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  • Wilson, H. M., R. M. Cruse, and C. L. Burras, 2011: Perennial grass management impacts on runoff and sediment export from vegetated channels in pulse flow runoff events. Biomass Bioenergy, 35, 429436, doi:10.1016/j.biombioe.2010.08.059.

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  • Zhu, P., Q. Zhuang, E. Joo, and C. Bernacchi, 2017: Importance of biophysical effects on climate warming mitigation potential of biofuel crops over the conterminous United States. Global Change Biol. Bioenergy, 9, 577590, doi:10.1111/gcbb.12370.

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  • Zhuang, Q., Z. Qin, and M. Chen, 2013: Biofuel, land and water: Maize, switchgrass or Miscanthus? Environ. Res. Lett., 8, 015020, doi:10.1088/1748-9326/8/1/015020.

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  • Zumkehr, A., and J. E. Campbell, 2013: Historical U.S. cropland areas and the potential for bioenergy production on abandoned croplands. Environ. Sci. Technol., 47, 38403847, doi:10.1021/es3033132.

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  • View in gallery

    (a) Domain and MODIS landscape representation for numerical simulation experiments. Region in CONUS (outlined in red) is used for model evaluation, as well as analysis of hydroclimatic impacts associated with perennial biofuel crop deployment. (b) Suitability of perennial biofuel crops over CONUS in four quartiles. Pixels within , , and of suitability were reclassified as low, moderate, high, and most suitable, respectively, based on Cai et al. (2011).

  • View in gallery

    Annual cycle of biophysical representation for existing land cover and perennial bioenergy crops. Daily varying values for (a) albedo (b) leaf area index (LAI) (m2 m−2), and (c) vegetation fraction (%) are displayed.

  • View in gallery

    Seasonally averaged albedo difference (Perennial100 − Control) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for Perennial25 − Control. (i)–(p) As in (a)–(h), but for LAI (m2 m−2). (q)–(x) As in (a)–(h) but for vegetation fraction (%). Red rectangles outline five subregions for time series calculations.

  • View in gallery

    Hovmöller diagrams of monthly averaged relative differences of near-surface temperature (K) (relative differences were derived by subtracting observations from control simulations, and then dividing by the corresponding observations) between (a)–(h) the eight control simulations (E1–E8) and the observational dataset t2_DW. (i)–(p) As in (a)–(h), but for the observational dataset t2_GC.

  • View in gallery

    Hovmöller diagrams of monthly averaged relative differences of precipitation (mm day−1) (relative differences were derived by subtracting observations from control simulations, and then dividing by the corresponding observations) between (a)–(h) the eight control simulations (E1–E8) and the observational dataset pr_DW. (i)–(p) As in (a)–(h), but for the observational dataset pr_UF.

  • View in gallery

    Taylor diagrams of seasonally averaged near-surface temperature between observations and control simulations over 10 years (2000–09) in (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for precipitation. Dots represent simulation skill relative to observed dataset of University of Delaware Air Temperature and Precipitation (i.e., DW), whereas triangles correspond to observed temperature and precipitation datasets of GHCN_CAMS Gridded 2-m Temperature and CPC U.S. Unified Precipitation (i.e., GC and UF), respectively. Hollow symbols represent the relationship between gridded observational datasets. Correlation coefficients between modeled and observed variables are shown in angular axes. Normalized standard deviation and centered root-mean-square error (RMSE) are proportional to the distance from the origin and the (1, 0) point, respectively.

  • View in gallery

    Seasonally averaged near-surface temperature difference (°C) (Perennial100_E1 − Control_E1) over one decade (2000–09) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for difference of Perennial100_E8 − Control_E8. (i)–(l) As in (a)–(d), but for difference of Perennial25_E1 − Control_E1. (m)–(p) As in (a)–(d) but for difference of Perennial25_E8 − Control_E8. Red rectangles outline five subregions for time series calculations.

  • View in gallery

    Annual cycle of surface temperature differences (°C), averaged only over grid cells undergoing land surface modification under Perennial100 scenario regions (a) 1, (b) 2, (c) 3, (d) 4, and (e) 5. (f)–(j) As in (a)–(e), but under Perennial25 scenario. Green and red lines indicate averaged annual cycle of simulated impact over the decadal period using ensemble member E1 and E8, respectively. Bands of one standard deviation above and below the mean annual cycle are shaded with the corresponding color.

  • View in gallery

    Seasonally averaged sensible flux difference (W m−2) (Perennial100_E1 − Control_E1) over one decade (2000–09) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for difference of Perennial100_E8 − Control_E8. (i)–(l) As in (a)–(d), but for difference of Perennial25_E1 − Control_E1. (m)–(p) As in (a)–(d), but for difference of Perennial25_E8 − Control_E8. Red rectangles outline five subregions for time series calculations.

  • View in gallery

    Annual cycle of sensible heat flux difference (W m−2) averaged only over grid cells undergoing land surface modification under Perennial100 scenario regions (a) 1, (b) 2, (c) 3, (d) 4, and (e) 5. (f)–(j) As in (a)–(e), but under Perennial25 scenario. Green and red lines indicate averaged annual cycle of simulated impact over the decadal period using ensemble member E1 and E8, respectively. Bands of one standard deviation above and below the mean annual cycle are shaded with the corresponding color.

  • View in gallery

    Seasonally averaged latent heat flux difference (W m−2) (Perennial100_E1 − Control_E1) over one decade (2000–09) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for difference of Perennial100_E8 − Control_E8. (i)–(l) As in (a)–(d), but for difference of Perennial25_E1 − Control_E1. (m)–(p) As in (a)–(d), but for difference of Perennial25_E8 − Control_E8. Red rectangles outline five subregions for time series calculations.

  • View in gallery

    Annual cycle of latent heat flux difference (W m−2) averaged only over grid cells undergoing land surface modification under Perennial100 scenario regions (a) 1, (b) 2, (c) 3, (d) 4, and (e) 5. (f)–(j) As in (a)–(e), but under Perennial25 scenario. Green and red lines indicate averaged annual cycle of simulated impact over decadal period using ensemble members E1 and E8, respectively. Bands of one standard deviation above and below the mean annual cycle are shaded with the corresponding color.

  • View in gallery

    Seasonally averaged soil moisture difference (m3 m−3) at 40–100-cm soil depth (Perennial100_E1 − Control_E1) over one decade (2000–09) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for difference of Perennial100_E8 − Control_E8. (i)–(l) As in (a)–(d), but for difference of Perennial25_E1 − Control_E1. (m)–(p) As in (a)–(d), but for difference of Perennial25_E8 − Control_E8. Red rectangles outline five subregions for time series calculations.

  • View in gallery

    Spatially averaged soil moisture difference (m3 m−3) at 40-cm to 1-m soil depth for grid cells undergoing land surface perturbation, for regions (a) 1, (b) 2, (c) 3, (d) 4, and (e) 5. Dark green and dark blue curves indicate ensemble member E1 and E8, respectively. Solid and dashed curves represent impact under Perennial100 scenario and Perennial25 scenario, respectively.

