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Data-Driven Assessment of Climate Change Impacts on Agriculture in the Southeast of the United States.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Assessment of Climate Change Impacts on Agriculture in the Southeast of the United States./
作者:	Nguyen, Hai.
面頁冊數:	1 online resource (154 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
標題:	Agricultural production. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29276555click for full text (PQDT)
ISBN:	9798841576143

Data-Driven Assessment of Climate Change Impacts on Agriculture in the Southeast of the United States.
Nguyen, Hai.

Data-Driven Assessment of Climate Change Impacts on Agriculture in the Southeast of the United States. - 1 online resource (154 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2022.

Includes bibliographical references

Agricultural production, in particularly crop production, and food security are already being affected and will continue to face negative consequences of climate change at the global and regional scales. Climate change impacts on crop production are multifold but two aspects remain top priority for researchers: crop productivity and crop water demand. These two aspects need to be examined, taking into consideration the uncertainties in climate change projections and crop modeling approaches. This study is a data-driven regional assessment of climate change impacts on crop yield and crop water demand for major food crops (maize and soybean) in the southeastern United States (U.S.), a less study but highly populated and lucrative agricultural region. One of the key challenges in examining the impacts of climate change on the environment and socioeconomic sectors is whether the changes are driven by the background internal climate variability (ICV) or by external, anthropogenic forcings. Therefore, it is important to improve the confidence level of detecting a climate signal from the background ICV when developing adaptation and mitigation strategies, as well as communicating the uncertainties in projecting future climate in certain locations to stakeholders. This study developed a statistical model using a high-resolution mean temperature dataset by Livneh et al. (2015) and a multi-model ensemble of statistically downscaled CMIP5 GCMs for RCP8.5 to identify the signal to noise ratio and better understand the climate trend uncertainty due to ICV as it pertains to recent historical climate trends in the conterminous US (CONUS). A large synthetic observation ensemble was created at a high spatial resolution (1/16th degree) in order to estimate the contribution of ICV to mean temperature variability across CONUS. Then standard bootstrapping procedures was used to estimate variance induced by ICV to estimate the ratio between the signal and the noise (SNR). The SNR map revealed which areas that have been or will be experiencing warming trends that is beyond the noise caused by ICV. The results from the large synthetic observation ensemble showed that historically, there is an overall 0.5oC increase in temperature (1961-2010), while under RCP8.5 scenario, CONUS-mean temperature will increase by a 1.27oC increase in the future (2011-2060). Historical increasing trend in mean temperature was only significant to in the southwestern and some part of southeastern and northeastern regions (SNR > 2). However, the increasing trend will become more prominent in most part in the CONUS for the summer time, where temperature might increase by 1.52oC (SNR between 4-7). This poses risks of drought and heat exposure for crop production in lower latitude regions. Mean temperature in winter months for the western regions may also experience significant increase (SNR between 2-6) by over 1oC, which may lead to decrease in snow pack in the spring.Drought is a recurring natural phenomenon that has devastating effects on various economic, social sectors, as well as natural ecosystems and biodiversity. The alarming trend of yield losses to drought in the US agriculture is rising over the years, despite increasing financial investments and technological development to abate drought impacts.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841576143Subjects--Topical Terms:

3559355
Agricultural production.
Index Terms--Genre/Form:

542853
Electronic books.

Data-Driven Assessment of Climate Change Impacts on Agriculture in the Southeast of the United States.
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500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
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502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2022.
504 $a Includes bibliographical references
520 $a Agricultural production, in particularly crop production, and food security are already being affected and will continue to face negative consequences of climate change at the global and regional scales. Climate change impacts on crop production are multifold but two aspects remain top priority for researchers: crop productivity and crop water demand. These two aspects need to be examined, taking into consideration the uncertainties in climate change projections and crop modeling approaches. This study is a data-driven regional assessment of climate change impacts on crop yield and crop water demand for major food crops (maize and soybean) in the southeastern United States (U.S.), a less study but highly populated and lucrative agricultural region. One of the key challenges in examining the impacts of climate change on the environment and socioeconomic sectors is whether the changes are driven by the background internal climate variability (ICV) or by external, anthropogenic forcings. Therefore, it is important to improve the confidence level of detecting a climate signal from the background ICV when developing adaptation and mitigation strategies, as well as communicating the uncertainties in projecting future climate in certain locations to stakeholders. This study developed a statistical model using a high-resolution mean temperature dataset by Livneh et al. (2015) and a multi-model ensemble of statistically downscaled CMIP5 GCMs for RCP8.5 to identify the signal to noise ratio and better understand the climate trend uncertainty due to ICV as it pertains to recent historical climate trends in the conterminous US (CONUS). A large synthetic observation ensemble was created at a high spatial resolution (1/16th degree) in order to estimate the contribution of ICV to mean temperature variability across CONUS. Then standard bootstrapping procedures was used to estimate variance induced by ICV to estimate the ratio between the signal and the noise (SNR). The SNR map revealed which areas that have been or will be experiencing warming trends that is beyond the noise caused by ICV. The results from the large synthetic observation ensemble showed that historically, there is an overall 0.5oC increase in temperature (1961-2010), while under RCP8.5 scenario, CONUS-mean temperature will increase by a 1.27oC increase in the future (2011-2060). Historical increasing trend in mean temperature was only significant to in the southwestern and some part of southeastern and northeastern regions (SNR > 2). However, the increasing trend will become more prominent in most part in the CONUS for the summer time, where temperature might increase by 1.52oC (SNR between 4-7). This poses risks of drought and heat exposure for crop production in lower latitude regions. Mean temperature in winter months for the western regions may also experience significant increase (SNR between 2-6) by over 1oC, which may lead to decrease in snow pack in the spring.Drought is a recurring natural phenomenon that has devastating effects on various economic, social sectors, as well as natural ecosystems and biodiversity. The alarming trend of yield losses to drought in the US agriculture is rising over the years, despite increasing financial investments and technological development to abate drought impacts.
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