National Climate Assessment Technical Report on the 

Impacts of Climate and Land Use and Land Cover Change

By Thomas Loveland, Rezaul Mahmood, Toral Patel-Weynand, Krista Karstensen, Kari Beckendorf, Norman Bliss, 

and Andrew Carleton

Open-File Report 2012–1155

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012 

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Suggested citation:

Loveland, Thomas, Mahmood, Rezaul, Patel-Weynand, Toral, Karstensen, Krista, Beckendorf, Kari, Bliss, Norman, 

and Carleton, Andrew, 2012, National climate assessment technical report on the impacts of climate and land use and 

land cover change: U.S. Geological Survey Open-File Report 2012–1155, 87 p.

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copyright owners to reproduce any copyrighted material contained within this report.
iii

Contents

Abstract ......................................................................................................................................................................... 1

1.0 Introduction.............................................................................................................................................................. 1

1.1 Land Use and Land Cover Definitions and Concepts........................................................................................... 2

1.2 An Overview of Climate and Land Use/Cover Forcings and Feedbacks.............................................................. 2

2.0 State of Knowledge.................................................................................................................................................. 4

2.1 Current, Past, and Future Land-Cover Understanding......................................................................................... 4

2.1.1 Current Land Cover in the United States....................................................................................................... 4

2.1.2 Past Land Cover............................................................................................................................................ 6

2.1.2.1 Southeast................................................................................................................................................ 7

2.1.2.2 Northwest................................................................................................................................................ 8

2.1.2.3 Great Plains............................................................................................................................................ 8

2.1.2.4 Northeast ................................................................................................................................................ 9

2.1.2.5 Midwest................................................................................................................................................... 9

2.1.2.6 Southwest............................................................................................................................................... 9

2.1.2.7 Land Cover Change Since 2000............................................................................................................10

2.1.3 Future Land Cover in the United States....................................................................................................11

2.2 Land-Use and Land-Cover Patterns and Conditions Affecting Weather and Climate .........................................12

2.2.1 Land Use and Land Cover Change Because of Agriculture and Changes in Weather and Climate.............12

2.2.1.1 Planetary Boundary Layer .....................................................................................................................12

2.2.1.2 Temperature ..........................................................................................................................................13

2.2.1.3 Precipitation...........................................................................................................................................15

2.2.2 Land use and Land Cover Because of Urbanization and Changes in Weather and Climate........................16

2.2.2.1 Temperature ..........................................................................................................................................16

2.2.2.2 Precipitation...........................................................................................................................................16

2.3 Land Use and Land Change and Biogeochemical Cycles ..................................................................................16

2.4 Combined Impacts of Land-Use and Land-Cover Change and Climate Change ................................................18

3.0 Recent Land-Use and Land-Cover and Climate Case Studies...............................................................................19

3.1 Climate Influences on Land-Use and Land-Cover and Water Resources ...........................................................19

3.2 Climate and Land-Use and Land-Cover Influences on Transportation and Energy Supply and Use ..................20

3.3 Climate Influences on Land Use and Land Cover and Agriculture......................................................................20

3.4 Climate Influences on Land Use and Land Cover and Ecosystems....................................................................20

3.5 Climate Influences on Land Use and Land Cover and Human Health ................................................................21

3.6 Climate Influences on Land Use and Land Cover and Society ...........................................................................22

4.0 A Regional and Sectoral Perspective of Climate and Land-Use and Land-Cover Interactions...............................22

4.1 Identification of Vulnerable Regions and Sectors................................................................................................23

4.2 Ongoing Land-Use Adaptations to Climate Change............................................................................................26

4.2.1 Agriculture ....................................................................................................................................................26

4.2.2 Forest ...........................................................................................................................................................28

4.2.3 Water............................................................................................................................................................29

4.2.4 Wetlands ......................................................................................................................................................31

4.4 LULC and Vegetation Conditions in the United States from 2009–11.................................................................31

5.0 A Design for an Ongoing Assessment of Climate and Land-Use and Land- Cover Impacts...................................38

5.1 Assessment Research and Development Needs................................................................................................39

5.2 Institutional Capabiltiies ......................................................................................................................................42

6.0 Concluding Observations........................................................................................................................................43

7.0 References .............................................................................................................................................................44
iv

Appendix A – Definitions of Major Terms......................................................................................................................66

Appendix B. Land Cover Class Descriptions ................................................................................................................67

Appendix C. 1973–2000 National Climate Assessment Southeast Region Change Statistics......................................68

Appendix D. 1973–2000 National Climate Assessment Northwest Region Change Statistics......................................69

Appendix E. 1973–2000 National Climate Assessment Great Plains Region Change Statistics ..................................70

Appendix F. 1973–2000 National Climate Assessment Northeast Region Change Statistics.......................................71

Appendix G. 1973–2000 National Climate Assessment Midwest Region Change Statistics ........................................72

Appendix H. 1973–2000 National Climate Assessment Southwest Region Change Statistics.....................................73

Appendix I. Summaries from Land-Use Land-Cover and Climate Case Studies ..........................................................74

Climate Influences on LCLU and Water Resources Case Study Summaries ...........................................................74

Baseflow, Drought, and Flood ...............................................................................................................................74

"The Roles of Precipitation Increases and Rural Land-Use Changes in Streamflow Trends in the 

Upper Mississippi River Basin" (Kochendorfer and Hubbart, 2010).....................................................74

"Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid 

regions: A review”(Price, 2011)..................................................................................................................74

"Effects of watershed land use and geomorphology on stream low flows during severe drought 

conditions in the southern Blue Ridge Mountains, Georgia and North Carolina, United State." 

(Price and others, 2011)................................................................................................................................75

"Hydrologic Effects of Urbanization and Climate Change on the Flint River Basin, Georgia" (Viger 

and others 2011).............................................................................................................................................75

"A simple approach to distinguish land-use and climate-change effects on watershed hydrology" 

(Tomer and Schilling, 2009).........................................................................................................................75

Water Supply.........................................................................................................................................................76

“Can forest management be used to sustain water-based ecosystem services in the face of climate 

change?”(Ford and others, 2011) ...............................................................................................................76

"A review of hydrological modeling of basin-scale climate change and urban development impacts" 

(Praskievicz and Chang, 2009) ....................................................................................................................76

"Evaluating water demands under climate change and transitions in the urban environment" 

(Polebitski and others, 2011)........................................................................................................................76

Water Quality.........................................................................................................................................................77

“Stream discharge and riparian land use influence in-stream concentrations and loads of 

phosphorus from central plains watershed." (Banner and others, 2009)...............................................77

"A century of changing land-use and water-quality relationships in the continental US" (Broussard 

and Turner, 2009) ..........................................................................................................................................77

"Nitrate in groundwater of the United States, 1991–2003" (Burow and others, 2010) .......................78

"Nitrogen loading to Great South Bay: Land use, sources, retention, and transport from land to 

bay" (Kinney and Valiela, 2011)..................................................................................................................78

Wetlands ...............................................................................................................................................................78

“Prairie wetland complexes as landscape: Functional units in a changing climate” (Johnson and 

others, 2010) ...................................................................................................................................................78

“Hydroclimatological impact of century-long drainage in midwestern United States: CCSM 

sensitivity experiments” (Kumar and others, 2010)..................................................................................79

“Adaptation of farming practices could buffer effects of climate change on northern prairie 

wetlands” (Voldseth and others, 2009).......................................................................................................79

Climate Influences on Land Use and Land Cover and Transportation and Energy Supply and Use Case Study 

Summaries................................................................................................................................................................80

Transportation .......................................................................................................................................................80

“Roadless areas and clean water” (DellaSala and others, 2011) ..........................................................80
v

“Assessing transportation infrastructure impacts on rangelands: Test of a standard rangeland 

assessment protocol” (Duniway and others, 2010)...................................................................................80

Energy Supply and Use.........................................................................................................................................80

“The land use-climate change-energy nexus” (Dale and others, 2011) ................................................80

“Energy sprawl or energy efficiency: Climate policy impacts on natural habitat for the United 

States of America” (McDonald and others, 2009) ....................................................................................81

“Assessing transportation infrastructure impacts on rangelands: Test of a standard rangeland 

assessment protocol” (Duniway and others, 2010)...................................................................................81

“Biofuels and sustainability” (Solomon, 2010).........................................................................................81

Climate Influences on Land Use and Land Cover and Agriculture Case Study Summaries .....................................82

Land Use Conversion to Cropland ........................................................................................................................82

“Changes in historical Iowa land cover as context for assessing the environmental benefits of 

current and future conservation efforts on agricultural lands” (Gallant and others, 2011)...............82

“Economics of Grassland Conversion to Cropland in the Prairie Pothole Region” (Rashford and 

others, 2011) ...................................................................................................................................................82

Cropland Sustainability..........................................................................................................................................82

“Fertilizer usage has increased in order to increase crop production” (Banner and others, 2009; 

Broussard and Turner, 2009; Burow and others, 2010)...........................................................................82

Increased Growing Season Temperatures............................................................................................................82

“Nonlinear temperature effects indicate severe damages to U.S. crop yield under climate change” 

(Schlenker and Roberts, 2009). ....................................................................................................................82

Climate Influences on Land Use and Land Cover and Ecosystems Case Study Summaries ...................................83

Greenhouse Gas Inventory and Biogenic Emissions ............................................................................................83

"Future land use and land cover influences on regional biogenic emissions and air quality in the 

United States" (Chen and others, 2009)......................................................................................................83

“Carbon changes in conterminous US forests associated with growth and major disturbances: 

1992–2001" (Zheng and others, 2011)........................................................................................................83

Vegetation Displacement ......................................................................................................................................84

“Projecting the distribution of forests in New England in response to climate change" (Tang and 

Beckage, 2010) ...............................................................................................................................................84

“Assessing ecosystem threats from global and regional change: Hierarchical modeling of risk to 

sagebrush ecosystems from climate change, land use and invasive species in Nevada, USA” 

(Bradley, 2010)...............................................................................................................................................84

"Potential of a national monitoring program for forests to assess change in high-latitude 

ecosystems." (Barret and Gray, 2011) ........................................................................................................84

Increased Wildland Fires.......................................................................................................................................85

“Trends in global wildfire potential in a changing climate" (Liu and others, 2010)...........................85

“Continued warming could transform GreaterYellowstone fire regimes by mid-21st century” 

(Westerling and others, 2011) ......................................................................................................................85

Climate Influences on Human Health Case Study Summaries .................................................................................85

West Nile Virus......................................................................................................................................................85

“Weather and land cover influences on mosquito populations in Sioux Falls, South Dakota” 

(Chuang and others, 2011) ...........................................................................................................................85

“Ecological niche of the 2003 West Nile virus epidemic in the Northern Great Plains of the United 

States” (Wimberly and others, 2008) ..........................................................................................................86

Extreme Heat Events ............................................................................................................................................86

“The great 2006 heat wave over California and Nevada: Signal of an increasing trend” (Gershunov 

and others, 2009)............................................................................................................................................86
vi

“Urban form and extreme heat events: Are sprawling cities more vulnerable to climate change than 

compact cities?” (Stone and others, 2010).................................................................................................86

Climate Influences on Land Use and Land Cover and Society Case Study Summaries...........................................87

“Urbanization in the Southeastern United States: Socioeconomic forces and ecological responses 

along and urban-rural gradient” (Nagy and Lockaby, 2011) .................................................................87

“Urban modification of thunderstorms: An observational storm climatology and model case study 

for the Indianapolis urban region” (Niyogi and others, 2011)................................................................87

“Land fragmentation under rapid urbanization: A cross-site analysis of southwestern cities” (York 

and others, 2011)............................................................................................................................................87

Figures

Figure 1. Land-cover patterns and National Climate Assessment regions for 2001 (Homer and others, 2004)........ 6

Figure 2. National Land Cover Database (NLCD) statistics for 2001 and 2006 (Fry and others, 2011).................. 10

Figure 3. National Land Cover Database (NLCD) 2001–06 land-cover change. The light colored areas in this map 

represent areas of change that occurred between the 2 years (Fry and others, 2011)................................................ 11

Figure 4. A coupled framework for assessing land change, vulnerability, and adaptability (McMahon and others, 

2005; modified from Turner and others, 2003). ........................................................................................................... 23

Figure 5. The Vegetation Drought Response Index (VegDRI) map for the 2-week period ending July 23, 2011, 

shows extensive droughts in the Southern United States (National Drought Mitigation Center, 2012)........................ 32

Figure 6. Shrub/scrub drought trends in the Great Plains region from 2009–11 (original analysis). ....................... 37

Figure 7. Cultivated crops drought trends in the Great Plains from 2009–11 (original analysis)............................. 38

Tables

Table 1. 2001 land-cover percentages for the United States and eight National Climate Assessment regions (Fry 

and others, 2011). ......................................................................................................................................................... 5

Table 2. Overall 1973–2000 land-cover change for the National Climate Assessment (NCA) regions (U.S. 

Geological Survey, 2012). ............................................................................................................................................. 7

Table 3. Summary of agricultural land-use and cover adaptations with corresponding literature citations. ........... 27

Table 4. Summary of forest-related land-use and cover adaptations with corresponding literature citations......... 28

Table 5. Summary of water resources and land-use and cover adaptations with corresponding literature 

citations. ................................................................................................................................................................. 30

Table 6. Summary of wetlands and land use and cover adaptations with corresponding literature citations......... 31

Table 7. Percentage of land in the National Climate Assessment (NCA) regions and by groups of National Land 

Cover Database (NLCD) land-cover classes that experienced moderate to extreme drought for the years 2009 to 

2011 (original analysis)................................................................................................................................................ 33

Table 8. Areas of land (square kilometers) in the land-cover groups and National Climate Assessment (NCA) 

regions during the growing season (original analysis). ................................................................................................ 33

Table 9. Condition of major land-cover types in the Northeast National Climate Assessment (NCA) region for 

2009–11(original analysis)........................................................................................................................................... 34

Table 10. Condition of major land-cover types in the Southeast National Climate Assessment (NCA) region for 

2009–11 (original analysis).......................................................................................................................................... 34

Table 11. Condition of major land-cover types in the Midwest National Climate Assessment (NCA) region for 2009–

11 (original analysis).................................................................................................................................................... 35

Table 12. Condition of major land-cover types in the Great Plains National Climate Assessment (NCA) region for 

2009–11 (original analysis).......................................................................................................................................... 35
vii

Table 13. Condition of major land-cover types in the Northwest National Climate Assessment (NCA) region for 

2009–11 (original analysis).......................................................................................................................................... 36

Table 14. Condition of major land-cover types in the Southwest National Climate Assessment (NCA) region for 

2009–11 (original analysis).......................................................................................................................................... 36
viii

Conversion Factors

SI to Inch/Pound 

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

hectare (ha) 2.471 acre

square kilometer (km2) 247.1 acre

hectare (ha) 0.003861 square mile (mi2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

cubic meter (m3) 264.2 gallon (gal) 

cubic centimeter (cm3) 0.06102 cubic inch (in3) 

cubic kilometer (km3) 0.2399 cubic mile (mi3) 

cubic meter (m3) 0.0008107 acre-foot (acre-ft) 

cubic hectometer (hm3) 810.7 acre-foot (acre-ft) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

Horizontal coordinate information is referenced to the insert datum name (and abbreviation) here, for instance, “North American 

Datum of 1983 (NAD 83)”
1

National Climate Assessment Technical Report on the 

Impacts of Climate and Land Use and Land Cover Change

By Thomas Loveland1, Rezaul Mahmood2, Toral Patel-Weynand3, Krista Karstensen1, Kari Beckendorf4, Norman 

Bliss4, and Andrew Carleton5

Abstract

This technical report responds to the recognition by the U.S. Global Change Research Program 

(USGCRP) and the National Climate Assessment (NCA) of the importance of understanding how land 

use and land cover (LULC) affects weather and climate variability and change and how that variability 

and change affects LULC. Current published, peer-reviewed, scientific literature and supporting data 

from both existing and original sources forms the basis for this report’s assessment of the current state 

of knowledge regarding land change and climate interactions. The synthesis presented herein documents 

how current and future land change may alter environment processes and in turn, how those conditions 

may affect both land cover and land use by specifically investigating, 

● The primary contemporary trends in land use and land cover, 

● The land-use and land-cover sectors and regions which are most affected by weather and climate 

variability,

● How land-use practices are adapting to climate change,

● How land-use and land-cover patterns and conditions are affecting weather and climate, and

● The key elements of an ongoing Land Resources assessment.

These findings present information that can be used to better assess land change and climate 

interactions in order to better assess land management and adaptation strategies for future environmental 

change and to assist in the development of a framework for an ongoing national assessment. 

1.0 Introduction

The 2003 Climate Change Science Program (CCSP) science strategy (Climate Change Science 

Program, 2003) initially recognized the complex links between LULC and weather and climate as one 

of the primary global climate change science themes. The CCSP LULC science strategy focused on the 

following question: How do climate variability and change affect land use and land cover, and what are 

the potential feedbacks of changes in land use and land cover to climate?

The objective of this assessment is to understand how current and predicted changes in land 

cover and land use will alter weather, climate, and other global environmental conditions and in turn, 

how those conditions will change land cover and land use. This interaction is a necessary basis for 

managing and adapting to future environmental changes. 

 

1

 U.S. Geological Survey, Earth Resources and Observation Science Center, Sioux Falls, South Dakota. 

2

 Western Kentucky University, Bowling Green, Kentucky. 

3

 U.S. Forest Service, Research and Development, Quantitative Sciences, Arlington, Virginia. 

4

 ASRC Research and Technology Solutions, on contract to U.S. Geological Survey, Earth Resources 

and Observation Science Center, Sioux Falls, South Dakota. 

5

 Pennsylvania State University, University Park, Pennsylvania. 
2

This report and the land use and land cover workshop report (Lebow and others, 2011) are

designed to supplement the state-of-knowledge of the current connections between LULC and weather 

and climate and to evaluate the general readiness of the Federal USGCRP agencies and broader science 

community to carry out an ongoing assessment of LULC and weather and climate impacts. The 

respective convening Federal sponsors, the U.S Forest Service (workshop report) and U.S. Geological 

Survey (technical report), consider both documents as complementary volumes for an understanding of 

the current LULC foundation for addressing weather and climate issues. 

Centralizing around LULC in a land-change-science context, this report focuses on the state-ofknowledge of climate impacts on LULC for the 2009–12 period in an effort to assess LULC and 

weather and climate interactions within the NCA regions, including those affecting natural resources. 

Land-change science, as defined by Turner and others, 2007, stresses the understanding of LULC 

dynamics within a coupled human-natural environment context that considers the causes, impacts, and 

consequences of LULC change. 

1.1 Land Use and Land Cover Definitions and Concepts

For the purposes of this report, land use includes the human activities and management practices 

for which land is used; land cover includes the status of vegetation, bare soil, developed structures (for 

example, building, roads, and other infrastructure), and water bodies including wetlands. See appendix 

A for additional definitions of major terms used in this report. Land use and land cover are linked but 

the relations are complex. Land-use practices determine cover, and subsequently, the effects of cover on 

weather and climate. Land-cover changes affect the exchange of water, energy, and gases with the 

atmosphere because of changes in surface albedo and transpiration, which modify surface temperature 

and latent heat flux and ultimately can cause changes in regional temperature and precipitation patterns 

(Dale, 1997). Land-cover changes also can initiate a wide range of other global changes including 

water-resources alterations and modifications to habitat. It is equally important to recognize that the 

resulting effects of climate change can force changes in land use, and potentially land cover, as landmanagement practices are implemented that mitigate the adverse effects of weather and climate 

variability and change. 

When considering LULC and weather and climate, several factors should be considered:

● LULC changes are local phenomena. However, the impacts of these changes can reach beyond 

the local scale and can accumulate and have global impacts. For example, tropical forest clearing 

reduces the ability to sequester carbon, which ultimately affects atmospheric chemistry 

worldwide. 

● LULC changes are, to a certain extent, driven by natural events but are mostly attributable to 

human activities on the Earth. 

● LULC change is typically motivated by economic goals, which are important for the growth of 

personal income and expanding the economies of communities, States, and nations. Because 

healthy economies depend on profitable land uses, mitigation strategies that reduce unwanted 

negative consequences must consider local to national economic implications. 

● LULC change can influence land-management practices by altering ecosystem goods and 

services and is also the catalyst for changes in atmospheric chemistry, weather and climate 

variability, and other processes.

1.2 An Overview of Climate and Land Use/Cover Forcings and Feedbacks

Land use and land cover (LULC) play critical roles in land surface-atmosphere interactions and 

thus influence climate. These impacts are evident on local, regional, and global climate. In addition, 
3

LULC has biogeophysical and biogeochemical impacts on climate. Biogeophysical mechanisms alter 

radiation balance, energy partitioning, and exchanges of energy, mass, and momentum between the land 

surface and the atmosphere. 

The radiation balance equation may be written as follows (Oke, 1987): 

Q* = K* + L* (1)

where, 

Q* is net all wave radiation,

K* is net short-wave radiation, and

L* is net long-wave radiation. 

Over dry land areas (light-colored surfaces) more short-wave radiation is reflected and contrasted with 

land surfaces having relatively high soil-moisture content (dark-colored surfaces), where more shortwave radiation is absorbed. The surface-water content also influences the net radiation balance. 

The energy balance relations can be expressed as follows (Oke, 1987): 

Q* = QE + QH + QG (2)

where QE, QH, and QG represent latent, sensible, and ground energy fluxes, respectively. 

On land surfaces with higher soil-moisture content most of the available energy is partitioned into latent 

as opposed to sensible flux. Higher latent energy flux (higher evapotranspiration) typically leads to 

lowering of temperature and increases atmospheric humidity, convection, and cloud development. Over 

drier land areas, such as semiarid or arid lands, sensible energy flux (surface and atmospheric warming) 

dominates the energy partitioning. Seasonal patterns, such as wet and dry seasons, influence the ways 

that LULC interacts with climate.

These impacts and their interactions reach a higher level of complexity because of the 

heterogeneity of the land surface, overlying vegetation cover, and vegetation condition. The types of 

vegetation cover (grass compared to tree, for example) alter the energy partitioning (lower compared to

higher latent energy flux). For example, given similar soil water and energy availability, there is more 

evapotranspiration from tree-covered land surfaces (greater latent energy flux) compared to grasscovered land surfaces. These partitioning differences also are dependent, particularly on plant 

physiology and their response to environmental stress. These may include excessively dry or unusually 

wet conditions. Vegetation type, stand architecture, and transition from one type of vegetation cover to 

another (for example, grass to forest) affect the transfer of energy and mass to the atmosphere and 

hence, convection and cloud development. Moreover, climate is not a passive player in the feedback 

with LULC. Intraannual and interannual climate variability (for example, dry compared to wet; healthy 

compared to stressed vegetation cover) affects the intensity of LULC forcing and the above mentioned 

processes and phenomena which, in turn, affect the atmospheric state and related phenomena. In some 

regions, this feedback loop is extended even at interannual time-scales.

Climate changes also affect LULC through direct effects on vegetation growth and structure and 

indirectly through human adaptation to climate change. For example, droughts may cause water stress in 

vegetation that may lead to more surface heating and less evapotranspiration. The stressed vegetation 

may be subject to fire, which will influence the radiation balance immediately after the fire, and perhaps 

vegetation with a different albedo will regrow. If drought causes crop failures for many years, an 

agricultural land use may be abandoned and a transition may follow to a grass land cover. The mutual 
4

interactions of land use and climate may be described as a feedback loop with each system influencing 

the other. The feedbacks may be very complex and occur at various scales in space and time. 

LULC changes can modify the interactions between land surfaces and the atmosphere. For 

example, with replacement of forests by barren surfaces, agriculture, or urban areas, an existing land 

surface-atmosphere feedback loop may be substituted with a modified loop. Land-use changes within a 

particular land-cover type may further complicate the ability to understand and describe the forcings and 

feedbacks. Replacement of rain fed agriculture (which already replaced natural grasslands) with 

irrigated agriculture is an example of such a change. In some cases, single-crop land use (one crop per 

year) has changed to multicrop land use (three crops per year). These types of changes may alter local 

and regional scale climate. Impacts at the global scale remain a topic of further investigation. 

2.0 State of Knowledge

2.1 Current, Past, and Future Land-Cover Understanding

LULC connections with weather and climate require general knowledge of past LULC patterns 

and changes, detailed data and information on current LULC patterns and dynamics, and an 

understanding of the plausible scenarios and resulting LULC patterns associated with change 20–100 

years into the future. To the extent possible, spatially explicit LULC products are used because they 

offer the ability to evaluate the geographic variability and complexity of climate and LULC impacts. A 

more complete summary of LULC products can be found in “Land Use and Land Cover National 

Stakeholder Workshop Technical Report” (Lebow and others, 2011).

2.1.1 Current Land Cover in the United States

The two most common spatially explicit maps of current land cover in the United States are the 

2001 and 2006 National Land Cover Database (NLCD) products (Homer and others, 2004; Fry and 

others, 2011), and the U.S. Forest Service (USFS)-U.S. Geological Survey (USGS) 2001 LANDFIRE 

land cover and vegetation dataset (Vogelmann and others, 2011). LANDFIRE provides detailed 

vegetation type and structure data sets for the entire United States, including Alaska and Hawaii, for use 

in fire management activities. The products are based on the National Vegetation Classification System 

and were produced through the analysis of Landsat imagery from 2001 and 2008. The 2008 vegetation 

updates are nearing completion. LANDFIRE datasets emphasize natural vegetation rather than general 

land cover. As a result, for assessments of LULC, LANDFIRE vegetation layers are best suited to 

assessments of climate impacts and feedbacks involving natural landscapes. 

The NLCD 2001 and 2006 land cover databases use Landsat 5 and 7 imagery to produce 30 

meter (m) resolution maps of land cover types, percent tree cover, and percent urban imperviousness. 

NLCD 2001 covers all 50 states, while the 2006 NLCD is limited to the conterminous United States. 

NLCD 2006 also quantifies land cover change between the years 2001 to 2006. A formal accuracy 

assessment is part of each dataset, though the NLCD 2006 accuracy assessment will not be completed 

until 2012. 

Because of the differences in content and coverage, the NLCD 2001 land-cover map provides 

the most comprehensive summary of land-cover composition in the United States (fig. 1). Based on 

2001 national land-cover statistics, shrub/scrub vegetation (including dwarf shrubs, moss, and lichen 

cover in Alaska) covers 39.2 percent of the United States, trees cover 23.2 percent, agriculture (cropland 

and pasture) covers 18.6 percent, open water covers 6.5 percent, developed and built-up lands cover 4.0 
5

percent, barren lands represent 2.6 percent, and snow and ice cover 0.9 percent of the 50 States. Table 1 

provides detailed land-cover statistics for the Nation and each of the eight NCA regions. 

Table 1. 2001 land-cover percentages for the United States and eight National Climate Assessment regions (Fry 

and others, 2011).

Land cover class Northeast Southeast Midwest

Great 

Plains Southwest Northwest Alaska Hawaii U.S.

Low intensity 

residential

4.1 4.8 4.6 2.7 1.4 1.7 0 3.1 2.4

High intensity 

residential

3.1 2.1 2.4 0.9 0.7 0.9 0.1 2.3 1.1

Commercial/

Industrial/ 

Transportation

1.8 0.6 0.7 0.3 0.5 0.3 0 0.8 0.4

Developed High 

Intensity

0.6 0.2 0.3 0.1 0.1 0.1 0 0.5 0.1

Pasture/Hay 5.5 11.2 11.6 7 1.3 2.3 0 2.2 5.2

Row crops 5.4 11.8 37.4 22.7 4.3 8.2 0 2.6 13.4

Dwarf shrub 0 0 0 0 0 0 17.2 0 3.4

Shrub/Scrub 3 4.2 0.5 16 50.4 34.8 21.2 22.7 22

Grassland/ Herbaceous 0.4 3.6 2.4 33.6 15.4 7.9 0.8 10.5 12.7

Sedge/Herbaceous 0 0 0 0 0 0 5.7 0 1.1

Lichens 0 0 0 0 0 0 0 0 0

Moss 0 0 0 0 0 0 0 0 0

Deciduous forest 22.7 18.8 20.2 3.6 2 0.7 3.4 0 7.6

Evergreen forest 13.3 15.5 2.5 6.5 17.1 35.4 15.5 22 13.4

Mixed forest 16.4 4.5 0.9 0.5 0.8 1.6 3.5 0 2.2

Woody wetlands 6.2 12 4.2 1.7 0.4 0.6 3.5 0.2 3.4

Emergent herbaceous 

wetlands

1.8 3.2 1.6 1 0.3 0.7 2.9 0.1 1.6

Open water 14.9 7.3 10.4 1.9 1.7 3.1 14.2 21.7 6.5

Perennial Ice/Snow 0 0 0 0 0 0.1 4.3 0 0.9

Bare Rock/ Sand/Clay 0.8 0.3 0.2 0.5 3.7 1.5 7.7 11.2 2.6

100 100 100 100 100 100 100 100 100
6

Figure 1. Land-cover patterns and National Climate Assessment regions for 2001 (Homer and others, 2004).

2.1.2 Past Land Cover

Accurate portrayal of past LULC is especially challenging. While the 2003 CCSP LULC change 

science strategy recommended development of a complete LULC history in the United States for use in 

climate research, progress has been limited. Several Federal agencies within the U.S. Department of 

Agriculture (USDA), including the Natural Resources Conservation Service and the Economic Research 

Service, have relatively systematic records of past land uses. Others have census or inventory 

information on particular sectors (Nusser and Goebel, 1997; Gillespie, 1999; National Agricultural 
7

Statistical Service, 2009). The USGS has several research LULC products that provide more 

information on past LULC (Waisanen and Bliss, 2002 for a summary of 1790 to 1997 changes in U.S. 

population and agriculture; Loveland and others, 2002 for a strategy for monitoring contemporary U.S. 

land cover change; Fry and others, 2011 information on 2001–06 U.S. land cover change). All of these 

USGS historical datasets cover the conterminous United States but coverage often does not include 

Alaska and Hawaii.