  • View in gallery

    Summer (JJA) averaged net radiation difference (W m−2) over one decade (2000–09) (a) Perennial100_E1 − Control_E1, (b) Perennial100_E8 − Control_E8, (c) Perennial25_E1 − Control_E1, and (d) Perennial25_E8 − Control_E8. (e)–(h) As in (a)–(d), but for net shortwave radiation (W m−2). (i)–(l) As in (a)–(d), but for net longwave radiation (W m−2).

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On the Long-Term Hydroclimatic Sustainability of Perennial Bioenergy Crop Expansion over the United States

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  • 1 School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona
  • | 2 School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona
  • | 3 Universidade de Santiago de Compostela, Galicia, Spain
  • | 4 School of Life Sciences, Arizona State University, Tempe, Arizona
  • | 5 Global Institute of Sustainability, Arizona State University, Tempe, Arizona
  • | 6 Department of Plant Biology, University of Illinois at Urbana–Champaign, Urbana, Illinois
  • | 7 Department of Agronomy, Iowa State University, Ames, Iowa
  • | 8 Climate and Ecosystems Science Division, Lawrence Berkeley National Laboratory, Berkeley, California
  • | 9 Global Change and Photosynthesis Research Unit, USDA-ARS, Urbana, Illinois
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Abstract

Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switchgrass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. The hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States are quantified using the Weather Research and Forecasting Model dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted using seasonally evolving biophysical representation of perennial bioenergy cropping systems within the LSM based on observational data. Deployment is carried out only over suitable abandoned and degraded farmlands to avoid competition with existing food cropping systems. Results show that near-surface cooling (locally, up to 5°C) is greatest during the growing season over portions of the central United States. For some regions, principal impacts are restricted to a reduction in near-surface temperature (e.g., eastern portions of the United States), whereas for other regions deployment leads to soil moisture reduction in excess of 0.15–0.2 m3 m−3 during the simulated 10-yr period (e.g., western Great Plains). This reduction (~25%–30% of available soil moisture) manifests as a progressively decreasing trend over time. The large-scale focus of this research demonstrates the long-term hydroclimatic sustainability of large-scale deployment of perennial bioenergy crops across the continental United States, revealing potential hot spots of suitable deployment and regions to avoid.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-16-0610.s1.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Matei Georgescu, matei.georgescu@asu.edu

Abstract

Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switchgrass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. The hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States are quantified using the Weather Research and Forecasting Model dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted using seasonally evolving biophysical representation of perennial bioenergy cropping systems within the LSM based on observational data. Deployment is carried out only over suitable abandoned and degraded farmlands to avoid competition with existing food cropping systems. Results show that near-surface cooling (locally, up to 5°C) is greatest during the growing season over portions of the central United States. For some regions, principal impacts are restricted to a reduction in near-surface temperature (e.g., eastern portions of the United States), whereas for other regions deployment leads to soil moisture reduction in excess of 0.15–0.2 m3 m−3 during the simulated 10-yr period (e.g., western Great Plains). This reduction (~25%–30% of available soil moisture) manifests as a progressively decreasing trend over time. The large-scale focus of this research demonstrates the long-term hydroclimatic sustainability of large-scale deployment of perennial bioenergy crops across the continental United States, revealing potential hot spots of suitable deployment and regions to avoid.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-16-0610.s1.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Matei Georgescu, matei.georgescu@asu.edu

1. Introduction

Bioenergy cropping systems are increasingly recognized as a plausible and sustainable substitute for fossil fuels because of their potential environmental and economic benefits (National Academy of Sciences 2009; U.S. Department of Energy 2011, 2016). The derivation of biofuels (e.g., biobutanol and ethanol) from such cropping systems could have a number of advantages, including mitigation of climate change through greenhouse gas reduction, provision of increasing energy demands, and stabilization of energy pricing (Clifton‐Brown et al. 2007; Campbell et al. 2008; Dondini et al. 2009; López-Bellido et al. 2014; Bagley et al. 2014; Hudiburg et al. 2015, 2016). Second-generation bioenergy crops (e.g., perennial grasses such as miscanthus and switchgrass) could serve as key alternatives to conventional feedstocks (e.g., maize) for biofuel production if planted on marginal lands (Campbell et al. 2008, 2013; Fargione et al. 2008; Field et al. 2008; Cai et al. 2011; Bagley et al. 2014; Hudiburg et al. 2015). Additionally, perennial bioenergy crops sequester carbon within the soil, and their use results in higher yields with lower nutrient input (e.g., reduced N2O) requirements relative to their annual counterparts, such as maize (Fargione et al. 2008; Miguez et al. 2008; Anderson-Teixeira et al. 2009, 2012; Dohleman and Long 2009; Smith et al. 2013; Zhuang et al. 2013; Gelfand et al. 2013; Bagley et al. 2014; Wagle and Kakani 2014; DeLucia 2015; Feng et al. 2015; Oikawa et al. 2015; Eichelmann et al. 2016; VanLoocke et al. 2016). Therefore, cultivating perennial bioenergy crops could be a more sustainable approach to meet increasing energy demand and mitigate anthropogenic climate change.

While biogeochemical effects (greenhouse gas uptake and emissions) of perennial bioenergy crops have been well documented (Dondini et al. 2009; Gelfand et al. 2013; Wagle and Kakani 2014), considerable uncertainties associated with biogeophysical impacts remain (Bagley et al. 2014; Caiazzo et al. 2014; Zhu et al. 2017). Large-scale deployment of perennial bioenergy crops, by virtue of their transition to an altered land use, modifies biogeophysical (e.g., direct impacts due to changes in the surface energy budget) processes. These changes could affect atmospheric boundary layer dynamics, mesoscale circulations, and regional climate (Weaver and Avissar 2001; Pielke 2005; Georgescu et al. 2009, 2011, 2013; Mahmood et al. 2010; VanLoocke et al. 2010; Levis et al. 2012; Murphy et al. 2012). Therefore, biogeophysical impacts associated with land-use conversion to perennial bioenergy cropping systems must be considered prior to large-scale deployment.

Recent work has examined biogeophysical impacts due to landscape conversion from annual to perennial bioenergy crops, noting changes mainly attributed to higher albedo, leaf area index (LAI), and enhanced evapotranspiration (ET) (Betts 2000; Hickman et al. 2010; VanLoocke et al. 2010; Georgescu et al. 2009, 2011; Le et al. 2011; Davin et al. 2014; Bagley et al. 2015; Eichelmann et al. 2016; Wagle et al. 2016; Zhu et al. 2017). In addition, the importance of field-scale studies has demonstrated the significance of appropriate biogeophysical representation in process-based models that can be used to examine scenario-based environmental implications. For example, Miller et al. (2016), via a multiyear observational campaign, conducted field-scale measurements to determine that perennial bioenergy crops have consistently higher values of albedo than annual crops during the growing season. This higher albedo can reduce the amount of solar energy received at the surface, affecting the partitioning of sensible, latent, and ground heat fluxes (Georgescu et al. 2011, 2013; Anderson-Teixeira et al. 2012; Anderson et al. 2013; Bagley et al. 2015; Miller et al. 2016). Studies have noted regional cooling (Georgescu et al. 2011; Le et al. 2011; Khanal et al. 2013; Goldstein et al. 2014; Feng et al. 2015) and the potential for increased precipitation (Georgescu et al. 2011; Khanal et al. 2013, 2014) associated with large-scale deployment of perennial bioenergy crops. These changes were attributable to enhanced ET due to the deeper and denser rooting systems extracting soil moisture from deeper soil depths (VanLoocke et al. 2010; Georgescu et al. 2011; Anderson et al. 2013; Hallgren et al. 2013; Ferchaud et al. 2015).

