Evaluating controls on coupled hydrologic and vegetation dynamics in ...

WATER RESOURCES RESEARCH, VOL. 49, 2552–2572, doi:10.1002/wrcr.20189, 2013

Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface-land surface processes model Chaopeng Shen,1 Jie Niu,2 and Mantha S. Phanikumar2 Received 20 November 2012; revised 23 February 2013; accepted 8 March 2013; published 28 May 2013.

[1] Understanding key controls on hydrologic dynamics is important for effectively allocating resources for data collection, reducing model dimensionality, and making modeling decisions. This work seeks to elucidate the physical factors responsible for the observed hydrologic patterns of a watershed in Michigan using an integrated hydrologic model, Process-based Adaptive Watershed Simulator and Community Land Model (PAWSþCLM). The model is tested using observed data for streamflows, soil temperature, groundwater table depths, and satellite-based observations of evapotranspiration (ET) and leaf area index (LAI). Numerical experiments are carried out to lump the effects of key controls, including land use types, nitrogen levels, groundwater redistribution, and soil texture, into different process indices. Using analysis of variance (ANOVA), we quantitatively determine the strengths of these controls on ET, net primary production (NPP), and other important variables. Groundwater flow is found to be the major control on runoff and infiltration, with soil texture ranking next, while vegetation type and nitrogen levels are found to dominate NPP, top soil temperature, and transpiration. Soil texture and groundwater are found to have comparable influence on soil moisture, which is in agreement with analysis of field data in the literature. All controls are found to colimit ET, which serves as the nexus for ecosystem-hydrology interactions. From the simulation results, we find that nitrogen significantly controls transpiration, through which it influences other hydrologic fluxes. While there is room for improving descriptions of the nitrogen cycle in the current version of CLM, these novel results call for an understanding of the interplay between hydrology and biogeochemistry. Additional analysis shows that the relative strengths of the controls examined in this work are fairly robust with respect to changes in parameters and spatial resolution. Citation: Shen, C., J. Niu, and M. S. Phanikumar (2013), Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface-land surface processes model, Water Resour. Res., 49, 2552–2572, doi:10.1002/wrcr.20189.

1.

Introduction

[2] Recently, there has been a surge of interest in identifying, understanding, and utilizing hydrologic and ecosystem patterns and controls in hydrologic predictions [Hwang et al., 2012; McDonnell et al., 2007; Thompson et al., 2011; Vivoni, 2012; Wagener et al., 2010]. Understanding the physical processes that control the variability of hydrologic variables is crucial for predictions and management [Joshi and Mohanty, 2010; Pan and Wang, 2009; Wilson et al., 2004]. The recognition of key patterns and controls can help reduce the dimensionality of the prediction problem [Sivapalan et al., 2011], facilitate predictions in unga1 Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, Pennsylvania, USA. 2 Department of Civil and Environmental Engineering, Michigan State University, East Lansing, Michigan, USA.

Corresponding author: C. Shen, Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA 16802, USA. ([email protected]) ©2013. American Geophysical Union. All Rights Reserved. 0043-1397/13/10.1002/wrcr.20189

uged basins [Sivapalan, 2003], and promote an in-depth understanding of the underlying principles. Topographyinduced groundwater flow leads to convergence of subsurface moisture, which crafts spatial patterns in hydrologic, geomorphologic, and vegetation processes. In some systems, groundwater flow plays a vital role in ecosystem functioning. However, despite intensive recent studies [e.g., Ivanov et al., 2008; Maxwell and Kollet, 2008a, 2008b; Miguez-Macho and Fan, 2012], the relative importance of groundwater flow as compared to other controls such as soil texture, land use, and vegetation characteristics remains unclear. [3] The coupling between hydrology and biogeochemistry has recently received growing attention [Chorover et al., 2011; Lohse et al., 2009; Manzoni and Porporato, 2011; Riveros-Iregui et al., 2012]. Strong controls and feedbacks have been identified between water, carbon, and nitrogen biogeochemistry in various environments [Burt and Pinay, 2005; D’Odorico et al., 2003; Lohse et al., 2009; Schimel et al., 1997]. The strong relationship between evapotranspiration (ET) and biological nitrogen fixation (BNF) [Cleveland et al., 1999] has long been recognized, so has the correlation between nitrogen

