2019
|
Qiu, Jiangxiao; Zipper, Samuel C.; Motew, Melissa; Booth, Eric G.; Kucharik, Christopher J.; Loheide, Steven P.: Nonlinear groundwater influence on biophysical indicators of ecosystem services. In: Nature Sustainability, vol. 2, no. 6, pp. 475-483, 2019, ISSN: 2398-9629. @article{Qiu2019,
title = {Nonlinear groundwater influence on biophysical indicators of ecosystem services},
author = {Jiangxiao Qiu and Samuel C. Zipper and Melissa Motew and Eric G. Booth and Christopher J. Kucharik and Steven P. Loheide},
url = {https://doi.org/10.1038/s41893-019-0278-2},
doi = {10.1038/s41893-019-0278-2},
issn = {2398-9629},
year = {2019},
date = {2019-06-01},
journal = {Nature Sustainability},
volume = {2},
number = {6},
pages = {475-483},
abstract = {Groundwater is a fundamental control on biophysical processes underpinning essential ecosystem services (ES). However, interactions and feedbacks among groundwater, climate and multiple ES remain less well understood. We investigated groundwater effects on a portfolio of food, water and biogeochemical ES indicators in an urbanizing agricultural watershed. Our results show that food production, water quality and quantity, and flood control are most sensitive to groundwater, with the strongest responses under wet and dry climate extremes. Climate mediates groundwater effects, such that several ES have synergies during dry climate, but trade-offs (groundwater increased some ES but declined others) under wet climate. There is substantial spatial heterogeneity in groundwater effects on ES, which is driven primarily by water table depth (WTD) and is also sensitive to soil texture and land cover. Most ES indicators respond nonlinearly to WTD when groundwater is within a critical depth (approximately 2.5thinspacem) of land surface, indicating that small WTD changes can have disproportionately large effects on ES in shallow groundwater areas. Within this critical WTD, increasingly shallow groundwater leads to nonlinear increases in surface flood risk, sediment erosion and phosphorus yield; nonlinear decreases in drainage to the deep vadose zone and thus groundwater recharge; and bidirectional responses of crop and grass production, carbon storage and nitrate leaching. Our study illustrates the complex role of groundwater in affecting multiple ES and highlights that strategically managing groundwater may enhance ES resilience to climate extremes in shallow groundwater settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Groundwater is a fundamental control on biophysical processes underpinning essential ecosystem services (ES). However, interactions and feedbacks among groundwater, climate and multiple ES remain less well understood. We investigated groundwater effects on a portfolio of food, water and biogeochemical ES indicators in an urbanizing agricultural watershed. Our results show that food production, water quality and quantity, and flood control are most sensitive to groundwater, with the strongest responses under wet and dry climate extremes. Climate mediates groundwater effects, such that several ES have synergies during dry climate, but trade-offs (groundwater increased some ES but declined others) under wet climate. There is substantial spatial heterogeneity in groundwater effects on ES, which is driven primarily by water table depth (WTD) and is also sensitive to soil texture and land cover. Most ES indicators respond nonlinearly to WTD when groundwater is within a critical depth (approximately 2.5thinspacem) of land surface, indicating that small WTD changes can have disproportionately large effects on ES in shallow groundwater areas. Within this critical WTD, increasingly shallow groundwater leads to nonlinear increases in surface flood risk, sediment erosion and phosphorus yield; nonlinear decreases in drainage to the deep vadose zone and thus groundwater recharge; and bidirectional responses of crop and grass production, carbon storage and nitrate leaching. Our study illustrates the complex role of groundwater in affecting multiple ES and highlights that strategically managing groundwater may enhance ES resilience to climate extremes in shallow groundwater settings. |
Haden, Adam C.; Kucharik, Christopher J.; Jackson, Randall D.; Marín-Spiotta, Erika: Litter quantity, litter chemistry, and soil texture control changes in soil organic carbon fractions under bioenergy cropping systems of the North Central U.S.. In: Biogeochemistry, vol. 143, no. 3, pp. 313-326, 2019, ISSN: 1573-515X. @article{vonHaden2019,
title = {Litter quantity, litter chemistry, and soil texture control changes in soil organic carbon fractions under bioenergy cropping systems of the North Central U.S.},
author = {Adam C. Haden and Christopher J. Kucharik and Randall D. Jackson and Erika Marín-Spiotta},
url = {https://doi.org/10.1007/s10533-019-00564-7},
doi = {10.1007/s10533-019-00564-7},
issn = {1573-515X},
year = {2019},
date = {2019-05-01},
journal = {Biogeochemistry},
volume = {143},
number = {3},
pages = {313-326},
abstract = {Soil organic carbon (SOC) storage is a critical component of the overall sustainability of bioenergy cropping systems. Predicting the influence of cropping systems on SOC under diverse scenarios requires a mechanistic understanding of the underlying processes driving SOC accumulation and loss. We used a density fractionation technique to isolate three SOC fractions that are conceptualized to vary in SOC protection from decomposition. The free light fraction (FLF) is particulate SOC that is present in the inter-aggregate soil matrix, the occluded light fraction (OLF) is contained within aggregates, and the heavy fraction (HF) is associated with minerals. We evaluated surface (0 to 10 cm depth) SOC fraction changes from baseline conditions 5 years after biofuel cropping system establishment at two temperate sites with contrasting soil textures. The biofuel cropping systems included no-till maize, switchgrass, prairie, and hybrid poplar. The FLF concentration (g fraction C g bulk soil−1) did not change significantly from baseline levels under any of the cropping systems at either site after 5 years. Except for poplar, OLF concentrations were reduced in all systems at the site with coarse-textured soils and maintained at the site with fine-textured soils. In poplar systems, OLF concentrations were maintained on coarse-textured soils and increased on fine-textured soils. The HF concentrations also increased under poplar on the coarse-textured soil. A structural equation model indicated that OLF concentrations increased with lower litter C:N, and HF concentrations increased with greater litter quantity and lower litter C:N mass ratios. C:N increased over time within all SOC fractions, suggesting that all pools are sensitive to land-use change on sub-decadal timescales. In agreement with modern SOC theory, our empirical results indicate that increasing litter input quantity and promoting plant species with low C:N litter may improve SOC storage in aggregate and mineral-associated soil fractions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Soil organic carbon (SOC) storage is a critical component of the overall sustainability of bioenergy cropping systems. Predicting the influence of cropping systems on SOC under diverse scenarios requires a mechanistic understanding of the underlying processes driving SOC accumulation and loss. We used a density fractionation technique to isolate three SOC fractions that are conceptualized to vary in SOC protection from decomposition. The free light fraction (FLF) is particulate SOC that is present in the inter-aggregate soil matrix, the occluded light fraction (OLF) is contained within aggregates, and the heavy fraction (HF) is associated with minerals. We evaluated surface (0 to 10 cm depth) SOC fraction changes from baseline conditions 5 years after biofuel cropping system establishment at two temperate sites with contrasting soil textures. The biofuel cropping systems included no-till maize, switchgrass, prairie, and hybrid poplar. The FLF concentration (g fraction C g bulk soil−1) did not change significantly from baseline levels under any of the cropping systems at either site after 5 years. Except for poplar, OLF concentrations were reduced in all systems at the site with coarse-textured soils and maintained at the site with fine-textured soils. In poplar systems, OLF concentrations were maintained on coarse-textured soils and increased on fine-textured soils. The HF concentrations also increased under poplar on the coarse-textured soil. A structural equation model indicated that OLF concentrations increased with lower litter C:N, and HF concentrations increased with greater litter quantity and lower litter C:N mass ratios. C:N increased over time within all SOC fractions, suggesting that all pools are sensitive to land-use change on sub-decadal timescales. In agreement with modern SOC theory, our empirical results indicate that increasing litter input quantity and promoting plant species with low C:N litter may improve SOC storage in aggregate and mineral-associated soil fractions. |