An important characteristic of historical LULC change in the United States is the considerable 

geographic variability in the types, rates, and causes of change. The USGS Land Cover Trends study 

provides an assessment of the 1973 to 2000 variability. Historical Landsat imagery and probabilitybased sampling were used to estimate change for each of the 84 conterminous ecoregions in the United 

States (Loveland and others, 2002). Based on this study, an estimated 8.6 percent of the conterminous 

United States changed from 1973 to 2000, but the ecoregional change varied from less than 1 percent in 

the southwest desert ecoregions to over 30 percent for several northwest and south-central forested 

ecoregions. Using the trends data, change rates for six conterminous NCA regions in the United States 

were calculated (table 2). For the 1973 to 2000 period, the Southeast NCA region had the highest rate of 

change (16.7 percent) because of active forest timber harvesting and replanting, and the Southwest NCA 

had the lowest rate of change (4.0 percent). The following text is a summary of land change in the six 

NCA regions. The order of the summary is from highest to lowest rates of NCA regional change. See 

appendix B for definitions of the land-cover classes discussed in the following sections. 

Table 2. Overall 1973–2000 land-cover change for the National Climate Assessment (NCA) regions 

(U.S. Geological Survey, 2012).

Region Percent Change

National 8.6

Southeast 16

Northwest 12.7

Great Plains 8.7

Northeast 6.9

Midwest 5.7

Southwest 4

2.1.2.1 Southeast

The Southeast region recorded the highest amount of land-cover change with 16 percent of the 

region changed from one land cover type to another (for example, agriculture to forest cover). To 

understand land-cover dynamics, it is important to understand both gross and net change. Gross change 

is the total amount of a given land cover changed, and net change is the aggregate of gains and losses 

affecting that category. For example, the Southeast experienced a 7.9 percent gross loss of forest cover 

but the net loss was only 2.5 percent. This is largely the result of active management (planting and 

harvesting rotations) of pine plantations (Drummond and Loveland, 2010). 

Overall, Southeast forest land cover declined from 48.3 percent of the region in 1973 to 45.8 

percent in 2000. Agriculture was the second leading land cover in the region (25.6 percent) and also 

declined from 1973 levels. The third leading land cover in the region was wetland with a decline from 

11.4 percent in 1973 to 10.7 percent in 2000. The major increases in cover were in the developed (urban 
8

and built-up) and mechanically disturbed land-cover classes, which increased steadily from 1973 to 

2000. 

Regarding gross land-cover change, forest cover (7.9 percent), mechanical disturbances (3.9 

percent), agriculture (3.7 percent), developed (2.3 percent), and wetland (1.3 percent) experienced the 

largest gross changes. The leading land cover transitions occurring in the Southeast region were:

● Forest cover to mechanically disturbed

● Agriculture to forest 

● Forest cover to developed 

● Mechanically disturbed to forest, and 

● Forest to agricultutre

Appendix C provides tabular summaries of Southeast land-cover change statistics. 

2.1.2.2 Northwest

Overall land-cover change was 12.7 percent between 1973 and 2000 (table 2). Forest cover 

declined from 42.7 percent of the region in 1973 to 40.3 in 2000 because of wildfires and other 

disturbances, urbanization, and active forest management (for example, harvesting and replanting). 

Nonmechanical disturbances (primarily wildfire disturbances in this region) increased by nearly 1.8 

percent, and developed cover increased by 0.5 percent (from 1.2 to 1.7 percent). The major land-cover 

conversions in the region were: 

● Forest to grassland/shrubland 

● Forest to mechanically disturbed

● Mechanically disturbed to forest 

● Forest to nonmechanically disturbed, and 

● Agriculture to grassland/shrubland 

The Northwest land-cover classes that experienced the largest gross change were: forest (6.0 percent), 

grassland/shrubland (4.6 percent), mechanically disturbed (2.7 percent), nonmechanically disturbed (2.1 

percent), and agriculture (1.9 percent). Appendix D provides detailed change statistics for this region. 

2.1.2.3 Great Plains

Overall land change in the Great Plains between 1973 and 2000 was 8.7 percent (table 2). The 

major changes were a 1.6-percent loss of agricultural cover and a nearly equal gain in 

grassland/shrubland cover. Developed cover increased by about 0.4 percent. The primary land cover 

conversions in the region were: 

● Agriculture to grassland/shrubland 

● Grassland/shrubland to agriculture 

● Forest to grassland/shrubland 

● Forest to mechanically disturbed, and 

● Agriculture to developed 

The major gross changes were: grassland/shrubland (5.3 percent), agriculture (4.8 percent), 

forest (1.5 percent), mechanically disturbed (0.50 percent), and water (0.46 percent). This region 

experienced several unique episodes of change beginning with a significant increase in agriculture from 

1973 to 1986 and then an even larger decline from 1986 to 2000. The later period of change was driven 

by the Conservation Reserve Program objectives (Drummond and others, 2012). 

The Great Plains NCA region illustrates the challenges in consistently summarizing and 

describing land-cover change. Some subregions, such as the High Plains (the subregion overlaying the 

High Plains aquifer) experienced much higher rates of change because of the intensification of 
9

irrigation, while other areas, such as the Flint Hills and Sand Hills of Nebraska, experienced very low 

rates of change because the land uses in those subregions had limited potential for more intensive land 

uses (Drummond and others, 2012). Appendix E provides detailed change statistics for this region.

2.1.2.4 Northeast

The overall land cover change in the Northeast between 1973 and 2000 was 6.9 percent (table 2). 

The two major changes were a 2.2 percent decrease in forest cover and a 1.4 percent increase in 

developed land. Grassland/shrubland cover increased 0.7 percent while agricultural cover decreased 0.9 

percent. The leading land cover conversions in the region were: 

● Forest to mechanically disturbed

● Forest to developed 

● Forest to grassland/shrubland 

● Agriculture to developed, and 

● Agriculture to forest 

The major gross land-cover changes were: forest (4.1 percent), agriculture (1.5 percent), 

mechanically disturbed (1.4 percent), developed (1.4 percent), and grassland/shrubland (1.1 percent). for 

An analysis of land cover change in the Southeast and Northeast regions can be found in Drummond 

and Loveland (2010). Appendix F includes detailed statistics on Northeast land-cover changes. 

2.1.2.5 Midwest

The overall amount of land cover change in the Midwest between 1973 and 2000 was 5.7 

percent (table 2). The predominant land-cover changes were the expansion of developed lands by an 

additional 1.3 percent of the region and a 1.4-percent decline of agriculture within the region. Forest 

cover declined by approximately 0.9 percent. The major gross changes were: agriculture (2.7 percent), 

forest (2.3 percent), grassland/shrubland (1.4 percent), developed (1.4 percent), and mechanically 

disturbed (0.8 percent). The leading land cover transformations in the region were:

● Agriculture to developed 

● Agriculture to grassland/shrubland 

● Forest to mechanically disturbed

● Forest to agriculture, and 

● Forest to grassland/shrubland 

Appendix G includes includes detailed statistics on Midwest land-cover changes. 

2.1.2.6 Southwest

The Southwest NCA region underwent the least amount of land-cover change with an overall 

amount of land change of 4.0 percent between 1973 and 2000 (table 2). Forest cover and agriculture 

decreased 0.5 and 0.4 percent, respectfully, and nonmechanical disturbances (for example, wildfire 

disturbances) and developed land each increased by 0.5 percent. The top leading land-cover conversions 

in the region were: 

● Agriculture to grassland/shrubland 

● Grassland/shrubland to agriculture 

● Forest to nonmechanically disturbed

● Grassland/shrubland to developed, and 

● Grassland/shrubland to nonmechanically disturbed 
10

The main gross land cover changes were: grassland/shrubland (2.5 percent), agriculture (1.7 percent), 

forest (0.8 percent), nonmechanically disturbed (0.6 percent), and developed (0.5 percent). Appendix H 

has detailed land-cover change statistics for the Southwest. 

2.1.2.7 Land Cover Change Since 2000

The USGS NLCD provides the most current spatially explicit information on land-cover change 

for the conterminous United States (Fry and others, 2011). The NLCD provides baseline land cover for 

2006 and 2001–06 land-cover change data (figs. 2 and 3). At this time, the accuracy of the baseline and 

land-cover change products is unknown, but results will be released in 2012. Preliminary results show 

that the overall amount of change between 2001 and 2006 was 1.67 percent, with the greatest changes in 

forest cover with a decrease of approximately 1 percent and developed land cover with an increase of 

0.4 percent (Fry and others, 2011). 

Figure 2. National Land Cover Database (NLCD) statistics for 2001 and 2006 (Fry and others, 2011).
11

Figure 3. National Land Cover Database (NLCD) 2001–06 land-cover change. The light colored areas in this map 

represent areas of change that occurred between the 2 years (Fry and others, 2011).

2.1.3 Future Land Cover in the United States

Several agency products are designed to aid in the achievement of specific mandates or 

objectives. Two reports relevant for a LULC-climate assessment are from the U.S. Environmental 

Protection Agency (EPA) and USGS. Both are based on the Intergovernmental Panel on Climate 

Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic 

storylines (Nakicenovic and Swart, 2000). The EPA has developed Integrated Climate and Land-Use 

Scenarios (ICLUS) based on four SRES and has used demographic model population estimates as a 

means to spatially distribute housing density across the conterminous United States for each scenario by 

decade to the year 2100 (U.S. Environmental Protection Agency, 2009). The EPA ICLUS emphasize 

built-up land uses rather than the broader set of resource-based land covers. 

For a broader characterization, the USGS land carbon project is using a set of scenarios based on 

the SRES to produce land-cover forecasts for all major land-cover types in the United States (Zhu and 
12

others, 2010). The USGS land carbon approach is to downscale the SRES to the Nation’s major 

ecological regions, and then refine the land-cover proportions using historical land-cover data and 

information on major land-change drivers developed as part of the USGS land cover trends project. The 

USGS-developed FOREcasting SCEnarios of Future Land Cover (FORE–SCE) model is used to 

develop spatially explicit maps of projected land cover, by ecoregion, on an annual basis through 2100 

(Sohl and Sayler, 2008). At this time, USGS scenarios and land-cover forecasts have only been 

completed for the central United States. The remaining regions are expected to be completed in late2013. 

2.2 Land-Use and Land-Cover Patterns and Conditions Affecting Weather and Climate

A number of studies were conducted over the last several decades highlighting the role of LULC 

change in global climate (Claussen and others, 2001; DeFries and others, 2002; Feddema and others, 

2005; Brovkin and others, 2006; Davin and others, 2007; Pitman and others, 2009; Davin and NobletDucoudré, 2010; Pongratz and others, 2010). However, a detailed discussion on global impacts is 

beyond the scope of this report. Here, the primary focus is on LULC change impacts on weather and 

climate in the United States . 

2.2.1 Land Use and Land Cover Change Because of Agriculture and Changes in Weather and Climate

2.2.1.1 Planetary Boundary Layer 

The Planetary Boundary Layer (PBL) is the lowest level of the troposphere and is directly 

affected by characteristics of the Earth’s surface and changes in LULC. In continental interior regions of 

the Northern Hemisphere, especially the central United States, LULC change-driven, rainfed, and 

irrigated agriculture adoption have significantly modified regional-scale energy and moisture budgets 

compared to the preexisting natural vegetation. Deforestation for agriculture and settlement in the 

eastern United States and Corn Belt regions over approximately the past 150 years has significantly 

modified physical climate parameters. The reduction of canopy height has reduced the aerodynamic 

roughness, which affects momentum exchange and the convective fluxes of heat and moisture, between 

the land surface and atmosphere and eventually cloud development (Allard and Carleton, 2010; Matyas 

and Carleton, 2010; and Carleton and others, 2008a, b). A similar reduction in leaf-area index over the 

same period increased the surface albedo, or short-wave reflectance, particularly in the Midwest, and 

also increased the heterogeneity (patchiness) of the land cover. This increased patchiness has altered the 

regional and smaller-scale surface energy budgets. A previously based modeling study (McPherson and 

Stensrud, 2005) demonstrated that the wheat belt of western Oklahoma has modified meso-scale 

horizontal and vertical wind fields, potential temperature, height of the PBL (lowered), and overall 

meso-scale circulation. 

Recent studies from the other parts of the world also report LULC change impacts on PBL. For 

example, a series of studies set in the Canadian Prairies clearly found increases in convective activity as 

a result of replacement of natural grassland by farming (Hanesiak and others, 2004; Raddatz, 2003; 

Raddatz and Cummine, 2003; Raddatz, 2007). Like many other regions of the world, Australia also 

experienced LULC change. It is found from observational studies that conversion of native vegetation 

cover to agriculture suppressed cloud development over modern vegetation cover; that is, native 

vegetation provided a favorable condition for cloud formation (Lyons and others, 1993; Lyons, 2002). 

Changes in surface roughness and albedo played an important role in this suppression. Moreover, this 

conversion of land cover also favors dust devil formation (Lyons and others, 2008). Sen Roy and others 
13

(2011) reported changes in wind field and equivalent potential temperature because of irrigated 

agriculture.

2.2.1.2 Temperature

As noted earlier, LULC change also can modify air temperature and near-surface moisture 

content. A study based on observed data, (Fall and others 2010) found that conversion of lands (for all 

types) to agriculture resulted in cooling. It was noted that conversion of barren areas and 

grasslands/shrublands was associated with a cooling of -0.121 degrees Celsius (°C) and -0.096°C, 

respectively. In addition, conversion of previously forested and barren areas to agriculture resulted in 

-0.061°C and -0.039°C, respectively. Previously, Hale and others (2006, 2008) analyzed LULC change 

and temperature data and indicated LULC change may have played an important role in temperature 

changes over the United States. In an earlier study, Kalnay and Cai (2003) reported decreases of 

maximum temperature over the Midwestern United States during the spring, summer, and autumn 

seasons. These changes were linked to rainfed, agricultural, land-use change. McPherson and others 

(2004) found low anomalies of maximum temperatures in the observed data for the rainfed, winter 

wheat growing area of western Oklahoma and also reported higher dewpoint temperatures (Td) over 

wheat growing areas compared to surrounding native grasslands in Oklahoma. These findings were 

further supported by Sandstrom and others (2004). Analysis of observed data suggested an increase in 

frequency of occurrence of days experiencing extreme Td (greater than or equal to 22°C) in the central 

United States (Sandstrom and others, 2004) and have suggested that increased evapotranspiration from 

croplands (LULC change) is the primary cause of this dew-point increase.

Two critical studies by Bonan (1997, 2001) reported impacts of agriculture on United States

temperatures. From a modeling study, Bonan (1997) reported up to 2°C cooling of summer temperature 

over the central United States and up to 1.5 grams/kilogram (g kg-1)increase in atmospheric moisture 

content in much of the United States. Bonan noted that these changes were caused by lowered surface 

roughness and stomatal resistance and increased albedo because of replacement of forests with modern 

vegetation (largely agriculture). In a subsequent study, Bonan (2001) analyzed near-surface 

temperatures in the United States and found lowering of the daily maximum temperature. A temporal 

correlation between the expansion of agriculture and lowered of temperature also was found by Bonan. 

Impacts of agriculture-related changes of weather and climate also have been experienced in 

other regions of the world. Based on an observational study, Gameda and others (2007) found 

significant reductions in mid-June to mid-July maximum air temperatures, and diurnal temperature 

range and solar radiation of 1.7°C decade-1, 1.1°C decade-1 and 1.2 M J m-2 decade, respectively, in the 

Canadian Prairies. These changes were linked to the increased latent heating associated with increased 

area under crop cultivation that forced lowering of temperature. In addition, observational data suggest -

0.30°C cooling in semi-arid Almeira, Spain (Campra and others, 2008), associated with pastureland 

changing to greenhouse farming and resultant strong negative radiative forcing (up to -34 watts per 

square meter (W m

-2)). To evaluate the impacts of historical land-cover changes on Australia’s regional 

climate, McAlpine and others (2007) conducted two sets of model experiments comparing the climatic 

impacts of modern day (1990) compared to pre-European land-cover conditions. A modeling study by 

McAlpine and others (2009) found 0.2–2.0°

C increase in summer temperature for eastern Australia and 

0.5°C for southwestern western Australia as a result of LULC change. It was suggested that increased 

summer temperatures in eastern Australia were linked to a large decrease in vegetation fraction 

(19 percent) and leaf area index (LAI) (23 percent) (McAlpine and others, 2009). 

Based on a modeling study, Beltrán-Przekurat and others (2011) found a large change (cooling) 

in maximum temperatures linked to LULC change in the Pampas region of Argentina. Simulations 
14

suggested large decreases in sensible energy flux and increases in latent energy flux. The study also 

noted a decrease in diurnal temperature range. The results were comparable to the observed trends in 

temperature over Argentina (Rusticucci and Barrucand, 2004; Nuñez and others, 2008). 

Modification of LULC to irrigated agriculture further intensified some of these impacts. For 

example, analyses of observed data suggest up to 1.41°C lowering (cooling) of maximum temperatures 

during the post-1945 period for the growing season (May through September) over irrigated locations in 

Nebraska (Mahmood and others, 2006). This finding is supported by the modeling studies by Mahmood 

and Hubbard (2002, 2004), Adegoke and others (2003), and Ozdogan and others (2010); the authors 

show up to a 36-percent increase in evapotranspiration over irrigated areas compared to grass. As 

discussed previously, increased evapotranspiration (latent energy flux) lowers sensible energy flux and 

thus temperature. In an effort to further verify these results, Mahmood and others (2008) investigated 

the impacts of irrigation on observed near-surface atmospheric moisture content using long-term 

observed Td data for Nebraska and found a 1.56°C increase in average growing season Td over irrigated 

areas. A 2.17°C increase in Td for peak growing season months also was reported (Mahmood and 

others, 2008). Earlier, Mahmood and others (2004) reported a cooling trend in long-term extreme 

maximum temperatures for irrigated locations in the same region. 

Similar studies on impacts of irrigation also reported a lowering of growing season temperature 

in California (Christy and others, 2006; Lobell and others, 2006a,b; Bonfils and Lobell, 2007; Kueppers 

and others, 2007, 2008; Lobell and Bonfils, 2008; Jin and Miller, 2011; Sorooshian and others, 2011). 

For example, based on a modeling study, Sorooshian and others (2011) reported 3–7°C cooling and 9–

20 percent increase in relative humidity over irrigated areas. This study also suggests that irrigated land 

use could lower daily maximum temperatures up to 1–4°C over surrounding nonirrigated areas. Jin and 

Miller (2011) reported cooling of daily maximum temperature during all seasons for irrigated locations 

with 0.30°C/decade reduction over summer. This modeling study further explained the overall findings. 

Gordon and others (2005) reported globally up to a 2,600 cubic kilometers per year (km3 yr

-1) 

increase in vapor flux because of irrigation, and northern India has been experiencing one of the most 

intense levels of vapor flux (500 millimeters per year (mm yr

-1)) because of irrigation (Gordon and 

others, 2005, figs. 3 and 4). Sen Roy and others (2007) also examined the impacts of irrigated 

agriculture on observed dry season temperature in northern India, analyzed observed temperature data, 

and supported the findings with regional modeling. This study found up to 0.34°C cooling of growing 

(dry) season maximum temperatures after irrigation was adopted. Up to a 0.53°C decrease of maximum 

temperature was reported for individual growing season months. This study reported increased latent 

energy flux and regional moisture flux because of irrigation. It was concluded that these lowered 

maximum temperatures were forced by increased evaporative cooling and decreased Bowen ratio (that 

is ratio of sensible to latent heat fluxes) associated with irrigation. The study also reported statistically 

significant long-term negative temperature trends. 

In a modeling study, Puma and Cook (2010) found notable regional cooling effects over 

southern and eastern Asia early in the 20th century. These impacts were intensified and became 

significant across the middle-latitude croplands during the mid-20th century. The results suggest winter 

season warming of parts of North America because of increased irrigation. Puma and Cook (2010) 

concluded that irrigation will play an important role in the future global climate.

Biggs and others (2008) also found lowered maximum temperature, increased latent and 

decreased sensible energy fluxes because of increased irrigation over the Krishna River Basin in 

southern India. Recent work by Douglas and others (2006, 2009) also reported decreased latent and 

increased sensible energy fluxes in India because of adoption of irrigated agriculture. Increased regional 

moisture content, lowered PBL height, and increased convectively available potential energy (CAPE)
15

also were reported by Douglas and others (2006, 2009). Lowering of temperature in Australia because 

of irrigated agriculture (thus an increase in latent energy flux) also is reported by Geerts (2002). 

2.2.1.3 Precipitation

In addition to temperature, a number of studies reported increases in precipitation because of the 

introduction of irrigated agriculture. For example, Barnston and Schickedanz (1984) noted an increase 

in precipitation because of irrigation over the Ogallala aquifer region and Southern Great Plains. The 

observational data-based study found that precipitation increased when low-level convergence and uplift 

were present within a synoptic setting and allows low-level moisture to rise and reach the base of 

clouds. As a result, the impact of irrigation was greater for prevailing rain-producing conditions that

typically allowed such uplift. In recent followup work, DeAngelis (2010) reported up to a 15–30-percent

increase in July precipitation over the easternmost part of the Ogallala aquifer. The influence of 

irrigation and these changes were extended as far east as Indiana. 

In regard to large-scale LULC, the Corn Belt depicts a seasonally invariant and relatively 

undifferentiated structure to the land cover. However, satellite-derived indices of vegetation activity, 

such as the Normalized Difference Vegetation Index (NDVI), reveal strong heterogeneity in the early 

warm season that is related to soil moisture content, crop stage of development (phenology), and 

vegetation type. The presence of long yet relatively narrow boundaries between extensive croplands and 

remnant forest serves to enhance convective cloudiness and rainfall during the warm season, supporting 

Anthes’ (1984) model results that widely-spaced rows of trees may initiate or intensify deep convection 

at least in the subtropics. This land-surface-convection coupling is most evident when the synoptic-scale 

atmospheric circulation favors weak winds in the free atmosphere. It is hypothesized that the large 

height differences between trees and crops can loft air parcels above the condensation level when 

humidity is high in the layer of air closest to the ground. The vegetation-boundary association with 

precipitation may be seasonally varying and tied to phenology differences between the croplands and 

surrounding forest areas. Early in the warm season, longer and wider boundaries help promote more 

precipitation, while later in the summer, shorter and narrower boundaries have associated high 

precipitation amounts. It is hypothesized that the seasonal switch in maximum NDVI between the 

forests and croplands changes the “pooling” of moisture along the vegetation boundaries. 

In a modeling study, Costa and others (2007) found that replacement of forests in Amazonia 

with soybean farmland significantly reduced precipitation compared to replacement with pastureland. 

This study suggests that lowering of albedo and latent heat flux resulted in a negative feedback

suppressing convection, cloudiness, and hence, precipitation. In followup research, Nair and others 

(2011) and Junkermann and others (2009) reported that replacement of native vegetation with 

agriculture also reduced rainfall. It was suggested that among other influences, changes in surface 

roughness play a critical role. Recently, Sen Roy and others (2011) analyzed observed data and found 

increased premonsoon rainfall over irrigated areas of northern India. Supporting modeling work found 

increases in latent heat flux, equivalent temperature, and lowered PBL that may have played important 

roles in this increase. Lee and others (2009) noted that LULC change in India has impacted early 

summer monsoon rainfall. and report increased anomalies in NDVI for March, April, and May because 

of increases in irrigation. In addition, significant decreases in July rainfall in central and southern India 

were linked to the greening vegetation as measured by an NDVI increase in the preceding March, April, 

and May period. Saeed and others (2009) also reported modification of premonsoon and monsoon 

precipitation in various regions of the Indian subcontinent because of irrigation.
16

2.2.2 Land use and Land Cover Because of Urbanization and Changes in Weather and Climate

2.2.2.1 Temperature

Urbanization provides examples of intense localized changes in LULC and its impact on weather 

and climate. One of the most well-known impacts of urbanization is the urban heat island (UHI) 

(Landsberg, 1970; Arnfield, 2003; Souch and Grimmond, 2006; Yow, 2007). UHI can be characterized 

by higher air temperatures compared to the surrounding rural area. It is found that UHI can be 

influenced by time of day, seasons, latitude, climate regime, and surrounding land cover (Arnfield, 

2003; Zhou and Shepherd, 2010. In a recent study, Imhoff and others (2010) found that ecological 

context influences summer-season daytime amplitude of the UHI. 

2.2.2.2 Precipitation

It has also been found, particularly in recent literature, that urbanization impacts convective 

precipitation (Shepherd and others, 2002, 2010; Shepherd, 2006; Niyogi and others, 2006; Kauffman 

and others, 2007; van den Heever and Cotton, 2007; Mote and others, 2007; Lei and others, 2008; Rose 

and others, 2008; Stallins and Rose, 2008; Trusilova and others, 2008; Hand and Shepherd, 2009; Shem 

and Shepherd, 2009; Kishtawal and others, 2010; Niyogi and others, 2010; Niyogi and others, 2011; 

Mitra and others, 2011). The impacts include changes in location of precipitation compared to preurbanization, enhancement in actual amounts of precipitation, and development of preferential location 

of precipitation as a result of urbanization. Overall, these studies suggest that urbanization modifies 

surface physical characteristics and energy partitioning and leads to changes in local atmospheric 

circulation and convection that eventually impact precipitation. 

2.3 Land Use and Land Change and Biogeochemical Cycles

Land-cover changes can influence climate change through biogeochemical cycles, especially the 

carbon (C) and nitrogen (N) cycles. Several primary greenhouse gases that influence climate change are 

directly involved in ecological systems. They have the following influence on the radiative forcing that 

causes global warming: 50 percent by carbon dioxide (CO2), 19 percent by methane (CH4), and 4 

percent by nitrous oxide (N2O) based on 1990 concentrations, excluding water vapor, and accounting 

for the residual time of each gas in the atmosphere (Houghton and others, 1990). There is considerable 

interest in offsetting some of the increase in greenhouse gases from burning fossil fuels by reducing the 

emissions of these gases from ecological, agricultural, and forest systems, or increasing the uptake of 

CO2 by these systems.

For the time scales of interest in climate change mitigation and adaptation (tens of years to 

centuries), the significant pools of carbon of interest are the atmosphere, vegetation, soils, and the 

oceans. At the current levels, the atmosphere has about 750 petagrams of carbon (Pg C), the soils have 

about 1,500 Pg C in the top 1 m or up to 2,300 Pg C in the top 3 m, and the terrestrial vegetation has 

850 Pg C (Eglin and others, 2010). The oceans have a very large pool and are important in the time 

scales of interest for absorbing about 1/3 of global CO2 emissions. 

Approximately 120 Pg C is taken from the atmosphere each year by terrestrial ecosystems 

through photosynthesis of which about half is quickly (seconds to days) released back to the atmosphere 

as CO2 by plant respiration, and the other half is released slowly by respiration or decomposition of 

plant and animal materials. A small amount of the annual gross primary production is incorporated into 

soil organic matter, which is often modeled as having three pools with turnover times of approximately 

1.5 year, 25 years, and 1,000 or more years (Parton and others, 1987). In modeling grassland soils using 

a simplified version of the Century model, Parton and others (2001) indicated that approximately 48 
17

percent of the soil organic carbon (SOC) was in the slow pool (10- to 30-year turnover time), 

approximately 50 percent of the SOC was in the passive pool (1,000- to 3,000-year turnover time), and 

about 2 percent was in the microbial pool (active decomposition). 

Most of the carbon released from soils is as CO2, with a smaller amount released as CH4. Both 

of these are greenhouse gases, and although there are fewer molecules released as CH4, the heating 

influence of each CH4 molecule on the radiation balance of the earth is about 25 times more than the 

influence of each CO2 molecule (McGuire and others, 2010; citing Intergovernmental Panel on Climate 

Change, 2007). Release of CH4 from soils generally is associated with wetland soils or rice cultivation. 

Releases of N2O generally are associated with agricultural activities such as nitrogen fertilization. N2O 

is even stronger per molecule than CH4 in its warming influence on the Earth’s radiation balance. 

Because the soil carbon pool is large in comparison to the biospheric and atmospheric pools, a 

small change in the soil pool can result in a larger proportional change in the atmospheric pool. 

Therefore, changes in soil processes resulting from LULC change may have an amplified impact on the 

atmospheric pool, where increasing levels of greenhouse gases are major drivers for climate change and 

global warming. If climate change stimulates a net release of greenhouse gases from soils, then it 

represents a positive feedback contributing to additional climate change. 

Land-cover change (for example, forest or grassland to agriculture) and land-use and 

management practices (for example, crop choice, fertilization rates, type of tillage, and use of irrigation) 

can change the soil conditions that affect the rates of release of SOC and N2O. The release rates also are 

influenced by the type of soil (for example, soil texture, water holding capacity, permeability, organic 

matter content, drainage, and erosion rate) and the seasonal patterns of soil moisture and temperature. 

The patterns of soil moisture and temperature are expected to change with changes in climate. Landmanagement practices also may be adjusted with climate change (for example, influences of drought or 

floods on agriculture) and by human attempts to mitigate the drivers of climate change (for example,

making biofuels with feedstocks from croplands, grasslands, or forests to use as alternatives to fossil 

fuels). 

Guo and Gifford (2002) provide a global review of the effects of land-cover change on soil 

carbon stocks. Meta analysis of 74 publications by Guo and Gifford gives the following results in terms 

of percentage change in SOC stocks: 

 -10 percent pasture to plantation (for example, plant trees), 

 -13 percent native forest to plantation, 

 -42 percent native forest to crop, 

 -59 percent pasture to crop, 

+8 percent native forest to pasture, 

+19 percent crop to pasture, 

+18 percent crop to plantation, and 

+53 percent crop to secondary forest. 

In general, conversion of native forest or grassland to cropland results in a large loss of soil 

carbon (presumably to the atmosphere and within the first few years after conversion). Conversely, 

converting cropland into forest or grassland can store atmospheric carbon in the soil, but it may take 70 

or more years and the new “equilibrium” may be at a lower level of SOC than in an ecosystem that was 

never disturbed. 

