Evapotranspiration, water use efficiency, and energy partitioning of a mature switchgrass stand

Agricultural and Forest Meteorology 217 (2016) 108–119

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet

Evapotranspiration, water use efficiency, and energy partitioning of a mature switchgrass stand Elke Eichelmann a,∗ , Claudia Wagner-Riddle a , Jon Warland a , Bill Deen b , Paul Voroney a a b

School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada N1G 2W1 Department of Plant Agriculture, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada N1G 2W1

a r t i c l e

i n f o

Article history: Received 5 May 2015 Received in revised form 2 November 2015 Accepted 7 November 2015 Available online 11 December 2015 Keywords: Switchgrass Eddy covariance Water budget Canopy conductance Latent heat flux Sensible heat flux

a b s t r a c t With the current interest in cellulosic biofuel feedstocks, switchgrass (Panicum virgatum L.) research has increased in North America over the past decades. However, implications of large-scale switchgrass production on local and regional water budgets are largely unknown. We conducted eddy covariance measurements over a large (60 ha) commercial switchgrass field in Southern Ontario, Canada, to determine evapotranspiration and energy fluxes during the sixth (2012) and seventh (2013) year since establishment of switchgrass. Annual total evapotranspiration was slightly smaller in the wet year of 2013 compared to the dry year of 2012 (562 ± 12 and 598 ± 9 mm, respectively; error estimate based on gap filling error). Canopy conductance was larger in the wet year (11.2 ± 1.5 mm s−1 in 2013; average ± standard error) than in the dry year (7.7 ± 0.9 mm s−1 in 2012), which suggests that switchgrass regulated stomatal opening to reduce water loss. Although this regulation reduced atmospheric CO2 fixation, it did not result in yield reduction. Water use efficiency of harvested dry matter was larger in 2012 compared to 2013 (1.82 ± 0.12 and 1.40 ± 0.11 g DM (kg H2 O)−1 , respectively; error estimates based on Gaussian error propagation). Water use efficiency based on gross ecosystem production (EWUE) in 2013 was 14.0 ± 0.6 g CO2 (kg H2 O)−1 , compared to 13.3 ± 0.3 g CO2 (kg H2 O)−1 in 2012 (error estimates based on Gaussian error propagation). Daily, monthly, and seasonal EWUE showed a trend to lower values in the dry year of 2012 compared to 2013. These results indicate that the switchgrass crop was able to reduce the water cost of aboveground biomass in the dry year by relocating carbon from belowground structures, but it fixed more atmospheric carbon per unit water evapotranspired in the wet year. Our findings show that mature switchgrass has the potential to withstand the impact of moderate drought or dry conditions. However, the impact of repeated drought and prolonged dry conditions on switchgrass and implications for regional water budgets are not clear yet. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Many countries have developed policies that require a certain percentage of transportation and heating fuel to be renewable to mitigate increasing CO2 emissions and associated climate change (Mabee, 2007). In the US and Canada, Renewable Fuel Standard (RFS) policies are implemented under their respective Clean Air

Abbreviations: BD, soil bulk density; BWUE, biome water use efficiency; EBC, energy balance closure; EC, eddy covariance; ET, evapotranspiration; EWUE, ecosystem water use efficiency; G, ground heat flux; GEP, gross ecosystem productivity; GPP, gross photosynthetic production; H, sensible heat flux; HWUE, harvest water use efficiency; NEE, net ecosystem exchange; Re, ecosystem respiration; Rn, net radiation; SOC, soil organic carbon; VPD, vapour pressure deficit; VWC, volumetric water content; WUE, water use efficiency; E, latent heat flux. ∗ Corresponding author. E-mail address: [email protected] (E. Eichelmann). http://dx.doi.org/10.1016/j.agrformet.2015.11.008 0168-1923/© 2015 Elsevier B.V. All rights reserved.

Acts. RFSs have led to increasing demand for biofuel feedstock production in recent years. Switchgrass (Panicum virgatum L.) is a North-American native perennial warm-season C4 grass that can be used as feedstock for cellulosic biofuel production under the RFS (Schnepf and Yacobucci, 2013). Although switchgrass has been grown as part of prairie ecosystems in the Conservation Reserve Program in the past decades (Lee et al., 2007), it has only gained importance from an agricultural perspective with the recent increase in biofuel feedstock demand (McLaughlin et al., 2002; Liebig et al., 2005). Switchgrass has been shown to have benefits over annual crops used for biofuel feedstocks, such as corn (Zea mays L.) and wheat (Triticum aestivum L.), like reduced fertilizer and pesticide input and high productivity across large regions of North-America (Bransby et al., 1998; McLaughlin and Walsh, 1998; McLaughlin and Adams Kszos, 2005; Parrish and Fike, 2005; Schmer et al., 2008). It has also been documented that switchgrass can improve soil quality by

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119

increasing soil organic carbon (SOC), improving water quality, and providing wildlife habitat (McLaughlin and Walsh, 1998). Impacts on biodiversity, soil and water quality are important ecosystem services that should be considered when assessing the effects of bioenergy feedstocks on the environment (VanLoocke et al., 2012; Rowe et al., 2009; Hill et al., 2006). Water cycling is one of the key components in ecosystems that links many biogeochemical and energy cycles (Sellers, 1997; Pielke et al., 1998; Wilson and Baldocchi, 2000). However, implications of large-scale production of perennial bioenergy crops on local water and energy balances are not well known (Rowe et al., 2009). One of the effects of climate change is an increase in the frequency of droughts and dry spells across large parts of North America, including many key regions for agricultural production (Dai et al., 2004; Cook et al., 2015). This will reduce agricultural water availability and increase competition for water resources (Steduto et al., 2007). Increased productivity often results in increased water use of a crop (Jackson et al., 2005). This has also been shown to be the case in switchgrass (Hickman et al., 2010). Switchgrass varieties differ in water requirements with upland varieties growing better under moderate soil moisture whereas lowland types performed best under high soil moisture (Porter, 1966). Switchgrass has been shown to have high WUE (Wagle and Kakani, 2014a; Skinner and Adler, 2010) and mature switchgrass has shown high productivity under moderate drought (Eichelmann et al., 2015). Some studies have shown switchgrass can survive extreme drought, but significant reduction of aboveground biomass occurs under drought (Barney et al., 2009; Knapp, 1984). It is important to assess the impact of large-scale switchgrass production on the local and regional water budget and consequences for switchgrass productivity to make decisions about its sustainability. With a deep and extensive root system, perennial plants like switchgrass can sequester additional carbon from the atmosphere, which is stored in belowground biomass and increases SOC in stable soil organic matter (McLaughlin et al., 2002). The process of sequestering additional carbon, which makes the ecosystem a net carbon sink, is an important ecosystem service. A study of a perennial bioenergy crop (Reed Canarygrass) in Finland has shown that net biome productivity (NBP) of the ecosystem indicated a carbon sink in wet years, but a carbon source during dry years (Shurpali et al., 2013). Similar results were observed for mature switchgrass for two growing seasons (defined as emergence to harvest) when moderate drought conditions resulted in lower gross primary production (GPP) (Eichelmann et al., 2015). Eichelmann et al. (2015) suggested that switchgrass was relocating belowground carbon from storage tissue to aboveground biomass during drought conditions. This behaviour would affect the water use efficiency of harvested biomass (HWUE) and of atmospheric carbon sequestration (EWUE) differently, since there is no atmospheric carbon sequestered when aboveground biomass is created with relocated carbon. Biome WUE (NBP per unit water lost) can indicate the efficiency of water use for carbon sequestration (VanLoocke et al., 2012). Information on WUE indices are needed when comparing the benefits of switchgrass and other bioenergy feedstocks and evaluating the effect of climatic conditions on ecosystem services. The objectives of this study were to determine the water budget of mature switchgrass and the variability of WUE indices between years and relate these to climatic conditions and biophysical properties of switchgrass such as canopy conductance. We conducted eddy covariance (EC) measurements of latent and sensible heat fluxes, as well as carbon dioxide exchange, on a mature switchgrass field on a commercial farm in Southern Ontario, Canada, over two consecutive years with contrasting climatic conditions. Additional energy fluxes like ground heat flux and net radiation were also measured for energy budget calculations. Latent energy flux measurements were converted to