Spawn, Seth A; Lark, Tyler J; Gibbs, Holly K: Carbon emissions from cropland expansion in the United States. In: Environmental Research Letters, vol. 14, no. 4, pp. 045009, 2019. @article{Spawn_2019,
title = {Carbon emissions from cropland expansion in the United States},
author = {Seth A Spawn and Tyler J Lark and Holly K Gibbs},
url = {https://doi.org/10.1088/1748-9326/ab0399},
doi = {10.1088/1748-9326/ab0399},
year = {2019},
date = {2019-04-01},
journal = {Environmental Research Letters},
volume = {14},
number = {4},
pages = {045009},
publisher = {IOP Publishing},
abstract = {After decades of decline, croplands are once again expanding across the United States. A recent spatially explicit analysis mapped nearly three million hectares of US cropland expansion that occurred between 2008 and 2012. Land use change (LUC) of this sort can be a major source of anthropogenic carbon (C) emissions, though the effects of this change have yet to be analyzed. We developed a data-driven model that combines these high-resolution maps of cropland expansion with published maps of biomass and soil organic carbon stocks (SOC) to map and quantify the resulting C emissions. Our model increases emphasis on non-forest—i.e. grassland, shrubland and wetland—above and belowground biomass C stocks and the response of SOC to LUC—emission sources that are frequently neglected in traditional C accounting. These sources represent major emission conduits in the US, where new croplands primarily replace grasslands. We find that expansion between 2008–12 caused, on average, a release of 55.0 MgC ha−1 (SDspatial = 39.9 MgC ha−1), which resulted in total emissions of 38.8 TgC yr−1 (95% CI = 21.6–55.8 TgC yr−1). We also find wide geographic variation in both the size and sensitivity of affected C stocks. Grassland conversion was the primary source of emissions, with more than 90% of these emissions originating from SOC stocks. Due to the long accumulation time of SOC, its dominance as a source suggests that emissions may be difficult to mitigate over human-relevant time scales. While methodological limitations regarding the effects of land use legacies and future management remain, our findings emphasize the importance of avoiding LUC emissions and suggest potential means by which natural C stocks can be conserved.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
After decades of decline, croplands are once again expanding across the United States. A recent spatially explicit analysis mapped nearly three million hectares of US cropland expansion that occurred between 2008 and 2012. Land use change (LUC) of this sort can be a major source of anthropogenic carbon (C) emissions, though the effects of this change have yet to be analyzed. We developed a data-driven model that combines these high-resolution maps of cropland expansion with published maps of biomass and soil organic carbon stocks (SOC) to map and quantify the resulting C emissions. Our model increases emphasis on non-forest—i.e. grassland, shrubland and wetland—above and belowground biomass C stocks and the response of SOC to LUC—emission sources that are frequently neglected in traditional C accounting. These sources represent major emission conduits in the US, where new croplands primarily replace grasslands. We find that expansion between 2008–12 caused, on average, a release of 55.0 MgC ha−1 (SDspatial = 39.9 MgC ha−1), which resulted in total emissions of 38.8 TgC yr−1 (95% CI = 21.6–55.8 TgC yr−1). We also find wide geographic variation in both the size and sensitivity of affected C stocks. Grassland conversion was the primary source of emissions, with more than 90% of these emissions originating from SOC stocks. Due to the long accumulation time of SOC, its dominance as a source suggests that emissions may be difficult to mitigate over human-relevant time scales. While methodological limitations regarding the effects of land use legacies and future management remain, our findings emphasize the importance of avoiding LUC emissions and suggest potential means by which natural C stocks can be conserved. |
Abel, David W; Holloway, Tracey; Martínez-Santos, Javier; Harkey, Monica; Tao, Madankui; Kubes, Cassandra; Hayes, Sara: Air Quality-Related Health Benefits of Energy Efficiency in the United States. In: Environmental Science & Technology, vol. 53, no. 7, pp. 3987-3998, 2019. @article{doi:10.1021/acs.est.8b06417,
title = {Air Quality-Related Health Benefits of Energy Efficiency in the United States},
author = {David W Abel and Tracey Holloway and Javier Martínez-Santos and Monica Harkey and Madankui Tao and Cassandra Kubes and Sara Hayes},
url = {https://doi.org/10.1021/acs.est.8b06417},
doi = {10.1021/acs.est.8b06417},
year = {2019},
date = {2019-03-05},
journal = {Environmental Science & Technology},
volume = {53},
number = {7},
pages = {3987-3998},
abstract = {While it is known that energy efficiency (EE) lowers power sector demand and emissions, study of the air quality and public health impacts of EE has been limited. Here, we quantify the air quality and mortality impacts of a 12% summertime (June, July, and August) reduction in baseload electricity demand. We use the AVoided Emissions and geneRation Tool (AVERT) to simulate plant-level generation and emissions, the Community Multiscale Air Quality (CMAQ) model to simulate air quality, and the Environmental Benefits Mapping and Analysis Program (BenMAP) to quantify mortality impacts. We find EE reduces emissions of NOx by 13.2%, SO2 by 12.6%, and CO2 by 11.6%. On a nationwide, summer average basis, ambient PM2.5 is reduced 0.55% and O3 is reduced 0.45%. Reduced exposure to PM2.5 avoids 300 premature deaths annually (95% CI: 60 to 580) valued at $2.8 billion ($0.13 billion to $9.3 billion), and reduced exposure to O3 averts 175 deaths (101 to 244) valued at $1.6 billion ($0.15 billion to $4.5 billion). This translates into a health savings rate of $0.049/kWh ($0.031/kWh for PM2.5 and $0.018/kWh for O3). These results illustrate the importance of capturing the health benefits of EE and its potential as a strategy to achieve air standards.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
While it is known that energy efficiency (EE) lowers power sector demand and emissions, study of the air quality and public health impacts of EE has been limited. Here, we quantify the air quality and mortality impacts of a 12% summertime (June, July, and August) reduction in baseload electricity demand. We use the AVoided Emissions and geneRation Tool (AVERT) to simulate plant-level generation and emissions, the Community Multiscale Air Quality (CMAQ) model to simulate air quality, and the Environmental Benefits Mapping and Analysis Program (BenMAP) to quantify mortality impacts. We find EE reduces emissions of NOx by 13.2%, SO2 by 12.6%, and CO2 by 11.6%. On a nationwide, summer average basis, ambient PM2.5 is reduced 0.55% and O3 is reduced 0.45%. Reduced exposure to PM2.5 avoids 300 premature deaths annually (95% CI: 60 to 580) valued at $2.8 billion ($0.13 billion to $9.3 billion), and reduced exposure to O3 averts 175 deaths (101 to 244) valued at $1.6 billion ($0.15 billion to $4.5 billion). This translates into a health savings rate of $0.049/kWh ($0.031/kWh for PM2.5 and $0.018/kWh for O3). These results illustrate the importance of capturing the health benefits of EE and its potential as a strategy to achieve air standards. |
Stull, Valerie J; Patz, Jonathan A: Research and policy priorities for edible insects. In: Sustainability Science, vol. 15, no. 2, pp. 1-13, 2019, ISBN: 1862-4057. @article{Stull2019,
title = {Research and policy priorities for edible insects},
author = {Valerie J Stull and Jonathan A Patz},
url = {https://link.springer.com/article/10.1007/s11625-019-00709-5},
doi = {https://doi.org/10.1007/s11625-019-00709-5},
isbn = {1862-4057},
year = {2019},
date = {2019-03-01},
journal = {Sustainability Science},
volume = {15},
number = {2},
pages = {1-13},
