by: tarun gill

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    02-Feb-2016
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Interpolation and evaluation of probable Maximum Precipitation (PMP) patterns using different methods. by: tarun gill. objectives. To convert vector based PMP to raster based PMP using different interpolation methods. Finding the accuracy of all the methods used. - PowerPoint PPT Presentation

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  • by: tarun gillInterpolation and evaluation of probable Maximum Precipitation (PMP) patterns using different methods

  • objectivesTo convert vector based PMP to raster based PMP using different interpolation methods. Finding the accuracy of all the methods used.

    Determining the best method for interpolation.

  • Interpolation Predicting values of a certain variable at unsampled location based on the measurement values at sampled locations.Different interpolation methodsDeterministic methodsUse mathematical functions based on the degree of similarity or degree of smoothingGeostatistical methodsUse Both mathematical and statistical functions based on spatial autocorrelation

  • Data usedProbable maximum precipitation mapsTheoretically the greatest depth of precipitation for a given duration that is physically possible over a drainage area at a certain time of year.Hmr-52 -Standard pmp estimates for united states east of the 105 meridian Areas -10,200,1000,5000,10000 sq.milesDuration-6,12,24,48,72hours

  • methodology

  • methodology

  • INVERSE DISTANCE WEIGHTED

    The further away the point the lesser its weight in defining the value at the unsampled location.

    Uses values of nearby points and their distancesWeight of each point is inversely proportional to its distance from that point.

  • Inverse distance weighted

  • Inverse distance weighted

  • Inverse distance weighted

  • splineFits a mathematical function to a specified number of nearest points.

    Unknown points are estimated by plotting their position on the spline

    minimizes overall surface curvatureRedundant values are often ignored

    Regularisedtension

  • spline

  • spline

  • spline

  • Ordinary krigingZ(s) = (s) + (s),

  • Trend analysis(si, sj) = sill - C(si, sj),

  • Ordinary kriging

  • Ordinary kriging

  • Ordinary kriging

  • comparison

  • Conclusion