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SHEN ET AL.: EVALUATING CONTROLS ON HYDROLOGIC AND VEGETATION DYNAMICS

availability, successional stage, soil moisture, and topography [Robertson et al., 1988]. In ecosystems where vegetation growth is limited by nutrient availability [Galloway et al., 2004; Wilson et al., 1999], higher inputs of nitrogen are expected to lead to a relatively more active ecosystem with higher productivity, thus altering ET and the hydrologic cycle. Presently, human activities are creating reactive N at unprecedented rates [Galloway et al., 2004], leading to increased nitrogen deposition [Dentener et al., 2006; Galloway et al., 2008]. The implications of this excess nitrogen and its interplay with the carbon and water cycles is worth detailed, mechanistic and integrated investigation. The relative importance of N is difficult to evaluate from field surveys alone due to the various time scales of the complex N biogeochemical reactions, its interactions with the carbon cycle, and its covariance with climatic input [e.g., see Burke et al., 1997]. On one hand, inclusion of nitrogen dynamics in global land surface simulations is relatively recent (see Bonan and Levis [2010], Reich et al. [2006], and Sokolov et al. [2008] for the effects of the C/N interactions), and the current hydrologic descriptions in these climate scale models are very crude, oversimplifying the subsurface and channel dynamics. On the other hand, with the exception of very few (TOPOG-IRM [Vertessy et al., 1996] and RHESSys [Band et al., 2001; Mackay and Band, 1997; Tague and Band, 2004]), ‘‘traditional’’ watershed-scale models often do not consider any carbon/nitrogen dynamics. Understanding the relative strengths of the water-carbon-nitrogen coupling helps identify unrecognized yet important linkages. [4] Besides synthesizing and mining observed data for correlations, one may also study the influences of separate processes using the simulations of physically based hydrologic models (PBHMs), which are ‘‘derived deductively from fundamental physical laws’’ [Beven, 2002]. Although PBHMs may never match the full complexity of reality, they have the potential to reveal patterns and pinpoint cause-effect relations. To reconcile differences and similarities in formulations and to aid scientific understanding of the complex processes involved, new models, approaches, and model intercomparison exercises are all expected to play an important role. Recent advances in PBHMs have focused on the linkages of terrestrial fluxes, ecohydrology, and the integral role played by surface and subsurface water dynamics. For an incomplete list, see the papers by Fatichi et al. [2012], Ferguson and Maxwell [2010], Goderniaux et al. [2009], Ivanov et al. [2008], Kollet and Maxwell [2008], Mackay and Band [1997], Miguez-Macho et al. [2007], and Niu et al. [2013]. A novel hydrologic model, Process-based Adaptive Watershed Simulator (PAWS), was recently introduced by Shen [2009] and Shen and Phanikumar [2010, hereinafter SP10]. PAWS was created with intermediate complexity, Geographical Information System (GIS) data interface and parameterization functionalities, attempting to strike a balance between physics and computational tractability at large scales. The model efficiently solves the governing equations for major hydrologic processes using some of the best available algorithms. By reducing the dimensionality of the fully threedimensional (3-D) subsurface problem using a quasi-3-D saturated groundwater domain and one-dimensional Richards’ equation to approximate soil columns in every grid

cell, the model significantly reduces the computational demand with little loss of physics. The computational efficiency of the PAWS model allows for long-term, large-scale simulations and makes parameter estimation and uncertainty analysis feasible. The PAWS model was tested extensively (SP10) with laboratory ‘‘recharge’’ experiments, analytical solutions, idealized test cases, and numerical solutions from models that solve the full 3-D Richards equation. The model achieved good performance in simulating hydrology in a medium-sized watershed in the U.S. midwest. Descriptions of land surface processes, including energy balance and vegetation growth cycles, were nonetheless simple in the original PAWS model. [5] The National Center for Atmospheric Research (NCAR) community land model (CLM), a process-based land surface model, was developed from grassroots collaboration among climate scientists [Collins et al., 2006; Dickinson et al., 2006; Lawrence et al., 2011; Niu and Yang, 2007; Oleson et al., 2010; Sakaguchi and Zeng, 2009; Zeng et al., 1998, 2002]. The model now encompasses a comprehensive suite of land surface processes including surface heat/momentum/vapor transfer, surface radiation balance, snow/soil heat transfer and freeze-thaw phase changes, photosynthesis and plant growth, as well as carbon and nitrogen fluxes, mostly using process-based descriptions. However, the flow modules in CLM, e.g., surface runoff and channel flow, subsurface flow, and characterization of geologic and soil properties, all tend to be overly simplified as compared to other components. CLM treats each computational element as independent columns, neglecting the lateral fluxes and hence their spatial interactions. As discussed in Anyah et al. [2008], Maxwell and Kollet [2008a], and Shen and Phanikumar [2010], the subsurface flow is an integral component of the hydrologic cycle that significantly influences local water fluxes and the climate through feedbacks. To further enhance the simulation capabilities of the PAWS model, we recently coupled the flow processes in PAWS to the land surface processes in CLM. This suite of modules brings together surface flow, 3-D subsurface flow, carbon/nitrogen dynamics, and land surface processes to offer a higher degree of model realism. [6] In this paper, we employ the PAWSþCLM model to study the water-energy-nutrient controls on hydrology in a humid continental climate watershed in the Great Lakes region of North America. Although progress has recently been made in understanding the hydrology of this region with a focus on the impacts of land cover/land use changes [Mao and Cherkauer, 2009] and projected climate change on hydrology [Mishra et al., 2010], the applications of PBHMs have the potential to offer additional insights into fundamental processes. Our objectives in this paper are (a) to quantify the relative importance of physical processes including land cover, soil texture, land use and groundwater flow on water, energy, and carbon fluxes using a process-based hydrologic model and (b) to examine the coupling strengths and mutual influences between hydrology and nitrogen biogeochemistry. The paper is organized as follows. After a brief description of the PAWSþCLM model, we present the results of extensive model testing using field and satellite-based observations. Using the welltested model, we show the rich spatiotemporal patterns of the hydrologic and vegetation dynamics in the basin. Then,

