Development of High Resolution Precipitation Extreme Dataset Using Spatial Interpolation Methods and Geostatistics in South Sulawesi, Indonesia

  • Amsari Mudzakir Setiawan Post Graduate School of Applied Climatology, Bogor Agricultural University (IPB), Campus of IPB Darmaga, Bogor, 16680, West Java, Indonesia.
  • Yonny Koesmaryono Post Graduate School of Applied Climatology, Bogor Agricultural University (IPB), Campus of IPB Darmaga, Bogor, 16680, West Java, Indonesia.
  • Akhmad Faqih Post Graduate School of Applied Climatology, Bogor Agricultural University (IPB), Campus of IPB Darmaga, Bogor, 16680, West Java, Indonesia.
  • Dodo Gunawan Indonesia Agency for Meteorology Climatology and Geophysics (BMKG), Jl Angkasa I No. 2 Kemayoran Jakarta Pusat, 10720, Indonesia
Keywords: Consecutive Dry Days, drought, geostatistics, spatial interpolation, precipitation extreme.


Consecutive Dry Days (CDD) is one of several precipitation extreme parameter suggested by Expert Team on Climate Change Detection and Indices (ETCDDI) to give a brief overview about climatic condition, especially related to climate change and drought occurrences. Daily precipitation data from observed Indonesia Agency for Meteorology, Climatology and Geophysics (BMKG) station network and Climate Hazzard group Infra-Red Precipitation with Station (CHIRPS) during 35 years (1981 – 2015) in South Sulawesi was used for CDD calculation. Three approaches were used for developing high resolution gridded (0.05o x 0.05o) precipitation extreme index over ungauged area, i.e. i) CHIRPS standardization with observed CDD (Std_CHIRPS); ii) spatial interpolation using nearest neighbours (NN) and invers distance weighted (IDW); and iii) geostatistics method using ordinary kriging (OK) and regression kriging (RK).Spatial and temporal assessments for each interpolation performance were done by applying leave one out cross validation method with actual CDD from observed station data. This study found that spatial and temporal distribution of CDD over the region are described by almost all interpolation methods, except Std_CHIRPS. Interpolation performance was reduced during La Niña periods or after El Niño years. Highest correlation coefficient value with lowest RMSE obtained by OK and RK.  Nevertheless, RK shows closest standard deviation value compared to observation data. Better interpolation performance achieved by RK compare to another method for CDD in South Sulawesi.


T. B. McKee, N. J. Doesken, and J. Kleist, “Drought Monitoring with Multiple Time Scales,” in Proceedings of the 9th AMS Conference on Applied Climatology, 1995, pp. 233–236.

S. Beguería, S. M. Vicente-Serrano, F. Reig, and B. Latorre, “Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring,” Int. J. Climatol., vol. 34, no. 10, pp. 3001–3023, 2014.

Y. Duan, Z. Ma, and Q. Yang, “Characteristics of consecutive dry days variations in China,” Theor. Appl. Climatol., pp. 1–9, 2016.

A. M. G. K. Tank, F. W. Zwiers, and X. Zhang, Guidelines on Analysis of Extremes in a Changing Climate in Support of Informed Decisions for Adaptation, TD No 1500., no. WCDMP-No.72. Geneva, Switzerland: World Meteorological Organization, 2009.

D. Gunawan, G. Gravenhorst, D. Jacob, and R. Podzun, “Rainfall variability studies in South Sulawesi using Regional Climate Model REMO,” J. Meteorol. dan Geofis., vol. 4, pp. 65–70, 2003.

A. M. Setiawan, “Mapping of South Sulawesi Rainfall Distribution using Arcview GIS [in Indonesian],” Makassar State University (UNM), Makassar, 2007.

J.-I. Hamada, M. D. Yamanaka, J. Matsumoto, S. Fukao, P. A. Winarso, and T. Sribimawati, “Spatial and Temporal Variations of the Rainy Season over Indonesia and their Link to ENSO,” J. Meteorol. Soc. Japan. Ser. II, vol. 80, no. 2, pp. 285–310, 2002.

P. Cantet, “Mapping the mean monthly precipitation of a small island using kriging with external drifts,” Theor. Appl. Climatol., vol. 127, no. 1–2, pp. 31–44, 2017.

