Abstract
Water resource variables are highly complex and vary both spatially and temporally. Understanding the variability and how it evolves has been an important scientific question in Ceará, Brazil. However, describing and determining the uncertainty and the variability in precipitation is still a challenge. Assessing the uncertainty around precipitation is key to develop robust and proactive planning. This study's main aim is to evaluate the underlying spatiotemporal variability of precipitation in the State of Ceará at different timescales by using standardized variability indices computed from different entropy measures. This methodology was applied to analyze 31 meteorological stations with daily time series from 1962 through 2006 while expanding the analysis to the remaining region using an interpolation method. The seasonal timescale analysis revealed that the dry season contributes more to the annual variability, and the change in intra-annual precipitation dynamics could vary with timescales. There were significant upward trends in entropy. Thus, for some stations, there was an increase in the uncertainty of rainfall. Also, there was an increase in variability amount and intensity throughout the decades at the monthly and seasonal timescales. Assessment of precipitation uncertainty within different timescales can benefit a broad community of scientists who are interested in arid-region and natural hazards.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability and material (data transparency)
Data was retrieved from the Brazilian National Water Agency (ANA) at http://www.snirh.gov.br/hidroweb/.
Code availability (software application or custom code)
The calculations and figures were made using the R software.
References
Agarwal A, Maheswaran R, Sehgal V, Khosa R, Sivakumar B, Bernhofer C (2016) Hydrologic regionalization using wavelet-based multiscale entropy method. J Hydrol 538:22–32. https://doi.org/10.1016/j.jhydrol.2016.03.023
Ahmad I, Zhang F, Tayyab M, Anjum MN, Zaman M, Liu J, Farid HU, Saddique Q (2018) Spatiotemporal analysis of precipitation variability in annual, seasonal and extreme values over upper Indus River basin. Atmos Res 213:346–360. https://doi.org/10.1016/j.atmosres.2018.06.019
Alves JMB, Servain J, Campos JNB (2009) Relationship between ocean climatic variability and rain-fed agriculture in northeast Brazil. Clim Res 38(3):225–236. https://doi.org/10.3354/cr00786
Andreoli RV, Kayano MT (2005) ENSO-related rainfall anomalies in South America and associated circulation features during warm and cold Pacific decadal oscillation regimes. Int J Climatol 25(15):2017–2030. https://doi.org/10.1002/joc.1222
Cheng L, Niu J, Liao D (2017) Entropy-based investigation on the precipitation variability over the Hexi Corridor in China. Entropy 19(12):660. https://doi.org/10.3390/e19120660
Cirilo JA, Montenegro SMGL, Campos JNB (2017) The Issue of Water in the Brazilian Semi-Arid Region. In: Bicudo CEM, Tundisi JG, Scheuenstuhl MCB. (eds) Waters of Brazil. Springer, Cham, pp 59–71. https://doi.org/10.1007/978-3-319-41372-3_5
Costa JA, Silva DD (2017) Distribuição espaço-temporal do Índice de anomalia de chuva para o Estado do Ceará. R Bras Geogr 10(4): 1002–1013. https://doi.org/10.26848/rbgf.v10.4.p1002-1013
Cunha APM, Tomasella J, Ribeiro-Neto GG, Brown M, Garcia SR, Brito SB, Carvalho MA (2018) Changes in the spatial–temporal patterns of droughts in the Brazilian Northeast. Atmos Sci Lett 19(10):e855. https://doi.org/10.1002/asl.855
Da Silva VDPR, Belo Filho AF, Almeida RSR, De Holanda RM, Campos JHBC (2016) Shannon information entropy for assessing space–time variability of rainfall and streamflow in semiarid region. Sci Total Environ 544:330–338. https://doi.org/10.1016/j.scitotenv.2015.11.082
De Jong P, Tanajura CAS, Sánchez AS, Dargaville R, Kiperstok A, Torres EA (2018) Hydroelectric production from Brazil’s São Francisco River could cease due to climate change and inter-annual variability. Sci Total Environ 634:1540–1553. https://doi.org/10.1016/j.scitotenv.2018.03.256
