Regional differences in the response of California’s rangeland production to climate and future projection
Rangelands support many important ecosystem services and are highly sensitive to climate change. Understanding temporal dynamics in rangeland gross primary production (GPP) and how it may change under projected future climate, including more frequent and severe droughts, is critical for ranching com...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2022-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/aca689 |
| _version_ | 1797747219437191168 |
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| author | Han Liu Yufang Jin Leslie M Roche Anthony T O’Geen Randy A Dahlgren |
| author_facet | Han Liu Yufang Jin Leslie M Roche Anthony T O’Geen Randy A Dahlgren |
| author_sort | Han Liu |
| collection | DOAJ |
| description | Rangelands support many important ecosystem services and are highly sensitive to climate change. Understanding temporal dynamics in rangeland gross primary production (GPP) and how it may change under projected future climate, including more frequent and severe droughts, is critical for ranching communities to cope with future changes. Herein, we examined how climate regulates the interannual variability of GPP in California’s diverse annual rangeland, based on the contemporary records of satellite derived GPP at 500 m resolution since 2001. We built Gradient Boosted Regression Tree models for 23 ecoregion subsections, relating annual GPP with 30 climatic variables, to disentangle the partial dependence of GPP on each climate variable. The machine learning results showed that GPP was most sensitive to growing season (GS) precipitation, with a reduction in GPP up to 200 g cm ^−2 yr ^−1 when GS precipitation decreased from 400 to 100 mm yr ^−1 in one of the driest subsections. We also found that years with more evenly distributed GS precipitation had higher GPP. Warmer winter minimum air temperature enhanced GPP in approximately two-thirds of the subsections. In contrast, average GS air temperatures showed a negative relationship with annual GPP. When the pre-trained models were forced by downscaled future climate projections, changes in the predicted rangeland productivity by mid- and end of century were more remarkable at the ecoregion subsection scale than at the state level. Our machine learning-based analysis highlights key regional differences in GPP vulnerability to climate and provides insights on the intertwining and potentially counteracting effects of seasonal temperature and precipitation regimes. This work demonstrates the potential of using remote sensing to enhance field-based rangeland monitoring and, combined with machine learning, to inform adaptive management and conservation within the context of weather extremes and climate change. |
| first_indexed | 2024-03-12T15:47:42Z |
| format | Article |
| id | doaj.art-297d67a4af1d4ba6985187bf77d49bd5 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:47:42Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-297d67a4af1d4ba6985187bf77d49bd52023-08-09T15:20:23ZengIOP PublishingEnvironmental Research Letters1748-93262022-01-0118101401110.1088/1748-9326/aca689Regional differences in the response of California’s rangeland production to climate and future projectionHan Liu0https://orcid.org/0000-0003-3655-5561Yufang Jin1https://orcid.org/0000-0002-9049-9807Leslie M Roche2https://orcid.org/0000-0003-2954-8056Anthony T O’Geen3Randy A Dahlgren4https://orcid.org/0000-0002-8961-875XDepartment of Land, Air and Water Resources, University of California , Davis, CA, United States of AmericaDepartment of Land, Air and Water Resources, University of California , Davis, CA, United States of AmericaDepartment of Plant Science, University of California , Davis, CA, United States of AmericaDepartment of Land, Air and Water Resources, University of California , Davis, CA, United States of AmericaDepartment of Land, Air and Water Resources, University of California , Davis, CA, United States of AmericaRangelands support many important ecosystem services and are highly sensitive to climate change. Understanding temporal dynamics in rangeland gross primary production (GPP) and how it may change under projected future climate, including more frequent and severe droughts, is critical for ranching communities to cope with future changes. Herein, we examined how climate regulates the interannual variability of GPP in California’s diverse annual rangeland, based on the contemporary records of satellite derived GPP at 500 m resolution since 2001. We built Gradient Boosted Regression Tree models for 23 ecoregion subsections, relating annual GPP with 30 climatic variables, to disentangle the partial dependence of GPP on each climate variable. The machine learning results showed that GPP was most sensitive to growing season (GS) precipitation, with a reduction in GPP up to 200 g cm ^−2 yr ^−1 when GS precipitation decreased from 400 to 100 mm yr ^−1 in one of the driest subsections. We also found that years with more evenly distributed GS precipitation had higher GPP. Warmer winter minimum air temperature enhanced GPP in approximately two-thirds of the subsections. In contrast, average GS air temperatures showed a negative relationship with annual GPP. When the pre-trained models were forced by downscaled future climate projections, changes in the predicted rangeland productivity by mid- and end of century were more remarkable at the ecoregion subsection scale than at the state level. Our machine learning-based analysis highlights key regional differences in GPP vulnerability to climate and provides insights on the intertwining and potentially counteracting effects of seasonal temperature and precipitation regimes. This work demonstrates the potential of using remote sensing to enhance field-based rangeland monitoring and, combined with machine learning, to inform adaptive management and conservation within the context of weather extremes and climate change.https://doi.org/10.1088/1748-9326/aca689ecosystem productivityclimate changerangelandsremote sensingGradient Boosted Regression TreesGCM |
| spellingShingle | Han Liu Yufang Jin Leslie M Roche Anthony T O’Geen Randy A Dahlgren Regional differences in the response of California’s rangeland production to climate and future projection Environmental Research Letters ecosystem productivity climate change rangelands remote sensing Gradient Boosted Regression Trees GCM |
| title | Regional differences in the response of California’s rangeland production to climate and future projection |
| title_full | Regional differences in the response of California’s rangeland production to climate and future projection |
| title_fullStr | Regional differences in the response of California’s rangeland production to climate and future projection |
| title_full_unstemmed | Regional differences in the response of California’s rangeland production to climate and future projection |
| title_short | Regional differences in the response of California’s rangeland production to climate and future projection |
| title_sort | regional differences in the response of california s rangeland production to climate and future projection |
| topic | ecosystem productivity climate change rangelands remote sensing Gradient Boosted Regression Trees GCM |
| url | https://doi.org/10.1088/1748-9326/aca689 |
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