Increases in carbon stored in vegetation or soil are often referred to as “carbon sequestration” 

because they remove carbon from the atmosphere for some period of time. Some of the increases in 

storage in vegetation are attributed to the “CO2 fertilization effect” in which higher levels of CO2 in the 
18

atmosphere cause plants to grow faster (CO2 is an input to photosynthesis, so more CO2 may lead to 

more photosynthesis). 

Eglin and others (2010) cite the global soil carbon stocks reported by Jobbagy and Jackson 

(2000) and assess the global vulnerability of SOC to climate and land-use changes. They note that with 

increasing CO2 in the atmosphere the size of the terrestrial sink (annual storage) also has been 

increasing from 1.8 PgC in the 1980s to 2.6 PgC in the 1990s and 3 PgC from 2000 to 2008. They also

note that the land biosphere carbon sink absorbs about 30 percent of the anthropogenic emissions from 

fossil fuel combustion and deforestation every year (Le Quere and others, 2009; Canadell and others, 

2007; Intergovernmental Panel on Climate Change, 2007). Although soil contains three times more C 

than vegetation, it is a two times smaller sink than living biomass (Reichstein, 2008 as cited by Eglin 

and others, 2010) because (1) when part of the biomass is harvested, the soil does not receive carbon 

from the harvested productivity, and (2) increased primary productivity adds C into biomass pools 

where it could be stored for decades as wood in forests and is not quickly passed into the soil. The 

carbon in soils may become more vulnerable to decomposition in a warmer world, and Reichstein 

(2008) estimates the potential loss of soil C under the future warming forecasted by climate models is 

six times larger than the current soil C sink.

Although there has been a lot of research on the potential for changes in land use and 

management to mitigate the increases of greenhouse gases in the atmosphere, there may be a limited 

potential for this. In one of the most complete analyses at the national level, Smith and others (2010) 

assessed how agricultural land-use changes influence SOC and greenhouse gas emissions in Great 

Britain. Transitions were considered between cropland, temporary grassland (less than 5 years under 

grass), permanent grass (more than 5 years under grass), and forest. It was shown that reversion to 

historical land-use patterns as present in 1930 could result in greenhouse gas emission reductions of up 

to 11 teragrams of carbon dioxide equivalent per year (Tg CO2-equivalent yr

-1), mostly because of an 

increased permanent grassland area. By contrast, cultivation of 20 percent of the permanent grassland 

area (as of 2004) for crop production could result in greenhouse gas emission increases of up to 14 Tg 

CO2-equivalent yr-1. It was concluded that changes between agricultural land uses in Great Britain is 

likely to be a limited option for greenhouse gas mitigation, mostly because it is unlikely that existing 

cropland would be transferred into grassland. In terms of total United Kingdom greenhouse gas 

emissions, even the most extreme feasible land-use change scenarios would offset only about 2 percent

of current national greenhouse gas emissions (Smith and others, 2010). 

The LandCarbon project of the USGS is evaluating carbon storage, carbon fluxes, and N2O 

fluxes in all forests, grasslands/shrublands, agricultural lands, wetlands and freshwater aquatic systems 

in the United States. The assessment uses remote sensing, statistical methods, and simulation models. 

The study includes a baseline time period (generally in the first half of the 2010s) and future projections 

from the baseline period to 2050. An initial report covers the Great Plains region (Zhu and others, 

2011). Among the conclusions is an estimated annual carbon sequestration rate for the region between 

20 and 99 TgC yr-1 from 2001 to 2005, and an estimate for the mean annual sequestration rate of 48 to 

66 TgC between baseline and 2050. The study methods include forecasting LULC change using 

Intergovernmental Panel on Climate Change (IPCC) scenarios, driven by the demand for agricultural 

commodities (including biofuels). The scenarios suggest a significant expansion of agricultural land (1.4 

to 9.2 percent of the total area by 2050, depending on which scenario is used in the calculation). 

2.4 Combined Impacts of Land-Use and Land-Cover Change and Climate Change 

A limited number of studies have been conducted on this topic over the last decade. In one of the 

earlier studies, Zhao and others (2001) showed LULC change did not get diminished with increasing 
19

CO2 impacts. This study also identified remote responses because of combined changes in LULC and 

CO2 levels. One of the four regions that showed remote response is North America and includes the 

United States. The remote responses include changes in temperature, which were largely influenced by 

the changes in circulations. In another modeling study, Pongratz and others (2010) reported that LULC 

change will lead to, on the average, small global cooling (-0.03 Kelvin (K)) while warming caused by 

CO2 will be greater (0.16–0.18 K). In another detailed model-based study Brovkin and others (2004) 

found net cooling over northern latitudes including most of the North America and the United States

because of the combined impact of LULC change and CO2. However, a relative decline in the role of 

LULC change compared to CO2 was noted. Additional studies conducted in Australia also show LULC 

change under increasing CO2 may mitigate impacts of the latter (Narisma and Pitman, 2004, 2006).

Additional research needs to be conducted to better understand the combined impacts. 

3.0 Recent Land-Use and Land-Cover and Climate Case Studies

Case studies selected from current literature (2009 through 2011) demonstrate historic, present, 

and potential future climate influences on LULC. The case studies were synthesized and organized into 

the following LULC/environment effect categories: water resources, transportation and energy supply 

and use, agriculture, ecosystems, human health. The individual case study summaries can be found in 

appendix I. 

3.1 Climate Influences on Land-Use and Land-Cover and Water Resources

Case studies addressing climate influences on LULC and water resources highlight five relevant 

topics: baseflow, drought and flood, water supply, water quality, and wetlands. Collectively, these case 

studies show the clear link between climate and LULC and the current state of water resources within 

the United States. 

The case studies on baseflow and flood and drought identified LULC and climate as two main 

drivers of altered baseflow (Kochendorfer and Hubbart 2010; Price 2011; Price and others 2011; Viger 

and others 2011). Baseflow, or in hydrologic terms, streamflow results from precipitation that infiltrates 

into the soil and eventually moves through the soil to the stream channel (also called low flow, National 

Oceanic and Atmospheric Administration, 2012). Recent changes in baseflow caused by LULC changes 

alter the natural habitat within watersheds. When natural habitat within a watershed is disturbed, the 

hydrologic functions such as infiltration and recharge are compromised leading to exacerbated floods in 

wet years and droughts in dry years. Tomer and Schilling (2009) present the difficulty of separating the 

climate influences from weather influences and land-use change on watershed hydrology and were able 

to separate interactions for a 25-year, small watershed experiment in Iowa. If applied regionally, the 

conclusions suggest that climate change has increased discharge from Midwest watersheds, especially 

since the 1970s. 

Praskievicz and Chang (2009) and Polebitski and others (2011) have studied climate change and 

urban development and the impacts on water supply and demand. These studies demonstrated that 

climate change will increase competition between land uses for water resources, particularly in areas of 

urban development and arid and mountainous regions of the United Sates. Ford and others (2011) 

looked at the impacts of LULC on forested watersheds and found that unmanaged watersheds had lower 

streamflow and a more stable water supply. 

Water quality can be impacted by several sectors (agriculture and energy supply and use), which 

ultimately compromises other sectors (water resources, human health, and society). Water-quality case 

studies suggest a positive relation between increased fertilizer usage and increased phosphorus, 
20

nitrogen, and nitrate runoff into streams or groundwater (Banner and others 2009; Burow and others 

2010; Broussard and Turner, 2009). Another study found that land-cover changes to natural vegetation 

reduced the watershed’s ability to retain nitrogen, and thus led to runoff and eutrophication (Kinney and 

Valiela, 2011). 

LULC change involving wetlands affects both water resources and ecosystems. Wetland 

functions are sensitive to agricultural practices and climate. Adaptations in agricultural management 

may help ameliorate climate change by retaining carbon sinks and water levels (Johnson and others, 

2010, Kumar and others, 2010 and Voldseth and others, 2009). 

3.2 Climate and Land-Use and Land-Cover Influences on Transportation and Energy Supply and 

Use

The impacts of transportation and energy supply and use sectors are similar. For example, 

transportation is essential to providing energy supply and use. DellaSala and others (2011), Duniway 

and others (2010), and McDonald and others (2009) have assessed the impact that transportation and 

energy activities have on the landscape. In Colorado, development of roads within undeveloped 

watersheds could potentially enable short-term energy supply (production of oil and gas); however, the 

long-term impact would reduce water supply and quality (DellaSala and others, 2011). Continued 

development of oil, gas, and renewable energy sources would require service roads to be developed in 

rangelands (Duniway and others, 2010). Although climate influences were not specifically mentioned in 

DellaSala and others (2011), Duniway and others (2010), and Dale and others (2011) suggested that 

analysis of landscape patterns could be used to enhance the understanding of interactions among land 

use, energy, and climate change. McDonald and others (2009) found that even if there is no cap-andtrade bill to reduce emissions, 206,000 km2

 of land will be affected by energy production by 2030. 

Ethanol has been used to help reduce emissions; however , Solomon (2010) found that corn-based 

ethanol was unsustainable and had significant environmental costs. Interactions between transportation 

and energy have significant influences on LULC and climate and are expected to increase as the demand 

for energy also increases. 

3.3 Climate Influences on Land Use and Land Cover and Agriculture

Agricultural production and land-use practices are influenced by climate, land, and water 

resources. Much of the world depends upon agriculture in the United States to supply many goods and 

services. As the world’s population continues to grow, so does the demand for increased agricultural 

production (Burow and others, 2010). Associated impacts of this demand include increased fertilizer 

usage, which results in increased concentrations of phosphorus and nitrogen loading in watersheds 

(Banner and others, 2009; Broussard and Turner, 2009; Burow and others, 2010). Land-use conversion 

to cropland, cropland sustainability, and increased growing season temperatures were identified as 

agricultural topics that were impacted by climate and land-use change. 

3.4 Climate Influences on Land Use and Land Cover and Ecosystems

Climate, LULC, and ecosystem issues include greenhouse gas inventory and biogenic emissions, 

vegetation displacement, and wildland fires. The United Nations Framework Convention on Climate 

Change (UNFCCC) requires all qualifying nations to report the status of their greenhouse gas (GHG) 

emissions and sinks annually (United National Framework Convention on Climate Change, 2012a, 

2012b). Zheng and others (2011) estimated the effects of major forest disturbances and net growth on 

carbon sequestration in the conterminous United States, based on the terminology and requirements for 
21

reporting to the UNFCCC for national GHG inventories. There are two types of emissions defined by 

the Environmental Protection Agency—anthropogenic and biogenic emissions (U.S. Environmental 

Protection Agency, 2012). Anthropogenic emissions are produced as a result of human activity that 

releases CO2 emissions into the atmosphere. One of the largest sources of anthropogenic CO2 emissions 

is the combustion of fossil fuels or fossil fuel-based products to produce electricity (U.S. Environmental 

Protection Agency, 2012). Biogenic emissions, in contrast, result from natural biological processes, 

such as the decomposition or combustion of vegetative matter. Biogenic CO2 emissions are part of a 

closed carbon loop. Biogenic CO2 emissions are balanced by the natural uptake of CO2 by growing 

vegetation, resulting in a net zero contribution of CO2 emissions to the atmosphere. Examples of 

biogenic emission sources include burning vegetation (biomass) to produce electricity or using plantbased biofuels for transport (U.S. Environmental Protection Agency, 2012). Studies have been done to 

quantify the effects of LULC on anthropogenic emissions; however, few studies have looked at the 

effects of LULC on biogenic emissions. Chen and others (2009) used a model to study LULC effects on 

future biogenic emissions within the United States. 

Projected climate change will have an impact on the distribution of native vegetation. It has been 

projected that the natural climate gradient of forest ecosystems will shift to higher elevations, thus 

displacing certain tree species (Tang and Beckage, 2010). Tang and Beckage (2010) studied the three 

major forest types in New England and modeled each forest’s distribution in response to climate change. 

In other ecosystems, climate change would impact native vegetation surviving and their ability to 

compete with invasive species. Bradley (2010) studied the risk of sagebrush loss associated with climate 

and land-use change. Barret and Gray (2011) presented the potential benefits of extending the Forest 

Inventory and Analysis (FIA) approach to the boreal forest region of Alaska and suggested that FIA 

monitoring could aid conservation decisions by providing information on the abundance and rarity of 

vascular plants, invasive species, biomass and carbon content of vegetation, shifting vegetation species 

distribution, disturbance frequency, type, impact, and wildlife habitat characteristics. 

Climate change is already altering natural fire regimes within the United States (Liu and others, 

2010; Westerling and others, 2011). Increased duration of fire seasons accompanied by drought 

conditions leads to increased wildfire potential. However, climate-driven changes in regional fire 

regimes are not well understood (Westerling and others, 2011). Liu and others (2010) and Westerling 

and others (2011) measured fire potential and predicted future fire trends in response to climate change. 

Westerling and others (2011) also mentioned that increased fire frequency would transform fire regimes 

and change vegetation types. 

3.5 Climate Influences on Land Use and Land Cover and Human Health

Projected climate change could/may have an impact on human health with LULC implications. 

Two health related topics identified in the literature as being linked to LULC and climate influences 

were West Nile virus and extreme heat events. Two studies examined the climate and land-use drivers 

of the Northern Great Plains because of the number of cases found within this region. Wimberly and 

others (2008) assessed environmental drivers of West Nile virus, and Chuang and others (2011) studied 

land cover and climatic variability of the host mosquitos. Extreme heat events are becoming a concern 

in large metropolitan areas because of increasing frequency. Gershunov and others (2009) studied the 

2006 heat wave over California and Nevada in context to the region’s climate over the last 60 years. 

Associations between metropolitan region and the frequency of extreme heat events over a five-decade 

period were examined using a sprawl index (Stone and others, 2010). 
22

3.6 Climate Influences on Land Use and Land Cover and Society

Climate and LULC can combine to cause negative feedbacks on society. For example, 

urbanization can affect regional weather and climate and cause land fragmentation. In the Southeastern 

United States, Nagy and Lockaby (2010) tested the hypothesis that a negative feedback would exist 

between environmental impacts and the rate and patterns of development. Niyogi and others (2011) 

studied the impact an urban region would have on altering the intensity, structure, and composition of an 

approaching thunderstorm. In the Southwestern United States, York and others (2011) investigated 

rapid urbanization to determine drivers that caused land fragmentation. 

4.0 A Regional and Sectoral Perspective of Climate and Land-Use and LandCover Interactions

Climate and land-system interactions are as unique as the regional and sectoral driving forces 

behind them. This section presents an integrated framework for assessing climate change and variability 

and land-change feedbacks, vulnerability, and resilience. The framework presented in figure 4 

(McMahon and others, 2005) acknowledges that variability and change in land systems should be 

identified, which is what this report presented in the previous section on case study analysis. The 

framework also acknowledges that consequences and implications of land change, such as vegetation 

response to drought, should be identified. To demonstrate this part of the framework, this section 

discusses regional and sectoral responses to drought from 2009 through 2011 by presenting results from 

the USGS’s Vegetation Drought Response Index (VegDRI) project (Brown and others, 2008). Lastly, 

the framework presents the need to understand human-environmental system vulnerability to climate 

and land change by investigating regional responses to change through adaptation strategies. While not 

conclusive, this section reviews possible adaptation strategies for the agricultural, forest, water, and 

wetland land-cover sectors. 
23

Figure 4. A coupled framework for assessing land change, vulnerability, and adaptability (McMahon and others, 

2005; modified from Turner and others, 2003).

4.1 Identification of Vulnerable Regions and Sectors

The vulnerability of LULC is based on the degree to which a coupled human-environmental 

system is susceptible to reduced function, goods, and services because of adverse effects of climate 

change, including climate variability and extremes. There are multiple feedbacks in vulnerable systems 

that are functions of their exposure, sensitivity, and adaptive capacity to the character, magnitude, and 

rate of climate change. In the figure 4 (yellow box), environmental and human influences are shown that 

can contribute to change in the environmental and human conditions in land systems (green box). There 

are many processes (blue box) operating on short-term (perturbations) or longer-term (stressors) time 

scales that influence how flora, fauna, individuals, and institutions use the land to exist and produce 

things (pink box). The capability of people and institutions to adapt may depend on the extent to which 

they have maintained an adaptive capacity (for example, set aside resources for responding to hazards). 

Some of the adaptive responses may be to change the ways in which humans influence the land system 

(orange box and yellow box). The boxes and arrows in this figure are conceptual because in a real 

system there are no clear boundaries between the primary human or ecological processes and the ways 

in which they mitigate or adapt to stress.
24

A regional vulnerability assessment would document how changes in climate and weather 

variability could change the exposure of ecological and social processes to perturbations and stressors. 

Mitigation efforts make use of some of the system’s resources to reduce the harmful stresses, and 

adaptation efforts make use of some of the system’s resources to reduce the harm or disruption to 

affected social or ecological subsystems. The systems which have sufficient adaptive capacity are 

considered resilient, whereas those without sufficient adaptive capacity are considered vulnerable. 

There are often tradeoffs between mitigation efforts (which may be long term) and adaptation 

efforts (which may be short term). For example, in an agricultural system, stressors could be droughts or 

excessive wetness. Mitigation could include decreasing greenhouse gas emissions to avoid increasing 

the rate of climate extremes in the future, although this is a long-term process and will not guarantee an 

immediate return. Adaptation could include crop insurance to compensate individual producers, moving 

agriculture away from sites prone to drought or flooding, and using crop varieties suited to the changed 

conditions. Systems that operate near 100 percent capacity often do not have the buffers needed to 

withstand new stressors. 

Considering climate and LULC change as stressors, the following is a list of regional 

vulnerabilities identified from recent scientific publications. The regional groups correspond to the NCA 

regions. 

● Northwest

o Earlier peak runoff in spring will lead to lower flows in summer when water demand is high 

(Praskievicz and Chang, 2009). 

o In Seattle, it was predicted that water pricing policy would decrease the demand; however, 

increasing temperature would increase the demand (Polebitski and others, 2011). 

● Southwest 

o Arid regions are likely to experience a decrease in annual runoff because of increased 

temperatures that could diminish snowpack (Praskievicz and Chang, 2009). 

o Development of inventoried roadless areas (IRAs) in Colorado could have long-term impacts 

on the water supply (DellaSala and others, 2011). 

o Continued development of renewable energy sources, including wind and solar, will require 

service road development in rangelands (Duniway, 2010). 

o The risk of sagebrush loss associated with land use and cheatgrass invasion was highest in 

portions of Nevada already dominated by cheatgrass and near lands used for agriculture 

(Bradley, 2010). 

o Continued warming could transform Greater Yellowstone ecosystems fire regime by the 

mid-21st century with a likelihood that forested ecosystems would be converted to nonforest 

vegetation and thus alter the fire return interval (Westerling and others, 2011. 

o Recent increases in California and Nevada heat waves are consistent with regional signs of 

global warming (Gershunov and others, 2009). 

o Five drivers of land fragmentation patterns were identified including: water provisioning, 

urban population dynamics, transportation, topography, and institutional factors. Water was 

identified as the key variable in understanding land change in the Southwest United States 

because all five study sites had major rivers dammed for storage or prevention of flooding 

(York and others, 2011). 

● Great Plains and Midwest

o Rural land-use change has played a significant role in streamflow alteration (Kochendorfer 

and Hubbart, 2010). 
25

o Watersheds that have been under agricultural land use for an extended period of time have 

increased baseflows because of improved cropping and tillage practices (Price, 2011). 

o Climate change is likely to exacerbate conflicts over competing land uses for water 

resources, such as urban and agriculture (Praskievicz and Chang, 2009). 

o Fertilizer usage has increased in order to increase crop production (Banner and others, 2009; 

Broussard and Turner, 2009; Burow and others, 2010). 

o Increased fertilizer usage has increased phosphorus, nitrogen, and nitrate runoff into streams 

and groundwater (Banner and others, 2009; Burow and others, 2010; Broussard and Turner, 

2009). 

o Corn, soybeans, and cotton increased yield threshold with increased temperatures of 29oC, 

30oC, and 32oC, respectively (Schlenker and Roberts, 2009). 

o Sensitivity of wetlands to warming temperatures within the Prairie Pothole Region (Johnson 

and others, 2010). 

o Agricultural drainage of Midwest wetlands have changed the carbon balance from carbon 

sinks to carbon sources and have affected the roles wetlands play in climatic feedbacks 

(Kumar and others, 2010). 

o Adaptations of agricultural practices can offset climate change effects on wetlands (Voldseth 

and others, 2009). 

o Temperate grasslands were identified as one of two most impacted habitats by future energy 

development (McDonald and others, 2009). 

o Increased market prices of corn and soybeans throughout the Midwest will contribute to a 

considerable decrease of lands enrolled in conservation programs (Gallant and others, 2011). 

o Grassland conversion to cropland was found to be driven by increased economic returns and 

the probability of grassland conversion to cropland will increase making grassland 

conservation more important (Rashford and others, 2011). 

o Incidences of West Nile virus are higher in the northern Great Plains than in other regions of 

the United States because the environmental conditions create a favorable ecological niche 

for Culex tarslis, a known vector of the West Nile virus (Wimberly and others, 2008). 

o Temporal and spatial distributions of two important mosquito species in South Dakota and 

their relations with land cover and weather will help to enhance the efficiency of vector 

control efforts and disease prevention (Chuang and others, 2011). 

o Thunderstorms were analyzed to capture varying storm events around the Indianapolis urban 

region and peripheral rural counties; it was found that 60 percent of storms changed structure 

over the Indianapolis area as compared to 25 percent over the rural regions (Niyogi and 

others, 2011). 

● Southeast 

o High-forest covered watersheds had lower baseflows, compared with areas of low- forest 

covered watersheds. Infiltration and recharge under undisturbed land cover is important in 

order to sustain low flows (Price, 2011). 

o Urbanization and climate change could potentially affect the freshwater resources of the Flint 

River System because of decreased streamflow caused by a decrease in surface-runoff and 

groundwater components (Viger and others, 2011). 

o Forest management of forested watersheds could potentially help mitigate extreme annual 

precipitation by decreasing flood risks because of pine forests ability to increase soil water 

storage (Ford and others, 2011). 
26

o Environmental conditions had little influence on rates and patterns of development in the 

west Georgia Piedmont region because of increasing land values, market concentrations, and 

road accessibility (Nagy and Lockaby, 2010). 

● Northeast

o Land cover shifts away from natural vegetation will increase nitrogen loading and lead to 

eutrophication of Great South Bay, New York (Kinney and Valiela, 2011). 

o Temperate deciduous forests along with temperate grasslands (Midwest) were identified as 

the two most impacted habitats by future energy development (McDonald and others, 2009). 

o Climate warming in the 21st century is likely to cause the extensive loss of boreal conifer 

forests, reduce the extent of northern hardwood deciduous forests, and result in large 

increases of mixed oak-hickory forest (Tang and Beckage, 2010). 

4.2 Ongoing Land-Use Adaptations to Climate Change

The importance of developing land-management options for climate change is anchored by an 

understanding of the regional variability of human-environmental interactions across the United States.

These interactions include but are not limited to the rates, driving forces, and implications of land 

change. As science-management partnerships are established, it becomes increasingly important to 

identify how specific land-use practices are responding to climate change and what specific adaptations 

should be considered for resource conservation. General land-cover classes that have an interface with 

the human environment and play a large role in resource conservation and management in the United 

States include agriculture, forest, water, and wetlands. The following sections summarize land-use 

adaptations by sector that were reported in recent publications. 

4.2.1 Agriculture

In addition to adapting to a changing climate, the agricultural sector will also face sustainment 

challenges associated with an increasing population. Agricultural management decisions may want to 

consider various land uses such as cropping systems and livestock in relation to climate variables such 

as warming and fluctuations in precipitation. 

Agricultural land managers should be aware of feedbacks associated with sectoral changes. For 

example, Voldseth and others (2009) examined the effects of climate and watershed cover on wetland 

water levels in eastern South Dakota to investigate if adaptation of agricultural land-use practices 

ameliorates effects of climate change on prairie wetlands, and how much climate change can be offset 

or absorbed through land-use management. This study concluded that cultivated crops and managed 

grasslands returned water levels that were equal or greater than unmanaged grassland under historical 

climate for the 2°

C temperature increase and the 2°C and 10 percent precipitation increase. Managed 

grasslands returned water levels that were equal or greater than unmanaged grasslands under historical 

climate for the 4°C temperature increase and the 2°C and 10 percent precipitation increase.

There are also economic impacts to consider when implementing climate adaptation strategies 

for the agricultural sector. For example, for livestock systems, Howden and others (2011) suggest that 

agricultural land-use adaptations to climate change include modifying grazing timing and paying special 

attention to continuously match stock rates with pasture production. The authors go on to discuss 

impacts on livestock agriculture by pointing out that more heat-tolerant livestock breeds often have 

lower levels of productivity. See table 3 for a summary of agriculture adaptations.
27

Table 3. Summary of agricultural land-use and cover adaptations with corresponding literature citations.

Land 

Cover Land Use

Climate 

Variable Adaptation Reference 

Agriculture Cropping Systems General Change Altering varieties and species

Howden and others, 2007; 

Delgado and others, 2011; 

Hallegatte, 2009; Yadav 

and others, 2011; 

Easterling, 1996; USGCRP, 

2000;Voldseth and others, 

2009

Altering fertilizer rates to maintain 

quality consistent with prevailing 

climate

Howden and others, 2007; 

USGCRP, 2000

Altering amounts and timing of 

irrigation

Howden and others, 

2007;Voldseth and others, 

2009

Alter timing or location of cropping 

activities (to include crop rotation and 

cover crop use)

Howden and others, 2007; 

Delgado and others, 2011; 

Easterling, 1996; USGCRP, 

2000

Increase soil C sequestration to 

improve soil functions Delgado and others, 2011

Increase N-use efficiencies for 

cropping systems Delgado and others, 2011

Diversify income by integrating other 

faming activities such as raising 

livestock Howden and others, 2007

Rainfall decrease

Adapt technologies to conserve soil 

moisture (because of crop reside 

retention)

Howden and others, 2007; 

Yadav and others, 2011

Increase irrigation efficiency

Delgado and others, 2011; 

Easterling, 1996; USGCRP, 

2000

Rainfall increase

Manage water to prevent water 

logging, erosion, and nutrient 

leaching Howden and others, 2007; 

Livestock systems General Change

Additional care to match stock rates 

with pasture production Howden and others, 2007; 

Altered rotation of pastures Howden and others, 2007; 

Modify times of grazing Howden and others, 2007; 

Alter forage and species/breeds Howden and others, 2007; 

Altered integration within mixed 

livestock/crop systems Howden and others, 2007; 

Reducing CH4 emissions from 

ruminants with feeding management, 

use of edible oils, and possible 

vaccinations Delgado and others, 2011

Use of supplementary feed and 

concentrates Howden and others, 2007
28

Land 

Cover Land Use

Climate 

Variable Adaptation Reference 

Warming

Increased need for management and 

infrastructure to ameliorate heatrelated reductions in productivity, 

fertility, and increases in mortality Howden and others, 2007

Tillage practices that incorporate crop 

residues would likely combat OM 

loss due to warming and improve soil 

quality USGCRP, 2000

General 

agricultural General change

Use more efficient power sources and 

renewable energy Delgado and others, 2011

Valuing agricultural commodities for 

their water footprint or environmental 

traits Delgado and others, 2011

Apply precision/target conservation 

to increase conservation effectiveness 

across spatial and temporal variability Delgado and others, 2011

Adapt farming practices to mitigate 

impacts of climate change on 

wetlands as a watershed management 

option Voldseth and others, 2009

4.2.2 Forest

Climate change may influence forest-management decisions to identify impacts and decisions 

that confer resilience and address vulnerability at multiple spatial scales (table 4). Management of forest 

for timber production may have to consider adaptation strategies such as altering rotation periods and 

harvesting patterns (Howden and others, 2011), whereas management of national forest land may put a 

stronger focus on managing ecosystem response to change and fire as both a natural and anthropogenic 

driver of change (Blate and others, 2009). 

Table 4. Summary of forest-related land-use and cover adaptations with corresponding literature citations.

Land 

Cover Land Use

Climate 

Variable Adaptation Reference 

Forest Planted Forests General Change Adapt management intensity

Howden and others, 

2007

Hardwood/softwood species mix

Howden and others, 

2007

Altering harvesting patterns within and 

between regions

Howden and others, 

2007

Alter rotation periods

Howden and others, 

2007

Salvage dead timber

Howden and others, 

2007

Shift species or areas more productive 

under new climatic conditions

Howden and others, 

2007
29

Land 

Cover Land Use

Climate 

Variable Adaptation Reference 

Make adjustments to altered wood size 

and quality

Howden and others, 

2007

Adjust fire management systems

Howden and others, 

2007

National 

Forests General Change Increase landscape diversity Littell and others, 2011

Maintain biological diversity Littell and others, 2011

Implement early detection/rapid response 

for exotic species and undesirable 

resource conditions

Littell and others, 2011; 

Blate and others, 2009

Treat large-scale disturbance as a 

management opportunity and integrate it 

in planning

Littell and others, 2011; 

Blate and others

Implement treatments that confer 

resilience at large spatial scales

Littell and others, 

2011;Mawdsley and 

others, 2009

Match engineering of infrastructure to 

expected future conditions

Littell and others, 2011; 

Blate and others, 2009

Promote education and awareness about 

climate change among resource staff and 

local public

Littell and others, 2011; 

Blate and others, 2009

Collaborate with a variety of partners on 

adaptation strategies and the promotion of 

ecoregional management

Littell and others, 2011; 

Blate and others, 2009

Reduce fuel loads in forests Blate and others, 2009

Increase use of wildland fire use Blate and others, 2009

Increase efforts to reduce current stress 

factors Blate and others, 2009

Develop silviculture treatments to reduce 

drought stress Blate and others, 2009

Review genetic guidelines for 

reforestation Blate and others, 2009

Provide technical assistance to urban 

foresters to sustain urban trees Blate and others, 2009

Develop corridors for species migration 

and habitat protection

Blate and others, 2009; 

Mawdsley and others, 

2009

Evaluate recreational impact on 

ecosystems under a changing climate Blate and others, 2009

4.2.3 Water

Water resources include our Nation’s surface and groundwater that will face climate-related 

challenges—irrigation demands supported, fresh drinking water provided to rural and urban 

communities, and habitat provided for nurturing ecosystems and their services (table 5). Waterresources management also includes mitigating consequences that affect water quality and quantity. For 

water quality, agricultural and urban runoff are often associated as principal sources of pollution in 

waterways. But as land cover changes, feedbacks and natural response should be considered when 

planning adaptation strategies. Kinney and Valiela (2011) used a nitrogen-loading model to: (1) estimate 

annual nitrogen deliverables to each subwatershed of Great South Bay, new York, (2) partition the 
30

contribution of nitrogen through disposal of wastewater, use of fertilizers, and atmospheric deposition 

on land, (3) estimate nitrogen transport through major land-use categories (natural vegetation, wetlands, 

agriculture, tuft, impervious surfaces), (4) calculate nitrogen retention within each subwatershed, and (5) 

model land-derived nitrogen loads from each subwatershed to Great South Bay. Wastewater-derived 

nitrogen was the dominant source to watershed surfaces followed by atmospheric deposition and 

fertilizer use. The authors concluded that as land cover shifts away from natural vegetation, 

eutrophication of Great South Bay will increase. 

