Interactions between hydrology and biogeochemistry within riparian wetlands

RIPARIAN WETLANDS: HYDROLOGY MEETS BIOGEOCHEMISTRY Interactions between hydrology and biogeochemistry within riparian wetlands Potential implications for internal biogeochemical process distributions and solute exports Dissertation zur Erlangung des Grades Doktor der Naturwissenschaften (Dr. rer. Nat.) an der Fakultät Biologie/Chemie/Geowissenschaften der Universität Bayreuth Vorgelegt von Sven Frei Geb. am 21. Juni 1979 in Augsburg Die vorliegende Dissertation wurde im Zeitraum von April 2008 bis Oktober 2012 unter der Betreuung von Dr. Jan H. Fleckenstein am Lehrstuhl für Hydrologie (Prof. Dr. Stefan Peiffer) der Universität Bayreuth angefertigt. Die Arbeiten im Rahmen der Dissertation wurden durch die Deutsche Forschungsgemeinschaft (DFG) gefördert im Rahmen des Projektes Fl 631/6-2, einem Teilprojekt innerhalb der DFG Forschergruppe FOR 562. Vollständiger Abdruck der von der Fakultät für Biologie, Chemie und Geowissenschaften der Universität Bayreuth genehmigten Dissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.). Dissertation eingereicht am: 05.10.2012 Zulassung durch die Prüfungskommission: 17.10.2012 Wissenschaftliches Kolloquium: 28.03.2013 Amtierender Dekan: Prof. Dr. Beate Lohnert Prüfungsausschuss: Dr. Jan H. Fleckenstein (Erstgutachter) Prof. Dr. Stefan Peiffer (Zweitgutachter) Prof. Dr. Michael Hauhs (Vorsitz) Prof. Dr. Bernd Huwe Prof. Dr. Egbert Matzner RIPARIAN WETLANDS: HYDROLOGY MEETS BIOGEOCHEMISTRY Interactions between hydrology and biogeochemistry within riparian wetlands Potential implications for internal biogeochemical process distributions and solute exports Vorfluternahe Feuchtgebiete: Hydrologie trifft Biogeochemie Interaktionen zwischen Hydrologie und Biogeochemie in vorfluternahen Feuchtgebieten Potentielle Auswirkungen für die interne biogeochemische Prozessverteilung und auf den Export gelöster Stoffe Extended Summary Acknowledgements I would like to thank Jan H. Fleckenstein for the supervision and helpful advice during all phases of this work. I would like to thank Stefan Peiffer for the opportunity, to work at the department of Hydrology and Klaus-Holger Knorr for his support and his excellent contributions to this work and the occasional climbing sessions. I would like to thank all members of the Hydrology department and all the assiduous student assistants for their help. Without their support, this work would not have been possible: Sandra Werb, Christopher Shope, Svenja Bartsch, Christiane Clemens, Christian Estop, Stefan Strohmeier, Jürgen Leonbacher, Christiane Neuman, Johannes Opitz and Sebastian Würzer. I would like to thank Rob McLaren, Young-Jin Park, Andrea Brookfield and Ed Sudicky at the University of Waterloo, Canada for their invaluable help with the ins and outs of the numerical code HydroGeoSphere. Furthermore, I would like to thank Daniel Partington School of Civil, Environmental and Mining Engineering (University of Adelaide) for the useful and productive cooperation. I would like to thank the helpful coordinators and technicians of the Research Group FOR 562. I would like to thank all people providing advice and helpful comments and sometimes the necessary distraction. Particularly, I want to thank Marianne Ruidisch, Martin Reichert, Sabine Thüns and Trang Tôi cám ơn gia đình Việtnam của tôi Chú Tụng và Cô Phương đã cho tôi một mái ấm gia đình nồng hậu ở Bayreuth và nhất là Cô Phương,người rất thường xuyên quan tâm chăm sóc tôi, tất cả họ đều đã đóng góp một phần quan trọng cho sự thành công trong công việc của tôi. I would like to thank my family and my parents Ingrid and Werner for the support during all phases of my studies. I would like to thank Hugo for the help, the patience and for the good time. [1] TABLE OF CONTENTS Table of Contents Table of Contents .................................................................................................................................... 1 List of Figures ......................................................................................................................................... 3 List of Tables .......................................................................................................................................... 4 Summary ................................................................................................................................................. 5 Zusammenfassung ................................................................................................................................... 7 1Introduction ..................................................................................................................................... 9 1.1Interactions between hydrology and biogeochemistry - an interdisciplinary challenge ......... 9 1.2Riparian Wetlands: Complex hydrology meets complex biogeochemistry .......................... 11 2Research Objectives and Hypotheses ........................................................................................... 15 3Materials and Methods .................................................................................................................. 17 3.1Study Site .............................................................................................................................. 17 3.2Hydrological Modeling ......................................................................................................... 19 3.2.1Virtual Wetland Modeling (Study 1, 2 and 3) ............................................................... 20 3.2.2Catchment Scale Modeling (Studies 4 + 5) ................................................................... 23 3.3Biogeochemical Modeling (Studies 2 + 3) ........................................................................... 27 3.3.1Coupling Hydrology and Biogeochemistry .................................................................. 27 3.3.2Implemented Reaction and Boundary Conditions ........................................................ 28 4Results and Discussion ................................................................................................................. 33 4.1Effects of micro-topography on surface-subsurface exchange and runoff generation in a virtual riparian wetland (Study 1) ..................................................................................................... 33 4.2Surface micro-topography causes hot spots of biogeochemical activity in wetland systems – a virtual modeling experiment. (Study 2) ......................................................................................... 36 4.3Representing effects of micro-topography on runoff generation and sub-surface flow patterns by using superficial rill storage height variations (Study 3). ............................................... 39 4.4Concentrations and fluxes of dissolved organic carbon in runoff from a forested catchment: insights from high frequency measurements (Study 4) ..................................................................... 42 4.5Interpreting flow generation mechanisms from integrated surface water-groundwater flow models of a riparian wetland and catchment (Study 5). .................................................................... 44 5Conclusions and Outlook .............................................................................................................. 47 [2] TABLE OF CONTENTS 6References ..................................................................................................................................... 49 7Appendix ....................................................................................................................................... 59 8Contributions to the included manuscripts .................................................................................... 63 Study 1:Effects of micro-topography on surface-subsurface exchange and runoff generation in a virtual riparian wetland ......................................................................................................................... 65 Study 2:Surface micro-topography causes hot spots of biogeochemical activity in wetland systems – a virtual modeling experiment. ............................................................................................................. 97 Study 3:Representing effects of micro-topography on runoff generation and sub-surface flow patterns by using superficial rill storage height variations .................................................................. 151 Study 4:Concentrations and fluxes of dissolved organic carbon in runoff from a forested catchment: insights from high frequency measurements ...................................................................................... 179 Study 5:Interpreting flow generation mechanisms from integrated surface water-groundwater flow models of a riparian wetland and catchment ....................................................................................... 207 Erklärung ............................................................................................................................................ 249 [3] LIST OF FIGURES List of Figures Figure 1: Traditional hydrologic and biogeochemical perspective on transport and reaction ............. 10 Figure 2: Conceptual model of the Lehstenbach catchment ................................................................ 18 Figure 3: Picture of the Schlöppnerbrunnen II field site. ..................................................................... 18 Figure 4: Geometry of the virtual wetland segment: a) planar reference model showing the main drainage direction and channel location; b) smoothed realization of the wetlands hummocky micro-topography; c) cross section (Y=5m) of the micro-topography model. ............................. 21 Figure 5: Finite element grid of the Lehstenbach catchment model.. .................................................. 23 Figure 6: Observed and simulated discharge values (estimated at the catchment outlet) for the calibration and validation periods of the catchment scale model.. ............................................... 26 Figure 7: Concept of the applied stream tube approach for representation of biogeochemistry along isolated subsurface flow paths (dashed line). ............................................................................... 28 Figure 8: Typical oxygen depth profile observed for a wetland site of the Lehstenbach catchment. .. 31 Figure 9: Six consecutive snapshots of the evolving surface flow networks during the largest flow event of the year (day 217 to day 218).. ....................................................................................... 33 Figure 10: a) Relationship between discharge and groundwater level for two peak flow events, observed for a small catchment located in British Colombia, Canada (modified after Fitzgerald et al. (2003)).b) Simulated relationship between groundwater level and channel discharge for the micro-topography model. .............................................................................................................. 35 Figure 11: Results of the biogeochemical simulations shown for the sulfate reduction process of the micro-topography scenario with the mean length 0.5m. ............................................................... 38 Figure 12: Snap shots taken at the end of a steady rainfall simulation showing the fully developed surface flow networks (yellow) which are generated in the micro-topography model as well as in the models with rill storage height variations (p-rs-low and p-rs-high) but not for the planar reference case. ............................................................................................................................... 40 Figure 13: Typical non-linear and hysteretic relationships between observed DOC concentrations in runoff and discharge ..................................................................................................................... 43 Figure 14: Calculated stream and overland flow generation, estimated by applying the “hydraulic mixing-cell” methodology to the Lehstenbach catchment model.. ............................................... 45 Figure A1: Soil retention functions used to represent variably saturated flow in the wetland soils and the regolithic aquifer of the catchment scale numerical model and the virtual wetland model... . 59 Figure A2: Saturated hydraulic conductivities Ksat assigned to the ten sub-layers SL1-SL10 of the wetland areas for the catchment scale model..... ........................................................................... 60 [4] LIST OF TABLES List of Tables Table 1: Critical concentrations which are controlling the sequential initialization of the redox sequence... ..................................................................................................................................... 29 Table A1: Overview of the parameterization of the catchment scale model to represent surface/subsurface flow and interactions for the three different zones (wetlands, upslope areas and stream areas).... ...................................................................................................................... 61 [5] SUMMARY Summary Interactions between hydrology and biogeochemistry at various spatio-temporal scales are important control mechanisms within terrestrial and aquatic ecosystems and exist among different compartments and transition interfaces. Understanding the fundamental mechanistic couplings between hydrological and biogeochemical processes and how these couplings feed back into ecosystem services and functions is an interdisciplinary challenge that must be addressed especially in the context of humanly mediated climate change. Riparian wetlands, as a transition zone between terrestrial and aquatic ecosystems, occupy large fractions of terrestrial ecosystems and provide important ecohydrological services. Due to their anoxic environments, riparian wetlands are able to store significant amounts of carbon as peat and act as an effective nutrient sink e.g. for sulfur, phosphorous and nitrogen. Riparian wetlands are characterized by highly dynamical interactions between hydrologically controlled transport mechanisms and biogeochemically controlled substrate availability, which governs nutrient cycling as well as the sink and source functions of wetlands. Generally, these interactions and their potential implications on ecosystem functions are only poorly understood. The representation of the tight couplings between hydrology and biogeochemistry in mechanistic models is a very challenging task because they have revealed a complexity which is often beyond the capabilities of current models. The objective of this thesis is to investigate interactions between hydrology and biogeochemistry in riparian wetlands and to understand their potential implications for internal biogeochemical process distributions and solute mobilization. Additionally, one major focus of the thesis is the attempt to represent such fundamental couplings in a process-based, hydrological/biogeochemical modeling approach. To this end, this thesis uses a combination of field and virtual experiments, as well as catchment-scale numerical modeling, performed for the Lehstenbach catchment, which was exemplarily chosen as main study site. Results from the virtual experiments show very complex small-scale hydrological dynamics within the riparian areas. Here, runoff generation processes are strongly influenced by the spatial structure of the wetland-typical micro-topography (hummocks and hollows). Surface flow is episodically generated by a highly dynamical, threshold-controlled process where extended surface flow networks drain large fractions of the wetland's area. During intensive rainstorm events these surface flow networks, which contribute to stream discharge due to a fill and spill mechanism, dominate runoff generation. These fast flow components are characterized by very low residence times (minutes to hours) and once they are activated, the surface flow networks are able to rapidly mobilize large amounts of solutes, like nitrate or dissolved organic carbon (DOC), out of the wetlands by bypassing deeper anoxic layers. The importance of fast flow components for the catchment-scale mobilization of DOC was further confirmed by field investigations and catchment-scale numerical modeling. High frequency measurements of DOC in runoff of the Lehstenbach catchment revealed that DOC export is [12] INTRODUCTION these reduction processes occur sequentially, known as the microbially influenced redox chain (Zehnder, 1988). The location of the redox-cline in wetlands, as the defined boundary between the reduced and oxidized environment, is tightly coupled to the location of the local water-table (Cirmo and McDonnell, 1997). Rapid fluctuations of the water table in response to onset of rainfall are a commonly observed phenomenon in wetland system (Cirmo and McDonnell, 1997; Devito and Hill, 1997; Devito and Hill, 1997). The rapid response of the water-table to rainfall is discussed in the literature as an effect of a large capillary fringe in nearsurface layers of soil or peat, where small amounts of rainfall or snowmelt may result in rapid upward movement of the water-table (Gillham, 1984; Heliotis and DeWitt, 1987). Water level manipulation experiments in the field (Knorr et al., 2009; Knorr and Blodau, 2009) have demonstrated that fluctuations of the water-table are directly linked to rapid changes in the predominant redox processes (i.e. iron(III) reduction, sulfate reduction and methanogenesis), the location of the redox-cline and the mineralization of organic material. At the landscape scale wetlands are commonly assumed to be effective sinks for solutes like sulfate or nitrate, because anaerobic conditions and large carbon supplies enhance reductive biogeochemical transformations like denitrification or sulfate reduction (Johnston, 1991). However, this perspective neglects that physically-controlled transport and biogeochemical transformation processes within wetlands are not static. Hydrology, biogeochemistry and their interactions are dynamic processes, especially in wetlands or riparian areas, which are frequently affected by rapid fluctuations in hydrological and meteorological boundary conditions (Cirmo and McDonnell, 1997; Knorr et al., 2009; Knorr and Blodau, 2009). Short and long term fluctuations of the hydrological and meteorological drivers have the potential to alter internal biogeochemical processes, which may constrain the sink and source functions of wetlands for certain minerals, gases and solutes (Knorr et al., 2009). Devito and Hill (1997) have shown that