finding 5.2 : key-message-5-2

Climate change affects land use and ecosystems. Climate change is expected to directly and indirectly impact land use and cover by altering disturbance patterns (medium confidence), species distributions (medium confidence), and the suitability of land for specific uses (low confidence). The composition of the natural and human landscapes, and how society uses the land, affects the ability of the Nation’s ecosystems to provide essential goods and services (high confidence).

This finding is from chapter 5 of Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II.

Process for developing key messages:

Chapter authors developed the chapter through technical discussions, literature review, and expert deliberation via email and phone discussions. The authors considered feedback from the general public, the National Academies of Sciences, Engineering, and Medicine, and federal agencies. For additional information about the overall process for developing the report, see Appendix 1: Process.

The topic of land-use and land-cover change (LULCC) overlaps with numerous other national sectoral chapters (for example, Ch. 6: Forests; Ch. 10: Ag & Rural; Ch. 11: Urban) and is a fundamental characteristic of all regional chapters in this National Climate Assessment. This national sectoral chapter thus focuses on the dynamic interactions between land change and the climate system. The primary focus is to review our current understanding of land change and climate interactions by examining how land change drives changes in local- to global-scale weather and climate and how, in turn, the climate drives changes in land cover and land use through both biophysical and socioeconomic responses. Where possible, the literature cited in this chapter is specific to changes in the United States.

Description of evidence base:

Much of the research assessing the impact of climate change on agriculture has been undertaken as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP),b84b193b-ca98-479c-b5ef-fe94e5ffd39c which has been understandably focused on productivity and food security.b84b193b-ca98-479c-b5ef-fe94e5ffd39c,21eb6d9e-0c16-47cd-81b4-95c61584731f,106d0098-646c-456e-8b53-38162cfe74f0,4a217f31-e1bf-4cf6-82ab-3a969a1b3b52,b600eba8-c12a-4403-90e2-bea74f5d0c7a Less effort has been devoted to understanding the impact of climate change on the spatial distribution of agriculture. Deryng et al. (2011)9a44e46a-1f5f-4f65-95a9-3be834c2b7c4 used one of the AgMIP crop models (PEGASUS) to show poleward and westward shifts in areas devoted to corn, soybean, and wheat production. Parker and Abatzoglou (2016)0a8508df-df59-4080-89a2-52bfeaca47e0 have reported a poleward migration of the USDA’s cold hardiness zones as a result of a warming climate. Several empirical studies have found an increase in wildland fires in the western United States over the last several decades,80cf45a9-2066-4434-ae46-0e8f53b2427d,de4a77df-03ba-4319-a13f-7fdefbb353a5,5bd55977-f882-4065-99c7-2fbb4945cb7b in which indicators of aridity correlate positively with the amount of area burned. Several studies have reported a decline in forest cover throughout the western United States and project future declines due to a warming climate and increasing aridity, as well as the concomitant likely increase in pest outbreaks and fire.37982de0-0e01-476f-b522-b8162d709134,b502bf1e-381e-4e41-9062-6a8d111e6712,878be8a3-989e-497d-af88-5417df6ab074,5ba92ba4-eb88-480e-91de-a442b293e649,15129c59-d64f-408a-8c67-582838d5565a Several studies have also reported a poleward shift in the forest communities of the eastern United States, resulting primarily from CO2 enrichment in a warming and wetter environment.c7860ce7-92b4-4743-a1e5-1f126ae04b58,37982de0-0e01-476f-b522-b8162d709134,26bd44a5-f9f3-49b5-9b96-5172e78da431,72d090b9-f1e4-4d82-86e6-feb8f0907105,59173115-7f0c-41a2-9422-d725f53df427,ca39a1dc-106b-4e84-a88e-6ff9f42ea77d

New information and remaining uncertainties:

Determining the impact of climate change on agriculture requires the integration of climate, crop, and economic models,e929c4c4-1ded-4644-9814-ab19f1a8b4eb each with its own sources of uncertainty that can propagate through the three models. Sources of uncertainty include the response of crops to the intermingled factors of CO2 fertilization, temperature, water, and nitrogen availability; species-specific responses; model parameterization; spatial location of irrigated areas; and other factors.21eb6d9e-0c16-47cd-81b4-95c61584731f,b600eba8-c12a-4403-90e2-bea74f5d0c7a,6402d058-2f17-4e9f-b28b-1d3da06ca739 The projection of recent empirical fire–climate relationships80cf45a9-2066-4434-ae46-0e8f53b2427d,de4a77df-03ba-4319-a13f-7fdefbb353a5,5bd55977-f882-4065-99c7-2fbb4945cb7b into the future introduces uncertainty, as the empirical results cannot account for future anthropogenic influences (for example, fire suppression management) and vegetation response to future fires.5bd55977-f882-4065-99c7-2fbb4945cb7b,f680e49e-d58f-45c2-8ad6-a7bc97c12ca0 Similarly, process-based models must account for vegetation response to fire, uncertainty in precipitation predictions from climate models, and spatiotemporal nonuniformity in human interactions with fire and vegetation.f680e49e-d58f-45c2-8ad6-a7bc97c12ca0 Many of the studies on climate-induced spatial migration of vegetation are based on dynamic global vegetation models, which are commonly based only on climate and soil inputs. These models aggregate species characteristics that are not uniform across all species represented and are generally lacking ecological processes that would influence a species’ range shift.00a8c280-09c0-4002-a8f5-53ef7b345fff,c910075b-e0af-46bb-bd30-1ed99f176cc9,8bb1a5f1-1b12-463f-8a1e-d72c1d982471,779de54f-5c3a-4b8e-8a36-ef4a0eb66638,10fa693a-fa23-47eb-b683-6a1e9c4b0851 Considerable uncertainties are associated with land-cover and land-use monitoring and projection.06e194ef-b57f-4a5e-b633-7e58386dcfd8,b8957392-913e-4d32-bfdb-544efbd5ccb9,ff9ffc0a-4f57-4c2f-9137-ecaa8b0fd5ee,3770dd69-0cf4-44c9-84a5-ed62a2e66841 Land-cover maps can be derived from remote sensing approaches; however, comprehensive approaches are typically characterized by coarse temporal resolution.437471ba-9fe3-4547-b193-7bf3ec00fbf3,f859d4bf-3716-4300-9779-6f6af8ce4c66,e0e4c3fb-f4f5-42a4-a64a-7e21d8eaa355,00bfab03-4a87-486b-90d2-4707410fe9f9 More recently, remote sensing has enabled annual classification over large areas (at national and global scales), but these efforts have been centered on a single land cover or disturbance type.a763e6c4-f1c0-41b0-954a-524bfdad6300,6a3ce882-e3f6-47c6-a9ae-dacb25c45e7f,c7b0af41-0488-4efa-9216-592dc6f30805 Comprehensive multitemporal mapping of land use is even more limited and is a source of considerable uncertainty in understanding land change and feedbacks with the climate system.

Assessment of confidence based on evidence:

There is high confidence that climate change will contribute to changes in agricultural land use; however, there is low confidence in the direction and magnitude of change due to uncertainties in the capacity to adapt to climate change. There is high confidence that climate change will impact urbanization in coastal areas, where sea level rise will continue to have direct effects. There is medium confidence that climate change will alter natural disturbance regimes; however, land management activities, such as fire suppression strategies, are likely to be of equal or greater importance. There is low confidence that climate change will result in changes to land cover resulting from changes in species distribution environmental suitability.

References :

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