This study assessed three methods for correcting daily precipitation data: Quantile Delta Mapping, Empirical Quantile Mapping (EQM), and Detrended Quantile Mapping (DQM) using 11 GCMs. EQM performed best overall, offering reliable corrections and lower uncertainty. The best bias correction method for each grid is selected differently depending on the weighting case. The best bias correction method can vary depending on factors such as climate and terrain.

This study proposed two new concepts for the previous Double Gamma Quantile Mapping (DGQM), such as the inclusion of flexible dividing point between two individual gamma functions and the use of more probability distributions. As a result, F-DDQM method performed the better bias correction for the GCMs very clearly. This new F-DDQM method can be also applied to the various fields such as the use of satellite climate data, reanalysis climate data and spatial downscaling or interpolation.

This study proposes a system for the scientific selection of evaluation indices and priority areas for non-point source control. We developed a framework to prioritize catchments in terms of the risk of non-point source pollution considering the characteristics of polluted runoff from a non-point source using a multi-criteria decision-making method.

This study evaluated the performance of 36 CMIP5 GCMs in simulating seasonal precipitation and maximum and minimum temperature over Pakistan using spatial metrics (SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency) for the period 1961–2005. NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 were identified as the most suitable GCMs for simulating all three climate variables over Pakistan.

This study proposes an improved group decision making (GDM) framework that combines VIKOR method with data fuzzification to quantify the spatial flood vulnerability, including multiple criteria. The combination of the GDM approach with the fuzzy VIKOR method can provide robust prioritization because it actively reflects the opinions of various groups and considers uncertainty in the input data.