Uncertainty in hydrological analysis of climate change: multi-parameter vs. multi-GCM ensemble predictions.

The quantification of uncertainty in the ensemble-based predictions of climate change and the corresponding hydrological impact is necessary for the development of robust climate adaptation plans. Although the equifinality of hydrological modeling has been discussed for a long time, its influence on the hydrological analysis of climate change has not been studied enough to provide a definite idea about the relative contributions of uncertainty contained in both multiple general circulation models (GCMs) and multi-parameter ensembles to hydrological projections. This study demonstrated that the impact of multi-GCM ensemble uncertainty on direct runoff projections for headwater watersheds could be an order of magnitude larger than that of multi-parameter ensemble uncertainty.

Predicting the impacts of climate change on nonpoint source pollutant loads from agricultural small watershed using artificial neural network.

This study described the development and validation of an artificial neural network (ANN) for the purpose of analyzing the effects of climate change on nonpoint source (NPS) pollutant loads from agricultural small watershed. The runoff discharge was estimated using ANN algorithm. The performance of ANN modelwas examined using observed data from s tudy watershed.