D. Ozturk and F. Kilic, “Geostatistical approach for spatial interpolation of meteorological data,” An Acad Bras Cienc (Annals Brazilian Acad. Sci., vol. 88, no. 884, pp. 2121–2136, 2016.

S. K. Adhikary, N. Muttil, and A. G. Yilmaz, “Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments,” Hydrol. Process., vol. 31, no. 12, pp. 2143–2161, 2017.

A. M. Setiawan, W. S. Lee, and J. Rhee, “Spatio-temporal characteristics of Indonesian drought related to El Niño events and its predictability using the multi-model ensemble,” Int. J. Climatol., vol. 37, no. 13, pp. 4700–4719, 2017.

A. M. Setiawan, Y. Koesmaryono, A. Faqih, and D. Gunawan, “Utilization of near real-time NOAA-AVHRR satellite output for El Niño induced drought analysis in Indonesia (Case study : El Niño 2015 induced drought in South Sulawesi),” Int. J. Remote Sens. Earth Sci., vol. 13, no. 2, pp. 87–94, 2016.

M. Martono and T. Wardoyo, “Impacts of El Niño 2015 and the Indian Ocean Dipole 2016 on rainfall in the Pameungpeuk and Cilacap regions,” Forum Geogr., vol. 31, no. 2, Dec. 2017.

L. S. Supriatin and M. Martono, “Impacts of Climate Change (El Nino, La Nina, and Sea Level) on the Coastal Area of Cilacap Regency,” Forum Geogr., vol. 30, no. 2, pp. 106–111, 2016.

S. Lestari, J.-I. Hamada, F. Syamsudin, Sunaryo, J. Matsumoto, and M. D. Yamanaka, “ENSO influences on rainfall extremes around Sulawesi and Maluku islands in the eastern Indonesian Maritime Continent,” SOLA, vol. 12, no. 1, pp. 37–41, 2016.

F. Kogan, W. Guo, A. Strashnaia, A. Kleshenko, O. Chub, and O. Virchenko, “Modelling and prediction of crop losses from NOAA polar-orbiting operational satellites,” Geomatics, Nat. Hazards Risk, vol. 7, no. 3, pp. 886–900, 2015.

C. Toté, D. Patricio, H. Boogaard, R. van der Wijngaart, E. Tarnavsky, and C. Funk, “Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique,” Remote Sens., vol. 7, no. 2, pp. 1758–1776, 2015.

Q. Dai, D. Han, and L. Zhuo, “Seasonal ensemble generator for radar rainfall using copula and autoregressive model,” 2015.

R. R. E. Vernimmen, A. Hooijer, Mamenun, E. Aldrian, and A. I. J. M. Van Dijk, “Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia,” Hydrol. Earth Syst. Sci., vol. 16, no. 1, pp. 133–146, Jan. 2012.

M. E. Moeletsi and S. Walker, “Evaluation of NASA satellite and modelled temperature data for simulating maize water requirement satisfaction index in the Free State Province of South Africa,” Phys. Chem. Earth, vol. 50–52, pp. 157–164, 2012.

J. L. Peña-Arancibia, A. I. J. M. van Dijk, L. J. Renzullo, and M. Mulligan, “Evaluation of Precipitation Estimation Accuracy in Reanalyses, Satellite Products, and an Ensemble Method for Regions in Australia and South and East Asia,” J. Hydrometeorol., vol. 14, no. 4, pp. 1323–1333, 2013.

C. Dyn, “Precipitation climatology over India : validation with observations and reanalysis datasets and spatial trends,” Clim. Dyn., 2015.

J. Kim and J. H. Ryu, “A heuristic gap filling method for daily precipitation series,” Water Resour. Manag., vol. 30, no. 7, pp. 2275–2294, 2016.

B. Jongjin, P. Jongmin, R. Dongryeol, and C. Minha, “Geospatial blending to improve spatial mapping of precipitation with high spatial resolution by merging satellite-based and ground-based data,” Hydrol. Process., vol. 30, no. 16, pp. 2789–2803, 2016.