Table 5. Summary of water resources and land-use and cover adaptations with corresponding literature citations.

Land 

Cover Land Use

Climate 

Variable Adaptation Reference 

Water Ecosystems Warming

Diversify and replicate 

habitats of special 

importance

Palmer and others, 

2009

Increase monitoring of 

populations at high risk

Palmer and others, 

2009

Enhance local monitoring 

and develop forecasts

Palmer and others, 

2009

Enhance technical assistance 

at local levels

Palmer and others, 

2009

Enhance protection of rivers 

(for example to enhance 

stormwater control and 

manage riparian corridors)

Palmer and others, 

2009

Use conjunctive 

groundwater/surface water 

management (for example 

drought protection areas, 

retrofit or remove dams)

Palmer and others, 

2009

Initiate restoration projects 

(for example to reconnect 

flood plains)

Palmer and others, 

2009

Hazard 

mitigation Warming

Increase off-channel storage 

basins or wetlands as ways 

to absorb high flows and 

maintain water quality

Palmer and others, 

2009

Agricultural General Change

More efficient water 

application methods (for 

example water pulsing) 

could decrease water needed 

for agriculture

Ojima and others, 

1999
31

4.2.4 Wetlands

Wetlands adaptations will be dependent on land use and climate feedbacks (table 6). For 

example, Voldseth and others (2009) suggest that adapting farming practices in response to climate 

change may influence wetland water levels and land use in the uplands affects the amount and routing 

of precipitation that reaches wetlands. Therefore, the type of faming practices employed could be used 

to shift wetland water levels toward drier or wetter conditions in response to climate change. 

Table 6. Summary of wetlands and land use and cover adaptations with corresponding literature citations.

Land Cover Land Use

Climate 

Variable Adaptation Reference 

Wetland Agricultural General Change

Conversion of high water use 

crops and unmanaged grassland 

to managed grassland produced 

higher wetland levels Voldseth and others, 2009

Land use in the upland affects 

the amount and routing of 

precipitation that reaches the 

wetland so the type of farming 

practices that are employed can 

be used to shift wetland water 

levels toward drier or wetter 

conditions in response to climate 

change Voldseth and others, 2009

Planting dense stands of grass to 

increase nesting cover in a 

wetland watershed can dry up 

wetlands to ensure that wetlands 

of all permanence types remain 

in the landscape after reversion 

to satisfy habitat needs Voldseth and others, 2009

4.4 LULC and Vegetation Conditions in the United States from 2009–11

Drought is one type of climate extreme that influences land use and impacts human livelihoods. 

The VegDRI (Vegetation Drought Response Index) data set produced by the USGS has been calculated 

for every 2-week period since 1989 (National Drought Mitigation Center, 2012). The index incorporates 

satellite data on vegetation productivity (Normalized Difference Vegetation Index, NDVI), climate data 

summarized as the Palmer Drought Severity Index (PDSI) and Standard Precipitation Index (SPI), and 

other biophysical variables such as soil available water capacity, land cover, irrigated lands, and 

elevation, to estimate drought-related vegetation stress for the conterminous United States (Brown and 

others, 2008). VegDRI expresses drought categories using the familiar Palmer Drought Severity Index 

legend. For example, figure 5 shows the VegDRI data for the two-week period ending July 25, 2011, 

indicating that much of the southern U.S. was in Moderate to Extreme Drought. Consequences of 

extensive drought may include crop failures, forest die-off, invasive plant species invasion, and water 

supply depletions. 
32

Figure 5. The Vegetation Drought Response Index (VegDRI) map for the 2-week period ending July 23, 2011, 

shows extensive droughts in the Southern United States (National Drought Mitigation Center, 2012). 

To investigate the interactions of the six conterminous NCA regions in the United States and 

selected groups of the NLCD land-cover classes, the VegDRI data were summarized for the growing 

seasons of 2009 through 2011. A growing season summary of VegDRI will embody longer-term 

drought effects but shorter-term droughts (for example, less than 2 months) may be missed. A summary 

of the percentage of the area for each land-cover group and region that is in the moderate to extreme 

drought categories is given in table 7. The average area of land in each of the land-cover categories is 

given by region in table 8 because there are substantial differences in the area of each of the categories. 

A more complete view is given in tables 9–14, which in addition to the “drought” category include 

information on the “normal” and “moist” conditions. In these tables, “Drought” includes the extreme 

drought, severe drought, and moderate drought categories “Normal” includes the mild drought, normal, 

and incipient moist spell categories and “Moist” includes the unusually moist spell, very moist spell, 

and extreme moist spell categories. The analysis was performed using provisional data with a pixel size 

of 1 square kilometer, so there is more uncertainty when the total areas are relatively small. For 

example, wetland areas in the Great Plains may be under-represented at this resolution, where many of 

the wetlands are small. Additionally, while a “developed” category is included in the land cover groups, 

identifying drought impacts across developed areas is not consistently performed in VegDRI models. 

The management of developed lands causes confusion in drought signals. The NLCD categories labeled 

Barren and Shore were excluded from this analysis because the VegDRI was not designed to evaluate 

these categories (54,957 km2

 nationally). 
33

Table 7. Percentage of land in the National Climate Assessment (NCA) regions and by groups of National Land 

Cover Database (NLCD) land-cover classes that experienced moderate to extreme drought for the years 2009 

to 2011 (original analysis). 

Land cover Year Northeast Southeast Midwest Great Plains Northwest Southwest 

Developed 2009 6.7 11.2 6.8 17.9 24.4 22.7

Developed 2010 4.2 8.8 4.8 4.3 6.4 8.4

Developed 2011 2.4 31.5 3.7 50.8 2.8 12

Forest 2009 3.8 5.8 13.9 15.2 24.3 23.3

Forest 2010 5 10 5.6 11.3 12.2 11.9

Forest 2011 1 26.2 4.8 29.9 6.4 18.7

Shrub/Scrub 2009 1.5 8.1 12.2 18.1 15.8 23.3

Shrub/Scrub 2010 4.3 11.8 5.9 4.5 17.9 11.1

Shrub/Scrub 2011 1.2 45.2 8.8 48.5 2.4 25.7

Grass/Pasture/Hay 2009 3.5 6.8 4.3 9.4 18.6 16

Grass/Pasture/Hay 2010 5 9.5 2 3.4 14.4 8

Grass/Pasture/Hay 2011 1.3 22.1 2.3 22.5 3.4 39.7

Cultivated crops 2009 4.3 10.7 6.1 11.5 12.8 16.8

Cultivated crops 2010 5.2 18.3 2.8 2.5 16.7 5.8

Cultivated crops 2011 3.1 37 3.1 21.4 1.8 17.3

Wetlands 2009 3 15.6 14.2 18.1 21 15.5

Wetlands 2010 4.7 15.2 5.7 12.8 14.5 11.8

Wetlands 2011 2.3 48.8 5 62.4 3.3 15

Table 8. Areas of land (square kilometers) in the land-cover groups and National Climate Assessment (NCA) 

regions during the growing season (original analysis).

Land cover group Northeast Southeast Midwest

Great 

Plains Northwest Southwest Total

Developed 29,424 54,489 43,434 25,347 7,896 20,046 180,636

Forest 381,616 576,589 352,536 213,895 248,853 281,702 2,055,192

Shrub/Scrub 4,122 22,780 692 425,991 207,433 743,434 1,404,451

Grass/Pasture/Hay 46,290 156,868 130,613 911,984 36,705 201,615 1,484,075

Cultivated crops 27,317 162,990 544,719 450,627 60,256 74,322 1,320,230

Wetlands 14,056 141,715 57,866 27,902 3,021 2,612 247,172

Total 502,825 1,115,431 1,129,860 2,055,746 564,164 1,323,731 6,691,756
34

Table 9. Condition of major land-cover types in the Northeast National Climate Assessment (NCA) region for 

2009–11(original analysis).

Land Cover Class Year Drought Normal Moist 

Developed 2009 6.66 84.08 9.26

Developed 2010 4.23 78.33 17.45

Developed 2011 2.37 59.62 38.01

Forest 2009 3.78 87.2 9.02

Forest 2010 5.04 87.43 7.53

Forest 2011 1.02 46.26 52.72

Shrub/Scrub 2009 1.49 85.16 13.36

Shrub/Scrub 2010 4.34 89.25 6.41

Shrub/Scrub 2011 1.23 34.11 64.67

Grass/Pasture/Hay 2009 3.47 90.33 6.2

Grass/Pasture/Hay 2010 5.02 88.6 6.37

Grass/Pasture/Hay 2011 1.27 48.39 50.33

Cultivated crops 2009 4.32 88.52 7.16

Cultivated crops 2010 5.2 83.17 11.64

Cultivated crops 2011 3.12 52.64 44.24

Wetlands 2009 2.99 81.07 15.94

Wetlands 2010 4.69 75.18 20.14

Wetlands 2011 2.27 49.73 48.01

Table 10. Condition of major land-cover types in the Southeast National Climate Assessment (NCA) region for 

2009–11 (original analysis).

Land Cover Class Year Drought Normal Moist 

Developed 2009 11.15 80.43 8.43

Developed 2010 8.83 75.19 15.98

Developed 2011 31.54 61.79 6.67

Forest 2009 5.75 83.64 10.61

Forest 2010 10.03 78.66 11.31

Forest 2011 26.15 65.53 8.32

Shrub/Scrub 2009 8.07 81.29 10.64

Shrub/Scrub 2010 11.76 74.73 13.52

Shrub/Scrub 2011 45.2 52.41 2.4

Grass/Pasture/Hay 2009 6.79 82.7 10.52

Grass/Pasture/Hay 2010 9.45 77.35 13.2

Grass/Pasture/Hay 2011 22.05 66.51 11.44

Cultivated crops 2009 10.72 78.25 11.03

Cultivated crops 2010 18.29 73.48 8.22

Cultivated crops 2011 37.02 55.35 7.63

Wetlands 2009 15.63 77.61 6.76

Wetlands 2010 15.22 74.2 10.57

Wetlands 2011 48.75 49.38 1.87
35

Table 11. Condition of major land-cover types in the Midwest National Climate Assessment (NCA) region for 2009–

11 (original analysis).

Land Cover Class Year Drought Normal Moist 

Developed 2009 6.75 72.56 20.69

Developed 2010 4.76 79.38 15.86

Developed 2011 3.7 60.25 36.05

Forest 2009 13.95 76.91 9.14

Forest 2010 5.62 80.81 13.58

Forest 2011 4.78 74.17 21.06

Shrub/Scrub 2009 12.18 82.12 5.7

Shrub/Scrub 2010 5.94 84.36 9.7

Shrub/Scrub 2011 8.82 80.43 10.75

Grass/Pasture/Hay 2009 4.33 76.5 19.18

Grass/Pasture/Hay 2010 2 61.27 36.73

Grass/Pasture/Hay 2011 2.29 78.42 19.29

Cultivated crops 2009 6.15 78.17 15.69

Cultivated crops 2010 2.77 67.59 29.64

Cultivated crops 2011 3.14 72.9 23.97

Wetlands 2009 14.21 74.73 11.07

Wetlands 2010 5.74 80.86 13.4

Wetlands 2011 4.99 77.96 17.05

Table 12. Condition of major land-cover types in the Great Plains National Climate Assessment (NCA) region for 

2009–11 (original analysis). 

Land Cover Class Year Drought Normal Moist 

Developed 2009 17.92 75.12 6.96

Developed 2010 4.27 77.53 18.2

Developed 2011 50.75 45.39 3.87

Forest 2009 15.2 77.96 6.83

Forest 2010 11.27 80.01 8.72

Forest 2011 29.92 56.75 13.33

Shrub/Scrub 2009 18.14 75.18 6.69

Shrub/Scrub 2010 4.53 75.29 20.17

Shrub/Scrub 2011 48.55 38.17 13.28

Grass/Pasture/Hay 2009 9.39 79.71 10.9

Grass/Pasture/Hay 2010 3.43 74.46 22.11

Grass/Pasture/Hay 2011 22.52 53.17 24.31

Cultivated crops 2009 11.45 77.49 11.06

Cultivated crops 2010 2.53 69.89 27.58

Cultivated crops 2011 21.44 58.35 20.21

Wetlands 2009 18.1 75.36 6.54

Wetlands 2010 12.79 74.08 13.13

Wetlands 2011 62.39 29.83 7.79
36

Table 13. Condition of major land-cover types in the Northwest National Climate Assessment (NCA) region for 

2009–11 (original analysis).

Land Cover Class Year Drought Normal Moist 

Developed 2009 24.37 73.59 2.03

Developed 2010 6.43 85.76 7.81

Developed 2011 2.77 69.92 27.31

Forest 2009 24.25 72.48 3.27

Forest 2010 12.2 80.71 7.09

Forest 2011 6.38 73.79 19.84

Shrub/Scrub 2009 15.81 78.87 5.33

Shrub/Scrub 2010 17.92 78.5 3.57

Shrub/Scrub 2011 2.41 72.97 24.62

Grass/Pasture/Hay 2009 18.6 76.29 5.1

Grass/Pasture/Hay 2010 14.35 81.22 4.43

Grass/Pasture/Hay 2011 3.41 73.13 23.46

Cultivated crops 2009 12.83 79.17 8

Cultivated crops 2010 16.7 78.58 4.72

Cultivated crops 2011 1.84 70.56 27.59

Wetlands 2009 20.99 73.24 5.77

Wetlands 2010 14.45 81.03 4.52

Wetlands 2011 3.26 72.77 23.97

Table 14. Condition of major land-cover types in the Southwest National Climate Assessment (NCA) region for 

2009–11 (original analysis).

Land Cover Class Year Drought Normal Moist 

Developed 2009 22.68 74.06 3.26

Developed 2010 8.48 83.16 8.35

Developed 2011 12.02 68.83 19.15

Forest 2009 23.31 73.27 3.42

Forest 2010 11.94 80.51 7.55

Forest 2011 18.68 67.74 13.58

Shrub/Scrub 2009 23.27 74.31 2.42

Shrub/Scrub 2010 11.05 82.81 6.14

Shrub/Scrub 2011 25.65 63.7 10.65

Grass/Pasture/Hay 2009 16.01 77.22 6.77

Grass/Pasture/Hay 2010 7.98 78.5 13.52

Grass/Pasture/Hay 2011 39.7 53.77 6.53

Cultivated crops 2009 16.78 73 10.23

Cultivated crops 2010 5.81 78.22 15.97

Cultivated crops 2011 17.32 62.16 20.52

Wetlands 2009 15.54 80.25 4.21

Wetlands 2010 11.75 80.78 7.47

Wetlands 2011 14.99 68.12 16.89
37

The percentages of the land area in drought reflect different impacts of drought for the analyzed 

land-cover types in the NCA regions. When the NCA regions are compared, results indicate that the 

drought in 2011 was particularly strong in the shrub/scrub areas of the Great Plains (48.5 percent of 

425,991 km2) (fig. 6) and the cultivated crops of the Southeast (37.0 percent of 162,990 km2) (fig. 7). 

The cultivated crops in 2011 in the Great Plains and Southwest regions also exhibited strong drought 

influences (21.4 percent and 17.3 percent, respectively) (table 7). The Grassland/pasture/hay category 

had major drought effects in 2011 in several regions (39.7 percent in the Southwest, 22.5 percent in the 

Great Plains, and 22.1 percent in the Southeast). Drought affected 48.8 percent of the 141,715 km2 of 

wetlands in the Southeast. Some categories experienced high percentages of land with drought 

influence, even though the total area of land was relatively small (for example, 62.4 percent of 27,902 

km2 of wetlands in the Great Plains). Summarizing drought effects across large regions will moderate 

the drought extremes that have occurred in smaller regions. For example, in 2011, the northern Great 

Plains were exceptionally wet while the southern tier experienced a record-breaking drought. 

Figure 6. Shrub/scrub drought trends in the Great Plains region from 2009–11 (original analysis).

0

10

20

30

40

50

60

70

80

Drought Normal Moist

Land cover that underwent drought condition, in percent

Great Plains Shrub/Scrub 2009 Annual

Great Plains Shrub/Scrub 2010 Annual

Great Plains Shrub/Scrub 2011 Annual

EXPLANATION
38

Figure 7. Cultivated crops drought trends in the Great Plains from 2009–11 (original analysis).

5.0 A Design for an Ongoing Assessment of Climate and Land-Use and LandCover Impacts

The goal of an assessment capability should focus on understanding and explaining how climate 

and LULC influence each other now and in the future. Understanding the connections and consequences 

of LULC and climate will require ongoing monitoring of LULC change, the translation of LULC into 

parameters relevant to meteorological and climatological processes, and the assessment of the real 

impacts that climate and LULC have on each other. An ongoing LULC climate assessment should 

address the following: 

● What are the primary contemporary trends in land use and land cover that affect, or are affected 

by, weather and climate?

● Of these trends, which sectors and regions are most affected by weather and climate variability? 

● What land uses and regions are most vulnerable to climate change?

● How are land-use practices adapting to climate change?

Assessment capabilities should include the means to evaluate the interactions of land use and 

management with climate change in a way that will help decision makers mitigate or adapt to the 

changes. This would require general principles on how the management may need to adapt in the 

context of changing climate rather than working from implicit assumptions on static climatic conditions 

(West and others, 2009). In particular, land managers and others may need to assess whether changes in 

climate would push advantageous LULC conditions beyond the point that they provide the necessary 

goods and services. Future LULC management strategies may require methods that incorporate an

evaluation of how climate and LULC change effects can combine to influence a wide range of social 

and economic benefits as well as ecological factors such as the migration of species, shifting mosaics of 

wetlands, and disturbances on climate-biology relations. The mechanisms of ecological response need to 

be incorporated into the design of the monitoring systems (Beever and Woodward, 2011b). 

0

10

20

30

40

50

60

70

80

Drought Normal Moist

Land cover that underwent drought condition, in percent

Great Plains Cultivated Crops 2009 Annual

Great Plains Cultivated Crops 2010 Annual

Great Plains Cultivated Crops 2011 Annual

EXPLANATION
39

5.1 Assessment Research and Development Needs

In order to address these questions, research and development may need to focus on a series of 

objectives that ensure assessment results are credible and relevant. Some specific foundational 

objectives for an ongoing assessment capability include the following: 

1. Improve the understanding of the connections between LULC and weather and climate. For 

effective modeling and assessment of climate and LULC forcings and feedbacks, research 

should be carried out that:

a. Improves the current understanding of how LULC and atmospheric interactions are 

linked at local to global scales. 

b. Validates these connections through an analysis of the historical record. Past changes 

could be identified in LULC that are attributable to changes in climate in order to project 

future changes in LULC that could result from changes in climate. 

Both the climate system and the human activities that result in LULC changes are complex 

systems that can only be observed with limited direct observations. For understanding both systems and 

how they interact, it is necessary to use models. Currently, many of the modeling systems are run to 

simulate the climate system with assumptions on how land-cover patterns will change with time

(Brovkin et al., 2006) or model land-cover change with assumptions on how climate will change with 

time (Sohl and Sayler, 2008). A current research objective is to better couple these models so that 

feedback between the systems can be incorporated. 

Any given model may be most appropriate for a limited range in space and time; so it may be 

necessary to use a suite of models to fully test system behaviors. The models are calibrated using 

observational data in historical periods, and the calibrated models may be used to project into the future 

(with increasing uncertainty through time). The ultimate purpose for the data collection and model 

simulations is to inform decision makers and the public about anticipated impacts on human and 

ecological systems (including agricultural, forest, wildlife, and human communities), so that activities to 

mitigate and adapt to the changes can be planned and undertaken. 

In order to provide input to climate and meteorological models, LULC forecast models must be 

spatially explicit, provide a means to parameterize key land-atmosphere interactions, and provide 

scenario-based forecasts for 50–100 years. There are several forecast models in use that can be used to 

project regional to national LULC patterns into the future (for example, FORE–SCE by Sohl and others, 

2010 that is being used for the USGS LandCarbon study on carbon management opportunities), but 

none of the models provide more than rudimentary handling of climate-model parameters. The most 

viable future LULC projections will be those based on well-defined and vetted scenarios. Current 

Federal agency scenario research has focused on using the IPCC SRES storylines to set the stage for 

downscaled land-change forecasts in the United States. The EPA Global Change Research Program's 

ICLUS project has established scenarios broadly consistent with the global-scale IPCC SRES storylines 

of population growth and economic development (U.S. Environmental Protection Agency, 2009). The 

EPA ICLUS forecast provides spatially explicit maps of housing density and the expansion of 

impervious surfaces based on SRES storylines. 

Because the SRES storylines were developed for use by climate change modelers to develop 

projections of future climate, they represent a reasonable starting point for a LULC climate assessment

in the United States. The USGS has also used SRES storylines to establish a nationally consistent 

library of future land change scenarios for use in addressing biological carbon sequestration 

opportunities (Zhu and others, 2010). The USGS effort addresses all major land-cover types found in 

the conterminous United States and is being developed for the ecoregions of the country. The SRES 
40

storylines provide the broad-level boundary conditions in the United States, and historical land-cover 

trends are used to establish the basis for future regional land changes. 

Improvements in land-change forecasts should benefit from the recently commissioned National 

Research Council (NRC) study on the Needs and Research for Land-Change Modeling, which is an 

important step toward improving future LULC forecasts. This study was recommended by the USGCRP 

Land Use Interagency Working group based on the LULC change science priorities specified in the 

2003 CCSP science strategy. The NRC study should provide a thorough review of the present status of 

spatially explicit land-change modeling approaches and describe future data and research needs so that 

model outputs can better assist the science, policy, and decision-support communities. 

2. Improve the coupling of LULC states and conditions within meteorological and climatological 

models. This will require:

a. The translation of LULC variables into quantitative parameters that directly relate to the 

physics and chemistry connected with the exchange of energy, water, and momentum 

between the land surface and the atmosphere. 

b. The ongoing development of multi-resolution LULC parameters needed to improve the 

accuracy of simulation models forecasts. LULC model parameters should become more 

current and accessible. This involves the ongoing development of Climate Data Records and 

Essential Climate Variables—data sets based on international standards that ensure relevant, 

stable measures needed to understand climate and climate impacts are developed (Global 

Climate Observing system, 2010).

c. Efforts to couple climate and LULC forecast models so that the dynamics of each component 

are part of the modeling process. 

d. Model inter-comparability studies should be used to determine strengths and weakness of 

different models and modeling approaches. 

Over the years, significant progress has been made in model design (physics and chemistry) that 

can address LULC changes and their interactions with weather and climate. Subsequently, both regional 

and global-scale modeling efforts have been undertaken to determine the impacts and interactions. 

However, it is evident that experimental design and modeling capabilities need further improvement. 

A number of in-situ and remote weather and climate observation platforms are currently 

available that can be used to identify signals of impacts of LULC change on atmospheric data. These 

include: U.S. Climate Reference Network (USCRN), new U.S. Regional Climate Reference Network

(USRCRN), and regional mesonets. The latter two could be excellent platforms for regional and localscale signals. In addition, satellite data can be used with the in-situ observations when available.

The International Global Energy and Water Cycle Experiment (GEWEX) is an example of a 

large collaborative research campaign in which many scientific organizations interact to achieve broad 

and integrative scientific goals. One such goal is to understand how land surface hydrology influences 

water availability and security (Trenbreth, 2011). One element of reaching the goal is to account for 

realistic land-surface complexity, including human influences such as land-use change and urbanization. 

Water quality, including water temperature and nutrient loadings, is affected by industrial and power 

plant use (National Research Council, 2001). The availability of water for human use and ecological 

systems will be affected by ecosystem responses to projected changing climate. Extremes of weather 

can cause water systems to be vulnerable, and good management and governance can increase 

resilience.

A range of multiscale geospatial land-cover-related datasets would cover the range of analysis 

functions—for example, model parameterization, land-condition monitoring, and impact evaluation.
41

Earth observations from global daily polar orbiter instruments such as the NOAA Advanced Very High 

Resolution Radiometers (AVHRR), NASA Moderate Resolution Imaging Spectroradiometer (MODIS) 

and NASA–NOAA Visible Infrared Imager Radiometer Suite (VIIRS) can provide current land 

parameters used in meteorological models (for example, surface albedo, surface temperature, leaf area 

index, and land cover). The current MODIS land products provide an important source for parameter 

datasets (Justice and others, 2002). These same data can also be used to monitor weekly changes in 

vegetation condition. The VegDRI product used earlier in this report to evaluate 2009–11 drought 

impacts on land cover is an example of a global daily imaging land condition product (Brown and 

others, 2008). Validation must be a component of all data activities.

Higher resolution land-cover characteristics and land-cover-change data are needed to identify 

and evaluate the specific cover types that are affected by weather and climate variability. Landsat-scale 

(30 m resolution) such as the NLCD dataset is suited to this application. However, an operational 

assessment would require land-cover information at a more frequent interval than NLCD. The planned 

USFS-USGS Land Cover Monitoring System concept that would provide Landsat-scale annual change 

data is a stronger, potential long-term candidate. Finally, land-use data are needed to understand impacts 

and mitigation opportunities. Sectoral products, such as Forest Inventory and Analysis data, are useful, 

but the absence of spatially explicit national land-use data is an issue. 

New data sources and analysis techniques that have been applied to land-cover analysis possibly 

could be extended to incorporate time series of both land surface and climate observations and then

could be used to analyze the interactions of LULC and climate (Knorn and others, 2009; Huang and 

others, 2010; Kennedy and others, 2010; Roy and others, 2010). Knowledge of such interactions could 

be used to assess the feedbacks between the land cover and climate systems and incorporated into the 

next generation of coupled LULC and climate models. 

3. Increase the understanding of the relations between climate and LULC impacts. This would 

require understanding how: 

a. Weather and climate variables differentially affect the different LULC types.

b. Different landscape variables (for example, ecoregions, topography, and land ownership) 

modify these relations.

c. LULC and ecosystems recover after disturbance events.

Specialties under various academic disciplines study LULC changes and their interactions with 

the weather and climate. Many of these groups have started to increase scholarly communications 

among themselves, which has enhanced the flow of knowledge in the recent years. However, further 

concerted efforts to expand these collaborations would provide maximum benefit to society. 

4. An assessment capability is needed that provides ongoing information on the impacts of weather 

conditions and climate trends on LULC. Reporting should focus on providing a clear 

understanding of the economic, social, and ecological impacts, and on the ways LULC changes 

in response to events and trends. Specific considerations include:

a. Distinguishing between short- and long-term climate patterns and their impacts on LULC.

This will allow decision makers at all levels to determine mitigation or coping mechanisms.

b. Establishing the capability to evaluate the impacts of extreme weather events on LULC, 

addressing the stresses on economic, social, and ecological systems. 

c. Providing an explanation on how local and regional climate and LULC impacts affect 

national and global economic, social, and ecological systems. 
42

d. Providing forecasts on the potential land-use impacts because of weather events and climate 

trends. These should include information on mitigation options and coping mechanisms. 

e. Describing how LULC changes related to weather and climate affect social and economic 

systems (for example, impacts on forest productivity, shortened/lengthened growing seasons 

for crop production). This should include the impacts of climate-induced LULC change on 

people's livelihoods. 

Regions that are currently experiencing rapid LULC changes and other ecologically sensitive 

and vulnerable areas could further be considered for weather and climate monitoring to better 

understand the pathways, mechanisms, and processes related to LULC change impacts on the 

atmosphere. Specifically, in addition to existing observation platforms, establishing weather and climate 

monitoring capabilities needs to be considered in some of the above noted areas. This effort could be 

completed in phases. 

The combination of LULC and climate may contribute to assessment of impacts of the capability 

of LULC systems to provide future goods and services. In order to detect evidence of land degradation, 

reference conditions can be used to determine deviation from a sustainable state. For example, Herrick 

and others (2010) show how data from the National Resources Inventory, along with remotely sensed 

imagery, soil surveys, and climate models, can be used to stratify landscapes in a way that allows the 

definition of reference conditions based on the long-term ecological potential of the land. Deviation 

from the reference conditions indicated possible land degradation. 

Although it is difficult to quantify the societal benefits of LULC practices and conditions, some 

research is providing a basis to understand these benefits by using more traditional measures of 

economic output. For example, Nelson and others (2009) have developed a modeling tool to predict 

changes in ecosystem services, biodiversity conservation, and commodity production levels and have 

applied the tool to the Willamette Basin in Oregon.

5. Maintaining an outreach capability that ensures rapid access to assessment inputs and outputs, 

interpretation of the results, and technical support for decision makers and scientists engaged in 

climate and LULC issues.

The communication of results and applications services used in assessing LULC and climate is 

critical to ensure the effective use of data, models, and analyses. This could include the provision of 

decision support tools tailored to the particular needs of different stakeholder groups. 

