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1. Introduction The Arctic National Wildlife Refuge (ANWR) was established by the Alaska National Interest Lands Conservation Act of 1980 and covers 19 million acres (77 000 km 2 ) in northeast Alaska. Proponents of development in the ANWR view its 1.6 million acre (6475 km 2 ) coastal plain as a promising onshore oil reserve ( Comay et al. 2018 ). Nonetheless, wildlife habitats in the ANWR are vulnerable to long-lasting effects from any disturbance, in part because short growing seasons in the
1. Introduction The Arctic National Wildlife Refuge (ANWR) was established by the Alaska National Interest Lands Conservation Act of 1980 and covers 19 million acres (77 000 km 2 ) in northeast Alaska. Proponents of development in the ANWR view its 1.6 million acre (6475 km 2 ) coastal plain as a promising onshore oil reserve ( Comay et al. 2018 ). Nonetheless, wildlife habitats in the ANWR are vulnerable to long-lasting effects from any disturbance, in part because short growing seasons in the
1. Introduction This study stems from a regional, multi-institutional project titled “Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop.” The U2U project seeks to develop decision support tools and climate resiliency–related resources for sustainable agriculture and improved profitability in the U.S. Corn Belt. One of the tasks underway is to develop historical and future agroclimatic assessments for U.S. Corn Belt ( www.Agclimate4U.org ) by
1. Introduction This study stems from a regional, multi-institutional project titled “Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop.” The U2U project seeks to develop decision support tools and climate resiliency–related resources for sustainable agriculture and improved profitability in the U.S. Corn Belt. One of the tasks underway is to develop historical and future agroclimatic assessments for U.S. Corn Belt ( www.Agclimate4U.org ) by
characteristics by incorporating an Urban Canopy Model (UCM). Our study is similar to the work of Georgescu et al. (2012 , 2013) in that we use the WRF + UCM to study the effects on regional climate in the Sun Corridor region. However, our goal is to examine the hydroclimate of the entire corridor region in detail, with our hypothesis being that detailed representation of the hydroclimate of the region can lead to better characterization of the impacts of urbanization on precipitation. To this end, we use
characteristics by incorporating an Urban Canopy Model (UCM). Our study is similar to the work of Georgescu et al. (2012 , 2013) in that we use the WRF + UCM to study the effects on regional climate in the Sun Corridor region. However, our goal is to examine the hydroclimate of the entire corridor region in detail, with our hypothesis being that detailed representation of the hydroclimate of the region can lead to better characterization of the impacts of urbanization on precipitation. To this end, we use
quantitatively, Karl and Knight (1998) analyzed long-term rainfall data during the period 1910–55 and found that the frequency of heavy precipitation events had increased over the United States by about 7% during said period. The change in heavy rainfall frequency has been primarily attributed to increasing greenhouse gases ( Meehl et al. 2000 ). Additionally, regional land-use/land-cover changes (LULCC) can also affect rainfall by altering mesoscale convection ( Pielke et al. 2007 , 2011 ). Urbanization
quantitatively, Karl and Knight (1998) analyzed long-term rainfall data during the period 1910–55 and found that the frequency of heavy precipitation events had increased over the United States by about 7% during said period. The change in heavy rainfall frequency has been primarily attributed to increasing greenhouse gases ( Meehl et al. 2000 ). Additionally, regional land-use/land-cover changes (LULCC) can also affect rainfall by altering mesoscale convection ( Pielke et al. 2007 , 2011 ). Urbanization
1. Introduction Terrestrial vegetation cover plays a key role in global energy, carbon, hydrological, and biogeochemical cycling. Connections of land-cover dynamics to regional temperature and precipitation patterns can be influenced by climate change on regional to global scales ( Feddema et al. 2005 ). There is a pressing need to document and interpret changes in vegetation cover worldwide, particularly in relation to changes in near-surface climate variables such as air temperature and
1. Introduction Terrestrial vegetation cover plays a key role in global energy, carbon, hydrological, and biogeochemical cycling. Connections of land-cover dynamics to regional temperature and precipitation patterns can be influenced by climate change on regional to global scales ( Feddema et al. 2005 ). There is a pressing need to document and interpret changes in vegetation cover worldwide, particularly in relation to changes in near-surface climate variables such as air temperature and
