This file can be imported into qgis from the layers panel and adding a delimited text layer. Our data is in the csv format with each column of the RP_ family representing the return period the Rainfall Correction Factor is estimated for.įig.1 -sample data points with key location and return period of RCF These RCF are as a result of intensive computational simulations run in matlab which is out of scope for this blog. We calculate the RCF for each point for a grid of lat,lon around the indian subcontinent. Not considering these factors can lead to reduced life time of the structure. These RCF are derived from Global Climate Models (GCMs) which models future scenarios. These RCF are useful for hydraulic structures such as dams, storm water drains, and spillways. In this blog, we create surface plots for Rainfall Correction Factors, which is indicative of how much the climate impacts a hydraulic structure based on the return period it is designed for. Kriging: A group of geostatistical techniques to interpolate the value of a random field at an unobserved location from observations of its value at a nearby location. It uses a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes through the input points. We can fix the radius of influence or the total sample points to weigh for cell value. The closer the point, the more weight assigned. IDW: Inverse Distance Weighted interpolation method estimates cell values by weighted average of sample data. TIN: Triangular Irregular Network forms contiguous, non-overlapping triangles by dividing the geographic space on set of sample points
Methods to perform spatial interpolation: The objective is to estimate the most probable value at an unknown location with a set of known points within the extent of sample points. This is where spatial interpolation comes into picture. Most of the time, we are equipped with a discrete set of sample points (of temperature, rainfall etc) and are tasked with generating a continuous surface.
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