Guarda Chuvas: program for access and visualization of historical rainfall data of the State of Pernambuco
DOI:
https://doi.org/10.33448/rsd-v9i11.9858Keywords:
Pernambuco; Precipitation; Interactive visualization; Interpolation methods.Abstract
Rainfall precipitation in Northeastern Brazil (NE) is characterized by high spatial and temporal variability. However, the availability of rainfall data is still limited in this region, for many reasons. Access to rainfall data could provide useful information to better understand rainfall distribution and support ecosystem management. In this paper, we present the program Guarda Chuvas, which makes viable access and visualization of historical rainfall data of the state of Pernambuco, NE Brazil, within a user-friendly environment. The trend surface analysis interpolation method was used to estimate values of monthly precipitation () for the state of Pernambuco on a grid with a resolution of 0.01 degree, totaling 81,544 monthly precipitation series spatially distributed over the state of Pernambuco. The program was developed in C language, with a graphical user interface developed using an application programming interface for Windows. The historical series provided by the program can be used as input for simulation models, and the program can support studies directed to the development of agricultural, water, environmental and socioeconomic policies for the state of Pernambuco. In addition to the regional interest in the data output from the program, the current approach should be found useful for applications in other parts of Brazil and the world.
References
Bier, A. A., & Ferraz, S. E. T. (2017). Comparação de Metodologias de Preenchimento de Falhas em Dados Meteorológicos para Estações no Sul do Brasil. Revista Brasileira de Meteorologia, 32(2), 215–226. https://doi.org/10.1590/0102-77863220008
Black, T. L. (1994). The New NMC Mesoscale Eta Model: Description and Forecast Examples. Weather and Forecasting, 9(2), 265–278. https://doi.org/10.1175/1520-0434(1994)009<0265:TNNMEM>2.0.CO;2
Borges, P. de A., Franke, J., da Anunciação, Y. M. T., Weiss, H., & Bernhofer, C. (2016). Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal, Brazil. Theoretical and Applied Climatology, 123(1–2), 335–348. https://doi.org/10.1007/s00704-014-1359-9
Cavalcanti, E. P., Silva, V. de P. R., & Sousa, F. de A. S. de. (2006). Programa computacional para a estimativa da temperatura do ar para a região Nordeste do Brasil. Revista Brasileira de Engenharia Agrícola e Ambiental, 10(1), 140–147. https://doi.org/10.1590/S1415-43662006000100021
Chen, J., Brissette, F. P., & Leconte, R. (2012). WeaGETS – a Matlab-based daily scale weather generator for generating precipitation and temperature. Procedia Environmental Sciences, 13, 2222–2235. https://doi.org/10.1016/j.proenv.2012.01.211
Ebert, E. E., Janowiak, J. E., & Kidd, C. (2007). Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models. Bulletin of the American Meteorological Society, 88(1), 47–64. https://doi.org/10.1175/BAMS-88-1-47
Fornberg, B., Larsson, E., & Wright, G. (2006). A new class of oscillatory radial basis functions. Computers & Mathematics with Applications, 51(8), 1209–1222. https://doi.org/10.1016/j.camwa.2006.04.004
Franke, R., & Nielson, G. (1980). Smooth interpolation of large sets of scattered data. International Journal for Numerical Methods in Engineering, 15(11), 1691–1704. https://doi.org/10.1002/nme.1620151110
Gebbers, R., & Bruin, S. (2010). Application of Geostatistical Simulation in Precision Agriculture. In Geostatistical Applications for Precision Agriculture (pp. 269–303). Springer Netherlands. https://doi.org/10.1007/978-90-481-9133-8_11
Gomes, O. M., Santos, C. A. C. dos, Souza, F. de A. S. de, Paiva, W. de, & Olinda, R. A. de. (2015). Análise comparativa da precipitação no estado da Paraíba utilizando modelos de regressão polinômial. Revista Brasileira de Meteorologia, 30(1), 47–58. https://doi.org/10.1590/0102-778620120454
Li, J., & Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics, 6(3–4), 228–241. https://doi.org/10.1016/j.ecoinf.2010.12.003
Luo, W., Taylor, M. C., & Parker, S. R. (2008). A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. International Journal of Climatology, 28(7), 947–959. https://doi.org/10.1002/joc.1583
Martin, T. N., Neto, D. D., Junior, P. A. V., & Manfron, P. A. (2008). Homogeneidade espaçotemporal e modelos de distribuição para a precipitação pluvial no estado de São Paulo. Ceres, 55(5).
