Abstract:Measuring total nitrogen (TN) and total phosphorus (TP) is important in managing heavy polluted urban waters in China. This study uses high spatial resolution IKONOS imagery with four multispectral bands, which roughly correspond to Landsat/TM bands 1-4, to determine TN and TP in small urban rivers and lakes in China. By using Lake Cihu and the lower reaches of Wen-Rui Tang (WRT) River as examples, this paper develops both multiple linear regressions (MLR) and artificial neural network (ANN) models to estimate TN and TP concentrations from high spatial resolution remote sensing imagery and in situ water samples collected concurrently with overpassing satellite. The measured and estimated values of both MLR and ANN models are in good agreement (R 2 > 0.85 and RMSE < 2.50). The empirical equations selected by MLR are more straightforward, whereas the estimated accuracy using ANN model is better (R 2 > 0.86 and RMSE < 0.89). Results validate the potential of using high resolution IKONOS multispectral imagery to study the chemical states of small-sized urban water bodies. The spatial distribution maps of TN and TP concentrations generated by the ANN model can inform the decision makers of variations in water quality in Lake Cihu and lower reaches of WRT River. The approaches and equations developed in this study could be applied to other urban water bodies for water quality monitoring. OPEN ACCESSWater 2015, 7 6552

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA) method combined with three statistical downscaling methods, which are support vector machine (SVM), BCC/RCG-Weather Generators (BCC/RCG-WG), and Statistics Downscaling Model (SDSM), is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB). The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error) shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.

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Macro-evolution is a new kind of high-level species evolution inspired by the dynamics of species extinction and diversification at large time scales. Immune algorithms are a set of computational systems inspired by the defense process of the biological immune system. By taking advantage of the macro-evolutionary algorithm and immune learning of artificial immune systems, this article proposes a macro-evolutionary multi-objective immune algorithm (MEMOIA) for optimizing multi-objective allocation of water resources in river basins. A benchmark test problem, namely the Viennet problem, is utilized to evaluate the performance of the proposed new algorithm. The study indicates that the proposed algorithm yields a much better spread of solutions and converges closer to the true Pareto frontier compared with The Non-dominated Sorting Genetic Algorithm and Improving the Strength Pareto Evolutionary Algorithm. MEMOIA is applied to a water allocation problem in the Dongjiang River basin in southern China, with three objectives named economic interests (OF 1 ), water shortages (OF 2 ) and the amount of organic pollutants in water (OF 3 ). The results demonstrate the capabilities of MEMOIA as well as its suitability as a viable alternative for enhanced water allocation and management in a river basin.

Purpose -With frequent floods occurring, and the fast economic development in China, attention must be paid to flood prevention, water supply, and forecasting precision. In particular, mid-and long-term runoff prediction is being paid more and more attention by researchers, and it is also the most difficult problem to solve. The purpose of this paper is to apply chaos phase space theory to forecast river run off. Design/methodology/approach -Because the hydrologic system is a complicated huge system, there exist high non-linear characteristics in the space-time change of hydrologic factors. According to theory of chaotic phase space, the paper established four models of the single-point, multi-point, lineal, and three-parameter D(m,t,k) models, they have stronger non-linear mapping function and much more information in the time series than traditional ways. Findings -The results of calculation show that the models are highly effective and worthy of popularization and application. It is reasonable and superior to use these models in mid-and long-term hydrologic prediction.Research limitations/implications -The method cannot reduce or eliminate the un-prediction parts caused by the inner random factors, such as the noise information of the observed data. Practical implications -The models are applied in the long-term runoff prediction of Baishan reservoir. Originality/value -The new approach of hydrology forecasting due to the theory of chaotic phase space. The paper is aimed at hydrology forecasting researches and engineers, especially those who dealt with the mid-and long-term prediction.