Eldrandaly KA, Abu-Zaid MS (2011) Comparison of Six GIS-based spatial interpolation methods for estimating air temperature in Western Saudi Arabia. J Environ Infor 18:38–45. https://doi.org/10.3808/jei.201100197
Eris E, Cavus Y, Aksoy H, Burgan HI, Aksu H, Boyacioglu H (2020) Spatiotemporal analysis of meteorological drought over Kucuk Menderes River Basin in the Aegean Region of Turkey. Theor Appl Climatol 142:1515–1530. https://doi.org/10.1007/s00704-020-03384-0
Ferreira PS, Souza WMD, Silva JFD, Gomes VP (2018) Variabilidade Espaço-temporal das Tendências de Precipitação na Mesorregião Sul Cearense e sua Relação com as Anomalias de TSM. Rev Bras De Meteorol 33(1):141–152. https://doi.org/10.1590/0102-7786331006
Fu T, Gao H, Liang H, Liu J (2021) Spatio-temporal precipitation changes and their localized predictors in the Taihang Mountain region, North China. Stoch Environ Res Risk Assess 35:665–679. https://doi.org/10.1007/s00477-021-01970-w
Garreaud RD, Vuille M, Compagnucci R, Marengo J (2009) Present-day South American climate. Palaeogeogr Palaeoclimatol Palaeoecol 281(3–4):180–195. https://doi.org/10.1016/j.palaeo.2007.10.032
Guntu RK, Rathinasamy M, Agarwal A, Sivakumar B (2020) Spatiotemporal variability of Indian rainfall using multiscale entropy. J Hydrol 587:124916. https://doi.org/10.1016/j.jhydrol.2020.124916
Guo A, Chang J, Wang Y, Huang Q, Guo Z (2017) Maximum entropy-copula method for hydrological risk analysis under uncertainty: a case study on the loess plateau. China Entropy 19(11):609. https://doi.org/10.3390/e19110609
IPCC Climate Change Synthesis Report (2014) Topic 2 future climate changes risks and impacts. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change eds. Pachauri RK, Meyer LA (Geneva, Switzerland).
Jemai S, Ellouze M, Abida H (2017) Variability of precipitation in arid climates using the wavelet approach: case study of watershed of Gabes in South-East Tunisia. Atmosphere 8(9):178. https://doi.org/10.3390/atmos8090178
Jhong BC, Huang J, Tung CP (2019) Spatial assessment of climate risk for investigating climate adaptation strategies by evaluating spatial-temporal variability of extreme precipitation. Water Resour Manage 33:3377–3400. https://doi.org/10.1007/s11269-019-02306-8
Kayano MT, Andreoli RV (2006) Relationships between rainfall anomalies over northeastern Brazil and the El Niño-Southern Oscillation. J Geophys Res 111:D13101. https://doi.org/10.1029/2005JD006142
Kayano MT, Andreoli RV, De Souza RAF (2020) Pacific and Atlantic multidecadal variability relations to the El Niño events and their effects on the South American rainfall. Int J Climatol 40:2183–2200. https://doi.org/10.1002/joc.6326
Kendall MG (1948) Rank correlation methods. C. Griffin, London
Khan MI, Liu D, Fu Q, Azmat M, Luo M, Hu Y, Zhang Y, Abrar FM (2016) Precipitation variability assessment of northeast China: Songhua River basin. J Earth Syst Sci 125:957–968. https://doi.org/10.1007/s12040-016-0715-9
Krige DG (1966) Two-dimensional weighted moving average trend surfaces for ore-evaluation. J South Afr Inst Min Metall 66:13–38
Li Q, Yang M, Wan G, Wang X (2016) Spatial and temporal precipitation variability in the source region of the Yellow River. Environ Earth Sci 75(7):594. https://doi.org/10.1007/s12665-016-5583-8
Liang CP, Hsu WS, Wang CYC, SW, Chen JS, (2019) The combined use of groundwater quality, drawdown index and land use to establish a multi-purpose groundwater utilization plan. Water Resour Manage 33:4231–4247. https://doi.org/10.1007/s11269-019-02360-2
Liu J, Liu T, Bao A, De Maeyer P, Feng X, Miller SN, Chen X (2016) Assessment of different modelling studies on the spatial hydrological processes in an arid alpine catchment. Water Resour Manage 30(5):1757–1770. https://doi.org/10.1007/s11269-016-1249-2
Mann HB (1945) Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245–259.
