cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1011924.v1
title: Examples of the effects of first-generation biofuel crops on rural livelihoods

description: <b>Table 1.</b> Examples of the effects of first-generation biofuel crops on rural livelihoods. <strong>Abstract</strong> Integrated assessment models suggest that the large-scale deployment of bioenergy could contribute to ambitious climate change mitigation efforts. However, such a shift would intensify the global competition for land, with possible consequences for 1.5 billion smallholder livelihoods that these models do not consider. Maintaining and enhancing robust livelihoods upon bioenergy deployment is an equally important sustainability goal that warrants greater attention. The social implications of biofuel production are complex, varied and place-specific, difficult to model, operationalize and quantify. However, a rapidly developing body of social science literature is advancing the understanding of these interactions. In this letter we link human geography research on the interaction between biofuel crops and livelihoods in developing countries to integrated assessments on biofuels. We review case-study research focused on first-generation biofuel crops to demonstrate that food, income, land and other assets such as health are key livelihood dimensions that can be impacted by such crops and we highlight how place-specific and global dynamics influence both aggregate and distributional outcomes across these livelihood dimensions. We argue that place-specific production models and land tenure regimes mediate livelihood outcomes, which are also in turn affected by global and regional markets and their resulting equilibrium dynamics. The place-specific perspective suggests that distributional consequences are a crucial complement to aggregate outcomes; this has not been given enough weight in comprehensive assessments to date. By narrowing the gap between place-specific case studies and global models, our discussion offers a route towards integrating livelihood and equity considerations into scenarios of future bioenergy deployment, thus contributing to a key challenge in sustainability sciences.
DOI: 10.6084/m9.figshare.1011925.v1
title: Possible livelihood outcomes from bioenergy deployment

description: <b>Table 2.</b> Possible livelihood outcomes from bioenergy deployment. Normal text denotes benefits or harms considered by integrated assessments, notably Golub <em>et al</em> (2012). Bold text denotes outcomes not included in integrated assessments. <strong>Abstract</strong> Integrated assessment models suggest that the large-scale deployment of bioenergy could contribute to ambitious climate change mitigation efforts. However, such a shift would intensify the global competition for land, with possible consequences for 1.5 billion smallholder livelihoods that these models do not consider. Maintaining and enhancing robust livelihoods upon bioenergy deployment is an equally important sustainability goal that warrants greater attention. The social implications of biofuel production are complex, varied and place-specific, difficult to model, operationalize and quantify. However, a rapidly developing body of social science literature is advancing the understanding of these interactions. In this letter we link human geography research on the interaction between biofuel crops and livelihoods in developing countries to integrated assessments on biofuels. We review case-study research focused on first-generation biofuel crops to demonstrate that food, income, land and other assets such as health are key livelihood dimensions that can be impacted by such crops and we highlight how place-specific and global dynamics influence both aggregate and distributional outcomes across these livelihood dimensions. We argue that place-specific production models and land tenure regimes mediate livelihood outcomes, which are also in turn affected by global and regional markets and their resulting equilibrium dynamics. The place-specific perspective suggests that distributional consequences are a crucial complement to aggregate outcomes; this has not been given enough weight in comprehensive assessments to date. By narrowing the gap between place-specific case studies and global models, our discussion offers a route towards integrating livelihood and equity considerations into scenarios of future bioenergy deployment, thus contributing to a key challenge in sustainability sciences.
DOI: 10.6084/m9.figshare.1011921
title: US thermoelectric power plant water withdrawals as percentage of US total water use