Changes in ET and soil moisture are directly associated with and have immediate implications for the regional hydrological cycle (VanLoocke et al. 2010; Seneviratne et al. 2010; Georgescu et al. 2011; Anderson et al. 2013). Increased ET, owing to soil moisture depletion at deeper depths, can lead to decreased surface runoff (McIsaac et al. 2010; Le et al. 2011; Wilson et al. 2011) and streamflow (Khanal et al. 2014). Concerns of surface runoff and streamflow reduction could contribute to water stress (Khanal et al. 2014) and have serious implications for regional water resources (McIsaac et al. 2010; VanLoocke et al. 2010; Khanal et al. 2013; Ferchaud et al. 2015).

Large-scale and long-term studies are therefore needed to better characterize hydroclimatic implications of perennial bioenergy crop expansion. For example, the previously noted cooling effect associated with perennial bioenergy crop deployment may only occur at the local and regional scale (Georgescu et al. 2009; 2011; VanLoocke et al. 2010; Hallgren et al. 2013). Over longer temporal scales, hydroclimatic impacts may be diminished due to natural climate variability (e.g., decadal time scale or longer). Khanal et al. (2014) showed that the mean increase of annual precipitation may be smaller than the interannual variability of changes in precipitation when cultivating perennial bioenergy crops. Given such uncertainties, it is evident that hydroclimatic consequences of large-scale deployment of perennial bioenergy crops require further research.

Deployment of perennial bioenergy crops over abandoned and degraded lands has been proposed as a sustainable strategy (Campbell et al. 2008, 2013; Gelfand et al. 2013; Bagley et al. 2014; Feng et al. 2015). The main advantage of such an approach is avoidance of competition between food and fuel production. Few studies have assessed the implications of perennial bioenergy crops over marginal land areas; to our knowledge, there have been no large-scale investigations to quantify hydroclimatic impacts owing to transition of abandoned and degraded farmlands to perennial bioenergy cropping systems. Here, we examine the hydroclimatic effects associated with perennial bioenergy crop deployment on abandoned and marginal land areas over the conterminous United States (CONUS) over a 10-yr contemporary climate period utilizing a coupled land–atmosphere model. We seek to answer the following questions:

  1. What are the large-scale hydroclimatic impacts associated with perennial bioenergy crop expansion?
  2. Are these impacts homogeneous in space and time?
  3. Can our numerical framework identify suitable hot spots of perennial bioenergy crop deployment?

By simulating deployment only over marginal or abandoned farmlands, this study portrays a more realistic depiction than previous studies for perennial-bioenergy-induced hydroclimatic consequences. This research evaluates the feasibility and long-term sustainability of large-scale deployment of perennial bioenergy crops across CONUS while simultaneously providing a framework of feedback assessment between land use and land cover change (LULCC) and water resources.

The manuscript is arranged as follows. Section 2 presents a description of model configuration and experimental design, observational gridded datasets employed for model evaluation, and the derivation of perennial bioenergy crop expansion scenarios. The results are presented and discussed in section 3. In this section, model results are evaluated against observational data, aimed at identifying an optimal model configuration for reproducing near-surface climate conditions. Following model evaluation, hydroclimatic impacts of perennial bioenergy crop deployment are assessed. Concluding remarks and suggestions for future work are discussed in section 4.

2. Methodology

We used the Weather Research and Forecasting Model version 3.6.1 (hereafter WRF) (Skamarock et al. 2008). WRF is a nonhydrostatic model that solves the nonlinear fully compressible atmospheric equations of motion, coupled to the Noah land surface model (Noah-LSM) (Chen and Dudhia 2001; Ek et al. 2003). This coupling provides the capability to study the interaction of perennial bioenergy crop-induced land use change and examine hydroclimatic response to vegetation forcing (Ek et al. 2003).

a. Experimental design of control simulations

Final Operational Global Analysis (FNL) data were acquired from the National Centers for Environmental Prediction for the year 2000 through the end of 2009 (NOAA/NCEP 2000). FNL data are reanalysis products combining information primarily from observational weather data and Global Forecast System (GFS) model outputs, archived at a spatial resolution of 1° × 1° with a frequency of 6 h (Research Data Archive; http://dx.doi.org/10.5065/D6M043C6). These FNL data were used to initialize and force the lateral boundaries for all WRF simulations (i.e., 2000–09).

All simulations used a grid spacing of 20 km, consisting of 310 and 190 grid points in the east–west and north–south directions, respectively, 30 levels in the vertical direction, and a 60-s time step. Numerical experiments were conducted continuously for a period of 10 years (2000 through the end of 2009), with 1-month spinup (starting from 1 December 1999) to allow for land surface conditions to reach equilibrium. Additionally, the 1-km modified IGBP MODIS 20-category land use/land cover (LULC) dataset was used to represent modern-day LULC within the Noah-LSM (Fig. 1a).

Fig. 1.
Fig. 1.

(a) Domain and MODIS landscape representation for numerical simulation experiments. Region in CONUS (outlined in red) is used for model evaluation, as well as analysis of hydroclimatic impacts associated with perennial biofuel crop deployment. (b) Suitability of perennial biofuel crops over CONUS in four quartiles. Pixels within , , and of suitability were reclassified as low, moderate, high, and most suitable, respectively, based on Cai et al. (2011).

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

An ensemble of eight sets of control simulations (hereafter E1–E8) was conducted to determine the optimal model configuration that best reproduces near-surface climatic conditions. These ensemble members varied by choice of microphysics scheme (Hong et al. 2004; Lim and Hong 2010), cumulus physics scheme (Grell 1993; Grell and Devenyi 2002; Kain 2004), and utility (i.e., on or off) of spectral nudging (Miguez‐Macho et al. 2004) (see Table 1). Spectral nudging corrects the systematic distortion of the large-scale flow due to the interaction with the lateral boundary conditions to derive smaller-scale processes by controlling large-scale atmospheric flow conditions in regional simulations (von Storch et al. 2000; Miguez‐Macho et al. 2004, 2005). We nudged wavenumbers 0–4 in the x direction and 0–3 in the y direction (i.e., wavelengths longer than 1200 km) only above the boundary layer (model level equivalent to about 1500 m) for u- and υ-winds, potential temperature, and geopotential height, with a relaxation time about 1 h (see Table 1).

Table 1.

Design of simulations. Eight Control simulations (E1–E8) that vary by choice of microphysics and cumulus physics schemes were performed. In addition, experiments with or without spectral nudging were conducted.