abstract = {Global communities increasingly struggle to provide ample healthful food for growing populations in the face of social and environmental pressures. Insect agriculture is one underexplored and innovative approach. Sustainable cultivation of nutrient-dense edible insects could help boost food access, support human nutrition, and mitigate key drivers of climate change. The edible insects industry is in its nascent stages, as relatively few entities have committed resources towards optimizing farming methods. Nevertheless, insect farming is poised to benefit food insecure populations, and the planet as a whole if more targeted research and conducive policies are implemented. The purpose of this paper is to outline the state of the science regarding edible insects, define a research agenda, and recommend policy action to support the growing industry. Edible insects are not a panacea for current challenges, but they have the potential to confer numerous benefits to people and the environment. Rigorous research is needed to establish optimal farming methods, strengthen food safety, understand health impacts of consumption, explore consumer acceptance, tackle ethical considerations, and investigate economic viability. A clear definition for insects as food, industry guidance support for obtaining generally regarded as safe designation, and collaboration by industry stakeholders to develop production standards will also help move the industry forward. Generating and galvanizing knowledge sharing networks, investing in critical interdisciplinary research, and advocating for conducive policies that support emerging entrepreneurs will be necessary to capitalize on the benefits of edible insects in the future.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Global communities increasingly struggle to provide ample healthful food for growing populations in the face of social and environmental pressures. Insect agriculture is one underexplored and innovative approach. Sustainable cultivation of nutrient-dense edible insects could help boost food access, support human nutrition, and mitigate key drivers of climate change. The edible insects industry is in its nascent stages, as relatively few entities have committed resources towards optimizing farming methods. Nevertheless, insect farming is poised to benefit food insecure populations, and the planet as a whole if more targeted research and conducive policies are implemented. The purpose of this paper is to outline the state of the science regarding edible insects, define a research agenda, and recommend policy action to support the growing industry. Edible insects are not a panacea for current challenges, but they have the potential to confer numerous benefits to people and the environment. Rigorous research is needed to establish optimal farming methods, strengthen food safety, understand health impacts of consumption, explore consumer acceptance, tackle ethical considerations, and investigate economic viability. A clear definition for insects as food, industry guidance support for obtaining generally regarded as safe designation, and collaboration by industry stakeholders to develop production standards will also help move the industry forward. Generating and galvanizing knowledge sharing networks, investing in critical interdisciplinary research, and advocating for conducive policies that support emerging entrepreneurs will be necessary to capitalize on the benefits of edible insects in the future. |
Limaye, Vijay S; Schöpp, Wolfgang; Amann, Markus: Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India. In: International Journal of Environmental Research and Public Health, vol. 16, no. 1, 2019, ISSN: 1660-4601. @article{ijerph16010060,
title = {Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India},
author = {Vijay S Limaye and Wolfgang Schöpp and Markus Amann},
url = {https://www.mdpi.com/1660-4601/16/1/60},
doi = {10.3390/ijerph16010060},
issn = {1660-4601},
year = {2019},
date = {2019-01-01},
journal = {International Journal of Environmental Research and Public Health},
volume = {16},
number = {1},
abstract = {Fine particulate matter (PM2.5, diameter ≤2.5 μm) is implicated as the most health-damaging air pollutant. Large cohort studies of chronic exposure to PM2.5 and mortality risk are largely confined to areas with low to moderate ambient PM2.5 concentrations and posit log-linear exposure-response functions. However, levels of PM2.5 in developing countries such as India are typically much higher, causing unknown health effects. Integrated exposure-response functions for high PM2.5 exposures encompassing risk estimates from ambient air, secondhand smoke, and active smoking exposures have been posited. We apply these functions to estimate the future cause-specific mortality risks associated with population-weighted ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. The loss in statistical life expectancy (SLE) is calculated based on risk estimates and baseline mortality rates. Losses in SLE are aggregated and weighted using national age-adjusted, cause-specific mortality rates. 2030 PM2.5 pollution in India reaches an annual mean of 74 μg/m3, nearly eight times the corresponding World Health Organization air quality guideline. The national average loss in SLE is 32.5 months (95% Confidence Interval (CI): 29.7-35.2, regional range: 8.5-42.0), compared to an average of 53.7 months (95% CI: 46.3-61.1) using methods currently applied in GAINS. Results indicate wide regional variation in health impacts, and these methods may still underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response functions suggests that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fine particulate matter (PM2.5, diameter ≤2.5 μm) is implicated as the most health-damaging air pollutant. Large cohort studies of chronic exposure to PM2.5 and mortality risk are largely confined to areas with low to moderate ambient PM2.5 concentrations and posit log-linear exposure-response functions. However, levels of PM2.5 in developing countries such as India are typically much higher, causing unknown health effects. Integrated exposure-response functions for high PM2.5 exposures encompassing risk estimates from ambient air, secondhand smoke, and active smoking exposures have been posited. We apply these functions to estimate the future cause-specific mortality risks associated with population-weighted ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. The loss in statistical life expectancy (SLE) is calculated based on risk estimates and baseline mortality rates. Losses in SLE are aggregated and weighted using national age-adjusted, cause-specific mortality rates. 2030 PM2.5 pollution in India reaches an annual mean of 74 μg/m3, nearly eight times the corresponding World Health Organization air quality guideline. The national average loss in SLE is 32.5 months (95% Confidence Interval (CI): 29.7–35.2, regional range: 8.5–42.0), compared to an average of 53.7 months (95% CI: 46.3–61.1) using methods currently applied in GAINS. Results indicate wide regional variation in health impacts, and these methods may still underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response functions suggests that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality. |
Xie, Yanhua; Lark, Tyler J.; Brown, Jesslyn F.; Gibbs, Holly K.: Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine. In: ISPRS Journal of Photogrammetry and Remote Sensing, vol. 155, pp. 136-149, 2019, ISSN: 0924-2716. @article{XIE2019136,
title = {Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine},
author = {Yanhua Xie and Tyler J. Lark and Jesslyn F. Brown and Holly K. Gibbs},
url = {https://www.sciencedirect.com/science/article/pii/S0924271619301728},
doi = {https://doi.org/10.1016/j.isprsjprs.2019.07.005},
issn = {0924-2716},
year = {2019},
date = {2019-01-01},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {155},
pages = {136-149},
abstract = {Accurate and timely information on the distribution of irrigated croplands is crucial to research on agriculture, water availability, land use, and climate change. While agricultural land use has been well characterized, less attention has been paid specifically to croplands that are irrigated, in part due to the difficulty in mapping and distinguishing irrigation in satellite imagery. In this study, we developed a semi-automatic training approach to rapidly map irrigated croplands across the conterminous United States (CONUS) at 30 m resolution using Google Earth Engine. To resolve the issue of lacking nationwide training data, we generated two intermediate irrigation maps by segmenting Landsat-derived annual maximum greenness and enhanced vegetation index using county-level thresholds calibrated from an existing coarse resolution irrigation map. The resulting intermediate maps were then spatially filtered to provide a training data pool for most areas except for the upper midwestern states where we visually collected samples. We then used random samples extracted from the training pool along with remote sensing-derived features and climate variables to train ecoregion-stratified random forest classifiers for pixel-level classification. For ecoregions with a large training pool, the procedure of sample extraction, classifier training, and classification was conducted 10 times to obtain stable classification results. The resulting 2012 Landsat-based irrigation dataset (LANID) identified 23.3 million hectares of irrigated croplands in CONUS. A quantitative assessment of LANID showed superior accuracy to currently available maps, with a mean Kappa value of 0.88 (0.75–0.99), overall accuracy of 94% (87.5–99%), and producer’s and user’s accuracy of the irrigation class of 97.3% and 90.5%, respectively, at the aquifer level. Evaluation of feature importance indicated that Landsat-derived features played the primary role in classification in relatively arid regions while climate variables were important in the more humid eastern states. This methodology has the potential to produce annual irrigation maps for CONUS and provide insights into the field-level spatial and temporal aspects of irrigation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Accurate and timely information on the distribution of irrigated croplands is crucial to research on agriculture, water availability, land use, and