Precipitation is a major input in hydrological models. Radar rainfall data compared with rain gauge measurements provide higher spatial and temporal resolutions. However, radar data obtained form reflectivity patterns are subject to various errors such as errors in reflectivity-rainfall Z-R relationships, variation in vertical profile of reflectivity, and spatial and temporal sampling among oth...

7 The hydrological budget of Himalayan rivers is dominated by monsoonal rainfall and 8 snowmelt, but their relative impact is not well established. Current knowledge of rainfall and 9 snow-cover distribution is unreliable because this remote region lacks a dense gauge network. 10 Here, we use a combination of remotely-sensed climate parameters that have been both 11 calibrated and validated to ...

7 The hydrological budget of Himalayan rivers is dominated by monsoonal rainfall and 8 snowmelt, but their relative impact is not well established. Current knowledge of rainfall and 9 snow-cover distribution is unreliable because this remote region lacks a dense gauge network. 10 Here, we use a combination of remotely-sensed climate parameters that have been both 11 calibrated and validated to ...

Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall prediction. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multilayer perceptron neural networks (MLP-NN). In fact, ...

The Multiplicative Discrete Random Cascade (MDRC) class of model is used to temporally disaggregate rainfall volumes through multiplying the volumes by random weights, which is repeated through multiple disaggregation levels. The model development involves the identification of probability density functions from which to sample the weights. The parameters of the probability density functions ar...

Modeling the acidity in rainfall at certain locations is a complex task because of different environmental conditions for different rainfall regimes and the large variability in the covariates involved. In this paper, concentration of acidity and major ions in the rainfall in UK is analyzed by assuming a bivariate pseudo-Gamma distribution. The model parameters are estimated by using the maximu...

The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from sa...

In this paper, fractal dimensional analysis is used to investigate the Tamil Nadu rainfall dynamics. Here, the time series data of Tamil Nadu rainfall (annual, south west and northeast) has been analyzed using Detrended fluctuation analysis (DFA). For estimating the 110 years rainfall data, the scaling exponent analysis is used. The result shows a varying degree of persistence over shorter and ...