Assessing the impacts of past and ongoing deforestation on rainfall patterns in South America.
Amazon
Cerrado
deforestation
machine learning
rainfall
Journal
Global change biology
ISSN: 1365-2486
Titre abrégé: Glob Chang Biol
Pays: England
ID NLM: 9888746
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
08
11
2022
accepted:
07
05
2023
medline:
15
8
2023
pubmed:
6
7
2023
entrez:
6
7
2023
Statut:
ppublish
Résumé
Despite recent advances in modeling forest-rainfall relationships, the current understanding of changes in observed rainfall patterns resulting from historical deforestation remains limited. To address this knowledge gap, we analyzed how 40 years of deforestation has altered rainfall patterns in South America as well as how current Amazonian forest cover sustains rainfall. First, we develop a spatiotemporal neural network model to simulate rainfall as a function of vegetation and climate inputs in South America; second, we assess the rainfall effects of observed deforestation in South America during the periods 1982-2020 and 2000-2020; third, we assess the potential rainfall changes in the Amazon biome under two deforestation scenarios. We find that, on average, cumulative deforestation in South America from 1982 to 2020 has reduced rainfall over the period 2016-2020 by 18% over deforested areas, and by 9% over non-deforested areas across South America. We also find that more recent deforestation, that is, from 2000 to 2020, has reduced rainfall over the period 2016-2020 by 10% over deforested areas and by 5% over non-deforested areas. Deforestation between 1982 and 2020 has led to a doubling in the area experiencing a minimum dry season of 4 months in the Amazon biome. Similarly, in the Cerrado region, there has been a corresponding doubling in the area with a minimum dry season of 7 months. These changes are compared to a hypothetical scenario where no deforestation occurred. Complete conversion of all Amazon forest land outside protected areas would reduce average annual rainfall in the Amazon by 36% and complete deforestation of all forest cover including protected areas would reduce average annual rainfall in the Amazon by 68%. Our findings emphasize the urgent need for effective conservation measures to safeguard both forest ecosystems and sustainable agricultural practices.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5292-5303Informations de copyright
© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
Références
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5(1), 170191. https://doi.org/10.1038/sdata.2017.191
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and Drainage Paper 56. FAO, 300(9), D05109.
Amigo, I. (2020). When will the Amazon hit a tipping point? Nature, 578(7796), 505-508.
Aragão, L. E. O. C., Malhi, Y., Roman-Cuesta, R. M., Saatchi, S., Anderson, L. O., & Shimabukuro, Y. E. (2007). Spatial patterns and fire response of recent Amazonian droughts. Geophysical Research Letters, 34(7), L07701. https://doi.org/10.1029/2006GL028946
Coe, M. T., Brando, P. M., Deegan, L. A., Macedo, M. N., Neill, C., & Silvério, D. V. (2017). The forests of the Amazon and Cerrado moderate regional climate and are the key to the future. Tropical Conservation Science, 10, 1940082917720671. https://doi.org/10.1177/1940082917720671
Cui, J., Lian, X., Huntingford, C., Gimeno, L., Wang, T., Ding, J., He, M., Xu, H., Chen, A., Gentine, P., & Piao, S. (2022). Global water availability boosted by vegetation-driven changes in atmospheric moisture transport. Nature Geoscience, 15(12), 982-988. https://doi.org/10.1038/s41561-022-01061-7
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A., & Hansen, M. C. (2018). Classifying drivers of global forest loss. Science, 361(6407), 1108-1111. https://doi.org/10.1126/science.aau3445
DiMiceli, C., Townshend, J., Carroll, M., & Sohlberg, R. (2021). Evolution of the representation of global vegetation by vegetation continuous fields. Remote Sensing of Environment, 254, 112271. https://doi.org/10.1016/j.rse.2020.112271