109

evapotranspiration (ET) to analyze the seasonal and annual water budget of switchgrass. 2. Materials and methods 2.1. Site characteristics The study was conducted from January 2012 to December 2013 on a commercial farm in Southern Ontario, Canada. The switchgrass field was approximately 20 km east from the shore of Lake Huron (43◦ 35 0.89 N 81◦ 26 47.55 W approximately 300 m a.s.l.). Fig. 1 shows the location of the field site in relation to the Great Lakes. This region is classified as Dfb (warm summer continental or hemiboreal climates) in the Köppen climate classification. The field was approximately 60 ha in size with a slight rise by 13 m elevation from the southern part of the field to the north-eastern corner. The field site consisted of a Grey-Brown Luvisol with the northern third of the field classified as a Harriston loam or Harriston silt loam and the southern part as a Perth clay loam (Table 1). The measurement instruments were located in the part of the field classified as loam or silt loam. Switchgrass of the variety Cave-In-Rock (upland variety) was planted on this field in spring 2006. The only management input since switchgrass establishment was fertilizer in the form of urea applied at 78 kg N ha−1 in May 2012 and 101 kg ha−1 in May 2013. In both study years switchgrass was cut in fall (on September 10, 2012 and September 25, 2013) and baled within one to three weeks. Dry biomass yield was 1.09 ± 0.07 kg m−2 in 2012 and 0.79 ± 0.06 kg m−2 in 2013 (mean ±standard error). Dry biomass yield was determined by collecting all plants growing within 1 m2 from five random locations across the field on August 24, 2012, and September 24, 2013. All samples were oven dried at 60 ◦ C for one week and dry biomass weight recorded. We also corrected the yield estimates provided by the farmer for moisture content at harvest and compared them with the dry biomass yield derived from the five subsamples. In both years the farmers dry yield estimates fell within one standard deviation of our dry yield estimate. Information on dry biomass yield for previous years is not available for this site. However, the farmer reported that according to his estimates 2012 was the highest yielding year in the 6-year history of the field. For a detailed description of plant sampling procedures, field layout, soil properties, and management practices please see Eichelmann et al. (2015). 2.2. Meteorological measurements The following sensors were set up to monitor environmental conditions during the two study years: air temperature and humidity probe (HMP60, Vaisala Inc., Helsinki, Finland), rain gauge (TR525 Tipping Bucket Rain Gage, Texas Electronics, Dallas, TX, USA), snow depth sensor (SR50A Sonic Ranging Sensor, Campbell Scientific, Logan, UT, USA), shortwave and longwave radiation sensor (CNR1, Kipp and Zonen, Delft, the Netherlands), low frequency wind direction and speed for quality control (Wind Monitor Model 05103, R.M. Young, Traverse City, MI, USA), soil temperature at 2.5 cm, 10 cm, and 20 cm depth (copper-constantan thermocouples, made in house), soil moisture at 0–5 cm, 10 cm, and 20 cm depth (CS616 Water Content Reflectometers, Campbell Scientific, Logan, UT, USA), ground heat flux at 5 cm depth (HFP01 Soil Heat Flux Plate, Hukseflux, Delft, the Netherlands), and infra-red soil surface temperature (IRt/c OS36, Omega, Stamford, CT, USA). All sensors measured 30 min averages except for the rain gauge, which reported 30 min total precipitation. Due to the presence of small rocks and gravel, it was not possible to conduct soil moisture measurements at levels lower than 20 cm. Soil temperature, surface

110

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119

Fig. 1. Left: Location of the field site in Southern Ontario, Canada, close to Lake Huron. Right: Close up satellite image of the field. The red pin indicates the position the EC measurement instruments. Satellite pictures taken from Google Earth. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table 1 Soil bulk density (BD) and particle size distribution by depth for the switchgrass field site in Southern Ontario after seven years of switchgrass management. Soil texture analysis was split in four soil cores for the part of the field classified as loam or silt loam, and four soil cores for the part classified as clay loam. Error margins for bulk density are standard errors from 8 individual samples per depth. Error margins for particle size distribution are standard errors from 4 individual samples per depth. Depth (cm)

BD (g cm−3 )

Particle size distribution (g kg−1 ) Clay

Silt

Loamy soil 0–10 10–20 20–30 30–60

1.33 1.49 1.48 1.61

± ± ± ±

0.04 0.03 0.06 0.05

225 215 231 268

± ± ± ±

7 8 29 20

Clay soil 293 313 351 376

± ± ± ±

37 33 47 63

temperature, soil moisture, and ground heat flux measurements were replicated in two locations about 5 m apart from each other. Due to malfunctioning of the rain gauge for extended periods throughout the two study years we were not able to derive reliable accumulated precipitation estimates for the study site. The values used in this paper were taken from an Environment Canada weather station which is located in Goderich, ON, about 30 km to the northwest of the study site. During times when precipitation measurements for our site were available, they compared very well with measurements from this station (R2 = 0.91 for correlation of available monthly totals).

2.3. Eddy flux measurements A detailed description of the EC setup, flux calculations and filtering can be found in Eichelmann et al. (2015). Latent and sensible heat fluxes, as well as CO2 fluxes were measured from January 10, 2012 to December 31, 2013 using the EC method (Baldocchi et al., 1988). The EC measurement system included a sonic anemometer (CSAT3, Campbell Scientific, Logan, UT, USA) and a CO2 and H2 O open path trace gas analyzer (LI-7500, LI-COR Inc., Lincoln, NE, USA) both measuring at a frequency of 10 Hz. For the first 6 months of 2012 the EC instrumentation was mounted at a height of 2.12 m and raised to 3.45 m for the remainder of the experiment. The sonic anemometer faced 275◦ from North at all times. After high frequency EC data were screened for invalid data points, the TK3 software package (Mauder and Foken, 2011) available through the University of Bayreuth was used to calculate half hourly eddy fluxes. This software applies the following corrections: spike removal (Vickers and Mahrt, 1997), high frequency loss correction (Moore, 1986; Kaimal et al., 1972), SND correction (Schotanus et al., 1983), WPL correction (Webb et al., 1980), coordinate rotation based on the planar fit model (Wilczak et al., 2001), and stability and turbulence tests (Foken and Wichura, 1996; Kaimal and Finnigan, 1994).

Sand

Loamy soil 482 493 479 502

± ± ± ±

25 23 28 30

Clay soil 480 468 450 478

± ± ± ±

21 22 22 30

Loamy soil 293 292 290 230

± ± ± ±

27 27 34 42

Clay soil 228 220 200 146

± ± ± ±

44 49 65 68

Additional filtering routines were implemented based on the site specifications. To avoid wind distortion by mounting structures and fluxes originating from the small forest in the north, fluxes were filtered depending on wind direction. A filtering routine selecting for friction velocity (u* ) after Papale et al. (2006) was also applied. For each year, u* thresholds were defined individually for the three periods between January 1 to emergence, emergence to harvest (growing season), and harvest to December 31. Instead of using the median of the six individual u* thresholds for all temperature classes (Papale et al., 2006), we used the mean to define the final u* threshold. Over the two years of measurements, the lowest u* threshold observed was 0.07 m s−1 and the highest was 0.14 m s−1 . Friction velocity thresholds during the growing seasons were typically lower than during the non-growing seasons. Fluxes in this study are reported according to the micrometeorological sign convention, where positive fluxes indicate a gain for the atmosphere and negative fluxes indicate a loss for the atmosphere. 2.3.1. Energy balance closure The quality of EC measurements is usually assessed by calculating the energy balance closure (EBC) using the following energy balance equation (Wilson et al., 2002): E + H = Rn − G − SSoil

(1)

where E and H are the latent and sensible heat fluxes measured with the EC system, Rn is net radiation, G is soil heat flux, and SSoil is the soil heat storage term for the soil layer above the soil heat flux plates. The EBC is the slope of the linear regression between the measured eddy fluxes (E + H) and the available energy (Rn − G − SSoil ). Leuning et al. (2012) demonstrated the importance of correct estimation of the energy storage terms for EBC calculation. Therefore, a storage term for sensible and latent heat storage in the air layer between the ground and the EC instruments was added to E and H before the EBC was calculated. Additionally, the soil heat storage term was split in two layers: One layer from 0 cm depth to 1 cm, and a second layer from 1 cm to the depth of the soil

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119

heat flux plates at 5 cm. The temperature of the surface layer was determined from infrared thermometer measurements of surface temperature. The temperature of the lower layer was determined from soil thermocouples buried at 2.5 cm depth. The soil storage terms were calculated following Meyers and Hollinger (2004): SSoil =

TSoil ∗ Cs ∗ D t

(2)

where TSoil is the change in soil temperature over the time period t, D is the depth of the soil layer, and Cs is the heat capacity of moist soil in J m−3 K−1 . Cs was calculated according to Cs = BD ∗ Cd + VWC ∗ w ∗ Cw