climate change. While agricultural land use has been well characterized, less attention has been paid specifically to croplands that are irrigated, in part due to the difficulty in mapping and distinguishing irrigation in satellite imagery. In this study, we developed a semi-automatic training approach to rapidly map irrigated croplands across the conterminous United States (CONUS) at 30 m resolution using Google Earth Engine. To resolve the issue of lacking nationwide training data, we generated two intermediate irrigation maps by segmenting Landsat-derived annual maximum greenness and enhanced vegetation index using county-level thresholds calibrated from an existing coarse resolution irrigation map. The resulting intermediate maps were then spatially filtered to provide a training data pool for most areas except for the upper midwestern states where we visually collected samples. We then used random samples extracted from the training pool along with remote sensing-derived features and climate variables to train ecoregion-stratified random forest classifiers for pixel-level classification. For ecoregions with a large training pool, the procedure of sample extraction, classifier training, and classification was conducted 10 times to obtain stable classification results. The resulting 2012 Landsat-based irrigation dataset (LANID) identified 23.3 million hectares of irrigated croplands in CONUS. A quantitative assessment of LANID showed superior accuracy to currently available maps, with a mean Kappa value of 0.88 (0.75–0.99), overall accuracy of 94% (87.5–99%), and producer’s and user’s accuracy of the irrigation class of 97.3% and 90.5%, respectively, at the aquifer level. Evaluation of feature importance indicated that Landsat-derived features played the primary role in classification in relatively arid regions while climate variables were important in the more humid eastern states. This methodology has the potential to produce annual irrigation maps for CONUS and provide insights into the field-level spatial and temporal aspects of irrigation. |
Lark, Tyler J; Larson, Ben; Schelly, Ian; Batish, Sapna; Gibbs, Holly K: Accelerated Conversion of Native Prairie to Cropland in Minnesota. In: Environmental Conservation, vol. 46, no. 2, pp. 155–162, 2019. @article{lark_larson_schelly_batish_gibbs_2019,
title = {Accelerated Conversion of Native Prairie to Cropland in Minnesota},
author = {Tyler J Lark and Ben Larson and Ian Schelly and Sapna Batish and Holly K Gibbs},
doi = {10.1017/S0376892918000437},
year = {2019},
date = {2019-01-01},
journal = {Environmental Conservation},
volume = {46},
number = {2},
pages = {155–162},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Burivalova, Z.; Miteva, D.; Salafsky, N.; Butler, R. A.; Wilcove, D. S.: Evidence Types and Trends in Tropical Forest Conservation Literature. In: Trends in Ecology & Evolution, vol. 34, no. 7, pp. 669-679, 2019, ISSN: 0169-5347. @article{BURIVALOVA2019669,
title = {Evidence Types and Trends in Tropical Forest Conservation Literature},
author = {Z. Burivalova and D. Miteva and N. Salafsky and R. A. Butler and D. S. Wilcove},
url = {https://www.sciencedirect.com/science/article/pii/S0169534719300825},
doi = {https://doi.org/10.1016/j.tree.2019.03.002},
issn = {0169-5347},
year = {2019},
date = {2019-01-01},
journal = {Trends in Ecology & Evolution},
volume = {34},
number = {7},
pages = {669-679},
abstract = {To improve the likelihood of conservation success, donors, policy makers, nongovernmental organizations (NGOs), and researchers are increasingly interested in making conservation decisions based on scientific evidence. A major challenge in doing so has been the wide variability in the methodological rigor of existing studies. We present a simple framework to classify different types of conservation evidence, which can be used to understand the strengths, weaknesses, and biases in the conservation effectiveness literature. We then apply this framework to evaluate the evidence for the efficacy of four important strategies in tropical forest conservation. Even though there has been an increase in methodologically rigorous studies over time, countries that are globally important in terms of their biodiversity are still heavily under-represented by any type of conservation effectiveness evidence.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To improve the likelihood of conservation success, donors, policy makers, nongovernmental organizations (NGOs), and researchers are increasingly interested in making conservation decisions based on scientific evidence. A major challenge in doing so has been the wide variability in the methodological rigor of existing studies. We present a simple framework to classify different types of conservation evidence, which can be used to understand the strengths, weaknesses, and biases in the conservation effectiveness literature. We then apply this framework to evaluate the evidence for the efficacy of four important strategies in tropical forest conservation. Even though there has been an increase in methodologically rigorous studies over time, countries that are globally important in terms of their biodiversity are still heavily under-represented by any type of conservation effectiveness evidence. |
Ziter, Carly D.; Pedersen, Eric J.; Kucharik, Christopher J.; Turner, Monica G.: Reply to Drescher: Interdisciplinary collaboration is essential to understand and implement climate-resilient strategies in cities. In: Proceedings of the National Academy of Sciences, vol. 116, no. 52, pp. 26155-26156, 2019. @article{doi:10.1073/pnas.1918746116,
title = {Reply to Drescher: Interdisciplinary collaboration is essential to understand and implement climate-resilient strategies in cities},
author = {Carly D. Ziter and Eric J. Pedersen and Christopher J. Kucharik and Monica G. Turner},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.1918746116},
doi = {10.1073/pnas.1918746116},
year = {2019},
date = {2019-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {116},
number = {52},
pages = {26155-26156},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Nocco, Mallika A.; Zipper, Samuel C.; Booth, Eric G.; Cummings, Cadan R.; Loheide, Steven P.; Kucharik, Christopher J.: Combining Evapotranspiration and Soil Apparent Electrical Conductivity Mapping to Identify Potential Precision Irrigation Benefits. In: Remote Sensing, vol. 11, no. 21, 2019, ISSN: 2072-4292. @article{rs11212460,
title = {Combining Evapotranspiration and Soil Apparent Electrical Conductivity Mapping to Identify Potential Precision Irrigation Benefits},
author = {Mallika A. Nocco and Samuel C. Zipper and Eric G. Booth and Cadan R. Cummings and Steven P. Loheide and Christopher J. Kucharik},
url = {https://www.mdpi.com/2072-4292/11/21/2460},
doi = {10.3390/rs11212460},
issn = {2072-4292},
year = {2019},
date = {2019-01-01},
journal = {Remote Sensing},
volume = {11},
number = {21},
abstract = {Precision irrigation optimizes the spatiotemporal application of water using evapotranspiration (ET) maps to assess water stress or soil apparent electrical conductivity (ECa) maps as a proxy for plant available water content. However, ET and ECa maps are rarely used together. We developed high-resolution ET and ECa maps for six irrigated fields in the Midwest United States between 2014-2016. Our research goals were to (1) validate ET maps developed using the High-Resolution Mapping of EvapoTranspiration (HRMET) model and aerial imagery via comparison with ground observations in potato, sweet corn, and pea agroecosystems; (2) characterize relationships between ET and ECa; and (3) identify potential precision irrigation benefits across rotations. We demonstrated the synergy of combined ET and ECa mapping for evaluating whether intrafield differences in ECa correspond to actual water use for different crop rotations. We found that ET and ECa have stronger relationships in sweet corn and potato rotations than field corn. Thus, sweet corn and potato crops may benefit more from precision irrigation than field corn, even when grown rotationally on the same field. We recommend that future research consider crop rotation, intrafield soil variability, and existing irrigation practices together when determining potential water use, savings, and yield gains from precision irrigation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Precision irrigation optimizes the spatiotemporal application of water using evapotranspiration (ET) maps to assess water stress or soil apparent electrical conductivity (ECa) maps as a proxy for plant available water content. However, ET and ECa maps are rarely used together. We developed high-resolution ET and ECa maps for six irrigated fields in the Midwest United States between 2014–2016. Our research goals were to (1) validate ET maps developed using the High-Resolution Mapping of EvapoTranspiration (HRMET) model and aerial imagery via comparison with ground observations in potato, sweet corn, and pea agroecosystems; (2) characterize relationships between ET and ECa; and (3) identify potential precision irrigation benefits across rotations. We demonstrated the synergy of combined ET and ECa mapping for evaluating whether intrafield differences in ECa correspond to actual water use for different crop rotations. We found that ET and ECa have stronger relationships in sweet corn and potato rotations than field corn. Thus, sweet corn and potato crops may benefit more from precision irrigation than field corn, even when grown rotationally on the same field. We recommend that future research consider crop rotation, intrafield soil variability, and existing irrigation practices together when determining potential water use, savings, and yield gains from precision irrigation. |
