Spatial interpolation methods for estimating monthly rainfall distribution in Thailand

Estimation of Missing Rainfall Data in Northeast Region of Thailand Using Spatial Interpolation Methods

Ground-based rainfall observations are the primary sources of precipitation data used in most developing countries. However, those observations are frequently damaged or incomplete, thus missing data is always a problem. This comparison study examines a number of spatial interpolation methods used to estimate missing monthly rainfall data in the northeast region of Thailand. The comparison was ...

Uncertainty Analysis of Interpolation Methods in Rainfall Spatial Distribution–A Case of Small Catchment in Lyon

Quantification of spatial and temporal patterns of rainfall is an important step toward developing regional water sewage models, the intensity and spatial distribution of rainfall can affect the magnitude and duration of water sewage. However, this input is subject to uncertainty, mainly as a result of the interpolation method and stochastic error due to the random nature of rainfall. In this s...

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

comparing interpolation methods for estimating spatial distribution of topsoil ph and ec (case study: karimabad rangelands, hamadan province, iran)

soil alkalinity and salinity are serious problems in arid and semi-arid regions, and therefore monitoring of soil ph and electrical conductivity (ec) is necessary in any region. the present study aimed to properly interpolate soil ph and ec as soil quality indices in a semi-arid mountainous area with annual precipitation of 342.4 mm. the study area is the karimabad rangelands in hamadan provinc...

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 ...