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we carry out numerical experiments to analyze the relative importance of the controlling processes on various fluxes and states and how they together produce the patterns.

2.

Model Description

[7] Since the mathematical and algorithmic details of the PAWS model have already been presented elsewhere (SP10), we will only provide a brief outline of the model here followed by short descriptions of the coupling with CLM. PAWS uses 3-D structured grids with the top layer representing the ground surface and the overland flow domain. Several key processes take place exclusively on the ground surface (e.g., overland flow, vegetation interception, depression storage) while others (e.g., ET) extend over the entire vertical depth of the soil column. To avoid confusion, we define our terminology as follows: the term ‘‘cell’’ is used to refer to the unit of the 2-D horizontal discretization of the ground surface (this corresponds to the ‘‘grid cell’’ concept in CLM 4.0). We use the word ‘‘column’’ to denote the totality of the soil matrix and pore spaces underneath a cell (under each cell, we only have one column, with a single set of moisture/thermodynamic states, as opposed to the possibility of multiple columns in CLM); the word ‘‘layer’’ refers to the vertical discretization of the soil column. 2.1. Flow Modules [8] The flow domain is divided into surface overland flow, channel flow, unsaturated soil water flow, and saturated groundwater. Processes in these domains are linked by the surface-unsaturated-saturated coupling method described in SP10. The ground surface is partitioned into the flow domain and the ponding domain. The ponding domain is updated together with the soil water states. Runoff rate from the ponding domain to the flow domain is determined by the Manning’s equation. Only water in excess of the interception depth can become runoff. Under flooding conditions, water in the flow domain may also backfill into the ponding domain. For the overland flow domain, we solve the 2-D diffusive wave equation using an efficient and stable Runge-Kutta finite volume (RKFV) scheme. A similar method is used for the channel network, which exchanges flow with the overland and the groundwater flow domains. The exchange between groundwater and channel flow is calculated based on the leakance concept [Gunduz and Aral, 2005], after the diffusive wave equation is solved. Wetlands are an important land cover type that modifies hydrologic responses. A new lowlandstorage module is developed as part of this work and is described in Appendix A (Figure 1 illustrates the conceptual model used by the module). [9] The subsurface is discretized into a series of 1-D soil columns connected to saturated groundwater layers at the bottom. The saturated-unsaturated soil water flow is governed by the Richards equation, which is solved using a modified Picard iteration approach [Celia et al., 1990; van Dam and Feddes, 2000]. The vadose zone module handles infiltration into the soil under normal conditions; however, under heavy rainfall conditions, we employ a modified form of the generalized Green and Ampt method [Jia, 1998]. We bring the dynamics of regional groundwater

Figure 1. Illustration of the lowland-storage module. This compartment is conceptualized as a storage compartment of the overland flow domain.