Motew, Melissa; Chen, Xi; Carpenter, Stephen R.; Booth, Eric G.; Seifert, Jenny; Qiu, Jiangxiao; Loheide, Steven P.; Turner, Monica G.; Zipper, Samuel C.; Kucharik, Christopher J.: Comparing the effects of climate and land use on surface water quality using future watershed scenarios. In: Science of The Total Environment, vol. 693, pp. 133484, 2019, ISSN: 0048-9697. @article{MOTEW2019133484,
title = {Comparing the effects of climate and land use on surface water quality using future watershed scenarios},
author = {Melissa Motew and Xi Chen and Stephen R. Carpenter and Eric G. Booth and Jenny Seifert and Jiangxiao Qiu and Steven P. Loheide and Monica G. Turner and Samuel C. Zipper and Christopher J. Kucharik},
url = {https://www.sciencedirect.com/science/article/pii/S0048969719334047},
doi = {https://doi.org/10.1016/j.scitotenv.2019.07.290},
issn = {0048-9697},
year = {2019},
date = {2019-01-01},
journal = {Science of The Total Environment},
volume = {693},
pages = {133484},
abstract = {Eutrophication of freshwaters occurs in watersheds with excessive pollution of phosphorus (P). Factors that affect P cycling and transport, including climate and land use, are changing rapidly and can have legacy effects, making future freshwater quality uncertain. Focusing on the Yahara Watershed (YW) of southern Wisconsin, USA, an intensive agricultural landscape, we explored the relative influence of land use and climate on three indicators of water quality over a span of 57 years (2014–2070). The indicators included watershed-averaged P yield from the land surface, direct drainage P loads to a lake, and average summertime lake P concentration. Using biophysical model simulations of future watershed scenarios, we found that climate exerted a stronger influence than land use on all three indicators, yet land use had an important role in influencing long term outcomes for each. Variations in P yield due to land use exceeded those due to climate in 36 of 57 years, whereas variations in load and lake total P concentration due to climate exceeded those due to land use in 54 of 57 years, and 52 of 57 years, respectively. The effect of land use was thus strongest for P yield off the landscape and attenuated in the stream and lake aquatic systems where the influence of weather variability was greater. Overall these findings underscore the dominant role of climate in driving inter-annual nutrient fluxes within the hydrologic network and suggest a challenge for land use to influence water quality within streams and lakes over timescales less than a decade. Over longer timescales, reducing applications of P throughout the watershed was an effective management strategy under all four climates investigated, even during decades with wetter conditions and more frequent extreme precipitation events.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Eutrophication of freshwaters occurs in watersheds with excessive pollution of phosphorus (P). Factors that affect P cycling and transport, including climate and land use, are changing rapidly and can have legacy effects, making future freshwater quality uncertain. Focusing on the Yahara Watershed (YW) of southern Wisconsin, USA, an intensive agricultural landscape, we explored the relative influence of land use and climate on three indicators of water quality over a span of 57 years (2014–2070). The indicators included watershed-averaged P yield from the land surface, direct drainage P loads to a lake, and average summertime lake P concentration. Using biophysical model simulations of future watershed scenarios, we found that climate exerted a stronger influence than land use on all three indicators, yet land use had an important role in influencing long term outcomes for each. Variations in P yield due to land use exceeded those due to climate in 36 of 57 years, whereas variations in load and lake total P concentration due to climate exceeded those due to land use in 54 of 57 years, and 52 of 57 years, respectively. The effect of land use was thus strongest for P yield off the landscape and attenuated in the stream and lake aquatic systems where the influence of weather variability was greater. Overall these findings underscore the dominant role of climate in driving inter-annual nutrient fluxes within the hydrologic network and suggest a challenge for land use to influence water quality within streams and lakes over timescales less than a decade. Over longer timescales, reducing applications of P throughout the watershed was an effective management strategy under all four climates investigated, even during decades with wetter conditions and more frequent extreme precipitation events. |
Haden, Adam C.; Marín-Spiotta, Erika; Jackson, Randall D.; Kucharik, Christopher J.: Soil microclimates influence annual carbon loss via heterotrophic soil respiration in maize and switchgrass bioenergy cropping systems. In: Agricultural and Forest Meteorology, vol. 279, pp. 107731, 2019, ISSN: 0168-1923. @article{VONHADEN2019107731,
title = {Soil microclimates influence annual carbon loss via heterotrophic soil respiration in maize and switchgrass bioenergy cropping systems},
author = {Adam C. Haden and Erika Marín-Spiotta and Randall D. Jackson and Christopher J. Kucharik},
url = {https://www.sciencedirect.com/science/article/pii/S0168192319303478},
doi = {https://doi.org/10.1016/j.agrformet.2019.107731},
issn = {0168-1923},
year = {2019},
date = {2019-01-01},
journal = {Agricultural and Forest Meteorology},
volume = {279},
pages = {107731},
abstract = {Heterotrophic soil respiration (RH) is the primary pathway of carbon (C) loss from litter and soil organic matter, and thus RH partially determines ecosystem C storage. Because RH is sensitive to soil temperature and moisture, aboveground factors that influence soil microclimate, such as plant structure and residue management, may in turn affect belowground C loss via RH, but this relationship has not been quantified. We examined multiyear soil microclimate differences to 1-m depth, measured seasonal trends of RH, and parameterized crop-specific microclimate-RH models to quantify the effect of soil microclimate differences on annual RH in temperate no-till maize and switchgrass bioenergy cropping systems. Summertime soil temperatures were typically warmer in maize compared to switchgrass, likely resulting from lower leaf area index (LAI) in maize. In contrast, winter soil temperatures were usually warmer in switchgrass than maize, due in part to more consistent snow retention within the switchgrass litter stubble. Daily soil temperature ranges were less extreme in the perennial switchgrass system compared to the annual no-till maize system. Soil moisture near the soil surface was usually lower in maize than switchgrass, but the opposite was true below about 50 cm. RH showed strong seasonal trends, with warmer and drier soil conditions generally leading to higher RH in both crops. Modeled scenarios indicated that the differences in crop-specific soil microclimates accounted for 4 to 17% of the annual RH flux, with the dominant soil microclimate effects on RH occurring during the summer. Thus, the soil microclimate serves as a strong coupling between aboveground properties and belowground C loss via RH in temperate agroecosystems. Agricultural management practices such as planting date, plant density, and residue management could be targeted to promote soil microclimates that reduce RH, thereby reducing gaseous belowground C losses and potentially enhancing ecosystem C storage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Heterotrophic soil respiration (RH) is the primary pathway of carbon (C) loss from litter and soil organic matter, and thus RH partially determines ecosystem C storage. Because RH is sensitive to soil temperature and moisture, aboveground factors that influence soil microclimate, such as plant structure and residue management, may in turn affect belowground C loss via RH, but this relationship has not been quantified. We examined multiyear soil microclimate differences to 1-m depth, measured seasonal trends of RH, and parameterized crop-specific microclimate-RH models to quantify the effect of soil microclimate differences on annual RH in temperate no-till maize and switchgrass bioenergy cropping systems. Summertime soil temperatures were typically warmer in maize compared to switchgrass, likely resulting from lower leaf area index (LAI) in maize. In contrast, winter soil temperatures were usually warmer in switchgrass than maize, due in part to more consistent snow retention within the switchgrass litter stubble. Daily soil temperature ranges were less extreme in the perennial switchgrass system compared to the annual no-till maize system. Soil moisture near the soil surface was usually lower in maize than switchgrass, but the opposite was true below about 50 cm. RH showed strong seasonal trends, with warmer and drier soil conditions generally leading to higher RH in both crops. Modeled scenarios indicated that the differences in crop-specific soil microclimates accounted for 4 to 17% of the annual RH flux, with the dominant soil microclimate effects on RH occurring during the summer. Thus, the soil microclimate serves as a strong coupling between aboveground properties and belowground C loss via RH in temperate agroecosystems. Agricultural management practices such as planting date, plant density, and residue management could be targeted to promote soil microclimates that reduce RH, thereby reducing gaseous belowground C losses and potentially enhancing ecosystem C storage. |