Table 1.

b. Observational data and model evaluation

Two different datasets—to account for uncertainties arising from different interpolation algorithms—of gridded observational representations of temperature and precipitation were used to evaluate simulated near-surface climate. For temperature, the University of Delaware’s air temperature dataset, version 3.01 (hereafter t2_DW; Willmott and Matsuura 1995) and the Global Historical Climatology Network (GHCN) and the Climate Anomaly Monitoring System (CAMS) (hereafter t2_GC; Fan and van den Dool 2008) were utilized with a spatial resolution of 0.5° × 0.5°. Analogously, two gridded observational datasets of precipitation were used: University of Delaware Precipitation, version 3.01 (hereafter pr_DW, with the same resolution as t2_DW; Legates and Willmott 1990), and the Climate Prediction Center’s (CPC’s) gridded Unified Gauge-Based Analysis of daily precipitation (hereafter pr_UF) with 0.25° × 0.25° longitude spatial resolution (Higgins et al. 2000; Chen and Knutson 2008). Datasets t2_DW, t2_GC, pr_DW, and pr_UF were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, from their website at http://www.esrl.noaa.gov/psd/. To conduct grid cell by grid cell comparisons with simulation results, these datasets were resampled to the coarsest resolution (0.5° × 0.5°) using bilinear interpolation. Regions outside CONUS were masked out to evaluate model performance only within the study area (see Fig. 1a).

Hovmöller and Taylor diagrams were utilized to evaluate simulated temperature and precipitation. Hovmöller diagrams (Hovmöller 1949) visualize spatial performance by averaging information across latitude bands. These diagrams were used to quantify monthly averaged relative differences (i.e., dimensionless values) between the eight control simulations (E1 through E8) and the aforementioned gridded observation datasets (differences were normalized by the corresponding observations). Additionally, Taylor diagrams were used to summarize simulation skill based on seasonally averaged differences between each ensemble member and observed 2-m temperature and precipitation. Taylor diagrams simultaneously illustrate normalized standard deviation, centered root-mean-squared error (RMSE), and correlation coefficient (Taylor 2001). Ideally, perfect agreement between simulated results and observations would fall on the (1,0) point. Based on Hovmöller and Taylor diagram metrics, two of the eight control ensemble members were selected as the most and least skillful, respectively, and served as baseline simulations using existing land cover (hereafter Control) against which simulations representing perennial bioenergy crop expansion were compared. Incorporation of bioenergy crops (see section 2c below) was done for both sets of model parameterization options (i.e., corresponding to the most and least skillful ensemble members) to examine whether the sensitivity to landscape change, and if so to what extent, depends on simulation skill.

c. Perennial bioenergy crop representation and deployment scenarios

We utilized a previously developed perennial bioenergy crop suitability dataset identifying potential areas for bioenergy crop deployment (Cai et al. 2011). These data provide global suitability locations over marginal and abandoned lands using soil productivity, land slope, soil temperature, a humidity index, and additional land use information. The most realistic scenario was chosen for our study (123 million hectares available for conversion to perennial bioenergy crops throughout the United States), including areas of marginal mixed crop and vegetation land, grassland, savanna, and scrubland with marginal productivity, while discounting current pastureland. The original suitability data were resampled from 1-km to 20-km grid spacing (to match the resolution of WRF simulations) using bilinear interpolation. Suitable locations were reclassified into four suitability classes using quartile classification (i.e., low, moderate, high, and most suitable) (Fig. 1b). Two deployment scenarios were selected using the identified suitability areas: upper 25th percentile (i.e., most suitable; hereafter Perennial25) and all suitable locations as identified by Cai et al. (2011; hereafter Perennial100). Our use of both deployment scenarios was made in order to examine the largest possible range in hydroclimatic impacts associated with this bioenergy crop pathway.

Within suitable locations, perennial bioenergy crop expansion was represented via modification of relevant biophysical parameters, including albedo, LAI, and vegetation fraction (Georgescu et al. 2009). Albedo values were modified based on field site observation values obtained from Miller et al. (2016). Seasonal profiles of albedo were determined by averaging daily albedo values across two perennial plant types (switchgrass and miscanthus) and across the observed years of 2010 and 2011.

Following the phenological evolution of observed albedo, LAI and vegetation fraction values were scaled using previously reported maximum and minimum values (e.g., Dohleman and Long 2009). Albedo, LAI, and vegetation fraction values were then incorporated into Noah by taking into account latitudinal dependencies, with shortened growing seasons to the north and lengthened growing seasons in southern regions. Specifically, albedo was depicted as
e1
where jday is the Julian day of the calendar, centerday is 197 (the assumed midpoint of the growing season and characterized as mid-July everywhere), 0.235 is the observed peak summertime albedo value and 0.16 is the observed minimum albedo value, and widthlai represents the extent of the growing season in days and is denoted as
e2
where maxwidthlai depicts the length of the growing season at 30°S (i.e., 3 months) and regulates the width of the bell-shaped curve characterizing the phenological evolution of LAI. LAI is represented as
e3
where maxlai = 6 (i.e., peak of the growing season), minlai = 0.1 (middle of winter when the crop is dormant), and we assume the maximum growing season LAI peaks at 30°N (i.e., maxwidthlai) and decreases linearly until 50°N, where it is equivalent to 0.75maxwidthlai.

Figure 2 shows the annual cycle of biophysical parameters for perennial bioenergy crops and existing land cover, averaged over all suitability grid cells. In general, albedo, LAI, and vegetation fraction for perennial bioenergy crops were higher than that of existing land cover from May to October. Spatial differences were apparent when examining seasonally averaged values of albedo, LAI, and vegetation fraction between Control and Perennial simulations (see Fig. 3). For albedo, the maximum difference occurs during June–August (JJA). During JJA, LAI and vegetation fraction are higher over the western plains by an average of 6 m2 m−2 and 75%, respectively. Differences in biophysical characteristics were more evident for Perennial100 compared to Perennial25 simulations. It is important to mention that no bioenergy cropping systems were irrigated in this work and that no modification of default rooting depth was made.

Fig. 2.
Fig. 2.

Annual cycle of biophysical representation for existing land cover and perennial bioenergy crops. Daily varying values for (a) albedo (b) leaf area index (LAI) (m2 m−2), and (c) vegetation fraction (%) are displayed.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

Fig. 3.
Fig. 3.

Seasonally averaged albedo difference (Perennial100 − Control) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for Perennial25 − Control. (i)–(p) As in (a)–(h), but for LAI (m2 m−2). (q)–(x) As in (a)–(h) but for vegetation fraction (%). Red rectangles outline five subregions for time series calculations.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

Two sets of experiments were conducted over CONUS based on model skill and deployment scenarios. These experiments used the best and least skilled ensemble members (see section 2b), based on the aforementioned model evaluation and pair of deployment scenarios (i.e., Perennial25 and Perennial100). All simulation experiments were conducted from 2000 through the end of 2009, with one month of spinup in December 1999, to allow the land surface state to equilibrate (see Table 2).

Table 2.

List of bioenergy crop sensitivity simulations.

Table 2.

To assess the sustainability of perennial bioenergy crop expansion, the Mann–Kendall modified trend test (for seasonal time series in the presence of serial correlation) was used to evaluate statistical significance of trends in soil moisture differences. This test determines the existence of a monotonic upward or downward (not necessarily linear) trend of soil moisture depletion over time (see Hamed and Rao 1998). Spatially averaged time series of soil moisture differences were aggregated from daily to monthly frequency to conduct the Mann–Kendall modified trend test. To compensate for the number of inferences, a Bonferroni adjustment was applied using a higher significance threshold for individual comparisons. Specifically, test-specific p values smaller than 0.001 characterized statistical significance in order to achieve a familywise type-I error rate (false positives) approximately equal to 5%.