(3)

where VWC is the volumetric water content for the top 5 cm, BD is the soil bulk density for the top 10 cm, Cd is the heat capacity of dry soil (840 J kg−1 K−1 , after Hanks and Ashcroft, 1980), w is the density of water, and Cw is the heat capacity of water. We only calculated the EBC when good quality measurements (i.e. measurements that passed all flagging and filtering routines) for all variables were available. Since the water content reflectometers cannot provide high quality VWC measurements in frozen soils and accurate estimation of energy used for freezing and thawing water in the soil layer is difficult, EBC was only calculated when soil temperature at depth between 0 and 20 cm was above 0 ◦ C. The energy balance closure values were 97% in 2012 and 90% in 2013. 2.3.2. Gap filling missing values After all filtering was performed, data coverage for the half hourly latent heat flux (E) series was 37% in 2012 and 32% in 2013. The longest gap in 2012 was just under seven days. In 2013 the longest gap was seven weeks which occurred in fall (October 3–November 19) due to malfunctioning of the LI-7500. Ninety-six percent of all gaps in each year were less than one day long. Two different methods for filling gaps in the latent heat flux series were tested (see Appendix A). The Regression procedure (Barr et al., 2006; Amiro et al., 2006) showed better performance in our test and was chosen to fill gaps in the latent heat flux record. In this method, gaps in the latent heat flux record are filled by using a linear regression between available energy (Rn − G) and latent heat flux (Barr et al., 2006; Amiro et al., 2006). A more detailed description of this gap filling method can be found in Appendix A. We also applied this procedure to the sensible heat fluxes with the modification that daytime and nighttime fluxes were not separated. We used the approach described by Moffat et al. (2007) to assess the performance of all gap filling procedures individually for each year and flux variable and derive error estimates for annual totals. In this approach, artificial gaps are created in each time series by removing (flagging) an additional 10% of the measured half hourly fluxes after all other filtering. This procedure is repeated 50 times using five different gap lengths scenarios to create 50 individual artificially flagged time series. These artificially flagged time series are then gap filled and annual totals are calculated for each of them. For more information on the different artificial gap scenarios and testing of the gap filling routines see Appendix A. The standard deviation of all annual totals calculated with the 50 artificial gap time series for each year was used to estimate the uncertainty of annual totals. When totals for only part of the year were calculated, we also calculated the same partial totals from the 50 artificial gap time series and again used the standard deviation of these results as uncertainty estimate for the partial totals. Introducing artificial gaps into the time series is a common approach for error estimation of eddy covariance data sets (Black et al., 2007; Richardson and Hollinger, 2007; Zeri et al., 2011; Wagle and Kakani, 2014). This approach only accounts for the error made by gap filling an incomplete time series and does not account for systematic or

111

random errors in the eddy fluxes. However, since the objective of this study was mainly to compare two years of eddy fluxes using the same instrumentation, set-up, and analysis, we assumed that any systematic or random errors were consistent between the two years, and, hence, did not affect the results. In the following, error estimates for time integrated evapotranspiration refer to values obtained based on the gap filling error estimates. CO2 fluxes were separated into gross photosynthetic production (GPP) and ecosystem respiration (Re) using the following equation: Fc GSdaytime = Re + GPP = Re − GEP

(4)

where FcGSdaytime are growing season daytime CO2 fluxes, Re is ecosystem respiration, GPP is gross photosynthetic production, and GEP is gross ecosystem productivity. All night-time and nongrowing season fluxes were assumed to be solely Re. Re fluxes were gap filled separately for the growing and non-growing season using a logistic equation dependent on temperature following Barr et al. (2004). Measured growing season daytime fluxes were partitioned into Re and GPP by calculating the daytime Re fluxes using the logistic equation used for gap filling and Eq. (4) (Barr et al., 2004). GPP fluxes were gap filled using a Michaelis–Menten type equation also following Barr et al. (2004). For a detailed description of the CO2 flux partitioning and gap filling routines refer to Eichelmann et al. (2015). 2.4. Canopy conductance Canopy conductance was derived to assess the impact of climatic conditions in both years on switchgrass water use patterns. Daytime canopy conductance was calculated by inverting the Penman–Monteith equation. gs =

ga (Rn−G)+a · cp · VPD · ga  · E

−

 

−1

(5)

where gs is canopy conductance, ga is aerodynamic conductance,  is the rate of change of the saturation vapour pressure with temperature, Rn is net radiation, G is ground heat flux, a is air density, cp is the specific heat capacity of air, VPD is vapour pressure deficit, and  is the psychrometric constant. The aerodynamic conductance was defined as (Monteith and Unsworth, 2008): u 1 = ra = raM + rb = 2 + 6.2 · u−0.67 ∗ ga u∗

(6)

where ra is the aerodynamic resistance, raM is the aerodynamic resistance for momentum transport, and rb is the additional aerodynamic resistance due to differences in roughness length for momentum and heat and water vapour transport (Monteith and Unsworth, 2008). Canopy conductance was only calculated for days on which no precipitation was recorded. For large rainfall events of more than 10 mm per day, we also excluded the day after the rainfall event from canopy conductance calculations. 2.5. Water use efficiency Water use efficiency (WUE) was calculated for the growing season for the switchgrass field in the following ways: • Harvest WUE (HWUE): total harvested dry biomass over total annual ET (VanLoocke et al., 2012). • Daily ecosystem WUE (EWUE): daily integral of daytime gross ecosystem productivity (GEP) over daily integral of daytime ET (Wagle and Kakani, 2014a). • Monthly EWUE: monthly integral of daytime GEP over monthly integral of daytime ET (Wagle and Kakani, 2014a).

112

• Seasonal EWUE: total growing season daytime GEP over total growing season daytime ET (Tubiello et al., 1999). • Seasonal regression EWUE: slope of regression between growing season monthly total GEP and growing season monthly total ET (Law et al., 2002). • Biome WUE (BWUE): net biome productivity (NBP) over total annual ET (VanLoocke et al., 2012). HWUE represents an agronomic perspective of water use efficiency where only harvested biomass is considered. Perennial plants like switchgrass have an extensive root system that has shown potential for enhancing carbon sequestration, which is considered an important ecosystem service (Garten and Wullschleger, 2000; Ma et al., 2000; McLaughlin et al., 2002; Frank et al., 2004; Liebig et al., 2008). This additional carbon sequestration as well as harvested biomass accumulation is accounted for when calculating EWUE (VanLoocke et al., 2012). BWUE only takes into account the additionally sequestered carbon (NBP) and allows for comparison of the WUE of carbon sequestration between different perennial plant species considered for bioenergy feedstock, like switchgrass and miscanthus (miscanthus × giganteus) (VanLoocke et al., 2012). We calculated EWUE on daily, monthly, and seasonal timescales. Higher time resolution for EWUE gives insight in changes of EWUE throughout the season and helps interpretation of how EWUE is influenced by changing environmental conditions, while seasonal EWUE gives an average EWUE performance across the whole growing season. Seasonal regression EWUE can be performed over multiple years and provides an estimate for average EWUE for a specific crop. If not stated otherwise, we report two values as significantly different from each other in this study if the values including their error intervals do not overlap.

Volumetric Water Content [%]

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119

30

20

10 01/12

07/12

01/13 Time (month/year)

07/13

01/14

Fig. 2. Average daily volumetric water content (solid green line) at 20 cm measured during the years 2012 and 2013 on a mature switchgrass field in Southern Ontario. Measurements are averages from two locations on the field. The red dashed line indicates 17%, an approximate wilting point for the soil at this site. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3. Results and discussion

10–20 W m−2 less than in 2013 (Table 2). High amounts of incoming solar radiation in June and July combined with very low precipitation in these months led to very dry soil moisture conditions during the growing season of 2012. Volumetric water content (VWC) at 20 cm depth was on average only 16% for June, July, and August 2012 (Fig. 2). During the same period in 2013 VWC averaged 20%. The wilting point for the field lay between 17 and 22% and the field capacity was determined to be 32–35%. (Soil Water Characteristics Calculator Tool, Version 6.02.74, http://hydrolab.arsusda.gob/ soilwater/Index.htm). Given that maximal daily VWC even after large rainfall events was only between 22 and 25% throughout the two years, the field capacity value calculated is probably overestimated. Average daily VWC at 20 cm stayed below 17% from June 27, 2012, to August 11, 2012, a time period of 47 days including the entire month of July (Fig. 2). During the year of 2013 average daily VWC at 20 cm dropped below 17% on a total of only 24 days with the longest continuous period below 17% being 11 days (Fig. 2). Soil moisture measurements for the 5 and 10 cm levels show the same trends (data not shown).

