Eutrophication has become the primary water quality issue for many urban landscape waters in the world. It is a focus in this paper which analyzes Enhanced Thematic Mapper images and quality observation data for 12 consecutive years in 20 parts of the urban landscape water in Xi'an City, China. A water quality model for urban landscape water based on Support Vector Machine (SVM) was established. Based on in situ monitoring data, the model is compared with water quality retrieving methods of multiple regression and back propagation neural network. Results show that the Genetic Algorithm-SVM (GA-SVM) method has better prediction accuracy than the inversion results of the neural network and the traditional statistical regression method. In short, GA-SVM provides a new method for remote sensing monitoring of urban water eutrophication and has more accurate predictions in inversion results [such as chlorophyll a (Chl-a)] in the Xi'an area. Additionally, remote sensing results highly agreed with in situ monitoring data, indicating that the technology is effective and less costly than in situ monitoring. The technology also can be used to evaluate large lake eutrophication.

With the acceleration of human economic activities and dramatic changes in climate, the validity of the stationarity assumption of flood time series frequency analysis has been questioned. In this study, a framework for flood frequency analysis is developed on the basis of a tool, namely, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We introduced this model to construct a non-stationary model with time and climate factor as covariates for the 50-year snowmelt flood time series in the Kenswat Reservoir control basin of the Manas River. The study shows that there are clear non-stationarities in the flood regime, and the characteristic series of snowmelt flood shows an increasing trend with the passing of time. The parameters of the flood distributions are modelled as functions of climate indices (temperature and rainfall). The physical mechanism was incorporated into the study, and the simulation results are similar to the actual flood conditions, which can better describe the dynamic process of snowmelt flood characteristic series. Compared with the design flood results of Kenswat Reservoir approved by the China Renewable Energy Engineering Institute in December 2008, the design value of the GAMLSS non-stationary model considers that the impact of climate factors create a design risk in dry years by underestimating the risk.

Aiming at the problems that need to be solved urgently in the current operation of a multireservoir in Kuitun River Basin, such as the uneven distribution of water resources in time and space, the large workload of manual operation calculation, and low coordination level, the paper takes the optimal operation of water resources in the basin as the main goal and carries out the research on the optimal operation model of the multireservoir in combination with the complex characteristics of local water resources system. Firstly, based on the generalization of hydraulic engineering in Kuitun River Basin, a water resources optimal operation model of the multireservoir is established and is solved by the graph theory. Then, the actual data of typical years were selected to test the model. The test results show that, compared with the actual water distribution, the water shortage rate of 2015 and 2016 in high flow years decreased by 98.57% and 100%, respectively; the water shortage rate of 2013 and 2014 in normal flow years decreased by 92.65% and 96.38%, respectively; and the water shortage rate of 2009 in a low flow year decreased by 87.78%. The model can provide the optimal operation scheme for the optimal operation of the multireservoir in the basin. And it can solve the problems such as the uneven distribution of water resources and the large workload of manual operation calculation and can provide technical support for the optimal operation of water resources of the multireservoir in Kuitun River Basin in the future.

Water resource carrying capacity (WRCC) is essential for characterizing the harmony between humans and water resources in an area. Investigation of the WRCC is useful for guiding the sustainable development of a region. The northern slope of the Tianshan Mountains is an important area for the economic development of Xinjiang, China. In recent years, the supply of water in the area barely satisfies the demand. To quantitatively evaluate the WRCC, data for four indicators including the water resources, social and economic development, and ecological environment of the area were utilized. The comprehensive weighting method, which combines the entropy and analytic hierarchy processes, was used to assess these indicators. A fuzzy comprehensive evaluation model was employed to evaluate the urban WRCC of the northern slope of the Tianshan Mountain for 2018. The results showed urban WRCC values varying between good and moderate for the northern slope of the Tianshan Mountains, and this indicates that the study area is in a loadable state. Although the water supply can meet the development of cities on the northern slope of the Tianshan Mountains to a certain extent at this stage, because it is located in the arid region of western China, the shortage and uneven distribution of water resources are one of the biggest limiting factors for the future development of this region. The findings of the present study provide a basis for the development, rational allocation, and sustainable utilization of urban water resources on the northern slope of the Tianshan Mountains.

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