly also their history (hysteresis
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effects). In meteorology, where such problems are more clear-cut, the endeavor of
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parameter prediction is related to the closure problem. Thus in general, there is no
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clear cut between system parameters and state variables, a problem increasingly
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recognized also in data assimilation.
assumptions,
which
are
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never
fulfilled,
are
used
to
justify
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The TR32 employs the pattern paradigm as an overarching concept to address the
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ubiquitous up- and downscaling issues in monitoring, modeling and data assimilation.
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More detailed information on this topic can be found in Vereecken et al. (2010) and a
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special issue in preparation for Water Resources Research. In the geosciences,
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patterns may be understood as repetitions of similar structures in system state
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variables and system parameters in space and time, with structures denoting
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identifiable objects. In the case of parameters such objects may be a single soil pore,
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a plant root, an individual plant, a plough mark, soil heterogeneity associated with a
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paleo-river system, a crop-cultivated field, a single hill, or an entire valley. Such
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objects can be rather static relative to the timescales on which fluxes occur within or
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between compartments. However, they can also be more dynamic as in the case of
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soil temperature and moisture in response to insolation or precipitation, the exchange
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of fluxes themselves, and any structure in the atmosphere ranging from single
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eddies, updrafts in the ABL and cumulus clouds to thunderstorms or cyclones. Most if
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not all of these structures and resulting patterns have characteristic scales on which
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they can be detected, but often disappear when changing the observation scale,
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such as e.g. cumulus clouds or cultivated fields whose characteristic patchy or
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rectangular shape, respectively, completely disappear when spatial resolution
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exceeds the 100 m scale. Thus the chaos approach to patterns with clear scale-
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variance relations usually does not apply. Studying how patterns influence fluxes and
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state variables across scales is a key goal of the TR32.
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The most appropriate way for modeling the terrestrial system requires the treatment
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of the soil-vegetation-atmosphere system as a continuum with spatial resolutions that
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allow the simulation of all relevant flow processes by the Navier-Stokes equations
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using Direct Numerical Simulation (DNS) models down to the sub-pore scale, and 8
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that thus reduce remaining exchanges at compartment boundaries to diffusive
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processes. This also takes into account small-scale processes such as the
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movement of plants in the turbulent airflow and water motion in pores. Patterns on
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this scale need not be considered in this approach as they turn up automatically as a
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natural consequence of the acting processes. While the direct approach is in
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principle possible, applications on scales required to predict terrestrial systems on
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the catchment scale are and will be prohibitive due to restricted computational
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capabilities for a very long time. Instead continuum scale modelling approaches are
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used, such as the Richards equation for flow of water in soils that captures micro-
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scale pore geometry in two macroscopic material properties, the water retention and
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hydraulic conductivity functions. Pore-scale models are used in TR32 to understand
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and derive these material properties in order to parameterize Richards equation. The
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atmosphere is usually modeled using RANS (Reynolds-Averaged Navier-Stokes)
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models, which require especially strong assumptions near boundaries. This is usually
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tackled with the Monin-Obukhow similarity assumption. As a consequence, the
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exchange between system compartments is parameterized by diffusion-like
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processes with coefficients and material properties estimated from experiments
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and/or statistics of the larger-scale flows. This is where patterns become important:
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since exchange processes between the compartments are driven by local gradients,
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any correlation between the patterns of the driving system state variables at the
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boundaries of the neighboring compartments (e.g. temperature at the surface and the
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lowest atmospheric model layer) will directly impact the fluxes and thus the system
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state. This also means that patterns are an inevitable element of any upscaling and
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downscaling concept applied in terrestrial system modeling, a quest taken up by the
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TR32 and illustrated in the remainder of this paper.