C. Funk et al., “The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes,” Sci. Data, vol. 2, p. 150066, Dec. 2015.

U. Schneider, A. Becker, M. Ziese, and B. Rudolf, “Global Precipitation Analysis Products of the GPCC,” pp. 1–13, 2012.

A. Becker et al., “A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present,” Earth Syst. Sci. Data, vol. 5, no. 1, pp. 71–99, Feb. 2013.

A. Yatagai, O. Arakawa, K. Kamiguchi, and H. Kawamoto, “A 44-Year Daily Gridded Precipitation Dataset for Asia,” Sola, vol. 5, pp. 3–6, 2009.

A. Yatagai, K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, “APHRODITE : Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges,” Bull. Am. Meteorol. Soc., vol. 93, no. 9, pp. 1401–1415, 2012.

J.-J. Wang, R. F. Adler, G. J. Huffman, and D. Bolvin, “An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation,” J. Clim., vol. 27, no. 1, pp. 273–284, 2014.

N. Hofstra, M. Haylock, M. New, P. Jones, and C. Frei, “Comparison of six methods for the interpolation of daily , European climate data,” vol. 113, no. November, 2008.

J. Q. Basconcillo et al., “Evaluation of spatial interpolation techniques for operational climate monitoring in the Philippines,” SOLA, vol. 13, no. 0, pp. 114–119, 2017.

Yanto, B. Livneh, and B. Rajagopalan, “Development of a gridded meteorological dataset over Java island, Indonesia 1985-2014,” Sci. Data, vol. 4, pp. 1–10, 2017.

L. Haimberger, “Homogenization of radiosonde temperature time series using innovation statistics,” J. Clim., vol. 20, no. 7, pp. 1377–1403, 2007.

A. Singh, R. K. Sahoo, A. Nair, U. C. Mohanty, and R. K. Rai, “Assessing the performance of bias correction approaches for correcting monthly precipitation over India through coupled models,” Meteorol. Appl., vol. 24, no. 3, pp. 326–337, 2017.

J. Li and A. D. Heap, A Review of Spatial Interpolation Methods for Environmental Scientists, Geoscience. Canberra: Geoscience Australia, 2008.

R. Webster and M. A. Oliver, Geostatistics for Environmental Scientists, Second Edi., no. 2. Chichester: John Wiley & Sons Ltd, 2007.

D. Shepard, “A two-dimensional interpolation function for irregularly-spaced data,” Proc. 1968 23rd ACM Natl. Conf., pp. 517–524, 1968.

E. H. Isaaks and R. M. Srivastava, An Introduction to Applied Geostatistics. New York: Oxford University Press, 1989.

D. G. Krige, “A statistical approach to some basic mine valuation problems on the Witwatersrand,” Journal of the Chemical, Metallurgical and Mining Society of South Africa, vol. 52, no. 6. pp. 201–215, 1952.

M. A. Oliver and R. Webster, “A tutorial guide to geostatistics: Computing and modelling variograms and kriging,” Catena, vol. 113, pp. 56–69, 2014.

N. A. C. Cressie, Special Topics in Statistics for Spatial Data, Revised Ed. New York: John Wiley & Sons, Inc, 1993.

F. J. Moral, “Comparison of different geostatistical approaches to map climate variables: Application to precipitation,” Int. J. Climatol., vol. 30, no. 4, pp. 620–631, 2010.

T. J. W. McGrill R. and W. A. Larsen, “Variations of box plots,” Am. Stat., vol. 32, no. 1, pp. 12–16, 1978.

K. E. Taylor, “Summarizing multiple aspects of model performance in a single diagram,” J. Geophys. Res. Atmos., vol. 106, no. D7, pp. 7183–7192, 2001.

S. Yuan and S. M. Quiring, “Comparison of three methods of interpolating soil moisture in Oklahoma,” Int. J. Climatol., vol. 37, no. 2, pp. 987–997, 2017.

Q. Wang, V. Rodriguez-Galiano, and P. M. Atkinson, “Geostatistical solutions for downscaling remotely sensed land surface temperature,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 2W7, pp. 913–917, 2017.

T. Hengl, G. B. M. Heuvelink, M. P. Tadić, and E. J. Pebesma, “Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images,” Theor. Appl. Climatol., vol. 107, no. 1–2, pp. 265–277, 2012.