wetlands are an efficient net sink for sulfate during high flow conditions where high water tables and anoxic conditions enhance reductive transformation processes e.g. denitrification or sulfate reduction. However, during extended drought periods and dropping water tables, redox conditions within wetlands change as wetland layers are being aerated, leading to increased mineralization and re-oxidation of reduced species like sulfide or ammonium, which are being flushed during storm runoff. Under these conditions, wetlands can turn into an episodic source for nitrate or sulfate (Devito and Hill, 1997). In catchments, upland areas and riparian wetlands are usually connected hydrologically, meaning that water originating from upland areas has to pass through the riparian wetlands first before it can reach the streams or rivers via subsurface flow. Groundwater from upland areas usually has a very different chemical signature compared to the pore water of the wetland. In comparison, pore water in the wetlands groundwater from upland areas is often enriched in oxidized species like sulfate, nitrate or oxygen, whereas in contrast to wetland areas carbon loadings are usually low. Along flow paths, where upland groundwater is exposed to the anoxic conditions within wetlands, compounds like nitrate or sulfate can be reduced efficiently (Hill et al., 2000; McMahon, 2001). However, intensive [13] INTRODUCTION rainfall or snowmelt may result in the generation of very fast flow components like surface or shallow subsurface flow ((Devito and Hill, 1997; Lischeid et al., 2007) within riparian wetlands. These fast flow components have very low subsurface residence times and the potential to rapidly transport water originating from hillslope areas to the streams by short-circuiting or bypassing the anoxic areas of wetlands (Wigington et al., 1990; Murdoch and Stoddard, 1992; Stoddard, 1994; DeWalle and Swistock, 1994). Under such conditions, the sink function of wetlands for nitrogen or sulfur can be deactivated temporarily. Attempts to describe and represent the complex processes and couplings between the hydrology and biogeochemistry of wetlands in mechanistic models is a challenging task, as processes and couplings are commonly at a level of complexity that is beyond the capabilities of current models (Hill, 1993; Waddington et al., 1993; Eshleman et al., 1994; Richardson et al., 2007a). Often, below ground processes within wetlands are treated as a black box (Kettunen et al., 1999; Updegraff et al., 2001; Chimner and Cooper, 2003) where only the transfer characteristics between input and output variables are being considered, neglecting underlying physical laws that govern system-internal hydrological and biogeochemical processes. To “unlock the black box” (Walling, 1983), it is necessary to gain an improved understanding of system-internal process mechanisms and fundamental mechanistic couplings between physical transport and biogeochemical reactions (Burt and Pinay, 2005), especially in such complex environments as riparian wetlands. This requires spatially-explicit, physically-based model structures (Burt and Pinay, 2005; Richardson et al., 2007b; Boano et al., 2010) which represent processes based on their actual governing physical laws and which, by definition, account for spatial organization of relevant hydrologic and biogeochemical parameters. Although fully distributed approaches have been heavily criticized because of the difficulties in adequately defining process equations and a unique, problem-specific parameterization (the “equifinality problem” presented in Beven, (1989) and Beven, (1993)), they offer flexible and extensive possibilities to test certain hypotheses (the “virtual experiment” concept presented in Weiler and McDonnell (2004)), which are related to the nature of interactions between hydrology and biogeochemistry in wetland systems. These approaches can be used to partially elucidate the black box and investigate the fate of those elements and solutes, which are affected by physical transport and biogeochemical transformation in wetland ecosystems. This thesis contributes to this line of work. [14] [15] RESEARCH OBJECTIVES AND HYPOTHESES 2 Research Objectives and Hypotheses This thesis aims at investigating fundamental interactions between hydrology and biogeochemistry in wetland ecosystems with the purpose to gain a better understanding of how nutrient cycling, internal biogeochemical process distributions, solute mobilization and solute export are affected by such interactions. A major focus of this thesis is to establish an interdisciplinary modeling framework where hydrological and biogeochemical processes are addressed equally and where fundamental interactions and feedback mechanisms between a wetland’s hydrology and biogeochemistry can be represented in a physically-based model. The five studies, which are presented as part of this thesis, use a combination of field investigations, virtual experiments and catchment scale numerical modeling to address the different research objectives and hypotheses. Study 1 focuses on the effects of surface micro-topography on hydrological process dynamics and interactions that govern surface-subsurface exchange and runoff generation in riparian wetlands. Specifically, study 1 uses a virtual experiment approach to investigate: (1) the role of a hummocky topography of wetlands on stream discharge generation; (2) the effect of micro-topography on typically-observed non-linear relationships between discharge and water table depth and (3) the connection between surface flow generation and climatic and hydrological boundary conditions. In study 2, the previously presented virtual wetland model, is subsequently used to develop a coupled hydrological/biogeochemical model which is being used in another virtual experiment to investigate how subsurface flow patterns, induced by micro-topography, affect hydrological transport and biogeochemical transformation processes of redox-sensitive solutes within wetlands. The main research hypothesis of study 2 is to explore whether a complex, three-dimensional subsurface flow field, as a result of micro-topography controlled surface/subsurface flow exchange, creates biogeochemical conditions that facilitate the formation of local process hot spots for wetland-typical redox reactions, even in soils with uniform soil properties. Representing small-scale interactions between hydrology and biogeochemistry of wetland ecosystems, as presented in study 2, in a coupled physically-based modeling approach has proven to be computationally very demanding, resulting in low computational efficiencies and extremely long simulation times. The main objective of study 3 therefore is to develop a technique how effects of micro-topography on sub-surface flow patterns, runoff generation and biogeochemical process patterns can be represented more efficiently in physically-based models. Once established, such an alternative representation can be used to account for effects of micro-topography in larger scale models like in watershed or regional models. Study 4 is mainly based on data from a field campaign on DOC export of a small forested watershed with riparian wetlands. The impacts of short term fluctuations in hydrological and meteorological boundary conditions on DOC variations in runoff are investigated. Here, the main research objectives are (1) to identify the spatial origin of DOC in runoff, [16] RESEARCH OBJECTIVES AND HYPOTHESES (2) to identify hydrological flow paths which are important for DOC mobilization and (3) to investigate implications of short term variations of DOC in runoff for the calculation of annual DOC export rates. Runoff generation mechanisms at the catchment scale are investigated in study 5, where a “Hydraulic Mixing-Cell” methology (HMC) is used to track overland and stream runoff generation mechanisms to attain a meaningful separation of streamflow hydrograph for the Lehstenbach. Objectives of study 5 are (1) to test whether the HMC method, developed and presented earlier by Partington et al. (2011), can principally be used in general to identify and quantify relevant runoff generation mechanisms in complex numerical flow models and more specifically (2) to investigate the spatial origin and relative contribution of different runoff components in the Lehstenbach catchment. [17] MATERIALS AND METHODS 3 Materials and Methods 3.1 Study Site Field experiments and numerical modeling were carried out in the Lehstenbach catchment. The catchment is located close to the city of Weisenstadt in north eastern Bavaria, Germany (50°08’38’’N, 11°51’41’’E). Elevations for the site vary between 877m above sea level for upslope areas and 690m above sea level for the outlet of the catchment. Mean annual precipitation, for the 4.2 km² large Lehstenbach catchment, is around 1150 mm with a mean temperature of ~5°C (Gerstberger, 2001). The main regional aquifer of the Lehstenbach catchment (around 40 m thick) is made up of regolithic material originating from weathering of the granitic bedrock (Lischeid et al., 2002). Hydrologically, the catchment can be separated into two distinct units as illustrated in Figure 2: Nearly one-third of the total area of the catchment can be classified as riparian wetlands surrounding all major streams. Peat forming wetlands have predominately developed in the topographic depressions towards the center of the bowl-shaped catchment, where converging groundwater flow (Figure 2) favors conditions that lead to the accumulation of peat. For the main wetlands, average peat thickness varies between 0.3m and 1.2m. The wetlands are locally separated from the deeper groundwater system by a basal clay layer of variable extent. Annual fluctuations of groundwater levels in the wetland’s main zones are limited to the upper 0.2 m, but may increase down to 0.8m below soil surface during very extended drought periods. Water content of the variably saturated zone within the wetlands is comparably high, which favors anoxic conditions (above 80% water saturation according to Paul et al. (2006), Estop-Aragonés et al. (2012) and Estop-Aragonés and Blodau (2012)). Extended areas of the wetlands, especially in the lower parts of the catchment close to the outlet (Schlöppnerbrunnen II), are characterized by a pronounced micro-topography (Figure 3); sequences of hollow and hummock structures, built by the wetland’s typical vegetation (Carex rostrata, C. Canesccens, Eriophorum vaginatum, Nardus stricta, Molinia coeruela, Agrostis sp., Sphagnum fallax, Brachythecium rivulare and Atrichum undulatum according to Knorr et al. (2008)). Such hummocky topographies are quiet common in peatlands (Nungesser, 2003) and evidence from chrono-stratigraphic studies indicates that such structures (hummocks and hollows) may persist relatively unchanged for centuries or even millennia (Godwin and Conway, 1939; Conway, 1948; Tolonen, 1971; Barber, 1981). Previous studies performed in the Lehstenbach catchment indicated that important mechanisms and processes controlling stream flow generation and solute export are located in the near-stream wetland areas (Lischeid et al., 2002; Alewell et al., 2007; Lischeid, 2008). Around two-thirds of the area of the Lehstenbach catchment is covered by forest (mainly Norway Spruce populations, (Gerstberger, 2001)). Hydrologic conditions in the forested areas, located mainly in the upslope areas of the catchment (Figure 2), clearly differ from those within the riparian wetlands. Long term groundwater observations for the upslope areas show permanently deep [18] MATERIALS AND METHODS groundwater levels, 5-10m below the land surface and an extended unsaturated zone with comparably low water contents. In contrast to the water saturated conditions within the wetlands, the upslope areas can be classified as aerated forest soils. The forested areas represent the main recharge zones for the deeper groundwater system, as reflected by downward hydraulic gradients in the unsaturated zone. There is no clear evidence for pronounced lateral flows above the groundwater table (interflow) in these areas with deep water table. Figure 2: Conceptual model of the Lehstenbach catchment. The overall hydrology of the catchment is controlled by the structure of the basin. Dark grey areas represent forested zones and light grey areas wetlands, which occupy almost 1/3 of the 4.2 km² catchment area. Figure 3: Picture of the Schlöppnerbrunnen II field site (located in the lower part of the catchment, close to the catchment’s outlet) taken during a storm flow event in spring 2009. The Schlöppnerbrunnen II site is characterized by a pronounced micro-topography (hollow and hummock structures) and belongs to the core wetland areas of the Lehstenbach catchment. [19] MATERIALS AND METHODS 3.2 Hydrological Modeling Hydrological modeling as part of this thesis was performed using a spatially-explicit, physically-based modeling concept, where surface and subsurface hydrology is represented using the code HydroGeoSphere (HGS, presented in Therrien et al. (2008)). HGS is a fully-integrated finite element surface-subsurface flow model. Variably saturated subsurface flow in porous media is simulated by solving the Richards equation in three dimensions (3D): 󰇛󰇜Γ    󰇛󰇜Eq. 1  󰇛󰇜 Eq. 2 Where  [-] represents the volumetric fraction of the total porosity occupied by the primary continuum (porous or fractured medium) and q [LT-1] the fluid flux.  [L3 L-3T-1] represents the volumetric fluid exchange between the subsurface domain and all other types of domains supported by the model (e.g. surface domain). Fluid exchange with the outside of the simulation domain is represented by Q [L3 L-3T-1], which is a volumetric flux per unit volume representing source (positive) and sinks (negative). θs [-] and Sw [-] represent the saturated water content and the degree of saturation respectively. Furthermore, the fluid flux q is given by Eq. 2 where  [-] represents the relative permeability of the medium as a function of the water saturation Sw, Ksat [LT-1] is the saturated hydraulic conductivity of the medium,  [L] is the pressure head and z [L] the elevation. For representation of variably saturated flow, commonly used functions incorporated into HGS are those presented in Van Genuchten (1980b) and Brooks and Corey (1964) or alternatively, soil retention characteristics can also be handled through the use of tabular data input if field measurements are available (Therrien et al., 2008). Overlandor stream flow in 2D is represented by the diffusion wave approximation to the depth-averaged dynamic wave equations (Therrien et al., 2008): 󰇛󰇜Γ  Eq. 3 Within the diffusive wave equation, here written in vectorial notation, do [L] represents the surface flow water depth; qo [LT-1] the water flux on the surface; [T-1] the fluid exchange rate with the subsurface; Qo [LT-1] the volumetric flow rate per unit area representing external sinks (negative) or sources (positive);  [-] the surface porosity and ho [L] the water surface elevation. Surface– subsurface coupling is implemented using the conductance concept: Γ  󰇛󰇜Eq. 4 [20] MATERIALS AND METHODS The conductance concept assumes that the exchange flux between the surface and the subsurface  [T-1] depends on the gradient across a coupling interface h-ho [L] (h [L] represents the subsurface water head and ho [L] the water surface elevation), the thickness of the interface  [L] (coupling length), its relative permeability  [-] and the vertical saturated hydraulic conductivity  [LT-1] (Therrien et al., 2008). All governing equations for surfaceand subsurface flow are solved simultaneously via a control volume, finite-element approach (Therrien et al., 2008). HGS has been applied over a wide range of spatial scales ranging from plot and river reach scales (Jones et al., 2006; Brookfield et al., 2009) over the scale of watersheds (Jones et al., 2008; Li et al., 2008) up to the scale of continents (Lemieux et al., 2008a; Lemieux et al., 2008b; Lemieux et al., 2008c). As part of this thesis, HGS was used to simulate hydrological flow processes and surface/subsurface flow interactions on two different scales: On the plot scale numerical flow modeling (using HGS) was used to represent the highly dynamic flow processes within the riparian wetlands (study 1, 2, 3) of the Lehstenbach catchment. An integrated perspective on hydrological flow processes, relevant for the catchment scale runoff generation and solute exports, was the motivation for setting up a numerical catchment scale flow model of the Lehstenbach area (study 4+5). 