Skip to main content

Advertisement

Log in

Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Rainfall is a climatic variable that dictates the daily rhythm of urban areas in Northeastern Brazil (NEB) and, therefore, understanding its dynamics is fundamental. The objectives of the study were (i) to validate the CHELSA product with data in situ, (ii) assess the spatial-temporality of the rains, and (iii) assess the trends and socio-environmental implications in the Metropolitan Region of Maceió (MRM). The monthly rainfall data observed between 1960 and 2016 were flawed and were filled with the imputation of data. These series were subjected to descriptive and exploratory statistics, statistical indicators, and the Mann–Kendall (MK) and Pettitt tests. CHELSA product was validated for MRM, and all stations obtained satisfactory determination coefficients (R2) and Pearson correlation (r). The standard error of the estimate (SEE), root mean square error (RMSE), and mean absolute error (MAE) were satisfactory. The highest annual rainfall accumulated occurred near the Mundaú and Manguaba lagoons. The Pettitt test identified that abrupt changes occur in El Niño and La Niña years (strong and weak). The monthly rain boxplots showed high variability in the rainy season (April–July). Outliers have been associated with extreme rainfall at MRM. The drought period was 5 months in all MRM seasons, except in Satuba and Pilar. The Mann–Kendall test and the Sen method showed a tendency for a significant increase in rainfall in Satuba and not significant in the Pilar, while in the others, there was a tendency for a decrease in rainfall. The MRM rainfall depends on physiographic factors, multiscale meteorological systems, and the coastal environment. These results will assist in planning conservationist practices, especially in areas of socio-environmental vulnerability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author on request.

References

  • Allen, R.G. (2002). SEBAL (Surface Energy Balance Algorithms for Land). Advance Training and Users Manual – IdahoImplementation, version 1.0, 97p.

  • Alves, J. M. B., Silva, E. M., Sombra, S. S., Barbosa, A. C. B., & Santos, A. C. S. (2017). Eventos Extremos Diários de Chuva no Nordeste do Brasil e Características Atmosféricas. Revista Brasileira de Meteorologia, 32(2), 227–233.

    Article  Google Scholar 

  • Amorim, M. C. C. T., & Monteiro, A. (2010). Episódios extremos de precipitação e fragilidade dos ambientes urbanos: exemplos de Portugal e do Brasil. Territorium, 17(1), 5–15.

    Article  Google Scholar 

  • ANA - Agência Nacional de Águas. (2020). Available in http://hidroweb.ana.gov.br

  • Armond, N. B., & Sant’anna Neto, J. O. (2019). The urban climate system and the impacts of flooding on Rio de Janeiro, Brazil. In: Henríquez, C.; Romero, H. (Org.). Urban Climates in Latin America. 1ª ed. Switzerland: Springer International Publishing, 1, 259–2801.

  • Arnbjerg-Nielsen, K., Willems, P., Olsson, J., Beecham, S., Pathirana, A., Bulow Gregersen, I., Madsen, H., & Nguyen, V. (2013). Impacts of climate change on rainfall extremes and urban drainage systems: a review. Water Science & Technology, 68(1), 16–28.

    Article  CAS  Google Scholar 

  • Bergemann, M., & Jakob, C. (2015). Global detection and analysis of coastline-associated rainfall using an objective pattern recognition technique. Journal of Climate, 28(18), 7225–7236.

    Article  Google Scholar 

  • Bergemann, M., & Jakob, C. (2016). How important is tropospheric humidity for coastal rainfall in the tropics? Geophysical Research Letters, 43(11), 5860–5868.

  • Brown, T. C., Mahat, V., & Ramirez, J. A. (2019). Adaptation to future water shortages in the United States caused by population growth and climate change. Earth’s Future, 7(3), 219–234.

    Article  Google Scholar 

  • Buuren, S. V., & Oudshoorn, K. G. (2011). Mice: multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 2011.

    Article  Google Scholar 

  • Calgaro, C., & Rech, M. J. (2017). Justiça ambiental, direitos humanos e meio ambiente: uma relação em construção. Revista de Direito e Sustentabilidade, 3(2), 1–16.

  • Camargo, A. P., & Sentelhas, P. C. (1997). Avaliação do desempenho de diferentes métodos de estimativas da evapotranspiração potencial no Estado de São Paulo. Brasil. Revista Brasileira de Agrometeorologia, 5(1), 89–97.

    Google Scholar 

  • Correia Filho, W. L. F., Oliveira Júnior, J. F., Santiago, D. B., Terassi, P. M. B., Teodoro, P. E., Gois, G., Blanco, C. J. C., Souza, P. H. A., Costa, M., & Santos, P. J. (2019a). Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product. Big Earth Data, 3(4), 315–337.

