Environmental Earth Sciences

, Volume 71, Issue 2, pp 885–894 | Cite as

Relationship between hydrogeological parameters for data-scarce regions: the case of the Araripe sedimentary basin, Brazil

  • Sávio de Brito FonteneleEmail author
  • Luiz Alberto Ribeiro Mendonça
  • José Carlos de Araújo
  • Maria Marlúcia Freitas Santiago
  • José Yarley de Brito Gonçalves
Original Article


This paper applies and validates a method for generating spatially distributed hydraulic conductivity (k) based on the specific capacity (Q s) for data-scarce regions. This method has been applied to the Araripe sedimentary basin, Brazil, and consists of four steps: (1) selection of (32) wells for which both k and Q s data are available; (2) estimation of k as a function of Q s for the (128) wells for which only specific capacity data are available; (3) spatial distribution of k using the kriging geostatistical tool; (4) validation of the method, using (17) representative wells with k measured data. The equation relating k and Q s showed a statistically significant linear relationship (R = 0.93), from which a database has been generated using kriging with the spherical model. The results showed a calibration coefficient of Nash and Sutcliffe (NS) of 0.54 and moderate spatial dependence ratio of 69 %. The validation process provided only a moderate efficiency (NS = 0.22), possibly due to the geological complexity of the focus system. Despite its limitations, the method indicates the possibility of application of ordinary kriging to generate reliable data from auxiliary variables, especially for the water management of data-scarce areas.


Hydraulic conductivity Specific capacity Linear relationship Kriging Geostatistic 



The authors acknowledge Capes—Brazilian Coordination for the Improvement of Higher Education Personnel—for the scholarship granted to the first author; CNPq—Brazilian National Council of Scientific and Technologic Development—for the financial support of the research (process 483270/2010-5); and COGERH—Water Resources Management Company of Ceará—in Crato, for providing important data.