3.2.1 Virtual Wetland Modeling (Study 1, 2 and 3) The conceptual idea behind the plot scale modeling is similar to the virtual experiments proposed by Weiler and McDonnell (2004). The objectives of the studies 1-3 are addressed through virtual modeling experiments. The numerical model is used as a virtual wetland, in which perfect process knowledge is assumed (see e.g. Zehe et al. (2005)). Virtual wetland modeling involves more than only one numerical flow model: Study 1 and 2 use different model scenarios with different, geostatistically generated 3D realizations of the hummocky micro-topography. Study 3 involves geostatistically derived, 2D representations of micro-topography, which were used in subsequent model scenarios. All numerical flow models (study 1-3) as part of the virtual wetland modeling approach were set up for the same spatial model domain (set up for a 10m x 20m x 2m plot) representing a synthetic section of a riparian wetland draining into a nearby stream segment (Figure 4). Virtual wetland modeling is described in detail in the method section of study 1 and only a brief summary about the applied techniques and methods is given in this section. [21] MATERIALS AND METHODS Figure 4: Geometry of the virtual wetland segment: a) planar reference model showing the main drainage direction and channel location; b) smoothed realization of the wetlands hummocky microtopography; c) cross section (Y=5m) of the micro-topography model. Representation of Micro-topography The spatial structure of the micro-topography for a typical wetland in the Lehstenbach catchment was represented using geostatistical indicator simulations based on Markov Chain models of transition probabilities (TPROGS-Transition PRObability Geostatistical Software presented in Carle and Fogg (1996)). The method was originally developed to realistically represent aquifer heterogeneity with discrete transitions between different hydrofacies (Carle and Fogg, 1996). TPROGS has been widely applied for groundwater flow and transport problems (e.g. Weissmann, 1999; Fleckenstein et al., 2006; Lee et al., 2007; Frei et al., 2009). For a realistic representation of micro-topography, the geostatistical model was conditioned with field data derived from several surveyed transects taken within a 30m x 30m plot of the Schlöppnerbrunnen II site located in the Lehstenbach catchment. The output of the indicator simulations was transferred into an artificial digital elevation model (DEM) by assigning the different indicators to certain elevation classes. The resulting DEM mimics the spatial structures of the wetlands micro-topography. The application of geostatistical simulations provided the possibility to work with multiple realizations of micro-topography based on either the same or different structural properties. A detailed description of the used geostatistical approach is given in the methods chapter of study 1. Study 1 and 2 use model scenarios where micro-topography is actually [28] MATERIALS AND METHODS was used to represent the whole 3D domain of the virtual wetland model, which resulted in ~1.450.000 different PHREEQC sub-section simulations per flow model. Figure 7: Concept of the applied stream tube approach for representation of biogeochemistry along isolated subsurface flow paths (dashed line). An isolated flow path is split into n different sub-sections. Each sub-section i represents a small reach of the flow path, for which the biogeochemical evolution, depending on the hydrological/biogeochemical boundary conditions, is simulated using PHREEQC (Parkhurst, 1995). Boundary and initial conditions are individually assigned for each PHREEQC subsection simulation. Between consecutive sub-sections, redox-sensitive solutes are exchanged were the ith sub-section uses the final redox chemical composition of the i-1th sub-section as initial condition. X, Y and Z represent the spatial coordinates at the beginning and the end of a sub-section; Δt represents the sub-section’s residence time. 3.3.2 Implemented Reaction and Boundary Conditions The biogeochemical model represents wetland-typical, redox-sensitive processes, which are implemented using different kinetic reactions. In particular, the following redox-sensitive processes are being simulated: aerobic respiration, denitrification, iron(III) reduction, sulfate reduction, iron(II) oxidation, ammonium oxidation, aerobic and anaerobic sulfide oxidation. Kinetics for all reduction processes (aerobic respiration, denitrification, iron(III) reduction, sulfate reduction) where microorganisms use different electron acceptors (oxygen, nitrate, iron(III) and sulfate) for turnover of organic material are formulated based on Monod kinetics (Monod, 1949). For reactions following Monod kinetics, as shown in Eq. 5, the kinetic rate Rk [ML-3T-1] is calculated as a function of the solutes concentration ck [ML-3] and the reaction specific constants μmax [ML-3T-1] and Ks,k [ML-3]. [29] MATERIALS AND METHODS     ,  Eq. 5 In the model, Monod kinetic constants for the different reduction processes are based on laboratory studies of biodegradation of organic chemicals (references are listed in Table 2 of study 2) and were adjusted as part of the calibration process. Finally, calibrated coefficients are listed in Table 2 of study 2. Oxidation processes (iron(II) oxidation, ammonium oxidation, anaerobic and aerobic sulfide oxidation) were formulated using higher order reaction kinetics as listed in Table 2 of study 2. In redox controlled systems like wetlands, reduction processes occur sequentially where microorganisms use oxygen as primary electron acceptor first, before nitrate, iron(III) and sulfate are being used. To represent this sequential behavior within the biogeochemical model, different conditions were formulated for which the different reduction processes are being initiated. In the approach presented here, these conditions are represented by critical concentrations for redox-sensitive solutes which control whether a redox process is initiated or not. For the different reduction processes, controlling critical concentrations are listed in Table 1. Table 1 must be red row-wise, where entries “>0” mean that the corresponding redox-sensitive reactant (column) must be available and “-“ means that this process does not depend on the presence of the redox-sensitive compound. For example iron(III) reduction in the biogeochemical simulation is only initiated if: (1) Dissolved oxygen concentrations fall below ; (2) Most of the nitrate is already depleted and actual concentrations fall below  ; and (3) The electron acceptor iron(III) is available. Table 1: Critical concentrations which are controlling the sequential initialization of the redox sequence. Values were derived from field observations. Table must be read row-wise (e.g. denitrification is initiated if 1) oxygen contents drop below Ccrit for oxygen and 2) if nitrate is present). = 5.0 x 10-6 mol/L;  = 4.0 x 10-7 mol/L; = 5.0 x 10-6 mol/L. The critical concentrations were formulated based on evaluation of depth profiles for redox-sensitive solutes which were taken at the Schlöpnerbrunnen II site in the Lehstenbach catchment (Knorr and Blodau, 2009; Knorr et al., 2009). Intervals for the activation of redox processes are overlapping, meaning that multiple processes can occur simultaneously which can be approved under laboratory as well as under field conditions (Knorr and Blodau, 2009; Knorr et al., 2009). Availability of oxygen can be seen as a key component, controlling the process composition within wetland ecosystems. Processes like aerobic respiration or nitrification only occur if oxygen is oxygen nitrate iron(III) Sulfate aerobic respiration >0 - - - denitrification   >0 - - iron(III) reduction      >0 - sulfate reduction        >0 [30] MATERIALS AND METHODS available. Other processes, like denitrification iron(III)- or sulfate-reduction are only initiated under anoxic conditions where oxygen concentrations are very low. Along a subsurface flow path, availability of oxygen varies as the hydrological boundary conditions change. Within the unsaturated zone, depleted oxygen is being replaced by diffusion of atmospheric oxygen and availability of oxygen for microbial catalyzed reactions is high. In the saturated zone dissolved oxygen concentrations are low because the resupply by diffusion is being inhibited by pore water, which acts as an effective diffusion barrier. Therefore, in the biogeochemical model oxygen availability was used as a key variable that either triggers or suppresses redox-sensitive processes. Along a sub-surface flow path, availability of oxygen was coupled to the transient pressure heads which were available as part of the virtual wetland modeling. For each PHREEQC sub-section simulation of a sub-surface flow path, the corresponding pressure head was estimated for the start location of the sub-section. Pressure heads were related to a certain oxygen concentration according to Figure 8. If the pressure head of the sub-section is located within zone 1 (unsaturated zone with negative pressure heads), the oxygen availability is at a maximum due to the uninhibited diffusion of atmospheric oxygen. Within zone 2 (saturated zone with positive pressure heads), oxygen contents are decreasing with increasing pressure heads representing increasing inhibition of oxygen diffusion with depth. Oxygen concentrations in sub-section simulations that are located either within zone 1 or 2 were set to a constant value reflecting that rapid resupply of oxygen prevents its depletion by oxygen consuming processes. For sub-sections that are located within zone 3 (deeper saturated zone with pressure heads above 0.25 m) oxygen is not assigned as a constant boundary condition. Instead, oxygen is set as an initial condition where the residual oxygen contents of the preceding sub-section are used as initialization. Within zone 3, where atmospheric diffusion is disrupted, oxygen can be totally depleted due to oxygen consuming processes. The relationship shown in Figure 8 was derived from observed oxygen-depth profiles taken at the Schlöppnerbrunnen II site in the Lehstenbach catchment (Knorr et al., 2009). Aside from an adequate electron acceptor (e.g. oxygen, nitrate, iron(III) or sulfate), microbial catalyzed reduction processes require a carbon source that is available to microorganisms. For the carbon rich systems studied here unlimited availability of carbon was assumed. [31] MATERIALS AND METHODS Figure 8: Typical oxygen depth profile observed for a wetland site of the Lehstenbach catchment. Profile was used to assign oxygen boundary conditions to the different PHREEQC sub-section simulations based on transient model output of the virtual wetland model. [32] [33] RESULTS AND DISCUSSION 4 Results and Discussion 4.1 Effects of micro-topography on surface-subsurface exchange and runoff generation in a virtual riparian wetland (Study 1) Results from the virtual wetland modeling indicate that hydrological dynamics and runoff generation processes within the riparian wetland are significantly affected by the wetland’s hummocky topography. Surface and subsurface runoff generation are influenced by distinct shifts between surface and sub-surface flow dominance resulting from the interplay between rainfall-induced fluctuations of the shallow water table and the surface micro-topography. Surface flows are characterized by a fill and spill mechanism, similar to what has been described for shallow subsurface drainage of hillslopes (Hopp and McDonnell, 2009). Here, surface depressions (hollows) are filled with water as soon as the groundwater level intersects with the land surface (e.g. during intensive rainstorm events).With increasing rainfall intensity ponded depressions start to interconnect, forming distinct surface flow networks which develop independently in space and time (as shown in Figure 9). These networks can rapidly drain large areas of the wetlands and at times (during very intensive rainstorm events) contribute up to 80% of the total discharge that is generated from wetlands. Figure 9: Six consecutive snapshots of the evolving surface flow networks during the largest flow event of the year (day 217 to day 218). The red lines separate different flow networks (1-3) that developed independently from each other. However, whether such surface flow networks develop in space and time and whether surface runoff is generated in the wetlands depends on the history of the system. For rainstorms occurring after extended dry periods in summer, surface flow networks may not be generated because groundwater levels in the wetland are too far below the land surface to generate surface ponding. On the contrary, a [34] RESULTS AND DISCUSSION rainstorm of the same or even lesser intensity may cause the generation of significant surface runoff if it occurs with wet preconditions. The simulated runoff dynamics can also explain observed non-linear and hysteretic relationships between the riparian groundwater level in the wetlands and discharge being generated from it (Figure 10). The dynamic runoff generation mechanism, which is controlled by micro-topography where the system rapidly shifts between surface and subsurface flow dominance, was identified as a main driver for the observed non-linear dynamics. Similar non-linear relationships between water table and discharge have been reported for wetlands and riparian zones in other parts of the world (e.g. Fitzgerald et al. (2003)). Understanding the mechanisms that govern hydrologic flow paths and stream flow generation in riparian zones is important, because nutrient transformation and export are integrally related to the hydrological dynamics (Gillham, 1984; Devito and Hill, 1997; Vidon and Hill, 2004; Lischeid et al., 2007). Although mobilization of solutes has not been explicitly simulated in study 1, the microtopographic controlled runoff generation can have significant implications for the export of solutes (e.g. DOC, nitrate or sulfate) from the wetlands. Fast flow components like rapid surface drainage due to the extensive surface flow networks or shallow subsurface flow have the potential to quickly (within minutes to hours) mobilize solutes from the uppermost layers (10 to 20 cm) of the wetlands. Field observations (Knorr and Blodau, 2009; Knorr et al., 2009) for the Lehstenbach catchment have shown that these superficial layers, which are typically unsaturated, are rich in oxic species that accumulate during drier periods such as nitrate or sulfate. During rainstorms, which trigger generation of rapid surface and shallow subsurface drainage, these species can be flushed from the system. Along these very fast flow pathways nitrate and/or sulfate are not being reduced because deeper, anoxic layers are being bypassed by the superficial runoff components. In its effect on the mobilization of redox-sensitive solutes, this mechanism operates the same way as other bypassing processes that have been described for the Lehstenbach catchment (Lischeid et al., 2007) and for other comparable ecosystems (Curtis et al., 2011). Similar dynamics apply to the mobilization of DOC because its concentrations are also highest in the uppermost layers where fresh organic material is available and the peat is less decomposed than in deeper layers (Clemens, 2011). The mechanistic understanding on how runoff is being generated on the small scale in the wetland areas and how the different flow components with their individual response and residence times contribute to stream flow generation is crucial to identify which flow pathways are important for solute mobilization. Findings from study 1 were subsequently used to develop a catchment-scale conceptional model for DOC mobilization presented as part of study 4. Moreover, findings that surface flow generation in the wetland areas is strongly influenced by micro-topography are important to simulate the catchment-scale hydrological dynamics (study 5), because rather than as sheet flow, surface flow in the catchment is generated in discrete surface flow networks in a threshold-controlled process, which must be accounted for in larger scale models (study 5). This was done by applying the rill storage concept developed as part of study 3. [35] RESULTS AND DISCUSSION Figure 10: a) Relationship between discharge and groundwater level for two peak flow events, observed for a small catchment located in British Colombia, Canada (modified after Fitzgerald et al. (2003)).b) Simulated relationship between groundwater level and channel discharge for the microtopography model. Blue filled circles represent times when no surface drainage occurs, red open circles represent conditions when surface drainage is being generated; different scales are used on the x-axis for better visibility of hysteretic behavior during low discharges; the sequence of days 217 to 219, representing an intense rain storm, is depicted by a line. [36] RESULTS AND DISCUSSION 4.2 Surface micro-topography causes hot spots of biogeochemical activity in wetland systems – a virtual modeling experiment. (Study 2) Results from particle tracking show that superficial micro-topographical structures of the wetland cause a complex subsurface flow field with shallow and deeper flow cells that transport water and solutes across the model domain (Figure 11 A). The spatial distribution of high points (hummoks) and depressions (hollows) results in small-scale patterns of inand exfiltration. Hummocks generally represent areas of preferential infiltration and hollows zones of preferential exfiltration (Figure 11 A). The coexisting deep and shallow flow system shows distinctly different flow velocities and subsurface residence times (Figure 11 B). The resulting complex redistribution of water in the subsurface and residence times, ranging from a few days to years, have significant effects on biogeochemical process patterns and the spatial distribution of redox-sensitive compounds in the wetlands. Biogeochemical simulations show the formation of local hot spots for redox processes within the wetlands. They are the result of the complex subsurface flow paths and the transport-limited availability of electron acceptors and donors. Hot spots for reduction of redox-sensitive species (e.g. denitrification, iron(III)- and sulfate reduction) are preferentially generated below local hummocks (Figure 11 C), whereas oxidation hot spots form in zones of upwelling water below hollows where older, reduced groundwater gets in contact with atmospheric oxygen (Figure 11 D). Findings from study 2 mechanistically prove the existence of localized zones of higher reactivity (hot spots) where most of the biogeochemical turnover is accomplished within wetland system. This has been observed before in various field studies (e.g. Jacks and Norrström, 2004; Paul et al., 2006; Knorr, 2009). Typically, the generation of such hot spots has been explained by the heterogeneous distribution of static, physical-chemical properties of the soil (Reeve et al., 2001; Holden and Burt, 2003) or labile carbon input in the rhizosphere (Crow and Wieder, 2005). However, results from the biogeochemical simulations in this study demonstrate that the occurrence of reactivity hot spots does not need to be associated with static physical-chemical soil heterogeneities a priori. Results have shown that hot spots could theoretically develop even in homogenous peat soils due to a highly dynamic flow system with (1) complex surface/subsurface flow interactions, where surface microtopography induces a subsurface flow field that defines a small-scale zonation of inand exfiltration areas and (2) a hydrological control of the biogeochemical boundary conditions that either facilitated or suppressed redox processes in exand infiltration areas. These results present a new perspective on biogeochemical transformation processes in riparian wetlands, which provides a dynamic framework to explain process heterogeneity in wetland soils and variability in process rates over space and time. Formation of biogeochemical hot spots as a result of the mechanisms presented in this study may furthermore explain how material heterogeneity is being generated within the subsurface. Biogeochemical hot spots may have the potential to alter the hydrodynamic properties of the peat or wetland soils. The precipitation of iron oxides for example, [37] RESULTS AND DISCUSSION which preferentially occurs at oxidation hot spots, can lead to a reduction of the effective porosity and a lower hydraulic conductivity, providing a negative feedback on oxygen penetration. Future work will have to address under which climatic conditions the simulated biogeochemical hot spots are stable, because shifts in climatic forcing due to climate change will probably affect the in study 1 simulated surface/subsurface flow interactions as well as the sub-surface flow field. This will in turn affect the oxygen availability and the biogeochemical process distributions within the wetlands. During extended drought periods