Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Mapping the effects of climate change on reference evapotranspiration in future scenarios in the Brazilian Semiarid Region - South America

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
اقرأ أكثر حفظ في قائمتي
  • معلومة اضافية
    • Contributors:
      FAPEMIG; CAPES; CNPQ
    • بيانات النشر:
      Universidade Federal de Pernambuco
    • الموضوع:
      2023
    • Collection:
      Portal de Periódicos - UFPE (Universidade Federal de Pernambuco)
    • الموضوع:
      Semiárido Brasileiro; Futuro
    • نبذة مختصرة :
      Brazil has the world's most populous semi-arid region and climate change represents significant ecological and socioeconomic challenges for this area. To better understand the impact of these changes, it is crucial to analyze the dynamics of climate variables and evapotranspiration (ETo), a critical climate variable. This study aimed to model ETo rates considering climate change scenarios in the Brazilian Semi-arid region (BSR). The modeling was based on tests of five machine learning algorithms: Bayesian Regularized Neural Networks (BRNN), Cubist, Earth, Linear Regression (LM), and Random Forest (RF). A dataset with 20 covariates was used to represent the current scenario. In the future prediction, covariates from two shared socio-economic pathways were used (SSPs 126 and 585). The best statistical performance was achieved by Cubist (R² = 0.98 and RMSE = 0.08 mm day-¹ in the holdout-test). The current daily average ETo is 4.77 mm day-¹, while in future scenarios, it can increase by 3.56% in SSP 126 and 15.51% in SSP 585. ETo rates are expected to expand territorially; ranges from > 0.60 mm day-¹ should increase 8% in SSP 126 and 40% in SSP 585. The applied model suggests that ETo may increase in future scenarios in the BSR, which could affect biodiversity levels and intensify social conflicts.Keywords: Machine Learning; Spatial Prediction; Cubist; Semi-arid Zone; Climate changes. Mapeamento dos Efeitos das Mudanças Climáticas na Evapotranspiração de Referência em Cenários Futuros no Semiárido Brasileiro - América do Sul RESUMOO Brasil possui a região semiárida mais populosa do mundo e as mudanças climáticas impõem desafios ecológicos e socioeconômicos significativos para esta área. Para entender melhor o impacto dessas mudanças, é crucial analisar a dinâmica das variáveis climáticas e a evapotranspiração (ETo), uma importante variável do clima. Este estudo teve como objetivo modelar taxas de ETo considerando cenários de mudanças climáticas no Semiárido Brasileiro (BSR). A modelagem foi baseada em testes de ...
    • File Description:
      application/pdf
    • Relation:
      https://periodicos.ufpe.br/revistas/rbgfe/article/view/256247/43770; https://periodicos.ufpe.br/revistas/rbgfe/article/downloadSuppFile/256247/42411; https://periodicos.ufpe.br/revistas/rbgfe/article/downloadSuppFile/256247/42412; Althoff, D., Bazame, H.C., Filgueiras, R., Dias, S.H.B., 2018. Heuristic methods applied in reference evapotranspiration modeling. Ciência e Agrotecnologia 42, 314–324. Althoff, D., Dias, S.H.B., Filgueiras, R., Rodrigues, L.N., 2020. ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018). Water Resources Research 56, 1–24. https://doi.org/10.1029/2020WR027562. Breiman, L., 2001. Random forests. Machine learning 45, 5–32. Burrell, A.L., Evans, J.P., De Kauwe, M.G., 2020. Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nat Commun 11, 3853. https://doi.org/10.1038/s41467-020-17710-7 Castro Oliveira, G., Arruda, D.M., Fernandes Filho, E.I., Veloso, G.V., Francelino, M.R., Schaefer, C.E.G.R., 2021. Soil predictors are crucial for modelling vegetation distribution and its responses to climate change. Science of The Total Environment 146680. https://doi.org/10.1016/j.scitotenv.2021.146680. Costa, J.F.C.B., Silva, R.M., Santos, C.A.G., Silva, A.M., Vianna, P.C.G., 2021. Analysis of the response of the Epitácio Pessoa reservoir (Brazilian semi-arid region) to potential future drought, water transfer and LULC scenarios. Natural Hazards 1–25. https://dx.doi.org/10.1007/s11069-021-04736-3. Cunha, A.P.M., Alvalá, R.C., Nobre, C.A., Carvalho, M.A., 2015. Monitoring vegetative drought dynamics in the Brazilian semi-arid region. Agricultural and forest meteorology 214, 494–505. https://doi.org/10.1016/j.agrformet.2015.09.010. Del Cerro, R.T.G., Subathra, M.S.P., Kumar, N.M., Verrastro, S., George, S.T., 2021. Modelling the daily reference evapotranspiration in semi-arid region of South India: A case study comparing ANFIS and empirical models. Information Processing in Agriculture 8, 173–184. https://dx.doi.org/10.1016/j.inpa.2020.02.003. Dias, H.B., Sentelhas, P.C., 2021. Assessing the performance of two gridded weather data for sugarcane crop simulations with a process-based model in Center-South Brazil. International Journal of Biometeorology 1–13. https://dx.doi.org/10.1007/s00484-021-02145-6. Dias, S.H.B., Filgueiras, R., Fernandes Filho, E.I., Arcanjo, G.S., Silva, G.H. da, Mantovani, E.C., Cunha, F.F. da, 2021. Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing. Plos one 16, e0245834. https://dx.doi.org/10.1371/journal.pone.0245834. Fan, J., Wu, L., Zhang, F., Xiang, Y., Zheng, J., 2016. Climate change effects on reference crop evapotranspiration across different climatic zones of China during 1956–2015. Journal of Hydrology 542, 923–937. https://doi.org/10.1016/j.jhydrol.2016.09.060. Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315. Gao, W., Zheng, C., Liu, X., Lu, Y., Chen, Y., Wei, Y., Ma, Y., 2022. NDVI-based vegetation dynamics and their responses to climate change and human activities from 1982 to 2020: A case study in the Mu Us Sandy Land, China. Ecological Indicators 137, 108745. Gomes, L.C., Faria, R.M., Souza, E., Veloso, G.V., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2019. Modelling and mapping soil organic carbon stocks in Brazil. Geoderma 340, 337–350. https://doi.org/10.1016/j.geoderma.2019.01.007 Grünzweig, J.M., Boeck, H.J., Rey, A., Santos, M.J., Adam, O., Bahn, M., Belnap, J., Deckmyn, G., Dekker, S.C., Flores, O., 2022. Dryland mechanisms could widely control ecosystem functioning in a drier and warmer world. Nature Ecology & Evolution 1–13. Hausfather, Z., Marvel, K., Schmidt, G.A., Nielsen-Gammon, J.W., Zelinka, M., 2022. Climate simulations: Recognize the ‘hot model’problem. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology: A Journal of the Royal Meteorological Society 25, 1965–1978. https://doi.org/10.1002/joc.1276. Houborg, R., McCabe, M.F., 2018. A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning. ISPRS Journal of Photogrammetry and Remote Sensing 135, 173–188. Huang, J., Ji, M., Xie, Y., Wang, S., He, Y., Ran, J., 2016. Global semi-arid climate change over last 60 years. Climate Dynamics 46, 1131–1150. https://doi.org/10.1007/s00382-015-2636-8. Jiang, F., Xie, X., Liang, S., Wang, Y., Zhu, B., Zhang, X., Chen, Y., 2021. Loess Plateau evapotranspiration intensified by land surface radiative forcing associated with ecological restoration. Agricultural and Forest Meteorology 311, 108669. Kang, Z., Qiu, B., Xiang, Z., Liu, Y., Lin, Z., Guo, W., 2022. Improving simulations of vegetation dynamics over the Tibetan Plateau: Role of atmospheric forcing data and spatial resolution. Advances in Atmospheric Sciences 39, 1115–1132. Kim, J., Choi, J., Choi, C., Park, S., 2013. Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea. Science of the Total Environment 452, 181–195. https://doi.org/10.1016/j.scitotenv.2013.02.005. Kuhn, M., Johnson, K., 2013. Applied predictive modeling. Springer. https://dx.doi.org/10.1007/978-1-4614-6849-3. Kuhn, M., Quinlan, R., 2018. Cubist: Rule-and instance-based regression modeling. R package version 0.2. 2. Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Team, R.C., 2020. Package ‘caret.’ The R Journal 223, 7. Leal Filho, W., Totin, E., Franke, J.A., Andrew, S.M., Abubakar, I.R., Azadi, H., Nunn, P.D., Ouweneel, B., Williams, P.A., Simpson, N.P., 2022. Understanding responses to climate-related water scarcity in Africa. Science of the Total Environment 806, 150420. Li, M., Chu, R., Sha, X., Islam, A.R.M.T., Jiang, Y., Shen, S., 2022. How Has the Recent Climate Change Affected the Spatiotemporal Variation of Reference Evapotranspiration in a Climate Transitional Zone of Eastern China? ISPRS International Journal of Geo-Information 11, 300. Liu, Y., Yao, X., Wang, Q., Yu, J., Jiang, Q., Jiang, W., Li, L., 2021. Differences in reference evapotranspiration variation and climate-driven patterns in different altitudes of the Qinghai–Tibet plateau (1961–2017). Water 13, 1749. Marengo, J.A., Galdos, M.V., Challinor, A., Cunha, A.P., Marin, F.R., Vianna, M. dos S., Alvala, R.C., Alves, L.M., Moraes, O.L., Bender, F., 2022. Drought in Northeast Brazil: A review of agricultural and policy adaptation options for food security. Climate Resilience and Sustainability 1, e17. Milborrow, S., Tibshirani, R., 2019. Package ‘earth’: Multivariate Adaptive Regression Splines. Nooni, I.K., Hagan, D.F.T., Wang, G., Ullah, W., Lu, J., Li, S., Dzakpasu, M., Prempeh, N.A., Lim Kam Sian, K.T., 2021. Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6. International Journal of Environmental Research and Public Health 18, 6760. Núñez-López, J.M., Cansino-Loeza, B., Sánchez-Zarco, X.G., Ponce-Ortega, J.M., 2022. Involving resilience in assessment of the water–energy–food nexus for arid and semi-arid regions. Clean Technologies and Environmental Policy 1–13. Oliver, M.A., Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information System, 4(3), 313-332. Orimoloye, I.R., Belle, J.A., Orimoloye, Y.M., Olusola, A.O., Ololade, O.O., 2022. Drought: A common environmental disaster. Atmosphere 13, 111. Pielke Jr, R., Burgess, M.G., Ritchie, J., 2022. Plausible 2005–2050 emissions scenarios project between 2° C and 3° C of warming by 2100. Environmental Research Letters 17, 024027. Rodriguez, P.P., Gianola, D., 2016. BRNN: Bayesian regularization for feed-forward neural networks. R package version 0.6. Salas-Martínez, F., Valdés-Rodríguez, O.A., Palacios-Wassenaar, O.M., Márquez-Grajales, A., 2021. Analysis of the Evolution of Drought through SPI and Its Relationship with the Agricultural Sector in the Central Zone of the State of Veracruz, Mexico. Agronomy 11, 2099. Santos, T.G., Battisti, R., Casaroli, D., Alves, J., Evangelista, A.W.P., 2021. Assessment of agricultural efficiency and yield gap for soybean in the Brazilian Central Cerrado biome. Bragantia 80, 1–11. https://doi.org/10.1590/1678-4499.20200352. Scott, R.L., Biederman, J.A., Hamerlynck, E.P., Barron‐Gafford, G.A., 2015. The carbon balance pivot point of southwestern US semi-arid ecosystems: Insights from the 21st century drought. Journal of Geophysical Research: Biogeosciences 120, 2612–2624. Shi, L., Feng, P., Wang, B., Liu, D.L., Yu, Q., 2020. Quantifying future drought change and associated uncertainty in southeastern Australia with multiple potential evapotranspiration models. Journal of Hydrology 590, 125394. https://doi.org/10.1016/j.jhydrol.2020.125394 Silveira, S.M.B., Silva, M.G., 2019. Conflitos socioambientais por água no Nordeste brasileiro: expropriações contemporâneas e lutas sociais no campo. Revista Katálysis 22, 342–352. Souza, C.M.P., Veloso, G.V., Mello, C.R., Ribeiro, R.P., Silva, L.A.P., Leite, M.E., Fernandes Filho, E.I., 2022. Spatiotemporal prediction of rainfall erosivity by machine learning in southeastern Brazil. Geocarto International 1–19. https://doi.org/10.1080/10106049.2022.2060318 Silva, C., Teixeira, A.C., Manzione, R., 2020. Utilização de Redes Neurais com Regularização Bayesiana na Modelagem de Evapotranspiração de Referência em Agroecossistemas Semiáridos. Revista Brasileira de Engenharia de Biossistemas 14, 73–84. Van Vuuren, D.P., Riahi, K., Calvin, K., Dellink, R., Emmerling, J., Fujimori, S., Kc, S., Kriegler, E., O’Neill, B., 2017. The Shared Socio-economic Pathways: Trajectories for human development and global environmental change. Global Environmental Change 42, 148–152. https://doi.org/10.1016/j.gloenvcha.2016.10.009 Wendt, K.A., Häuselmann, A.D., Fleitmann, D., Berry, A.E., Wang, X., Auler, A.S., Cheng, H., Edwards, R.L., 2019. Three-phased Heinrich Stadial 4 recorded in NE Brazil stalagmites. Earth and Planetary Science Letters 510, 94–102. https://doi.org/10.1016/j.epsl.2018.12.025.; Wu, T., Zhang, W., Jiao, X., Guo, W., Hamoud, Y.A., 2021. Evaluation of stacking and blending ensemble learning methods for estimating daily reference evapotranspiration.Computers and Electronics in Agriculture 184, 106039. https://doi.org/10.1016/j.compag.2021.10603 Xavier, A.C., King, C.W., Scanlon, B.R., 2016. Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology 36, 2644–2659. https://doi.org/10.1002/joc.4518. Xing, X., Qian, J., Chen, X., Chen, C., Sun, J., Wei, S., Yimamaidi, D., Zhanar, Z., 2022. Analysis of Effects of Recent Changes in Hydrothermal Conditions on Vegetation in Central Asia. Land 11, 327. Yoo, J., Kwon, H.-H., Lee, J.-H., Kim, T.-W., 2016. Influence of evapotranspiration on future drought risk using bivariate drought frequency curves. KSCE Journal of Civil Engineering 20, 2059–2069. https://doi.org/10.1007/s12205-015-0078-9. Zhang, G., Gan, T.Y., Su, X., 2022. Twenty-first century drought analysis across China under climate change. Climate Dynamics 59, 1665–1685.; https://periodicos.ufpe.br/revistas/rbgfe/article/view/256247
    • الرقم المعرف:
      10.26848/rbgf.v16.2.p1001-1012
    • Rights:
      Direitos autorais 2023 Revista Brasileira de Geografia Física ; https://creativecommons.org/licenses/by/4.0
    • الرقم المعرف:
      edsbas.1477C69B