Nocco, Mallika A.; Smail, Robert A.; Kucharik, Christopher J.: Observation of irrigation-induced climate change in the Midwest United States. In: Global Change Biology, vol. 25, no. 10, pp. 3472-3484, 2019. @article{https://doi.org/10.1111/gcb.14725,
title = {Observation of irrigation-induced climate change in the Midwest United States},
author = {Mallika A. Nocco and Robert A. Smail and Christopher J. Kucharik},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.14725},
doi = {https://doi.org/10.1111/gcb.14725},
year = {2019},
date = {2019-01-01},
journal = {Global Change Biology},
volume = {25},
number = {10},
pages = {3472-3484},
abstract = {Abstract Irrigated agriculture alters near-surface temperature and humidity, which may mask global climate change at the regional scale. However, observational studies of irrigation-induced climate change are lacking in temperate, humid regions throughout North America and Europe. Despite unknown climate impacts, irrigated agriculture is expanding in the Midwest United States, where unconfined aquifers provide groundwater to support crop production on coarse soils. This is the first study in the Midwest United States to observe and quantify differences in regional climate associated with irrigated agricultural conversion from forests and rainfed agriculture. To this end, we established a 60 km transect consisting of 28 stations across varying land uses and monitored surface air temperature and relative humidity for 31 months in the Wisconsin Central Sands region. We used a novel approach to quantify irrigated land use in both space and time with a database containing monthly groundwater withdrawal estimates by parcel for the state of Wisconsin. Irrigated agriculture decreased maximum temperatures and increased minimum temperatures, thus shrinking the diurnal temperature range (DTR) by an average of 3°C. Irrigated agriculture also decreased the vapor pressure deficit (VPD) by an average of 0.10 kPa. Irrigated agriculture significantly decreased evaporative demand for 25% and 66% of study days compared to rainfed agriculture and forest, respectively. Differences in VPD across the land-use gradient were highest (0.21 kPa) during the peak of the growing season, while differences in DTR were comparable year-round. Interannual variability in temperature had greater impacts on differences in DTR and VPD across the land-use gradient than interannual variability in precipitation. These regional climate changes must be considered together with increased greenhouse gas emissions, changes to groundwater quality, and surface water degradation when evaluating the costs and benefits of groundwater-sourced irrigation expansion in the Midwest United States and similar regions around the world.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Irrigated agriculture alters near-surface temperature and humidity, which may mask global climate change at the regional scale. However, observational studies of irrigation-induced climate change are lacking in temperate, humid regions throughout North America and Europe. Despite unknown climate impacts, irrigated agriculture is expanding in the Midwest United States, where unconfined aquifers provide groundwater to support crop production on coarse soils. This is the first study in the Midwest United States to observe and quantify differences in regional climate associated with irrigated agricultural conversion from forests and rainfed agriculture. To this end, we established a 60 km transect consisting of 28 stations across varying land uses and monitored surface air temperature and relative humidity for 31 months in the Wisconsin Central Sands region. We used a novel approach to quantify irrigated land use in both space and time with a database containing monthly groundwater withdrawal estimates by parcel for the state of Wisconsin. Irrigated agriculture decreased maximum temperatures and increased minimum temperatures, thus shrinking the diurnal temperature range (DTR) by an average of 3°C. Irrigated agriculture also decreased the vapor pressure deficit (VPD) by an average of 0.10 kPa. Irrigated agriculture significantly decreased evaporative demand for 25% and 66% of study days compared to rainfed agriculture and forest, respectively. Differences in VPD across the land-use gradient were highest (0.21 kPa) during the peak of the growing season, while differences in DTR were comparable year-round. Interannual variability in temperature had greater impacts on differences in DTR and VPD across the land-use gradient than interannual variability in precipitation. These regional climate changes must be considered together with increased greenhouse gas emissions, changes to groundwater quality, and surface water degradation when evaluating the costs and benefits of groundwater-sourced irrigation expansion in the Midwest United States and similar regions around the world. |
Chen, Xi; Motew, Melissa M.; Booth, Eric G.; Zipper, Samuel C.; Loheide, Steven P.; Kucharik, Christopher J.: Management of minimum lake levels and impacts on flood mitigation: A case study of the Yahara Watershed, Wisconsin, USA. In: Journal of Hydrology, vol. 577, pp. 123920, 2019, ISSN: 0022-1694. @article{CHEN2019123920,
title = {Management of minimum lake levels and impacts on flood mitigation: A case study of the Yahara Watershed, Wisconsin, USA},
author = {Xi Chen and Melissa M. Motew and Eric G. Booth and Samuel C. Zipper and Steven P. Loheide and Christopher J. Kucharik},
url = {https://www.sciencedirect.com/science/article/pii/S0022169419306407},
doi = {https://doi.org/10.1016/j.jhydrol.2019.123920},
issn = {0022-1694},
year = {2019},
date = {2019-01-01},
journal = {Journal of Hydrology},
volume = {577},
pages = {123920},
abstract = {Lake level regulation is commonly used to manage water resources and mitigate flood risk in watersheds with linked river–lake systems. In this study, we first assess exposure, in terms of both population and land area, to flooding impacts in the Yahara Watershed’s chain of four lakes in southern Wisconsin as affected by minimum lake level management. A flooding exposure assessment shows that the areas surrounding the upstream lakes, Mendota and Monona, have dense urban areas with high populations that are exposed to flooding; Waubesa has low elevations along its lakeshore, resulting in a large potential flooding area; and the most downstream lake, Kegonsa, has a large area of surrounding cropland that is exposed to flooding but impacts a limited population. We then use a linked modeling framework of a land surface model (Agro-IBIS) and a hydrologic-routing model (THMB) to simulate daily lake level over a study period of 1994–2013 in the Yahara Watershed with different minimum lake level management strategies. Modeling results show that the peak lake levels and corresponding exposed land area and population to flooding will decrease under a lower target minimum lake level. However, at the same time, the number of days that the lake level is below winter minimum will increase, which may adversely affect ecosystem health. In addition, our sensitivity analysis indicates that reducing target minimum lake levels will help mitigate flood risk in terms of both flood magnitude and frequency. Nevertheless, this must be balanced against the need to maintain adequately high lake levels for ecosystem services and recreational functions of the lakes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lake level regulation is commonly used to manage water resources and mitigate flood risk in watersheds with linked river–lake systems. In this study, we first assess exposure, in terms of both population and land area, to flooding impacts in the Yahara Watershed’s chain of four lakes in southern Wisconsin as affected by minimum lake level management. A flooding exposure assessment shows that the areas surrounding the upstream lakes, Mendota and Monona, have dense urban areas with high populations that are exposed to flooding; Waubesa has low elevations along its lakeshore, resulting in a large potential flooding area; and the most downstream lake, Kegonsa, has a large area of surrounding cropland that is exposed to flooding but impacts a limited population. We then use a linked modeling framework of a land surface model (Agro-IBIS) and a hydrologic-routing model (THMB) to simulate daily lake level over a study period of 1994–2013 in the Yahara Watershed with different minimum lake level management strategies. Modeling results show that the peak lake levels and corresponding exposed land area and population to flooding will decrease under a lower target minimum lake level. However, at the same time, the number of days that the lake level is below winter minimum will increase, which may adversely affect ecosystem health. In addition, our sensitivity analysis indicates that reducing target minimum lake levels will help mitigate flood risk in terms of both flood magnitude and frequency. Nevertheless, this must be balanced against the need to maintain adequately high lake levels for ecosystem services and recreational functions of the lakes. |