    Article  Google Scholar 

  • Correia Filho, W. L. F., Santiago, D. B., Oliveira Júnior, J. F., & Silva Junior, C. A. (2019b). Impact of urban decadal advance on land use and land cover and surface temperature in the city of Maceió. Brazil. Land Use Policy, 87(1), 1–11.

    Google Scholar 

  • Crosman, E., & Horel, J. (2010). Sea and lake breezes: A review of numerical studies. Boundary Layer Meteorology, 137(1), 1–29.

    Article  Google Scholar 

  • Cunha, B. L. J. E., Rufino, I. A. A., Silva, B. B., & Chaves, I. B. (2012). Dinâmica da cobertura vegetal para a Bacia de São João do Rio do Peixe, PB, utilizando-se sensoriamento remoto. Revista Brasileira de Engenharia Agrícola e Ambiental, 16(5), 539–548.

    Article  Google Scholar 

  • Da Silva, D. F., Sousa, F. A. S., & Kayano, M. T. (2007). Avaliação Dos Impactos Da Poluição Nos Recursos Hídricos Da Bacia Do Rio Mundaú (AL e PE). Revista de Geografia, 24(3), 209–222.

    Google Scholar 

  • Da Silva, D. F., Sousa, F. A. S., & Kayano, M. T. (2010). Escalas Temporais da Variabilidade Pluviométrica na Bacia Hidrográfica do Rio Mundaú. Revista Brasileira de Meteorologia, 25(3), 147–155.

    Article  Google Scholar 

  • Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R., & Mearns, L. O. (2000). Climateextremes: Observations, modeling, and impacts. Science, 289(5487), 2068–2074.

    Article  CAS  Google Scholar 

  • Fialho, E. S., Fernandes, L. A., & Correa, W. S. C. (2019). Climatologia Urbana:Conceitos, Metodologias e Técnicas. Revista Brasileira de Climatologia, 15, 47–85.

    Google Scholar 

  • Freitas, E. D., & Silva Dias, P. L. (2005). Alguns Efeitos de Áreas Urbanas Na Geração de uma Ilha de Calor. Revista Brasileira de Meteorologia, 20(3), 355–366.

    Google Scholar 

  • Gocic, M., & Trajkovic, S. (2013). Analysis of precipitation and drought data in Serbia over the period 1980-2010. Journal of Hydrology, 494(32–42).

  • Gois, G., Delgado, R. C., Oliveira-Júnior, J. F., Teodoro, P. E., & Souza, T. C. O. (2016). EVI2 index trend applied to the vegetation of the state of Rio de Janeiro based on non-parametric tests and Markov chain. Bioscience Journal, 32(4), 1049–1058.

    Article  Google Scholar 

  • Gois, G., Freitas, W., Terassi, P. M. B., Olveira-Júnior, J. F., & Portz, A. (2019). Variabilidade Anual e Mensal da Chuva e da Temperatura do ar no Município de Resende, Rio de Janeiro. Revista Brasileira de Climatologia, 24(15), 67–88.

    Google Scholar 

  • Gois, G., Oliveira-Júnior, J. F., Silva, E. B., Maia, J. L. M., Aleluia, I. S. S., Teodoro, P. E. (2017). Carbon monoxide trend in the city of Rio de Janeiro via Mann-Kendall and Cusum tests. Bioscience Journal (On Line), 33(5), 1332–1339.

  • Guimarães, S. O., Costa, A. A., Vasconcelos Júnior, F. C., Silva, E. M., Sales, D. C., Araújo Júnior, L. M., & Souza, S. G. (2016). Projeções de Mudanças Climáticas sobre o Nordeste Brasileiro dos Modelos do CMIP5 e do CORDEX. Revista Brasileira de Meteorologia, 3(3), 337–365.

    Article  Google Scholar 

  • Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2), 135–143.

    Article  Google Scholar 

  • Harrell, F. E. Jr. (2001). Regression modeling strategies with applications to linear models, logistic regression and survival analysis. (p. 522p). Springer-Verlag.

    Book  Google Scholar 

  • Hopkins, W. G. (2007). A new view of statistics: Correlation coefficient. http://www.sportsci.org/resource/stats/correl.html. Accessed January 18, 2007

  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213.

    Article  Google Scholar 

  • IBGE - Instituto Brasileiro de Geografia e Estatística. (2017). Classificação e caracterização dos espaços rurais e urbanos do Brasil: uma primeira aproximação / IBGE, Coordenação de Geografia. Rio de Janeiro: IBGE, 84p. n. 11.

