## 1. Introduction

[2] Catchments are the key landscape elements for the analysis of water fluxes. This is not least because the convergence of flow into a stream allows for easy monitoring of water fluxes at the catchment outlet. Catchments are also the natural filters that control how variability in atmospheric conditions is translated into variations in streamflow. Governing flow equations, such as the Darcy equation or Richards' equation, have all been derived at scales several orders of magnitude smaller than the catchment scale and without taking into account variability in natural porous media or nonmatrix flow. The modeling of catchment processes is complicated due to several factors. Not only are model parameters spatially heterogeneous and hard to measure; also the optimal model structure may change with the scale at which models are applied [*Kirchner*, 2006].

[3] Traditionally, hydrological models have been developed around the central assumption that the functional behavior of hydrological systems can be predicted solely from physical properties of the system, combined with the governing flow equations and initial and boundary conditions. Over the past decades, the limitations of this approach have become clear. System properties cannot be easily determined a priori at the appropriate scale, and model parameters need to be optimized in order to achieve a satisfactory correspondence between observed and simulated fluxes. Observed fluxes are however not yet routinely used to infer information concerning model structure. It has been suggested that a downward approach, where time series characteristics are used to derive model structures rather than to optimize parameters of a given model structure, should play a more prominent role in model development and process identification [*Sivapalan et al.*, 2003].

[4] In a recent study, *Kirchner* [2009] proposed to represent catchments as simple dynamical systems, for which the model structure (i.e., the conceptualization of the system properties) can be directly inferred from observed changes in streamflow during recession. Here, simple refers to the fact that the combined effect of all subsurface flow processes on the catchment streamflow can be represented as resulting from a single state variable. The only (but necessary) assumption is that runoff is solely dependent on the total water storage in the catchment. This approach yielded good results for two catchments at Plynlimon in Mid Wales [*Kirchner*, 2009]. Moreover, the simplicity of the system allowed for “doing hydrology backward,” i.e., estimation of fluxes at the land surface from variations in streamflow. Recession analysis has been widely used to study properties of groundwater systems [*Hall*, 1968; *Tallaksen*, 1995; *Lyon et al.*, 2009], and recently also evapotranspiration recession has been used to study unsaturated zone properties [*Teuling et al.*, 2006]. However it has been known for decades that the recession rate also depends on evapotranspiration fluxes.

[5] The link between the rate of evapotranspiration at the land surface and the rate of streamflow recession, through the amount of water stored in the subsurface, was already acknowledged in the 1970s by *Federer* [1973] and *Daniel* [1976]. This link was further explored by *Wittenberg and Sivapalan* [1999] for a catchment in southwest Western Australia, where the recession rate showed a marked seasonal cycle that could be attributed to evapotranspiration. It was however not until the study of *Kirchner* [2009] that these processes were explicitly linked in a simple framework without the need for calibration or baseflow separation. The method presented by *Kirchner* [2009] offers some interesting possibilities for new approaches to catchment hydrology that were not explored in depth before, but it relies on the assumption of hydraulic connectivity between the main dynamical saturated and unsaturated stores in the catchment. Whether this assumption is justified can be tested by applying the method to catchments with different climate and subsurface conditions.

[6] Here, we test whether the Swiss Rietholzbach catchment (S. I. Seneviratne et al., manuscript in preparation, 2010) behaves like a first-order dynamical system. Like the Plynlimon catchments, Rietholzbach can be classified as humid. However, the Rietholzbach catchment receives considerably less precipitation than Plynlimon does, and the observational record at Rietholzbach contains some severe droughts (e.g., 1976, 1991, 2003). In the presentation that follows, we first focus on the discharge sensitivity function, which describes the sensitivity of discharge to changes in catchment storage. This function fully characterizes the first-order system. Next, we infer catchment storage from streamflow, and compared it to other estimates including unique observations from a weighing lysimeter. Discharge is also simulated. Finally, the discharge sensitivity function is used to infer infiltration (precipitation and snowmelt) rates from fluctuations in streamflow.