Ziter, Carly D.; Pedersen, Eric J.; Kucharik, Christopher J.; Turner, Monica G.: Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. In: Proceedings of the National Academy of Sciences, vol. 116, no. 15, pp. 7575-7580, 2019. @article{doi:10.1073/pnas.1817561116,
title = {Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer},
author = {Carly D. Ziter and Eric J. Pedersen and Christopher J. Kucharik and Monica G. Turner},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.1817561116},
doi = {10.1073/pnas.1817561116},
year = {2019},
date = {2019-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {116},
number = {15},
pages = {7575-7580},
abstract = {As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10-, 30-, 60-, and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (∼7 km length, sampled 3–12 times each) spanning a range of impervious and tree canopy cover (0–100%, each) in a midsized city in the Upper Midwest United States. Variability in daytime air temperature within the urban landscape averaged 3.5 °C (range, 1.1–5.7 °C). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale and was greatest at the size of a typical city block (60–90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1 °C (range, 1.2–3.0 °C), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10-, 30-, 60-, and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (∼7 km length, sampled 3–12 times each) spanning a range of impervious and tree canopy cover (0–100%, each) in a midsized city in the Upper Midwest United States. Variability in daytime air temperature within the urban landscape averaged 3.5 °C (range, 1.1–5.7 °C). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale and was greatest at the size of a typical city block (60–90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1 °C (range, 1.2–3.0 °C), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming. |
Nocco, Mallika A.; Ruark, Matthew D.; Kucharik, Christopher J.: Apparent electrical conductivity predicts physical properties of coarse soils. In: Geoderma, vol. 335, pp. 1-11, 2019, ISSN: 0016-7061. @article{NOCCO20191,
title = {Apparent electrical conductivity predicts physical properties of coarse soils},
author = {Mallika A. Nocco and Matthew D. Ruark and Christopher J. Kucharik},
url = {https://www.sciencedirect.com/science/article/pii/S0016706117322565},
doi = {https://doi.org/10.1016/j.geoderma.2018.07.047},
issn = {0016-7061},
year = {2019},
date = {2019-01-01},
journal = {Geoderma},
volume = {335},
pages = {1-11},
abstract = {Precision agriculture informed by electromagnetic induction surveys could reduce groundwater withdrawals and nitrogen leaching from coarse soils. However, coarse, nonsaline soils often have extremely narrow ranges of mapped apparent electrical conductivity (ECa) and the efficacy of ECa for predicting soil physical properties is uncertain in this context. For this reason, it is also uncertain as to whether electromagnetic induction surveys are valuable for guiding precision agriculture on coarse, nonsaline soils. Additionally, the need to ground-truth electromagnetic induction surveys for individual agricultural fields with soil sampling and statistical model development hampers adoption of precision agriculture at the regional scale. Our research objectives were to quantify the variation in mapped ECa and develop statistical relationships between ECa and soil physical properties both within and across several agricultural fields in the Wisconsin Central Sands, a distinct hydropedological region with coarse, glaciolacustrine soils. We used nonparametric correlation analyses to identify associations and quantile regression, a statistical approach with no assumptions of normality or homoscedasticity, to identify predictive relationships between ECa and soil physical properties. We found strong, significant (p < 0.05) correlative and predictive relationships between ECa and topsoil (0–0.3 m) particle size fraction, organic matter content, and field capacity within and across several fields. Yet, we did not observe many significant relationships between ECa and subsoil (0.5–0.6 m) physical properties, which we attribute to heterogeneous soil layering and the low depth resolution of our soil sampling approach. Our findings demonstrate that proximal sensing of ECa can identify intrafield variability in soil properties under extremely narrow observed ECa ranges (0–11 mS m−1). Moreover, we found that interfield quantile regression models predicted soil physical properties across several agroecosystems. Heteroscedasticity was present in interfield ECa relationships with physical properties, which resulted in the need for different quantile regression models across the conditional distribution. The flexibility for accommodating heteroscedasticity in soils and simplicity of modeled functions make quantile regression a promising approach for developing interfield or regional models of ECa to predict soil physical properties in distinct, hydropedological regions with coarse soils.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Precision agriculture informed by electromagnetic induction surveys could reduce groundwater withdrawals and nitrogen leaching from coarse soils. However, coarse, nonsaline soils often have extremely narrow ranges of mapped apparent electrical conductivity (ECa) and the efficacy of ECa for predicting soil physical properties is uncertain in this context. For this reason, it is also uncertain as to whether electromagnetic induction surveys are valuable for guiding precision agriculture on coarse, nonsaline soils. Additionally, the need to ground-truth electromagnetic induction surveys for individual agricultural fields with soil sampling and statistical model development hampers adoption of precision agriculture at the regional scale. Our research objectives were to quantify the variation in mapped ECa and develop statistical relationships between ECa and soil physical properties both within and across several agricultural fields in the Wisconsin Central Sands, a distinct hydropedological region with coarse, glaciolacustrine soils. We used nonparametric correlation analyses to identify associations and quantile regression, a statistical approach with no assumptions of normality or homoscedasticity, to identify predictive relationships between ECa and soil physical properties. We found strong, significant (p < 0.05) correlative and predictive relationships between ECa and topsoil (0–0.3 m) particle size fraction, organic matter content, and field capacity within and across several fields. Yet, we did not observe many significant relationships between ECa and subsoil (0.5–0.6 m) physical properties, which we attribute to heterogeneous soil layering and the low depth resolution of our soil sampling approach. Our findings demonstrate that proximal sensing of ECa can identify intrafield variability in soil properties under extremely narrow observed ECa ranges (0–11 mS m−1). Moreover, we found that interfield quantile regression models predicted soil physical properties across several agroecosystems. Heteroscedasticity was present in interfield ECa relationships with physical properties, which resulted in the need for different quantile regression models across the conditional distribution. The flexibility for accommodating heteroscedasticity in soils and simplicity of modeled functions make quantile regression a promising approach for developing interfield or regional models of ECa to predict soil physical properties in distinct, hydropedological regions with coarse soils. |
Kontgis, Caitlin; Schneider, Annemarie; Ozdogan, Mutlu; Kucharik, Christopher; Tri, Van Pham Dang; Duc, Nguyen Hong; Schatz, Jason: Climate change impacts on rice productivity in the Mekong River Delta. In: Applied Geography, vol. 102, pp. 71-83, 2019, ISSN: 0143-6228. @article{KONTGIS201971,
title = {Climate change impacts on rice productivity in the Mekong River Delta},
author = {Caitlin Kontgis and Annemarie Schneider and Mutlu Ozdogan and Christopher Kucharik and Van Pham Dang Tri and Nguyen Hong Duc and Jason Schatz},
url = {https://www.sciencedirect.com/science/article/pii/S014362281730454X},