3. Results

a. Model evaluation

In general, model skill was superior for temperature compared to precipitation across all simulated years and ensemble members. Hovmöller diagrams (Figs. 4 and 5) show minimal variability in simulated near-surface temperature (i.e., at 2 m above ground) but high variability for precipitation across ensemble members. Monthly averaged temperature biases were small compared to both observational datasets (Fig. 4). However, temperatures biases varied according to latitude and time of year. During summer, simulated temperatures exhibited a positive bias primarily over southern areas, whereas during winter simulated temperatures exhibited a negative bias over northern locations. Ensemble members E4 and E8 performed best in simulating temperature especially during summer, whereas ensemble members E1 and E5 exhibited the largest warm bias (Fig. 4). Overall, ensemble member E8 (see Table 1; with Microphysics WDM6) produced the best correspondence to wintertime temperatures while demonstrating minimal summertime warm biases, whereas ensemble member E1 (with microphysics WSM3) displayed the largest underestimate of near-surface temperatures.

Fig. 4.
Fig. 4.

Hovmöller diagrams of monthly averaged relative differences of near-surface temperature (K) (relative differences were derived by subtracting observations from control simulations, and then dividing by the corresponding observations) between (a)–(h) the eight control simulations (E1–E8) and the observational dataset t2_DW. (i)–(p) As in (a)–(h), but for the observational dataset t2_GC.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

Fig. 5.
Fig. 5.

Hovmöller diagrams of monthly averaged relative differences of precipitation (mm day−1) (relative differences were derived by subtracting observations from control simulations, and then dividing by the corresponding observations) between (a)–(h) the eight control simulations (E1–E8) and the observational dataset pr_DW. (i)–(p) As in (a)–(h), but for the observational dataset pr_UF.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

Unlike temperature, monthly averaged simulated precipitation biases were highly variable. Figure 5 shows normalized precipitation differences generally up to 5 times greater than observed precipitation, which was more prevalent when compared with the second observed dataset. Additionally, precipitation biases were greater over latitudinal belts below 30°N or above 45°N. The disparity between simulated precipitation and observation datasets is largely explained by the different algorithms utilized to create the gridded observational datasets themselves. Despite this disparity, ensemble members E4 and E8, which used the Grell-3D cumulus scheme and spectral nudging, performed better than the other ensemble members (Fig. 5). Ensemble members E1 and E5, which used the Kain–Fritsch cumulus scheme without spectral nudging, performed worse.

In addition to evaluating Hovmöller diagrams, Taylor diagrams (which permit simultaneous assessment of multiple statistical metrics) also show high model skill in simulating temperature, but only moderate model skill for precipitation (Fig. 6). For near-surface temperature, considerable clustering among all ensemble members is evident, indicative of reduced near-surface temperature sensitivity to choice of model physics (Figs. 6a–d). All ensemble members show similar standard deviation, correlation coefficients near 0.96, and centered RMSE ranging from 0.25° to 0.4°C.

Fig. 6.
Fig. 6.

Taylor diagrams of seasonally averaged near-surface temperature between observations and control simulations over 10 years (2000–09) in (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for precipitation. Dots represent simulation skill relative to observed dataset of University of Delaware Air Temperature and Precipitation (i.e., DW), whereas triangles correspond to observed temperature and precipitation datasets of GHCN_CAMS Gridded 2-m Temperature and CPC U.S. Unified Precipitation (i.e., GC and UF), respectively. Hollow symbols represent the relationship between gridded observational datasets. Correlation coefficients between modeled and observed variables are shown in angular axes. Normalized standard deviation and centered root-mean-square error (RMSE) are proportional to the distance from the origin and the (1, 0) point, respectively.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

For simulated precipitation, considerable spread among the ensemble members is evident, indicating enhanced sensitivity to the choice of physics parameterizations employed here (Figs. 6e–h). The standard deviation of the simulated precipitation values was 0.9 to 1.5 times greater than that of the observations. Centered RMSE values ranged between 0.5 to 1 mm day−1. Correlation coefficients for all ensemble members were lowest during summer and fall (generally between 0.65 to 0.8), coinciding with the period of time when large-scale synoptic forcing is absent and precipitation is convectively driven. Nevertheless, ensemble members E4 and E8 consistently performed better than other members, especially during the convective season, exhibiting correlation coefficients in excess of 0.8, lowest standard deviation ratio of 1 relative to that of observations, as well as lowest centered RMSE of 0.7. Ensemble members E1 and E5 had the least model skill in simulating precipitation; this was especially evident during the summer (e.g., these ensemble members had a lowest correlation coefficient of 0.65).

Based on the aforementioned results, ensemble member E8, which used the Grell-3D cumulus scheme, WDM6 microphysics parameterization and spectral nudging turned on, performed the best, whereas, ensemble member E1, which used the Kain–Fritsch cumulus scheme, WSM3 microphysics parameterization and spectral nudging turned off, performed the worst. In the following analysis, ensemble members E8 and E1 were identified as the best and least skilled members, respectively. Both ensemble members (i.e., E8 and E1) were modified to incorporate bioenergy crops (see section 2c) to assess whether the sensitivity to landscape change depends on simulation skill, and if so to what extent.

b. Hydroclimatic impacts

1) Temperature

We present results as differences in 10-yr seasonally averaged hydroclimatic variables between the perennial bioenergy crop simulations and the contemporary landscape utilized in control simulations. Overall, seasonal averages of near-surface temperature differences illustrate cooling associated with deployment of perennial bioenergy crops (Fig. 7). Maximum simulated cooling occurs during the peak of perennial bioenergy crop greenness (JJA) for all deployment scenarios. During this period, near-surface temperature decreases dramatically over the southern Great Plains with maximum cooling on the order of 5°C for the full deployment scenario (i.e., Perennial100_E1 and Perennial100_E8, corresponding to Figs. 7c and 7g). The Pacific Coast and western mountains (designated as regions 1 and 2, respectively) exhibit moderate temperature decreases of approximately 2°–4°C. This cooling is gradually attenuated from the central plains to the U.S. Northeast (i.e., within regions 4 and 5, respectively). Under the reduced deployment scenario (i.e., Perennial25_E1 and Perennial25_E8), near-surface cooling associated with perennial bioenergy crop deployment is more localized and primarily restricted to regions 4 and 5. Within these regions, the maximum cooling is restricted to approximately 3°C during summer months (Figs. 7i–p). Only minimal differences in simulated cooling were evident when comparing ensemble member E1 and E8 results (i.e., cf. Figs. 7c and 7g), indicating that the simulated near-surface temperature sensitivity to bioenergy crop deployment was independent of model performance.

Fig. 7.
Fig. 7.