  1. Acheampong SY, Hess JW (1998) Hydrogeologic and hydrochemical framework of the groundwater system in the southern Voltaian sedimentary basin, Ghana. Hydrogeol J 6:527–537Google Scholar
  2. Benson RC, Yuhr L (1993) Spatial sampling considerations and their applications to characterizing fractured rock and karst systems. Environ Geol 22:296–307. doi: 10.1007/BF00767501 CrossRefGoogle Scholar
  3. CAGECE (Companhia de Água e Esgoto do Estado do Ceará) (1984) Captação de Juazeiro do Norte—Condição de exploração dos poços. Governo do Estado do Ceará, FortalezaGoogle Scholar
  4. Carvalho JRP, Assad ED (2005) Spatial analysis of precipitation data in São Paulo state: comparison of interpolation methods. Eng Agríc 25:377–384CrossRefGoogle Scholar
  5. SRH (Secretaria de Recursos Hídricos do Estado do Ceará) (2005) Implantação do sistema de monitoramento/gestão de uma área piloto do aquífero Missão Velha, na Bacia Sedimentar do Araripe. Fortaleza, pp 8–13Google Scholar
  6. Chen LH, Chen CT, Pan YG (2010) Groundwater level prediction using SOMRBFN multisite model. J Hydrol Eng 8:624–631CrossRefGoogle Scholar
  7. Cooper Junior HH, Jacob CE (1946) A generalized graphical method of evaluating formation constants and summarizing well-filed history. Trans Am Geophys Un 27:526–534CrossRefGoogle Scholar
  8. de Andrade ARS, Guerrini IA, Garcia CJB, Katez I, Guerra HOC (2005) Spatial variability of the soil density in the irrigation management. Ciênc Agrotec 29:322–329CrossRefGoogle Scholar
  9. de Araújo JC, Piedra JIG (2009) Comparative hydrology: analysis of a semiarid and a humid tropical watershed. Hydrol Process 23:1169–1178CrossRefGoogle Scholar
  10. de Lima JS S, Oliveira PC, de Oliveira RB, Xavier AC (2008) Geostatistic methods used in the study of soil penetration resistance in tractor traffic trail during wood harvesting. Rev Árvore 32:931–938. doi: 10.1590/S0100-67622008000500018 CrossRefGoogle Scholar
  11. Dupuit J (1863) Études théoriques et pratiques sur le mouvement des eaux dans lês canaux découverts et à travers lês terrains perméables, 2nd edn. Dunod, ParisGoogle Scholar
  12. Fabbri P (1997) Transmissivity in the geothermal Euganean basin: a geostatistical analysis. Ground Water 35:881–887. doi: 10.1111/j.1745-6584.1997.tb00156.x CrossRefGoogle Scholar
  13. Faraco MA, Uribe-Opazo MA, da Silva EAA, Johann JA, Borssoi JA (2008) Selection criteria of spatial variability models used in thematical maps of soil physical attributes and soybean yield. Rev Bras Ciênc Solo 32:463–476CrossRefGoogle Scholar
  14. Grego CR, Vieira SR (2005) Spatial variability of soil physical properties on an experimental plot. Rev Bras Ciênc Solo 29:169–177CrossRefGoogle Scholar
  15. Hamm SY, Cheong JY, Jang S, Jung CY, Kim BS (2005) Relationship between transmissivity and specific capacity in the volcanic aquifers of Jeju Island, Korea. J Hydrol 310:111–121CrossRefGoogle Scholar
  16. Huntley D, Nommensen R, Steffey D (1992) Use of specific capacity to assess transmissivity in fractured-rock aquifers. Ground Water 30:396–402. doi: 10.1111/j.1745-6584.1992.tb02008.x CrossRefGoogle Scholar
  17. IBGE (Instituto Brasileiro de Geografia e Estatística) (2010) Banco de dados. Accessed 20 February 2011
  18. Isaaks EH, Srivastava M (1989) An introduction to applied geostatistics. Oxford University Press, New YorkGoogle Scholar
  19. Jacob CE (1950) Flow of groundwater Engineering Hydraulics. Wiley, New YorkGoogle Scholar
  20. Jalludin M, Razack M (2004) Assessment of hydraulic properties of sedimentary and volcanic aquifer systems under arid conditions in the Republic of Djibouti (Horn of Africa). Hydrogeol J 12:159–170CrossRefGoogle Scholar
  21. Li J, Heap AD (2011) A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecol Inform 6:228–241CrossRefGoogle Scholar
  22. Liu G, Craig JR, Soulis ED (2011) Applicability of the Green-Ampt infiltration model with shallow boundary conditions. J Hydrol Eng 16:266–274CrossRefGoogle Scholar
  23. Mace RE (1997) Determination of transmissivity from specific capacity test in a karst aquifer. Ground Water 35:738–742. doi: 10.1111/j.1745-6584.1997.tb00141.x CrossRefGoogle Scholar
  24. Machado CJF, Santiago MFS, Mendonça LAR, Firschron H, Mendes Filho J (2007) Hydrochemical and flow modeling of aquitard percolation in the Cariri Valley-Northeast Brazil. Aquat Geochem 13:187–196CrossRefGoogle Scholar
  25. Malveira VTC, de Araújo JC, Güntner A (2012) Hydrological impact of a high-density reservoir network in the semiarid North-Eastern Brazil. J Hydrol Eng 17:109–117. doi: 10.1061/(ASCE)HE.1943-5584.0000404 CrossRefGoogle Scholar
  26. Mc Bratney AB, Webster R (1986) Choosing functions for semi-variograms of soil properties and fitting them to sampling estimates. J Soil Sci 37:617–639. doi: 10.1111/j.1365-2389.1986.tb00392.x CrossRefGoogle Scholar
