Coupled Model

The coupled subsurface-land surface model ParFlow.CLM has been described in detail in several previous studies [e.g., Maxwell and Miller, 2005; Kollet and Maxwell, 2008; Maxwell

and Kollet, 2008]. This coupled model consists of a groundwater flow model ParFlow and a

land surface model CLM. ParFlow, an integrated, parallel, variably saturated groundwater flow model solves the Richards’ equation in three spatial dimensions using a finite volume scheme with two point flux approximation in space and an implicit backward Euler scheme in time. ParFlow integrates surface flow by applying a free surface overland flow boundary condition at the land surface [Kollet and Maxwell, 2006]. In this study, a terrain following vertical grid along with a variable vertical spatial discretization is used honoring the topographic slopes in an approximate fashion for ParFlow [Maxwell, 2013].

The Common Land Model (CLM) simulates the mass and energy fluxes at the land surface. It needs precipitation rate, radiation, atmospheric temperature, pressure, wind, and water vapor data as atmospheric forcing input. In the coupled modeling framework, ParFlow is coupled to CLM over the first ten downward vertical model layers starting at the land surface. ParFlow, which replaces the simplified soil moisture and runoff formulations in CLM, simulates the distribution of soil moisture in the subsurface and sends this information to CLM. In return, CLM calculates the source/sink of soil moisture (e.g., infiltration from precipitation, soil

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evaporation, and plant transpiration) for ParFlow. The two coupled model components communicate at every time step based on an operator splitting approach for the exchange of fluxes and the shallow soil moisture distribution.

4.2.3 Rur model setup

The coupled subsurface-land surface model ParFlow.CLM is applied over a model domain encompassing the Rur catchment. A uniform lateral grid resolution (∆x=∆y) of 1 km is considered with 168 ×168 cells in x and y dimensions, respectively. A total subsurface depth of 50 m is discretized ranging from 4×10-2m at the land surface to 2×100m at the bottom of the model domain using the aforementioned terrain following grid implementation. A free surface overland flow boundary condition is applied at the land surface following Kollet and

Maxwell [2006].

Global Land Cover 2000 digital database (GLC2000, European Commission, Joint Research Centre, 2003) is used to represent the spatially distributed vegetation cover (Figure 1b) over the model domain. The plant parameters are derived following the International Geosphere- Biosphere Program (IGBP) standard. The soil texture information of the shallow subsurface (Figure 4.1c) is derived from the Digital Soil Map of the World (DSMW) provided by the Food and Agricultural Organization of UNO (FAO) and the Euro-soil database information [e.g., Dolfing and Scheltens, 1999]. Considering spatial homogeneity, soil hydraulic parameters for the deeper subsurface are adapted from Gleeson et al. [2011]. Saturation pressure head relationship for different soil types are represented using van Genuchten function [van Genuchten, 1980], with parameter values following Schaap and Leij [1998]. A simulation period of three years (2009-2011) with a time resolution of one hour is considered in this study. COSMO-DE re-analysis data set provided by the German Weather Service is used to obtain the atmospheric variables (e.g., precipitation rate, radiation, temperature, barometric pressure, wind speed, and humidity) to force ParFlow.CLM. Because COSMO-DE operates at a lateral grid resolution of 2.8 km, downscaling of the atmospheric variables to the model grid resolution of 1 km is performed applying linear interpolation. In order to achieve a realistic initial condition, a model spin-up was performed by forcing ParFlow.CLM repeatedly with the hourly atmospheric information over 2009 until a dynamic equilibrium was achieved. A detailed description of model setup, input data, and spin-up process can be found in Rahman et al. [2014]

54 4.2.4 Experimental setup

In the experimental setup, three different model configurations, namely, dynamic, constant, and free drainage boundary condition were considered to identify the influence of the LBC on the variability of land surface mass and energy fluxes in the framework of DBF concept. Note, except for the treatment of the LBC, these model configurations are identical in terms of model inputs, initial and boundary conditions. In the dynamic boundary condition (DBC) configuration, WTD is allowed to evolve freely through time. In contrast, the constant boundary condition (CBC) configuration maintains the initial WTD at individual horizontal model grid cells throughout the simulation period. In the free drainage boundary condition (FD) configuration, gravity drainage is implemented at the bottom of the model domain that allows moisture to drain gravitationally through the lower boundary. Model runs are performed for three consecutive years (2009-2011) considering each of the aforementioned configurations and the mass and energy fluxes of the coupled water and energy cycles are simulated. Differences in simulation results are analyzed using continuous wavelet transform (Appendix B) techniques ensuing the model runs and discussed in context of DBF concept.

4.3 Results and discussion

According to the DBF concept, atmosphere and groundwater act as the upper and lower boundaries, respectively, for the land surface processes. The boundary condition that dominates the exchange processes and induces variability in the land surface energy fluxes is determined by the time and space localized availability of soil moisture and energy. At small time scales (e.g., daily), variability of land surface processes is generally governed by atmospheric forcing. This high frequency variability of the land surface processes influences

WTD under soil moisture limited conditions because of groundwater contribution towards

daily ET [e.g., Gribovszki et al., 2010; Fahle and Dietrich, 2014], which is analogous to periodic pumping. A the large time scales, the influence of this depletion of groundwater storage can be observed on land surface processes at monthly to multi-month time scales governed by groundwater table dynamics [Rahman et al., 2014].

In this section, the proposed DBF concept is tested by analyzing the differences in model results from the three aforementioned configurations. Temporal dynamics of fluxes and states from the three model configurations are analyzed applying continuous wavelet transform technique (Appendix B). Because DBC, CBC, and FD configurations are identical except for

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the LBCs, differences in the variability of the fluxes from these configurations can be attributed directly to groundwater dynamics.