for example, which are predicted by climate models for the temperate zones (McCarty et al., 2001), biogeochemical hot spots are likely to vanish as the system gradually shifts towards a more homogenous process distributions. Here, the dropping groundwater may be responsible for the reversal of the hydraulic gradients under depressions, switching from upwelling to infiltrating conditions. In turn oxidation hot spots will diminish because resupply of reduced species from upwelling groundwater is disrupted. The effect of the biogeochemical process patchiness on solute exports (e.g. nitrate or sulfate) out of the wetland areas has also to be investigated further. Because of model limitations it was so far not possible to link the internal biogeochemical process distributions to the runoff generation mechanisms presented in study 1 in order to explicitly simulate solute exports under conditions of hot spot formation. Such an integrated simulation would also help to further improve the in study 4 presented conceptual model on catchment-scale solute mobilization. [44] RESULTS AND DISCUSSION 4.5 Interpreting flow generation mechanisms from integrated surface watergroundwater flow models of a riparian wetland and catchment (Study 5). The Hydraulic Mixing-Cell (HMC) methodology (Partington et al., 2011 and Partington et al., 2012) has proven to be a useful tool for assessment of catchment functioning and separation of flow hydrographs. Applied to the catchment scale model of the Lehstenbach, the HMC method elucidated the complexity in the spatiotemporal distribution of the different runoff generation mechanisms. The different flow components which were identified to dominate runoff generation for the Lehstenbach catchment are, (1) groundwater discharge to the stream network (GW-CH), (2) direct rainfall entering the streams (RF-CH) and (3) stream inputs due to saturated overland flow from the wetland areas. Overland flow from the riparian wetlands was further sub-divided into a surface flow fraction originating from groundwater exfiltration (GW-WL) and overland flow generated from rainfall falling onto entirely water saturated areas of the wetlands (RF-WL). Relative contributions of the five different runoff generation mechanisms are tracked in time and space by the HMC routine. The HMC routine was applied for a large storm event (13th21st July, 2001) as well as for the entire 2001 hydrological year (11/01/2000 – 10/31/2001). Results for the storm event are shown in Figure 14. The GW-CH component (panel A) dominates runoff generation over large areas of the stream network prior to the storm event during low flow conditions. At the peak of the storm, GW-CH generation is of minor importance as other generation mechanisms are activated (RF-CH, GW-WL and RF-WL in panel B, C and D, respectively). Most overland flow that contributs to stream discharge during the storm event is generated due to rainfall, which is directly falling onto the fully water saturated wetland areas as indicated by the high relative fraction of the RF-WL component in panel B. On an annual basis, total stream water leaving the catchment at the outlet (shown in Figure 5), according to the HMC analysis, consists of 67.9% of water originating from groundwater inputs (GW-CH), 12.6% of direct rainfall to the stream network and 19.5% of saturated overland flow from the wetland areas (GW-WL + RF-WL). However, overland flow was identified to be only relevant during very intensive rainstorm events and is only generated in significant proportions in the areas of wetland that are close to the catchment outlet. According to the HMC analysis, no overland flow is generated in the forested areas because rainfall quickly infiltrates there and recharges the underlying regolithic aquifer. [45] RESULTS AND DISCUSSION Figure 14: Calculated stream and overland flow generation, estimated by applying the “hydraulic mixing-cell” methodology to the Lehstenbach catchment model. The flow generation components tracked are: a) groundwater discharge to the channel (GW-CH), b) rainfall to the channel (RF-CH), c) groundwater discharge to the wetlands (GW-WL), and d) rainfall to the wetlands (RF-WL). Relative contributions (colored scales ranging from 0 to 1) were tracked for a typical storm flow event. [46] RESULTS AND DISCUSSION However, the response times of subsurface flow entering the stream network (GW-CH) to rainfall seems to be very fast. This can be explained by the “pressure wave propagation” mechanism (Germann et al., 1990; Lischeid, 2008), where increasing hydraulic pressure in the upslope areas mobilizes groundwater further downslope (e.g. in the center of the bowl-shape catchment). Interestingly, surface flow components from the wetland areas (GW-WL + RF-WL), which are usually regarded as very fast flow components, show a clearly delayed response to rainfall inputs. This can partly be explained by the threshold-controlled “fill and spill” surface flow generation mechanisms described in study 1 where small scale depressions first have to be filled with water before any surface flow towards the stream is generated. The threshold-controlled surface flow generation caused by the micro-topography has been accounted for in the catchment-scale model by applying the rill-storage height concept presented in study 3. Whether simulated fluxes from the individual runoff generation processes as estimated by the HMC analysis actually match with the more complex reality is questionable due to simplifications in the model. For example, direct rainfall inputs to the stream network are presumably negligible as streams cover only a minor fraction of the catchment area (Lischeid, 2008). In the catchment scale model, however, stream segments occupy comparatively large fractions of the total area because the resolution of the numerical mesh was too coarse to adequately represent the narrow stream channels. This explains the large fractions of the RF-CH component (12.6% of total discharge per year) in the simulated discharge. Nevertheless, the HMC method in combination with numerical modeling provides a valuable tool to assess whether or not a catchment model behaves in the expected way or, more importantly, the way the catchment processes are conceptualized. In that sense it is a promising and useful tool for a “soft calibration” based on understanding of catchment functioning from real observations. A future application of the HMC method in combination with the catchment-scale model of the Lehstenbach area could be to separate runoff components originated from forested sites and wetland areas. Water originating from the wetlands and forested sites have very different chemical signatures (e.g. oxygen saturation, redox states or DOC loadings) which are being mixed within the stream or the hyporheic zone. Applying the HMC analysis to track how much water in stream runoff is originated from the wetlands and forested sites would improve our understanding on the relative contributions of different flow paths to stream discharge and solute exports. [47] CONCLUSIONS AND OUTLOOK 5 Conclusions and Outlook Findings from this thesis have shown that the combination of field investigations, virtual experiments and catchment scale numerical modeling has proven to be a very useful combination to investigate and explore scale-dependent runoff generation processes and couplings between hydrology and biogeochemistry. On the catchment scale, couplings between hydrology and biogeochemistry were identified to be very important for the mobilization of DOC. Flow components, relevant for the generation of runoff in the Lehstenbach catchment, contribute differently to the mobilization of DOC. Deep groundwater flow originated from the forested upslope areas was identified to be generally low in DOC, mainly because percolating water for forested sites is being efficiently depleted in DOC due to sorption and biogeochemical decomposition processes. Fast flow components like surface flow or interflow, which would have the ability to bypass soil layers where sorption and decomposition occur, could not be verified for the forested areas, neither in field investigations nor in numerical simulations. Field investigations and numerical modeling indicate that the potential for DOC mobilization is highest for flow components located within the riparian wetlands. Mobilization of DOC within the riparian wetlands is controlled by the interplay of (1) the transmissivity feedback mechanism controlling the depth dependent dynamics and timescales of subsurface transport, (2) a thresholdcontrolled surface flow generation where, episodically, large amounts of surface water are rapidly being mobilized in extended surface flow networks and (3) the depth dependent availability of DOC caused by the lateral variation of DOC production and the non-uniform biogeochemical transformation and degradation processes. Episodically, the activation of fast flow components in shallow layers and/or on the surface is responsible for the mobilization of large amounts of DOC, which can explain observed short term variations of DOC concentrations in runoff. This conceptual view on how DOC is being mobilized at the catchment-scale relates physical controlled mobilization pathways to the biogeochemical substrate availability and includes scalebridging insights on DOC mobilization and runoff production. Hydrological and biogeochemical process interactions, identified to be relevant for the mobilization of DOC in the Lehstenbach catchment, are, in our opinion, of general significance and can be transferred to similar ecosystems. However, this conceptional view on how DOC is being mobilized in the Lehstenbach catchment has to be further improved and verified. Recent field investigations (Knorr, 2012) e.g. show that timescales of complexation and de-complexation of DOC with dissolved iron in addition to iron reduction/oxidation cycles significantly control the availability of DOC, especially in the superficial layers of the wetlands. This so far has not been accounted for in the developed conceptual model. Also, the significance of the interplay between different hydrological flow paths with their individual response and residence times combined with the spatial heterogeneity of biogeochemical conditions [48] CONCLUSIONS AND OUTLOOK (forested sites vs. wetlands) on nutrient cycling and solute mobilization is, in general, so far only still poorly understood and must be further addressed in future work. First preliminary results for nitrate (unpublished data) for example hint that the mobilization processes across the catchment differ significantly from those identified for DOC, mainly because spatial sources of nitrate and biogeochemical transformation processes along the flow paths are different for nitrate compared to DOC. On the small scale, results from the developed hydrological and biogeochemical model, where subsurface transport processes and kinetically controlled redox-sensitive reactions are represented equally, highlight how complex couplings between hydrology and biogeochemistry can be within wetland ecosystems. One of the most interesting results of this thesis is that biogeochemical hot spots can form even in homogenous peat or wetland soils, simply as a result of the interactions between a highly dynamic, three-dimensional subsurface flow system induced by micro-topography and the hydrologically controlled biogeochemical boundary conditions that either facilitate or suppress redoxsensitive processes. Results from this modeling approach offer a new perspective on biogeochemical transformation processes in riparian wetlands which provides a dynamic framework to explain process heterogeneity in wetland soils and variability in process rates over time and space. A next step would be to approve that the simulated mechanisms and interactions between hydrology and biogeochemistry actually can result in the formation of biogeochemical hot spots under field conditions. This is a challenging task because characterization of subsurface flow patterns in situ, necessary to investigate interactions between hydrology and biogeochemistry, is very difficult and would require improved experimental settings. However, the framework presented as part of this thesis may be helpful to develop such novel in situ experiments. Because of various limitations and simplifications, the hydrological/biogeochemical modeling approach so far is restricted to relative simple test case scenarios. Future work will have to address these shortcomings and improve the modeling framework stepwise in order for it to be applied to more realistic systems and to address topics like the interplay between different static (e.g. soil properties, vegetation patterns) and dynamic controls (e.g. flow, temperature and vegetation dynamics) of spatial and temporal variations in biogeochemical process activities in wetlands. Finally, this thesis has shown that interdisciplinary research efforts, combining the knowledge of hydrologists and biogeochemists, offer new perspectives on how ecosystems are functioning. 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Interactions between biogeochemistry and hydrologic systems. Annual Review of Environment and Resources 34, 65– 96. McCarty, J.J., Canziani, O.F., Leary, N.A., Dikken, D.J., White, K.S., 2001. IPCC Climate Change 2001: Impacts, Adaptation and Vulnerability. The Third Assessment Report of Working Group [60] APPENDIX Figure A2: Saturated hydraulic conductivities Ksat assigned to the ten sub-layers SL1-SL10 of the wetland areas for the catchment scale model. Ksat values are exponentially decreasing (as indicated by the linear decrease using a logarithmic X-axis) with depth to mimic the transmissivity feedback mechanism. Values for used Ksat-values within the wetland areas are based on the study of Jacks and Norrström, (2004). [61] APPENDIX Table A1: Overview of the parameterization of the catchment scale model to represent surface/subsurface flow and interactions for the three different zones (wetlands, upslope areas and stream areas). a. Subsurface Wetlands Upslope Areas saturatedhydraulic conductivity[m/d] variablewith depth(see FigureA2) 0.24  porosity[‐]0.5 0.4 specificstorage[m‐1]0.0001 0.0001 b. SurfaceWetlands Upslope AreasStream surfacestorage[m]0.1;0.5;1.00.010.0 couplinglength[m]0.10.10.0001 frictionslopesXandY[m‐1/3s]8.1x10‐71.9x10‐64.0x10‐7 c. EvapotranspirationWetlands Upslope Areas leafAreaIndex[‐]3.06.5 rootdepth[m] (quadraticdecayfunction)0.83.0  evaporationdepth[m] (quadraticdecayfunction)0.50.5    [62] [63] CONTRIBUTIONS TO THE INCLUDED MANUSCRIPTS 8 Contributions to the included manuscripts Study 1 Effects of micro-topography on surface-subsurface exchange and runoff generation in a virtual riparian wetland – a modeling study. Authors: Sven Frei, Gunnar Lischeid and Jan H. Fleckenstein Sven Frei: concepts, modeling, interpretation and discussion of results, manuscript preparation Gunnar Lischeid: comments on manuscript, field site coordinator, field data Jan H. Fleckenstein: concepts, discussion of result, manuscript preparation Study 2 Surface micro-topography causes hot spots of biogeochemical activity in wetland systems – a virtual modeling experiment. Authors: Sven Frei, Klaus-Holger Knorr, Stefan Peiffer and Jan H. Fleckenstein Sven Frei: concepts, modeling, interpretation and discussion of results, manuscript preparation Klaus-Holger Knorr: field data, concepts, interpretation and discussion of results, manuscript preparation Stefan Peiffer: comments on manuscript, discussion of results Jan H. Fleckenstein: interpretation and discussion of results, manuscript preparation Study 3 Representing effects of micro-topography on runoff generation and subsurface flow patterns by using superficial rill storage height variations. Authors: Sven Frei, and Jan H. Fleckenstein Sven Frei: concepts, modeling, interpretation and discussion of results, manuscript preparation Jan H. Fleckenstein: concepts, discussion of results, manuscript preparation [64] CONTRIBUTIONS TO THE INCLUDED MANUSCRIPTS Study 4 Concentrations and fluxes of dissolved organic carbon in runoff from a forested catchment: Insights from high frequency measurements Authors: Stefan Strohmeier, Klaus-Holger Knorr, Martin Reichert, Sven Frei, Jan H. Fleckenstein, Stefan Peiffer and Egbert Matzner Stefan Strohmeier: concepts, interpretation and discussion of results, manuscript preparation, modeling Klaus-Holger Knorr: interpretation and discussion of results, laboratory work Martin Reichert: field work, laboratory work Sven Frei: modeling, interpretation and discussion of results, comments on manuscript Jan H. Fleckenstein: comments on manuscript Stefan Peiffer: comments on manuscript Egbert Matzner: manuscript preparation, concepts, interpretation and discussion of results Study 5 Interpreting flow generation mechanisms from integrated surface water-groundwater flow models of a riparian wetland and catchment. Authors: Daniel Partington, Philip A. Brunner, Sven Frei, Craig T. Simmons, Adrian D. Werner, René Therrien, Holger R. Maier, Graeme C. Dandy and Jan H. Fleckenstein Daniel Partington: concepts, coding, modeling, interpretation and discussion of results, manuscript preperation Philip A. Brunner: concepts, discussion of results, comments on manuscript Sven Frei: development of flow models, discussion of results, manuscript preperation Craig T. Simmons: discussion of results, comments on manuscript Adrian D. Werner: comments on manuscript René Therrien: concepts, discussion of results, comments on manuscript Holger R. Maier: comments on manuscript Graeme C. Dandy comments on manuscript Jan H. Fleckenstein comments on manuscript [65] STUDY 1 Study 1 Effects of micro-topography on surface-subsurface exchange and runoff generation in a virtual riparian wetland – a modeling study. By Sven Frei, Gunnar Lischeid and Jan H. Fleckenstein Published in Advances in Water Resources 33 (2010) 1388-1401 [66] [67] STUDY 1 Published in Advances in Water Resources 33 (2010) 1388-1401 Effects of micro-topography on surface-subsurface exchange and runoff generation in a virtual riparian wetland – a modeling study. Frei1, S., G. Lischeid2 and J. H. Fleckenstein3 1 Department of Hydrology, University of Bayreuth, Germany 2 Leibnitz Centre for Agricultural Landscape Research, (ZALF), Germany 3 Department Hydrogeology, Helmholtz-Center for Environmental Research – UFZ, Germany Abstract In humid upland catchments wetlands are often a prominent feature in the vicinity of streams and have potential implications on runoff generation and nutrient export. Wetland surfaces are often characterized by distinct micro-topography (hollows and hummocks). The effects of such microtopography on surface-subsurface exchange and runoff generation for a 10 by 20 m synthetic