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2 Monitoring of the Rur Catchment
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The development of techniques to map and understand patterns and to use this to
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model and predict the terrestrial system requires a real counterpart for analysis and
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testing. Due to its proximity to the cooperating institutes, TR32 identified the Rur
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catchment at the western border of Germany to Belgium and the Netherlands (Figure
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1 (Rur Catchment Monitoring)) as its central observation site. The Rur catchment has
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been heavily instrumented in strong cooperation with the TERENO program of the
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Helmholtz Association (http://teodoor.icg.kfa-juelich.de), which was developed in
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parallel to TR32 as a network of terrestrial observatories distributed over Germany
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(Zacharias et al., 2011). Precipitation as the main atmospheric driver for soil moisture
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patterns is monitored in the Rur catchment in five minute intervals by the twin dual-
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polarized X-band Doppler radars BoXPol in Bonn and JuXPol on the Sophienhöhe, a
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hill created from open-cast mining and jutting roughly 200 m out of the surrounding
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terrain, and at one minute resolution by a third reflectivity-only X-Band radar
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(Rainscanner) in the southwestern corner of the catchment. The area is also covered
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by four C-Band radars of the German national weather service (Deutscher
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Wetterdienst, DWD), which have been recently upgraded to polarimetry. Precipitation
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patterns derived from sets of scanning radars are usually plagued with artifacts
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imposed by the observation method via beam-clutter, beam blocking, and
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attenuation. Such effects have been minimized by the so-called R(A) methodology
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developed by Ryzhkov et al. (2014). Detailed knowledge on the atmospheric state is
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available in near-real time from the Jülich Observatory for Cloud Evolution (JOYCE,
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http://www.geomet.uni-koeln.de/allgemein/forschung/joyce/), which is equipped with
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a unique array of state-of-the-art active and passive remote sensing and in-situ
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instruments. The continuous and temporally highly-resolved measurements focus on 10
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the atmospheric boundary layer and allow the characterization of the diurnal cycle of
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turbulence, water vapor, stability and cloudiness (Schween et al., 2011). A specific
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feature of this atmospheric observatory is the use of scanning measurements that
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aim to capture atmospheric patterns and their relation to the land surface (see
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supplement). Fluxes of sensible heat, evapotranspiration, CO2, and momentum are
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monitored by five fixed Eddy Covariance (EC) stations and by one roving EC-station.
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Two sub-catchments (forest and grassland) are equipped with wireless extensive soil
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moisture networks with hundreds of sensors in various depths (Bogena et al., 2010),
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which allow for the examination of seasonal and event-scale spatial soil moisture
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dynamics, the validation of models (Cornelisson et al., 2014, see also modeling
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section) and remote sensing data (e.g. Hasan et al., 2014). We found clockwise
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hysteretic soil moisture dynamics at the event-scale during intense precipitation
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events that rapidly wetted the topsoil (Rosenbaum et al., 2012). Cosmic-ray soil
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moisture probes (Zreda et al., 2008) supplement the installed soil moisture networks
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(Bogena et al., 2013) and allow the characterization of temporal soil moisture
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dynamics over the entire catchment (Baatz et al., 2014). At few specific sites, a
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whole suite of geophysical methods like Nuclear Magnetic Resonance (NMR, e.g.
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Perlo et al., 2013), Spectral Induced Polarization (e.g. Kemna et al., 2013),
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Electromagnetic Induction (EMI, e.g. Mester et al., 2011), and Ground-Penetrating
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Radar (e.g. Busch et al., 2013) are employed to develop and test methods for
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probing the structure and composition of the subsurface. Recent results from the
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inversion of Electro-Magnetic Induction observations (Figure 2 (EMI results), see also
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von Hebel et al., 2014 and Rudolph et al., in preparation) clearly show the linkage
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between sub-soil patterns in terms of sand, silt and gravel originating from paleo-
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rivers with both the EMI information and the visual impact on vegetation and Leaf
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Area Index after an extended drought period. Methods known from medicine such as 11
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Magnetic Resonance Imaging have first been developed in the laboratory and are
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now being applied in the field to measure soil water content with high vertical
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resolution near the soil surface. Observations also address ecosystem exchange
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processes on various scales for agricultural surfaces (e.g. Langensiepen et al.,
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2012). The role of soil patterns for soil carbon pools (Bornemann et al., 2010, 2011)
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and soil heterotrophic respiration is analyzed using Mid-Infrared Spectroscopy
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(MIRS) and geostatistical modeling (Herbst et al., 2012), respectively. In several
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intensive measurement campaigns, aircraft-borne instruments are used to measure
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the spatio-temporal structure of the ABL including the CO2 concentration in order to
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analyze the relation between surface flux patterns and corresponding patterns in the
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atmosphere. Vegetation, soil moisture and land use over the whole catchment is
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monitored via aircraft and satellites (Hoffmeister et al., 2012; Koyama et al., 2010). In
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addition, vegetation assimilation activity states are observed using ground-based
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fluorescent-related techniques and airborne instrumentation (Figure 3 (Sun-Induced
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Fluourescence)) that is a prototype for future space-based mission (Damm et al.,
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2011). The observations document the large between and within field variability of
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plant photosynthetic activity, and may in future be used for directly quantifying plant
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transpiration. Patterns have also been investigated within the soil moisture
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monitoring activities with a particular focus on the scaling properties for agricultural
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land (Korres et al., 2013). The dependence of evapotranspiration on soil states, soil
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and root properties, and meteorological conditions is analyzed using sap-flow
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devices installed in trees and crops.