5.2 Institutional Capabiltiies

An ongoing assessment of LULC and weather and climate connections would require the 

participation of a number of USGCRP members as well as academic and industry researchers. Key 

agency participants and contributions could include the following: 

EPA – land change scenario information, 

NASA – land surface parameterizations derived from remote sensing, land cover and 

atmospheric research , 

NOAA – meteorological and climatological expertise, atmospheric observations, in situ 

instrument records, 

NSF – research support, participation of key observation networks such as NEON, 

USDA – a wide range of datasets and assessments, including USFS Forest Inventory and 

Analysis and forest cover products, Natural Resources Conservation Service National Resources 

Inventory data, National Agricultural Statistical Service annual crop area and type data and agricultural 

census results, and 
43

USGS – land-cover map products, land-change scenarios, land-change modeling and forecasts, 

climate and land-use research. 

The integration of Federal capabilities will be challenging and will require a fresh approach. 

Beever and Woodward (2011a), for example, suggest that climate and land monitoring that 

effectively supports resource management might ideally be structured to address the actual spatial and 

temporal scales of relevant processes, rather than the artificial boundaries of individual landmanagement units. 

In summary, assessment and monitoring systems for understanding the changing relations 

between land use, land cover, and climate should make use of information from multiple scales of space 

and time. Field plot measurements, such as the FIA and NRI, flux towers for detailed understanding of 

carbon dynamics, soil surveys, and long-term ecological research sites are needed for detailed 

understanding of processes. Remotely sensed data, particularly the Landsat time series integrated with 

higher and lower resolution data, can provide information on the spatial magnitude and directions of 

change. Integration of these data sources in models allows bridging the gaps in observation across space 

and time, and allow simulation of processes that are not directly observable. Testing of multiple 

scenarios may make it possible to separate the influences of different processes (for example, land 

management compared to climate change or weather extremes) as they influence ecosystems and human 

activities. New methods of analysis, in which entire time series of images can be analyzed at once, 

provide new possibilities in classifying land-cover change. It is possible that the algorithms used in such 

analyses could be adapted to analyze joint time series of climate change, weather extremes, and landcover change to separate and investigate the interactions of these variables. Knowledge of such 

interactions could be coded into coupled models of the climate and LULC interactions could be used to 

forecast scenarios of future system behavior. Such forecasts could help identify critical weaknesses in 

existing planning for mitigation and adaptation. The assessment system should include continued 

contact with groups that represent decision makers for urban and regional planning, agricultural and 

forest land management, biodiversity conservation, and ecological research, so that the models are 

sensitive to the types of policy choices that will be needed in the future. The research should be 

coordinated with national and international campaigns that have complementary interests, such as 

National Ecological Observatory Network (NEON), Global Energy and Water Balance Experiment 

(GEWEX), and the Global Earth Observation System of Systems (GEOSS). 

The basic inputs needed for an operational LULC-climate assessment capability are available, 

but some inputs will require some improvements or changes in specifications in order to provide the 

timeliness and geographic coverage required. The real challenge will be the identification of a Federal 

host to lead the assessment process. NOAA, USDA, or the USGS are the logical candidates based on 

their mission objectives and current investments in climate and LULC.

6.0 Concluding Observations

Projected climate variability and change will challenge natural resources managers. Management 

challenges can be compromised when LULC change reduces, modifies, or complicates strategies for 

managing climate change. As a result, maintaining the societal and ecological benefits drawn from the 

Nation’s land resources—land cover and land use— requires an integrated understanding of the bidirectional links between LULC and weather and climate. That understanding will likely come from an 

assessment capability assembled from the various USGCRP agency activities related to LULC and 

climate change. In this report, identification of the foundations of such a capacity—the state of science 

and the availability of the basic elements that might be included in a monitoring and assessment 

activity—was begun. 
44

The foundation of climate-LULC understanding, the scientific investigations addressing those 

bidirectional links, is growing rapidly. Studies on the basic mechanics and processes governing the 

exchange of water and energy between the land surface and the atmosphere are maturing and being used 

in both experimental and operational forecasting. Years of land-atmosphere interaction research has led 

to significant maturity in the ability to analyze the role land cover plays in weather and climate 

formation. The contemporary land-cover parameters used in those models are also available. As a result, 

it is increasingly feasible to simulate local to regional LULC influences on weather and climate 

formation. The simulation of future LULC-climate connections is more complicated because most 

future LULC projections are not dynamically linked to climate and meteorology models. This is an area 

where more research and development are needed.

The national investments in Earth observations can provide the means to identify LULC stresses, 

to map the condition of LULC across the Nation, and determine how different regions are changing or 

adapting to different weather and climate conditions. Where there is considerable capacity to provide 

near real-time monitoring of LULC responses to climate, there is currently no systematic effort to 

monitor and evaluate those climate-driven LULC changes.

Considering the questions outlined in Section 5.0 that could drive a LULC-climate assessment 

capability, a general conclusion is that the basic elements needed for monitoring and assessment exist, 

though not necessarily in a format that is optimized for the assessment of LULC-climate impacts and 

feedbacks. The challenge will be the integration of capabilities, the enhancement of the different 

elements, and the maturing of assessment frameworks. Attention to the spatial and temporal scales of 

analysis, the geospatial framework for monitoring, assessing, and reporting LULC-climate issues, the 

detailed specifications and the validation of all inputs, outputs, and model assumptions would be 

needed. There are some obvious areas where improvements would be required. Most activities do not 

specifically address the issues occurring in Alaska, Hawaii, and the Territories of the United States. 

Most observational capabilities are more adept in monitoring croplands and forests rather than cities and 

rangelands. Efforts to integrate in situ and remotely sensed data would require more attention.

Improvements in model coupling would be required. Perhaps most important for LULC assessments is 

the improvement in geospatial representation of land-use practices. This is problematic because land-use 

inputs are needed to address local social and economic issues. The next steps are to move beyond 

independent case studies and a rich assortment of technical tools and data into a designed, integrated 

framework for ongoing national assessments. 

7.0 References

Adegoke, J.O., Pielke Sr., R.A., Eastman, J., Mahmood, R., and Hubbard, K.G., 2003, Impact of 

irrigation on midsummer surface fluxes and temperature under dry synoptic conditions—a regional 

atmospheric model study of the U.S. high plains: Monthly Weather Review, v. 131, no. 3, p. 556-564. 

(Also available online at http://dx.doi.org/10.1175/1520-0493(2003)131<0556:ioioms>2.0.co;2.)

Allard, J., and Carleton, A., 2010, Mesoscale associations between midwest land surface properties and 

convective cloud development in the warm season: Physical Geography, v. 31, no. 2, p. 107-136. 

(Also available online at http://dx.doi.org/10.2747/0272-3646.31.2.107.) 

Anthes, R.A., 1984, Enhancement of convective precipitation by mesoscale variations in vegetative 

covering in semiarid regions: Journal of Climate and Applied Meteorology, v. 23, no. 4, p. 541-554. 

(Also available online at http://dx.doi.org/10.1175/1520-0450(1984)023<0541:eocpbm>2.0.co;2.) 
45

Arnfield, A.J., 2003, Two decades of urban climate research—a review of turbulence, exchanges of 

energy and water, and the urban heat island: International Journal of Climatology, v. 23, no. 1, p. 1-

26. (Also available online at http://dx.doi.org/10.1002/joc.859.) 

Banner, E.B.K., Stahl, A.J., and Dodds, W.K., 2009, Stream discharge and riparian land use influence 

in-stream concentrations and loads of phosphorus from central plains watersheds: Environmental 

Management, v. 44, no. 3, p. 552-565. (Also available online at http://dx.doi.org/10.1007/s00267-009-

9332-6.) 

Barnston, A.G., and Schickedanz, P.T., 1984, The effect of irrigation on warm season precipitation in 

the southern Great Plains: Journal of Climate and Applied Meteorology, v. 23, no. 6, p. 865-888. 

(Also available online at http://dx.doi.org/10.1175/1520-0450(1984)023<0865:teoiow>2.0.co;2.) 

Barrett, T.M., and Gray, A.N., 2011, Potential of a national monitoring program for forests to assess 

change in high-latitude ecosystems: Biological Conservation, v. 144, no. 5, p. 1285-1294. (Also 

available online at http://dx.doi.org/10.1016/j.biocon.2010.10.015.) 

Beever, E.A., and Woodward, A., 2011, Design of ecoregional monitoring in conservation areas of 

high-latitude ecosystems under contemporary climate change: Biological Conservation, v. 144, no. 5, 

p. 1258-1269. (Also available online at http://dx.doi.org/10.1016/j.biocon.2010.06.022.) 

Beever, E.A., and Woodward, A., 2011, Ecoregional-scale monitoring within conservation areas, in a 

rapidly changing climate: Biological Conservation, v. 144, no. 5, p. 1255-1257. (Also available online 

at http://dx.doi.org/10.1016/j.biocon.2011.04.001.) 

Beltrán-Przekurat, A., Pielke Sr., R.A., Eastman, J.L., and Coughenour, M.B., 2011, Modelling the 

effects of land-use/land-cover changes on the near-surface atmosphere in southern South America: 

International Journal of Climatology. (Also available online at http://dx.doi.org/10.1002/joc.2346.) 

Biggs, T.W., Scott, C.A., Gaur, A., Venot, J.-P., Chase, T., and Lee, E., 2008, Impacts of irrigation and 

anthropogenic aerosols on the water balance, heat fluxes, and surface temperature in a river basin: 

Water Resources Research, v. 44, no. 12, p. W12415. (Also available online at 

http://dx.doi.org/10.1029/2008wr006847.) 

Blake, D., Brockett, P., Cox, S., and MacMinn, R., 2011, Longevity risk and capital markets—the 2009-

2010 update: North American Actuarial Journal, v. 15, no. 2, p. 141-149. (Also available online at 

http://www.soa.org/library/journals/north-american-actuarial-journal/2011/no-2/naaj-2011-vol15-

no2.aspx.) 

Blate, G.M., Joyce, L.A., Littell, J.S., McNulty, S.G., Millar, C.I., Moser, S.C., Neilson, R.P., 

O'Halloran, K., and Peterson, D.L., 2009, Adapting to climate change in United States national 

forests: Unasylva, v. 60, no. 231-232, p. 57-62. (Also available online at 

http://www.fao.org/docrep/011/i0670e/i0670e00.htm.) 

Bonan, G.B., 1997, Effects of land use on the climate of the United States: Climatic Change, v. 37, no. 

3, p. 449-486. (Also available online at http://dx.doi.org/10.1023/a:1005305708775.) 
46

Bonan, G.B., 2001, Observational evidence for reduction of daily maximum temperature by croplands 

in the midwest United States: Journal of Climate, v. 14, no. 11, p. 2430-2442. (Also available online 

at http://dx.doi.org/10.1175/1520-0442(2001)014<2430:oefrod>2.0.co;2.) 

Bonfils, C., and Lobell, D., 2007, Empirical evidence for a recent slowdown in irrigation-induced 

cooling: Proceedings of the National Academy of Sciences, v. 104, no. 34, p. 13582-13587. (Also 

available online at http://dx.doi.org/10.1073/pnas.0700144104.) 

Bradley, B.A., 2010, Assessing ecosystem threats from global and regional change—hierarchical 

modeling of risk to sagebrush ecosystems from climate change, land use and invasive species in 

Nevada, USA: Ecography, v. 33, no. 1, p. 198-208. (Also available online at 

http://dx.doi.org/10.1111/j.1600-0587.2009.05684.x.) 

Broussard, W., and Turner, R.E., 2009, A century of changing land-use and water-quality relationships 

in the continental US: Frontiers in Ecology and the Environment, v. 7, no. 6, p. 302-307. (Also 

available online at http://dx.doi.org/10.1890/080085.) 

Brovkin, V., Claussen, M., Driesschaert, E., Fichefet, T., Kicklighter, D., Loutre, M., Matthews, H., 

Ramankutty, N., Schaeffer, M., and Sokolov, A., 2006, Biogeophysical effects of historical land cover 

changes simulated by six Earth system models of intermediate complexity: Climate Dynamics, v. 26, 

no. 6, p. 587-600. (Also available online at http://dx.doi.org/10.1007/s00382-005-0092-6.) 

Brovkin, V., Sitch, S., Von Bloh, W., Claussen, M., Bauer, E., and Cramer, W., 2004, Role of land 

cover changes for atmospheric CO2 increase and climate change during the last 150 years: Global 

Change Biology, v. 10, no. 8, p. 1253-1266. (Also available online at 

http://dx.doi.org/10.1111/j.1365-2486.2004.00812.x.) 

Brown, J., Wardlow, B., Tadesse, T., Hayes, M., and Reed, B., 2008, The Vegetation Drought Response 

Index (VegDRI)—a new integrated approach for monitoring drought stress in vegetation: GIScience 

& Remote Sensing, v. 45, no. 1, p. 16-46. (Also available online at http://dx.doi.org/10.2747/1548-

1603.45.1.16.) 

Burow, K.R., Nolan, B.T., Rupert, M.G., and Dubrovsky, N.M., 2010, Nitrate in groundwater of the 

United States, 1991-2003: Environmental Science and Technology, v. 44, no. 13, p. 4988-4997. (Also 

available online at http://dx.doi.org/10.1021/es100546y.) 

Campra, P., Garcia, M., Canton, Y., and Palacios-Orueta, A., 2008, Surface temperature cooling trends 

and negative radiative forcing due to land use change toward greenhouse farming in southeastern 

Spain: Journal of Geophysical Research-Atmospheres, v. 113, no. D18, p. D18109. (Also available 

online at http://dx.doi.org/10.1029/2008jd009912.) 
47

Canadell, J.G., Le Quéré, C., Raupach, M.R., Field, C.B., Buitenhuis, E.T., Ciais, P., Conway, T.J., 

Gillett, N.P., Houghton, R.A., and Marland, G., 2007, Contributions to accelerating atmospheric CO2

growth from economic activity, carbon intensity, and efficiency of natural sinks: Proceedings of the 

National Academy of Sciences, v. 104, no. 47, p. 18866-18870. (Also available online at 

http://dx.doi.org/10.1073/pnas.0702737104.) 

Carleton, A.M., Arnold, D.L., Travis, D.J., Curran, S., and Adegoke, J.O., 2008, Synoptic circulation 

and land surface influences on convection in the midwest U.S. “corn belt” during the summers of 

1999 and 2000. Part I—composite synoptic environments: Journal of Climate, v. 21, no. 14, p. 3389-

3415. (Also available online at http://dx.doi.org/10.1175/2007jcli1578.1.) 

Carleton, A.M., Travis, D.J., Adegoke, J.O., Arnold, D.L., and Curran, S., 2008, Synoptic circulation 

and land surface influences on convection in the midwest U.S. “corn belt” during the summers of 

1999 and 2000. Part II—role of vegetation boundaries: Journal of Climate, v. 21, no. 15, p. 3617-

3641. (Also available online at http://dx.doi.org/10.1175/2007jcli1584.1.) 

Chen, J., Avise, J., Guenther, A., Wiedinmyer, C., Salathe, E., Jackson, R.B., and Lamb, B., 2009, 

Future land use and land cover influences on regional biogenic emissions and air quality in the United 

States: Atmospheric Environment, v. 43, no. 36, p. 5771-5780. (Also available online at 

http://dx.doi.org/10.1016/j.atmosenv.2009.08.015.) 

Choi, W., 2008, Catchment-scale hydrological response to climate-land-use combined scenarios—a case 

study for the Kishwaukee River basin, Illinois: Physical Geography, v. 29, no. 1, p. 79-99. (Also 

available online at http://dx.doi.org/10.2747/0272-3646.29.1.79.) 

Christy, J.R., Norris, W.B., Redmond, K., and Gallo, K.P., 2006, Methodology and results of 

calculating central California surface temperature trends—evidence of human-induced climate 

change?: Journal of Climate, v. 19, no. 4, p. 548-563. (Also available online at 

http://dx.doi.org/10.1175/jcli3627.1.) 

Chuang, T.W., Hildreth, M.B., Vanroekel, D.L., and Wimberly, M.C., 2011, Weather and land cover 

influences on mosquito populations in Sioux Falls, South Dakota: Journal of Medical Entomology, v. 

48, no. 3, p. 669-679. (Also available online at http://dx.doi.org/10.1603/ME10246.) 

Claussen, M., Brovkin, V., and Ganopolski, A., 2001, Biogeophysical versus biogeochemical feedbacks 

of large-scale land cover change: Geophysical Research Letters, v. 28, no. 6, p. 1011-1014. (Also 

available online at http://dx.doi.org/10.1029/2000gl012471.) 

Climate Change Science Program, 2003, Strategic plan for the U.S. climate change science program: 

Washington, D.C., 202 p. (Also available online at http://purl.access.gpo.gov/GPO/LPS64573.) 

Costa, M.H., Yanagi, S.N.M., Souza, P.J.O.P., Ribeiro, A., and Rocha, E.J.P., 2007, Climate change in 

Amazonia caused by soybean cropland expansion, as compared to caused by pastureland expansion: 

Geophysical Research Letters, v. 34, no. 7, p. L07706. (Also available online at 

http://dx.doi.org/10.1029/2007gl029271.) 
48

Cuo, L., Lettenmaier, D.P., Alberti, M., and Richey, J.E., 2009, Effects of a century of land cover and 

climate change on the hydrology of the Puget Sound basin: Hydrological Processes, v. 23, no. 6, p. 

907-933. (Also available online at http://dx.doi.org/10.1002/hyp.7228.) 

Dale, V.H., 1997, The relationship between land-use change and climate change: Ecological 

Applications, v. 7, no. 3, p. 753-769. (Also available online at http://dx.doi.org/10.1890/1051-

0761(1997)007[0753:trbluc]2.0.co;2.) 

Dale, V.H., Efroymson, R.A., and Kline, K.L., 2011, The land use-climate change-energy nexus: 

Landscape Ecology, v. 26, no. 6, p. 755-773. (Also available online at 

http://dx.doi.org/10.1007/s10980-011-9606-2.) 

Davin, E.L., and de Noblet-Ducoudré, N., 2010, Climatic impact of global-scale deforestation—

radiative versus nonradiative processes: Journal of Climate, v. 23, no. 1, p. 97-112. (Also available 

online at http://dx.doi.org/10.1175/2009jcli3102.1.) 

Davin, E.L., de Noblet-Ducoudré, N., and Friedlingstein, P., 2007, Impact of land cover change on 

surface climate—relevance of the radiative forcing concept: Geophysical Research Letters, v. 34, no. 

13, p. L13702. (Also available online at http://dx.doi.org/10.1029/2007gl029678.) 

DeAngelis, D., 2010, Foreword to the Siberian lakes special issue: Aquatic Ecology, v. 44, no. 3, p. 

479-479. (Also available online at http://dx.doi.org/10.1007/s10452-010-9337-5.) 

Defries, R.S., Bounoua, L., and Collatz, G.J., 2002, Human modification of the landscape and surface 

climate in the next fifty years: Global Change Biology, v. 8, no. 5, p. 438-458. (Also available online 

at http://dx.doi.org/10.1046/j.1365-2486.2002.00483.x.) 

Delgado, J.A., Groffman, P.M., Nearing, M.A., Goddard, T., Reicosky, D., Lal, R., Kitchen, N.R., Rice, 

C.W., Towery, D., and Salon, P., 2011, Conservation practices to mitigate and adapt to climate 

change: Journal of Soil and Water Conservation, v. 66, no. 4, p. 118A-129A. (Also available online at 

http://dx.doi.org/10.2489/jswc.66.4.118A.) 

DellaSala, D.A., Karr, J.R., and Olson, D.M., 2011, Roadless areas and clean water: Journal of Soil and 

Water Conservation, v. 66, no. 3, p. 78A-84A. (Also available online at 

http://dx.doi.org/10.2489/jswc.66.3.78A.) 

Douglas, E.M., Beltrán-Przekurat, A., Niyogi, D., Pielke Sr., R.A., and Vörösmarty, C.J., 2009, The 

impact of agricultural intensification and irrigation on land–atmosphere interactions and Indian 

monsoon precipitation—a mesoscale modeling perspective: Global and Planetary Change, v. 67, no. 

1–2, p. 117-128. (Also available online at http://dx.doi.org/10.1016/j.gloplacha.2008.12.007.) 

Douglas, E.M., Niyogi, D., Frolking, S., Yeluripati, J.B., Pielke Sr., R.A., Niyogi, N., Vörösmarty, C.J., 

and Mohanty, U.C., 2006, Changes in moisture and energy fluxes due to agricultural land use and 

irrigation in the Indian monsoon belt: Geophysical Research Letters, v. 33, no. 14, p. L14403. (Also 

available online at http://dx.doi.org/10.1029/2006gl026550.) 
49

Drummond, M.A., Auch, R.F., Karstensen, K.A., Sayler, K.L., Taylor, J.L., and Loveland, T.R., 2012, 

Land change variability and human–environment dynamics in the United States Great Plains: Land 

Use Policy, v. 29, no. 3, p. 710-723. (Also available online at 

http://dx.doi.org/10.1016/j.landusepol.2011.11.007.) 

Drummond, M.A., and Loveland, T.R., 2010, Land-use pressure and a transition to forest-cover loss in 

the eastern United States: BioScience, v. 60, no. 4, p. 286-298. (Also available online at 

http://dx.doi.org/10.1525/bio.2010.60.4.7.) 

Duniway, M.C., Herrick, J.E., Pyke, D.A., and Toledo P, D., 2010, Assessing transportation 

infrastructure impacts on rangelands—test of a standard rangeland assessment protocol: Rangeland 

Ecology and Management, v. 63, no. 5, p. 524-536. (Also available online at 

http://dx.doi.org/10.2111/REM-D-09-00176.1.)

Easterling, W.E., 1996, Adapting North American agriculture to climate change in review: Agricultural 

and Forest Meteorology, v. 80, no. 1, p. 1-53. (Also available online at 

http://dx.doi.org/10.1016/0168-1923(95)02315-1.) 

Eglin, T., Ciais, P., Piao, S.L., Barre, P., Bellassen, V., Cadule, P., Chenu, C., Gasser, T., Koven, C., 

Reichstein, M., and Smith, P., 2010, Historical and future perspectives of global soil carbon response 

to climate and land-use changes: Tellus, Series B: Chemical and Physical Meteorology, v. 62, no. 5, p. 

700-718. (Also available online at http://dx.doi.org/10.1111/j.1600-0889.2010.00499.x.) 

Fall, S., Diffenbaugh, N.S., Niyogi, D., Pielke Sr., R.A., and Rochon, G., 2010, Temperature and 

equivalent temperature over the United States (1979-2005): International Journal of Climatology, v. 

30, no. 13, p. 2045-2054. (Also available online at http://dx.doi.org/10.1002/joc.2094.) 

Feddema, J.J., Oleson, K.W., Bonan, G.B., Mearns, L.O., Buja, L.E., Meehl, G.A., and Washington, 

W.M., 2005, The importance of land-cover change in simulating future climates: Science, v. 310, no. 

5754, p. 1674-1678. (Also available online at http://dx.doi.org/10.1126/science.1118160.) 

Ford, C.R., Laseter, S.H., Swank, W.T., and Vose, J.M., 2011, Can forest management be used to 

sustain water-based ecosystem services in the face of climate change?: Ecological Applications, v. 21, 

no. 6, p. 2049-2067. (Also available online at http://dx.doi.org/10.1890/10-2246.1.) 

Fry, J.A., Xian, G., Jin, S., Dewitz, J.A., Homer, C.G., Yang, L., Barnes, C.A., Herold, N.D., and 

Wickham, J.D., 2011, Completion of the 2006 National Land Cover Database for the conterminous 

United States: Photogrammetric Engineering and Remote Sensing, v. 77, no. 9, p. 858-864. (Also 

available online at http://asprs.org/Photogrammetric-Engineering-and-Remote-Sensing/PE-RSJournals.html.) 

Gallant, A.L., Sadinski, W., Roth, M.F., and Rewa, C.A., 2011, Changes in historical Iowa land cover 

as context for assessing the environmental benefits of current and future conservation efforts on 

agricultural lands: Journal of Soil and Water Conservation, v. 66, no. 3, p. 67A-77A. (Also available 

online at http://dx.doi.org/10.2489/jswc.66.3.67A.) 
50

Gameda, S., Qian, B., Campbell, C.A., and Desjardins, R.L., 2007, Climatic trends associated with 

summerfallow in the Canadian prairies: Agricultural and Forest Meteorology, v. 142, no. 2–4, p. 170-

185. (Also available online at http://dx.doi.org/10.1016/j.agrformet.2006.03.026.) 

Geerts, B., 2002, On the effects of irrigation and urbanisation on the annual range of monthly-mean 

temperatures: Theoretical and Applied Climatology, v. 72, no. 3, p. 157-163. (Also available online at 

http://dx.doi.org/10.1007/s00704-002-0683-7.) 

Gershunov, A., Cayan, D.R., and Iacobellis, S.F., 2009, The great 2006 heat wave over California and 

Nevada—signal of an increasing trend: Journal of Climate, v. 22, no. 23, p. 6181-6203. (Also 

available online at http://dx.doi.org/10.1175/2009jcli2465.1.) 

Gillespie, A.J.R., 1999, Rationale for a national annual forest inventory program: Journal of Forestry, v. 

97, no. 12, p. 16-20. (Also available online at 

http://www.ingentaconnect.com/content/saf/jof/1999/00000097/00000012/art00007.) 

Global Climate Observing System (GCOS), 2010, Implementation plan for the global observing system 

for climate in support of the UNFCCC: Geneva, Switzerland, World Meteorological Organization, 

180 p. (Also available online at http://www.wmo.int/pages/prog/gcos/index.php?name=Publications.) 

Gordon, L.J., Steffen, W., Jönsson, B.F., Folke, C., Falkenmark, M., and Johannessen, Å., 2005, Human 

modification of global water vapor flows from the land surface: Proceedings of the National Academy 

of Sciences of the United States of America, v. 102, no. 21, p. 7612-7617. (Also available online at 

http://dx.doi.org/10.1073/pnas.0500208102.) 

Guo, L.B., and Gifford, R.M., 2002, Soil carbon stocks and land use change—a meta analysis: Global 

Change Biology, v. 8, no. 4, p. 345-360. (Also available online at http://dx.doi.org/10.1046/j.1354-

1013.2002.00486.x.) 

Hale, R.C., Gallo, K.P., and Loveland, T.R., 2008, Influences of specific land use/land cover 

conversions on climatological normals of near-surface temperature: Journal of Geophysical ResearchAtmospheres, v. 113, no. D14, p. D14113. (Also available online at 

http://dx.doi.org/10.1029/2007jd009548.) 

Hale, R.C., Gallo, K.P., Owen, T.W., and Loveland, T.R., 2006, Land use/land cover change effects on 

temperature trends at U.S. Climate Normals stations: Geophysical Research Letters, v. 33, no. 11, p. 

L11703. (Also available online at http://dx.doi.org/10.1029/2006gl026358.) 

Hallegatte, S., 2009, Strategies to adapt to an uncertain climate change: Global Environmental Change, 

v. 19, no. 2, p. 240-247. (Also available online at http://dx.doi.org/10.1016/j.gloenvcha.2008.12.003.) 

Hand, L.M., and Shepherd, J.M., 2009, An investigation of warm-season spatial rainfall variability in 

Oklahoma City—possible linkages to urbanization and prevailing wind: Journal of Applied 

Meteorology and Climatology, v. 48, no. 2, p. 251-269. (Also available online at 

http://dx.doi.org/10.1175/2008jamc2036.1.) 
51

Hanesiak, J.M., Raddatz, R.L., and Lobban, S., 2004, Local initiation of deep convection on the 

Canadian prairie provinces: Boundary-Layer Meteorology, v. 110, no. 3, p. 455-470. (Also available 

online at http://dx.doi.org/10.1023/B:BOUN.0000007242.89023.e5.) 

Herrick, J.E., Lessard, V.C., Spaeth, K.E., Shaver, P.L., Dayton, R.S., Pyke, D.A., Jolley, L., and 

Goebel, J.J., 2010, National ecosystem assessments supported by scientific and local knowledge: 

Frontiers in Ecology and the Environment, v. 8, no. 8, p. 403-408. (Also available online at 

http://dx.doi.org/10.1890/100017.) 

Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M., 2004, Development of a 2001 National Land 

Cover Database for the United States: Photogrammetric Engineering and Remote Sensing, v. 70, no. 

7, p. 829-840. (Also available online at http://asprs.org/Photogrammetric-Engineering-and-RemoteSensing/PE-RS-Journals.html.) 

Houghton, J.T., Jenkins, G.J., and Ephrums, J.J., 1990, Climate change—the IPCC scientific 

assessment: Cambridge, U.K., Cambridge University Press, 410 p. (Also available online at 

http://www.ipcc.ch/publications_and_data/publications_ipcc_first_assessment_1990_wg1.shtml#.T7T

56vWrRk4.) 

Howden, N.J.K., Burt, T.P., Worrall, F., Mathias, S., and Whelan, M.J., 2011, Nitrate pollution in 

intensively farmed regions—what are the prospects for sustaining high-quality groundwater?: Water 

Resources Research, v. 47, p. W00L02. (Also available online at 

http://dx.doi.org/10.1029/2011wr010843.) 

Howden, S.M., Soussana, J.F., Tubiello, F.N., Chhetri, N., Dunlop, M., and Meinke, H., 2007, Adapting 

agriculture to climate change: Proceedings of the National Academy of Sciences of the United States 

of America, v. 104, no. 50, p. 19691-19696. (Also available online at 

http://dx.doi.org/10.1073/pnas.0701890104.) 

Huang, C., Goward, S.N., Masek, J.G., Thomas, N., Zhu, Z., and Vogelmann, J.E., 2010, An automated 

approach for reconstructing recent forest disturbance history using dense Landsat time series stacks: 

Remote Sensing of Environment, v. 114, no. 1, p. 183-198. (Also available online at 

http://dx.doi.org/10.1016/j.rse.2009.08.017.) 

Imhoff, M.L., Zhang, P., Wolfe, R.E., and Bounoua, L., 2010, Remote sensing of the urban heat island 

effect across biomes in the continental USA: Remote Sensing of Environment, v. 114, no. 3, p. 504-

513. (Also available online at http://dx.doi.org/10.1016/j.rse.2009.10.008.) 