section of a riparian wetland were investigated in a virtual modeling experiment. A reference model with a planar surface was run for comparison. The geostatistically simulated structure of the microtopography replicates the topography of a peat-forming riparian wetland in a small mountainous catchment in South-East Germany (Lehstenbach). Flow was modeled with the fully integrated surface-subsurface code HydroGeoSphere. Simulation results show that the specific structure of the wetland surface results in distinct shifts between surface and subsurface flow dominance. Surface depressions fill and start to drain via connected channel networks in a threshold-controlled process, when groundwater levels intersect the land surface. These networks expand and shrink in a spill and fill mechanism when the shallow water table fluctuates around the mean surface elevation under variable rainfall inputs. The micro-topography efficiently buffers rainfall inputs and produces a hydrograph that is characterized by subsurface drainage during most of the year and only temporarily shifts to surface flow dominance (> 80% of total discharge) during intense rainstorms. In contrast the hydrograph in the planar reference model is much “flashier” and more controlled by surface runoff. A non-linear, hysteretic relationship between groundwater level and discharge observed at the study site was reproduced with the micro-topography model. Hysteresis was also observed in the relationship between surface water storage and discharge, but over a relatively narrow range of surface water storage values. Therefore it was concluded that surface water storage was a better predictor for the occurrence of surface runoff than groundwater levels. [68] STUDY 1 1 Introduction Riparian zones contain dynamic interfaces between groundand surface water flowpaths [10, 27]. It is important to understand the mechanisms that govern hydrologic flowpaths and stream flow generation in riparian zones because nutrient transformations and export are integrally related to the hydrologic dynamics [8, 53, 18, 34]. However, these dynamics can be quite complex [53, 27] and are generally poorly understood [49, 28]. In humid temperate climates riparian zones are often occupied by wetlands [34, 36, 27]. Rapid surface and shallow subsurface flows typically dominate runoff generation in riparian wetlands during rainstorms [8, 34]. Gibson et al. [17] showed that runoff dynamics highly depend on surface storage and interactions between surface water and shallow groundwater. Kværner and Kløve [27] identified distinctly different runoff generation processes with shifts between subsurface and surface flow dominance for low and high flow events. Non-linear relationships between riparian water table depth and stream flow have often been observed [5, 15, 38, 44]. For catchments dominated by matrix flow these relationships have been attributed to the transmissivity feedback mechanism [4, 3, 44]. Stream flow originating from matrix flow increases exponentially, when the water table rises into soil layers with progressively increasing lateral hydraulic conductivity [3, 44]. In systems where shifts between matrix flow and surface flow dominance occur, additional dynamics and non-linearities have been observed (e.g. [27]). Peat-forming wetlands are often characterized by a hummocky topography with sequences of high points (hummocks) and depressions (hollows) at the sub-meter scale, which will affect runoff generation during transitions between surface and subsurface flow dominance. Effects of microtopography on infiltration and runoff generation processes were first investigated by Dunne et al. [9]. They showed that hill slope runoff was controlled by an intricate interplay between rainfall intensity, surface flow depth, vegetation cover and the specific micro-topography of the slope. Microtopography can attenuate and delay surface flows [36, 27], because surface depressions first need to be filled until a specific surface water storage threshold is exceeded and then surface flow towards the stream channel can be initiated [2, 12]. Tromp-van Meerveld and McDonnell [51] and Tromp-van Meerveld and McDonnell [52] termed similar threshold dynamics in the generation of subsurface stormflows on bedrock surfaces with micro-topography the "fill and spill mechanism". Qu and Duffy [40] reported distinct double peaks in hydrographs from single rainfall events, which they ascribed to complex interactions between small scale micro-topography controlled surface runoff in the wetland and subsurface flow. Several modeling studies have addressed the effects of micro-topography on runoff dynamics. Dunne et al. [9] used a conceptual approach to simulate overland flow and infiltration processes for uniform [69] STUDY 1 sinusoidal micro-topography. They demonstrated that micro-topography resulted in significant spatial variability of infiltration and surface flows. Esteves et al. [11] and Fiedler and Ramirez [12] used finite difference solutions to the two-dimensional depth-averaged dynamic wave equations to simulate overland flow and infiltration processes on small plots with micro-topography. Both studies showed that micro-topography strongly affects flow directions, flow velocities and flow depths and resulted in surface flow along well defined micro-channels. Connectivity indicators for surface flow on plots with micro-topography were systematically investigated with a numerical model by Antoine et al. [2]. Each of the aforementioned modeling studies were restricted to surface flows and infiltration and did not account for feedbacks between surface and subsurface flow, an important process in wetlands [8, 17]. An exception was the study by Qu and Duffy [40], who used a finite element coupled surfacesubsurface flow model to simulate a series of rainfall events for a 0.08 km2 watershed in Pennsylvania. They demonstrated how small scale topography can control local surface saturation and subsequent connectivity of surface flow paths leading to stream flow generation. However the spatial resolution of the Qu and Duffy [40] model was too coarse to account for micro-topography on the sub-meter scale. Hopp and McDonnell [20] modeled the effects of bedrock micro-topography on subsurface storm flow generation from hillslopes. Our work evaluates the complex hydrologic dynamics of a riparian wetland with micro-topography through a virtual modeling experiment. The purpose of the simulations is to examine process dynamics rather than calibration of a model to a specific field site. We argue that to accurately describe these dynamics a numerical model has to account for overland flow, variably saturated subsurface flow and complex interactions between the surface and subsurface domains. A fullyintegrated modeling approach simultaneously solves all of the equations that govern the complex interactions between surface and subsurface. Efficient numerical models that use the fully-integrated approach have become available in recent years (e.g. [25, 50]). The fully integrated, threedimensional numerical flow model HydroGeoSphere [50] is used here to examine hydrologic dynamics in a virtual riparian wetland with distinct micro-topography (hummocks and hollows). The micro-topographic relief is geostatistically generated for a 10m x 20m area at a resolution of approximately 0.1m based on surveyed micro-topography in a riparian wetland of the small experimental Lehstenbach catchment located in Germany (Figure 1). The wetlands in the catchment, which have a hummocky surface topography, can be classified as fens. The relative elevation differences between hollows and hummocks range between 0.2-0.4 m and the hollows are generally inter-connected. Inflows from deeper groundwater are locally diminished by a basal clay layer. At several locations lateral inflows from adjacent hillslopes are intercepted by small stream channels bounding the wetlands. Most small streams have their headwaters in the wetlands and practically all the water that reaches the streams either originates in or passes through the wetlands. [76] STUDY 1 condition at the channel outlet. All other boundaries were set to no-flow boundaries with the exception of the upper model surface where variable rainfall rates are applied. The initial groundwater elevation was prescribed as 0.5 m above the horizontal base of the model with an equilibrium pressure distribution above the water table. Daily precipitation was applied to the model surface based on the rainfall record from the 2000 hydrologic year (November 1999 through October 2000). The surface domain was initialized with a zero depth of ponded water representing dry initial conditions. The friction slope for surface flow calculations is described using Manning's equation. Manning's roughness coefficients for the peat surface were uniformly assigned as 0.03 m-1/3s for x and y; a value reported for densely vegetated surfaces [45]. [77] STUDY 1 3 Results 3.1 Dynamics of runoff generation for steady rainfall To investigate the general dynamics of discharge generation under increasing wetness, a simulation with a constant rainfall rate of 0.008 m/d was run until the steady state discharge at the channel outlet was attained. The rainfall rate represented conditions for a moderate to intense rainstorm (exceeded on about 40 days per year for a typical hydrologic year) and ensured that surface flow networks could develop before the final steady state was reached. Figure 4 shows the development of channel discharge and the water table (evaluated at an observation well at the up-stream end of the model domain – see Figure 3) for the planar (upper panel) and the micro-topography model (lower panel) respectively. In the initial stage of both simulations channel discharge gradually increased from subsurface inflows caused by increasing hydraulic gradients towards the channel. The increase was more rapid in the planar model compared to the micro-topography model. The slower and slightly undulating increase in the latter case was caused by the progressive formation of ponds when the water table intersects local surface depressions. At this point the build-up of subsurface gradients towards the channel was slowed. The same input of subsurface heads below the ponds increased less rapidly as if the same amount of water had infiltrated (due to the porosity). Surface flow in the planar model, indicated by a steep increase in channel discharge, occurs after approximately 16 days. Discharge subsequently increased rapidly until the system attained a state of equilibrium with constant discharge around day 24. In the micro-topography model isolated ponds at the surface developed connected channel networks, which eventually spilled into the main channel segment around day 45. The subsequent rapid increase in discharge displayed several kinks, which represented the development and maturation of different surface flow networks. The networks eventually all provided water to the channel when equilibrium was reached around day 50. Groundwater levels at equilibrium (evaluated at the location in the upslope center of the domain – see Figure 3) were about 0.18 m below the land surface for the micro-topography model and at the land surface for the planar model. [78] STUDY 1 Figure 4: Hydrographs and development of the local groundwater level for a simulation with constant rainfall (0.008 m/d). Results for the planar model are shown at the top and for the micro-topography model on the bottom. Channel discharge and groundwater level are evaluated at the channel outlet and in an observation well (as shown in Figure 3). [79] STUDY 1 3.2 Runoff dynamics and flow components for variable rainfall Figure 5 shows the simulated discharge hydrograph at the main channel outlet (lower right corner of the domain – see Figure 3) for the micro-topography model. Discharge is separated into a surface and a subsurface flow component. The model-forcing daily precipitation record is depicted on the top axis. The separation of flow components was achieved by placing "hydrograph nodes", which tracked all flow through a node in the grid, along the edged of the channel segment and at the channel outlet (see Figure 3). The surface flow hydrograph nodes tracked surface flow into the channel at each time step of the simulation. The subsurface flow components tracked all flow that exited the model domain (sum of surface and subsurface flows). The difference between the two components represented subsurface flows into the channel segment. Steady rainfall in recharged groundwater, the groundwater levels increased, and the hydraulic gradients to the stream increased, resulting in increased subsurface flows. After initial wetting of the system, surface flow via surface channel networks, was initiated on day 125. Maximum discharge was simulated for day 217 after the most intensive rainfall event in the annual record (48 mm/d). Simulated discharge was generated via subsurface flow during most of the year. Only on 52 of the 365 simulated days was surface flow observed in the model. On these 52 days, surface flow accounted for up to 85% of total channel discharge (see Fitzgerald et al. [17] for a field example). Figure 5: Simulated, yearly hydrograph for the micro-topography model. Precipitation at the field site for the hydrologic year 2000 (10/31/1999 – 11/1/2000) is shown on the top. Surface and subsurface fractions of total channel discharge are shown in green and black respectively. [80] STUDY 1 Figure 6 (panel a) shows a typical situation during periods with low to intermediate rainfall intensities. Water was already ponded in local depressions (hollows) at the surface. However, ponded areas are not all interconnected and surface drainage into the channel segment was inhibited by the micro-topography. Only during high rainfall rates (panel b) did pond areas start to become interconnected and form extended surface flow networks and micro-channels. Under these conditions a large fraction of the wetland surface drained into the adjacent channel. Drainage into the channel occured at two distinct locations (Figure 6). Similar patterns were observed in the fen located at the field site during a rainstorm in the spring of 2009 (Figure 7). Figure 6: Snapshots of the evolving surface flow networks for a) moderate flow conditions (day 180) and b) during peak flow (day 218). Blue zones indicate ponded surface water and yellow arrows stream traces in the surface flow networks. Snapshots show simulated results. Figure 7: Picture of the field site taken during a storm-flow event in spring 2009. Channel location is marked by a line. [81] STUDY 1 Figure 8 shows the simulated discharge hydrograph for the planar model. Compared to the microtopography model, the hydrograph generally showed higher peak discharges. Surface flows were generated much earlier (around day 55) and occured more frequently compared to the microtopography model (75 of 365 simulated days). During the relatively dry summer period between day 150 and 217, rainfall intensities during the six different events were high enough to generate surface drainage. The micro-topography model, in comparison, did not show any surface drainage during this period. In the planar model surface drainage was not inhibited by micro-topographic structures and could occur as sheet flow as soon as the water table intersected the land surface. In the planar model surface flow contributed up to 95% of the total discharge during individual events. Figure 8: Simulated, yearly hydrograph for the planar reference model. Precipitation at the field site for the hydrologic year 2000 (10/31/1999 – 11/1/2000) is shown on the top. Surface and subsurface fractions of total channel discharge are shown in green and black respectively. 3.3 Non-linearities and hysteresis No unique groundwater level or rainfall rate could be associated with the development of surface flow networks and the onset of surface flows. In contrast the amount of ponded surface water, necessary to initiate flow to the channel via the surface flow networks and micro-channels, was narrowly defined. Figure 9 (upper plot) shows the relationship between surface discharge and surface water storage (total amount of ponded surface water in m³ stored in local depression and flow networks). Figure 9 (upper plot) summarizes results for the 365 day simulation for the micro-topography model. The different loops represent different trajectories for single rainfall events. The trajectory for the most [82] STUDY 1 intense rainstorm of the simulated year is discussed in more detail (marked by a line in Figure 9 upper plot). This precipitation event (48 mm/d) occurred right after an extended drier period (day 150 to 217, see Figure 5) on day 217 followed by only 7.2 mm/d on day 218. In the beginning of the rain storm infiltrating rainwater exclusively recharged groundwater (no surface discharge). With rising groundwater levels, local depressions were filled with water and increasingly more water was stored on the soil surface (increasing surface storage without surface flow in the channel). Later, the filled depressions start to interconnect until a critical surface storage value (~5.6m³) was exceeded. The resulting flow network was subsequently large enough to provide first surface flow to the channel. The surface flow rapidly increased until it reached a stable rate of ~3.8 m³/d. After that surface flow abruptly increased as surface storage exceeded another critical value (~7.2m³). This was caused by a second surface flow network that developed and drained independently from the first one. That is illustrated in Figure 10 by different snapshots, taken for five different time steps. [83] STUDY 1 Figure 9: Relationship between surface storage and channel discharge for the micro-topography model (upper panel) and the planar model (lower panel). The black line depicts the peak flow event around day 218. Scrit(max) - Scrit(min) (upper and lower panels) represents the critical range of surface water storage, within which surface flows occur in the yearly simulations. [84] STUDY 1 Figure 10: Six consecutive snapshots of the evolving surface flow networks during the largest flow event of the year (day 217 to day 218). The red lines separate different flow networks (1-3) that developed independently from each other. The lines on the model surface in Figure 10 delineate different surface flow networks, determined by an analysis of overland flow stream traces. The first snapshot shows the situation right at the beginning of the rainstorm on day 217. Due to the preceding drier period only a few, isolated depressions were filled with water. After the onset of rainfall, additional depressions were filled with water and the isolated ponded areas began to interconnect (second snapshot). After 12 hours and 10 minutes (third snapshot) networks 1 and 2 reach their maximum extent, although surface drainage to the channel has not yet been initiated. Although the third snapshot seems to suggest that there were small ponded flow bridges connecting the two networks, an analysis of stream traces showed that there was no surface water exchange. After 12 hours and 20 minutes (fourth snapshot), the first flow network started to drain into the channel causing the first increase in total runoff (Figure 9 upper panel). 