Skip to main content
Log in

A New Method for Estimation the Regional Precipitation

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

In this article we propose a new method - the Most Probable Precipitation Method (MPPM) - for estimating the precipitation at regional scale. Comparisons with the Thiessen polygons methods (TPM), inverse distance weighting interpolation (IDW) and ordinary kriging (OK) on annual, monthly, seasonal and annual maximum monthly precipitation are provided. In all cases MPPM performs better than IDW and OK, and in most of them, better than TPM.

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

References

  • Abtew W, Obeysekera J, Shih G (1993) Spatial analysis for monthly rainfall in South Florida. J Am Water Resour Assoc 29(2):179–188

    Article  Google Scholar 

  • Barbulescu A, Bautu E (2010) Mathematical models of climate evolution in Dobrudja. Theor Appl Climatol 100(1–2):29–44

    Article  Google Scholar 

  • Barbulescu A, Deguenon J (2014) Models for trend of precipitation in Dobrudja. Environ Eng Manag J 13(4):873–880

    Google Scholar 

  • Barbulescu A, Deguenon J (2015) About the variations of precipitation and temperature evolution in the Romanian Black Sea Littoral. Rom Rep Phys 7(2):625–637

    Google Scholar 

  • Barbulescu A, Deguenon J, Teodorescu D (2011) Study on water resources in the Black Sea region. Nova Publishers, USA

    Google Scholar 

  • Barbulescu A, Maftei C, Bautu E (2010) Modeling the hydro-meteorological time series. Applications to Dobrudja region. LAP Lambert Academic Publishing, Germany

    Google Scholar 

  • Basistha A, Arya DS, Goel NK (2008) Spatial distribution of rainfall in indian himalayas a case study of uttarakhand region. Water Resour Manag 22:1325–1346

    Article  Google Scholar 

  • Buttafuoco G, Caloiero T, Coscarelli R (2011) Spatial patterns of variability for rain fields at different timescales: an application in southern Italy. Eur Water 36:3–13

    Google Scholar 

  • Campling P, Gobin A, Feyen J (2001) Temporal and spatial rainfall analysis across a humid tropical catchment. Hydrol. Process 15:359–375

    Article  Google Scholar 

  • Chang CL, Lo SL, Yu SL (2005) Applying fuzzy theory and genetic algorithm to interpolate precipitation. J Hydrol 314(1-4):92–104

    Article  Google Scholar 

  • Chang CL, Lo SL, Yu SL (2006) The parameter optimization in the inverse distance method by genetic algorithm for estimating precipitation. Environ Monit Assess 117:145–155

    Article  Google Scholar 

  • Cheng C-d, Cheng S-j, Wen J-c, Lee Ju-h (2012) Effects of raingauge distribution on estimation accuracy of areal rainfall. Water Resour Manag 26:1–20

    Article  Google Scholar 

  • Chiles J-P, Delfiner P (2012) Geostatistics. Modeling Spatial Uncertainty, 2nd. Wiley, Hoboken

    Book  Google Scholar 

  • Cressie N (1998) Statistics for spatial data. Wiley Interscience, New York

    Google Scholar 

  • Daly C, Neilson RP, Phillips DL (1994) A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33(2):140–158

    Article  Google Scholar 

  • Garcia M, Peters-Lidard CD, Goodrich DC (2008) Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States. Water Resour Res 44:W05S13

    Google Scholar 

  • Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228(1-2):113–129

    Article  Google Scholar 

  • Hutchinson MF (1995) Interpolating mean rainfall using thin plate smoothing splines. Int J Geogr Inf Sci 9:385–403

    Article  Google Scholar 

  • Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci - UK 34:1044–1055

    Article  Google Scholar 

  • Ly S, Charles C, Degre A (2013) Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale: a review. Biotechnol Agron Soc Environ 17(2):392–406

    Google Scholar 

  • Maftei C, Barbulescu A (2012) Statistical analysis of precipitation time series in the Dobrudja region. MAUSAM 63(4):553–564

    Google Scholar 

  • Matheron G (1981) Splines and Kriging: Their Formal Equivalence 8. Syracuse University Geologists Contributions

  • Simian D, Stoica F (2012) A general frame for building optimal multiple SVM kernels. Lect Notes Comput Sci 7116:256–263

    Article  Google Scholar 

  • Shope CL, Maharjan GR (2015) Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution. Adv Meteorol 2015: Article ID 174196

  • St-Hilaire A, Ouarda TBMJ, Lachance M, BObee B, Gaudet J, Gignac C (2003) Assessment of the impact of meteoro logical network density on the estimation of basin precipitation and runoff: a case study. Hydrol Proc 17:3561–3580

    Article  Google Scholar 

  • Szolgay J, Parajka J, Kohnova S, Hlavcova K (2009) Comparison of mapping approaches of design annual maximum daily precipitation. Atmos Res 92:289–307

    Article  Google Scholar 

  • Tabios GQ, Salas JD (1985) A comparative analysis of techniques for spatial interpolation of precipitation. Water Resour Bull 21:265–380

    Google Scholar 

  • Thiessen AH (1911) Precipitation averages for large areas. Mon Weather Rev 39(7):1082–1084

    Google Scholar 

  • Tigkas D, Vangelis H, Tsakiris (2013) The RDI as a composite climatic index. Eur Wat 41:17–22

    Google Scholar 

  • Tsakiris et al (2013) A system-based paradigm of drought analysis for operational management. Water Resour Manag 27:5281–5297

    Article  Google Scholar 

  • Wang S, Huang GH, Lin QG, Li Z, Zhang H, Fan YR (2014) Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario, Canada. Int J Climatol 33(14):3745–3751

    Article  Google Scholar 

  • World Meteorological Organization (2008) Guide to Hydrological Practices, WMO-No.164, 6th. WMO, Geneva

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alina Barbulescu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barbulescu, A. A New Method for Estimation the Regional Precipitation. Water Resour Manage 30, 33–42 (2016). https://doi.org/10.1007/s11269-015-1152-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-015-1152-2

Keywords

Navigation