Intergovernmental Panel on Climate Change (IPCC), 2007, Summary for policymakers, in Metz, B., ed., 

Climate change 2007—mitigation, contribution of Working Group III to the Fourth Assessment 

Report of the Intergovernmental Panel on Climate Change: Cambridge, U.K., Cambridge University 

Press, p. 1-23. (Also available online at http://www.ipcc.ch/pdf/assessment-report/ar4/wg3/ar4-wg3-

spm.pdf.) 
52

Jin, J., and Miller, N.L., 2011, Regional simulations to quantify land use change and irrigation impacts 

on hydroclimate in the California Central Valley: Theoretical and Applied Climatology, v. 104, no. 3-

4, p. 429-442. (Also available online at http://dx.doi.org/10.1007/s00704-010-0352-1.) 

Jobbágy, E.G., and Jackson, R.B., 2000, The vertical distribution of soil organic carbon and its relation 

to climate and vegetation: Ecological Applications, v. 10, no. 2, p. 423-436. (Also available online at 

http://dx.doi.org/10.1890/1051-0761(2000)010[0423:tvdoso]2.0.co;2.) 

Johnson, W.C., Werner, B., Guntenspergen, G.R., Voldseth, R.A., Millett, B., Naugle, D.E., Tulbure, 

M., Carroll, R.W.H., Tracy, J., and Olawsky, C., 2010, Prairie wetland complexes as landscape 

functional units in a changing climate: BioScience, v. 60, no. 2, p. 128-140. (Also available online at 

http://dx.doi.org/10.1525/bio.2010.60.2.7.) 

Junkermann, W., Hacker, J., Lyons, T., and Nair, U., 2009, Land use change suppresses precipitation: 

Atmospheric Chemistry and Physics, v. 9, no. 17, p. 6531-6539, available only online at 

http://dx.doi.org/10.5194/acp-9-6531-2009. 

Justice, C.O., Townshend, J.R.G., Vermote, E.F., Masuoka, E., Wolfe, R.E., Saleous, N., Roy, D.P., and 

Morisette, J.T., 2002, An overview of MODIS land data processing and product status: Remote 

Sensing of Environment, v. 83, no. 1–2, p. 3-15. (Also available online at 

http://dx.doi.org/10.1016/s0034-4257(02)00084-6.) 

Kalnay, E., and Cai, M., 2003, Corrigendum—impact of urbanization and land-use change on climate: 

Nature, v. 423, no. 102, p. 528-531. (Also available online at http://dx.doi.org/10.1038/nature01952.) 

Kampe, T.U., Johnson, B.R., Kuester, M., and Keller, M., 2010, NEON—the first continental-scale 

ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure: 

Journal of Applied Remote Sensing, v. 4, p. 043510-043524. (Also available online at 

http://dx.doi.org/10.1117/1.3361375.) 

Kaufmann, R.K., Seto, K.C., Schneider, A., Liu, Z., Zhou, L., and Wang, W., 2007, Climate response to 

rapid urban growth—evidence of a human-induced precipitation deficit: Journal of Climate, v. 20, no. 

10, p. 2299-2306. (Also available online at http://dx.doi.org/10.1175/jcli4109.1.) 

Kennedy, R.E., Yang, Z., and Cohen, W.B., 2010, Detecting trends in forest disturbance and recovery 

using yearly Landsat time series—1. LandTrendr — Temporal segmentation algorithms: Remote 

Sensing of Environment, v. 114, no. 12, p. 2897-2910. (Also available online at 

http://dx.doi.org/10.1016/j.rse.2010.07.008.) 

Kinney, E.L., and Valiela, I., 2011, Nitrogen loading to Great South Bay—land use, sources, retention, 

and transport from land to bay: Journal of Coastal Research, v. 27, no. 4, p. 672-686. (Also available 

online at http://dx.doi.org/10.2112/JCOASTRES-D-09-00098.1.) 

Kishtawal, C.M., Niyogi, D., Tewari, M., Pielke Sr., R.A., and Shepherd, J.M., 2010, Urbanization 

signature in the observed heavy rainfall climatology over India: International Journal of Climatology, 

v. 30, no. 13, p. 1908-1916. (Also available online at http://dx.doi.org/10.1002/joc.2044.) 
53

Knorn, J., Rabe, A., Radeloff, V.C., Kuemmerle, T., Kozak, J., and Hostert, P., 2009, Land cover 

mapping of large areas using chain classification of neighboring Landsat satellite images: Remote 

Sensing of Environment, v. 113, no. 5, p. 957-964. (Also available online at 

http://dx.doi.org/10.1016/j.rse.2009.01.010.) 

Kochendorfer, J.P., and Hubbart, J.A., 2010, The roles of precipitation increases and rural land-use 

changes in streamflow trends in the upper Mississippi River basin: Earth Interactions, v. 14, no. 20, p. 

1-12. (Also available online at http://dx.doi.org/10.1175/2010ei316.1.) 

Kueppers, L.M., Snyder, M.A., and Sloan, L.C., 2007, Irrigation cooling effect—regional climate 

forcing by land-use change: Geophysical Research Letters, v. 34, no. 3, p. L03703. (Also available 

online at http://dx.doi.org/10.1029/2006gl028679.) 

Kueppers, L.M., Snyder, M.A., Sloan, L.C., Cayan, D., Jin, J., Kanamaru, H., Kanamitsu, M., Miller, 

N.L., Tyree, M., Du, H., and Weare, B., 2008, Seasonal temperature responses to land-use change in 

the western United States: Global and Planetary Change, v. 60, no. 3–4, p. 250-264. (Also available 

online at http://dx.doi.org/10.1016/j.gloplacha.2007.03.005.) 

Kumar, S., Merwade, V., Lee, W., Zhao, L., and Song, C., 2010, Hydroclimatological impact of 

century-long drainage in midwestern United States—CCSM sensitivity experiments: Journal of 

Geophysical Research-Atmospheres, v. 115, no. D14, p. D14105. (Also available online at 

http://dx.doi.org/10.1029/2009jd013228.) 

Landsberg, H.E., 1970, Man-made climatic changes: Science, v. 170, no. 3964, p. 1265-1274. (Also 

available online at http://dx.doi.org/10.1126/science.170.3964.1265.) 

Lebow, Beth, Patel-Weynand, Toral, Loveland, Thomas, Guildin, Richard, and Cantral, Ralph, 2011, 

National climate assessment—Land use land cover national stakeholder workshop technical report: 

Salt Lake City, Utah, November 9 to December 1, 2011. 

Le Quere, C., Raupach, M.R., Canadell, J.G., Marland, G., and et al., 2009, Trends in the sources and 

sinks of carbon dioxide: Nature Geoscience, v. 2, no. 12, p. 831-836. (Also available online at 

http://dx.doi.org/10.1038/ngeo689.) 

Lee, E., Chase, T.N., Rajagopalan, B., Barry, R.G., Biggs, T.W., and Lawrence, P.J., 2009, Effects of 

irrigation and vegetation activity on early Indian summer monsoon variability: International Journal 

of Climatology, v. 29, no. 4, p. 573-581. (Also available online at http://dx.doi.org/10.1002/joc.1721.) 

Lei, M., Niyogi, D., Kishtawal, C., Pielke Sr., R.A., Beltrán-Przekurat, A., Nobis, T.E., and Vaidya, 

S.S., 2008, Effect of explicit urban land surface representation on the simulation of the 26 July 2005 

heavy rain event over Mumbai, India: Atmospheric Chemistry and Physics, v. 8, no. 20, p. 5975-5995, 

available only online at http://dx.doi.org/10.5194/acp-8-5975-2008. 
54

Levasseur, A., Lesage, P., Margni, M., Deschěnes, L., and Samson, R., 2010, Considering time in 

LCA—dynamic LCA and its application to global warming impact assessments: Environmental 

Science and Technology, v. 44, no. 8, p. 3169-3174. (Also available online at 

http://dx.doi.org/10.1021/es9030003.) 

Littell, J.S., Peterson, D.L., Millar, C.I., and O'Halloran, K.A., 2012, U.S. national forests adapt to 

climate change through science-management partnerships: Climatic Change, v. 110, no. 1-2, p. 269-

296. (Also available online at http://dx.doi.org/10.1007/s10584-011-0066-0.) 

Liu, Y., Stanturf, J., and Goodrick, S., 2010, Trends in global wildfire potential in a changing climate: 

Forest Ecology and Management, v. 259, no. 4, p. 685-697. (Also available online at 

http://dx.doi.org/10.1016/j.foreco.2009.09.002.) 

Lobell, D.B., Bala, G., Bonfils, C., and Duffy, P.B., 2006a, Potential bias of model projected 

greenhouse warming in irrigated regions: Geophysical Research Letters, v. 33, no. 13, p. L13709. 

(Also available online at http://dx.doi.org/10.1029/2006gl026770.) 

Lobell, D.B., Bala, G., and Duffy, P.B., 2006b, Biogeophysical impacts of cropland management 

changes on climate: Geophysical Research Letters, v. 33, no. 6, p. L06708. (Also available online at 

http://dx.doi.org/10.1029/2005gl025492.) 

Lobell, D.B., and Bonfils, C., 2008, The effect of irrigation on regional temperatures—a spatial and 

temporal analysis of trends in California, 1934–2002: Journal of Climate, v. 21, no. 10, p. 2063-2071. 

(Also available online at http://dx.doi.org/10.1175/2007jcli1755.1.) 

Long, J., Nelson, T., and Wulder, M., 2010, Regionalization of landscape pattern indices using 

multivariate cluster analysis: Environmental Management, v. 46, no. 1, p. 134-142. (Also available 

online at http://dx.doi.org/10.1007/s00267-010-9510-6.) 

Loveland, T.R., Sohl, T.L., Stehman, S.V., Gallant, A.L., Sayler, K.L., and Napton, D.E., 2002, A 

strategy for estimating the rates of recent United States land cover changes: Photogrammetric 

Engineering and Remote Sensing v. 68, no. 10, p. 1091-1099. (Also available online at 

http://asprs.org/Photogrammetric-Engineering-and-Remote-Sensing/PE-RS-Journals.html.) 

Lyons, T.J., 2002, Clouds prefer native vegetation: Meteorology and Atmospheric Physics, v. 80, no. 1, 

p. 131-140. (Also available online at http://dx.doi.org/10.1007/s007030200020.) 

Lyons, T.J., Nair, U.S., and Foster, I.J., 2008, Clearing enhances dust devil formation: Journal of Arid 

Environments, v. 72, no. 10, p. 1918-1928. (Also available online at 

http://dx.doi.org/10.1016/j.jaridenv.2008.05.009.) 

Lyons, T.J., Xinmei, H., Schwerdtfeger, P., Hacker, J.M., Foster, I.J., and Smith, R.C.G., 1993, Land–

atmosphere interaction in a semiarid region—the bunny fence experiment: Bulletin of the American 

Meteorological Society, v. 74, no. 7, p. 1327-1334. (Also available online at 

http://dx.doi.org/10.1175/1520-0477(1993)074<1327:liiasr>2.0.co;2.) 
55

Mahmood, R., Foster, S.A., Keeling, T., Hubbard, K.G., Carlson, C., and Leeper, R., 2006, Impacts of 

irrigation on 20th century temperature in the northern Great Plains: Global and Planetary Change, v. 

54, no. 1–2, p. 1-18. (Also available online at http://dx.doi.org/10.1016/j.gloplacha.2005.10.004.) 

Mahmood, R., and Hubbard, K.G., 2002, Effect of time of temperature observation and estimation of 

daily solar radiation for the northern Great Plains, USA: Agronomy Journal, v. 94, no. 4, p. 723-733. 

(Also available online at https://www.agronomy.org/publications/aj/abstracts/94/4/723.) 

Mahmood, R., and Hubbard, K.G., 2004, An analysis of simulated long-term soil moisture data for three 

land uses under contrasting hydroclimatic conditions in the northern Great Plains: Journal of 

Hydrometeorology, v. 5, no. 1, p. 160-179. (Also available online at http://dx.doi.org/10.1175/1525-

7541(2004)005<0160:aaosls>2.0.co;2.) 

Mahmood, R., Hubbard, K.G., and Carlson, C., 2004, Modification of growing-season surface 

temperature records in the northern great plains due to land-use transformation—verification of 

modelling results and implication for global climate change: International Journal of Climatology, v. 

24, no. 3, p. 311-327. (Also available online at http://dx.doi.org/10.1002/joc.992.) 

Mahmood, R., Hubbard, K.G., Leeper, R.D., and Foster, S.A., 2008, Increase in near-surface 

atmospheric moisture content due to land use changes—evidence from the observed dewpoint 

temperature data: Monthly Weather Review, v. 136, no. 4, p. 1554-1561. (Also available online at 

http://dx.doi.org/10.1175/2007mwr2040.1.) 

Matyas, C., and Carleton, A., 2010, Surface radar-derived convective rainfall associations with midwest 

US land surface conditions in summer seasons 1999 and 2000: Theoretical and Applied Climatology, 

v. 99, no. 3, p. 315-330. (Also available online at http://dx.doi.org/10.1007/s00704-009-0144-7.) 

Mawdsley, J.R., O'Malley, R., and Ojima, D.S., 2009, A review of climate-change adaptation strategies 

for wildlife management and biodiversity conservation: Conservation Biology, v. 23, no. 5, p. 1080-

1089. (Also available online at http://dx.doi.org/10.1111/j.1523-1739.2009.01264.x.) 

McAlpine, C.A., Syktus, J., Deo, R.C., Lawrence, P.J., McGowan, H.A., Watterson, I.G., and Phinn, 

S.R., 2007, Modeling the impact of historical land cover change on Australia's regional climate: 

Geophysical Research Letters, v. 34, no. 22, p. L22711. (Also available online at 

http://dx.doi.org/10.1029/2007gl031524.) 

McAlpine, C.A., Syktus, J., Ryan, J.G., Deo, R.C., McKeon, G.M., McGowan, H.A., and Phinn, S.R., 

2009, A continent under stress—interactions, feedbacks and risks associated with impact of modified 

land cover on Australia's climate: Global Change Biology, v. 15, no. 9, p. 2206-2223. (Also available 

online at http://dx.doi.org/10.1111/j.1365-2486.2009.01939.x.) 

McDonald, R.I., Fargione, J., Kiesecker, J., Miller, W.M., and Powell, J., 2009, Energy sprawl or 

energy efficiency—climate policy impacts on natural habitat for the United States of America: PLoS 

ONE, v. 4, no. 8, p. 1-11, available only online at http://dx.doi.org/10.1371/journal.pone.0006802. 
56

McGuire, A.D., Hayes, D.J., Kicklighter, D.W., Manizza, M., Zhuang, Q., Chen, M., Follows, M.J., 

Gurney, K.R., McClelland, J.W., Melillo, J.M., Peterson, B.J., and Prinn, R.G., 2010, An analysis of 

the carbon balance of the arctic basin from 1997 to 2006: Tellus, Series B: Chemical and Physical 

Meteorology, v. 62, no. 5, p. 455-474. (Also available online at http://dx.doi.org/10.1111/j.1600-

0889.2010.00497.x.) 

McMahon, G., Benjamin, S.P., Clarke, K., Findley, J.E., Fisher, R.N., Graf, W.L., Gundersen, L.C., 

Jones, J.W., Loveland, T.R., Roth, K.S., Usery, E.L., and Wood, N.J., 2005, Geography for a 

changing world—a science strategy for the geographic research of the U.S. Geological Survey, 2000-

2015: U.S. Geological Survey Circular 1281, 54 p. (Also available online at 

http://pubs.er.usgs.gov/publication/cir1281.) 

McPherson, R.A., and Stensrud, D.J., 2005, Influences of a winter wheat belt on the evolution of the 

boundary layer: Monthly Weather Review, v. 133, no. 8, p. 2178-2199. (Also available online at 

http://dx.doi.org/10.1175/mwr2968.1.) 

McPherson, R.A., Stensrud, D.J., and Crawford, K.C., 2004, The impact of Oklahoma's winter wheat 

belt on the mesoscale environment: Monthly Weather Review, v. 132, no. 2, p. 405-421. (Also 

available online at http://dx.doi.org/10.1175/1520-0493(2004)132<0405:tiooww>2.0.co;2.) 

Meyer, M.D., 2010, Greenhouse gas and climate change assessment: Journal of the American Planning 

Association, v. 76, no. 4, p. 402-412. (Also available online at 

http://dx.doi.org/10.1080/01944363.2010.504808.) 

Mitra, C., Shepherd, J.M., and Jordan, T., 2011, On the relationship between the premonsoonal rainfall 

climatology and urban land cover dynamics in Kolkata City, India: International Journal of 

Climatology. (Also available online at http://dx.doi.org/10.1002/joc.2366.) 

Mote, T.L., Lacke, M.C., and Shepherd, J.M., 2007, Radar signatures of the urban effect on 

precipitation distribution—a case study for Atlanta, Georgia: Geophysical Research Letters, v. 34, no. 

20, p. L20710. (Also available online at http://dx.doi.org/10.1029/2007gl031903.) 

Nagy, R.C., and Lockaby, B.G., 2011, Urbanization in the southeastern United States—socioeconomic 

forces and ecological responses along an urban-rural gradient: Urban Ecosystems, v. 14, no. 1, p. 71-

86. (Also available online at http://dx.doi.org/10.1007/s11252-010-0143-6.) 

Nair, U.S., Wu, Y., Kala, J., Lyons, T.J., Pielke Sr., R.A., and Hacker, J.M., 2011, The role of land use 

change on the development and evolution of the west coast trough, convective clouds, and 

precipitation in southwest Australia: Journal of Geophysical Research-Atmospheres, v. 116, no. D7, 

p. D07103. (Also available online at http://dx.doi.org/10.1029/2010jd014950.) 

Nakicenovic, N., and Swart, R., eds., 2000, Special report on emissions scenarios—a special report of 

Working Group III of the Intergovernmental Panel on Climate Change: Cambridge, U.K., Cambridge 

University Press, 570 p. (Also available online at 

http://www.ipcc.ch/ipccreports/sres/emission/index.php?idp=0.) 
57

Narisma, G.T., and Pitman, A.J., 2004, The effect of including biospheric responses to CO2 on the 

impact of land-cover change over Australia: Earth Interactions, v. 8, no. 5, p. 1-28. (Also available 

online at http://dx.doi.org/10.1175/1087-3562(2004)008<0001:teoibr>2.0.co;2.) 

Narisma, G.T., and Pitman, A.J., 2006, Exploring the sensitivity of the Australian climate to regional 

land-cover-change scenarios under increasing CO2 concentrations and warmer temperatures: Earth 

Interactions, v. 10, no. 7, p. 1-27. (Also available online at http://dx.doi.org/10.1175/ei154.1.) 

National Agricultural Statistical Service (NASS), 2009, 2007 census of agriculture, v. 1, U.S. summary 

and state reports: Washington, D.C., U.S. Department of Agriculture, National Agricultural Statistics 

Service, 639 p. (Also available online at 

http://www.agcensus.usda.gov/Publications/2007/Full_Report/.) 

National Drought Mitigation Center (NDMC), 2012, VegDRI animated time series: National Drought 

Mitigation Center Web page at http://vegdri.unl.edu/TimeSeries.aspx. (Accessed May 9, 2012.) 

National Oceanic and Atmospheric Administration (NOAA), 2012, National Weather Service Glossary: 

National Oceanic and Atmospheric Administration Web page at http://weather.gov/glossary/. 

(Accessed May 1, 2012.) 

National Research Council (NRS), 2001, Grand challenges in environmental sciences: Washington, 

D.C., National Academy Press, 96 p. (Also available online at 

http://www.nap.edu/catalog/9975.html.) 

Nelson, E., Mendoza, G., Regetz, J., Polasky, S., Tallis, H., Cameron, D.R., Chan, K.M.A., Daily, G.C., 

Goldstein, J., Kareiva, P.M., Lonsdorf, E., Naidoo, R., Ricketts, T.H., and Shaw, M.R., 2009, 

Modeling multiple ecosystem services, biodiversity conservation, commodity production, and 

tradeoffs at landscape scales: Frontiers in Ecology and the Environment, v. 7, no. 1, p. 4-11. (Also 

available online at http://dx.doi.org/10.1890/080023.) 

Niyogi, D., Holt, T., Zhong, S., Pyle, P.C., and Basara, J., 2006, Urban and land surface effects on the 

30 July 2003 mesoscale convective system event observed in the southern Great Plains: Journal of 

Geophysical Research-Atmospheres, v. 111, no. D19, p. D19107. (Also available online at 

http://dx.doi.org/10.1029/2005jd006746.) 

Niyogi, D., Kishtawal, C., Tripathi, S., and Govindaraju, R.S., 2010, Observational evidence that 

agricultural intensification and land use change may be reducing the Indian summer monsoon rainfall: 

Water Resources Research, v. 46, no. 3, p. W03533. (Also available online at 

http://dx.doi.org/10.1029/2008wr007082.) 

Niyogi, D., Pyle, P., Lei, M., Arya, S.P., Kishtawal, C.M., Shepherd, M., Chen, F., and Wolfe, B., 2011, 

Urban modification of thunderstorms—an observational storm climatology and model case study for 

the Indianapolis urban region: Journal of Applied Meteorology and Climatology, v. 50, no. 5, p. 1129-

1144. (Also available online at http://dx.doi.org/10.1175/2010JAMC1836.1.) 
58

Nuñez, M.N., Ciapessoni, H.H., Rolla, A., Kalnay, E., and Cai, M., 2008, Impact of land use and 

precipitation changes on surface temperature trends in Argentina: Journal of Geophysical ResearchAtmospheres, v. 113, no. D6, p. D06111. (Also available online at 

http://dx.doi.org/10.1029/2007jd008638.) 

Nusser, S.M., and Goebel, J.J., 1997, The National Resources Inventory—a long-term multi-resource 

monitoring programme: Environmental and Ecological Statistics, v. 4, no. 3, p. 181-204. (Also 

available online at http://dx.doi.org/10.1023/a:1018574412308.) 

Oke, T.R., 1987, Boundary layer climates (2d ed.): London, U.K., John Wiley & Sons, 435 p. 

Ozdogan, M., Rodell, M., Beaudoing, H.K., and Toll, D.L., 2010, Simulating the effects of irrigation 

over the United States in a land surface model based on satellite-derived agricultural data: Journal of 

Hydrometeorology, v. 11, no. 1, p. 171-184. (Also available online at 

http://dx.doi.org/10.1175/2009jhm1116.1.) 

Palmer, M.A., Lettenmaier, D.P., Poff, N.L., Postel, S.L., Richter, B., and Warner, R., 2009, Climate 

change and river ecosystems—protection and adaptation options: Environmental Management, v. 44, 

no. 6, p. 1053-1068. (Also available online at http://dx.doi.org/10.1007/s00267-009-9329-1.) 

Pan, Y., Birdsey, R., Hom, J., and McCullough, K., 2009, Separating effects of changes in atmospheric 

composition, climate and land-use on carbon sequestration of U.S. mid-Atlantic temperate forests: 

Forest Ecology and Management, v. 259, no. 2, p. 151-164. (Also available online at 

http://dx.doi.org/10.1016/j.foreco.2009.09.049.) 

Parton, W.J., Morgan, J.A., Kelly, R.H., and Ojima, D.S., 2001, Modeling C responses to environmental 

change in grassland systems, in Follett, R.F., Kimble, J.M., and Lal, R., eds., The potential of U.S. 

grazing lands to sequester carbon and mitigate the greenhouse effect: Boca Raton, Fla., Lewis 

Publishers, CRC Press, p. 371-398. (Also available online at 

http://www.crcnetbase.com/isbn/9781566705547.) 

Parton, W.J., Schimel, D.S., Cole, C.V., and Ojima, D.S., Analysis of factors controlling soil organic 

matter levels in Great Plains grasslands: Soil Science Society of America Journal, v. 51, no. 5, p. 

1173-1179. (Also available online at http://dx.doi.org/10.2136/sssaj1987.03615995005100050015x.) 

Pitman, A.J., de Noblet-Ducoudré, N., Cruz, F.T., Davin, E.L., Bonan, G.B., Brovkin, V., Claussen, M., 

Delire, C., Ganzeveld, L., Gayler, V., van den Hurk, B.J.J.M., Lawrence, P.J., van der Molen, M.K., 

Müller, C., Reick, C.H., Seneviratne, S.I., Strengers, B.J., and Voldoire, A., 2009, Uncertainties in 

climate responses to past land cover change—first results from the LUCID intercomparison study: 

Geophysical Research Letters, v. 36, no. 14, p. L14814. (Also available online at 

http://dx.doi.org/10.1029/2009gl039076.) 

Polebitski, A.S., Palmer, R.N., and Waddell, P., 2011, Evaluating water demands under climate change 

and transitions in the urban environment: Journal of Water Resources Planning and Management, v. 

137, no. 3, p. 249-257. (Also available online at http://dx.doi.org/10.1061/(ASCE)WR.1943-

5452.0000112.) 
59

Pongratz, J., Reick, C.H., Raddatz, T., and Claussen, M., 2010, Biogeophysical versus biogeochemical 

climate response to historical anthropogenic land cover change: Geophysical Research Letters, v. 37, 

no. 8, p. L08702. (Also available online at http://dx.doi.org/10.1029/2010gl043010.) 

Praskievicz, S., and Chang, H., 2009, A review of hydrological modelling of basin-scale climate change 

and urban development impacts: Progress in Physical Geography, v. 33, no. 5, p. 650-671. (Also 

available online at http://dx.doi.org/10.1177/0309133309348098.) 

Price, K., 2011, Effects of watershed topography, soils, land use, and climate on baseflow hydrology in 

humid regions—a review: Progress in Physical Geography, v. 35, no. 4, p. 465-492. (Also available 

online at http://dx.doi.org/10.1177/0309133311402714.) 

Price, K., Jackson, C.R., Parker, A.J., Reitan, T., Dowd, J., and Cyterski, M., 2011, Effects of watershed 

land use and geomorphology on stream low flows during severe drought conditions in the southern 

Blue Ridge Mountains, Georgia and North Carolina, United States: Water Resources Research, v. 47, 

p. W02516. (Also available online at http://dx.doi.org/10.1029/2010WR009340.) 

Puma, M.J., and Cook, B.I., 2010, Effects of irrigation on global climate during the 20th century: 

Journal of Geophysical Research-Atmospheres, v. 115, no. D16, p. D16120. (Also available online at 

http://dx.doi.org/10.1029/2010jd014122.) 

Raddatz, R.L., 2003, Agriculture and tornadoes on the Canadian prairies—potential impact of 

increasing atmospheric CO2 on summer severe weather: Natural Hazards, v. 29, no. 2, p. 113-122. 

(Also available online at http://dx.doi.org/10.1023/a:1023626806353.) 

Raddatz, R.L., 2007, Evidence for the influence of agriculture on weather and climate through the 

transformation and management of vegetation—illustrated by examples from the Canadian prairies: 

Agricultural and Forest Meteorology, v. 142, no. 2–4, p. 186-202. (Also available online at 

http://dx.doi.org/10.1016/j.agrformet.2006.08.022.) 

Raddatz, R.L., and Cummine, J.D., 2003, Inter-annual variability of moisture flux from the prairie agroecosystem—impact of crop phenology on the seasonal pattern of tornado days: Boundary-Layer 

Meteorology, v. 106, no. 2, p. 283-295. (Also available online at 

http://dx.doi.org/10.1023/a:1021117925505.) 

Rashford, B.S., Walker, J.A., and Bastian, C.T., 2011, Economics of grassland conversion to cropland 

in the Prairie Pothole region: Conservation Biology, v. 25, no. 2, p. 276-284. (Also available online at 

http://dx.doi.org/10.1111/j.1523-1739.2010.01618.x.) 

Reichstein, M., 2008, Impacts of climate change on forest soil carbon—principles, factors, models, 

uncertainties, in Freer-Smith, P.H., Broadmeadow, M.S.J., and Linch, J.M., eds., Forestry and climate 

change: Wallingford, U.K., CABI Books, p. 127-135. 
60

Rose, L.S., Stallins, J.A., and Bentley, M.L., 2008, Concurrent cloud-to-ground lightning and 

precipitation enhancement in the Atlanta, Georgia (United States), urban region: Earth Interactions, v. 

12, no. 11, p. 1-30. (Also available online at http://dx.doi.org/10.1175/2008ei265.1.) 

Roy, D.P., Ju, J., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E., 

and Zhang, C., 2010, Web-enabled Landsat Data (WELD)—Landsat ETM+ composited mosaics of 

the conterminous United States: Remote Sensing of Environment, v. 114, no. 1, p. 35-49. (Also 

available online at http://dx.doi.org/10.1016/j.rse.2009.08.011.) 

Rusticucci, M., and Barrucand, M., 2004, Observed trends and changes in temperature extremes over 

Argentina: Journal of Climate, v. 17, no. 20, p. 4099-4107. (Also available online at 

http://dx.doi.org/10.1175/1520-0442(2004)017<4099:otacit>2.0.co;2.)

Saeed, F., Hagemann, S., and Jacob, D., 2009, Impact of irrigation on the South Asian summer 

monsoon: Geophysical Research Letters, v. 36, no. 20, p. L20711. (Also available online at 

http://dx.doi.org/10.1029/2009gl040625.) 

Sandstrom, M., Lauritsen, R., and Changnon, D., 2004, A central-U.S. summer extreme dew-point 

climatology (1949-2000): Physical Geography, v. 25, no. 3, p. 191-207. (Also available online at 

http://dx.doi.org/10.2747/0272-3646.25.3.191.) 

Schlenker, W., and Roberts, M.J., 2009, Nonlinear temperature effects indicate severe damages to U.S. 

crop yields under climate change: Proceedings of the National Academy of Sciences, v. 106, no. 37, 

p. 15594-15598. (Also available online at http://dx.doi.org/10.1073/pnas.0906865106.) 