7 hours and 10 minutes later (fifth snapshot), the second flow network was activated and starts to spill into the channel resulting in the second rapid increase in discharge (Figure 9 upper panel). Consequently peak discharge (at the end of day 217) occured when both networks were connected to the channel. At that time, the third zone was still not connected to either of the two other networks. This area was characterized by depressions, which remained isolated from the flow networks and where the ponded surface water was immobile during events. The process of growing (during rainfall events) and shrinking networks (during flow recessions) was responsible for the different clockwise loops that can be seen in the relationship shown in Figure 9 (upper panel). In contrast to the wetting process the drying cycle during flow recessions proceeds much more uniformly (Figure 9 upper panel), because it was not characterized by the same stepwise threshold behavior as the wetting [85] STUDY 1 process. This distinctly different behavior of the system during wetting and drying resulted in the observed hysteresis loops. The same behavior was also evident in the development of subsurface and surface flow throughout the event (Figure 12). Subsurface flow showed a gradual increase during wetting of the system, whereas surface flows were initiated at distinct thresholds when specific flow network started to spill into the channel. In contrast recession of surface flow was much more gradual. Figure 11: Simulated relationship between groundwater level and channel discharge for the microtopography model. Blue filled circles represent times when no surface drainage occurs, red open circles represent conditions when surface drainage is being generated, different scales are used on the x-axis for better visibility of hysteretic behavior during low discharges, the sequence of days 217 to 219, representing an intense rain storm, is depicted by a line. [92] STUDY 1 Figure 14: Relationship between discharge and groundwater level for two peak flow events observed for a small catchment located in British Colombia, Canada (modified after Fitzgerald et al. (2003)). 4.3 Limitations and constraints The virtual systems simulated here are a simplification of complex field situations. Peat soils are rarely homogeneous and hydraulic conductivities are usually non-uniform. Retention characteristics of peat soils can be hysteretic. Hence non-linear response of real systems may be caused by several reasons. Furthermore higher effective hydraulic conductivities in some peat soils (e.g. caused by preferential flow) may so efficiently drain a wetland that for a given rainfall rate surface ponding never occurs. Significant inflows from adjacent hillslopes or from deeper groundwater may affect the dynamics of flow in the peat. Most of these aspects were intentionally excluded from this study to highlight the effects of the micro-topography. Although this limits the degree to which the results can be generalized, it provides a new insight into the process dynamics caused by distinct surface microtopography, which is not uncommon for peat-forming wetlands. The structure of micro-topography, hydraulic conductivities of the peat and rainfall rates were taken from a riparian fen in an experimental watershed in Germany and are believed to be representative for other hummocky riparian wetlands in humid climates. Therefore simulation results can provide new insights into the dynamics of runoff generation in such systems that may help to explain other observed non-linear system responses (e.g. [13]). 5 Conclusions Hydrologic systems typically show complex non-linear stream flow response to rainfall inputs. Deciphering the processes that cause the observed response is usually difficult due to the strongly [93] STUDY 1 non-linear behavior of hydrologic systems [57]. Using physically-based numerical models as controlled replicates of natural systems to conduct "virtual experiments" [55] can be a useful tool to elucidate individual processes and their interdependencies (see also Zehe et al. [57]). This approach was used here to investigate the effects of surface micro-topography on runoff generation in a virtual riparian wetland in a humid climate. Simulation results reveal complex threshold processes with stepwise expansions and contractions of surface flow networks that govern stream flow generation. Distinctly different behavior of the system during wetting and drying results in a pronounced clockwise hysteresis in the non-linear relationship between stream flow and riparian groundwater level that resembles similar relationships observed in the field. Simulations for different microtopographies and for a planar reference model show clear differences in the shape of the non-linear relationship and demonstrate how stream flow is moderated by the micro-topography. The planar model does not show significant hysteresis in the stream flow-water table relationship. Results from a model with smaller mean length of the micro-topographic structures (1/2 of the original model) suggest that for decreasing size of the structures the response of the system approaches that of the planar model. A comparison of the model results with results presented by Fitzgerald et al. ([13]) from a field study in a humid riparian wetland in Canada, suggests that the simulated dynamics might provide a consistent explanation for the observed behavior of the system. We hypothesize that the simulated hydrologic dynamics in wetlands with a defined micro-topography can result in a large range of subsurface residence times and dynamic mixing between surface and subsurface water of different age and potentially impact water quality. Preliminary particle tracking simulations, which will be presented in a follow-up paper, support this hypothesis. To what degree the simulated dynamics could provide a new framework to interpret the common variability in stream water chemistry during events that is described in Kirchner's double paradox [24] remains to be investigated. Future work will also address to what degree simplified conceptual representations of surface structures in numerical models (e.g. by defining a rill storage height for larger model cells) can mimic the effects of the micro-topography on surface flow and surface-subsurface exchange. Ackonwledgements The authors would like to thank the anonymous reviewers for constructive comments, which helped to improve the final manuscript. This study was funded by the German Research Foundation (DFG, grant FL 631/6-2). Their financial support is greatly appreciated. The authors also thank Rob MacLaren, Young-Jin Park, Andrea Brookfield and Ed Sudicky at the University of Waterloo, Canada for their invaluable help with the ins and outs of the numerical code HydroGeoSphere. [94] STUDY 1 References [1] Alewell, C.; Paul, S.; Lischeid, G.; Storck, F. R. 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J.; McDonnell, J. J. (2006), Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis, Water Resources Research 42(2):W02411. [53] Vidon, P. G.F.; Hill, A. R. (2004), Landscape controls on nitrate removal in stream riparian zones, Water Resources Research 40(3):W03201, doi:10.1029/2003WR002473. [54] Vogel, H. J.; Hoffmann, H.; Roth, K. (2005), Studies of crack dynamics in clay soil:: I. Experimental methods, results, and morphological quantification, Geoderma 125(3-4):203–211. [55] Weiler, M.; McDonnell, J. (2004), Virtual experiments: a new approach for improving process conceptualization in hillslope hydrology, Journal of Hydrology 285(1-4):3–18. [56] Weissmann, G.S. ,(1999) ,Toward new models of subsurface heterogeneity: An alluvial fan sequence stratigraphic framework with transition probability geostatistics, unpublished Ph.D. thesis, Hydrologic Sciences Graduate Group, Universtiy of California, Davis. [57] Zehe, E.; Becker, R.; Bárdossy, A.; Plate, E. (2005), Uncertainty of simulated catchment runoff response in the presence of threshold processes: Role of initial soil moisture and precipitation, Journal of Hydrology 315(1-4):183–202. [58] Zehe, E.; Blöschl, G. (2004), Predictability of hydrologic response at the plot and catchment scales: Role of initial conditions, Water Resour. Res 40(10):W10202. [59] Zehe, E.; Elsenbeer, H.; Lindenmaier, F.; Schulz, K.; Blöschl, G. (2007), Patterns of predictability in hydrological threshold systems, Water Resour. Res 43(7):W07434. [60] Zehe, E.; Sivapalan, M. (2009), Threshold behaviour in hydrological systems as (human) geoecosystems: manifestations, controls, implications, Hydrol. Earth Syst. Sci 131273–1297. [97] STUDY 2 Study 2 Surface micro-topography causes hot spots of biogeochemical activity in wetland systems – a virtual modeling experiment. By Sven Frei, Klaus-Holger Knorr, Stefan Peiffer and Jan H. Fleckenstein Published in Journal of Geophysical Research Letters - Biogeosiences (in press) [98] [99] STUDY 2 Published in Journal of Geophysical Research Letters – Biogeosiences (in press) Surface micro-topography causes hot spots of biogeochemical activity in wetland systems – a virtual modeling experiment. Frei1, S., Knorr1, K.H., Peiffer1, S., and Fleckenstein2. J.H. 1 Department of Hydrology, University of Bayreuth, Germany 2 Department Hydrogeology, Helmholtz-Center for Environmental Research – UFZ, Germany Abstract Wetlands provide important ecohydrological services by regulating fluxes of nutrients and pollutants to receiving waters, which can in turn mitigate adverse effects on water quality. Turnover of redoxsensitive solutes in wetlands has been shown to take place in distinct spatial and temporal patterns, commonly referred to as hot spots and hot moments. Despite the importance of such patterns for solute fluxes the mechanistic understanding of their formation is still weak and their existence is often explained by variations in soil properties and diffusive transport only. Here we show that surface micro-topography in wetlands can cause the formation of biogeochemical hot spots solely by the advective redistribution of infiltrating water as a result of complex subsurface flow patterns. Surface and subsurface flows are simulated for an idealized section of a riparian wetland using a fully integrated numerical code for coupled surface-subsurface systems. Biogeochemical processes and transport along advective subsurface flow paths are simulated kinetically using the biogeochemical code PHREEQC. Distinct patterns of biogeochemical activity (expressed as reaction rates) develop in response to micro-topography induced subsurface flow patterns. Simulated vertical pore water profiles for various redox-sensitive species resemble profiles observed in the field. This mechanistic explanation of hot-spot formation complements the more static explanations that relate hot spots solely to spatial variability in soil characteristics and can account for spatial as well as temporal variability of biogeochemical activity, which is needed to assess future changes in the biogeochemical turnover of wetland systems. [100] STUDY 2 1 Introduction Wetlands provide important ecohydrological services in many mountainous headwater catchments. They store significant amounts of carbon as peat, and act as effective nutrient sinks e.g. for sulfur, phosphorus and nitrogen [Le Kellogg and Bridgham, 2003; Paul et al., 2006; Tauchnitz et al., 2010]. Redox conditions and the corresponding biogeochemical processes in these wetlands largely control the source and sink functions of peat-soil dominated catchments [Bishop et al., 2004; Lischeid et al., 2007]. Process activities in such wetlands are spatially nonuniform, though, and have been found to form distinct hot spots [Jacks and Norrström, 2004], i. e. areas or patches that show disproportionally high reaction rates relative to the surrounding areas [McClain et al., 2003; Morris and Waddington, 2011]. Such hot spots are not easily identified in the scatter of spatiotemporal datasets and hence their relevance for net matter turnover is assumed to be underestimated [Richardson et al., 2007; McClain et al., 2003; Vidon et al., 2010]. Various studies have observed large variations in the spatial distribution of redox-sensitive solutes within wetland soils [Jacks and Norrström, 2004; McMahon and Chapelle, 2008] on the scale of transects (10-50m) [Jacks and Norrström, 2004] as well as in the meter and sub-meter range [Knorr and Blodau, 2009; Mitchell and Branfireun, 2005; Wachinger et al., 2000]. It seems obvious that complex transport and transformation processes within the subsurface are main drivers for the observed spatial heterogeneity in solute concentrations. Although studies have pointed at potential effects of subsurface flow dynamics in wetlands on solute concentrations, e.g. by enhanced mixing due to hydraulic gradient reversals [Reeve et al., 2006] and the formation of hot spots has conceptually been linked to transport processes [McClain et al., 2003] transport and biogeochemical transformations are rarely combined mechanistically to explain such phenomena. Recent studies in wetlands have mainly attributed the formation of hot spots to lateral variations in local physico-chemical variables such as soil texture, composition, moisture or temperature [Bruland and Richardson, 2005; Morris and Waddington, 2011] or the local availability of certain reactants such as nitrate or DOC [Bruland et al., 2006]. Differences in these properties may e.g. arise from different degrees of peat decomposition, peat compaction, vegetation or surface microtopography [Gafni and Kenneth, 1990; Cheng et al., 2011; Bruland and Richardson, 2005]. This perspective, however, does not consider that microbial processes are dynamic and dependent on variable hydrologic and biogeochemical boundary conditions. The close links between the mechanisms controlling biogeochemical activity in wetlands and the hydrological processes occurring within the wetland have been highlighted in several studies [Morris and Waddington, 2011; Mitchell and Branfireun, 2005]. Field studies [Knorr et al., 2009; Knorr and Blodau, 2009] demonstrated a rapid change of predominant redox processes (i.e. iron(III)-, sulfate reduction and methanogenesis) in a wetland exposed to fluctuations of hydrological boundary conditions during manipulation of the water level. Wetlands in mountainous catchments are often characterized by rapidly fluctuating but [101] STUDY 2 shallow water levels [Devito and Hill, 1997; Lischeid et al., 2007]. Such hydrological conditions facilitate fast flow components like saturation excess overland flow and shallow subsurface flows [Frei et al., 2010; Holden and Burt, 2003]. The dynamics of these flow components are important controls on mobilization of dissolved solutes (e.g. dissolved organic carbon or nitrate) from wetlands [Alewell et al., 2007; Lischeid et al., 2007; Hinton et al., 1998; Dosskey and Bertsch, 1994] but their effect on the biogeochemical processes and distribution of redox-sensitive solutes is still poorly understood and rarely addressed [Shabaga and Hill, 2010]. Partly this is because it is nearly impossible to directly investigate and characterize the complex, dynamic subsurface hydrology in the field. Therefore the interpretation of field observations (e.g. depth profiles for redox-sensitive solutes) may be poorly constrained, e.g. if biogeochemical turnover rates are calculated based on the assumption that resupply of dissolved electron acceptors/donors within riparian wetlands is only diffusion limited [Beer and Blodau, 2007; Clymo and Bryant, 2008]. This simplification may hold true for some sites [Beer and Blodau, 2007] and for defined lab incubations [Knorr and Blodau, 2009], but it neglects that transport and turnover of redox-sensitive solutes at many natural sites occurs within a complex, three-dimensional (3D) subsurface flow field that is subject to variable boundary conditions. This results in distinct flow paths along which biogeochemical reactions can occur, controlled by the individual kinetics of each process [Knorr and Blodau, 2009; Hill, 2000; Brovelli et al., 2011]. An improved mechanistic model for the formation and occurrence of biogeochemical hot spots therefore needs to account for flow and transport processes and how they are affected by changes in hydrologic boundary conditions. This is of particular importance if such a model is used to assess the effects of climate change where induced shifts in the frequency of intense rainstorms or extended droughts [Huntington, 2006] have the potential to significantly alter the boundary conditions within wetlands. Virtual experiments [Weiler and McDonnell, 2004, 2006] have proven to be a suitable tool to investigate complex hydrologic processes and feedback mechanisms between hydrology and biogeochemistry [Frei et al., 2010; Boano et al., 2010; Jakobsen, 2007]. In this study, we use virtual modeling experiments to investigate how complex subsurface flow patterns induced by surface microtopography affect the subsurface transport of redox-sensitive solutes and the resulting spatial distribution of biogeochemical process activities within a hummocky wetland. We test the hypothesis that the complex subsurface flow-field creates biogeochemical conditions in the subsurface that facilitate the formation of local process hot spots even in soils with uniform soil properties. To address this objective, the numerical simulations of complex surface and subsurface flow processes in the hypothetical section of the riparian wetland with pronounced micro-topography (hollows and hummocks) as described by Frei et al., [2010], is combined with advective particle tracking and multi-species biogeochemical simulations in a sequential stream tube approach. The main redox reactions typically found in peat-forming wetlands are simulated along individual subsurface flow [108] STUDY 2 Figure 2: Typical oxygen depth profile based on observations from a riparian wetland site in the Lehstenbach catchment. Profile was used to assign oxygen boundary conditions to the different PHREEQC sub-section simulations based on transient flow model output. All represented reductive processes (detailed information are given in the next paragraph) are treated as reactions catalyzed by microorganisms, comparable to e.g. the