This package contains the data and scripts used in "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling" (Jiang et al., 2022). The ARW_SWAT.zip file contains the SWAT simula
Data and code in support of paper "Urban Land Expansion Amplifies Surface Warming More in Dry Climate than in Wet Climate: A Global Sensitivity Study".
Dataset of the article "LB-SCAM: A learning-based method for efficient large-scale sensitivity analysis and tuning of single column atmosphere model (SCAM)".