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, 26–40. https://doi.org/10.1016/j.jaridenv.2016.12.009
Parton, W. J., Schimel, D. S., Cole, C. V., & Ojima, D. S. (1987). Analysis of Factors Controlling Soil Organic Matter Levels in Great Plains Grasslands. Soil Science Society of America Journal, 51(5), 1173–1179. https://doi.org/10.2136/sssa j1987.03615995005100050015x
Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia da pesquisa científica. Santa Maria, RS: UFSM, NTE. https://repositorio.ufsm.br/bitstream/ handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1
Semenov, M. A., & Barrow, E. M. (1997). Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change, 35(4), 397–414. https://doi.org/10.1023/A:1005342632279
Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 1968 23rd ACM National Conference On -, 517–524. https://doi.org/10.1145/800186.810616
Sibson, R. (1980). A vector identity for the Dirichlet tessellation. Mathematical Proceedings of the Cambridge Philosophical Society, 87(1), 151–155. https://doi.org/10.1017/S0305004100056589
Silva, A. S. A., Borko, S., Cezar, M. R. S., & P., S. V. (2019). Comparison of Interpolation Methods for Spatial Distribution of Monthly Precipitation in the State of Pernambuco, Brazil. Journal of Hydrologic Engineering, 24(3), 4018068. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001743
Silva, V. P. R., Sousa, F. A. S., Cavalcanti, E. P., Souza, E. P., & Silva, B. B. (2006). Teleconnections between sea-surface temperature anomalies and air temperature in northeast Brazil. Journal of Atmospheric and Solar-Terrestrial Physics, 68(7), 781–792. https://doi.org/10.1016/j.jastp.2005.12.002
Stöckle, C. O., Campbell, G., & Nelson, R. (1999). ClimGen Manual. Biological Systems Engineering Department. Washington State University Pullman, WA.
Tabios, G. Q., & Salas, J. D. (1985). A comparative analysis of techniques for spatial interpolation of precipitation. Journal of the American Water Resources Association, 21(3), 365–380. https://doi.org/10.1111/j.1752-1688.1985.tb00147.x
Thiessen, A. H. (1911). Precipitation averages for large areas. Monthly Weather Review, 39(7), 1082–1089. https://doi.org/10.1175/1520-0493(1911)39<1082b:PAFLA>2.0.CO;2
Vicente-Serrano, S., Saz-Sánchez, M., & Cuadrat, J. (2003). Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature. Climate Research, 24, 161–180. https://doi.org/10.3354/cr024161
Virgens Filho, J. S., Oliveira, R. B. de, Leite, M. de L., & Tsukahara, R. Y. (2013). Desempenho dos modelos CLIGEN, LARS-WG e PGECLIMA_R na simulação de séries diárias de temperatura máxima do ar para localidades do estado do Paraná. Engenharia Agrícola, 33(3), 538–547. https://doi.org/10.1590/S0100-69162013000300010
Wang, S., Huang, G. H., Lin, Q. G., Li, Z., Zhang, H., & Fan, Y. R. (2014). Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario, Canada. International Journal of Climatology, 34(14), 3745–3751. https://doi.org/10.1002/joc.3941
Webster, R., & Oliver, M. A. (2007). Geostatistics for Environmental Scientists. John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470517277
Xie, Y., Chen, T., Lei, M., Yang, J., Guo, Q., Song, B., & Zhou, X. (2011). Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis. Chemosphere, 82(3), 468–476. https://doi.org/10.1016/j.chemosphere.2010.09.053
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