JWARP  Vol.9 No.8 , July 2017
Comparative Evaluation of Spatial Interpolation Methods for Estimation of Missing Meteorological Variables over Ethiopia
Abstract: In developing countries like Ethiopia where there is abundant water resources potential and also luck of reliable meteorological quality data, it expected to face the problem of missing meteorological data. Therefore, in conducting any water resources studies in any river basin for water resource project planning and management (like small scale irrigation), the first step before starting data analysis is to fill up the missing values of the meteorological variables (like rainfall, temperature, sunshine, wind speed etc.) which are required to start the study. One way of filling these missing variables is using datasets from other stations in the surrounding and applying appropriate spatial interpolation methods. A lot of studies have been conducted around the world to identify which method is the best to be applied to particular study area among the available spatial interpolation techniques. But when we come to Ethiopia, the study area, few or no studies are conducted to recommend the best performed method. Therefore, the objective of this paper is to conduct comparative evaluation of five interpolation techniques Nearest Neighbour (NN), Inverse Distance Weighting Average (IDWA), Modified Inverse Distance Weighting Average (MIDWA), Kriging Method (KM) and Thin Plate Spline (TPS) for estimation of four climatic variables (rainfall, mean temperature, wind speed and sunshine fraction) over complex topography of Ethiopia. Performance assessment is done using Mean Error (ME), Mean Absolute Error (MAE), Mean Relative Error (MRE) and Root Mean Square Error (RMSE); and the number of the meteorological stations selected for validation is ten (10) and these are distributed over the study area taking into account the variation of elevation ranging from 860 m (Awash) to 2420 m (Debremarkos) above sea level. The radial distances of 100 km and 200 km were selected and it was found that 100 km radial distance was not appropriate to compare all methods as some variables could not be estimated by KM and TPS. Therefore, 200 km was selected for further analysis and the result showed that NN, IDWA, and MIDWA were best methods relative to the remaining two methods (KM and TPS) for all variables and all stations except at Dire Dawa and Addis Ababa-Bole for estimation of wind speed using all methods except NN, and rainfall using TPS, respectively. Hence, NN, IDWA, and MIDWA methods could be used for estimation of missing meteorological variables over Ethiopia whenever necessary.
Cite this paper: Boke, A. (2017) Comparative Evaluation of Spatial Interpolation Methods for Estimation of Missing Meteorological Variables over Ethiopia. Journal of Water Resource and Protection, 9, 945-959. doi: 10.4236/jwarp.2017.98063.

[1]   Presti, R.Lo., Barca, E. and Passarella, G. (2010) A Methodology for Treating Missing Data Applied to Daily Rainfall Data in the Candelaro River Basin (Italy). Environmental Monitoring and Assessment, 160, 1-22.

[2]   Xia, Y., Fabian, P., Stohl, A. and Winterhalter, M. (1999) Forest Climatology: Estimation of Missing Values for Bavarian, Germany. Agricultural and Forest Meteorology, 96, 131-144.

[3]   Caldera, H.P.G.M., Piyathisse, V.R.P.C. and Nandalal, K.D.W. (2016) A Comparison of Methods of Estimating Missing Daily Rainfall Data. Engineer, XLIX, 1-8.

[4]   Burhanuddin, S.N.Z.A., Denis, S.M. and Ramli, N.M. (2015) Geometric Median for Missing Rainfall Data Imputation. AIP Conference Proceedings, 1643.

[5]   Villazón, M.F. and Willems, P. (2010) Filling Gaps and Daily Disaccumulation of Precipitation Data for Rainfall-Runoff Model. BALWOIS 2010, Ohrid, 25-29 May 2010.

[6]   Wong, K.J., Fung, K.W. and Che, C. (2012) A Comparative Analysis of Soft Computing Techniques Used to Estimate Missing Precipitation Records. 2012 19th ITS Biennial Conference, Bangkok, 18-21 November 2012.

[7]   Ramos-Calzado, P., Gomez-Camacho, J., Perez-Bernal, F. and Pita-Lopez, M.F. (2008) A Novel Approach to Precipitation Series Completion in Climatological Datasets: Application to Andalusia. International Journal of Climatology, 28, 1525-1534.

[8]   Li, J. and Heap, A.D. (2008) A Review of Spatial Interpolation Methods for Environmental Scientists. Geosciences Australia, Record 2008/23, 137 p.