doi = {https://doi.org/10.1016/j.apgeog.2018.12.004},
issn = {0143-6228},
year = {2019},
date = {2019-01-01},
journal = {Applied Geography},
volume = {102},
pages = {71-83},
abstract = {Rice is consumed by more people than any other grain. Globally, Vietnam is one of the largest exporters of rice, with the majority of production occurring in the tropical, low-lying Mekong River Delta. Agriculture in the Mekong River Delta is susceptible to yield losses from rising temperatures, sea level rise, and land use change as urban expansion replaces productive farmland. Most studies that assess climate change impacts to rice paddy yields are conducted at global- or continental-scales, and use general information on management practices to simulate production. Here, we use management information from farmers and published information on soils collected in Can Tho, a centrally-located province in the Mekong Delta. These data, along with projected mid-century (2040–2069) climate data for the RCP4.5 and RCP8.5 greenhouse gas emissions scenarios, are used to drive the Decision Support System for Agrotechnology Transfer (DSSAT) platform to project future rice paddy yields using the CERES-Rice model. The results indicate that yields decline for all three rice-growing seasons in Can Tho city for both emissions scenarios when CO2 fertilization is not considered (5.5–8.5% annually on average depending on the emissions scenario). Increasing irrigation and fertilizer did not offset these losses, but simulated CO2 fertilization did compensate for yield declines caused by increasing temperatures (yields were modeled to be up 23% higher when CO2 fertilization is considered). However, we caution that estimated yield gains from CO2 fertilization are optimistic, and these modeled values do not consider rises in ozone, which can diminish yields. Continued and future dam construction could negatively affect agriculture in the region, and current government policies prohibit rice paddy farmers from diversifying their livelihoods to adapt to these changes. Monitoring rice agroecosystems at a fine-scale, as this study does, is necessary to best capture the impact that varying management practices can have on local yields. When these differences are captured, future impacts of climate change can be modeled more effectively so that local policymakers can make informed decisions about how to offset yield losses and use farmland more efficiently.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rice is consumed by more people than any other grain. Globally, Vietnam is one of the largest exporters of rice, with the majority of production occurring in the tropical, low-lying Mekong River Delta. Agriculture in the Mekong River Delta is susceptible to yield losses from rising temperatures, sea level rise, and land use change as urban expansion replaces productive farmland. Most studies that assess climate change impacts to rice paddy yields are conducted at global- or continental-scales, and use general information on management practices to simulate production. Here, we use management information from farmers and published information on soils collected in Can Tho, a centrally-located province in the Mekong Delta. These data, along with projected mid-century (2040–2069) climate data for the RCP4.5 and RCP8.5 greenhouse gas emissions scenarios, are used to drive the Decision Support System for Agrotechnology Transfer (DSSAT) platform to project future rice paddy yields using the CERES-Rice model. The results indicate that yields decline for all three rice-growing seasons in Can Tho city for both emissions scenarios when CO2 fertilization is not considered (5.5–8.5% annually on average depending on the emissions scenario). Increasing irrigation and fertilizer did not offset these losses, but simulated CO2 fertilization did compensate for yield declines caused by increasing temperatures (yields were modeled to be up 23% higher when CO2 fertilization is considered). However, we caution that estimated yield gains from CO2 fertilization are optimistic, and these modeled values do not consider rises in ozone, which can diminish yields. Continued and future dam construction could negatively affect agriculture in the region, and current government policies prohibit rice paddy farmers from diversifying their livelihoods to adapt to these changes. Monitoring rice agroecosystems at a fine-scale, as this study does, is necessary to best capture the impact that varying management practices can have on local yields. When these differences are captured, future impacts of climate change can be modeled more effectively so that local policymakers can make informed decisions about how to offset yield losses and use farmland more efficiently. |
Nemet, Gregory F.: How solar energy became cheap: A model for low-carbon innovation. Routledge, 2019. @book{nokey,
title = {How solar energy became cheap: A model for low-carbon innovation},
author = {Gregory F. Nemet},
year = {2019},
date = {2019-01-01},
publisher = {Routledge},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
|
Kang, Yanghui; Özdoğan, Mutlu: Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach. In: Remote Sensing of Environment, vol. 228, pp. 144-163, 2019, ISSN: 0034-4257. @article{KANG2019144,
title = {Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach},
author = {Yanghui Kang and Mutlu Özdoğan},
url = {https://www.sciencedirect.com/science/article/pii/S0034425719301427},
doi = {https://doi.org/10.1016/j.rse.2019.04.005},
issn = {0034-4257},
year = {2019},
date = {2019-01-01},
journal = {Remote Sensing of Environment},
volume = {228},
pages = {144-163},
abstract = {Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. However, it remains challenging to efficiently scale this approach to large areas while maintaining reliable prediction at field scales. In this paper, we explored the factors limiting the generalization of the data assimilation approach and found that the accuracy of crop model prediction and the systematic model errors can significantly affect the performance of data assimilation and the yield estimation. To address these issues, we propose a hierarchical data assimilation framework, which enables maize yield estimation at field levels across large areas for the Midwestern US with no a priori knowledge about the management of individual fields. This approach applies data assimilation algorithms at two spatial scales. At the county scale, we adopted a Markov Chain Monte Carlo algorithm to recalibrate uncertain and sensitive model parameters based on aggregated Leaf Area Index (LAI) time series derived from Landsat images and county-level yield statistics. Using the county-specific models, we assimilated LAI time series into crop model simulations using Ensemble Kalman Filter for individual fields or pixels. This method was validated by multiple field-level maize yield datasets across major production states in the US Midwest. The Root Mean Squared Error ranges from 1.4 to 2.3 ton/ha, and the percentage error is between 9% and 21%. The hierarchical data assimilation framework provides a novel solution that downscales county-level yield statistics to 30-meter resolution yield maps, which can inform between and within field maize yield variability. This study contributes valuable insights towards practical large-scale crop yield mapping at high resolutions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. However, it remains challenging to efficiently scale this approach to large areas while maintaining reliable prediction at field scales. In this paper, we explored the factors limiting the generalization of the data assimilation approach and found that the accuracy of crop model prediction and the systematic model errors can significantly affect the performance of data assimilation and the yield estimation. To address these issues, we propose a hierarchical data assimilation framework, which enables maize yield estimation at field levels across large areas for the Midwestern US with no a priori knowledge about the management of individual fields. This approach applies data assimilation algorithms at two spatial scales. At the county scale, we adopted a Markov Chain Monte Carlo algorithm to recalibrate uncertain and sensitive model parameters based on aggregated Leaf Area Index (LAI) time series derived from Landsat images and county-level yield statistics. Using the county-specific models, we assimilated LAI time series into crop model simulations using Ensemble Kalman Filter for individual fields or pixels. This method was validated by multiple field-level maize yield datasets across major production states in the US Midwest. The Root Mean Squared Error ranges from 1.4 to 2.3 ton/ha, and the percentage error is between 9% and 21%. The hierarchical data assimilation framework provides a novel solution that downscales county-level yield statistics to 30-meter resolution yield maps, which can inform between and within field maize yield variability. This study contributes valuable insights towards practical large-scale crop yield mapping at high resolutions. |