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3 Modeling the Terrestrial System on Different Scales
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The TR32 employs a cross-scale, multi-compartment modeling approach to upscale 12
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the water, energy, and CO2 fluxes in the terrestrial system from the local to the
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catchment scale based on a numerical approach combined with stochastic
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techniques. The analysis of the simulations with grids that honor the respective
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scales reveals the role of patterns on the fluxes in the system and helps to design a
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general upscaling framework that quantifies information transfer between scales due
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to non-linear interactions in the system. Accordingly, the TR32 uses and develops
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physics-based process models and model platforms for all relevant scales. For
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example, pore-scale models using Lattice Boltzmann methods have been used for
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jointly simulating multi-phase flow and NMR relaxation behavior (Figure 4 (Lattice
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Boltzmann Simulations)) in order to improve the interpretation of NMR observations
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in terms of water retention and hydraulic conductivity properties that are essential for
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describing water flow in continuum models (Mohnke and Klitzsch, 2010). On a
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somewhat larger scale, a soil-root model is developed in order to derive improved
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parameterizations for root density and distribution in the soil, as well as their effect on
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the soil moisture profiles and respiration as a function of larger-scale soil state
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variables and parameters (see Box Soil-Root Model). Large-Eddy-Simulation (LES)
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models (see below) with highly resolved land surface and subsurface models for
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energy fluxes, water flow, and carbon dynamics are used for resolving patterns and
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heterogeneities on numerical grids of the order of centimeters (vertical) to decimeters
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(horizontal) for land surface and subsurface, while the atmosphere from the LES is
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assumed homogeneous on this scale. Patterns on scales of tens to several hundred
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meters, which are mainly characterized by land use patterns and subsurface
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structures generated e.g. from geomorphologic processes like erosion or river
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evolution are addressed by simulations with LES-ALM (Shao et al., 2012) derived
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from the WRF-NOAH modeling platform and the Terrestrial System Modeling
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Platform (TerrSysMP, see Box 2 TerrSysMP) designed for the regional climate scale 13
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with resolutions from about 100 meters to kilometers, but also by 3D hydrological
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models like HydroGeoSphere (HGS, see e.g. Cornelissen et al., 2014).
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Compared to Parflow in TerrSysMP, which requires a rectangular grid definition, the
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finite-element model HGS is particularly useful for small-scale soil, land use, and
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topography applications. The comparison of the mean simulated and observed soil
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moisture dynamic of a 27 hectares headwater sub-catchment of the Rur for the
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period 2010/2011 (Figure 5 (HGS Dynamics)) shows the ability of HGS to capture
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long-term dynamics in a reasonable manner, although this model failed to reproduce
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short-term dynamics probably due to a missing preferential flow component. The
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general spatial patterns between simulations and observations are similar (Figure 6
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(HGS Soil Patterns)) and are determined by the complex interactions between soil,
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topography, and vegetation.
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With LES-ALM we investigate the propagation of land surface heterogeneity in the
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atmospheric boundary layer. Here we present example results for the natural land
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surface at the Selhausen-Merken field site (for model settings see Shao et al.
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(2013)). The evolutions in space and time of the patterns of e.g. sensible and latent
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heat fluxes, temperature, humidity and turbulent kinetic energy are analyzed using
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wavelet decomposition and averaging over multiple time scales. Figure 7 (LES
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Pattern Propagation) shows the variation of sensible heat flux (H) patterns with
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height in a convective boundary layer with an inversion at about 1.6 km above
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ground. H patterns and wavelet energy spectra substantially differ on different height
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levels and for different averaging times. Close to the surface the H patterns (and
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those of other quantities not shown) bear great similarity to the land use pattern even
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without time averaging, but time averaging enhances the similarity. For large heights 14
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the resemblance decreases for small time intervals as turbulent patterns emerge, as
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the hexagonal cells at 32 m. With increasing averaging times, however, the land
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surface flux pattern clearly re-emerge with large-scale features of land surface
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pattern persisting over a considerable depth of the ABL with enhanced energy
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according to the wavelets spectra for the 30 minutes averaged H patterns. The
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persistency of a land surface pattern in the atmospheric boundary layer depends on
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its scale and strength, and on the capacity of turbulence to diffuse the land surface
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signals. Close to the surface with low eddy diffusivity the land surface dominates the
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pattern of atmospheric quantities. At higher levels turbulence rapidly diffuses the land
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surface footprints by its own patterns governed by domain averaged buoyancy flux
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and inversion height. Convective eddies have typical time scales τt of 103s and
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spatial scales lt of 103m. A land surface pattern has in general multiple scales but has
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often a dominant scale ls. Given an averaging time of atmospheric quantities of τa for
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a given height, land surface heterogeneity persists if ls >> lt, and the land surface
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pattern is most visible if τa >> τt.