description: <b>Table 1.</b> US thermoelectric power plant water withdrawals as percentage of US total water use. <strong>Abstract</strong> Here, we assess current stress in the freshwater system based on the best available data in order to understand possible risks and vulnerabilities to regional water resources and the sectors dependent on freshwater. We present watershed-scale measures of surface water supply stress for the coterminous United States (US) using the water supply stress index (WaSSI) model which considers regional trends in both water supply and demand. A snapshot of contemporary annual water demand is compared against different water supply regimes, including current average supplies, current extreme-year supplies, and projected future average surface water flows under a changing climate. In addition, we investigate the contributions of different water demand sectors to current water stress. On average, water supplies are stressed, meaning that demands for water outstrip natural supplies in over 9% of the 2103 watersheds examined. These watersheds rely on reservoir storage, conveyance systems, and groundwater to meet current water demands. Overall, agriculture is the major demand-side driver of water stress in the US, whereas municipal stress is isolated to southern California. Water stress introduced by cooling water demands for power plants is punctuated across the US, indicating that a single power plant has the potential to stress water supplies at the watershed scale. On the supply side, watersheds in the western US are particularly sensitive to low flow events and projected long-term shifts in flow driven by climate change. The WaSSI results imply that not only are water resources in the southwest in particular at risk, but that there are also potential vulnerabilities to specific sectors, even in the 'water-rich' southeast.
DOI: 10.6084/m9.figshare.1011921.v1
title: US thermoelectric power plant water withdrawals as percentage of US total water use

description: <b>Table 1.</b> US thermoelectric power plant water withdrawals as percentage of US total water use. <strong>Abstract</strong> Here, we assess current stress in the freshwater system based on the best available data in order to understand possible risks and vulnerabilities to regional water resources and the sectors dependent on freshwater. We present watershed-scale measures of surface water supply stress for the coterminous United States (US) using the water supply stress index (WaSSI) model which considers regional trends in both water supply and demand. A snapshot of contemporary annual water demand is compared against different water supply regimes, including current average supplies, current extreme-year supplies, and projected future average surface water flows under a changing climate. In addition, we investigate the contributions of different water demand sectors to current water stress. On average, water supplies are stressed, meaning that demands for water outstrip natural supplies in over 9% of the 2103 watersheds examined. These watersheds rely on reservoir storage, conveyance systems, and groundwater to meet current water demands. Overall, agriculture is the major demand-side driver of water stress in the US, whereas municipal stress is isolated to southern California. Water stress introduced by cooling water demands for power plants is punctuated across the US, indicating that a single power plant has the potential to stress water supplies at the watershed scale. On the supply side, watersheds in the western US are particularly sensitive to low flow events and projected long-term shifts in flow driven by climate change. The WaSSI results imply that not only are water resources in the southwest in particular at risk, but that there are also potential vulnerabilities to specific sectors, even in the 'water-rich' southeast.
DOI: 10.6084/m9.figshare.1011910
title: The ordinal date at which SCA is first below 50% according to time-lapse camera observations and interpolated estimates of SCA derived from Landsat and MODIS over four vegetation types at Daring Lake

description: <b>Table 1.</b> The ordinal date at which SCA is first below 50% according to time-lapse camera observations and interpolated estimates of SCA derived from Landsat and MODIS over four vegetation types at Daring Lake. <strong>Abstract</strong> The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA &gt; 50%.
DOI: 10.6084/m9.figshare.1011911
title: Uncertainty in 1 May–30 June (MJ) and snow season (SS) estimates of NEE by VPRM both with (<sub>s</sub>) and without (<sub>0</sub>) representations of the influences of snow on respiration and GEE

description: <b>Table 2.</b> Uncertainty in 1 May–30 June (MJ) and snow season (SS) estimates of NEE by VPRM both with (<sub>s</sub>) and without (<sub>0</sub>) representations of the influences of snow on respiration and GEE. Results are indicated for the Daring Lake MT calibration site, as well as the Ivotuk validation site, for years 2004–2007. Mean absolute error (MAE) values are indicated first, followed by root mean squared error (RMSE) values in brackets. All error rates were calculated by comparing daily average eddy covariance NEE to daily average model NEE. <strong>Abstract</strong> The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA &gt; 50%.
DOI: 10.6084/m9.figshare.1011912
title: Uncertainty in 1 May–30 June (MJ) and snow season (SS) estimates of NEE by VPRM at Daring Lake and Ivotuk, using formulations through which respiration is calculated year-round as a linear or piecewise linear (pwl) function of <em>T</em><sub>soil</sub>

