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It is increasingly recognized that climate change could impose both direct and indirect impacts on the quality of the water environment. Previous studies have mostly concentrated on evaluating the impacts of climate change on non-point source pollution in agricultural watersheds. Few studies have assessed the impacts of climate change on the water quality of river basins with complex point and non-point pollution sources. In view of the gap, this paper aims to establish a framework for stochastic assessment of the sensitivity of water quality to future climate change in a river basin with complex pollution sources. A sub-daily soil and water assessment tool (SWAT) model was developed to simulate the discharge, transport, and transformation of nitrogen from multiple point and non-point pollution sources in the upper Huai River basin of China. A weather generator was used to produce 50 years of synthetic daily weather data series for all 25 combinations of precipitation (changes by − 10, 0, 10, 20, and 30%) and temperature change (increases by 0, 1, 2, 3, and 4 °C) scenarios. The generated daily rainfall series was disaggregated into the hourly scale and then used to drive the sub-daily SWAT model to simulate the nitrogen cycle under different climate change scenarios. Our results in the study region have indicated that (1) both total nitrogen (TN) loads and concentrations are insensitive to temperature change; (2) TN loads are highly sensitive to precipitation change, while TN concentrations are moderately sensitive; (3) the impacts of climate change on TN concentrations are more spatiotemporally variable than its impacts on TN loads; and (4) wide distributions of TN loads and TN concentrations under individual climate change scenario illustrate the important role of climatic variability in affecting water quality conditions. In summary, the large variability in SWAT simulation results within and between each climate change scenario highlights the uncertainty of the impacts of climate change and the need to incorporate extreme conditions in managing water environment and developing climate change adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

Copula-based bivariate frequency analysis can be used to investigate the changes in flood characteristics in the Huai River Basin that could be caused by climate change. The univariate distributions of historical flood peak, maximum 3-day and 7-day volumes in 1961-2000 and future values in 2061-2100 projected from two GCMs (CSIRO-MK3.5 and CCCma-CGCM3.1) under A2, A1B and B1 emission scenarios are analyzed and compared. Then, bivariate distributions of peaks and volumes are constructed based on the copula method and possible changes in joint return periods are characterized. Results indicate that the Clayton copula is more appropriate for historical and CCCma-CGCM3.1 simulating flood variables, while that of Frank and Gumbel are better fitted to CSIRO-MK3.5 simulations. The variations of univariate and bivariate return periods reveal that flood characteristics may be more sensitive to different GCMs than different emission scenarios. Between the two GCMs, CSIRO-MK3.5 evidently predicts much more severe flood conditions in future, especially under B1 scenario, whereas CCCma-CGCM3.1 generally suggests contrary changing signals. This study corroborates that copulas can serve as a viable and flexible tool to connect univariate marginal distributions of flood variables and quantify the associated risks, which may provide useful information for risk-based flood control. [ABSTRACT FROM AUTHOR]

A comparison study of meteorological, hydrological and agricultural drought responses to climate change resulting from different General Circulation Models (GCMs), emission scenarios and hydrological models is presented. Drought variations from 1961-2000 to 2061-2100 in Huai River basin above Bengbu station in China are investigated. Meteorological drought is recognized by the Standardized Precipitation Index (SPI) while hydrological drought and agricultural drought are indexed with a similar standardized procedure by the Standardized Runoff Index (SRI) and Standardized Soil Water Index (SSWI). The results generally approve that hydrological and agricultural drought could still pose greater threats to local water resources management in the future, even with a more steady background to meteorological drought. However, the various drought responses to climate change indicate that uncertainty arises in the propagation of drought from meteorological to hydrological and agricultural systems with respect to alternative climates. The uncertainty in hydrological model structure, as well as the uncertainties in GCM and emission scenario, are aggregated to the results and lead to much wider variations in hydrological and agricultural drought characteristics. Our results also reveal that the selection of hydrological models can induce fundamental differences in drought simulations, and the role of hydrological model uncertainty may become dominating among the three uncertainty sources while recognizing frequency of extreme drought and maximum drought duration. [ABSTRACT FROM AUTHOR]