343 344
Results on pattern interactions emerging from this effort eventually feed into
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TerrSysMP (see Box 1), which includes a downscaling scheme for predicting near-
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surface atmospheric variables at the scale of its higher-resolved landsurface and
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subsurface scheme (Schomburg et al., 2010, 2012). This scheme is currently
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advanced to achieve a better reconstruction of the spatiotemporal patterns at the fine
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scale. Figure 8 (Atmospheric Downscaling)) illustrates the performance of a new
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algorithm based on multi-objective Genetic Programming (GP). This machine
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learning method allows for physical consistency checks (in contrast to alternative
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methods that rely on the output of artificial neural networks) and offers the possibility
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to quantify the quality of a downscaling rule based on several aspects, such as 15
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spatial structure, spatially distributed variance and spatio-temporal correlation of the
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fields. The regional climate length scale corresponds to the smallest grid size that
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can be computed using TerrSysMP, with the Monin-Obukhov similarity theory for the
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ABL currently being the limiting approximation. Patterns resolved include differences
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in land use (bare soil/cropped soil and differences in crops), soil type, groundwater
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table depth, and topography.
360 361
TerrSysMP simulations with increasing spatial resolution and thus grid sizes over the
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whole Rur area include calculations of both water and carbon dynamics from the
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subsurface into the atmosphere. The goal is to identify the major spatial and temporal
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scales at which two-way feedbacks from the subsurface and the atmosphere impact
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water and energy states and fluxes at the land surface and vice versa. Using, for
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example, time localized wavelet spectra (Figure 9 (WTD-LE Cross-Wavelets)),
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Rahman et al. (2014) were able to show that net radiation (Rnet) induces physically
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intuitive diurnal variability in evapotranspiration (ET), which results in a cyclic
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pumping effect depleting groundwater storage under dry conditions. In turn,
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groundwater depletion and associated lowering of the shallow groundwater table
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leads to a negative feedback on ET at the monthly time scale, which increases to
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multi-months in case of extended dry periods. Applying similar concepts in the space
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domain, the simulation results suggest that in summer, the structure in ET is mainly
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determined by the spatial water table configuration, while ET can be predicted from
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the spatial structure of Rnet during cooler months. This hierarchy and interactions of
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space and time scales is used to derive a theoretical framework to upscale fluxes
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and to account for the role of patterns, which may include e.g. a coarse graining
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approach based on wavelets and information theory.
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4 Data Assimilation
381 382
Aside from appropriate boundary conditions, the prediction of the state of the
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terrestrial system requires knowledge concerning the initial state. Data assimilation,
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i.e. the convolution of observations with a given model state, is the method of choice
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for this endeavor. In contrast to atmospheric models, terrestrial system models are
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faced with the additional problem of a priori unknown parameters of the surface, soil
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and subsoil, which vary strongly in space and even time due to violations of
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homogeneity assumptions in the models or even physical hysteresis effects. Thus,
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parameters need to be included into the data assimilation process. Most
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observations of the terrestrial system are rather indirect (any remote sensing
391
technique) and depend on additional characteristics of the system, which are often
392
ignored by the terrestrial models because of their minor role for the evolution of the
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system state variables (e.g. the dependence of the measured neutron intensity by the
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cosmic ray probe on litter layers). Accordingly, as many observations as possible
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should be utilized in data assimilation in order to constrain the very large degree of
396
freedom.
397 398
Currently, the TR32 uses the Local Ensemble Transform Kalman Filter (LETKF)
399
(Hunt et al., 2007) for updating both model state variables and parameters of the land
400
surface model in a column-based approach. The implementation allows assimilating
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brightness temperature measured by satellites with the CMEM-operator (de Rosnay
402
et al., 2011), land surface temperature with a dual source operator (Kustas and
403
Anderson, 2009) and neutron counts with the COSMIC-operator (Shuttleworth et al.,
404
2013), besides direct soil moisture and soil temperature measurements. Further
405
details are given in the online supplement. The catchment tomography approach, in 17
406
which localized precipitation events are considered as transmitters and runoff gauges
407
act as integrating receivers, is explored as an alternative approach to determine
408
catchment properties and their impact on water fluxes.
409 410
5 Outlook
411 412
With our observation and monitoring capabilities set up to the optimum and the fully
413
developed integrated terrestrial system models for the meter and kilometer scales in
414
place, the ensuing last DFG-funded phase of the TR32 will focus on model-data
415
fusion via combined parameter estimation and state variable assimilation employing
416
the Data Assimilation Research Testbed (DART, Anderson et al., 2009) in order to
417
address predictability of the terrestrial system on the catchment scale. We envision a
418
complete high-resolution reanalysis data set of a real mesoscale terrestrial system on
419
the sub-kilometer scale, which will be of interest for the wider scientific community.
