GIS-Based Rainfall Estimator Evaluation and Interpolation Analysis Using ArcGIS

Joe Yuan Mambu

doi:10.31154/cogito.v4i1.118.230-242

Authors

Joe Yuan Mambu Universitas Klabat

DOI:

https://doi.org/10.31154/cogito.v4i1.118.230-242

Abstract

We have been always trying to predict the weather to minimize risk, produce strategy, and other decision making situation. To achieve this, monitoring method need to be used to gather data. Rainfall monitoring is one of the area that widely used and one of the mostly used method is satellite-based rainfall monitoring. However, there are limitation to apply to the Satellite Rainfall (SR) estimation specifically on its accuracy and lack of certainty. Thus, study that directed to measure the inaccuracies of SR measurements is needed by using data of distribution of Actual Rainfall (AR). The SR data is taken from the National Oceanic and Atmospheric Administration (NOAA)’s Hydro—Estimator while the AR data was from the National Institute of Water and Atmospheric Research (NIWA). Through the statistics and interpolation analysis using ArcGIS, the study shows a prominent result of SR estimation accuracy in the sample area and thus may opens up more similar implementation as well as stands as a good benchmark for future improvement of the method. This study also shows how interpolation method through a GIS software could provide a significant result on a geographical related studies. Keywords : GIS, Geospatial Analysis, Interpolation Analysis, Arcgis

References

J. Lee Rodgers, and W. A. Nicewander, “Thirteen ways to look at the correlation coefficient,” The American Statistician, vol. 42, no. 1, pp. 59-66, 1988.

J. Neter, M. H. Kutner, and W. Wasserman, Applied linear statistical models: Richard D. Irwin, Inc., 1983.

D. Algarni, and I. Hassan, “Comparison of thin plate spline, polynomial C-Function and Shepard's interpolation techniques with GPS derived DEM.,” Internation Journal of Applied Earth Observation and Geoinformation, vol. 3, no. 2, 2001.

S. Erdogan, “A comparision of interpolation methods for producing digital elevation models at the field scale,” Earth surface processes and landforms, vol. 34, no. 3, pp. 366-376, 2009.

Z.-h. Lin, X.-g. MO, H.-x. LI, and H.-b. LI, “Comparison of Three Spatial Interpolation Methods for Climate Variables in China [J],” Acta Geographica Sinica, vol. 1, pp. 005, 2002.

M. J. Noori, H. H. Hassan, and Y. T. Mustafa, “Spatial Estimation of Rainfall Distribution and Its Classification in Duhok Governorate Using GIS,” Journal of Water Resource & Protection, vol. 6, no. 2, 2014.

C. A. Bridges, “Technical Note: Estimating Forest Site Productivity Using Spatial Interpolation,” Southern journal of applied forestry, vol. 32, no. 4, pp. 187-189, 2008.

NIWA. "Climate stations and instruments," 26 May, 2018; https://www.niwa.co.nz/our-services/instruments/instrumentsystems/products/climate-stations.

NOAA. "STAR Satellite Rainfall Estimates - Hydro-Estimator," 25 May, 2018; https://www.star.nesdis.noaa.gov/smcd/emb/ff/HydroEst.php.

R. A. Scofield, “The NESDIS operational convective precipitation-estimation technique,” Monthly Weather Review, vol. 115, no. 8, pp. 1773-1793, 1987.

G. A. Vicente, R. A. Scofield, and W. P. Menzel, “The operational GOES infrared rainfall estimation technique,” Bulletin of the American Meteorological Society, vol. 79, no. 9, pp. 1883-1898, 1998.

R. A. Scofield, and R. J. Kuligowski, “Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events,” Weather & Forecasting, vol. 18, no. 6, 2003.