flow into the soil columns by writing a separate mass balance equation for the last grid cell, whose thickness changes as the water table fluctuates. The bottom of the last cell extends to the top of the bedrock. Lateral flow exchange calculated by the groundwater flow equation is fed into the Richards equation, and the recharge flux obtained from the solution of the Richards equation is in turn passed to the groundwater flow equation as a source term. As described in SP10, this coupling method produces physically consistent (in that the soil moisture profile is consistent with the groundwater head) and stable solutions and compares well with solutions based on the fully 3-D variably saturated Richards equation models for different conditions (e.g., conditions corresponding to infiltration excess, saturation excess, and return flow). 2.2. (PAWS1CLM): Coupled Subsurface and Land Surface Processes [10] The processes included in CLM have been detailed in Oleson et al. [2010]. For the sake of completeness, we provide a brief overview of CLM processes incorporated in the coupled model in this section and then discuss linkages. PAWS is coupled to the latest release of CLM (version 4.0), with a prognostic crop model [Lawrence et al., 2011]. The soil hydrology and river routing routines in CLM are replaced by the corresponding procedures in the PAWS model. The coupling details are described in section 2.2.2. 2.2.1. Introduction to CLM Processes [11] For canopy radiative fluxes, the two-stream approximation of Dickinson [1983] and Sellers [1985] is used to keep track of incoming, transmitted, reflected and absorbed direct or indirect radiation, over multiple solar wavebands. The plants are parameterized by detailed ecophysiological descriptors to capture the optical variability among plant species, especially trees. Canopy scaling/integration of sunlight penetration is based on the specific leaf area (SLA, the ratio of leaf area to leaf mass) concept [Thornton and Zimmermann, 2007], which is based on the assumption that

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SLA increases linearly as a function of overlying leaf area index (LAI), and the two-big-leaf canopy model, which treats the canopy as a sun-lit leaf and a sun-shaded leaf [Dai et al., 2004]. CLM employs the resistance concept, rather than the widely used Penman-Monteith (PM) approach to calculate momentum/heat/vapor transfer. Although the PM equation assumes a wet-bulb approximation for vegetation surface temperature, the resistance approach explicitly solves for the leaf temperature to satisfy the coupled latent and sensible heat transfer equations. Aerodynamic resistances are calculated based on the Monin-Obukov similarity theory [Kundu and Cohen, 2010; Zeng et al., 1998]. Canopy resistances are solved simultaneously with photosynthesis using the Farquhar model for C3 plants [Farquhar et al., 1980] or the model of Collatz [Collatz et al., 1992] for C4 plants. As classified in Arora [2002], this is a biochemical approach that provides a process-based description of CO2 assimilation and may be more suitable for the assessment of future climate scenarios. The water stress function couples hydrology with ecosystem dynamics and is parameterized as described in section 2.2.2. [12] Soil and snow temperatures are updated by solving the heat conduction equation. The SNICAR (SNow, ICe, and Aerosol Radiative model) module [Toon et al., 1989] included in CLM 4.0 keeps track of the mass balance of aerosols and impurities in the snowpack. Snow aging impacts effective grain size and consequently snow albedo. Soil freezing is calculated by a freezing-point depression formulation of Niu and Yang [2006]. This formulation allows supercooled water and ice to coexist in a wide range of below-freezing temperatures. The soil hydraulic conductivities are scaled down by an impermeability factor depending on the fraction of ice in the soil water content. [13] The carbon and nitrogen cycle on the land surfaces are fully incorporated with the biogeochemical vegetation dynamics and phenology module of Biome-BGC [Thornton and Rosenbloom, 2005; White et al., 1997]. The photosynthesized carbon is sent into a central carbon pool, which is first spent on maintenance respiration by live compartments (leaf, fine root, live coarse root, and live stem if applicable). Surplus carbon is then allocated to growth and phenological carbon pools (leaf, dead steam, root, and storage/ transfer pools) depending on the allometric relationships prescribed for different plant species. This process is strongly controlled by available nitrogen. Growth respiration is consumed when allocation occurs. Offsetting, gap mortality, and fragmentation of coarse wood debris send carbon and nitrogen into the litter pools. Carbon and nitrogen (C/N) then trickle down a cascade of litter/soil organic matter (SOM) pools with varying turnover rates. The displayed structure of vegetation (LAI, tree height) is prognostically updated based on the carbon stored in different pools. Different plant species are parameterized with different phenological traits (e.g., evergreen, stress-deciduous, seasonal deciduous, etc). Nitrogen transformations are book kept in plant pools, three litter pools, four SOM pools, and a soil mineral pool. Simulated nitrogen-specific dynamics include deposition, fixation, leaching, and uptake. [14] It is worth mentioning that although CN (Carbon and Nitrogen) module in CLM is a complex model with respect to the number of pools as compared to many other