  • IBGE - Instituto Brasileiro de Geografia e Estatística. (2019). Censo Agropecuário. https://www.ibge.gov.br/estatisticas-novoportal/economicas/agricultura-e-pecuaria/9117-producao-agricola-municipal-culturas-temporarias-e-permanentes.html?=&t=downloads. Access in 21 October 2019.

  • IBGE - Instituto Brasileiro de Geografia e Estatística. (2020). Censos 2000 e 2010, https://ww2.ibge.gov.br/home/estatistica/populacao/censo2010/indicadores_sociais_municipais/. Access in 13 March 2020.

  • Junger, W., & de Leon, A. P. (2018). MTSDI: Multivariate Time Series Data Imputation. R package version 0.3.5. https://CRAN.R-project.org/package=mtsdi

  • Karger, D. N., & Zimmermann, N. E. (2019). Climatologies at High Resolution for the Earth Land Surface Areas CHELSA V1. 2: Technical Specification.

  • Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific Data, 4, 170122.

    Article  Google Scholar 

  • Kendall, M. G. (1975). Rank Correlation Methods. . Griffin.

    Google Scholar 

  • Kousky, V. E. (1979). Frontal influences on Northeast Brazil. Monthly Weather Review, 107(9), 1140–1153.

    Article  Google Scholar 

  • Kousky, V. E., & Gan, M. A. (1981). Upper tropospheric cyclone vortices in the tropical South Atlantic. Tellus, 33(6), 538–551.

    Article  Google Scholar 

  • Li, L., Tan, Y., Ying, S., Yu, Z., Li, Z., & Lan, H. (2014). Impact of land cover and population density on land surface temperature: case study in Wuhan. China. Journal of Applied Remote Sensing, 8(1), 084993.

    Article  Google Scholar 

  • Lyra, G. B., Oliveira-Júnior, J. F., Gois, G., Cunha-Zeri, G., & Zeri, M. (2017). Rainfall variability over Alagoas under the influences of SST anomalies. Meteorology and Atmospheric Physics, 129(1), 157–171.

    Article  Google Scholar 

  • Lyra, G. B., Oliveira-Júnior, J. F., & Zeri, M. (2014). Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Alagoas state, Northeast of Brazil. International Journal of Climatology, 34(13), 3546–3558.

    Article  Google Scholar 

  • Mann, H. B. (1945). Non-parametric tests against trend. Econometrica, 13(3), 245–259.

    Article  Google Scholar 

  • Marengo, J. A., Nobre, C. A., Seluchi, A. E., Cuartas, A., Alves, L. M., Mendiondo, E. M., Obregón, G., & Sampaio, G. (2015). A seca e a crise hídrica de 2014–2015 em São Paulo. Revista USP, 106, 31–44.

    Article  Google Scholar 

  • Markham, B. L., & Barker, L. L. (1987). Thematic mapper bandpass solar exoatmospherical irradiances. International Journal of Remote Sensing, 8(3), 517–523.

    Article  Google Scholar 

  • Martins, T. A. L., Bonhomme, M., & Adolphe, L. (2013). Análise do impacto da morfologia urbana na demanda estimada de energia das edificações: um estudo de caso na cidade de Maceió. AL. Ambiente Construído, 13(4), 213–233.

    Article  Google Scholar 

  • Mello, C. R., & Silva, A. M. (2009). Modelagem estatística da precipitação mensal e anual e no período seco para o estado de Minas Gerais. Revista Brasileira de Engenharia Agrícola e Ambiental, 13(1), 68–74.

    Article  Google Scholar 

  • Molion, L. C. B., & Bernardo, S. O. (2002). Uma revisão da dinâmica das chuvas no Nordeste Brasileiro. Revista Brasileira de Meteorologia, 17(1), 1–10.

    Google Scholar 

  • Moreira, A. B., Santos, P. F. C., Soares, D. B., & Nóbrega, R. S. (2017). Eventos extremos e a cidade: estudo de caso dos impactos causados por um evento climático em área urbana. Revista Brasileira de Geografia Física, 10(6), 1730–1745.

    Article  Google Scholar 

  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885–900.

    Article  Google Scholar 

  • Nash, J. E., & Sutcliffe, J. V. (1970). River Flow Forecasting through Conceptual Model. Part 1—A Discussion of Principles. Journal of Hydrology, 10(3), p. 282-290.

  • NOAA/CPC - National Oceanic and Atmospheric Administration/Climate Prediction. (2020). Center. Disponível em: http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. Access in 27 March 2020.