420
The TR32 will employ this data set for the development of an extended view on
421
patterns in terrestrial systems. To this goal we will extend the object-based view
422
currently being developed for the atmospheric sciences for the analysis of e.g. the
423
structure and life cycles of convective systems or atmospheric rivers (e.g. Sellars et
424
al., 2013) to include pattern linkages between the surface and subsurface and
425
structures of the atmospheric boundary layer. This will require the consideration and
426
inclusion of the different time scales on which processes act in the contributing
427
compartments. Following our initial LES-based results on the propagation of patterns
428
from the land surface to the ABL, this requires the consideration of objects living on
429
timescales related to sub-objects of the slowest component like groundwater or soil
430
moisture.
431 18
432
Box 1 (Soil Root Model)
433 434
Plant roots play an important role in the terrestrial water cycle as they take up water
435
from the soil that is pumped up back into the atmosphere by vegetation. Since plant
436
roots increase the depth of the soil layer from which water can be taken up and
437
transpired back into the atmosphere, the depth of the root zone is an important
438
structural feature of terrestrial systems. Root zone depths depend on the interaction
439
between vegetation, climate, and soil (Schenk and Jackson, 2005) and show a
440
seasonal dynamics. The plasticity of root systems to changing environmental
441
conditions is therefore an additional important feature of terrestrial systems. Besides
442
root zone depths, the root density distribution is also important since water is
443
expected to be taken up more easily from soil layers with a high root density. The
444
structure of the plant root system therefore has an imprint on soil water distributions,
445
with soil layers with a high root density drying out more rapidly. However, how root
446
water uptake changes when part of the root zone dries out during dry spells is a
447
source of uncertainty in models. Neglecting compensatory uptake from deeper soil
448
layers in simulation models is considered to be the reason for the underestimation of
449
transpiration during dry spells (Wang and Dickinson, 2012). In order to improve the
450
prediction of root water uptake dynamics, a biophysical model that couples flow and
451
transport processes in the soil and plant root system while spatially resolving water
452
fluxes to single roots has been developed (Javaux et al., 2008). Simulations by this
453
model (Figure Box 1 (Soil-Root Model)) were used to infer upscaling rules that can
454
be used to parameterize larger-scale simulation models that do not resolve single
455
plant roots (Javaux et al., 2013; Couvreur et al., 2012). Processes like root growth
456
and regulation of transpiration by hormonal signals that are produced in the root zone
457
as a function of soil environmental conditions have been implemented in the model to 19
458
investigate whether these processes lead to a fundamentally different behavior of
459
root water uptake. In order to verify the behavior of the model, a field research facility
460
has been constructed in which root growth, soil water content and soil water
461
potential, and plant transpiration are non-invasively measured in different soil types
462
with different water holding capacity and for different water application regimes.
463
20
464
BOX 2 (TerrSysMP)
465 466
The TR32 Terrestrial System Modeling Platform (TerrSysMP, Figure Box 1
467
(TerrSysMP), Shrestha et al., 2014) couples the hydrological model ParFlow
468
(Lawrence Livermore Laboratory, e.g. Kollet and Maxwell, 2008; Kollet et al., 2010),
469
the land surface scheme Community Land Model (CLM, Version 3.5 (Oleson et al.,
470
2008) of NCAR, and the weather forecasting and regional climate model COSMO of
471
DWD and the COSMO-community (e.g. Baldauf et al., 2011) using the OASIS3
472
coupler (Ocean Atmosphere Sea Ice Soil, Version 3, e.g. Valke et al., 2012)
473
developed under the coordination of CERFACS (Centre Européen de Recherche et
474
de Formation Avancée en Calcul Scientifique). OASIS uses a dynamical two-way
475
approach including down- and upscaling algorithms for fluxes and state variables
476
between computational grids of different resolution (Shrestha et al., 2014). The
477
upscaling algorithm uses the mosaic or explicit sub-grid approach (Avissar and
478
Pielke, 1989) in which high-resolution land surface fluxes are averaged to the coarser
479
resolution of the atmosphere before they are passed over to the atmospheric model.