process model for methane production in wetlands as shown by Segers and Kengen [1998]. These types of reactions depend on the presence of (a) an adequate electron acceptor (e.g. oxygen, nitrate, iron(III) or sulfate) and (b) a source of labile carbon that is available to microorganisms. As a simplification to reduce model complexity, we considered the electron acceptor as the limiting factor for the presence of the individual catalyzed redox-reactions. We think that this is a reasonable approximation, since the supply of labile carbon (e.g. acetate) may be assumed to be coupled to the organic matter mineralization rate, as usually no intermediates (e.g. from fermentation) accumulate [Segers and Kengen, 1998]. The biogeochemical simulations were thus performed based on that concept, implementing an unlimited carbon source as BC for all sub-section simulations and limiting process rates solely by their kinetic parameters. By dynamically assigning the biogeochemical boundary conditions to each individual sub-section the whole sequence of sub-section simulations for one subsurface flow path, can be viewed as a continuous simulation of the redox-chemical evolution of a small water parcel that carries dissolved redox-sensitive solutes and is transported along that specific flow path. [109] STUDY 2 Implemented Reactions and Kinetics For each sub-section, PHREEQC simulates redox processes as kinetic reactions based on the assigned boundary (BCi) and initial conditions (ICglobal/FCi-1). Implemented processes are shown in Table 2. All reduction processes are formulated based on Monod kinetic reactions according to Equation 4: kks k k k kCK C dt dC R  , max,  (4) Here Rk [ML-3 T -1] is the kinetic rate of the corresponding reduction reaction k (  4,3,2,1k) according to Table 2. kmax,  [ML-3T-1] represents the maximal specific growth rate (for k=1 aerobic respiration, k=2 de-nitrification, k=3 iron(III)- reduction, k=4: sulfate reduction) and Ks,k [ML-3] represents the substrate saturation constant (i.e. substrate concentration for k=1 oxygen, k=2 nitrate, k=3 iron(III), k=4 sulfate at half kmax,  ). Ck [ML-3] is the corresponding concentration of the electron acceptor (for k=1: oxygen, k=2: nitrate, k=3: iron(III); k=4: sulfate). Monod kinetic coefficients ( kmax,  and Ks,k) for all reduction processes are based on values reported for biodegradation of organic chemicals in aquifers [Appelo and Postma, 2005; Bekins et al., 1998; Schirmer et al., 1999; MacQuarrie et al., 1990; Eckert and Appelo, 2002; Kelly et al., 1996; Goldsmith and Balderson, 1988] and were later modified and adjusted as part of the calibration process. Simulated depth profiles for redox-sensitive compounds (nitrate, sulfate and iron(II)) were calibrated by systematic variation of the Monod coefficients to best fit observed data taken at the study site [Knorr and Blodau, 2009; Knorr et al., 2009]. Calibrated Monod coefficients are listed in Table 2. For all processes where organic carbon is being decomposed, organically bound nitrogen is being released according to the Redfield ratio [Redfield, 1934]. Oxidation processes (k=5 iron(II) oxidation, k=6 nitrification, k=7 aerobic sulfide oxidation and k=8 anaerobic sulfide oxidation) were formulated using higher order reaction kinetics as listed in Table 2. [110] STUDY 2 Table 2: Implemented processes and the equivalent reaction specific kinetic rate. Reduction processes are formulated based on Monod type reaction kinetics. Process Rate Coefficients Reference aerobic respiration according to equation 4 1max,  = 1.6 x 10-9 mol/Ls Ks,1 = 2.9 x 10-6 mol/L modified and calibrated after [Appelo and Postma, 2005; Bekins et al., 1998; Schirmer et al., 1999; MacQuarrie et al., 1990; Eckert and Appelo, 2002; Kelly et al., 1996; Goldsmith and Balderson, 1988] denitrification according to equation 4 2max,  = 1.06 x 10-9 mol/Ls Ks,2 = 2.0 x 10-6 mol/L modified and calibrated after [Appelo and Postma, 2005; Bekins et al., 1998; Schirmer et al., 1999; MacQuarrie et al., 1990; Eckert and Appelo, 2002; Kelly et al., 1996; Goldsmith and Balderson, 1988] iron(III) reduction according to equation 4 3max,  = 1.5 x 10-12 mol/Ls Ks,3 = 2.94 x 10-6 mol/L modified and calibrated after [Appelo and Postma, 2005; Bekins et al., 1998; Schirmer et al., 1999; MacQuarrie et al., 1990; Eckert and Appelo, 2002; Kelly et al., 1996; Goldsmith and Balderson, 1988] sulfate reduction according to equation 4 3max,  = 0.5 x 10-10 mol/Ls Ks,4 = 2.5 x 10-6 mol/L modified and calibrated after [Appelo and Postma, 2005; Bekins et al., 1998; Schirmer et al., 1999; MacQuarrie et al., 1990; Eckert and Appelo, 2002; Kelly et al., 1996; Goldsmith and Balderson, 1988] iron(II) oxidation )()()( 2 25 5 5   FecOpOHaA d t dC R A5 = 8 x 10 13 min-1atm-1 [Appelo and Postma, 2005; Stumm and Morgan, 1995] ammonium oxidation )()( 246 6 6OcNHcA dt dC R  A6 =5 x 106 (mol/L)-1 a-1 [Billen, 1982; van Cappellen and Wang, 1996] aerobic sulfide oxidation )()( 27 7 7OcHScA d t dC R  A7 = 1.6 x 105 (mol/L)-1 a-1 [Millero et al., 1987; van Cappellen and Wang, 1996] anaerobic sulfide oxidatio oxidation )()( 3 8 8 8   FecHScA d t dC R A8 = 8 x 103 (mol/L)-1 a1 [Pyzik and Sommer, 1981; van Cappellen and Wang, 1996] [111] STUDY 2 In redox controlled systems like wetlands, reduction processes can be expected to occur sequentially due to thermodynamic reasons (e.g. [Achtnich et al., 1995]). Oxygen is used as primary electron acceptor, and after depletion nitrate, subsequently iron(III) and finally sulfate are being reduced. Further electron acceptors, such as manganese [Nealson and Saffarini, 1994] or organic molecules [Lovley et al., 1996] were not considered in this study. To make sure that the reduction processes proceed sequentially in the biogeochemical simulations, specific redox conditions were defined. These conditions are represented by critical concentrations for redox-sensitive solutes which control whether a redox process can be initiated or not. Critical concentrations Ccrit [ML-3] for oxygen, nitrate and Iron(III) were derived based on observed depth profiles for redox-sensitive compounds [Knorr and Blodau, 2009; Knorr et al., 2009; Estop-Aragonés and Blodau, 2012] For example, the critical concentration for oxygen CcritO2 is the residual concentration of oxygen under which denitrification is being initiated, which was estimated from observed depth profiles and field data. Critical concentrations for oxygen, nitrate and iron(III) are listed in Table 3. The rows of Table 3 represent the conditions under which the different reduction processes are initiated. Entries must be read row-wise, where entries “>0” mean that the corresponding redox-sensitive reactant (column) must be present and “-“ means that this process is independent from the presence of this specific compound. For example iron(III) reduction in the biogeochemical simulation is initiated if: (1) Dissolved oxygen concentrations fall below CcritO2; (2) Most of the nitrate is already depleted where concentrations for nitrate fall below CcritNO3; (3) The electron acceptor iron(III) is available. Intervals for the activation of reduction processes are overlapping which means that multiple processes can occur simultaneously in the simulation; this was also observed in laboratory and under field conditions [Knorr and Blodau, 2009; Knorr et al., 2009]. Table 3:. Critical concentrations which are controlling the sequential initialization of the redox sequence. Values were derived from field observations. Table must be read row wise (e.g. denitrification is initiated if 1. oxygen contents drop below Ccrit derived for oxygen and 2. if nitrate is present). Oxygen Nitrate iron(III) sulfate aerobic respiration >0 - - - denitrification < CcritO2 >0 - - iron(III) reduction < CcritO2 < CcritNO3 >0 - sulfate reduction < CcritO2 < CcritNO3 < CcritFe3+ >0 CcritO2 5.0 x 10-6 mol/L CcritNO3 4.0 x 10-7 mol/L CcritFe3+ 5.0 x 10-6 mol/L [112] STUDY 2 Simplifying Model Assumptions To reduce the complexity of the represented system and to maintain a tractable model the following simplifying assumptions were made: (1) Soil specific parameters (saturated hydraulic conductivity, porosity and retention curves for variably saturated flow) are uniform within the model domain to separate the effects of micro-topography on subsurface flow dynamics from possible impacts of heterogeneity. (2) By simulating biogeochemical reactions along isolated subsurface flow paths, it is assumed that there is no interaction between different flow paths where water and/or solutes are exchanged due to hydrodynamic dispersion (mechanic dispersion + diffusion). (3) Subsurface flow paths are derived based on a transient flow field resulting from yearly model runs. Particle tracking is performed for a 25 year period by repeating the yearly output of the flow model twenty-five times. This assumes that there are no inter-annual changes in the basic properties of the subsurface flow field (distribution of flow paths and RTs). (4) In the biogeochemical simulations availability of DOC, as the primary electron source for microbially catalyzed reactions (aerobic respiration, denitrification, iron(III)- and sulfate reduction) was assumed to be non-limiting. (5) Effects of vegetation and its potential influence on subsurface flow and redox processes, i.e. due to root respiration or exudation and evapotranspiration, are not considered. (6) Iron(III) species in the biogeochemical simulations are treated as solutes only, which are advectively transported within the subsurface domain and not as immobile solids bound to the peat matrix. (7) In the biogeochemical simulations, bioavailability of all involved species is not affected by e.g. complexation with DOC. [113] STUDY 2 3 Results 3.1 Subsurface flow patterns Subsurface flow paths for the two micro-topography models and the planar reference model are shown in Figure 3 (A-D). For the model with a mean-length of 0.5m (ml-0.5m) flow-paths are shown for the entire 3D model domain (A) as well as for the 2D transect located accross the center of the 3D model domain (dashed line in A). The 2D flow fields for the transects represent projections of the 3D flow paths into a 2D plane (flow components in the y directions are neglected). In contrast to the planar reference model, both micro-topography models showed complex distributed subsurface flow paths where coexisting shallow and deep flow cells developed in 3D. This is a common phenomenon caused by topography and was first described by Toth, [1962] for regional groundwater flow systems but can be found for flows in systems with pronounced topography over a range of scales [Wörman et al., 2006; Stonedahl et al., 2010]. Shallow flow cells are most pronounced for the model with a mean length of the surface structures of 0.5m (B) and are associated with the dominant surface structures (largest hummocks). Areas characterized by shallow flow cells are outlined with red dotted lines in Figure 3B. Water infiltrating in these areas relatively quickly returns to the land surface, travels shorter distances and is characterized by short subsurface residence times (Figure 4 A and B). In contrast deeper flow cells, which develop for areas where water infiltrates deep into the subsurface predominately at locations that are located far away from the channel segment, have longer travel distances (often spanning the entire extent of the model domain) and show significantly longer residence times as also reflected in the water ages (residence time in the subsurface since infiltration) plotted for the central 2D transect in Figure 5B. Deeper flow cells are controlled by the general hydraulic gradient across the model domain. The flow field for the micro-topography model with a mean length of 0.25m (ml-0.25m) shows no clear separation between shallow and deep flow cells because the topographic variations are too small to create sufficient variations in subsurface hydraulic potentials that could induce significant shallow flow cells (Figure 3 C). Similarly in the planar reference model (Figure 3 D) flow paths are relatively uniform in space with flow directions almost parallel to the planar land surface. [114] STUDY 2 Figure 3: Subsurface flow paths derived from particle tracking. A) Flow paths for the 3D domain of the micro-topography realization with a mean length of 0.5 m. B)-D) Flow paths projected to a cross section at the center of the 3D domain (yellow dashed line in A) for the two micro-topography models and the planar reference model. Outlined areas (red dotted lines) in B) represent the typical down and upwelling movement of the shallow flow system induced by surface micro-topography. Yellow dotted lines represent two flow paths (infiltrating at X = 0.4 m and X = 6.8 m) reflecting long and short subsurface residence times for which the biogeochemical evolution is shown in Figure 4. The model domain is 10 m x 20 m x 2 m. [115] STUDY 2 3.2 Biogeochemical evolution along flow paths Figure 4 (E-H) depicts exemplarily the results of the biogeochemical simulations, shown for two selected subsurface flow paths of the ml-0.5m micro-topography model. The two flow paths, beginning at location X = 0.4 m and X = 6.8 m (shown as yellow dotted lines in Figure 3B), represent the deep and shallow flow cells respectively. Results for the deep flow path are shown in Figure 4 A, C, E, G and for the shallow one in Figure 4 B, D, F, H. Both flow paths start in the unsaturated zone where pressure heads are negative (C and D). For the unsaturated zone, dissolved oxygen concentrations are constantly high (E and F) due to unlimited diffusive supply of atmospheric oxygen. Aerobic respiration is the dominant process within the unsaturated zone. The high turnover of organic material and the associated release of organically bound nitrogen within the unsaturated zone results in increasing concentrations of ammonium (G and H), which is in turn oxidized to nitrate due to nitrification (E and F). When the flow paths reach the saturated zone (pressure heads become positive), oxygen contents are decreasing and turnover due to aerobic respiration with associated release and oxidation of ammonium are slowed down (E and F). Oxygen contents are initially fluctuating in the saturated zone because of pressure head variations (i.e. water table fluctuations due to rain events), which are coupled to the oxygen boundary condition as shown in Figure 2. Once the flow paths reach a depth below the water table of about 0.25 m (pressure heads >= 0.25 m) oxygen become limiting and is completely depleted after ~90 days for the deep and after ~100 days for the shallow flow path. Under anoxic conditions, increasing concentrations of reduced species (e.g. iron(II) or sulfide) indicates that the system sequentially shifts to de-nitrification, iron(III)- and sulfate reduction (Figure 4 G and H). After 250 days, the deep flow path is in a completely reduced state where all oxidized species are depleted (Figure 4 E and G) and conditions remains reduced until the flow path reemerges at the surface and the water exfiltrates. The shallow flow path reaches completely reduced conditions after 200 days, but shortly before exfiltration oxygen becomes available again and oxidation processes are reactivated (Figure 4 F and H). The reason why reoxidation only occures at the end of the shallow flow path is related to the corresponding exfiltration location. The shallow flow path ends in a shallow, water filled depression where ponded water heights are low enough (pressure heads < 0.25 m) for atmospheric oxygen to diffuse into the uppermost layers of the peat so that oxygen is in contact with the upwelling reduced water. In contrast, the deep flow path which exfiltrates into the stream channel, where ponded water depths are too large to allow resupply of oxygen by diffusion; no reoxidation of reduced species is observed. Animation 1 and 2 (auxiliary material) show similar results where redox conditions are changing along two isolated subsurface flow paths (deep and shallow) extracted from the 3D model domain. [116] STUDY 2 Figure 4: Results of biogeochemical simulations along two different flow paths. A,C,E,G represent a deep flow path with long subsurface residence time and B,D,E,F a flow path of the shallow flow system. Subsurface flow velocities, pressure heads, flow depths and travel distances as shown in A,B,C,D were derived from numerical flow modeling and were used as hydrologic boundary conditions for the biogeochemical simulations. Additionally, oxygen availability (E, F) was coupled to the pressure head dynamics (C,D), individually for each flow path. E and F show the evolution for oxidized species (nitrate, iron(III) and sulfate) in time normalized to their corresponding initial concentrations Ct=0 and G and H the evolution of reduced species (ammonium, iron(II) and sulfide) normalized to their final concentrations Ct=max. How redox condtions are changing in time is also shown in Animation 1 and 2 (auxiliary material). [117] STUDY 2 3.3 Spatial patterns of hot spots In the previous paragraph, results of the biogeochemical simulations for two selected subsurface flow paths were presented in the time domain. A representation in space is depicted in Figure 5 and was generated by interpolating local species concentrations and reaction rates from the biogeochemical model for all flow paths into the 3D spatial domain of the flow model. In Figure 5 the results for the process of sulfate reduction in the model with ml = 0.5 m are presented as an example and plotted for the central transect aligned along Y = 5 m (dashed line in Figure 3 A). Panel C and D shows simulated sulfate reduction and sulfide oxidation rates whereas panel E and F show the corresponding concentrations of the reaction product (sulfide) and educt (sulfate). Flow paths are shown in panel A and the age of subsurface water (residence time in the subsurface since infiltration derived from particle tracking) is depicted in panel B. In the cross section, areas of intensive sulfate reduction (hot spots) are visible as well as areas where sulfate reduction is practically inactive (panel C). The latter areas are mainly associated with zones of upwelling subsurface water that is in a reduced state and depleted of sulfate (plot E and F). They are preferentially located below local depressions. For areas of infiltration, preferentially located below local hummocks, hot spots (panel C) for sulfate reduction can develop because the infiltrating water, originating from the oxygenated unsaturated zone, is rich in sulfate which can be reduced when more reducing conditions are encountered at increasing depth (panel E). This general pattern with local reduction hot spots below hummocks and an inhibition or absence of reduction processes below depressions, is also