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. The finding suggests that the selection of appropriate GCMs should be much more emphasized than that of a parameter set among behavioral ones. When projecting soil moisture and groundwater, on the other hand, the hydrological modeling equifinality was more influential than the multi-GCM ensemble uncertainty. Overall, the uncertainty of GCM projections was dominant for relatively rapid hydrological components while the uncertainty of hydrological model parameterization was more significant for slow components. In addition, uncertainty in hydrological projections was much more closely associated with uncertainty in the ensemble projections of precipitation than temperature, indicating a need to pay closer attention to precipitation data for improved modeling reliability. Uncertainty in hydrological component ensemble projections showed unique responses to uncertainty in the precipitation and temperature ensembles.

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. The simulationresults agreed well with observed values during calibration and validation periods. NPS pollutant loads were calculated from load-discharge relationship driven by long-term monitoring data. LARS-WG (Long Ashton Research Station-Weather Generator) model was used to generate rainfall data. The calibrated ANN model and load-discharge relationship with the generated data from LARS-WGwere applied to analyze the effects of climate change on NPS pollutant loads from the agricultural small watershed. The results showed that the ANN model provided valuable approach i n estimating future runof f discharge, and the NPS pollutantloads.