vegetation. Biogeochemical feedbacks from regional land-cover changes have been discussed in the context of global climate change in several studies (e.g., Carter et al. 2007 ; Arneth et al. 2010 ), where it is found that the biogeochemical feedback is too big to be ignored in climate change studies. Biogeophysical effects occur due to changes in the physical properties of the land surface, such as changes in albedo, soil properties, and roughness. The biogeophysical effects include changes in
vegetation. Biogeochemical feedbacks from regional land-cover changes have been discussed in the context of global climate change in several studies (e.g., Carter et al. 2007 ; Arneth et al. 2010 ), where it is found that the biogeochemical feedback is too big to be ignored in climate change studies. Biogeophysical effects occur due to changes in the physical properties of the land surface, such as changes in albedo, soil properties, and roughness. The biogeophysical effects include changes in
environmentally and economically beneficial ( U.S. EPA 2007 ). The review also concludes that barring a few exceptions, LID methods generally result in cost savings of 15% to 80%. Anticipated future climate includes warming temperature and changes in the amount and intensity of precipitation ( IPCC 2014 ). If realized, these changes will have direct effects on stormwater and may require adaptation of the existing stormwater infrastructure. Effects are likely to vary significantly in different regions of the
environmentally and economically beneficial ( U.S. EPA 2007 ). The review also concludes that barring a few exceptions, LID methods generally result in cost savings of 15% to 80%. Anticipated future climate includes warming temperature and changes in the amount and intensity of precipitation ( IPCC 2014 ). If realized, these changes will have direct effects on stormwater and may require adaptation of the existing stormwater infrastructure. Effects are likely to vary significantly in different regions of the
watersheds, while the second allows us to address three subquestions: (i) Does the variability (i.e., range) of simulated watershed responses to climate change differ when driven by downscaled versus nondownscaled GCM information? (ii) Does using downscaled data lead to the identification of regional patterns of streamflow and water quality variability not found using nondownscaled GCM output, for example, small-scale orographic effects? (iii) Do the simulated watershed responses to climate change depend
watersheds, while the second allows us to address three subquestions: (i) Does the variability (i.e., range) of simulated watershed responses to climate change differ when driven by downscaled versus nondownscaled GCM information? (ii) Does using downscaled data lead to the identification of regional patterns of streamflow and water quality variability not found using nondownscaled GCM output, for example, small-scale orographic effects? (iii) Do the simulated watershed responses to climate change depend
phosphorus, slowing regeneration by woody species from 5 to 20 yr following abandonment ( Fearnside and Guimarães 1996 ; Müller et al. 2004 ). Woody plant regeneration on abandoned land is important for regional carbon budgets as uptake of carbon dioxide from the atmosphere by these forests regrowing is estimated to account for 0.06 Pg C yr −1 over the last two decades ( Houghton et al. 2000 ; Hirsch et al. 2004 ). Though less dramatic than the sudden release of carbon from deforestation, regrowing
phosphorus, slowing regeneration by woody species from 5 to 20 yr following abandonment ( Fearnside and Guimarães 1996 ; Müller et al. 2004 ). Woody plant regeneration on abandoned land is important for regional carbon budgets as uptake of carbon dioxide from the atmosphere by these forests regrowing is estimated to account for 0.06 Pg C yr −1 over the last two decades ( Houghton et al. 2000 ; Hirsch et al. 2004 ). Though less dramatic than the sudden release of carbon from deforestation, regrowing
out since the late 1980s, and a series of achievements has also been made. However, the majority of the literature has been focused on the global or national scale, and NPP is highly variable in space and time because of the differences or inconsistencies in natural and anthropogenic factors ( Loescher et al. 2003 ; Matsushita et al. 2004 ; Peng et al. 2008 ). A clear and concise description for the correlation between regional NPP and climate change is still crucial for the research on the
out since the late 1980s, and a series of achievements has also been made. However, the majority of the literature has been focused on the global or national scale, and NPP is highly variable in space and time because of the differences or inconsistencies in natural and anthropogenic factors ( Loescher et al. 2003 ; Matsushita et al. 2004 ; Peng et al. 2008 ). A clear and concise description for the correlation between regional NPP and climate change is still crucial for the research on the