  • Nóbrega, R. S., Farias, R. F. L., & Santos, C. A. C. (2015). Variabilidade temporal e espacial da precipitação pluviométrica em Pernambuco através de índices de extremos climáticos. Revista Brasileira de Meteorologia, 30(2), 171–180.

    Article  Google Scholar 

  • Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1–24.

    Google Scholar 

  • Oliveira Júnior, J. F., Gois, G., Silva, E. B., Teodoro, P. E., Johann, J., & Silva Junior, C. A. (2019). Non-parametric tests, multivariate analysis and descriptive and exploratory statistics applied to reported dengue cases in Brazil. Environmental Monitoring and Assessment, 191(1), 473–491.

    Article  Google Scholar 

  • Oliveira Júnior, J. F., Lyra, G. B., Gois, G., Brito, T. T., & Moura, N. S. H. (2012). Análise de homogeneidade de séries pluviométricas para determinação do índice de seca IPP no estado de Alagoas. Floresta e Ambiente, 19, 101–112.

    Article  Google Scholar 

  • Oscar Júnior, A. C. (2015). Extremos atmosféricos e desastres hidrometeorológicos em Duque de Caxias (RJ). Revista Brasileira de Climatologia, 17(11), 189–205.

    Google Scholar 

  • Panagos, P., Karydas, C. G., Gitas, J. Z., & Montanarela, L. (2012). Monthly soil erosion monitoring based on remotely sensed biophysical parameters: a case study in Strymonas river basin towards a functional pan-European service. International Journal of Digital Earth, 5(6), 461–487.

    Article  Google Scholar 

  • Paredes-Trejo, F. J., Barbosa, H. A., & Lakshmi Kumar, T. V. (2017). Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil. Journal of Arid Environments, 139(1), 26–40.

    Article  Google Scholar 

  • PBMC - Painel Brasileiro de Mudanças Climáticas. (2012). Sumário Executivo do Volume 1 – Base Científica das Mudanças Climáticas. Contribuição do Grupo de Trabalho 1 para o 1° Relatório de Avaliação Nacional do Painel Brasileiro de Mudanças Climáticas. Volume Especial para Rio+20. Rio de Janeiro, Brasil, 34.

  • Pettitt, A. N. (1979). A non-parametric approach to the change-point problem. Applied Statistics. Journal of the Royal Statistical Society, 28(2), 126–135.

  • Pingale, S. M., Khare, D., Jat, M. K., & Adamowski, J. (2014). Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centers of the arid and semi-arid state of Rajasthan. India. Atmospheric Research, 1(1), 73–90.

    Article  Google Scholar 

  • Pontes da Silva, B. F., Fedorova, N., Levit, V., Peresetsky, A., & Brito, B. M. (2011). Sistemas Sinóticos Associados às Precipitações Intensas no Estado de Alagoas. Revista Brasileira de Meteorologia, 26(3), 323–338.

    Article  Google Scholar 

  • QGIS Development Team. (2014). QGIS geographic information system version 2.14. Open source geospatial Foundation project.

  • R Development Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.r-project.org. ISBN 3-900051-07-0.

  • Rao, V. B., Lima, M. C., & Franchito, S. H. (1993). Seasonal and interannual variations of rainfall over Eastern Northeast Brazil. Journal of Climate, 6(9), 1754–1763.

    Article  Google Scholar 

  • Rouse J, Haas R, Schell J, Deering D, & Harlan J, (1974). Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation: Final Report [online]. Greenbelt: NASA/GSFC, 371 p. Available from: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19740008955.pdf

  • Salton, F. G., Morais, H., Caramori, H., & Borrozzino, E. (2016). Climatologia dos Episódios de Precipitação em Três Localidades no Estado do Paraná. Revista Brasileira de Meteorologia, 31(4), 626–638.

    Article  Google Scholar 

  • Sant’anna Neto, J. L. (2005). Decálogo da Climatologia do Sudeste Brasileiro. Revista Brasileira de Climatologia, 1, 3–60.

    Article  Google Scholar 

  • Santiago, D. B., & Gomes, H. B. (2016). Estudo de Ilhas de Calor no Município de Maceió/AL, por meio de Dados Orbitais do Landsat5. Revista Brasileira de Geografia Física, 9(3), 793–803.

    Google Scholar 

  • Santos, Y. S., Silva, E. B., Oliveira-Júnior, J. F., Santos, P. J., & Costa, L. M. B. (2018). Diagnóstico da Morbidade e Mortalidade dos Casos de Leptospirose no Nordeste Brasileiro entre 2000 A 2015. Enciclopédia Biosfera, 15(27), 107–118.