3.1. Environmental conditions

3.2. Energy partitioning

During the two study years the field site experienced contrasting environmental conditions. Conditions in 2012 were on average warmer (annual mean 9.6 ◦ C) and drier (annual precipitation 533 mm) than 30-year annual means (7.5 ◦ C, 998 mm) with part of the growing season being classified as a moderate drought (level D1) by Agriculture & Agri-Food Canada. The year 2013 represented conditions similar to the 30-year normal for temperature and precipitation (7.4 ◦ C, 868 mm). Mean monthly net radiation during the growing season (emergence to harvest) was similar for both years in May, but June and July 2012 received 10–20 W m−2 more and August and September

Partitioning of net radiation into the components of sensible, latent, and ground heat flux showed similar patterns during the two growing seasons (Fig. 3 and Table 2). In May, sensible and latent heat flux played an equal role in dispersing available energy. This pattern shifted for June and July when evapotranspiration from switchgrass plants increased and latent heat flux was approximately twice the magnitude of sensible heat flux during the midday hours. In August, sensible heat flux increased and latent heat flux decreased slightly so that the two became comparable in magnitude again. Wagle and Kakani (2014a) found that even during the main active growing season in June and July, sensible heat flux was the

Table 2 Monthly mean sensible heat flux (H), latent heat flux (E), ground heat flux (G), Net Radiation (Rn), monthly sum of precipitation, and evapotranspiration (ET) during the two study years. Error margins of annual ET are estimates of the error made by gap filling incomplete time series. Month

H (W m−2 )

E (W m−2 )

G (W m−2 )

Rn (W m−2 )

2012

2013

2012

2013

2012

2013

2012

January February March April May June July August September October November December Annual

−10 −4 16 47 45 23 30 34 29 5 −5 −7

−13 −10 3 25 40 29 24 41 33 10 −17 −10

9 19 34 32 60 105 104 78 53 35 20 12

13 10 24 52 61 92 96 82 48 26 14 11

N/A N/A N/A N/A 9 15 12 6 N/A −2 −7 −6

−4 −3 0 5 12 9 6 4 −2 −5 −9 −5

−10 8 55 86 125 154 161 127 83 32 11 1

Precipitation (mm)

ET (mm)

2013

2012

2013

2012

2013

−1 3 37 85 123 141 141 140 93 40 −2 −1

49 26 25 17 41 44 38 37 88 83 34 51 533

51 41 18 132 63 50 89 68 51 164 86 55 868

9 19 37 33 65 110 112 84 55 38 21 13 598 ± 9

14 9 26 54 66 96 104 89 51 28 15 12 562 ± 12

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119 Time of Day (hours)

−2

Energy Flux (W m )

0 600 500

5

10

15

20

Time of Day (hours) 0

5

10

15

20

Time of Day (hours) 0

10

15

20

Time of Day (hours) 0

5

10

15

20

August 2012

July 2012

June 2012

May 2012

5

113

400

400

300

300

200

200

100

100

0

0

600 −2

Energy Flux (W m )

600 500

500

600 August 2013

July 2013

June 2013

May 2013

500

400

400

300

300

200

200

100

100

0 0

0 20 5 10 15 Time of Day (hours)

0

10 20 5 15 Time of Day (hours)

10 15 5 20 Time of Day (hours)

0

0

5 15 10 20 Time of Day (hours)

Fig. 3. Diurnal cycle of energy partitioning into net radiation (blue), sensible heat flux (green), latent heat flux (red), and ground heat flux (black) for each month of the growing season 2012 (top panels) and 2013 (bottom panels). Values are pooled over the entire month for each half hour of the day. Error bars show standard errors for each half hour. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

dominant turbulent flux on a switchgrass field in Oklahoma, USA. They concluded that latent heat exchange was limited for their field due to a severe drought. Our results of increased partitioning of energy towards latent heat in June and July represent more regular growing season behaviour (Wever et al., 2002) and indicate that latent heat flux was not drastically limited by reduced soil moisture in the top 20 cm. A shift to increased sensible heat and decreased latent heat towards the end of the growing season has been documented in grassland and prairie ecosystems before (Wever et al., 2002; Ham and Knapp, 1998; Saigusa et al., 1998) and was probably caused by onset of plant senescence and associated decrease in canopy conductance (Ham and Knapp, 1998, see also Fig. 5). In 2012 the decrease in latent heat was more pronounced than in 2013 and was probably aggravated by reduced VWC and dry conditions (Wever et al., 2002). Peak ground heat flux did not show large changes throughout the growing seasons and ranged between 50 and 90 W m−2 .

We applied individual linear regressions between net radiation and sensible heat, latent heat, and ground heat flux for each growing season. Net radiation was partitioned 32 and 33% to sensible heat, 38 and 36% to latent heat, and 15 and 14% to ground heat flux during the growing season of 2012 and 2013, respectively (R2 = 0.71–0.84). This indicates that even when averaged across the growing season, the energy partitioning of the system was still slightly dominated by latent heat. Wagle and Kakani (2014a) reported energy partitioning of 39% to sensible heat, 28% to latent heat, and 14% to ground heat flux for the switchgrass field in Oklahoma. Although the system in Oklahoma was dominated by sensible heat due to the effect of drought, the respective magnitudes for partitioning into sensible, latent, and ground heat flux are comparable to our study. 3.3. ET vs. precipitation Daily evapotranspiration during January and February of both years was on average 0.44 ± 0.03 mm d−1 (average ±standard 18

700

16 −1

800

Rain 2012 ET 2012 Rain 2013 ET 2013

Canopy Conductance (mm s )

Precipitation or Evapotranspiration (mm)

900

600 500 400 300 200

12 10 8 6

100 0 01/01

14

01/04

01/07 Time (day/month)

01/10

01/01

Fig. 4. Cumulative evapotranspiration (solid lines) and precipitation (dashed lines) for the years 2012 (grey) and 2013 (black) on a mature switchgrass field in Southern Ontario.

4 01/05

01/06

01/07 01/08 Time (day/month)

01/09

01/10

Fig. 5. Monthly mean canopy conductance calculated from half hourly daytime values for a mature switchgrass field in Southern Ontario for the years 2012 (grey) and 2013 (black). Error bars show the standard error for each monthly average.

114

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119

error). From March on, daily evapotranspiration increased until it reached a peak in July in each year. Peak daily evapotranspiration was 5.2 ± 0.3 mm d−1 on July 6, 2012, and 4.8 ± 0.4 mm d−1 on July 19, 2013. Average daily evapotranspiration during the peak months of June and July was 3.64 ± 0.78 and 3.27 ± 0.90 mm d−1 in 2012 and 2013, respectively. After reaching peak values in July, daily evapotranspiration decreased gradually for the rest of the year until it reached average daily values of 0.42 ± 0.03 mm in December again. These measured values compare well with non-growing season daily values for switchgrass in Pennsylvania and Oklahoma (0.5 mm d−1 ; Skinner and Adler, 2010; Wagle and Kakani, 2014a) and prairie grassland (<0.5 mm d−1 ; Wever et al., 2002). Growing season values fall within the 3–6 mm d−1 range for switchgrass reported in previous studies (Skinner and Adler, 2010; Wagle and Kakani, 2014a), except for lower values recorded during a severe drought in July (2 mm d−1 ; Wagle and Kakani, 2014a). Average latent heat flux and total evapotranspiration was similar between the two years in May and August (Fig. 3 and Table 2). Even though the region experienced moderate drought during the growing season of 2012, evapotranspiration on the switchgrass field was larger in 2012 June and July compared to 2013 (Table 2). The increased evapotranspiration in these two months in 2012 was probably due to increased net radiation compared to 2013 and indicates that the crop did not experience water stress during the moderate drought. The lack of reduced ET under moderate drought conditions agrees with the carbon budget findings which showed no effects on biomass accumulation in 2012 (Eichelmann et al., 2015). Frank et al. (2004) observed significant amounts of root biomass down to 110 cm depth in field grown switchgrass with more than 50% of the root biomass located below 30 cm. They also observed significant increase in root biomass with switchgrass age. In 2012, the switchgrass crop was in its sixth year since establishment and probably had a well-developed root system which allowed it to access water stored deep in the soil profile that was not provided from precipitation in this year. This is supported by the fact that annual total evapotranspiration exceeded annual precipitation in 2012 by 65 ± 9 mm (Table 2), which can also be seen in the cumulative evapotranspiration and precipitation curves in Fig. 4. We were not able to determine water loss through drainage or run-off at our site. Previous studies have found that soil under more than five years of switchgrass cultivation tended to have increased infiltration rates and field moisture capacity compared to annual crops (Bharati et al., 2002; Bonin et al., 2012). Switchgrass root characteristics and low soil bulk density under switchgrass cultivation can lead to reduced water loss through run-off or drainage (Bharati et al., 2002; Bonin et al., 2012). Measured bulk densities (Table 1) were similar to bulk densities under switchgrass in the study by Bonin et al. (2012). Therefore, water losses through drainage and run-off were likely small for our mature switchgrass field and precipitation and available water were strongly coupled. Cumulative evapotranspiration exceeded precipitation at the beginning of June in 2012 (Fig. 4). The gap between cumulative evapotranspiration and precipitation increased through the very dry months of June and July in 2012. Even though more regular precipitation resumed in fall 2012, total annual evapotranspiration still exceeded precipitation (Fig. 4 and Table 2). In 2013 cumulative evapotranspiration never exceeded cumulative precipitation. Cumulative evapotranspiration showed very similar behaviour in both years (Fig. 4). Annual total evapotranspiration was slightly smaller in 2013 compared to 2012 (Table 2). Total growing season evapotranspiration was similar between the years with 391 ± 5 mm in 2012 and 398 ± 11 mm in 2013, even though the growing season in 2012 was shorter. Lower total growing season (May–September) values (331 mm–358 mm) were reported by Skinner and Adler (2010) associated with