Sen Roy, S., Mahmood, R., Niyogi, D., Lei, M., Foster, S.A., Hubbard, K.G., Douglas, E.M., and Pielke 

Sr., R.A., 2007, Impacts of the agricultural Green Revolution-induced land use changes on air 

temperatures in India: Journal of Geophysical Research-Atmospheres, v. 112, no. D21, p. D21108. 

(Also available online at http://dx.doi.org/10.1029/2007JD008834.) 

Sen Roy, S., Mahmood, R., Quintanar, A., and Gonzalez, A., 2011, Impacts of irrigation on dry season 

precipitation in India: Theoretical and Applied Climatology, v. 104, no. 1, p. 193-207. (Also available 

online at http://dx.doi.org/10.1007/s00704-010-0338-z.) 

Shem, W., and Shepherd, M., 2009, On the impact of urbanization on summertime thunderstorms in 

Atlanta—two numerical model case studies: Atmospheric Research, v. 92, no. 2, p. 172-189. (Also 

available online at http://dx.doi.org/10.1016/j.atmosres.2008.09.013.) 

Shepherd, J.M., 2006, Evidence of urban-induced precipitation variability in arid climate regimes: 

Journal of Arid Environments, v. 67, no. 4, p. 607-628. (Also available online at 

http://dx.doi.org/10.1016/j.jaridenv.2006.03.022.) 

Shepherd, J.M., Pierce, H., and Negri, A.J., 2002, Rainfall modification by major urban areas—

observations from spaceborne rain radar on the TRMM satellite: Journal of Applied Meteorology, v. 

41, no. 7, p. 689-701. (Also available online at http://dx.doi.org/10.1175/1520-

0450(2002)041<0689:rmbmua>2.0.co;2.) 
61

Shepherd, M., Carter, M., Manyin, M., Messen, D., and Burian, S., 2010, The impact of urbanization on 

current and future coastal precipitation—a case study for Houston: Environment and Planning B—

Planning and Design, v. 37, no. 2, p. 284-304. (Also available online at 

http://dx.doi.org/10.1068/b34102t.) 

Smith, P., Bhogal, A., Edgington, P., Black, H., Lilly, A., Barraclough, D., Worrall, F., Hillier, J., and 

Merrington, G., 2010, Consequences of feasible future agricultural land-use change on soil organic 

carbon stocks and greenhouse gas emissions in Great Britain: Soil Use and Management, v. 26, no. 4, 

p. 381-398. (Also available online at http://dx.doi.org/10.1111/j.1475-2743.2010.00283.x.) 

Smith, W.B., Miles, P.D., Perry, C.H., and Pugh, S.A., 2009, Forest resources of the United States, 

2007: U.S. Department of Agriculture, Forest Service, General Technical Report WO-78, 336 p., 

available only online at http://www.nrs.fs.fed.us/pubs/7334. 

Sohl, Terry, and Sayler, Kristi., 2008, Using the FORE-SCE model to project land-cover change in the 

southeastern United States: Ecological Modelling, v. 219, no. 1–2, p. 49-65. (Also available online at 

http://dx.doi.org/10.1016/j.ecolmodel.2008.08.003.) 

Solomon, B.D., 2010, Biofuels and sustainability: Annals of the New York Academy of Sciences, v. 

1185, p. 119-134. (Also available online at http://dx.doi.org/10.1111/j.1749-6632.2009.05279.x.) 

Sorooshian, S., Li, J., Hsu, K.-l., and Gao, X., 2011, How significant is the impact of irrigation on the 

local hydroclimate in California's Central Valley? Comparison of model results with ground and 

remote-sensing data: Journal of Geophysical Research-Atmospheres, v. 116, no. D6, p. D06102. (Also 

available online at http://dx.doi.org/10.1029/2010jd014775.) 

Souch, C., and Grimmond, S., 2006, Applied climatology—urban climate: Progress in Physical 

Geography, v. 30, no. 2, p. 270-279. (Also available online at 

http://dx.doi.org/10.1191/0309133306pp484pr.) 

Stallins, J.A., and Rose, L.S., 2008, Urban lightning—current research, methods, and the geographical 

perspective: Geography Compass, v. 2, no. 3, p. 620-639. (Also available online at 

http://dx.doi.org/10.1111/j.1749-8198.2008.00110.x.) 

Stehman, S.V., and Selkowitz, D.J., 2010, A spatially stratified, multi-stage cluster sampling design for 

assessing accuracy of the Alaska (USA) National Land Cover Database (NLCD): International 

Journal of Remote Sensing, v. 31, no. 7, p. 1877-1896. (Also available online at 

http://dx.doi.org/10.1080/01431160902927945.) 

Stone, B., Hess, J.J., and Frumkin, H., 2010, Urban form and extreme heat events—are sprawling cities 

more vulnerable to climate change than compact cities?: Environmental Health Perspectives, v. 118, 

no. 10, p. 1425-1428. (Also available online at http://dx.doi.org/10.1289/ehp.0901879.) 

Tang, G., and Beckage, B., 2010, Projecting the distribution of forests in New England in response to 

climate change: Diversity and Distributions, v. 16, no. 1, p. 144-158. (Also available online at 

http://dx.doi.org/10.1111/j.1472-4642.2009.00628.x.) 
62

Thompson, W.L., Miller, A.E., Mortenson, D.C., and Woodward, A., 2011, Developing effective 

sampling designs for monitoring natural resources in Alaskan national parks—an example using 

simulations and vegetation data: Biological Conservation, v. 144, no. 5, p. 1270-1277. (Also available 

online at http://dx.doi.org/10.1016/j.biocon.2010.09.032.) 

Tomer, M.D., and Schilling, K.E., 2009, A simple approach to distinguish land-use and climate-change 

effects on watershed hydrology: Journal of Hydrology, v. 376, no. 1-2, p. 24-33. (Also available 

online at http://dx.doi.org/10.1016/j.jhydrol.2009.07.029.) 

Trenberth, K.E., 2011, Challenges in GEWEX, International Global Energy and Water Cycle 

Experiment project office: GEWEX News, v. 21, no. 4, p. 2-3. (Also available online at 

http://www.gewex.org/gewex_nwsltr.html.) 

Trusilova, K., Jung, M., Churkina, G., Karstens, U., Heimann, M., and Claussen, M., 2008, 

Urbanization impacts on the climate in Europe—numerical experiments by the PSU–NCAR 

Mesoscale Model (MM5): Journal of Applied Meteorology and Climatology, v. 47, no. 5, p. 1442-

1455. (Also available online at http://dx.doi.org/10.1175/2007jamc1624.1.) 

Turner, B.L., Kasperson, R.E., Matson, P.A., McCarthy, J.J., Corell, R.W., Christensen, L., Eckley, N., 

Kasperson, J.X., Luers, A., Martello, M.L., Polsky, C., Pulsipher, A., and Schiller, A., 2003, A 

framework for vulnerability analysis in sustainability science: Proceedings of the National Academy 

of Sciences, v. 100, no. 14, p. 8074-8079. (Also available online at 

http://dx.doi.org/10.1073/pnas.1231335100.) 

Turner, B.L., Lambin, E.F., and Reenberg, A., 2007, The emergence of land change science for global 

environmental change and sustainability: Proceedings of the National Academy of Sciences, v. 104, 

no. 52, p. 20666-20671. (Also available online at http://dx.doi.org/10.1073/pnas.0704119104.) 

U.S. Environmental Protection Agency (US EPA), 2009, Land-use scenarios—national-scale housingdensity scenarios consistent with climate change storylines (final report): EPA/600/R-08/076F, 137 p., 

available only online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=203458. 

U.S. Environmental Protection Agency (US EPA), 2012, Green power defined: U.S. Environmental 

Protection Agency Web page at http://www.epa.gov/greenpower/gpmarket/index.htm. (Accessed May 

1, 2012.) 

U.S. Geological Survey (USGS), 2012, Land Cover Trends Project: U.S. Geological Survey database 

available online at http://landcovertrends.usgs.gov/download/dlMap.html. (Accessed May 2, 2012.) 
63

U.S. Global Change Research Program, 2000, US National Assessment of the Potential Consequences 

of Climate Variability and Change Sector: Agriculture: U.S. Global Change Research Program, 

accessed May 16, 2012 available at 

http://www.usgcrp.gov/usgcrp/Library/nationalassessment/15AG.pdf. 

United Nations Framework Convention on Climate Change (UNFCCC), 2012a, Land use, land-use 

change and forestry: Draft Decision /CMP.6, 9 p. (Also available online at 

http://unfccc.int/files/meetings/cop_16/application/pdf/cop16_lulucf.pdf.) 

United Nations Framework Convention on Climate Change (UNFCCC), 2012b, Outcome of the work of 

the ad hoc working group on long-term cooperative action under the convention: Draft Decision 

/CP.16, 29 p. (Also available online at 

http://unfccc.int/files/meetings/cop_16/application/pdf/cop16_lca.pdf.) 

van den Heever, S.C., and Cotton, W.R., 2007, Urban aerosol impacts on downwind convective storms: 

Journal of Applied Meteorology and Climatology, v. 46, no. 6, p. 828-850. (Also available online at 

http://dx.doi.org/10.1175/jam2492.1.) 

Verburg, P.H., Neumann, K., and Nol, L., 2011, Challenges in using land use and land cover data for 

global change studies: Global Change Biology, v. 17, no. 2, p. 974-989. (Also available online at 

http://dx.doi.org/10.1111/j.1365-2486.2010.02307.x.) 

Viger, R.J., Hay, L.E., Markstrom, S.L., Jones, J.W., and Buell, G.R., 2011, Hydrologic effects of 

urbanization and climate change on the Flint River basin, Georgia: Earth Interactions, v. 15, no. 20, p. 

1-25. (Also available online at http://dx.doi.org/10.1175/2010ei369.1.) 

Vogelmann, J.E., Kost, J.R., Tolk, B., Howard, S., Short, K., Chen, X., Huang, C., Pabst, K., and 

Rollins, M.G., 2011, Monitoring landscape change for LANDFIRE using multi-temporal satellite 

imagery and ancillary data: IEEE Journal of Selected Topics in Applied Earth Observations and 

Remote Sensing, v. 4, no. 2, p. 252-264. (Also available online at 

http://dx.doi.org/10.1109/JSTARS.2010.2044478.) 

Voldseth, Richard, Johnson, W. Carter, Guntenspergen, Glenn, Gilmanov, Tagir, and Millett, Bruce, 

2009, Adaptation of farming practices could buffer effects of climate change on northern prairie 

wetlands: Wetlands, v. 29, no. 2, p. 635-647. (Also available online at http://dx.doi.org/10.1672/07-

241.1.) 

Waisanen, P.J., and Bliss, N.B., 2002, Changes in population and agricultural land in conterminous 

United States counties, 1790 to 1997: Global Biogeochemical Cycles, v. 16, no. 4, p. 1137. (Also 

available online at http://dx.doi.org/10.1029/2001gb001843.) 

West, J.M., Julius, S.H., Kareiva, P., Enquist, C., Lawler, J.J., Petersen, B., Johnson, A.E., and Shaw, 

M.R., 2009, U.S. natural resources and climate change—concepts and approaches for management 

adaptation: Environmental Management, v. 44, no. 6, p. 1001-1021. (Also available online at 

http://dx.doi.org/10.1007/s00267-009-9345-1.) 
64

Westerling, A.L., Turner, M.G., Smithwick, E.A.H., Romme, W.H., and Ryan, M.G., 2011, Continued 

warming could transform Greater Yellowstone fire regimes by mid-21st century: Proceedings of the 

National Academy of Sciences, v. 108, no. 32, p. 13165-13170. (Also available online at 

http://dx.doi.org/10.1073/pnas.1110199108.) 

Wickham, J.D., Stehman, S.V., Fry, J.A., Smith, J.H., and Homer, C.G., 2010, Thematic accuracy of the 

NLCD 2001 land cover for the conterminous United States: Remote Sensing of Environment, v. 114, 

no. 6, p. 1286-1296. (Also available online at http://dx.doi.org/10.1016/j.rse.2010.01.018.) 

Wimberly, M.C., Hildreth, M.B., Boyte, S.P., Lindquist, E., and Kightlinger, L., 2008, Ecological niche 

of the 2003 West Nile virus epidemic in the northern Great Plains of the United States: PLoS ONE, v. 

3, no. 12, p. 1-7, available only online at http://dx.doi.org/10.1371/journal.pone.0003744. 

Yadav, S.S., Redden, R.J., Hatfield, J.L., Lotze-Campen, H., and Hall, A.E., eds., 2011, Crop adaptation 

to climate change: Cambridge, U.K., John Wiley & Sons, 632 p. (Also available online at 

http://onlinelibrary.wiley.com/book/10.1002/9780470960929.) 

York, A.M., Shrestha, M., Boone, C.G., Zhang, S., Harrington, J.A., Jr., Prebyl, T.J., Swann, A., Agar, 

M., Antolin, M.F., Nolen, B., Wright, J.B., and Skaggs, R., 2011, Land fragmentation under rapid 

urbanization—a cross-site analysis of southwestern cities: Urban Ecosystems, v. 14, no. 3, p. 429-455. 

(Also available online at http://dx.doi.org/10.1007/s11252-011-0157-8.) 

Yow, D.M., 2007, Urban heat islands—observations, impacts, and adaptation: Geography Compass, v. 

1, no. 6, p. 1227-1251. (Also available online at http://dx.doi.org/10.1111/j.1749-8198.2007.00063.x.) 

Zhao, M., Pitman, A.J., and Chase, T.N., 2001, Climatic effects of land cover change at different carbon 

dioxide levels: Climate Research, v. 17, no. 1, p. 1-18. (Also available online at 

http://dx.doi.org/10.3354/cr017001.) 

Zheng, D., Heath, L.S., Ducey, M.J., and Smith, J.E., 2011, Carbon changes in conterminous US forests 

associated with growth and major disturbances—1992-2001 (vol 6, 0140212, 2011): Environmental 

Research Letters, v. 6, no. 1, p. 1, available only online at http://dx.doi.org/10.1088/1748-

9326/6/1/019502. 

Zhou, Yan, and Shepherd, J. Marshall, 2010, Atlanta’s urban heat island under extreme heat conditions 

and potential mitigation strategies: Natural Hazards, v. 52, no. 3, p. 639-668. (Also available online at 

http://dx.doi.org/10.1007/s11069-009-9406-z.) 

Zhu, Zhiliang, ed., Bouchard, Michelle, Butman, David, Hawbaker, Todd, Li, Zhengpeng, Liu, Jinxun, 

Liu, Shuguang, McDonald, Cory, Reker, Ryan, Sayler, Kristi, Sleeter, Benjamin, Sohl, Terry , 

Stackpoole, Sarah, Wein, Anne, and Zhu, Zhiliang, 2011, Baseline and projected future carbon 

storage and greenhouse-gas fluxes in the Great Plains region of the United States: U.S. Geological 

Survey Professional Paper 1787, 28 p. (Also available online at 

http://pubs.er.usgs.gov/publication/pp1787.) 
65

Zhu, Zhiliang, ed., Bergamaschi, Brian, Bernknopf, Richard, Clow, David, Dye, Dennis, Faulkner, 

Stephen, Forney, William, Gleason, Robert, Hawbaker,Todd, Liu, Jinxun, Liu, Shuguang, Prisley, 

Stephen, Reed, Bradley, Reeves, Matthew, Rollins, Matthew, Sleeter, Benjamin, Sohl, Terry, 

Stackpoole, Sarah, Stehman, Stephen, Striegl, Robert, Wein, Anne, and Zhu, Zhiliang, 2012, A 

method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of 

the United States under present conditions and future scenarios: U.S. Geological Survey Scientific 

Investigations Report 2010-5233, 190 p. (Also available online at 

http://pubs.er.usgs.gov/publication/sir20105233.) 
66

Appendix A – Definitions of Major Terms

Land change: The patterns, processes, and consequences of changes in land use, land condition, and land 

cover at multiple spatial and temporal scales that is associated with the interaction between human 

activities and natural systems. (U.S. Geological Survey Geography Science Sythesis Team, written 

commun., 2010). 

Land use: The human utilization of environmental resources for economic, societal, or environmental 

development. 

Land cover: The observed biophysical characteristics of the Earth’s surface.

Land condition: The observable state of the Earth’s surface used for monitoring and assessing resilience 

and sustainability as a function of both natural and human processes. 

Weather: The state of the atmosphere in a given geographic region and specific point in time with 

respect to its effects on human-environmental systems and variables such as temperature and moisture. 

(Adapted from National Oceanic and Atmospheric Administration, 2012 and National Aeronautics and 

Space Administration, 2012). 

Climate: The long-term, average weather pattern for a given region.

Risk: The probability of a consequence from a known or forecasted change in climate or land cover, 

use, or condition. 

Vulnerability: The degree to which a coupled human-environmental system is susceptible to reduced 

function, goods, and services because of adverse effects of climate change, including climate variability 

and extremes. Vulnerability is a function of the exposure, sensitivity, and adaptive capacity of the 

system given the character, magnitude, and rate of climate change. (Krista Karstensen and Nathan 

Wood, U.S. Geological Survey, written commun., 2012). 
67

Appendix B. Land Cover Class Descriptions

Water – Areas persistently covered with water, such as streams, canals, lakes, reservoirs, bays, or 

oceans. 

Developed – Areas of intensive use with much of the land covered with structures or anthropogenic 

impervious surfaces (for example, high-density residential, commercial, industrial, roads) or less 

intensive uses where the land-cover matrix includes both vegetation and structures (for example, lowdensity residential, recreational facilities, cemeteries, parking lots, utility corridors), including any land 

functionally related to urban or built-up environments (for example, parks, golf courses). 

Mining – Areas with extractive mining activities that have a substantial surface expression. This includes 

(to the extent that these features can be detected) mining buildings, quarry pits, overburden, leach, 

evaporative, tailings, or other related components. 

Barren – Land comprised of soils, sand, or rocks where less than 10 percent of the area is vegetated. 

Barren lands are usually naturally occurring.

Forest – Tree-covered land where the tree cover density is greater than 10 percent. Note that cleared 

forest land (clear-cuts) is mapped according to current cover (for example, mechanically disturbed or 

grassland/shrubland). 

Grassland/Shrubland – Land predominately covered with grasses, forbs, or shrubs. The vegetated cover 

must comprise at least 10 percent of the area.

Agriculture – Land in either a vegetated or an unvegetated state used for the production of food and 

fiber. This includes cultivated and uncultivated croplands, hay lands, pasture, orchards, vineyards, and 

confined livestock operations. Note that forest plantations are considered forests regardless of the use of 

the wood products. 

Wetland – Land where water saturation is the determining factor in soil characteristics, vegetation types, 

and animal communities. Wetlands usually contain water and vegetated cover.

Ice and Snow – Land where the accumulation of snow and ice does not completely melt in the summer 

period (for example, alpine glaciers and snowfields).

Nonmechanically disturbed – Land in an altered and often unvegetated state that because of disturbances 

by nonmechanical means, is in transition from one cover type to another. Nonmechanical disturbances 

are caused by fire, wind, floods, animals, and other similar phenomena. 

Mechanically disturbed – Land in an altered and often unvegetated state that, because of disturbances by 

mechanical means, is in transition from one cover type to another. Mechanical disturbances include 

forest clear-cutting, earthmoving, scraping, chaining, reservoir drawdown, and other similar humaninduced changes. 
68

Appendix C. 1973–2000 National Climate Assessment Southeast Region Change 

Statistics
69

Appendix D. 1973–2000 National Climate Assessment Northwest Region Change 

Statistics
70

Appendix E. 1973–2000 National Climate Assessment Great Plains Region 

Change Statistics
71

Appendix F. 1973–2000 National Climate Assessment Northeast Region Change 

Statistics
72

Appendix G. 1973–2000 National Climate Assessment Midwest Region Change 

Statistics
73

Appendix H. 1973–2000 National Climate Assessment Southwest Region 

Change Statistics
74

Appendix I. Summaries from Land-Use Land-Cover and Climate Case Studies

Climate Influences on LCLU and Water Resources Case Study Summaries

Baseflow, Drought, and Flood

"The Roles of Precipitation Increases and Rural Land-Use Changes in Streamflow Trends in the Upper 

Mississippi River Basin" (Kochendorfer and Hubbart, 2010) 

Precipitation and streamflow trends were examined for 48 United States Hydro-Climatic Data 

Network (HCDN) streams in the upper Mississippi water resource region from 1939 through 2008. 

Using the concept of precipitation of elasticity of flow, the observed magnitudes of statistically 

significant increases in mean and low flows were up to a factor of three greater than those expected 

from observed increases in precipitation alone. Peak flows increased less than expected. The authors 

suggest that the differences between the expected and observed changes in streamflow can be explained 

by regional, rural land-use changes. Results emphasized that precipitation trends alone cannot explain 

the direction and magnitude of flow trends citing specifically how soil and water conservation efforts 

have impacted rural land change. Another major conclusion applicable to land change was that all seven 

streams in the Driftless Area, an area with a history of widespread soil conservation drivers of land 

change, had decreasing peak flows despite an increase in precipitation. Because of the fact that the study 

streams were relatively free of significant changes in developed areas and flow regulation, the authors 

suggest that rural land-use change has played a significant role in streamflow alterations. Moreover, 

they suggest that the assumption of flow trends in HCDN streams being exclusively because of climate 

change is likely incorrect for the upper Mississippi River Basin and any other agriculturally dominated 

region in the United States. 

"Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid regions: 

A review”(Price, 2011)

Studies on climatic and land change influences on baseflow are split. Many studies associate 

higher watershed forest cover with lower baseflows, attributed to high evapotranspiration (ET) rates of 

forests, while other studies suggest that baseflow increased with higher watershed forest cover because 

of higher infiltration and recharge. Moreover, the effects of specific land-cover effects (developed and 

agriculture) are also varied because of additions of imported water and variable background conditions. 

The author reviewed a variety of studies in humid, temperate, and tropical regions and suggested that 

there is a research need to address land and climate change effects and human impacts on baseflow.

Urbanization – There were many inconsistencies in the studies examined by the author, likely 

stemming from the variety of factors controlling baseflow discharge and system response to 

urbanization. 

Agriculture – As with urbanization, baseflow response to agricultural land use may be positive 

or negative depending on the management practice. Despite inconsistency in the literature, two main 

inferences were made by the author: (1) watersheds that have been under agricultural land use for 

extended periods show baseflow increases in response to improved cropping and tillage practices, and 

(2) comparison of baseflows under agricultural land use compared to other land uses is precluded by the 

variety of management practices and variables uses of irrigation.
75

"Effects of watershed land use and geomorphology on stream low flows during severe drought 

conditions in the southern Blue Ridge Mountains, Georgia and North Carolina, United State." (Price 

and others, 2011) 

The objective of this study was to assess the influence of land use and watershed geomorphic 

characteristics on low-flow variability in the southern Blue Ridge Mountains of North Carolina and 

Georgia. Two low-flow seasons, coinciding with a severe drought period in the Southeastern United 

States, were monitored in 35 small-scales to mesocscale watersheds 93 to 146 km2. A comprehensive 

suite of watershed characteristics, including factors of topography, channel network morphometry, soil, 

land use, and precipitation were used in multiple regression analysis of low-flow variability within the 

35 watersheds. The results of this study indicate that low flows in the southern Blue Ridge Mountains 

are affected most strongly by factors of geomorphology, particularly drainage density, topographic 

variability, amount of watershed colluvium, and percentage of the stream network that is first order. 

Additionally, low flow in groups of lower and higher forest-cover watersheds were compared. There is a 

significant body of literature that has demonstrated lower baseflow within higher watershed forest 

cover, this study also demonstrated a consistent positive relation between low flow and higher forest 

cover. This study highlights the importance of infiltration and recharge under undisturbed land cover in 

sustaining low flows, and bears noteworthy implications for environmental flows and water resource 

sustainability. These results also suggest that as development continues, additional land-use change will 

carry a negative impact on reduced low flow, thus issues of environmental flows will be reduced as 

forest is converted to nonforest developed lands. 

"Hydrologic Effects of Urbanization and Climate Change on the Flint River Basin, Georgia" (Viger and 

others 2011) 

This study used the Precipitation-Runoff Modeling System (PRMS) to examine the potential 

effects of long-term urbanization on the freshwater resources of the Flint River System. Climate inputs 

included precipitation and temperature output from five, downscaled General Circulation Models 

(GCMs) (one current and four future) and land cover. The central tendency of streamflow simulated in 

the climate-change scenarios showed a slight decrease in overall streamflow relative to simulations 

under current conditions, mostly caused by decreases in the surface-runoff and groundwater 

components. The addition of forecasted urbanization of land surfaces from the Forecasting Scenarios of 

Future Land-Cover Model (FORE–SCE) to the hydrologic simulation mitigated the decreases in 

streamflow by increasing surface runoff. The authors did note that there was a large degree of 

uncertainty about forecasted precipitation in interpreting the results of the study as the Flint River 

streamflow is extremely sensitive to precipitation adding that it may be more sensitive to precipitation 

than to increases in impervious surfaces. 

"A simple approach to distinguish land-use and climate-change effects on watershed hydrology" (Tomer 

and Schilling, 2009) 

This paper notes that it is difficult to separate the influences of climate (long-term averages) 

from weather cycles (drought and excess precipitation) and land-use change on watershed hydrology 

(annual hydrologic budgets). For a 25-year, small-watershed experiment in Iowa, the authors were able 

to separate some of these interactions. If applied regionally, the conclusions suggest that climate change 

has increased discharge from Midwest watersheds, especially since the 1970s. By inference, climate 

change has increased susceptibility of nutrients to water transport, exacerbating Gulf of Mexico 

hypoxia. 
76

Water Supply

 “Can forest management be used to sustain water-based ecosystem services in the face of climate 

change?”(Ford and others, 2011) 

Forested watersheds provide ecosystem services related to water supply and can have their 

structure, function, and streamflow altered by land use and land cover. The goal of this study was to 

quantify interactions among forest management, climate, and streamflow. It was hypothesized that 

climate impacts may be mitigated or exacerbated by forest management practices that alter land cover, 

depending upon how land cover changes impact hydrologic functions. A retrospective analysis and 

synthesis of long-term climate and streamflow data from six watersheds, all with different management 

histories, were studied to determine whether streamflow responded to variation in annual temperature 

and extreme precipitation compared to unmanaged watersheds. It was found that the streamflow 

response to climate was affected by nearly all land uses examined. Watershed response differed 

significantly in extreme wet and dry years. Deciduous stands that were converted to pine altered the 

streamflow response to extreme wet years. The increase of soil water storage may reduce flood risks in 

wet years but could exacerbate drought during a dry year. Forest management could potentially mitigate 

extreme annual precipitation associated with climate change; however, management effects need to be 

analyzed spatially to determine potential risks and vulnerabilities. 

"A review of hydrological modeling of basin-scale climate change and urban development impacts" 

(Praskievicz and Chang, 2009) 

This study reviewed the components of hydrologic modeling of climate change and urbanization 

at the basin scale. The most significant effect of increasing urbanization is the increase in impervious 

surfaces that contributes to increases in surface runoff, flashiness of storm hydrographs, degradation of 

water quality, and decreased groundwater recharge and evapotranspiration. For geographic and land use 

and land cover relevance, the authors discuss how the projected impacts of climate change on basin 

hydrology depend on the geography of the study area (that is, arid regions are likely to experience a 

decrease in annual runoff), how basin size may influence hydrologic sensitivity to urban development 

(that is, smaller basins experiencing greater impacts than larger ones), and the location of the 

development (that is, increased impervious surfaces in headwater regions tend to have more impact than 

development downstream). There are studies that suggest that land-use change will affect water 

resources more significantly than climate change (Cuo and others, 2009), and there are studies that 

suggest future climate changes are more significant than land-use change in determining hydrological 

impacts (Choi, 2008). 

For vulnerability and water management related studies, the authors suggest that climate change 

is likely to exacerbate conflicts over competing land-uses for water resources (that is, urban and 

agricultural). This problem maybe the most severe in regions where projected increased temperatures 

would diminish snowpack and shift the timing of peak runoff to earlier in the spring, which could lead 

to lower flows in the summer when water demand is generally high (that is, mountains in the Western 

United States).

"Evaluating water demands under climate change and transitions in the urban environment" (Polebitski 

and others, 2011) 

This study discusses how population growth, land use, pricing policy, and climate change affect 

residential water demands in the Puget Sound Region. The authors used a water demand model coupled 
77

with an urban simulation model (UrbanSim) to generate water demands over a 30-year time span 

(2000–30) using four different scenarios (including a baseline). 

Scenario 1: Demonstrated the importance of incorporating urban growth and development in 

determining water demands especially in single-family homes. The authors suggest that changes in 

development patterns, such as conversion of larger single-family lots into smaller townhomes can have 

a significant influence on water demand. 

Scenarios 2 and 3: The scenarios influenced the summer demand differently. While Seattle has 

one of the Nation’s lowest outdoor watering rates, the city still consumes about 1.4 times more water 

during the summer than in the winter. In the scenarios, the water pricing policy decreases the demand 

whereas increasing temperatures increase the demand. The authors note that the increases in summer 

water consumption because of climate change may be ameliorated by pricing policies and conservation 

policies, and offer as support that Seattle has been experiencing a decreasing water demand over the 

past two decades because of conservation programs, responses to price increases, increasing fixture 

efficiency, and two curtailment periods. 

An interesting perspective offered in the conclusion of this study was that housing density may 

prove to be essential in understanding effects of climate change on water demands – a factor that is 

currently not incorporated into many forecast models. 

Water Quality 

“Stream discharge and riparian land use influence in-stream concentrations and loads of phosphorus 

from central plains watershed." (Banner and others, 2009) 

This study investigated linkages between land-cover use and discharge of total phosphorus (TP) 

concentration loads in 25 Kansas streams. A long-term monitoring dataset maintained by the Kansas 

Department of Health and Environment was used to extract data from collection points that were 

colocated with USGS gaging stations. Median TP concentration was strongly correlated (R2 = 76

percent) with the presence of cropland within the riparian zones. This study found that on average 88 

percent of the TP loading occurred during high flows, which occurred 10 percent of the time. The 

authors suggest that in order to reduce TP during baseflow conditions, management activities may be 

best focused on reducing cropland uses near perennial streams. 