evident for all other redox-sensitive species (e.g. see plots in the supplement Figure A1-A7). In comparison, oxidation processes (iron(III)- , aerobic sulfide oxidation) show a reversed pattern, where local hot spots are preferentially generated below depressions where older upwelling water, rich in reduced species, comes in contact with atmospheric oxygen (panel D). In infiltrating areas, oxidation processes are practically inactive as the freshly infiltrated water carries predominantly oxidized species. [124] STUDY 2 to be highly variable in space and time in wetlands with a hummocky topography, depending on the climatic boundary conditions [Frei et al., 2010]. During intensive rainfall events, surface storage and runoff generation in wetlands with shallow water table can be controlled by a dynamic fill and spill mechanism [Frei et al., 2010]. Depressions are filled with water due to rising groundwater levels during onset of rainfall. With lasting rainfall, isolated ponded depressions start to interconnect with each other building extended surface flow networks [Frei et al., 2010; Antoine et al., 2009]. These surface flow networks can efficiently drain large fractions of the wetland's surface. At times more than 80% of the generated stream discharge may originate from this type of surface flow [Frei et al., 2010]. During high water table conditions, fast diffusion of atmospheric oxygen into the subsurface system is limited to areas of high elevation (hummocks), which remain unsaturated at the surface. During water table recessions and decreasing surface ponding, diffusion of atmospheric oxygen, below depressions with lower surface ponding, becomes more effective in terms of increasing rates for resupply, which triggers oxidation processes for upwelling conditions. Generally field data on oxygen supply in wetlands, its coupling to water table dynamics and peat properties are scarce [Aragonès and Blodau., 2012], stressing the importance of virtual modeling studies. A special condition can develop during extended dry periods, where depressions become disconnected from the declining water table. Below these disconnected depressions hydraulic gradients may reverse, switching from upwelling to infiltrating conditions. In turn oxidation hot spots will diminish because resupply of reduced species from upwelling groundwater is disrupted. It is reasonable to assume that during droughts hot spot patterns will become less pronounced and may eventually vanish as the system gradually shifts towards a more homogenous distribution of process activities. In real wetland systems probably more than one mechanism will be responsible for the formation of biogeochemical hot spots [McClain et al., 2003] and a clear separation of the influence of one specific process is almost impossible under field conditions. The simulations presented here, however, demonstrate that heterogeneous process patterns in hummocky wetlands can be explained by the complex re-distribution of redox-sensitive solutes in space as being controlled by micro-topography induced, subsurface transport processes and alternating biogeochemical boundary conditions. Furthermore, the presented concept shows that biogeochemical hot spots can be generated without reference to material heterogeneities which often are hardly observable in horizontally relatively homogenous peat soils [Morris and Waddington, 2011; Holden and Burt, 2003; Reeve et al., 2001; Reeve et al., 2006; Clymo, 1984]. Of course the presented concept neglects important aspects of real field conditions. Effects of the wetlands vegetation like root water uptake and its influence on subsurface flow or the special biogeochemical conditions within the rhizosphere [Crow and Wieder, 2005; Knorr et al., 2008; Wachinger et al., 2000] are not considered as well as the potential effects of dispersion the availability of electron acceptors and donors. Hydrodynamic dispersion may cause a smearing effect where the boundaries between hot spots and surrounding areas are not as sharp and [125] STUDY 2 clearly expressed as in an advectively dominated system, because solutes are also re-distributed along concentration gradients (diffusion) and transversally and longitudinally along the advective flow directions (dispersion). The biogeochemical simulations were performed using 5-day time steps, which was necessary because of computational constraints during the flow modeling (e.g. memory overflow, storage limitations). However, it is known that hydrological events at time scales of hours (e.g. single rainstorm events) can influence the biogeochemical processes within wetlands, as e.g. demonstrated for pulses of N2O emission [Goldberg et al., 2010] or high instantaneous CO2 production [Deppe et al., 2010] after wetting. Dynamics at these time scales, however, were not the main focus of this work and at this point cannot be fully accounted for in the present modeling approach because of computational limitations. Further it is known that organic carbon in wetlands typically consists of a fraction of labile components that can be easily utilized by micro-organisms (mostly within shallow layers) and more recalcitrant components (more abundant in deeper layers) [Yavitt and Lang, 1990; Reiche et al., 2010; Moore et al., 2007]. Labile organic carbon is not uniformly available as is assumed in our approach. However, there are two main reasons why we think that our assumption of unlimited carbon supply is nonetheless reasonable. Firstly, labile organic carbon availability is higher in shallow peat layers, in which most of the modeled processes occur, mostly due to inputs from the vegetation and high fermentation activity in the rhizosphere [Knorr et al., 2008; Wachinger et al., 2000; Reiche et al., 2010]. Secondly, we did not include methanogenesis, for which the supply of electron donors will be the key control, as the ubiquitous CO2 may serve as electron acceptor [Achtnich et al., 1995]. Field observations suggested that if alternative electron acceptors were present, the respective process proceeded, while under methanogenic conditions, respiratory activity slowed down and partly ceased [Beer and Blodau, 2007; Knorr et al., 2009]. Nevertheless, the process rate, constant in this case, depends on the quality of organic matter used and is not universal but substrate specific. The application of the Redfield ratio to simulate release of organic bound nitrogen due to decomposition of organic material in terrestrial ecosystems was probably a weak model assumption. Recent literature reported that C:N:P ratios in terrestrial ecosystems vary depending on vegetation types, but on the global scale average at about 186:13:1 for soil biomass and 60:7:1 for soil microbial biomass [Cleveland and Liptzin, 2007]. In our biogeochemical model we assumed that the majority of organic carbon available to microbes originates from vegetation and fermented plant material processed by microorganisms. The Redfield ratio is, however, narrower than the global average observed for soil biomass (106:16:1 compared to 186:13:1) and nitrogen release would be overestimated by our model. That means that the concentrations of ammonia, rates of nitrification and thus also nitrate pools available for denitrification may also be overestimated. Nevertheless, this should translate into slightly longer phases of nitrification or subsequent denitrification only, thus not fundamentally altering spatial patterns of the model output. [126] STUDY 2 4.2 Comparison with field observations Despite these simplifications, the presented model is capable of reproducing spatial variations in pore water concentrations of redox-sensitive solutes in the field (Figure 10). Vertical concentration profiles were measured in pore water from six different locations at the Lehstenbach field site, for an area, which is comparable in size to the spatial domain of the flow model (10m x 20m) [Goldberg et al., 2010; Knorr et al., 2009]. Simulated maxima in nitrate concentrations are found at a depth of ~0.1m and not directly at the surface, which agrees with measured data. The observed shift of nitrate concentration maxima has been explained as a result of plant uptake from the upper layers, as plant cover often leads to rapid depletion of nitrate concentrations [Silvan et al., 2005]. However our biogeochemical simulations suggest an additional explanation for the increased nitrate concentrations at shallow depth: As shown for the cross sections (Figure 6 C) high nitrification rates are limited to a relatively thin layer where turnover of ammonium to nitrate is highest. This layer of higher reactivity is the result of the vertical transport of water, which is being enriched with ammonium as it passes the unsaturated zone. Because nitrification rates under aerobic conditions depend on the local availability of ammonium, higher ammonium concentrations result in higher nitrification rates, which can be found directly above the de-nitrification zone where anaerobic conditions trigger rapid nitrate reduction. Similar findings were reported for different field studies [Regina et al., 1999; Goldberg et al., 2010]. Measured depth profiles as shown in Figure 10 are often used to calculate biogeochemical turnover rates based on a simplified approach treating wetlands as diffusion limited systems where the resupply of dissolved electron acceptors/donors is solely controlled by diffusion [Beer and Blodau, 2007; Clymo and Bryant, 2008]. However, model results show that advective transport can be an important component especially for slightly sloping wetlands with micro-topography and can significantly affect the spatial availability and re-distribution of electron acceptors and donors within the subsurface. Vertical concentration profiles simulated in this study suggest that depth variations in the concentrations of redox-sensitive solutes observed in the field are probably the result of a complex interplay between three-dimensional advective transport processes and biogeochemical reactions, which are in turn controlled by micro-topography moderated interactions between surface and subsurface flow processes and do not arise from pure diffusion and reactions alone. [127] STUDY 2 Figure 10: Observed and simulated variations of depth profiles for redox-sensitive species (nitrate, iron(II) and sulfate). Grey areas represent envelopes for predicted depth profiles and the black lines (mean +/- standard deviation) actual field observations taken simultaneously at six different locations for an area which is comparable to the model 20 m x 10 m domain at the field site in the Lehstenbach catchment. [128] STUDY 2 5 Conclusions and Implications At the landscape scale, riparian wetlands are commonly assumed to be zones of enhanced biogeochemical transformations (e.g. denitrification) due to anaerobic conditions and large carbon supplies [Johnston, 1991]. Field studies, however, have shown that biogeochemical conditions within wetlands can be quite diverse where most of the biogeochemical turnover may be accomplished in localized zones of higher reactivity (hot spots) [Paul et al., 2006; Knorr et al., 2009; Knorr and Blodau, 2009; Fenner et al., 2011]. Under field conditions, different processes and mechanisms can lead to the formation of hot spots [McClain et al., 2003] depending on the scale of interest. However, explaining such hot spots solely by the heterogeneous distribution of static, physico-chemical properties of the soil [Reeve et al., 2001; Holden and Burt, 2003] may be too simplistic. Our simulations indicate that biogeochemical hot spots can form even in homogeneous peat soils as a result of a dynamic subsurface flow system with (1) complex surface/subsurface interactions where surface micro-topography induces a subsurface flow field that is characterized by a small-scale zonation of inand exfiltration and (2) hydrological controls of the biogeochemical boundary conditions that either facilitate or suppress redox processes in exand infiltration areas. Hence the occurrence of reactivity hot spots does not need to be associated with static heterogeneities in physico-chemical soil properties a priori. In fact, the formation of biogeochemical hot spots in wetland systems may have the potential to alter the hydrodynamic properties of the peat and therefore, typically observed material heterogeneity may result from processes described in this study. The precipitation of iron oxides e.g., which preferentially occurs at oxidation hot spots, can lead to a reduction of the effective porosity and a lower hydraulic conductivity, providing a negative feedback on oxygen penetration; or in areas of reduction hot spots e.g. iron sulfides may become enriched that could be reoxidized upon more severe drying. Our results offer a new perspective on biogeochemical transformation processes in riparian wetlands that provides a dynamic framework to explain process heterogeneity in wetland soils and variability in process rates over time and space. Future work will have to address the interplay between different static (e.g. soil properties, vegetation patterns) and dynamic controls (e.g. flow, temperature & vegetation dynamics) of spatial and temporal variations in biogeochemical process activities in wetlands. It is clear that a mechanistic understanding of the links between hydrologic dynamics and biogeochemical transformations will be crucial for an assessment of climate change impacts on wetland functions and associated ecosystems services. The work presented here can serve as starting point for such an assessment by providing an explorative, mechanistic modeling framework to investigate potential shifts in hydrological and biogeochemical processes including changes in feedback mechanisms caused by changes in climatic forcing. [129] STUDY 2 Acknowledgments This study was funded by the German Reasearch Foundation (DFG, grant FL 631/6-2). Their financial support is greatly appreciated. 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Lowrance, J. Okay, D. Scott and S. Sebestyen (2010), Hot Spots and Hot Moments in Riparian Zones: Potential for Improved Water Quality Management1, JAWRA Journal of the American Water Resources Association 46(2), 278–298. [140] STUDY 2 – SUPPLEMENTARY MATERIAL Figure A5: Results of the biogeochemical simulations shown for the nitrification of the microtopography scenario with the mean length 0.5 m. PHREEQC simulations were performed along the flow paths shown in A. Results were interpolated into the 2D cross sections. B shows the age distribution in years of subsurface flow derived from backward particle tracking. Nitrification rates in mol/Ls are shown in C. Ammonium concentrations and nitrate concentrations in mol/L are shown in D and E respectively. Dissolved oxygen concentrations in mol/L are shown in F. [141] STUDY 2 – SUPPLEMENTARY MATERIAL Figure A6: Results of the biogeochemical simulations shown for the aerobic sulfide oxidation of the micro-topography scenario with the mean length 0.5 m. PHREEQC simulations were performed along the flow paths shown in A. Results were interpolated into the 2D cross sections. B shows the age distribution in years of subsurface flow derived from backward particle tracking. Aerobic sulfide oxidation rates in mol/Ls are shown in C. Sulfide concentrations and sulfate concentrations in mol/L are shown in D and E respectively. Dissolved oxygen concentrations in mol/L are shown in F. [142] STUDY 2 – SUPPLEMENTARY MATERIAL . Figure A7: Results of the biogeochemical simulations shown for the anaerobic sulfide oxidation of the micro-topography scenario with the mean length 0.5 m. PHREEQC simulations were performed along the flow paths shown in A. Results were interpolated into the 2D cross sections. B shows the age distribution in years of subsurface flow derived from backward particle tracking. Anaerobic sulfide oxidation rates in mol/Ls are shown in C. Sulfide concentrations, iron(III) and iron(II) concentrations in mol/L are shown in D-F respectively. [143] STUDY 2 – SUPPLEMENTARY MATERIAL Figure A8: Top view for the micro-topography scenarios and the planar reference. Micro-topography is shown in categories, red for hummock and blue for hollow structures. Additionally, for the planar reference the linear slope is shown. Black areas to the ride sides represent areas of aerobic respiration hot spots relative to their surroundings. Aerobic respiration can only occur if oxygen is present. Below hummocks a variably saturated zone with high oxygen contents is stable where preferential aerobic respiration occurs. Zones below hollows usually are water saturated where no oxygen is available for aerobic respiration. [144] STUDY 2 – SUPPLEMENTARY MATERIAL Figure A9: Top view for the micro-topography scenarios and the planar reference. Micro-topography is shown in categories, red for hummock and blue for hollow structures. Additionally, for the planar reference the linear slope is shown. Black areas to the ride sides represent areas of preferential iron(III) reduction (hot spots) relative to their surroundings. The patchy pattern develops because Iroon(III) reduction preferentially occurs below hummock structures because of higher iron(III) abundance. Below hollows upwelling water is rich in reduced iron species (Iron(II)). [145] STUDY 2 – SUPPLEMENTARY MATERIAL Figure A10: Top view for the micro-topography scenarios and the planar reference. Micro-topography is shown in categories, red for hummock and blue for hollow structures. Additionally, for the planar reference the linear slope is shown. Black areas to the ride sides represent areas of preferential ammonium oxidation (hot spots) relative to their surroundings. [146] STUDY 2 – SUPPLEMENTARY MATERIAL Figure A11: Top view for the micro-topography scenarios and the planar reference. Micro-topography is shown in categories, red for hummock and blue for hollow structures. Additionally, for the planar reference the linear slope is shown. Black areas to the ride sides represent areas of preferential iron(II) oxidation (hot spots) relative to their surroundings. [147] STUDY 2 – SUPPLEMENTARY MATERIAL Figure A12: Top view for the micro-topography scenarios and the planar reference. Micro-topography is shown in categories, red for hummock and blue for hollow structures. Additionally, for the planar reference the linear slope is shown. Black areas to the ride sides represent areas of preferential sulfide oxidation (hot spots) relative to their surroundings. [148] STUDY 2 – SUPPLEMENTARY MATERIAL Figure A13:. Fence plots showing the zones of preferential sulfate reduction for the whole 3D domain of the mean length 0.25 m model. [149] STUDY 2 – SUPPLEMENTARY MATERIAL Figure A14: Fence plots showing the zones of preferential sulfate reduction for the whole 3D domain of the planar reference model