Evaluating Rainfall Interpolation Over The Netherlands

Different applications(e.g. urban flood early warning system or agriculture management)need rainfall information at different spatial resolutions. On the other hand, most of the meteorological departments only produce one set of precipitation data at fixed spatial resolution(e.g. KNMI provides rainfall data at 1km resolution). This study tries to derive rainfall data at different scales. One common method to obtain gridded rainfall data at different specific spatial resolutions is interpolation. The gridded rainfall data that are interpolated from in situ rain gauge varies depending on many factors. One of such impacting factors is the different interpolation methods that are used to produce gridded data, since each method has its own benefits and drawbacks. It is therefore needed to understand the uncertainty that may be caused by different interpolation methods. In this thesis, two geostatistical algorithms(ordinary kriging, thin plate spline) and one deterministic algorithm(inverse distance weighting) have been used to interpolate daily rainfall data at five different resolutions(1km, 3km, 8km, 12 km, 25km) over the Netherlands from 2003 to 2013.Meanwhile, the grid data was resampled at 1km to other four resolutions and compared with interpolated data. Moreover, the scale factor may have influence on the interpolation method when interpolating rainfall measurement which means different interpolation algorithms may suit for specific scales. So there is also a need to understand the uncertainty may be caused by different spatial scales. As an extension of research, monthly data has been interpolated to see how the temporal scale may affect the interpolation results. In addition, the interpolated data were used to validate satellite data.The main objective of this thesis is to identify the optimal interpolation method vs spatial-scale pair for generating reliable rainfall datasets over the Netherlands, meanwhile generate the relevant reference data which is prepared for generating a long term dataset.Results contain interpolated rainfall data and resample rainfall data at 1km, 3km, 8km,12 km and 25 km resolutions. Through comparing with the observed data from 32 automatic meteorological stations we found that for long-term daily rainfall interpolation, IDW interpolation is suitable at 1km, 3km and 8km and resampling method is suitable at 12 km and25km. Ordinary kriging is preferred on monthly rainfall interpolation.