Marengo JA, Torres RR, Alves LM (2017) Drought in Northeast Brazil—past, present, and future. Theor Appl Climatol 129:1189–1200. https://doi.org/10.1007/s00704-016-1840-8
Maruyama T, Kawachi T, Singh VP (2005) Entropy-based assessment and clustering of potential water resources availability. J Hydrol 309(1–4):104–113. https://doi.org/10.1016/j.jhydrol.2004.11.020
Maurer EP, Lettenmaier DP, Mantua NJ (2004) Variability and potential sources of predictability of North American runoff. Water Resour Res 40:W09306. https://doi.org/10.1029/2003WR002789
Meshram SG, Singh VP, Meshram C (2017) Long-term trend and variability of precipitation in Chhattisgarh State. India Theor Appl Climatol 129(3):729–744. https://doi.org/10.1007/s00704-016-1804-z
Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Mishra AK, Özger M, Singh VP (2009) An entropy-based investigation into the variability of precipitation. J Hydrol 370(1–4):139–154. https://doi.org/10.1016/j.jhydrol.2009.03.006
Qin FY, Jia GS, Yang J, Na YT, Hou MT (2018) Spatiotemporal variability of precipitation during 1961–2014 across the Mongolian Plateau. J Mt Sci 15(5):992–1005. https://doi.org/10.1007/s11629-018-4837-1
Ramarao MVS, Sanjay J, Mujumdar KR, M, Bazaz A, Revi A, (2019) On observed aridity changes over the semiarid regions of India in a warming climate. Theor Appl Climatol 136:693–702. https://doi.org/10.1007/s00704-018-2513-6
Roushangar K, Alizadeh F (2018) Entropy-based analysis and regionalization of annual precipitation variation in Iran during 1960–2010 using ensemble empirical mode decomposition. J Hydroinform 20(2):468–485. https://doi.org/10.2166/hydro.2018.037
Santos CACD, Brito JIBD, Rao TVR, Menezes HEA (2009) Tendências dos índices de precipitação no Estado do Ceará. Rev Bras De Meteorol 24(1):39–47. https://doi.org/10.1590/S0102-77862009000100004
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech 27(3):379–423
Singh VP (1997) The use of entropy in hydrology and water resources. Hydrol Process 11(6):587–626
Singh PK, Chudasama H (2021) Pathways for climate change adaptations in arid and semi-arid regions. J Clean Prod 284:124744. https://doi.org/10.1016/j.jclepro.2020.124744
Su HT, You GJY (2014) Developing an entropy-based model of spatial information estimation and its application in the design of precipitation gauge networks. J Hydrol 519:3316–3327. https://doi.org/10.1016/j.jhydrol.2014.10.022
Tongal H, Sivakumar B (2019) Entropy analysis for spatiotemporal variability of seasonal, low, and high streamflows. Stoch Environ Res Risk Assess 33(1):303–320. https://doi.org/10.1007/s00477-018-1615-0
Villalta DE, De Guenni LB, Sajo-Castelli AM (2020) Spatio-temporal modelling of hydro-meteorological derived risk using a Bayesian approach: a case study in Venezuela. Stoch Environ Res Risk Assess 34(3):513–529. https://doi.org/10.1007/s00477-020-01783-3
Wu Y, Liu S, Yan W, Xia J, Xiang W, Wang K, Luo Q, Fu W, Yuan W (2016) Climate change and consequences on the water cycle in the humid Xiangjiang River Basin. China Stoch Environ Res Risk Assess 30(1):225–235. https://doi.org/10.1007/s00477-015-1073-x