[9]   Hijmans, R.J., Cameron, S.E., Parra, J.L. and Jones, P.G. (2005) Very High Resolution Interpolated Climate Surfaces for Global Land Areas. International Journal of Climatology, 25, 1965-1978.

[10]   Jarvis, C.H. and Stuart, N. (2001) A Comparison among Strategies for Interpolating Maximum and Minimum Daily Air Temperatures. Part II: The Interaction between Numbers of Guiding Variables and the Type of Interpolation Method. Journal of Applied Meteorology, 40, 1075-1084.<1075:ACASFI>2.0.CO;2

[11]   Mardikis, M.G., Kalivas, D.P. and Kollias, V.J. (2005) Comparison of Interpolation Methods for Prediction of Reference Evapotranspiration—An Application in Greece. Water Resources Management, 19, 251-278.

[12]   Coulibaly, M. and Becker, S. (2007) Spatial Interpolation of Annual Precipitation in South Africa-Comparison and Evaluation of Methods. Journal of Water International, 32, 494-502.

[13]   Eklundh, L. and Pilesjo, P. (1990) Regionalization and Spatial Estimation of Ethiopian Mean Annual Rainfall. International Journal of Climatology, 10, 473-494.

[14]   Derib, S.D. and Diekkruger, B. (2011) Comparison of Spatial Interpolation Methods for Filling Daily Rainfall Missing Data, Blue Nile Basin, Ethiopia. Development on Margin, Bonn, 5-7 October 2011.

[15]   Grieser, J., Gommes, R. and Bernardi, M. (2006) New Local Climate Estimator of FAO. Geophysical Research Abstracts, 8, Article ID: 08305.

[16]   Awulachew, S.B., Yilma, A.D., Loulseged, M., Loiskandl, W., Ayana, M. and Alamirew, T. (2007) Water Resources and Irrigation Development in Ethiopia. International Water Management Institute, Colombo.

[17]   Worldometeres (2016) Ethiopia Population.

[18]   Seleshi, Y. and Zanke, U. (2004) Recent Changes in Rainfall and Rainy Days in Ethiopia. International Journal of Climatology, 24, 973-983.

[19]   Golkhatmi, N.S., Sanaeinejad, S.H., Ghahraman, B. and Pazhand, H.R. (2012) Extended Modified Inverse Distance Method for Interpolation-Rainfall. International Journal of Engineering Inventions, 1, 57-65.

[20]   Hutchinson, M.F. (1995) Interpolating Mean Rainfall Using Thin Plate Smoothing Splines. International Journal of Geographic Information Systems, 9, 385-403.

[21]   Apaydin, H., Sonmez, F.K. and Yildirim, Y.E. (2004) Spatial Interpolation Techniques for Climate Data in the GAP Region in Turkey. Climate Research, 28, 31-40.

[22]   Hutchinson, M.F., Mckenney, D.W., Lawrence, K., Pedlar, J.H., Hopkinson, R.F., Milewsha, E. and Papadopol, P. (2009) Development and Testing of Canada-Wide Interpolated Spatial Models of Daily Minimum-Maximum Temperature and Precipitation for 1961-2003. Journal of Applied Meteorology and Climatology, 48, 725-741.

[23]   Gomez, M.R.S. (2007) Spatial and Temporal Rainfall Gauge Data Analysis and Validation with TRMM Microwave Radiometer Surface Rainfall Retrievals. Master’s Thesis, International Institute for Geo-Information Science and Earth Observation, Enschede.

[24]   Wong, D.W., Yuan, L. and Perlin, S.A. (2004) Comparison of Spatial Interpolation Methods for the Estimation of Air Quality Data. Journal f Exposure Analysis and Environmental Epidemiology, 14, 404-415.

[25]   Stahl, K., Moore, R.D., Floyer, J.A., Asplin, M.G. and McKendry, I.G. (2006) Comparison of Approaches for Spatial Interpolation of Daily Air Temperature in a Large Region with Complex Topography and Highly Variable Station Density. Agricultural and Forest Meteorology, 139, 224-236.

[26]   Thornton, P.E., Running, S.W. and White, M.A. (1997) Generating Surfaces of Daily Meteorological Variables over Large Regions of Complex Terrain. Journal of hydrology, 190, 214-251.