models [Manzoni and Porporato, 2009], there are still large uncertainties with the biogeochemical cycling in CLM. The CN module is a compartment model and does not track chemical species (except for the distinction between cellulose and lignin pools), microbial community, age/residence time, and vertical distribution of C/N, all of which can play important roles [e.g., see Berg and Laskowski, 2005; Fang et al., 2005; Maggi and Porporato, 2007; Maggi et al., 2008; Wutzler and Reichstein, 2008; Xu-Ri and Prentice, 2008]. The allometric coefficients, base turnover rates (base rates are then adjusted by temperature and water functions), nitrogen deposition/fixation rates, and denitrification rates all face uncertainties. There are also uncertainties associated with the appropriate spatiotemporal scales at which these rates should be applied [Manzoni and Porporato, 2009]. Data sets to validate the internal dynamics of CN cycling models are generally lacking. 2.2.2. Coupling CLM With PAWS [15] The coupling between PAWS and CLM is done in a mass-conservative, theoretically consistent, and tightly integrated fashion. At initialization, vertical and horizontal discretization information in PAWS, which depends on topography, soil, and geology, is ported to CLM so that both have the same definitions of computational units. At each time step, climate forcing is passed into CLM to compute interception, throughflow, surface radiation processes, surface energy balance, photosynthesis, soil temperature, and snow processes. Then, the ET demand (both transpiration and soil evaporation) as a function of depths is passed into PAWS as a source term for the unsaturated zone. Also passed along are the soil temperature, ice content, canopy storage, ground precipitation, dew, and snowmelt amounts. The soil hydrology module in CLM is replaced with its PAWS counterpart, which then solves the Richards equation together with ET, groundwater flow, and runoff based on the coupling scheme described in SP10. Overland flow and streamflow are also calculated in PAWS. The resulting soil moisture states are then converted into the soil water state variables in CLM and supplied back into CLM for ecosystem updates and calculations in the next time step. [16] In this paper, we will focus on the results obtained by running the (PAWSþCLM) model in the ‘‘offline’’ mode, in which the climate forcings are based on observations from conventional land-based stations. PAWS uses a structured grid; therefore, the CLM discretization is configured such that there is a one-to-one mapping between a PAWS cell and a CLM grid cell, and there is only one column in each CLM grid cell. At the same time, subcell heterogeneity on the plant functional types (PFT) level is supported as PAWS also adopts a similar approach. In SP10, this subcell heterogeneity of land use/land cover types was called representative plant types (RPT). To conform to the CLM literature, we adopt the term PFT in this paper. In the coupled model, PAWS provides the front-end processing utilities that reclassify raw land use data sets into CLM PFTs. [17] Some changes to the CLM processes are necessary to make the two models consistent in theory. Since the soil water flow processes are primarily computed in PAWS, the combined model uses the van Genuchten formulation for soil water retention relationships as in the original PAWS model. Field capacity, saturated water content, and wilting

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SHEN ET AL.: EVALUATING CONTROLS ON HYDROLOGIC AND VEGETATION DYNAMICS Table 1. Model Calibration Parameters Symbol (Unit) K (m/day) KS (m/day) N A (1/m) ice Kr (m/day) l (m)

Parameter Meaning Groundwater hydraulic conductivity Soil saturated hydraulic conductivity van Genuchten parameter van Genuchten parameter Parameter in Lai and Katul [2000] root efficiency function Scale-dependent freezing fraction parameter as in equation (11) in Niu and Yang [2006] River leakances Length of flow path for runoff contribution to overland flow domain

points are set by the PAWS model. The soils characterizations are derived from the SSURGO database (Soil Survey Geographic database from the Natural Resources Conservation Service [Soil Survey Staff, 2010]), which is a high resolution, comprehensive data set. The soil resistances to evaporation are reformulated according to Neitsch et al. [2005]. The root water uptake efficiency (or root resistance in CLM) adopted by PAWS, that is, the Lai and Katul [2000] formulation, is used. This formulation contains a tunable parameter (Table 1) that modulates the root efficiency-water stress function. When coupled to atmospheric models, the climate input data is normally forced at 30 m above canopy. With land-based climatic stations, however, the observation height is between 1 and 2 m. Therefore, the aerodynamic resistances to vapor/heat/momentum transfer are formulated using a neutral stability assumption. When wind flow velocity is low ( F

Source

Sum of Squares

1,783 5,775 4,398 7,172 135 49 37 70 117 24

0 0 0 0 0 0 0 0 0 1.30E-06

N level PFT SWSI LatI N level PFT N level SWSI N level LatI PFT SWSI PFT LatI SWSI LatI Error Total