description: <b>Table 3.</b> Uncertainty in 1 May–30 June (MJ) and snow season (SS) estimates of NEE by VPRM at Daring Lake and Ivotuk, using formulations through which respiration is calculated year-round as a linear or piecewise linear (pwl) function of <em>T</em><sub>soil</sub>. Mean absolute error (MAE) values are indicated first, followed by root mean squared error (RMSE) values in brackets. MAE and RMSE were calculated from daily average observations. <strong>Abstract</strong> The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA &gt; 50%.
DOI: 10.6084/m9.figshare.1011912.v1
title: Uncertainty in 1 May–30 June (MJ) and snow season (SS) estimates of NEE by VPRM at Daring Lake and Ivotuk, using formulations through which respiration is calculated year-round as a linear or piecewise linear (pwl) function of <em>T</em><sub>soil</sub>

description: <b>Table 3.</b> Uncertainty in 1 May–30 June (MJ) and snow season (SS) estimates of NEE by VPRM at Daring Lake and Ivotuk, using formulations through which respiration is calculated year-round as a linear or piecewise linear (pwl) function of <em>T</em><sub>soil</sub>. Mean absolute error (MAE) values are indicated first, followed by root mean squared error (RMSE) values in brackets. MAE and RMSE were calculated from daily average observations. <strong>Abstract</strong> The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA &gt; 50%.
DOI: 10.6084/m9.figshare.1011913
title: Estimates of annual net carbon exchange (gC m<sup>−2</sup> yr<sup>−1</sup>) by different VPRM formulations

description: <b>Table 4.</b> Estimates of annual net carbon exchange (gC m<sup>−2</sup> yr<sup>−1</sup>) by different VPRM formulations. <strong>Abstract</strong> The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA &gt; 50%.
DOI: 10.6084/m9.figshare.1011913.v1
title: Estimates of annual net carbon exchange (gC m<sup>−2</sup> yr<sup>−1</sup>) by different VPRM formulations

description: <b>Table 4.</b> Estimates of annual net carbon exchange (gC m<sup>−2</sup> yr<sup>−1</sup>) by different VPRM formulations. <strong>Abstract</strong> The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA &gt; 50%.
DOI: 10.6084/m9.figshare.1011910.v1
title: The ordinal date at which SCA is first below 50% according to time-lapse camera observations and interpolated estimates of SCA derived from Landsat and MODIS over four vegetation types at Daring Lake

description: <b>Table 1.</b> The ordinal date at which SCA is first below 50% according to time-lapse camera observations and interpolated estimates of SCA derived from Landsat and MODIS over four vegetation types at Daring Lake. <strong>Abstract</strong> The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA &gt; 50%.
DOI: 10.6084/m9.figshare.1011911.v1
title: Uncertainty in 1 May–30 June (MJ) and snow season (SS) estimates of NEE by VPRM both with (<sub>s</sub>) and without (<sub>0</sub>) representations of the influences of snow on respiration and GEE

description: <b>Table 2.</b> Uncertainty in 1 May–30 June (MJ) and snow season (SS) estimates of NEE by VPRM both with (<sub>s</sub>) and without (<sub>0</sub>) representations of the influences of snow on respiration and GEE. Results are indicated for the Daring Lake MT calibration site, as well as the Ivotuk validation site, for years 2004–2007. Mean absolute error (MAE) values are indicated first, followed by root mean squared error (RMSE) values in brackets. All error rates were calculated by comparing daily average eddy covariance NEE to daily average model NEE. <strong>Abstract</strong> The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA &gt; 50%.