low aboveground biomass. Considerably longer growing season (May–November) in Oklahoma resulted in 450 mm evapotranspiration (Wagle and Kakani, 2014a), which is close to 464 ± 6 mm and 432 ± 11 mm obtained for the same period in this study in 2012 and 2013, respectively. The results from the evapotranspiration measurements reinforce the suggestion that the switchgrass crop did not experience significant water stress in 2012. If evapotranspiration had been limited through drought in 2012, a significantly smaller evapotranspiration, especially during the driest month of July (based on soil moisture), would be expected compared to the year 2013 when water was more readily available to the plants. 3.4. Canopy conductance Monthly average canopy conductance was calculated for the two growing seasons (Fig. 5). Average canopy conductance across the whole growing season was 7.7 ± 0.9 mm s−1 in 2012, and 11.2 ± 1.5 mm s−1 in 2013 (average of daily canopy conductance ±standard error). Ham and Knapp (1998) reported a similar canopy conductance of around 9.5 mm s−1 at the end of August for a prairie site in Kansas. During the months of June and August canopy conductance for the 2012 growing season was significantly lower (9.0 ± 1.0 and 6.5 ± 0.6 mm s−1 , respectively) than for 2013 (12.6 ± 1.4 and 12.1 ± 4.4 mm s−1 , respectively; Fig. 5). In July average canopy conductance was not significantly different between the two years, due to high variability, but had a trend to lower values in 2012 (9.3 ± 3.1 and 12.0 ± 1.7 mm s−1 for 2012 and 2013, respectively; Fig. 5). The lower values during part of the growing season of 2012 would suggest that the switchgrass crop was limiting water loss through decreased stomatal opening, although this is not supported by the measurements of evapotranspiration and latent heat flux from the field (see Section 3.3). Wever et al. (2002) also reported lower canopy conductance for a Canadian grassland ecosystem during dry periods with limited soil moisture supply. But contrary to our findings, they observed a reduction in latent heat flux compared to periods with higher available soil moisture (Wever et al., 2002). 3.5. Water use efficiency Due to a significantly larger biomass yield in 2012 (based on ANOVA with significance level of 0.05; p-value = 0.00064, df = 1, F = 17.86) but only small differences in annual ET, water use efficiency (WUE) based on harvested dry matter (DM) was larger in 2012 (1.82 ± 0.12 g DM (kg H2 O)−1 ) than in 2013 (1.40 ± 0.11 g DM (kg H2 O)−1 ; error estimates based on Gaussian error propagation). A larger HWUE in the dry year indicates that the switchgrass stand was able to reduce the water cost of biomass production for that year through stomatal control. The carbon budget for this switchgrass stand showed that during the dry year of 2012 some of the carbon used for aboveground biomass production probably originated from carbon stored in crown and root tissue in previous years (Eichelmann et al., 2015). Relocating carbon from belowground storage tissue to aboveground biomass would have less evapotranspiration associated with it and would increase WUE from an agronomic perspective. Measured HWUE values were in the upper range or higher compared to values derived for switchgrass in a modelling study (0.5–1.5 g DM (kg H2 O)−1 ; VanLoocke et al., 2012). Zhuang et al. (2013) reported slightly larger HWUE of 1.9 g DM (kg H2 O)−1 from modelling estimates for switchgrass grown on land currently under maize production in the US. Hickman et al. (2010) on the other hand reported a considerably lower HWUE of 1.0 g DM (kg H2 O)−1 for switchgrass in Illinois, USA.

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119

Water Use Efficiancy (g CO2/kg H2O)

25

20

15

10

5

0 01/05

01/06

01/07 01/08 Time (day/month)

01/09

01/10

Fig. 6. Daily ecosystem water use efficiency averaged over seven day periods for a mature switchgrass field calculated from daytime GEP over daytime ET for the 2012 (grey) and 2013 (black) growing seasons.

Literature values for HWUE often only consider growing season ET for the calculation. To compare HWUE of switchgrass with literature values of other crops considered as feedstock for bioenergy, such as maize, wheat, and miscanthus, we calculated HWUE based on growing season ET, obtaining 2.8 and 2.0 g DM (kg H2 O)−1 for 2012 and 2013, respectively. Zwart and Bastiaanssen (2004) reported a global average HWUE (using growing season ET) for maize of 1.8 g DM (grain) (kg H2 O)−1 and 1.1 g DM (grain) (kg H2 O)−1 for wheat. Hickman et al. (2010) measured a HWUE of 1.9 g DM (kg H2 O)−1 for both, miscanthus and maize based on grain yield. However, if HWUE for maize is calculated on a total aboveground biomass basis, HWUE increases to 3.0 g DM (kg H2 O)−1 (Hickman et al., 2010). HWUE values from the mature switchgrass field exceed all cited literature values, with the exception of HWUE for maize using total aboveground biomass. Since complete maize residue removal can have adverse environmental impacts, it is generally not recommended (Blanco-Canqui and Lal, 2009). EWUE is based on GEP and represents the amount of atmospheric CO2 fixed per kg of water evapotranspired over a given time period. EWUE increased rapidly at the start of the growing season during the month of May in 2012 (Fig. 6). Although the initial increase was similar in both years, EWUE increased more slowly at the end of May in 2013 compared to 2012. However, by mid June values in both years were comparable again at between 15 and 16 g CO2 (kg H2 O)−1 . In 2013 EWUE increased even further to a short peak in early July of up to 21.5 g CO2 (kg H2 O)−1 (Fig. 6). After the peak in 2013 values stayed around 17 g CO2 (kg H2 O)−1 until early August when they started to decline slowly. From mid June 2012 EWUE stayed around 16 g CO2 (kg H2 O)−1 until it started to decrease slowly at the end of July. EWUE was larger in 2012 than 2013 at the start of the growing season in May and early June, but it was larger in 2013 than 2012 for most of the time from mid June to the end of the growing season (Fig. 6). Values of monthly EWUE for the two growing season also show these trends (Table 3). The pattern of peak EWUE occurring in early June, high values (12–15 g CO2 (kg H2 O)−1 ) for the rest of June and July and gradually declining EWUE in August and September was also observed by Skinner and Adler (2010). Maximum daily EWUE values of 26 g CO2 (kg H2 O)−1 measured during the second growing season of a switchgrass field in Oklahoma (Wagle and Kakani, 2014a) are also consistent with maximum daily EWUE of 22

115

and 26 g CO2 (kg H2 O)−1 recorded in this study in 2012 and 2013, respectively (data not shown). Monthly EWUE also showed a trend to lower values in the dry year of 2012 compared to 2013 (Table 3) which suggests that dry conditions and drought can reduce EWUE. This is consistent with the observation of Wagle and Kakani (2014a) who reported a decrease in monthly EWUE in July and August due to the effect of a severe drought. On a seasonal basis, however, the differences between the two growing seasons are not as pronounced as for monthly EWUE. Seasonal EWUE on our mature switchgrass field was 13.3 ± 0.3 and 14.0 ± 0.6 g CO2 (kg H2 O)−1 in 2012 and 2013, respectively (error estimates based on Gaussian error propagation). However, the seasonal EWUE value reported by Wagle and Kakani (2014a) was 11.7 g CO2 (kg H2 O)−1 , lower than our values due to the effect of severe drought. Values of EWUE calculated for our switchgrass ecosystem also exceed values for biweekly EWUE of 6.2–7.7 g CO2 (kg H2 O)−1 for a northern temperate grassland (Wever et al., 2002). We calculated the seasonal regression EWUE combined for both years. The slope of a regular linear regression of monthly GEP versus monthly ET for the months of May–September 2012 and 2013 is 17.7 ± 6.5 g CO2 (kg H2 O)−1 (±95% confidence interval on slope) with an R2 of 0.81. This is considerably larger than the value of 9.9 g CO2 (kg H2 O)−1 reported for a switchgrass field by Wagle and Kakani (2014a) and the values of 3.4 g CO2 (kg H2 O)−1 for grassland ecosystems, 3.1 g CO2 (kg H2 O)−1 for annual crops (corn, soybean, winter wheat) and 5.4 g CO2 (kg H2 O)−1 for a corn/soybean rotation reported by Law et al. (2002). However, the intercept for our linear regression was −374.1 ± 544 g CO2 m−2 (±95% confidence interval), which was very large. For the grassland ecosystems the intercept was only −67.88 g CO2 m−2 and for the annual crops it was −31.60 g CO2 m−2 Law et al. (2002). Although Wagle and Kakani (2014a) do not state the intercept for their regression, when we reproduced the regression from the monthly GEP and ET data listed we derived an intercept of −10.98 g CO2 m−2 . The larger intercept in our study would suggest that our monthly ET measurements contain a significant contribution of soil evaporation, since ET is positive (21 mm) when GEP is zero. Especially during the month of May soil evaporation is probably large. In May the canopy is still sparse since switchgrass only emerged in early to mid May in both years. If we force the linear regression through zero to reduce the effect of soil evaporation on evapotranspiration, we get a slope of 13.5 ± 2.33 g CO2 (kg H2 O)−1 (R2 = 0.78). This is closer to, but still larger than the value of 9.9 g CO2 (kg H2 O)−1 reported by Wagle and Kakani (2014a). The large differences (13–18 g CO2 (kg H2 O)−1 ) we observed in seasonal EWUE calculated for our field using different methods suggest that a standardization of WUE calculation should be implemented to facilitate comparison of WUE between studies. Even though the values for different EWUE metrics for the dry year of 2012 were lower on the mature switchgrass field than during the regular year of 2013, they still exceed most other values reported for EWUE for young switchgrass stands (Skinner and Adler, 2010; Wagle and Kakani, 2014a). This suggests that water cycling dynamics for mature switchgrass stands can differ from young stands during the establishment phase, a finding in agreement with the carbon cycling dynamics (Eichelmann et al., 2015). Wagle and Kakani (2014a) note that the higher water use efficiency found for switchgrass stands compared to annual crops shows potential for switchgrass biomass production in drought prone regions. Our findings extend this potential to mature stands with a higher EWUE and highlight that mature stands can also be resilient to drought and dry conditions. However, climatic conditions can also influence WUE as can be seen in the differences in WUE for the same switchgrass stand between the two years in our study. Annual temperatures during the study in Pennsylvania (9.7–10.0 ◦ C; Skinner and Adler, 2010) were comparable to the first