84,307,345 2,594,772 742,608 253,061 24,127,980 2,326,108 632,842 62,980 10,551 5 1,281,601 116,339,853

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Percent of Variance Explained (%) 72.47 2.23 0.64 0.22 20.74 2.00 0.54 0.05 0.01 0.00 1.10 100.00

F

Prob > F

25,590 1,575 1,352 461 2,441 706 192 38 6 0

0 0 0 0 0 0 0 0 2.56E-04 9.27E-01


Figure 15. ANOVA analysis results show the relative strengths controls of physical processes on hydrologic and vegetation dynamics (Rf, runoff; Inf, infiltration; ET, evapotranspiration; Tp, transpiration; NPP, net primary productivity; t10cm, top 10 cm soil temperature ; 10cm, top 10 cm soil moisture; Interactions, for simplicity, the cross terms between different factors are combined into one interaction term). ‘‘Original’’ stands for ANOVA test carried out without global parameter changes described in section 3.4.3. In ‘‘K 0.1’’ ANOVA test, all simulations (S2, S3, and S4) have their K scaled by 0.1. Similarly, 0.1 is added to N in all simulations in ‘‘N þ 0.1’’ ANOVA test. ‘‘Double resolution’’ means the spatial step size is halved (number of cells quadrapled). (a) Original, (b) K 0.1, (c) double resolution, (d) N þ 0.1. 10% and LatI: 15%). Soil nitrogen level (as a result of ecosystem spin-up to equilibrium using different BNF rates) plays an important role in ET, confirming previous observations of positive correlation between BNF and ET. As is clear from Figure 15, this influence is mainly exerted through its control on transpiration (Tp). It can then be inferred that groundwater and soil texture are much more dominant controls on soil evaporation. The influence of N levels also propagates to hydrologic fluxes and temperature, although not in a strong fashion. The findings suggest that in order to accurately predict spatiotemporal distributions of ET (or latent heat flux), the ecosystem dynamics including nitrogen availability must be carefully considered. [47] In contrast, LatI and SWSI have comparable significance for 10cm and each explain about 40% of the variance. As a central variable for hydrologic and biogeochemical processes, soil moisture has been the most intensively studied in the literature among the variables analyzed in this paper. Interestingly, our results have been corroborated by analysis of field data [Famiglietti et al., 1998; Joshi and Mohanty, 2010; Pan and Wang, 2009; Wilson et al., 2004], where soil and topography are found to be of similar importance (although soil/vegetation impacts are lumped in Wilson et al. [2004], and it is not clear how much variance they each explain). Therefore, for hydrologic and biogeochemical processes, which depend on soil moisture in a strongly nonlinear fashion, it is important to consider both local soil textural properties and regional groundwater flow. Qiu et al. [2001] report a larger control of mean soil moisture by land use. The actual controls of vegetation on soil moisture are expected to be higher than explained in the model. Further study should

consider more diversified vegetation and the effects of hydraulic lift by vegetation or macropores created by vegetation roots. Our study intentionally breaks the correlation that naturally exists between topography and vegetation types [e.g., Brown, 1994]. We also note a large error for 10cm, indicating that the complex dynamics of moisture cannot be explained by simple linear models of the variables. The complex controls of soil moisture have been reported by Zhu and Lin [2011], who conclude that terrain attributes are more important in wetter places among other dynamics. [48] It must be noted that, for NPP, although LatI and SWSI appear unimportant compared to vegetation types and soil nitrogen content, both have significant influence once we hold PFT and N levels constant across the basin, as we have shown in S3. 3.4.3. Robustness of the Analysis [49] A reasonable question to ask is whether the relative importance of different processes will stay the same when we perform the same exercise with a different model, a different spatial resolution or in another region where climatic input and physical parameters (e.g., aquifer transmissivities) are different. Intuitively, the results should change when physical parameters are altered since, if we imagine a basin with very low aquifer transmissivities, the influence of groundwater flow dynamics should be very low. Although the effect of different model formulations should also be examined in the future, in this paper, we assess the effect of the parameters and spatial resolution. To quantitatively assess this impact, we redo the ANOVA analysis in section 3.4.2 with exactly one of the following changes applied: (a) the conductivity of the unconfined aquifer