Seasonally averaged near-surface temperature difference (°C) (Perennial100_E1 − Control_E1) over one decade (2000–09) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for difference of Perennial100_E8 − Control_E8. (i)–(l) As in (a)–(d), but for difference of Perennial25_E1 − Control_E1. (m)–(p) As in (a)–(d) but for difference of Perennial25_E8 − Control_E8. Red rectangles outline five subregions for time series calculations.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

To better examine hydroclimatic impacts over time, time series plots of temperature differences are calculated for each of the five subregions depicted in Fig. 7 (Fig. 8). These subregions include the Pacific Coast (subregion 1), western mountains (subregion 2), western Great Plains (subregion 3), central/eastern United States (subregion 4), and Gulf Coast (subregion 5). Across all subregions, cooling occurs from May to October, coinciding with the higher albedo of perennial bioenergy crops (Fig. 2a). Under the full deployment scenario, maximum cooling ranges between 3°–5°C over region 3 (i.e., western Great Plains), whereas regions 4 (central/eastern United States) and 5 (Gulf Coast) illustrate a maximum cooling ranging between 1° and 2°C under the reduced deployment scenario. In terms of ensemble member performance, E8 and E1 overlap considerably. However, E8 displays less variability in annual cycle differences as indicated by the narrower standard deviation band when compared to E1 (Fig. 8). Despite this small difference, uncertainty due to model physics parameterization is secondary to the simulated signal of cooling impact. Moreover, we consider the simulated thermal impacts robust as temperature differences and the associated annual variability consistently exhibits cooling, with only small exceptions evident for reduced deployment experiments for some regions (e.g., region 1).

Fig. 8.
Fig. 8.

Annual cycle of surface temperature differences (°C), averaged only over grid cells undergoing land surface modification under Perennial100 scenario regions (a) 1, (b) 2, (c) 3, (d) 4, and (e) 5. (f)–(j) As in (a)–(e), but under Perennial25 scenario. Green and red lines indicate averaged annual cycle of simulated impact over the decadal period using ensemble member E1 and E8, respectively. Bands of one standard deviation above and below the mean annual cycle are shaded with the corresponding color.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

2) Surface energy balance

Similar to simulated temperature patterns, sensible heat flux associated with perennial bioenergy crops also decreases under both deployment scenarios (see Fig. 9). This decrease is maximized during the summer months especially under the full deployment scenario. Under this scenario, peak reduction in sensible heat flux, ranging between 40 and 70 W m−2, was evident over western and central portions of the United States (regions 1, 2, and 3). Under the reduced deployment scenario, the reduction in sensible heat was moderated to only 20 W m−2. This reduction was most noticeable in the central/eastern U.S. and Gulf Coast areas (regions 4 and 5), unlike the full deployment scenario, which exhibited greatest decrease in sensible heat along or west of the 100th meridian.

Fig. 9.
Fig. 9.

Seasonally averaged sensible flux difference (W m−2) (Perennial100_E1 − Control_E1) over one decade (2000–09) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for difference of Perennial100_E8 − Control_E8. (i)–(l) As in (a)–(d), but for difference of Perennial25_E1 − Control_E1. (m)–(p) As in (a)–(d), but for difference of Perennial25_E8 − Control_E8. Red rectangles outline five subregions for time series calculations.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

The temporally varying nature of sensible heat flux differences for the individual subregions also indicates lower sensible heat fluxes associated with perennial bioenergy crops during the growing season (Fig. 10). For regions 1–3, the greatest decrease occurs from May to mid-June. In regions 4 and 5, sensible heat flux is more gradually reduced and remains nearly constant for the majority of the growing season. The reduction in sensible heat flux for regions 4 and 5 coincides with reduced temperature differences for these two regions. Under the full deployment scenario, sensible heat decreases by a maximum of 45 W m−2 in region 3. Under the reduced deployment scenario, the decrease in sensible heat is minimized to 15–25 W m−2.

Fig. 10.
Fig. 10.

Annual cycle of sensible heat flux difference (W m−2) averaged only over grid cells undergoing land surface modification under Perennial100 scenario regions (a) 1, (b) 2, (c) 3, (d) 4, and (e) 5. (f)–(j) As in (a)–(e), but under Perennial25 scenario. Green and red lines indicate averaged annual cycle of simulated impact over the decadal period using ensemble member E1 and E8, respectively. Bands of one standard deviation above and below the mean annual cycle are shaded with the corresponding color.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

Despite consistent decreases in sensible heat, latent heat fluxes associated with perennial bioenergy crop expansion exhibits geographically dependent changes (Fig. 11). During the growing season, latent heat fluxes increase, by up to 55 W m−2, over the Pacific coast, western mountains, and western Great Plains regions (regions 1, 2, and 3) under the full deployment scenario. However, over eastern portions of the United States (regions 4 and 5), latent heat fluxes decrease, generally between 15 and 25 W m−2 for full and reduced deployment scenarios. In addition, according to time series plots of latent heat flux differences (Fig. 12), regions 1, 2, and 3 display higher latent heat fluxes associated with perennial bioenergy crops through early portions of the summer, followed by a gradual decrease until October. Over regions 4 and 5, latent heat flux differences are small during the growing season. Notably, decreases in latent heat fluxes are evident from April to May and from October to November, coinciding with lower LAI and vegetation fraction values for perennial bioenergy crops relative to the existing land cover (see Figs. 2b,c).

Fig. 11.
Fig. 11.

Seasonally averaged latent heat flux difference (W m−2) (Perennial100_E1 − Control_E1) over one decade (2000–09) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for difference of Perennial100_E8 − Control_E8. (i)–(l) As in (a)–(d), but for difference of Perennial25_E1 − Control_E1. (m)–(p) As in (a)–(d), but for difference of Perennial25_E8 − Control_E8. Red rectangles outline five subregions for time series calculations.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

Fig. 12.
Fig. 12.

Annual cycle of latent heat flux difference (W m−2) averaged only over grid cells undergoing land surface modification under Perennial100 scenario regions (a) 1, (b) 2, (c) 3, (d) 4, and (e) 5. (f)–(j) As in (a)–(e), but under Perennial25 scenario. Green and red lines indicate averaged annual cycle of simulated impact over decadal period using ensemble members E1 and E8, respectively. Bands of one standard deviation above and below the mean annual cycle are shaded with the corresponding color.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

3) Soil moisture

Changes in soil moisture associated with perennial bioenergy crops are inversely related with latent heat flux changes. Soil moisture changes are evident in both shallow (10–40 cm; see Fig. S1 in the online supplemental material) and deeper (40–100 cm; Fig. 13) soil depth levels. Under the full deployment scenario, soil moisture was reduced over western and central portions of the United States (regions 1, 2, and 3) during summer and fall. Within these regions, volumetric soil moisture decreased by up to 0.17 and 0.20 m3 m−3 for shallow and deeper soil depths, respectively. In the central/eastern United States (region 4), unlike other regions, soil moisture increased by up to 0.07 and 0.10 m3 m−3 for shallow and deeper soil depths, respectively. Soil moisture differences were minimal under the reduced deployment scenario with minor changes manifested in regions 4 and 5, respectively (<0.05 m3 m−3).

Fig. 13.
Fig. 13.