480
In addition, a downscaling scheme following Schomburg et al. (2010, 2012) is
481
implemented which downscales atmospheric variables of the lowest layer to the
482
higher-resolved land surface model. The scheme involves three steps: (1) spline
483
interpolation while conserving mean and lateral gradients of the coarse field, (2)
484
deterministic downscaling rules developed via statistics and genetic programming to
485
exploit empirical relationships between atmospheric variables and surface variables;
486
(3) addition of high-resolution variability (i.e. noise) in order to restore spatial
487
variability. All component models can be run in stand-alone or arbitrary coupled
488
mode. When CLM is coupled with ParFlow, both models share the same upper soil
489
layers. ParFlow takes over the complete hydrological calculations while sources and 21
490
sinks by rainfall and evapotranspiration are provided by CLM. The simulation (Figure
491
Box 2 (TerrSysMP)) shows the 3D distribution of the relative soil saturation along
492
with the turbulent eddy heat fluxes. A clear spatial structure can be observed at the
493
land surface, with topographic convergent zones (i.e., river corridors) experiencing
494
higher saturation values. In the atmosphere, patterns seem mainly controlled by the
495
vegetation distribution. For instance, higher values of turbulent eddy fluxes can be
496
identified over forested areas characterized by higher sensible heat fluxes. This can
497
be explained by the increased available energy (via lower albedo) over such areas
498
compared e.g. to crops. Note also that this effect tends to be amplified over steeper
499
terrain with its lower level of soil saturation due to a more efficient lateral drainage.
500 501
22
502 503
References (main text and boxes):
504 505
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506
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507
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510
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Curdt, C., D. Hoffmeister, C. Jekel, G. Waldhoff and G. Bareth, 2012: Scientific
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24
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T. Pellarin, J. Polcher, and J. P. Wigneron, 2009: AMMA Land Surface Model
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ALMIP-MEM, J. Geophys Res.-Atmos., 114, D05108, DOI: 10.1029/2008JD010724.
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Langensiepen, M., M. Kupisch, M.T. van Wijk, F. Ewert, 2012: Analyzing transient
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Hebel, C. van, S. Rudolph, A. Mester, J. A. Huisman, P. Kumbhar, H. Vereecken,
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Herbst, M., L. Bornemann, A. Graf, G. Welp, H. Vereecken, W. Amelung, 2012: A
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Hoffmeister, D., G. Waldhoff, C. Curdt, N. Tilly, J. Bendig and G. Bareth, 2013:
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Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for
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Javaux, M., T. Schröder, J. Vanderborght, and H. Vereecken, 2008: Use of a three-
590
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Javaux, M., V. Couvreur, J. Vanderborght, and H. Vereecken, 2013: Root Water
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Uptake: From Three-Dimensional Biophysical Processes to Macroscopic Modeling
595
Approaches. Vadose Zone Journal 12(4), doi:10.2136/vzj2013.02.0042.
596 597
Kemna, A., Binley, A., Cassiani, G., Niederleithinger, E., Revil, A., Slater, L.,
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Williams, K., Flores-Orozco, A., Haegel, F., Hoerdt, A., Kruschwitz, S., Leroux, V.,
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Titov, K., and E. Zimmermann, 2012: An overview of the spectral induced
600
polarization method for near-surface applications. Near Surface Geophysics. 11 (7),
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602 603
Kollet S., R. M. Maxwell, 2008: Capturing the influence of groundwater dynamics on
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land surface processes using an integrated, distributed watershed model. Water
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Resour Res 44:W02,402.doi:10.1029/ 2007WR006004
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Kollet, S. J., R. M. Maxwell, C. S. Woodward, S. Smith, J. Vanderborght, H.
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Vereecken and C. Simmer, 2010: Proof of concept of regional scale hydrologic
609
simulations at hydrologic resolution utilizing massively parallel computer resources,
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Water Resour. Res. 46, W04201, doi:10.1029/2009WR008730.
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Korres, W., T.G. Reichenau, and K. Schneider, 2013: Patterns and scaling properties
613
of surface soil moisture in an agricultural landscape: An ecohydrological modeling
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study. Journal of Hydrology, 498, 89, doi:10.1016/j.jhydrol.2013.05.050.
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Koyama, C.N., W. Korres, P. Fiener, K. Schneider, 2010: Variability of Surface Soil
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Moisture Observed from Multitemporal C-Band Synthetic Aperture Radar and Field
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Data. Vadose Zone Journal, 9, 1014.
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Kustas, W. and M. Anderson: 2009: Advances in thermal infrared remote sensing for
621
land surface modeling. Agricultural and Forest Meteorology, 149, 2071-2081.
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Mester, A., van der Kruk, J., Zimmermann, E., Vereecken, H., 2011: Quantitative
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Two-Layer Conductivity Inversion of Multi-Configuration Electromagnetic Induction
625
Measurements. Vadose Zone J., 10, 4, 1319-1330. DOI: 10.2136/vzj2011.0035.
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27
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Mohnke, O., and N. Klitzsch, 2010: Microscale simulations of NMR relaxation in
628
porous media considering internal field gradients. Vadose Zone Journal, 9, 846–857.
629 630
Oleson, K. W., G. Y. Niu, Z. L. Yang, D. M. Lawrence, P. E. Thornton, P. J.
