Our resources

Merging of multiple satellite datasets is a simple yet effective way to reduce prediction error.

The R package, namely WAvelet System Prediction (WASP), is based on a discrete wavelet transform (DWT)-based variance transformation method (Jiang et al., 2020).

The Robust Multivariate Bias Correction (RoMBC) package is a robust package designed to deal with the distribution and dependence biases in a multivariate time series.

Understanding the impacts of short-term climate variability on drinking water source quality

Building a flood warning framework for ungauged locations using low resolution, open access remotely sensed surface soil moisture, precipitation, soil and topographic information.

The Multivariate Bias Correction (MBC) package is a complete package designed to deal with the distribution and, auto and cross dependence biases in a multivariate time series at multiple time steps.

The Multisite Rainfall Downscaling (MRD) is a tool that conditionally simulates the rainfall occurrence and amount by using the modified Markov model-kernel density estimate (MMM-KDE).

Soil moisture is an important variable in the coupled hydrologic and climate system. 

A new automated workflow based computationally efficient hydrologic modeling application is developed for soil moisture and runoff simulation. 

Many problems in hydrology and agricultural science require extensive records of rainfall from multiple locations.

Bayesian inference provides an ideal platform for assessing parameter uncertainty for complex physical models, such as conceptual hydrological models.