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everywhere in the basin is reduced by a factor of 10. Therefore, the basin average unconfined aquifer K becomes 1.3 m/day; (b) the van Genuchten parameter n is increased by 0.1, thereby increasing the nonlinearity of the soil water retention and unsaturated conductivities ; (c) the horizontal cell size is reduced in half (resolution is doubled), thereby quadrupling the number of cells in the basin. [50] The results are shown in Figure 15. Indeed, the variances attributed to each factor do shift after making the changes (Figure 15a). The influence of LatI reduces for all variables, implying a less dominant groundwater system. The relative roles of other factors, especially SWSI, increased accordingly to make up for the drop in LatI. This means the relative importance of groundwater dynamics will shift when we move to a geologic configuration that extends beyond the current range of variability. With change (b), however, the changes are relatively minor, with SWSI explaining slightly more variance in Rf and Inf. The shifts in the relative importance due to a change in the range of variability have also been previously noted by field data analysis [Zhu and Lin, 2011]. With the help of a numerical model, however, the relative importance of controls at each site can be analyzed prior to extensive data collection. [51] The spatial resolution has a relatively weak influence on the results of the analysis. For ET, Rf, and Inf, LatI now plays a more important role when resolution is doubled, scoring 20%, 74%, and 87%, respectively. This is due to the better resolution of the effects of topography relative to the original simulation. For t10cm and 10cm, however, SWSI increased slightly, presumably due to a larger range of SWSI as higher resolution allows more distinct soil classes to be simulated. However, the general trend stays the same as in the lower resolution simulations.

4.

Discussion and Conclusions

[52] The knowledge and the mechanistic understanding obtained in this study may help guide future modeling and data-gathering decisions. For example, many previous efforts focused on downscaling satellite soil moisture observations [e.g., Busch et al., 2012; Mascaro et al., 2010; Pellenq et al., 2003], and our results indicate that both topography and soil texture should be environmental predictors in such downscaling methods as they are important controls for soil moisture. For another example, for gross primary production (GPP/NPP) estimation, at least for the humid continental climatic region examined here, plant type and nutrient availability far outweigh groundwater dynamics and soil water supply properties in terms of their relative importance. It is therefore important to obtain soils data sets with better carbon/nitrogen storage estimations. For quantification of terrestrial-atmospheric vapor/ heat exchange, groundwater dynamics, soil properties, and ecosystem processes are all important. Because both shortranged and long-ranged patterns of groundwater flow exist, it is important to consider regional groundwater dynamics and subsurface processes in land surface models. This means that for large-scale simulations that aim at studying terrestrial-atmospheric vapor/energy exchange, many simplified cell-based hydrologic formulations (such as the TOPMODEL-based formulations in original CLM and

Noah LSM) are likely to produce unreliable estimates. Although similar conclusions have been drawn in the literature [Anyah et al., 2008; Fan et al., 2007; Maxwell and Kollet, 2008a], our study is unique in this climatic region. [53] In spite of the limitations of the current nitrogen cycling schemes in CLM4.0, the results clearly indicate that the nitrogen availability heavily influences not only vegetation growth but also ET, through which other hydrologic fluxes are affected. Conventional hydrologic models often do not consider the nutrient dynamics mechanistically. For assessment of climate change on regional hydrology and vegetation, nitrogen dynamics should be considered. The results in this paper suggest that we need to take an integrative view of the ecosystem-hydrology interactions in order to make better predictions. It is important for hydrologists to continue expanding the domain of sciences in hydrologic simulations to identify previously undiscovered but potentially important linkages, echoing the call for more crossinterdisciplinary integration [Wagener et al., 2010] and encouraging grass-roots community collaborations. [54] For many variables studied in Figure 15 (e.g., ET, Tp, NPP, and t10cm), the error terms account for less than 10% of the total variance. Consequently, it is possible to predict these variables (at least their annual averages) with a fair degree of accuracy by just using simple linear models of the main processes (land use, nitrogen, groundwater flow, and soil water retention) and their interactions. However, the variables with higher percentage of error (e.g., 10cm) will need to be addressed by more complex models detailing the space-time interactions of factors. [55] As with other similar studies, the conclusions in the paper are somewhat tied to the model employed. The ability of a numerical model to represent linkages and interactions depends on its formulations. Considering the large uncertainties associated with descriptions of the nitrogen cycle in CLM, the numerical value of its relative importance will likely shift when improved N cycling schemes are employed. Given that a large range of BNF rates (altered by a) are used to get carbon nitrogen states, the true influence of nitrogen is likely smaller than estimated. Future simulations supported by novel observations will be crucial for confirming the results. PAWSþCLM considers water to move only vertically in the unsaturated zone. As a result, the model does not produce accurate results when significant interflow exists. The model currently does not track nutrient transport within groundwater and surface water. Also, the analysis in the paper does not include the effects of microtopography, micrometeorological conditions, and aspect of the slope. In the future, it would be interesting to compare results from other models using the approach described here. The effect of very deep soils on the coupling algorithm in PAWS seems to be mild given the good match with observed groundwater heads and wellcaptured baseflow in the hydrograph. In the future, very high resolution simulations will likely provide more insight on the effect of spatial resolution. Scale-aware or multiscale simulation approaches are called for to help with the issue of spatial scaling. [56] To summarize, we have described the application of a novel process-based hydrologic model PAWSþCLM to a watershed in the humid continental climate region of the U.S. Midwest to understand the relative importance of

