Climate change and water resources： Case study of Eastern Monsoon Region of China

This paper addresses the impact of climate change on the water cycle and resource changes in the Eastern Monsoon Region of China （EMRC）. It also represents a summary of the achievements made by the National Key Basic Research and Development Program （2010CB428400）, where the major research focuses are detection and attribution, extreme floods and droughts, and adaptation of water resources management. Preliminary conclusions can be summarized into four points： 1） Water cycling and water resource changes in the EMRC are rather complicated as the region is impacted by natural changes relating to the strong monsoon influence and also by climate change impacts caused by CO2 emissions due to anthropogenic forcing; 2） the rate of natural variability contributing to the influence on precipitation accounts for about 70%, and the rate from anthropogenic forcing accounts for 30% on average in the EMRC. However, with future scenarios of increasing CO2 emissions, the contribution rate from anthropogenic forcing will increase and water resources management will experience greater issues related to the climate change impact; 3） Extreme floods and droughts in the EMRC will be an increasing trend, based on IPCC-AR5 scenarios; 4） Along with rising temperatures of 1 ~C in North China, the agricultural water consumption will increase to about 4% of total water consumption. Therefore, climate change is making a significant impact and will be a risk to the EMRC, which covers almost all of the eight major river basins, such as the Yangtze River, Yellow River, Huaihe River, Haihe River, and Pearl River, and to the South-to-North Water Diversion Project （middle line）. To ensure water security, it is urgently necessary to take adaptive countermeasures and reduce the vulnerability of water resources and associated risks.

Differences in parameter estimates derived from various methods for the ORYZA(v3) Model

Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods(SCE-UA, GA and PEST) and two Bayesian-based methods(GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA(v3) Model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method(MCMC_P_(max)) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation.

Bayesian multi-model projections of extreme hydroclimatic events under RCPs scenarios

A Bayesian multi-model inference framework was used to assess the changes in the occurrence of extreme hydroclimatic events in four major river basins in China （i.e., Liaohe River Basin, Yellow River Basin, Yangtze River Basin, and Pearl River Basin） under RCP2.6, RCP4.5, and RCP8.5 scenarios using multiple global climate model projections from the IPCC Fifth Assessment Report. The results projected more summer days and fewer frost days in 2006-2099. The ensemble prediction shows the Pearl River Basin is projected to experience more summer days than other basins with the increasing trend of 16.3, 38.0, and 73.0 d per 100 years for RCP2.6, RCP4.5 and RCP8.5, respectively. Liaohe River Basin and Yellow River Basin are forecasted to become wetter and warmer with the co-occurrence of increases in summer days and wet days. Very heavy precipitation days （R20, daily precipitation ≥20 mm） are projected to increase in all basins. The R20 in the Yangtze River Basin are projected to have the highest change rate in 2006-2099 of 1.8, 2.5, and 3.8 d per 100 years for RCP2.6, RCP4.5 and RCP8.5, respectively.