Seasonally averaged soil moisture difference (m3 m−3) at 40–100-cm soil depth (Perennial100_E1 − Control_E1) over one decade (2000–09) for (a) DJF, (b) MAM, (c) JJA, and (d) SON. (e)–(h) As in (a)–(d), but for difference of Perennial100_E8 − Control_E8. (i)–(l) As in (a)–(d), but for difference of Perennial25_E1 − Control_E1. (m)–(p) As in (a)–(d), but for difference of Perennial25_E8 − Control_E8. Red rectangles outline five subregions for time series calculations.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

Although time-averaged changes in soil moisture raise concerns associated with water depletion within the soil column, time series analyses of soil moisture provide insight into the progressive trend of these effects. Time series of soil moisture differences show seasonal and annual trends of soil moisture depletion, most notably at deeper soil depths (40–100 cm), with statistically significant decreasing trends in regions 2 and 3 under the full deployment scenario (Fig. 14, Table 3). In terms of seasonal differences, soil moisture associated with perennial bioenergy crops decreases during the growing season and then partially recharges from November until the following April over regions 1, 2, 3, and 5 (Figs. 14a–c, e). This evolution of soil moisture differences is inversely related to changes in latent heat flux (for regions 1, 2, and 3) and is partially coincident with large-scale rainfall reduction (for region 5, see Figs. S2 and S3). Under full bioenergy crop deployment, these differences are most noticeable with decreased soil moisture reaching 0.12 m3 m−3 over regions 2 and 3. Over the simulated decade and for these regions (western mountains and western Great Plains), soil moisture is depleted by roughly one-third of the initial soil moisture availability. Moreover, soil moisture decreases progressively with each subsequent year for regions 2 and 3 under the full deployment scenario (with familywise type-I error rate < 0.05 for simultaneous testing of all soil moisture difference trends; see Table 3). These progressive drying trends, however, are not evident in regions 1 (Pacific Coast), 4 (central/eastern United States), and 5 (Gulf Coast).

Fig. 14.
Fig. 14.

Spatially averaged soil moisture difference (m3 m−3) at 40-cm to 1-m soil depth for grid cells undergoing land surface perturbation, for regions (a) 1, (b) 2, (c) 3, (d) 4, and (e) 5. Dark green and dark blue curves indicate ensemble member E1 and E8, respectively. Solid and dashed curves represent impact under Perennial100 scenario and Perennial25 scenario, respectively.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

Table 3.

Relative changes of soil moisture at the end of the 10th simulation year (Perennial minus Control), normalized by the corresponding initial soil moisture at shallow and deeper soil depths. Asterisks indicate statistically significant monotonic trends with 95% familywise confidence (p value < 0.001 for each test under the Bonferroni correction for multiple hypothesis tests), based on the Mann–Kendall test for serially correlated measurements.

Table 3.

4) Radiation balance

Changes in net radiation balance play an important role in driving the aforementioned hydroclimatic impacts. Overall, net radiation decreased, with the largest reduction occurring during summer (Figs. 15a–d). These changes are largely responsible for the previously discussed changes in temperature and sensible heat flux. Under the full deployment scenario, the largest reduction in net radiation (up to 60 W m−2) occurs over the southern Great Plains (mainly within region 3). Under the reduced deployment scenario, net radiation decreases 20–30 W m−2, primarily over the central/eastern United States (region 4) and Gulf Coast (region 5). According to time series plots of spatially averaged net radiation differences, these decreases mainly occurred from mid-April to mid-October (Fig. S4).

Fig. 15.
Fig. 15.

Summer (JJA) averaged net radiation difference (W m−2) over one decade (2000–09) (a) Perennial100_E1 − Control_E1, (b) Perennial100_E8 − Control_E8, (c) Perennial25_E1 − Control_E1, and (d) Perennial25_E8 − Control_E8. (e)–(h) As in (a)–(d), but for net shortwave radiation (W m−2). (i)–(l) As in (a)–(d), but for net longwave radiation (W m−2).

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0610.1

The large-scale net radiation reduction is dominated by the decrease of shortwave radiation at the surface (Figs. 15e–h), resulting from enhanced surface reflectivity (Figs. 3c,g). Summer net shortwave decreases up to 50 W m−2 over the southern Great Plains (the same region with maximum net radiation depletion), whereas the reduction of summer net longwave radiation peaks at roughly 12 W m−2 over southeastern areas of the United States.

4. Discussion and conclusions

Here we investigate hydroclimatic impacts of perennial bioenergy crop expansion over CONUS using continuous ensemble-based WRF simulations (2000 through 2009) and a suite of realistic deployment scenarios. Our results demonstrate that converting abandoned and degraded farmlands to perennial bioenergy croplands can lead to significantly cooler temperatures and potentially unintended consequences of soil moisture depletion for some U.S. regions. Temperature decreases associated with perennial bioenergy crop deployment are largest over the Great Plains, generally 4°–5°C lower during the growing season compared to the unperturbed landscape (Figs. 7 and 8). Simulated soil moisture associated with perennial bioenergy crops shows a progressive decrease for some regions, most notably at deeper soil depths (40–100 cm). This decrease is most apparent under the full deployment scenario over the western plains, with soil moisture depleted by ~35% relative to the initial soil moisture availability (see Figs. 13 and 14). However, we note that, in general, smaller differences were evident under the reduced deployment scenario, although even in such instances soil moisture reduction was apparent (e.g., region 3; Table 3). Therefore, large-scale perennial bioenergy crop expansion over abandoned farmlands could have undesirable regional hydroclimatic consequences, but these effects are reduced for most areas undergoing small-scale deployment.

Biophysical parameters, including albedo, vegetation fraction, and LAI, were shown to serve as key factors characterizing hydroclimatic impacts due to perennial bioenergy crop expansion, in agreement with previous work focused on hypothetical landscape transitions (Georgescu et al. 2011; Davin et al. 2014; Zhu et al. 2017). Perennial bioenergy crops have higher albedo, vegetation fraction, and LAI values from May to October, compared to the existing land cover (see Figs. 2 and 3). Higher albedo values can lead to a decrease in net radiation absorbed at the surface, which in turn lowers sensible heat flux, and consequently, reduces near-surface temperatures. This finding is in agreement with previous work (Hallgren et al. 2013; Caiazzo et al. 2014) that also noted regional cooling associated with perennial bioenergy crop expansion. In addition, some studies have also attributed near-surface cooling to enhanced ET (e.g., VanLoocke et al. 2010) resulting from increased LAI (Le et al. 2011), and deeper rooting system of perennial bioenergy crops (e.g., Georgescu et al. 2011). In this study, radiative forcing appears to be the key regional cooling driver as rooting depths were not modified, based on our assumption of similar magnitude of mean rooting depths of perennial bioenergy crops and existing land cover (Monti and Zatta 2009; Ferchaud et al. 2015).

Unlike previous studies (VanLoocke et al. 2010; Le et al. 2011; Khanal et al. 2013; Abraha et al. 2015), changes in latent heat flux associated with perennial bioenergy crop expansion varied spatially (i.e., increased latent heat fluxes over some regions but minimal changes, or even decreases, over other regions). Simulated latent heat flux and associated ET (not shown) increased over the western United States (where existing landscapes had minimal vegetation cover), but was reduced over central/eastern United States and Gulf Coast (regions 4 and 5 of Fig. 11). Over eastern portions of the United States, the reduction in radiative forcing served as the principal driver behind this effect, as opposed to western portions of the United States, where radiative forcing impacts were secondary to changes in vegetation abundance and greenness. Over eastern U.S. regions, increases in vegetation fraction and LAI were less compared to increases in these parameters over other regions (Fig. 3). For example, vegetation fraction increased by 10%–20% over regions 4 (central/eastern United States) and 5 (Gulf Coast), while vegetation fraction increased by more than 50% over regions 1 through 3. The relative contribution to latent heat flux changes over eastern portions of the United States was dominated by disproportionately greater changes in albedo relative to vegetation fraction and LAI. Therefore, the spatial variability of latent heat flux changes described here benefits from a comprehensive assessment of bioenergy crop deployment over a diversity of landscapes, rather than the hypothetical and unrealistic annuals-to-perennials modification studied to date.