Duku, C., & Hein, L. (2021). The impact of deforestation on rainfall in Africa: A data-driven assessment. Environmental Research Letters, 16(6), 064044. https://doi.org/10.1088/1748-9326/abfcfb
Ellison, D., Morris, C. E., Locatelli, B., Sheil, D., Cohen, J., Murdiyarso, D., Gutierrez, V., Noordwijk, M. V., Creed, I. F., Pokorny, J., Gaveau, D., Spracklen, D. V., Tobella, A. B., Ilstedt, U., Teuling, A. J., Gebrehiwot, S. G., Sands, D. C., Muys, B., Verbist, B., … Sullivan, C. A. (2017). Trees, forests and water: Cool insights for a hot world. Global Environmental Change, 43, 51-61. https://doi.org/10.1016/j.gloenvcha.2017.01.002
Feng, X., Porporato, A., & Rodriguez-Iturbe, I. (2013). Changes in rainfall seasonality in the tropics. Nature Climate Change, 3(9), 811-815. https://doi.org/10.1038/nclimate1907
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. https://doi.org/10.1002/qj.3803
Hoffmann, W. A., Schroeder, W., & Jackson, R. B. (2003). Regional feedbacks among fire, climate, and tropical deforestation. Journal of Geophysical Research: Atmospheres, 108(D23), 4721. https://doi.org/10.1029/2003JD003494
Holden, Z. A., Swanson, A., Luce, C. H., Jolly, W. M., Maneta, M., Oyler, J. W., Warren, D. A., Parsons, R., & Affleck, D. (2018). Decreasing fire season precipitation increased recent western US forest wildfire activity. Proceedings of the National Academy of Sciences of the United States of America, 115(36), E8349-E8357. https://doi.org/10.1073/pnas.1802316115
INPE. (2022). PRODES-Monitoramento da Floresta Amazônica Brasileira por Satélite. INPE databaseTerraBrasilis (INPE.br). http://terrabrasilis.dpi.inpe.br/app/dashboard/deforestation/biomes/amazon/increments
Kalamandeen, M., Gloor, E., Mitchard, E., Quincey, D., Ziv, G., Spracklen, D., Spracklen, B., Adami, M., Aragão, L. E. O. C., & Galbraith, D. (2018). Pervasive rise of small-scale deforestation in Amazonia. Scientific Reports, 8(1), 1600. https://doi.org/10.1038/s41598-018-19358-2
Khanna, J., Medvigy, D., Fueglistaler, S., & Walko, R. (2017). Regional dry-season climate changes due to three decades of Amazonian deforestation. Nature Climate Change, 7(3), 200-204. https://doi.org/10.1038/nclimate3226
Konapala, G., Mishra, A. K., Wada, Y., & Mann, M. E. (2020). Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nature Communications, 11(1), 3044. https://doi.org/10.1038/s41467-020-16757-w
Lawrence, D., & Vandecar, K. (2015). Effects of tropical deforestation on climate and agriculture. Nature Climate Change, 5(1), 27-36. https://doi.org/10.1038/nclimate2430
Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M., & Börner, J. (2021). Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nature Communications, 12(1), 2591. https://doi.org/10.1038/s41467-021-22840-7
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., & Gomis, M. (2021). Climate change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2.
Mataveli, G., de Oliveira, G., Chaves, M. E., Dalagnol, R., Wagner, F. H., Ipia, A. H., Silva-Junior, C. H., & Aragão, L. E. (2022). Science-based planning can support law enforcement actions to curb deforestation in the Brazilian Amazon. Conservation Letters, 15, e12908.
Meier, R., Schwaab, J., Seneviratne, S. I., Sprenger, M., Lewis, E., & Davin, E. L. (2021). Empirical estimate of forestation-induced precipitation changes in Europe. Nature Geoscience, 14(7), 473-478. https://doi.org/10.1038/s41561-021-00773-6
Myneni, R., Knyazikhin, Y., & Park, T. (2015). MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006. https://doi.org/10.5067/MODIS/MCD15A3H.006
Ratajczak, Z., Carpenter, S. R., Ives, A. R., Kucharik, C. J., Ramiadantsoa, T., Stegner, M. A., Williams, J. W., Zhang, J., & Turner, M. G. (2018). Abrupt change in ecological systems: Inference and diagnosis. Trends in Ecology & Evolution, 33(7), 513-526. https://doi.org/10.1016/j.tree.2018.04.013
Reynolds, R., & Banzon, V. (2008). NOAA optimum interpolation 1/4 degree daily sea surface temperature (OISST) analysis, version 2. NOAA National Centers for Environmental Information, 10, V5SQ8XB5.