116

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119

Table 3 Monthly GEP, daytime ET, and EWUE during the growing season. Month

May 2012 June 2012 July 2012 August 2012 September 2012

GEP (g CO2 m−2 )

EWUE (g CO2 kg H2 O−1 )

ET (mm)

2012

2013

2012

2013

2012

2013

483 1503 1700 1082 197

295 1293 1725 1339 591

59 106 109 80 48

62 91 97 84 47

8.2 14.1 15.7 13.6 4.1

4.8 14.3 17.7 15.9 12.6

year (9.6 ◦ C), but warmer than the average during the second year in our study (7.4 ◦ C). Annual temperature during the study in Oklahoma was considerably warmer than in our study (16.9 ◦ C; Wagle and Kakani, 2014a). These climatic differences could also have led to differences in WUE between the studies. More studies on WUE of mature switchgrass stands in a variety of climatic conditions are needed to draw general conclusions on the water cycling dynamics of mature switchgrass. The switchgrass field in this study was a carbon source in 2012 and a small carbon sink in 2013 (NBP = −389 ± 165 and 216 ± 165 g CO2 m−2 in 2012 and 2013, respectively; error estimates based on gap filling error) (Eichelmann et al., 2015). Therefore, WUE of carbon sequestration (BWUE) was negative in 2012 (−0.65 ± 0.28 g CO2 (kg H2 O)−1 ) and positive but small in 2013 (0.38 ± 0.29 g CO2 (kg H2 O)−1 ; error estimates based on Gaussian error propagation). In their modelling study, VanLoocke et al. (2012) found a maximum BWUE of 1.5 g CO2 (kg H2 O)−1 for the Midwest US. The smaller BWUE for the mature switchgrass stand highlights the finding that mature switchgrass provides only a small carbon sink in some years and is probably carbon neutral on average (Eichelmann et al., 2015). When comparing the different WUE metrics, HWUE, EWUE, and BWUE, we find that HWUE was larger in the dry year of 2012, but EWUE and BWUE were smaller in the dry year. This is due to the differences in definition of these metrics. In 2012 the switchgrass was able to produce more biomass per kg H2 O evaporated than in 2013. From an ecosystem perspective, however, the crop in 2013 was able to fix more atmospheric CO2 per kg H2 O evaporated than the 2012 crop. This is in line with the findings for canopy conductance, showing that canopy conductance was reduced through part of the 2012 growing season. The switchgrass did probably limit stomatal opening in 2012, which reduced atmospheric CO2 fixation. Due to higher air temperature in 2012 and slightly larger VPD, latent heat flux was, however, not significantly reduced by the small limitation of stomatal opening. Biomass accumulation was larger in 2012 due to supply of additional carbon from belowground storage tissue. This is evident in the BWUE, which was negative in 2012 (the system was a carbon source). 4. Summary and conclusion We quantified latent and sensible heat fluxes, evapotranspiration, canopy conductance, and water use efficiency on different time scales for two contrasting growing seasons during year six and seven of a mature switchgrass stand. The first growing season (2012) experienced a warm and dry climate with parts of the growing season classified as moderate drought, while the second growing season (2013) was close to the 30-year average for precipitation and temperature. Partitioning of net radiation across each growing season was dominated by latent heat flux (37%), followed closely by sensible heat flux (32.5%). Ground heat flux played only a minor role in dispersing energy with 14.5%. In both years, latent heat flux was the dominant turbulent flux in June and July, while sensible and

latent heat flux were of comparable magnitude in May and August. Latent heat flux and energy partitioning did not show typical signs of the crop being impacted by drought in 2012. Daily evapotranspiration was very small in the winter months from December to February. It increased in spring reaching peak values in early to mid July, after which it gradually declined again. Annual evapotranspiration exceeded annual precipitation in 2012, but precipitation exceeded ET in 2013. Cumulative ET showed very similar behaviour between the two years and annual ET was slightly higher in the dry and warm year of 2012 than in 2013. These findings suggest that the mature switchgrass stand was able to access water deeper down in the soil profile with its extensive root system and was able to mitigate the impact of a moderate drought in 2012. Canopy conductance was slightly larger in 2013 than in 2012, which suggests that the switchgrass crop regulated stomatal opening somewhat in 2012 to reduce water loss. This regulation did, however, not cause a large reduction in water loss compared to 2013, nor was a yield reduction observed. Results for HWUE, EWUE, and BWUE also show that although the switchgrass crop did produce more biomass per kg H2 O in 2012, it did fix less atmospheric carbon per kg H2 O when compared to 2013. By using carbon stored in belowground structures for aboveground biomass production in 2012 (Eichelmann et al., 2015), the switchgrass stand was able to reduce the water cost of aboveground biomass production. Our findings from the energy and water budget for a mature switchgrass crop grown in a continental climate show that mature switchgrass can mitigate the impact of moderate drought and has potential to be grown under dry conditions. However, it should also be considered that evapotranspiration exceeded precipitation during dry conditions in this study, therefore lowering the amount of water stored in the system. The long-term effect of this outcome on the regional water budget in case of more frequent dry years should be further investigated, especially since dry spells and droughts will become more frequent with climate change. Acknowledgement This research was funded by an NSERC Discovery grant and NSERC Discovery Accelerator Supplement Award. The authors would like to thank Don Nott of Nott Farms (Ont) Ltd. for making his switchgrass field available for this research and for his cooperation throughout the project. Appendix A. Gap filling latent heat fluxes We tested two different methods for filling gaps in the latent heat flux series. The first method was based on the procedure described by Reichstein et al. (2005) called Marginal Distribution Sampling (MDS) which consisted of a combination of Mean Diurnal Variation and Look-Up tables. Missing values were filled

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119

117

1

1 0.8

Relative RSME

0.8

R

2

0.6

0.4

0.2

0

0.6 0.4 0.2

Very short

Medium Short Long Artificial Gap Scenario

Very long

0

Very short

(a)

Medium Short Long Artificial Gap Scenario

Very long

(b)

Fig. A.7. Results for R2 (a) and relative root mean square error (b) of comparing measured vs gap filled values for two different gap filling algorithms for the year 2012. The first box indicates results for MDS gap filling (red), the second box indicates results for Regression gap filling (blue) for each artificial gap filling scenario (very short to very long gaps). The line inside each box marks the median, boxes show the interquartile range, and whiskers display the most extreme data points not considered outliers. Outliers are plotted separately. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

based on measured values with similar meteorological conditions (global radiation, air temperature, and vapour pressure deficit) within a variable time window. If values could not be filled using this type of Look-Up table, they were filled using a Mean Diurnal Variation approach. The second gap filling method tested for latent heat fluxes was based on a linear regression between available energy (Rn − G) and latent heat flux (Barr et al., 2006; Amiro et al., 2006). This method will be called ‘Regression’ in the following discussion. The regression was performed on a 240 point window that was moved through the data series 48 points at a time (Barr et al., 2006; Amiro et al., 2006). Daytime and nighttime were treated separately, but nighttime values were not set to zero as in Barr et al. (2006) and Amiro et al. (2006). Nighttime was defined as the time between astronomical sunset and astronomical sunrise when global radiation levels were below 20 W m−2 . To test which of the two gap filling procedures performed better on our data series, we followed the procedure outlined by Moffat et al. (2007) and created artificial gaps by removing (flagging) an additional 10% of the measured half hourly fluxes after all other filtering. We introduced artificial gaps of different length according to five different scenarios: • • • • •

Very short gaps of single half hours. Short gaps of four consecutive hours. Medium gaps of 1.5 days. Long gaps of 12 days. Very long gaps of seven weeks during the non-growing season.