631
Lawrence, R. Stöckli, R. E. Dickinson, G. B. Bonan, S. Levis, A. Dai, and T. Qian,
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633
hydrological cycle. J. Geophys. Res., 113, doi: 10.1029/2007JG000563
634 635
Perlo, J., E. Danieli, B. Blümich, and F. Casanova, 2013: Optimized slim-line logging
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NMR tool to measure soil moisture in situ. Journal of Magnetic Resonance. 233, 74-
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638 639
Rahman M., S. Kollet, and M. Sulis, 2014: The concept of dual-boundary forcing in
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land surface-subsurface interactions of the terrestrial hydrologic and energy cycles,
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642 643
Rosenbaum, U., H. R. Bogena, M. Herbst, J. A. Huisman, T. J. Peterson, A.
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Weuthen, A. W. Western, and H. Vereecken, 2012: Seasonal and event dynamics of
645
spatial soil moisture patterns at the small catchment scale. Water Resour. Res., 48,
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647 648
Ryzhkov, A., M. Diederich, P. Zhang, and C. Simmer, 2014: Potential utilization of
649
specific attenuation for rainfall estimation, mitigation of partial beam blockage, and
650
radar networking. Journal of Atmospheric and Oceanic Technology, 31, 599-619.
651
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Schenk, H.J. and Jackson, R.B., 2005: Mapping the global distribution of deep roots
653
in relation to climate and soil characteristics. Geoderma 126(1-2), 129-140.
654 655
Schomburg, A., V. Venema, R. Lindau, F. Ament, and C. Simmer, 2010: A
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657
transfer models. Tellus, 62, 4, p.242-258, doi 10.1111/j.1600-0889.2010.00466.
658 659
Schomburg, A., V. Venema, R. Lindau, F. Ament, and C. Simmer, 2012:
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Disaggregation of screen-level variables in a numerical weather prediction model
661
with an explicit simulation of subgrid-scale land-surface heterogeneity. Meteorology
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676 29
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Soil Moisture Interaction Code (COSMIC) for use in data assimilation. Hydrology and
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682
consistent Terrestrial System Modeling Platform based on COSMO, CLM and
683
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Vereecken H., S. Kollet, and C. Simmer, 2010: Patterns in Soil–Vegetation–
690
Atmosphere Systems: Monitoring, Modeling, and Data Assimilation. Vadose Zone J.,
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694
Observation, Modelling, Climatology, And Climatic Variability. Reviews of Geophysics
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699
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700
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701
2011: A Network of Terrestrial Environmental Observatories in Germany. Vadose
702
Zone J., 10, 3, 955-973, DOI: 10.2136/vzj2010.0139
703 704
Zreda, M., D. Desilets, T. P. A. Ferre, and R. L. Scott, 2008: Measuring soil moisture
705
content non-invasively at intermediate spatial scale using cosmic-ray neutrons.
706
Geophysical Research Letters, 35, L21402
707
31
708
Figure captions (main text and boxes)
709 710
Figure 1 (Rur Catchment Monitoring): Rur catchment including instrumentation and
711
precipitation radar coverage from TR32 and TERENO.
712 713
Figure 2 (EMI results): a) satellite-derived Leaf Area Index (LAI) distribution in Mai
714
2011 estimated after a two-month drought period over the lower right field in e). b)
715
and c) EMI ECa measurements with depth sensitivity up to 0.5 and 1.8 m,
716
respectively, measured in June 2012. d) Photo taken in August 2013 (camera
717
position and view delineated in a) – c)) that indicates stressed (A and C) and
718
unstressed (B) regions in a sugar beet field. e) ECa measurements for a larger area
719
with green colors indicating high and red colors low values. Red dashed lines
720
indicate paleo river channels. f) is a blowup of the Selhausen testsite where the EMI
721
measurement lines are clearly visible. Open areas indicate man-made structures. g)
722
quasi 3-D EC distribution of the subsurface obtained by a 3-layer inversion of multi-
723
configuration ECa measurements using a Maxwell forward model at every grid point
724
(modified from von Hebel et al., 2014 and Rudolph et al., in preparation).
725 726
Figure 3 (Sun-Induced Fluorescence): Air-borne observation of sun-induced
727
fluorescence from Sept 2012 600 meters above ground using the high-performance
728
imaging spectrometer HyPlant. HyPlant allows the quantification of the emitted red
729
fluorescence of active chlorophyll in the oxygen absorption line at 760nm, which is
730
directly related to the efficiency of photosynthesis. Thus the map illustrates the
731
variable photosynthetic rates of the different vegetation types. Highest fluorescence
732
signals come sugar beet while other vegetation types were already approaching
733
autumn senescence. 32
734 735
Figure 4 (Lattice Boltzmann simulations): Lattice Boltzmann simulations at the pore
736
scale: a) Fluid distribution in the pore space of a sand sample (air in blue and water
737
in red) at a water saturation of 0.6 and b) simulated (lines) and measured (circles)
738
NMR saturation recovery data of the same sample for water saturations between 0.3
739
and 1 (color coded).