Yang H, Xiao H, Guo C, Sun Y (2019a) Spatial-temporal analysis of precipitation variability in Qinghai Province, China. Atmos Res 228:242–260. https://doi.org/10.1016/j.atmosres.2019.06.005
Yang X, Yang Y, Li K, Wu R (2019b) Estimation and characterization of annual precipitation based on spatiotemporal kriging in the Huanghuaihai basin of China during 1956–2016. Stoch Environ Res Risk Assess 34:1407–1420. https://doi.org/10.1007/s00477-019-01757-0
Zhang Q, Zheng Y, Singh VP, Xiao M, Liu L (2016) Entropy-based spatiotemporal patterns of precipitation regimes in the Huai River basin. China Int J Climatol 36(5):2335–2344. https://doi.org/10.1002/joc.4498
Zhang L, Li H, Liu D, Fu Q, Li M, Faiz MA, Khan MI, Li T (2019) Identification and application of the most suitable entropy model for precipitation complexity measurement. Atmos Res 221:88–97. https://doi.org/10.1016/j.atmosres.2019.02.002
Zheng Y, He Y, Chen X (2017) Spatiotemporal pattern of precipitation concentration and its possible causes in the Pearl River basin, China. J Clean Prod 161:1020–1031. https://doi.org/10.1016/j.jclepro.2017.06.156
Zhou F, Xu Y, Chen Y, Xu CY, Gao Y, Du J (2013) Hydrological response to urbanization at different spatio-temporal scales simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta region. J Hydrol 485:113–125. https://doi.org/10.1016/j.jhydrol.2012.12.040
Acknowledgements
This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico—Brasil (CNPq), the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES), and the Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico (FUNCAP).
Funding
This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico—Brasil (CNPq), the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES), and the Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico (FUNCAP).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
A—Mean Standard Error (MSE) and Mean Square Standard Error (MSSE) of the Kriging prediction for the different timescales.
Timescale of the data | MSE | MSSE |
|---|---|---|
Annual SVIME | 0.020 | 0.975 |
Mean Annual Rainfall | 0.023 | 0.914 |
SVIME for January | 0.014 | 0.958 |
SVIME for February | −0.007 | 0.943 |
SVIME for March | 0.005 | 1.076 |
SVIME for April | 0.028 | 1.088 |
SVIME for May | −0.001 | 1.018 |
SVIME for June | 0.007 | 1.499 |
SVIME for July | 0.007 | 1.122 |
SVIME for August | 0.022 | 0.964 |
SVIME for September | 0.030 | 0.967 |
SVIME for October | −0.028 | 1.177 |
SVIME for November | −0.019 | 1.057 |
SVIME for December | 0.047 | 0.953 |
SVIME for the rainy season | −0.017 | 0.990 |
SVIME for the dry season | 0.015 | 1.009 |
Daily SVIAE | 0.025 | 0.954 |
Monthly SVIAE | 0.018 | 1.043 |
Seasonal SVIAE | 0.031 | 1.426 |
Monthly SVIIE | 0.067 | 1.098 |
Seasonal SVIIE | −0.001 | 1.783 |
Rights and permissions
About this article
Cite this article
Rolim, L.Z.R., Oliveira da Silva, S.M. & de Souza Filho, F. Analysis of precipitation dynamics at different timescales based on entropy theory: an application to the State of Ceará, Brazil. Stoch Environ Res Risk Assess 36, 2285–2301 (2022). https://doi.org/10.1007/s00477-021-02112-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-021-02112-y