The first four scenarios followed the procedure outlined by Moffat et al. (2007). The last scenario was included to assess the impact of a seven week gap that occurred during the fall of 2013. For each of the five artificial gap scenarios, 10 different implementations were created with the gaps at different locations within the time series. This resulted in 50 different artificially flagged time series for each flux variable and each year of measurements. The two different gap filling algorithms were applied to each of the 50 time series in each year. Statistical parameters were calculated to compare the original measurements with the gap filling results (Moffat et al., 2007). The Regression gap filling procedure consistently showed a slightly better performance than MDS. This can be seen by comparing the R2 values and relative root mean square errors from the testing routine for the year 2012 (Fig. A.7).

The same behaviour was found in the other statistical parameters: absolute root mean square error, mean absolute error, and bias error (data not shown).

References Amiro, B.D., Barr, A.G., Black, T.A., Iwashita, H., 2006. Carbon, energy and water fluxes at mature and disturbed forest sites, Saskatchewan, Canada. Agric. For. Meteorol. 136, 237–251, http://dx.doi.org/10.1016/j.agrformet.2004.11.012 http://www.sciencedirect.com/science/article/pii/S0168192305002017. Baldocchi, D., Hicks, B., Meyers, T.P., 1988. Measuring biosphere–atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology 69, 1331–1340. Barney, J.N., Mann, J.J., Kyser, G.B., Blumwald, E., Van Deynze, A., DiTomaso, J.M., 2009. Tolerance of switchgrass to extreme soil moisture stress: ecological implications. Plant Sci. 177, 724–732, http://dx.doi.org/10.1016/j.plantsci. 2009.09.003 http://linkinghub.elsevier.com/retrieve/pii/S0168945209002519. Barr, A.G., Black, T.A., Hogg, E.H., Kljun, N., Morgenstern, K., Nesic, Z., 2004. Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agric. For. Meteorol. 126, 237–255, http://dx.doi.org/10.1016/j.agrformet.2004.06.011 http://linkinghub.elsevier. com/retrieve/pii/S0168192304001686. Barr, A.G., Morgenstern, K., Black, T.A., Mccaughey, J.H., Nesic, Z., 2006. Surface energy balance closure by the eddy-covariance method above three boreal forest stands and implications for the measurement of the CO2 flux. Agric. For. Meteorol. 140, 322–337, http://dx.doi.org/10.1016/j.agrformet.2006.08.007 http://linkinghub.elsevier.com/retrieve/pii/S0168192306002292. Bharati, L., Lee, K.H., Isenhart, T.M., Schultz, R.C., 2002. Soil–water infiltration under crops, pasture, and established riparian buffer in Midwestern USA. Agrofor. Syst. 56, 249–257. Black, K., Bolger, T., Davis, P., Nieuwenhuis, M., Reidy, B., Saiz, G., Tobin, B., Osborne, B., 2007. Inventory and eddy covariance-based estimates of annual carbon sequestration in a Sitka spruce (Picea sitchensis (Bong.) Carr.) forest ecosystem. Eur. J. For. Res. 126, 167–178, http://dx.doi.org/10.1007/s10342005-0092-4. Blanco-Canqui, H., Lal, R., 2009. Crop residue removal impacts on soil productivity and environmental quality. CRC Crit. Rev. Plant Sci. 28, 139–163. Bonin, C., Lal, R., Schmitz, M., Wullschleger, S., 2012. Soil physical and hydrological properties under three biofuel crops in Ohio. Acta Agric. Scand. Sect. B – Soil Plant Sci. 62, 595–603. Bransby, D., McLaughlin, S., Parrish, D., 1998. A review of carbon and nitrogen balances in switchgrass grown for energy. Biomass Bioenergy 14, 379–384 http://linkinghub.elsevier.com/retrieve/pii/S0961953497100745. Cook, B.I., Ault, T.R., Smerdon, J.E., 2015. Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci. Adv. 1, e1400082, http://dx. doi.org/10.1126/sciadv.1400082 http://advances.sciencemag.org/content/1/1/ e1400082.abstract. Dai, A., Trenberth, K.E., Qian, T., 2004. A global dataset of palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming. J. Hydrometeorol. 5, 1117–1130. Eichelmann, E., Wagner-Riddle, C., Warland, J., Deen, B., Voroney, P., 2015. Carbon dioxide exchange dynamics over a mature switchgrass stand. GCB Bioenergy, http://dx.doi.org/10.1111/gcbb.12259.

118

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119

Foken, T., Wichura, B., 1996. Tools for quality assessment of surface-based flux measurements. Agric. For. Meteorol. 78, 83–105, http://dx.doi.org/10.1016/ 0168-1923(95)02248-1 http://linkinghub.elsevier.com/retrieve/pii/ 0168192395022481. Frank, A., Berdahl, J., Hanson, J., 2004. Biomass and carbon partitioning in switchgrass. Crop Sci. 44, 1391–1396 http://ddr.nal.usda.gov/handle/10113/ 8468. Garten, C., Wullschleger, S., 2000. Soil carbon dynamics beneath switchgrass as indicated by stable isotope analysis. J. Environ. Qual. 29, 645–653 http://cat. inist.fr/?aModele=afficheN&cpsidt=1345103. Ham, J.M., Knapp, A.K., 1998. Fluxes of CO2 , water vapor, and energy from a prairie ecosystem during the seasonal transition from carbon sink to carbon source. Agric. For. Meteorol. 89, 1–14. Hanks, R.J., Ashcroft, G.L., 1980. Applied Soil Physics: Soil Water and Temperature Application. Springer, http://dx.doi.org/10.1007/978-1-4684-0184-4. Hickman, G.C., VanLoocke, A., Dohleman, F.G., Bernacchi, C.J., 2010. A comparison of canopy evapotranspiration for maize and two perennial grasses identified as potential bioenergy crops. GCB Bioenergy 2, 157–168, http://dx.doi.org/10. 1111/j.1757-1707.2010.01050.x. Hill, J., Nelson, E., Tilman, D., Polasky, S., Tiffany, D., 2006. Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels. Proc. Natl. Acad. Sci. U. S. A. 103, 11206–11210, http://dx.doi.org/10.1073/pnas. 0604600103 http://www.pubmedcentral.nih.gov/articlerender. fcgi?artid=1544066&tool=pmcentrez&rendertype=abstract. Jackson, R.B., Jobbágy, E.G., Avissar, R., Roy, S.B., Barrett, D.J., Cook, C.W., Farley, K.A., le Maitre, D.C., McCarl, B.A., Murray, B.C., 2005. Trading water for carbon with biological carbon sequestration. Science 310 (80), 1944–1947. Kaimal, J.C., Finnigan, J.J., 1994. Atmospheric Boundary Layer Flows: Their Structure and Measurement, Oxford University Press http://www.loc.gov/ catdir/enhancements/fy0602/92020792-t.html. Kaimal, J.C., Wyngaard, J.C., Izumi, Y., Coté, O.R., 1972. Spectral characteristics of surface-layer turbulence. Q. J. R. Meteorol. Soc. 98, 563–589, http://dx.doi.org/ 10.1002/qj.49709841707. Knapp, A.K., 1984. Water relations and growth of three grasses during wet and drought years in a tallgrass prairie. Oecologia 65, 35–43. Law, B.E., Falge, E., Gu, L., Baldocchi, D.D., Bakwind, P., Berbigier, P., Davis, K., Dolman, A.J., Falk, M., Fuentes, J., Goldstein, A., Granier, A., Grelle, A., Hollinger, D., Janssens, I., Jarvis, P., Jensen, N.O., Katul, G., Mahli, Y., Matteucci, G., Meyers, T., Monson, R., Munger, W., Oechel, W., Olson, R., Pilegaard, K., Paw, U., Thorgeirsson, K., Valentini, H., Verma, R., Vesala, S., Wilson, T., Wofsy, K.S., 2002. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agric. For. Meteorol. 113, 97–120, http://dx.doi.org/ 10.1016/S0168-1923(02)00104-1. Lee, D.K., Doolittle, J.J., Owens, V.N., 2007. Soil carbon dioxide fluxes in established switchgrass land managed for biomass production. Soil Biol. Biochem. 39, 178–186. Leuning, R., van Gorsel, E., Massman, W.J., Isaac, P.R., 2012. Reflections on the surface energy imbalance problem. Agric. For. Meteorol. 156, 65–74, http://dx. doi.org/10.1016/j.agrformet.2011.12.002. Liebig, M., Johnson, H., Hanson, J., Frank, A., 2005. Soil carbon under switchgrass stands and cultivated cropland. Biomass Bioenergy 28, 347–354, http://dx.doi. org/10.1016/j.biombioe.2004.11.004. Liebig, M.A., Schmer, M.R., Vogel, K.P., Mitchell, R.B., 2008. Soil carbon storage by switchgrass grown for bioenergy. BioEnergy Res. 1, 215–222, http://dx.doi.org/ 10.1007/s12155-008-9019-5. Ma, Z., Wood, C.W., Bransby, D.I., 2000. Soil management impacts on soil carbon sequestration by switchgrass. Biomass Bioenergy 18, 469–477 http:// linkinghub.elsevier.com/retrieve/pii/S0961953400000131. Mabee, W.E., 2007. Policy options to support biofuel production. In: Olsson, L. (Ed.), In: Biofuels. Advances in Biochemical Engineering/Biotechnology, vol. 108. Springer, Berlin, Heidelberg, pp. 329–357. Mauder, M., Foken, T., 2011. Documentation and Instruction Manual of the Eddy-Covariance Software Package TK3, Technical Report 46. University of Bayreuth, Bayreuth. McLaughlin, S., Walsh, M., 1998. Evaluating environmental consequences of producing herbaceous crops for bioenergy. Biomass Bioenergy 14, 317–324 http://www.sciencedirect.com/science/article/pii/S0961953497100666. McLaughlin, S.B., Adams Kszos, L., 2005. Development of switchgrass (Panicum virgatum) as a bioenergy feedstock in the United States. Biomass Bioenergy 28, 515–535, http://dx.doi.org/10.1016/j.biombioe.2004.05.006 http://linkinghub. elsevier.com/retrieve/pii/S0961953404001904. McLaughlin, S.B., De La Torre Ugarte, D.G., Garten, C.T., Lynd, L.R., Sanderson, M.A., Tolbert, V.R., Wolf, D.D., 2002. High-value renewable energy from prairie grasses. Environ. Sci. Technol. 36, 2122–2129 http://www.ncbi.nlm.nih.gov/ pubmed/12038820. Meyers, T.P., Hollinger, S.E., 2004. An assessment of storage terms in the surface energy balance of maize and soybean. Agric. For. Meteorol. 125, 105–115. Moffat, A.M., Papale, D., Reichstein, M., Hollinger, D.Y., Richardson, A.D., Barr, A.G., Beckstein, C., Braswell, B.H., Churkina, G., Desai, A.R., Falge, E., Gove, J.H., Heimann, M., Hui, D., Jarvis, A.J., Kattge, J., Noormets, A., Stauch, V.J., 2007. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agric. For. Meteorol. 147, 209–232, http://dx.doi.org/10.1016/j. agrformet.2007.08.011 http://linkinghub.elsevier.com/retrieve/pii/ S016819230700216X. Monteith, J.L., Unsworth, M.H., 2008. Principles of Environmental Physics, 3rd ed. Elsevier Inc.