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vegetation type, nitrogen availability, groundwater dynamics, and soil texture. The model shows good comparison with various observation data sets. The background hydrology of the basin is revealed as one limited by energy and water at different times during the annual cycle, with saturation excess being the dominant runoff generating mechanism. Groundwater plays an important role in the watershed, controlling saturation excess and lowland exfiltration. Through a series of hypothetical simulations based on the model, we use ANOVA to quantify the influence and relative importance of the controlling processes. The results confirm the basin, which has an average unconfined aquifer conductivity of 13 m/day, as a groundwater-dominated system. Groundwater flow has been found to be the major control on runoff and infiltration, explaining more than 70% of the variance. Soil texture ranks next. Vegetation type and nitrogen levels are found to determine NPP, top 10 cm soil temperature, and transpiration. Interestingly, all factors are found to significantly control ET, which serves as the nexus for ecosystem-hydrology interactions. Nitrogen is shown to be a major control of ecosystem variables, but it also significantly controls transpiration, through which it influences other hydrologic fluxes. While keeping in mind the limitations with the CN cycling in CLM, the results indicate that hydrologists need to continue expanding the domains of sciences in hydrologic simulations to discover important linkages. The analysis method used in the paper is shown to be relatively robust with respect to changes in parameter values and spatial resolution, but when we move to a region whose parameters extend beyond the current range of variability, the results are expected to shift.

Appendix A: Lowland-Storage Module [57] Here we describe a new enhancement to the model. Wetlands are an important land cover type that exerts strong influence on hydrology, ecosystem functioning, and water quality. A flexible lowland-storage module is added to the original PAWS model to keep track of the exchanges between wetlands, overland flow, and groundwater. The wetland is conceptualized as a lowland-storage compartment of the overland flow domain, as shown in Figure 1. To accommodate the wetland compartment, the mass balance equation for the flow domain is modified as follows: H zw þ hf =fw @hf ¼ fw Kw Ew þ Fg rðhof uÞ; @t zw

(A1)

in which hf is the flow depth in the flow domain, H is the groundwater head, Ew is the evaporation from lowland storage, fw is the areal fraction of lowland storage, Fg is runoff from the ponding domain, u is overland flow velocity vector, Kw is the bottom conductivity, zw is the bottom elevation of the lowland storage, zw is the maximum of zwH or thickness of bed material layer, and hof is the overland flow depth in excess of the lowland-storage capacity. Unit of the equation is m/day. We solve equation (A1) in two fractional steps. The first one is an implicit step that calculates the groundwater exchange and obtains intermediate states hf . The second step takes hof ¼ max 0; hf dw , where dw is the bucket depth of the depression storage, and sends result to

the 2-D diffusive wave overland flow solver as described in SP10 to solve for the convective term, rhof u. The exchange between groundwater and lowland storage becomes either exfiltration (Ex) or infiltration (Inf), depending on the direction. Equation (A1) can also be applied to describe transient shallow concentrated flows, paddy fields, or potholes. In addition, the lowland storages in different locations are connected via overland flow paths. When flooding occurs, river water may flood the overland flow domain, and in turn, fill the lowland-storage compartments. The threshold for backfill to happen is termed as hback, fw, and dw, all of which can be inferred from analyzing land cover maps and fine-resolution digital elevation model (DEM). [58] Acknowledgments. J.N. and M.S.P. are supported by the NOAA Center of Excellence for Great Lakes and Human Health. We thank Sam Levis, NCAR, for valuable advice related to CLM and Ben Ong, Institute for Cyber-Enabled Research (iCER) at MSU for help with high-performance computing. We thank three anonymous reviewers whose constructive and detailed comments have greatly helped in improving the manuscript.

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