  27. Mendonça LAR, Frischkorn H, Santiago MF, Mendes Filho J (2005) Isotope measurements and ground water flow modeling using MODFLOW for understanding environmental changes caused by a well field in semiarid Brazil. Environ Geol 47:1045–1053CrossRefGoogle Scholar
  28. CPRM (Companhia de Pesquisa de Recursos Minerais) (2010) SIAGAS—Sistema de Informações de Águas Subterrâneas. Accessed 10 March 2010
  29. DNPM (Departamento Nacional de Produção Mineral) (1996) Projeto avaliação hidrogeológica da bacia sedimentar do Araripe. Recife, p 103Google Scholar
  30. Montebeller CA, Ceddia MB, de Carvalho DF, Vieira SR, Franco EM (2007) Spatial variability of the rainfall erosive potential in the state of Rio de Janeiro, Brazil. Eng Agríc 27:426–435. doi: 10.1590/S0100-69162007000300011 CrossRefGoogle Scholar
  31. Nash JE, Sutcliffe JV (1970) River flow forecasting models: model calibration and uncertainty through conceptual models I: a discussion of prediction. J Hydrol 103:282–290CrossRefGoogle Scholar
  32. Panosso AR, Pereira GT, Marques Júnior J, La Scala Júnior N (2008) Spatial variability of CO2 emission on Oxisol soils cultivated with sugar cane under different management practices. Eng Agríc 28:227–236CrossRefGoogle Scholar
  33. Patriarche D, Castro MC, Goovaerts P (2005) Estimating regional hydraulic conductivity fields—a comparative study of geostatistical methods. Math Geol 37:587–613CrossRefGoogle Scholar
  34. Razack M, Huntley D (1991) Assessing transmissivity from specific capacity in a large and heterogeneous alluvial aquifer. Ground Water 29:856–861. doi: 10.1111/j.1745-6584.1991.tb00572.x CrossRefGoogle Scholar
  35. Razack M, Lasm T (2006) Geostatistical estimation of the transmissivity in a highly fractured metamorphic and crystalline aquifer (Man-Danane region, western Ivory Coast). J Hydrol 325:164–178. doi: 10.1016/j.jhydrol.2005.10.014 CrossRefGoogle Scholar
  36. Rotzoll K, El-Kadi AI (2008) Estimating hydraulic conductivity from specific capacity for Hawaii aquifers, USA. Hydrogeol J 16:969–979CrossRefGoogle Scholar
  37. Sá FT (2004) As águas subterrâneas no município de Barbalha, Ceará, Brasil. Dissertation, Federal University of CearáGoogle Scholar
  38. Srivastav SK, Lubczynski MW, Biyani AK (2007) Upscaling of transmissivity, derived from specific capacity: a hydrogeomorphological approach applied to the Doon valley aquifer system in India. Hydrogeol J 15:1251–1264. doi: 10.1007/s10040-007-0207-8 CrossRefGoogle Scholar
  39. Theis CV (1935) The relation between the lowering of the piezometric surface and the rate and the duration of discharge of a well using groundwater storage. Trans Am Geophys Un 16:519–524CrossRefGoogle Scholar
  40. Thiem G (1906) Hidrologische methoden. J. M. Gebhardt’s Verlag, LeipzigGoogle Scholar
  41. Trabelsi F, Tarhouni J, Mammou AB, Ranieri G (2011) GIS-based subsurface databases and 3-D geological modeling as a tool for the set up of hydrogeological framework: Nabeul–Hammamet coastal aquifer case study (Northeast Tunisia). Environ Earth Sci. doi: 10.1007/s12665-011-1416-y Google Scholar
  42. Trangmar BB, Yost RS, Uehara G (1986) Application of geostatistics to spatial studies of soil properties. Adv Agron 38:45–94. doi: 10.1016/S0065-2113(08)60673-2 CrossRefGoogle Scholar
  43. Verbovsek T (2008) Estimation of transmissivity and hydraulic conductivity from specific capacity and specific capacity index in dolomite aquifers. J Hydrol Eng 13:817–823CrossRefGoogle Scholar
  44. Verbovsek T, Veselic M (2008) Factors influencing the hydraulic properties of wells in dolomite aquifers of Slovenia. Hydrogeol J 16:779–795. doi: 10.1007/s10040-007-0250-5 CrossRefGoogle Scholar
  45. Vieira SR (2000) Geoestatística em estudos de variabilidade espacial do solo. In: Novais RF, Alvarez VH, Schaefer GR (eds) Tópicos em ciência do solo. Sociedade Brasileira de Ciência do Solo, Viçosa, pp 1–54Google Scholar
  46. Zhao H, Ma F, Li G, Zhang Y, Guo J (2012) Study of the hydrogeological characteristics and permeability of the Xinli seabed gold mine in Laizhou Bay, Jiaodong Peninsula, China. Environ Earth Sci 65:2003–2014. doi: 10.1007/s12665-011-1181-y CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sávio de Brito Fontenele
    • 1
    Email author
  • Luiz Alberto Ribeiro Mendonça
    • 2
  • José Carlos de Araújo
    • 1
  • Maria Marlúcia Freitas Santiago
    • 3
  • José Yarley de Brito Gonçalves
    • 4
  1. 1.Department of Agricultural EngineeringUniversidade Federal do CearáFortalezaBrazil
  2. 2.Civil EngineeringUniversidade Federal do CearáJuazeiro do NorteBrazil
  3. 3.Department of PhysicsUniversidade Federal do Ceará Campus do PiciFortalezaBrazil
  4. 4.Water Resources Management Company (COGERH) of CearáCratoBrazil

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