Salazar, A., Baldi, G., Hirota, M., Syktus, J., & McAlpine, C. (2015). Land use and land cover change impacts on the regional climate of non-Amazonian South America: A review. Global and Planetary Change, 128, 103-119. https://doi.org/10.1016/j.gloplacha.2015.02.009
Schaaf, C., & Wang, Z. (2015). MCD43D51 MODIS/Terra+aqua BRDF/albedo black sky albedo shortwave daily L3 global 30arcsec CMG V006. https://doi.org/10.5067/MODIS/MCD43D51.006
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting (Montreal, Canada) 29th Annual Conference on Neural Information Processing Systems, NIPS 2015.
Sierra, J. P., Junquas, C., Espinoza, J. C., Segura, H., Condom, T., Andrade, M., Molina-Carpio, J., Ticona, L., Mardoñez, V., Blacutt, L., Polcher, J., Rabatel, A., & Sicart, J. E. (2022). Deforestation impacts on Amazon-Andes hydroclimatic connectivity. Climate Dynamics, 58(9), 2609-2636. https://doi.org/10.1007/s00382-021-06025-y
Smith, C., Baker, J. C. A., & Spracklen, D. V. (2023). Tropical deforestation causes large reductions in observed precipitation. Nature, 615(7951), 270-275. https://doi.org/10.1038/s41586-022-05690-1
Song, X.-P., Hansen, M. C., Potapov, P., Adusei, B., Pickering, J., Adami, M., Lima, A., Zalles, V., Stehman, S. V., Di Bella, C. M., Conde, M. C., Copati, E. J., Fernandes, L. B., Hernandez-Serna, A., Jantz, S. M., Pickens, A. H., Turubanova, S., & Tyukavina, A. (2021). Massive soybean expansion in South America since 2000 and implications for conservation. Nature Sustainability, 4(9), 784-792. https://doi.org/10.1038/s41893-021-00729-z
Souza, C. M., Shimbo, J. Z., Rosa, M. R., Parente, L., Alencar, A. A., Rudorff, B. F. T., Hasenack, H., Matsumoto, M., Ferreira, L. G., Souza-Filho, P. W. M., de Oliveira, S. W., Rocha, W. F., Fonseca, A. V., Marques, C. B., Diniz, C. G., Costa, D., Monteiro, D., Rosa, E. R., Vélez-Martin, E., … Azevedo, T. (2020). Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and earth engine. Remote Sensing, 12(17), 2735 https://www.mdpi.com/2072-4292/12/17/2735
Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L., & Marsham, J. H. (2018). The effects of tropical vegetation on rainfall. Annual Review of Environment and Resources, 43(1), 193-218. https://doi.org/10.1146/annurev-environ-102017-030136
Spracklen, D. V., & Garcia-Carreras, L. (2015). The impact of Amazonian deforestation on Amazon basin rainfall. Geophysical Research Letters, 42(21), 9546-9552. https://doi.org/10.1002/2015GL066063
Staal, A., Fetzer, I., Wang-Erlandsson, L., Bosmans, J. H. C., Dekker, S. C., van Nes, E. H., Rockström, J., & Tuinenburg, O. A. (2020). Hysteresis of tropical forests in the 21st century. Nature Communications, 11(1), 4978. https://doi.org/10.1038/s41467-020-18728-7
Staal, A., Flores, B. M., Aguiar, A. P. D., Bosmans, J. H. C., Fetzer, I., & Tuinenburg, O. A. (2020). Feedback between drought and deforestation in the Amazon. Environmental Research Letters, 15(4), 044024. https://doi.org/10.1088/1748-9326/ab738e
Staal, A., Tuinenburg, O. A., Bosmans, J. H. C., Holmgren, M., van Nes, E. H., Scheffer, M., Zemp, D. C., & Dekker, S. C. (2018). Forest-rainfall cascades buffer against drought across the Amazon. Nature Climate Change, 8(6), 539-543. https://doi.org/10.1038/s41558-018-0177-y
te Wierik, S. A., Cammeraat, E. L. H., Gupta, J., & Artzy-Randrup, Y. A. (2021). Reviewing the impact of land use and land-use change on moisture recycling and precipitation patterns. Water Resources Research, 57(7), e2020WR029234. https://doi.org/10.1029/2020WR029234
UNEP-WCMC, & IUCN. (2022). Protected planet: The World Database on Protected Areas (WDPA).
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., … SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261-272. https://doi.org/10.1038/s41592-019-0686-2