740 741
Figure 5 (HGS Dynamics): Soil moisture simulation results for the 27 ha headwater
742
sub-catchment of the Rur catchments at 25 m spatial resolution for the period 2010
743
and 2011.
744 745
Figure 6 (HGS Soil Moisture Patterns): Spatial distribution of absolute soil moisture
746
(vol. %) on 13.01.2011 (upper figure) and 30.5.2011 (lower figure) for measured (top)
747
and simulated data for 25 m. The values in brackets refer to the mean standard
748
deviation of the kriging algorithm.
749 750
Figure 7 (LES Pattern Propagation): Shown in column (a) are patterns of
751
instantaneous sensible heat flux (deviation from domain average) in Wm-2 for 1300Z
752
at levels of 2, 8, 32 and 512m. Column (b) is as (a) but for patterns of sensible heat
753
flux averaged over 30 minutes. Column (c) shows the Haar wavelet energy spectra.
754
The sensible heat fields are Haar-decomposed with window sizes of 2, 4, 8, 16 and
755
32Δx (with Δx = 60 m).
756 757
Figure 8 (Atmospheric Downscaling): Disaggregation of a temperature field on an
758
almost cloud-free night. Under such conditions temperature inversions cause cold air
759
to drain into the valleys, which leads to pronounced channel structures in the 33
760
temperature field with substantial variability contained in the fine scales: (a) shows
761
the temperature field simulated by the COSMO (atmospheric component of
762
TerrSysMP) at coarse resolution (2.8 km); (b) shows the downscaled field at 400 m
763
resolution resulting from the GP based disaggregation algorithm; (c) shows the
764
reference field from a high-resolution (400 m) COSMO run.
765 766
Figure 9 (WTD-LE Cross-Wavelets): Cross-wavelet spectrum of evapotranspiration,
767
LE, and water table depth, WTD, simulated from TerrSysMP (CLM coupled to
768
ParFlow and driven with COSMO output) for the year 2009.
769 770
Figure Box 1 (Soil-Root Model): a) Installation of 54 8-m long transparent rhizotubes
771
(9 at 6 depths), b) recording images of roots along rhizotubes using a BTC2 video
772
microscope (Bartz Technology Corporation, Carpinteria, CA, USA), c) examples of
773
recorded images in rhizotubes at different depths from which the evolution of the
774
relative root density distribution during the growing season is derived, d) three-
775
dimensional simulation of root water uptake by a root architecture using the coupled
776
soil-root model R-SWMS (Javaux et al., 2008): color scale represents the soil water
777
pressure head (PH) and transparent cyan colors represent water uptake by root
778
segments.
779 780
Figure Box 2 (TerrSysMP Structure)): a) Schematic of TerrSysMP (modified after
781
Shrestha et al., 2014) showing the fluxes and state variables exchanged between the
782
three model components COSMO (atmosphere), CLM (landsurface and subsurface),
783
and ParFlow (subsurface hydrology) via the OASIS coupler. SW, LWdn, Rain, T, P,
784
QV and U (downward solar and terrestrial radiation, precipitation, screen
785
temperature, air pressure, specific humidity, and wind, respectively) are passed from 34
786
COSMO to CLM while CLM passes back SH, LH, TAU, LWup, and Albedo (sensible
787
and latent heat fluxes, friction velocity, upward terrestrial radiation, and albedo,
788
respectively). CLM share the upper soil layers with ParFlow within which qrain and qe
789
(infiltration and plant transpiration) are passé from CLM to ParFlow, which transmits
790
back Sw and Ψ (soil moisture and pressure head, respectively). b) Turbulent eddy
791
heat flux (km s-1) and relative soil saturation (-) along the Rur catchment. c) Main land
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use classes in the catchment. d) Meridional cross-section of turbulent eddy heat flux
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and relative soil saturation.
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35
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Supplement for Online Display
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Supplement 1: The Jülich ObservatorY for Cloud Evolution (JOYCE)
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A suite of ground-based remote sensing instruments is operated from the roof of
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Forschungszentrum Jülich (FZJ) GmbH (50.908547°N, 6.413536°E, 105m MSL) next
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to a 120 meter meteorological mast. Continuous long-term measurements
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(http://gop.meteo.uni-koeln.de/~hatpro/dataBrowser) with high-temporal resolution (