We posit that a lack of statistically significant monotonic trends in soil moisture (Fig. 14; Table 3) accompanied by areas of regional cooling can be a determining factor in identifying suitable hot spots of bioenergy crop deployment. Perennial bioenergy crop expansion, therefore, could be sustainable in regions 4 and 5 (central/eastern United States and Gulf Coast states) based on the amount of soil moisture available during the annual cycle and the minimal to positive soil moisture changes simulated over the decadal time scale examined. Moreover, sections of Wisconsin and Missouri, extending eastward through the Ohio River Valley, could be posited as favorable locations for deployment due to seasonal soil moisture recharge (Fig. 13). Our results indicate statistically significant decreasing trends in soil moisture (up to 35% of initial soil moisture content) for regions 2 and 3 over the 10-yr simulation period (see Table 3), highlighting these areas as potentially unsuitable. Although we do not observe a statistically significant trend in soil moisture for region 1 (i.e., California) the incomplete recovery of differences relative to the Control scenario during the winter season does raise water resource concerns vis-à-vis depletion/interaction with the water table, which requires further investigation. However, it is worth noting that benefits may still exist as a decrease in runoff would lead to less soil erosion and therefore could improve water quality over potential unsuitable areas.

We characterize the simulated large-scale hydroclimatic impacts associated with perennial bioenergy crop expansion as robust since the two sets of experiments (i.e., E8 and E1) converged to similar conclusions. Over most perennial bioenergy crop deployment regions, the best (i.e., E8) and least (i.e., E1) skilled ensemble members yielded similar results in terms of the magnitude and extent of regional cooling, changes in latent and sensible heat fluxes, and soil moisture impacts. Additionally, the overlaid climate variability ranges associated with the mean annual cycle of subregionally averaged cooling and changes of surface energy balance components between the aforementioned two ensemble members provide further confidence in our results. It is important to mention that the predicted temperature in our simulations exhibits reduced scattering compared to precipitation. This suggests that the errors observed in precipitation, owing to utility of different cloud microphysics parameterizations, do not have significant impact on the dynamics simulated by WRF. If these errors were important, they would have affected the dynamics through temperature changes caused by the release or absorption of latent heat. Consequently, the scattering in temperature and precipitation would have been closely correlated. However, this was not observed in our simulations, consistent with previous research (e.g., Done et al. 2005; Okalebo et al. 2016). Nevertheless, from a purely physics and model development perspective additional insights characterizing the parameterization aspects leading to quantitative determination detailing differences in simulated results (e.g., what particular aspects of parameterized features contributes to this variability) is an important research avenue for pursuit, but is beyond the focus of this manuscript.

As with any modeling study, it is important to acknowledge inherent assumptions and caveats. First, our imposed LULCC assumed a consistent mapping between the IGBP MODIS LULC dataset and abandoned and degraded farmland regions obtained from Cai et al. (2011). However, it is not clear that the IGBP MODIS dataset will identify these precise areas as such (i.e., as low-yielding, unproductive marginal lands). It is possible that development of an alternative abandoned and degraded farmland dataset that relies on the MODIS LULC dataset will yield some shifts in the presumed areas of deployment. It is precisely for this reason that our methodological approach accounted for widely varying deployment scenarios, highlighting the sensitivity associated with the largest possible range in hydroclimatic impacts associated with multiple landscape modification pathways. Second, our incorporation of perennial bioenergy crop biophysical characteristics observed over the Midwest for all abandoned and degraded farmlands, including west of the 100th meridian, is likely leading to an overestimate of simulated impacts for regions 1 and 2. For example, it is possible that for drier regions the actual LAI of perennial bioenergy crops may be more reflective of local soil moisture conditions, which would generate a reduced growing season LAI than used here. This important point emphasizes the need for future research with a fully coupled Earth system model that includes a dynamically evolving interannual biophysical representation of perennial bioenergy cropping systems, wherein the maturity of the plant functional type is dependent on ambient environmental conditions rather than the presumed periodic depiction utilized here. Finally, the use of Noah LSM could also affect the results of this study, more specifically latent heat flux effects. Because Noah LSM calculates surface energy fluxes over a combined surface layer of vegetation and soil surface (rather than separately), it cannot explicitly simulate photosynthetically active radiation (PAR), canopy temperature, and associated surface fluxes that modulate the regional water cycle (Niu et al. 2011). Moreover, Noah LSM assumes four soil layers with spatially invariant thickness that extends only to 2 m, omitting the interaction with subsurface hydrology and groundwater storage (Niu et al. 2011; Cai et al. 2014). Utility of other land surface models, such as the land component of the Community Earth System Model (CLM4) with more complex structure and process physics (Gent et al. 2010; Lawrence et al. 2012), Noah-MP with multiple parameterization schemes (Niu et al. 2011; Yang et al. 2011), and LEAF-2, which accounts for groundwater and river dynamics (e.g., Miguez-Macho et al. 2007) should provide a more refined representation of impacts owing to perennial bioenergy crop expansion.

Future work should focus on impacts using high-resolution simulations to examine the response to extreme events at finer scales (e.g., Wagner et al. 2017), especially in the proposed suitable hot spots revealed in the present study, and should also utilize different datasets to account for the variability in abandoned and degraded farmland area characterization (e.g., Zumkehr and Campbell 2013). Further research is required to investigate the sustainability of this proposed alternative energy pathway in terms of crop yields using an ecosystem model [such as the terrestrial agricultural version of the Integrated Biosphere Simulator (Agro-IBIS); Kucharik 2003] to quantify implications for the amount of biomass that can be produced. Utilizing such ecosystem models to examine the potential impact of decreased soil moisture on biomass yield is a necessary element of a comprehensive environmental assessment of bioenergy crop deployment, and is the subject of future research. Last, socioecological assessments are necessary to systematically evaluate potential effects on livelihoods and human well-being (Gasparatos et al. 2011; Creutzig et al. 2013, 2015; Hess et al. 2016). These assessments are inherently linked to economic drivers of land use change and could result in growth of biofuel processing plants that do not occur over abandoned and degraded farmlands, since the underlying cause of landscape change may be motivated by financial rather than environmental considerations. Therefore, while we have quantified the hydroclimatic sustainability of perennial bioenergy crop expansion in this paper, a fully comprehensive assessment characterizing the sustainability of perennial bioenergy crop deployment, in general, requires interdisciplinary assessments that integrate natural and social components.

Finally, the principal highlights of this research establish a framework of feedback assessment between LULCC and water resource impacts where analogous energy pathways involving landscape modification are being considered (e.g., natural landscape conversion to oil palm in Indonesia). Via identification of suitable hot spots of bioenergy crop deployment, due to simultaneous regional-scale cooling in conjunction with minimal adverse effects on soil moisture, we also identify areas wherein cultivation can effectively reduce projected warming due to large-scale climate change.

Acknowledgments

This work was funded by NSF Grant EAR-1204774.

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