    Article  Google Scholar 

  • Seifert, E. (2014). OriginPro 9.1: Scientific Data Analysis and Graphing Software Software Review.

  • Sepúlveda, S. A., & Petley, D. N. (2015). Regional trends and controlling factors of fatal landslides in Latin America and the Caribbean. Natural Hazards and Earth System Sciences, 15(1), 1821–1833.

    Article  Google Scholar 

  • Sheng, C., Li, W. B., Du, Y. D., Mao, C. Y., & Zhang, L. (2015). Urbanization effect on precipitation over the Pearl River Delta based on CMORPH data. Advances in Climate Change Research, 6(1), 16–22.

    Article  Google Scholar 

  • Silva Junior, C. A., Costa, G. M., Rossi, F. S., Vale, J. C. E., Lima, R. B., LIMA, M., Oliveira Júnior, J. F., Teodoro, P. E., & Santos, R. C. (2019). Remote sensing for updating the boundaries between the Brazilian Cerrado-Amazonia biomes. Environmental Science & Policy, 101(1), 383–392.

  • Silva, J. L., & Samora, P. (2019). Os impactos da crise hídrica sobre a população do município de Campinas/SP (2012–2016). URBE - Revista Brasileira de Gestão Urbana, 11, e20170210.

    Article  Google Scholar 

  • Stoof, C. R., Ferreira, A. J. D., Mol, W., Berg, J. V. D., Kort, A. D., Drooger, S., Slingerland, E. C., Mansholt, A. U., Ferreira, C. S. S., & Ritsema, C. J. (2015). Soil surface changes increase runoff and erosion risk after a low–moderate severity fire. Geoderma, 239, 58–67.

    Article  Google Scholar 

  • Tenório, R. S., Moraes, M. C. S., & Sauvageot, H. (2012). Raindrop Size Distribution and Radar Parameters in Coastal Tropical Rain Systems of Northeastern Brazil. Journal of Applied Meteorology and Climatology, 51(1), 1960–1970.

    Article  Google Scholar 

  • Tenório, R. S., Kwon, B. H., Moraes, M. C. S., & Lee, G. (2010). Tropical Rainfall Characteristics at the Eastern Coast of South America. Asia-Pacific Journal Atmospheric Science, 46(1), 415–423.

    Google Scholar 

  • Vieira, R. M. D. S. P., Cunha, A. P. M. D. A., Alvalá, R. C. D. S., Carvalho, V. C., FerrazNeto, S., & Sestini, M. F. (2013). Land use and land cover map of a semiarid region of Brazil for meteorological and climatic models. Revista Brasileira de Meteorologia, 28(2), 129–138.

    Article  Google Scholar 

  • Wang, L., Qu, J. J., & Hao, X. (2008). Forest fire detection using the normalized multi-band drought index (NMDI) with satellite measurements. Agricultural and Forest Meteorology, 148(11), 1767–1776.

    Article  Google Scholar 

  • Wilks, D. S. (1995). Statistical methods in the atmospheric sciences: an introduction, 59. (p. 467). Academic Press.

    Google Scholar 

  • Willmott, C. J. (1981). On the validation of models. Physical Geography, 2(2), 184–194.

    Article  Google Scholar 

  • Willmott, C. J., Ackleson, S. G., Davis, R. E., Feddema, J. J., Klink, K. M., Legates, D. R., Rowe, C. M., & O’Donnell, J. (1985). Statistics for the evaluation and comparison of models. Journal of Geophysical Research, 90(C5), 8995–9005.

    Article  Google Scholar 

  • Wu, C., & Huang, G. (2015). Changes in heavy precipitation and floods in the upstream of the Beijiang River basin, South China. International Journal of Climatology, 35(10), 2978–2992.

    Article  Google Scholar 

  • Yengoh, G. T., Fogwe, Z. N., & Armah, F. A. (2017). Floods in the Douala metropolis, Cameroon: attribution to changes in rainfall characteristics or planning failures? Journal of Environmental Planning and Management, 60(2), 204–230.

    Article  Google Scholar 

  • Zanella, M. E., Sales, M. C. L., & Abreu, N. J. (2009). Análise das precipitações diárias intensas e impactos gerados em Fortaleza - CE. Geousp, 25(1), 53–68.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos Antonio da Silva Junior.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Oliveira-Júnior, J.F., Correia Filho, W.L.F., de Barros Santiago, D. et al. Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications. Environ Monit Assess 193, 263 (2021). https://doi.org/10.1007/s10661-021-09043-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-021-09043-9

Keywords

Navigation