drought monitoring is a fundamental component of drought risk management. it is normally performed using various drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables. in many instances, drought indices are used for monitoring purposes. geostatistical methods allow the interpolation of spatially referenced data and the prediction of values...

drought monitoring is a fundamental component of drought risk management. it is normally performed usingvarious drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables.in many instances, drought indices are used for monitoring purposes. geostatistical methods allow the interpolationof spatially referenced data and the prediction of values fo...

A total of 21 gauges across the mountainous leeward portion of the island of Oʻahu, Hawaiʻi, were used to compare rainfall interpolation methods and assess rainfall spatial variability over a 34-month monitoring period from 2005 to 2008. Traditional and geostatistical interpolation methods, including Thiessen polygon, inverse distance weighting (IDW), linear regression, ordinary kriging (OK), a...

Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. No...

     Drought monitoring is a fundamental component of drought risk management. It is normally performed using various drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables. In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolation of spatially referenced data and the prediction of v...

Drought monitoring is a fundamental component of drought risk management. It is normally performed usingvarious drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables.In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolationof spatially referenced data and the prediction of values fo...

This paper analyzes the validity of temperature maps obtained by means of single and mixed interpolation methods. In this context, several interpolation methods are used for the temperature mapping: inverse distance square (IDS), ordinary kriging (OK) and co-kriging (CK) and mixed methods (combined global, local and geostatistical methods). And the validity of the maps is checked through the cr...

groundwater is the main water resource for domestic and agricultural uses in iran. so real-time mapping of groundwater depth can guide to have a proper exploitation of groundwater. this study was conducted to investigate the spatial variability of depth to groundwater in barkhar plain in esfahan using geostatistical methods. in this regard, the measurements of groundwater depth between july and...

Precipitation data provide a crucial input for examining hydrological issues, including watershed management and mitigation of the effects floods, drought, landslides. However, they are collected frequently from scarce often insufficient network ground-based rain-gauge stations to generate continuous precipitation maps. Recently, maps derived satellite have not been sufficiently linked rain gau...

Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simulations. In contrast, Machine Learning Algorithms (MLA) (1) free of such limitations, (2) can incorporate information from multiple sources therefore emerge with increasing interest in real-time resource automation...