Moore, C.J., 1986. Frequency response corrections for eddy correlation systems. Bound. Layer Meteorol. 37, 17–35. Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch, W., Longdoz, B., Rambal, S., Valentini, R., Vesala, T., Yakir, D., 2006. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3, 571–583, http://dx.doi.org/10.5194/bg-3-571-2006. Parrish, D., Fike, J., 2005. The biology and agronomy of switchgrass for biofuels. CRC Crit. Rev. Plant Sci. 24, 423–459, http://dx.doi.org/10.1080/ 07352680500316433. Pielke, R.A., Avissar, R., Raupach, M., Dolman, A.J., Zeng, X., Denning, A.S., 1998. Interactions between the atmosphere and terrestrial ecosystems: influence on weather and climate. Glob. Change Biol. 4, 461–475. Porter, C.L., 1966. An analysis of variation between upland and lowland switchgrass, Panicum virgatum L., in central Oklahoma. Ecology 47, 980 http:// www.jstor.org/stable/1935646. Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., Valentini, R., 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439, http://dx.doi.org/10.1111/j. 1365-2486. 2005.001002.x. Richardson, A.D., Hollinger, D.Y., 2007. A method to estimate the additional uncertainty in gap-filled NEE resulting from long gaps in the CO2 flux record. Agric. For. Meteorol. 147, 199–208, http://dx.doi.org/10.1016/j.agrformet. 2007.06.004 http://linkinghub.elsevier.com/retrieve/pii/S0168192307001645. Rowe, R.L., Street, N.R., Taylor, G., 2009. Identifying potential environmental impacts of large-scale deployment of dedicated bioenergy crops in the UK. Renew. Sustain. Energy Rev. 13, 271–290. Saigusa, N., Oikawa, T., Liu, S., 1998. Seasonal variations of the exchange of CO2 and H2 O between a grassland and the atmosphere: an experimental study. Agric. For. Meteorol. 89, 131–139. Schmer, M.R., Vogel, K.P., Mitchell, R.B., Perrin, R.K., 2008. Net energy of cellulosic ethanol from switchgrass. Proc. Natl. Acad. Sci. U. S. A. 105, 464–469 http:// www.pnas.org/content/105/2/464.short. Schnepf, R., Yacobucci, B.D., 2013. Renewable fuel standard (RFS): overview and issues. Congr. Res. Serv. Rep., pR40155 http://www.fas.org/sgp/crs/misc/ R40155.pdf. Schotanus, P., Nieuwstadt, F.T.M., De Bruin, H.A.R., 1983. Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Bound. Layer Meteorol. 26, 81–93. Sellers, P.J., 1997. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275 (80), 502–509. Shurpali, N.J., Biasi, C., Jokinen, S., Hyvönen, N., Martikainen, P.J., 2013. Linking water vapor and CO2 exchange from a perennial bioenergy crop on a drained organic soil in eastern Finland. Agric. For. Meteorol. 168, 47–58, http://dx.doi. org/10.1016/j.agrformet.2012.08.006 http://linkinghub.elsevier.com/retrieve/ pii/S0168192312002675. Skinner, R.H., Adler, P.R., 2010. Carbon dioxide and water fluxes from switchgrass managed for bioenergy production. Agric. Ecosyst. Environ. 138, 257–264, http://dx.doi.org/10.1016/j.agee.2010.05.008 http://linkinghub.elsevier.com/ retrieve/pii/S0167880910001489. Steduto, P., Hsiao, T.C., Fereres, E., 2007. On the conservative behavior of biomass water productivity. Irrig. Sci. 25, 189–207. Tubiello, F.N., Lin, G., Druitt, J.W., Marino, B.D.V., 1999. Ecosystem-level evapotranspiration and water-use efficiency in the desert biome of biosphere 2. Ecol. Eng. 13, 263–271. VanLoocke, A., Twine, T.E., Zeri, M., Bernacchi, C.J., 2012. A regional comparison of water use efficiency for miscanthus, switchgrass and maize. Agric. For. Meteorol. 164, 82–95, http://dx.doi.org/10.1016/j.agrformet.2012.05. 016. Vickers, D., Mahrt, L., 1997. Fetch limited drag coefficients. Bound. Layer Meteorol. 85, 53–79. Wagle, P., Kakani, V.G., 2014a. Growing season variability in evapotranspiration, ecosystem water use efficiency, and energy partitioning in switchgrass. Ecohydrology 7, 64–72. Wagle, P., Kakani, V.G., 2014. Seasonal variability in net ecosystem carbon dioxide exchange over a young switchgrass stand. Glob. Change Biol. Bioenergy 6, 339–350, http://dx.doi.org/10.1111/gcbb.12049. Webb, E.K., Pearman, G.I., Leuning, R., 1980. Correction of flux measurements for density effects due to heat and water vapour transfer. Q. J. R. Meteorol. Soc. 106, 85–100, http://dx.doi.org/10.1002/qj.49710644707. Wever, L.A., Flanagan, L.B., Carlson, P.J., 2002. Seasonal and interannual variation in evapotranspiration, energy balance and surface conductance in a northern temperate grassland. Agric. For. Meteorol. 112, 31–49, http://dx.doi.org/10. 1016/S0168-1923(02)00041-2 http://linkinghub.elsevier.com/retrieve/pii/ S0168192302000412. Wilczak, J.M., Oncley, S.P., Stage, S.A., 2001. Sonic anemometer tilt correction algorithms. Bound. Layer Meteorol. 99, 127–150. Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D.D., Berbigier, P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom, A., Law, B.E., Kowalski, A., Meyers, T.P., Moncrieff, J., Monson, R., Oechel, W., Tenhunen, J., Valentini, R., Verma, S., 2002. Energy balance closure at FLUXNET sites. Agric.

E. Eichelmann et al. / Agricultural and Forest Meteorology 217 (2016) 108–119 For. Meteorol. 113, 223–243 http://www.sciencedirect.com/science/article/pii/ S0168192302001090. Wilson, K.B., Baldocchi, D.D., 2000. Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America. Agric. For. Meteorol. 100, 1–18. Zeri, M., Anderson-Teixeira, K., Hickman, G., Masters, M., DeLucia, E., Bernacchi, C.J., 2011. Carbon exchange by establishing biofuel crops in Central Illinois. Agric. Ecosyst. Environ. 144, 319–329, http://dx.doi.org/10.1016/j.agee.2011.09.006.

119

Zhuang, Q., Qin, Z., Chen, M., 2013. Biofuel, land and water: maize, switchgrass or Miscanthus? Environ. Res. Lett. 8, 015020, http://dx.doi.org/10.1088/17489326/8/1/015020 http://stacks.iop.org/1748-9326/8/i=1/ a=015020?key=crossref.59656f38e7c47d9ab8bee263172e892e. Zwart, S.J., Bastiaanssen, W.G.M., 2004. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manag. 69, 115–133.