RAINFALL INTERPOLATION

Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates

The accurate assessment of spatiotemporal rainfall variability is a crucial and challenging task in many hydrological applications, mainly due to the lack of a sufficient number of rain gauges. The purpose of the present study is to investigate the spatiotemporal variations of annual and monthly rainfall over Fujian province in China by combining the Bayesian maximum entropy (BME) method and sa...

Third generation rainfall climatologies: satellite rainfall and topography provide a basis for smart interpolation

This short paper discusses a technique for producing ‘improved rainfall estimates’ (IRE) for Africa. The central objective of IRE is to make reasonable use of the high resolution mean fields, moderate density gauge data, and timely satellite rainfall data typically available at the national/regional level in Africa. In general, traditional rainfall interpolation schemes can be classified into t...

Comparing rainfall interpolation techniques for small subtropical urban catchments

This study focuses on evaluating the suitability of three interpolation methods in terms of their accuracy, for small urban catchments. The Bridgewater Creek catchment in south Brisbane is the region of study. Thirteen storm events measured over 24-hours are interpolated using an inverse-distance weighted average method, thin plate smoothing spline, and an ordinary kriging technique. The delete...

Spatial Interpolation Using Copula for non-Gaussian Modeling of Rainfall Data

‎One of the most useful tools for handling multivariate distributions of dependent variables in terms of their marginal distribution is a copula function‎. ‎The copula families capture a fair amount of attention due to their applicability and flexibility in describing the non-Gaussian spatial dependent data‎. ‎The particular properties of the spatial copula are rarely ...

Interpretable Fuzzy Systems for Monthly Rainfall Spatial Interpolation and Time Series Prediction