"A century of changing land-use and water-quality relationships in the continental US" (Broussard and 

Turner, 2009) 

The relationships between various land-use practices and riverine nitrate-nitrogen (NN) 

concentrations from the early 1990s through the end of the 20th century were studied in order to 

determine if there was a statistical significance in the quantifiable association between agricultural landuse and riverine NN concentrations. It was found that 63 rivers monitored over the past century for NN 

concentrations were three to four times higher at the end of the century. The most noticeable change of 

increased NN concentrations was in the Mississippi River Basin and the Midwest region. Agricultural 

cropland planted with corn was the most important agricultural driver of riverine NN concentrations, 

according to the factors considered in this study. This study predicts that the continued expansion of 

modern corn agriculture will likely increase the NN concentration in rivers and streams, particularly in 

watersheds where corn cropland occupies more than 25 percent of the total area. The authors suggest 

that change in land-use practices, such as increasing cropland diversity or increasing the area of 

perennial crops could help improve water quality by reducing NN loading. 
78

 "Nitrate in groundwater of the United States, 1991–2003" (Burow and others, 2010) 

An increase in fertilizer usage has been necessary for increasing crop production for food to 

support an increasing world population. In the United States, the amount of Nitrogen(N)entering the 

environment from the application of N fertilizer has increased 20-fold since 1945. With groundwater 

supplying 33 percent of water used for public drinking, contamination of this water by increased nitrates 

can affect human health. The objective of this paper was to identify the most influential factors 

controlling groundwater nitrate concentrations across a wide range of environmental setting in the 

United States. This study used an extensive data set from the USGS National Water-Quality Assessment 

(NAWQA). During 1991 through 2003, 5,101 wells were sampled in 51 study areas throughout the 

United States. The NAWQA method for assessing groundwater focuses on the water-quality conditions 

of shallow groundwater beneath agricultural and urban land-use settings and in major aquifers in each 

study area. Nitrate concentrations were highest in shallow groundwater beneath agricultural land use in 

areas with well-drained soils and oxic geochemical conditions. Nitrate concentrations were lowest in 

deep groundwater where ground water is reduced, or where groundwater is older and, hence, 

concentrations reflect historically low N application rates. 

"Nitrogen loading to Great South Bay: Land use, sources, retention, and transport from land to bay" 

(Kinney and Valiela, 2011). 

A nitrogen-loading model was used to (1) estimate annual nitrogen deliverables to each 

subwatershed of Great South Bay (GSB), (2) partition the contribution of nitrogen through disposal of 

wastewater, use of fertilizers, and atmospheric deposition on land, (3) estimate nitrogen transport 

through major land-use categories (natural vegetation, wetlands, agriculture, tuft, impervious surfaces), 

(4) calculate nitrogen retention within each subwatershed, and (5) model land-derived nitrogen loads 

from each subwatershed to GSB. Wastewater-derived nitrogen was the dominant source to watershed 

surfaces followed by atmospheric deposition and fertilizer use. There was a high within watershed 

nitrogen retention (77 percent) linked to areas of natural vegetation. The authors concluded that as land 

cover shifts away from natural vegetation, eutrophication of GSB will increase. 

Wetlands 

“Prairie wetland complexes as landscape: Functional units in a changing climate” (Johnson and 

others, 2010) 

The approach examined the consequences of climate change on wetland complexes in the Prairie 

Pothole Region (PPR) using WETLANDSCAPE (WLS), which is a climate-driven, process-based, 

deterministic simulation model. Generally, the simulations showed that all three permanence types of 

wetlands lost significant hydroperiod under warming scenarios unless accompanied by a minimum 

increase in precipitation of 5–7 percent per degree of warming. The model suggests that the 

vulnerability of the members of prairie wetland complexes to the climate warming defined by the study 

generally increase in the following order: temporary wetlands, semipermanent wetlands, seasonal 

wetlands. Principal conclusions as written by the authors are: (1) prairie wetlands in general are highly 

sensitive to climate warming; (2) wetlands in the drier, western PPR are most vulnerable to climate 

warming; (3) members of the wetland complex will respond differently to climate change, and longerhydroperiod wetlands are perhaps the most sensitive; (4) shortened wetland hydroperiods will severely 

affect vertebrates because of their longer life-cycle requirements; (5) in a greenhouse climate more of 

the PPR will be too dry or without functional wetlands and nesting habitat to support historic levels of 
79

waterfowl breeding; and (6) adaptation of farming practices in wetland watersheds may buffer the 

effects of climate change on wetlands.

“Hydroclimatological impact of century-long drainage in midwestern United States: CCSM sensitivity 

experiments” (Kumar and others, 2010) 

This study aimed to fill the research gap by analyzing the effect of wetland drainage on Midwest 

hydroclimatology testing two hypotheses: (1) large-scale artificial drainage has significantly changed 

the energy and water fluxes in the Midwest, thus affecting precipitation and temperature in the region, 

and (2) the impact of past LCLU change on Midwest hydroclimatology is comparable to or even greater 

than the impact of greenhouse gas emission-based climate change in the region. The study conducted 

four sensitivity experiments to investigate the effects of climate and LULC changes in the Midwest 

using a community climate system model (CCSM3). It is important to note that “climate change” in this 

study refers to greenhouse gas emission-based climate change.

Large-scale drainage activities in the Midwest (Illinois, Indiana, Iowa, Michigan, Minnesota, 

Missouri, Ohio, and Wisconsin) have resulted in the loss of more than 85 percent of its original 

wetlands. Moreover, agricultural drainage of Midwest wetlands has changed the carbon balance of the 

region from carbon sink to carbon source and has affected the roles wetlands play in climatic feedbacks 

(that is albedo and Bowen ratio). The results of the modeling suggested that wetland drainage has 

resulted in significant changes in the energy budget (sensible and latent heat flux) and radiation budget 

(long-wave radiation), particularly from May to October. As a result, the authors state that the climate 

has become warmer and convective precipitation has decreased during summer months. However, 

climate change did not result in any significant changes in other important variables (that is,

precipitation, latent heat flux, long-wave radiation). Results from this study highlight the importance of 

LULC change compared to climate change in the Midwest over the past one and half centuries. 

“Adaptation of farming practices could buffer effects of climate change on northern prairie wetlands” 

(Voldseth and others, 2009) 

The authors used WETSIM 3.2 to examine the effects of climate and watershed cover on 

wetland water levels in eastern South Dakota to answer the questions: Can adaptation of agricultural 

land-use practices ameliorate effects of climate change on prairie wetlands? and How much climate 

change can be offset or absorbed through land-use management? The considered land-use practices 

were unmanaged grassland, managed grassland, and cultivated crops. Climate scenarios were developed 

by adjusting historical climate combinations of 2°C and 4°C air temperature and +/- 10 percent

precipitation. Water levels in wetlands surrounded by managed grasslands were significantly greater 

than those surrounded by unmanaged grassland. Management reduced the proportion of years the 

wetland went dry and the frequency of dry periods. Cultivated crops and managed grasslands returned 

water levels that were equal or greater than unmanaged grassland under historical climate for the 2°

C 

temperature increase and the 2°C and 10 percent precipitation increase. Managed grasslands returned 

water levels that were equal or greater than unmanaged grassland under historical climate for the 4°C 

temperature increase and the 2°

C and 10 percent precipitation increase. The authors state that few 

empirical data exist to verify the results of such land-use simulations.
80

Climate Influences on Land Use and Land Cover and Transportation and Energy Supply and Use

Case Study Summaries

Transportation

“Roadless areas and clean water” (DellaSala and others, 2011) 

In the United States, inventoried roadless areas (IRAs) are lands without roads exceeding 2,000 

ha (5,000 acres) and that have been inventoried by the USFS. This paper assessed the importance of 

preserving IRAs as developing them could severely impact water quality and supply. Colorado has been 

seeking Federal permission to develop IRAs for logging, skiing, mining, and producing oil and gas. 

However, IRAs in Colorado occur in the headwaters of all major drainages, which cover about a onethird of the upper watersheds in the State. If road building was allowed in these intact watersheds it 

could reduce the ability to provide clean water to downstream communities. It was projected that the 

demand for water in Colorado will triple by 2050. Development of IRAs in Colorado would provide 

short-term opportunities but long-term impacts on water supply and quality. The authors suggest the use 

of cost-benefit analyses to better inform project management on decisions that may reduce the water 

quality. 

“Assessing transportation infrastructure impacts on rangelands: Test of a standard rangeland 

assessment protocol” (Duniway and others, 2010) 

The increase of on- and off-road vehicles, in recent decades, is because of a variety of factors 

including increased availability of all-terrain vehicles, infrastructure development (oil, gas, renewable 

energy, and ex-urban), and recreational activities. With continued development of renewable energy 

sources, including wind and solar, additional service roads are anticipated to be developed in 

rangelands. The goal of this study was to test the application of the Interpreting Indicators of Rangeland 

Health (IIRH) for detecting impacts on rangelands because of roads, trails, and pipelines. Results 

indicate that IIRH can be used for detecting areas impacted by off-highway vehicle use and energy use. 

These methods could potentially be integrated with remote sensing technologies in order to assess 

landscape scale impacts of transportation networks. 

Energy Supply and Use

“The land use-climate change-energy nexus” (Dale and others, 2011) 

Land use, climate change, and energy influence each other through a complex set of processes 

that need to be documented, modeled, and calibrated. This study explored the use of landscape ecology 

on patterns and processes and how they could be used to provide insight into the interaction between 

climate change, energy choices, and land-use activities. Major research issues within the land useclimate change-energy nexus were discussed in order to address integrated patterns and processes. It 

was found that climate influences the potential output, relative efficiencies, and sustainability of 

alternative energy sources. Landscape ecology could be used to enhance the understanding of 

interactions among land use, energy, and climate change. Understanding how climate change alters 

landscape conditions is important because management decisions should be based on scientific study 

that consider both the short-term and long-term implications of climate change.
81

“Energy sprawl or energy efficiency: Climate policy impacts on natural habitat for the United States of 

America” (McDonald and others, 2009) 

Climate change has been acknowledged as a potential threat to biodiversity and human wellbeing, which has led the United States to consider a cap-and-trade system to regulate emissions. 

Scenarios of a cap-and-trade system’s effects on United States’ energy production and impacts on 

habitat because of energy sprawl were evaluated. In all scenarios, temperate deciduous forests and 

temperate grasslands were identified as the most impacted habitats by future energy development. It was 

found that regardless of a cap-and-trade bill, 206,000 km2 of land will be affected by energy production 

by 2030. The results demonstrated that a potential side effect to reducing emissions would increase 

habitat impacts. The possibility of widespread energy sprawl increases the need for energy conservation, 

appropriate siting, sustainable production practices, and compensatory mitigation offsets. 

“Assessing transportation infrastructure impacts on rangelands: Test of a standard rangeland 

assessment protocol” (Duniway and others, 2010) 

The increase of on- and off-road vehicles, in recent decades, is because of a variety of factors 

including increased availability of all-terrain vehicles, infrastructure development (oil, gas, renewable 

energy, and ex-urban), and recreational activities. With continued development of renewable energy 

sources, including wind and solar, additional service roads are expected to be developed in rangelands. 

The goal of this study was to test the application of the Interpreting Indicators of Rangeland Health 

(IIRH) for detecting impacts on rangelands because of roads, trails, and pipelines. Results indicate that 

IIRH can be used for detecting areas impacted by off-highway vehicle use and energy use. These 

methods could potentially be integrated with remote sensing technologies in order to assess landscape 

scale impacts of transportation networks. 

“Biofuels and sustainability” (Solomon, 2010) 

There has been considerable controversy in North America, Europe, and Southeast Asia on the 

sustainably of the biofuel industry. This study focused on ethanol manufactured from cornstarch in the 

United States and from sugarcane in Brazil, and biodiesel produced from rapeseed oil in Germany and 

France. Recent literature in ecological economics and sustainability science were reviewed and several 

common criteria for sustainable biofuels were identified: scale (resource assessment, land availability, 

and land use practices), efficiency (economic and energy), equity (geographic distribution of resources 

and the “food versus fuel” debate), socio-economic issues, and environmental effects and emissions. A 

number of analyses found that corn-based ethanol was unsustainable and had significant environmental 

costs. Sugarcane-based ethanol and biodiesel were difficult to determine their sustainability, because of 

significant environmental challenges. The only biofuel found to be produced and consumed on a 

sustainable basis was cellulosic ethanol. Cellulosic ethanol produces a large resource and land base for 

feedstock, higher energy return on investment, potentially greater economic efficiency, equitable 

resource distribution, little or no conflict with food resources, and lower greenhouse gas emissions. It is 

important to consider sustainable biofuels especially with growing concerns about food security, 

distribution, greenhouse gas emissions, soil erosion, water pollution, and water supply in arid regions. 
82

Climate Influences on Land Use and Land Cover and Agriculture Case Study Summaries

Land Use Conversion to Cropland

“Changes in historical Iowa land cover as context for assessing the environmental benefits of current 

and future conservation efforts on agricultural lands” (Gallant and others, 2011) 

Two prominent conservation programs, from the 2008 Farm Bill, that authorized the USDA to 

provide financial incentives for agricultural producers in order to reduce environmental impacts 

included the Wetlands Reserve Program (WRP) and the Conservation Reserve Program (CRP). Current 

and future success of WRP and CRP practices depends on incentives for landowners to enroll or stay 

enrolled in conservation programs. With increased prices of corn and soybeans there has been a 

substantial decrease in CRP throughout the Midwest. This study analyzed land-cover changes since the 

mid-1800s in Iowa to better understand the full range of benefits from WRP and CRP efforts in Iowa. 

The contemporary Iowa landscape is extensively managed for agriculture and has significantly changed 

from the grassland-, woodland-, and wetland-covered landscape it once was in the mid-1800s. These 

landscape-scale changes were linked to conversion of grasslands and forest to agriculture and draining 

of wetlands. The resulting land-cover analysis of Iowa provides a description of how humans have 

altered the landscape since the mid-1800s. The spatial and temporal changes in the amount and 

distribution of grasslands, woodlands, and wetlands can provide the USDA with a context to evaluate 

benefits of conservation programs. 

“Economics of Grassland Conversion to Cropland in the Prairie Pothole Region” (Rashford and 

others, 2011) 

Much of the remaining grassland in the Prairie Pothole Region (PPR) is privately owned, and its 

conversion to cultivated cropland is largely driven by economics. This study investigated land-use 

change in the PPR and explored the economic and landscape characteristics related to conservation. The 

study modeled 183 counties in Iowa, Minnesota, North Dakota, South Dakota, and Montana in order to 

estimate the probability of grassland conversion to cropland. Results of the model demonstrated that 

conversion probabilities are spatially heterogeneous as a function of soil quality and are driven by 

changes in economic returns of alternate land uses. 

Cropland Sustainability 

“Fertilizer usage has increased in order to increase crop production” (Banner and others, 2009; 

Broussard and Turner, 2009; Burow and others, 2010). 

Increased Growing Season Temperatures

“Nonlinear temperature effects indicate severe damages to U.S. crop yield under climate change” 

(Schlenker and Roberts, 2009). 

The study pairs a panel of county-level yields for corn, soybeans, and cotton with a new finescale weather dataset that incorporates the whole distribution of temperatures within each day and 

across all days in the growing season. The data spanned most United States counties from 1950 to 2005. 

Results show that yields increase with temperature up to 29°

C for corn, 30°C for soybeans, and 32°C for 

cotton, but temperatures above these thresholds are harmful. The nonlinear relations established in this 

study revealed that (1) the relation between yield and temperature observed in cooler northern States is 
83

similar to that observed in southern States, and (2) the relation between yield and temperature observed 

between 1950 and 1977 was the same as observed between 1978 and 2005. An important caveat of the 

study was the inability to account for CO2 concentrations. 

Climate Influences on Land Use and Land Cover and Ecosystems Case Study Summaries

Greenhouse Gas Inventory and Biogenic Emissions

"Future land use and land cover influences on regional biogenic emissions and air quality in the United 

States" (Chen and others, 2009) 

This study applied a regional modeling system driven by global models to study the land use and 

land cover effects on future biogenic emissions and their impacts on ozone and biogenic secondary 

organic aerosols (BSOA). Under the IPCC SRES A2 business-as-usual storyline, the modeling system 

was applied to a matrix of four model scenarios including: Case 1-present day 1990 through 1999, and 

three future cases covering 2045 through 2054; Case 2 -present day LULC; Case 3 -projected-future 

LULC; and Case 4 - future LULC with designed regions of tree planting for carbon sequestration. 

Results of the study showed changing future meteorology under Case 2 increased average isoprene and 

monoterpene emission rates by 26 percent and 20 percent becuse of higher temperature and solar 

insolation. Under Case 3 predicted isoprene and monoterpene emissions decreased by 52 percent and 31 

percent respectively, primarily because of the projected expansion of cropland. The reduction of 

isoprene and monoterprene was less under Case 4 at 31 percent and 14 percent when future LULC 

changes were accompanied by regions of tree plantings. Overall, the results demonstrated that on a 

regional basis, changes in LULC can offset temperature driven increases in biogenic emissions and, 

thus, LULC projection is an important factor to consider in the study of future regional air quality. 

“Carbon changes in conterminous US forests associated with growth and major disturbances: 1992–

2001" (Zheng and others, 2011) 

Forest ecosystems are an important part of the global carbon cycle, therefore, the United Nations 

Framework Convention and Climate Change (UNFCCC) agreed that qualifying nations report their 

greenhouse gas (GHG) emissions and sinks annually. The goal of this study was to estimate the effects 

of major forest disturbances and net growth on carbon sequestration in the conterminous United States

in context of the terminology and needs for reporting to the UNFCCC for national GHG inventories. 

Forest area and carbon changes were estimated using remote-sensing based land-cover change map, 

forest fire data from the Monitoring Trends in Burn Severity Program, and forest growth and harvest 

data from the USFW Forest Inventory and Analysis Program. Natural and human-associated 

disturbances reduced the forest ecosystem’s carbon sink by 36 percent from 1992 to 2001, compared to 

that without disturbances. Among the three identified disturbances (land-cover change, harvesting, and 

forest wildfires) forest-related land-cover change contributed 33 percent of the total effect in reducing 

the forest carbon potential sink, while harvests and fires accounted for 63 percent and 4 percent of the 

total effect, respectively. The southern region of the United States was a small net carbon source, 

whereas, the greater Pacific Northwest region was a strong net sink. Results from this study fit 

reasonably well at an aggregated level with other related estimates of the current forest GHG inventory 

in the United States, suggesting that further research using this approach is acceptable. 
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Vegetation Displacement

“Projecting the distribution of forests in New England in response to climate change" (Tang and 

Beckage, 2010) 

Three major forest types in New England were modeled to project potential distributions in 

response to expected climate change. The three dominant forest types in New England include: boreal 

conifer, northern deciduous hardwood, and mixed oak-hickory forests. Historically, the distributionof 

these forest types have corresponded with a natural climate gradient. Boreal conifer forests are found at 

higher elevations in northern regions of New England, northern deciduous hardwoods are found in the 

central uplands, and mixed oak-hickory are found at lower elevations in the more southerly regions. 

Using the process-based BIOME4 vegetation model and nine future climate change scenarios, the future 

distribution of the three primary forest types were simulated to determine the potential magnitude of 

spatial displacement of the forest types by mid and late 21st century. The model projected that climate 

warming in the 21st century is likely to cause the extensive loss of boreal conifer forests, reduce the 

extent of northern hardwood deciduous forests, and result in large increases of mixed oak-hickory 

forest. 

“Assessing ecosystem threats from global and regional change: Hierarchical modeling of risk to 

sagebrush ecosystems from climate change, land use and invasive species in Nevada, USA” (Bradley, 

2010) 

A lack of spatially explicit models of risk to ecosystems makes it difficult for science to inform

conservation planning and management. Bradley presents a series of modeling approaches to (1) 

estimate the likelihood of change to climatic habitat of sagebrush, (2) develop a statewide landscapescale risk assessment associated with land use and the invasion of cheatgrass, and (3) model risk of 

pinyon-juniper woodland expansion into sagebrush ecosystems. The study found that the risk of 

sagebrush loss associated with land use and cheatgrass invasion was highest in portions of Nevada 

already dominated by cheatgrass and near lands used for agriculture. Regionally, there was considerable 

spatial heterogeneity in future climatic suitability for sagebrush. Wyoming, eastern Idaho, central 

Oregon, and northern Nevada had the lowest predicted risk of climate change. In Nevada, only a small 

portion of the State was assessed to contain low risk sagebrush, with 97 percent of the sagebrush at risk 

from one or more of the factors modeled. 

"Potential of a national monitoring program for forests to assess change in high-latitude ecosystems."

(Barret and Gray, 2011) 

This paper presents the potential benefits of extending the Forest Inventory and Analysis (FIA) 

approach to the boreal forest region of Alaska. The authors recognize that climate change is expected to 

impact many components of boreal ecosystems, but for most indicators, the direction and magnitude of 

change are difficult to predict because of complex interactions among system components. The authors 

suggest that the status and trend information provided by FIA monitoring could be helpful to 

conservation decisions by providing information on the abundance and rarity of vascular plants, 

invasive species, biomass and carbon content of vegetation, shifting vegetation species distribution, 

disturbance frequency, type, impact, and wildlife habitat characteristics.
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Increased Wildland Fires

 “Trends in global wildfire potential in a changing climate" (Liu and others, 2010) 

Global wildfire potential and projected future climate change trends because of greenhouse 

effects were investigated. In this study fire potential was measured by the Keetch-Byram Drought Index, 

which was calculated using observed maximum temperature and precipitation and future climate 

conditions at the end of this century (2070–2100) measured by general circulation models. Future 

increases in fire potential were found in five regions: the United States, South America, Eurasia, 

southern Africa, and Australia. The increased fire potential is primarily caused by warming in midlatitude regions of the United States, Eurasia, and Australia and by the combination of warming and 

drying in other regions. The overall results suggest global increases in wildfire potential, which would

require increased resources and management efforts for disaster prevention and recovery. 

“Continued warming could transform GreaterYellowstone fire regimes by mid-21st century” 

(Westerling and others, 2011) 

In response to climate change, wildfire regimes, fire frequency, extent, severity, and seasonality

are expected to increase. However, potential climate-driven changes in regional fire regimes are not well 

understood. This study considers how the occurrence, size, and spatial location of large fires greater 

than 200 hectares in the Greater Yellowstone ecosystem (GYE) might be altered by climate change. A 

suite of statistical models that related monthly climate data (1972 to 1999) to the occurrence and size of 

fires was developed, cross-validated, and then used with global climate models to predict fire 

occurrence and area burned in the GYE through 2009. It was found that continued warming could 

transform GYE fire regimes by the mid-21st century. The magnitude of predicted increases in fire 

occurrences and area burned suggested that there is a likelihood that the forested area in the GYE will 

be converted to nonforest vegetation and thus alter the fire return interval. The authors suggest that the 

climate-fire system is a tipping element that may qualitatively change the ecosystem processes in GYE 

and other subalpine or boreal forests. 

Climate Influences on Human Health Case Study Summaries

West Nile Virus

“Weather and land cover influences on mosquito populations in Sioux Falls, South Dakota” (Chuang 

and others, 2011) 

Spatial and temporal patterns of two mosquito populations (Culex tarsalis Coquillett and Aedes 

vexans Meigen) were examined with their relations to land cover types and climatic variability in Sioux 

Falls, S.Dak. South Dakota is dominated by two species of mosquitos, Cx. tarsalis and Ae. vexans, with 

the first a known vector of West Nile virus (WNV) and the second a suspected bridge vector of WNV. It 

was found that Cx. tarsalis was positively correlated with grass/hay, and Ae. vexans was positively 

correlated with wetlands. Seasonal weather patterns had an effect on the abundance of both mosquito 

species. Precipitation 2–3 weeks before mosquito sampling and warm temperature during the collection 

week had a positive influence on Ae. vexans. Warm temperatures 2 weeks prior along with increased 

precipitation 3–4 weeks before mosquito sampling had a positive influence on Cx. tarsalis. This study 

helped to improve the understanding of mosquito ecology in particular to weather conditions and 

landscape characteristics that make the Northern Great Plains an environmental niche. Understanding 
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temporal and spatial distributions of two important mosquito species and their relations with land cover 

and weather may help to enhance the efficiency of vector control efforts and disease prevention. 

“Ecological niche of the 2003 West Nile virus epidemic in the Northern Great Plains of the United 

States” (Wimberly and others, 2008) 

Incidences of West Nile virus (WNV) have remained higher in the northern Great Plains than in 

other regions of the United States. Yet, the reasons for the sustained high risk of WNV in this region 

have not been determined. To assess the environmental drivers of WNV in the Great Plains, countylevel spatial patterns of human cases during the 2003 epidemic were analyzed across seven States. This 

study found that WNV incidence increased with mean May-July temperatures and also increased with 

the percentage of irrigated cropland and population living in rural areas. There is evidence that the 

environmental conditions across the northern Great Plains create a favorable ecological niche for Culex 

tarslis Coquillett, a known vector of the WNV. 

Extreme Heat Events

“The great 2006 heat wave over California and Nevada: Signal of an increasing trend” (Gershunov 

and others, 2009) 

This case study took a comprehensive look at the July 2006 heat wave over California and 

Nevada in context to the region’s climate over the last six decades. The great California and Nevada 

heat waves can be classified into daytime or nighttime events depending on whether atmospheric 

conditions are dry or humid. Nighttime and daytime heat waves can be distinguished by the anomaly of 

moist atmosphere during nighttime events. The entire region has experienced a positive trend in heat 

wave activity, which has been expressed strongly in nighttime temperature extremes. Recently, the last 

two nighttime heat waves were also strongly expressed in daytime temperatures. These changes in 

regional heat wave activities are related to the presence of an increasing Pacific moisture source to 

southwestern California, west of Baja, California. Temperature records from the last 59 years indicate 

that the couplings of daytime and nighttime heat waves have increased. The authors suggest that the 

recent increase in California and Nevada heat waves are consistent with regional signs of global 

warming. 

“Urban form and extreme heat events: Are sprawling cities more vulnerable to climate change than 

compact cities?” (Stone and others, 2010) 

Extreme heat events (EHEs) are becoming more frequent in large cities in the United States and 

are responsible for a greater annual number of climate-related fatalities. This heat effect is a 

phenomenon through which cities exhibit higher temperatures than the surrounding countryside, in 

particular, low-density, sprawling patterns of urban development. This study examined the association 

between urban form at the level of the metropolitan region and the frequency of EHEs over a fivedecade period. A sprawl index for 53 metropolitan regions in the United Sates was used to measure the 

association between urban form in 2000 and the mean annual rate of change in EHEs between 1956 and 

2005. It was found that the frequency of EHEs increased significantly on an annual basis in large 

metropolitan regions of the United States. The rate of increase in EHEs was found to be higher in more 

sprawling than compact metropolitan regions. The authors suggest that their findings have clear 

implications for public health officials and urban planners to incorporate land-use patterns into models 

that project climate change impacts over time in urban areas. 
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Climate Influences on Land Use and Land Cover and Society Case Study Summaries

“Urbanization in the Southeastern United States: Socioeconomic forces and ecological responses along 

and urban-rural gradient” (Nagy and Lockaby, 2011) 

Results from the west Georgia (WestGA) project evaluated the causes and consequences of 

urbanization associated with a midsize city (less than 200,000 population). The hypothesis tested was 

that negative feedback would occur as a result of environmental impacts that could alter the rate of 

development or its spatial distribution. Collectively, the WestGA project found that urbanization had 

greatly altered aquatic and terrestrial ecosystems through vegetation clearing, impervious surfaces, 

changes in hydrology, and degradation of water quality. With increasing land values, market 

concentrations, road accessibility, and forest to urban conversion, it was foundthat residents of 

developing and urban areas were unaware of the surrounding degraded ecosystems. Consequently, the 

hypothesis was rejected suggesting that environmental conditions had little influences on rates and 

patterns of development. The authors state that quantified ecological response to development along the 

urban-rural gradient could help predict changes that will occur with increased urbanization in the 

WestGA Piedmont regions. 

“Urban modification of thunderstorms: An observational storm climatology and model case study for 

the Indianapolis urban region” (Niyogi and others, 2011) 

To understand the impact of urban area on thunderstorms, 10 years of summertime 

thunderstorms were analyzed to capture varying storm events that were initiated around the Indianapolis 

urban region and peripheral rural counties. It was hypothesized that urban regions would alter the 

intensity and composition /structure of approaching thunderstorms because of land-surface 

heterogeneity. It was found that 60 percent of storms changed structure over the Indianapolis area as 

compared to 25 percent over the rural regions. Radar-base climatology indicated that storms split close 

to the upwind urban region and merge again downwind. A larger portion of small storms and larger 

storms was found downwind from the urban region, and midsize storms dominated the upwind region. 

A case study of a typical storm on June 13, 2005, was observed, and results indicated that urban regions 

have strong climatological influences on regional thunderstorms. 

“Land fragmentation under rapid urbanization: A cross-site analysis of southwestern cities” (York and 

others, 2011) 

To better understand land fragmentation in the Southwestern United States, national land cover 

data were analyzed from 1992 to 2001 in five southwestern cities associated with Long Term Ecological 

Research (LTER) sites. Two general fragmentation trends were observed: (1) expansion of the 

urbanized area leading to fragmentation in the exurban and peri-urban regions and (2) decreased 

fragmentation associated with infill in the previously developed urban areas. Three fragmentation 

patterns that reflected western growth and urbanization were identified as riparian, polycentric, and 

monocentric. Five drivers of land fragmentation patterns were identified including: water provisioning, 

urban population dynamics, transportation, topography, and institutional factors (these factors are 

components within the socio-ecological system or external drivers). Results from this study identified 

water as the key variable in understanding land change in the Southwestern United States because all 

five study sites had dammed major rivers for storage or prevention of flooding.