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.
Xinjiang has a serious wind erosion problem due to its fragile ecological condition and sensitivity to climate change. Wind erosion climatic erosivity is a measure of climatic factors influencing wind erosion; evaluating its spatiotemporal variations and relationship with the large-scale circulation pattern can contribute to the understanding of the climate change effect on wind erosion risk. Thus, this study quantified the wind erosion climatic erosivity and examined the connections between climatic erosivity and climate indices using trend analysis, geo-statistical analysis, and cross-wavelet analysis based on the observed daily meteorological data from 64 weather stations in Xinjiang, China during 1969–2019 (50 years). The results indicated that the climatic erosivity showed a significant downward trend at seasonal and annual scales over the past 50 years. Strong seasonality in the C-factor was found, with its highest values in the spring and summer and its lowest values in the winter. The average climatic erosivity was weaker during El Niño events than during La Niña events. The impact of El Niño events on climatic erosivity in Xinjiang continued from the beginning of the event to two months after the end of the events. The La Niña events had a lag effect on the climatic erosivity in Xinjiang, with a lag period of 4 months. From a statistical perspective, the El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) indices showed relationships to the climatic erosivity in Xinjiang in terms of their correlation and periodicity. The relationships between the climatic erosivity and ENSO were not clearly positive or negative, with many correlations advanced or delayed in phase. The NAO and AO indices showed a consistent in-phase relationship with climatic erosivity on significant bands, whereas the profound mechanisms involved in this require further study. The results of this study provide a preliminary perspective on the effect of large-scale atmospheric circulation on wind erosion risk in arid and semi-arid regions.
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.
In order to effectively monitor water quality, this paper proposes a data fusion method based on Dempster-Shafer evidence theory to detect pollutants in water. Our proposed water quality monitoring system is organized as a hierarchical structure, and the whole monitoring area is divided into several parts. The water quality monitoring system includes an online monitoring module and an offline monitoring module. In particular, each monitoring area has a cluster that contains several wireless sensor nodes to collect data and communicate with other sensor nodes. Furthermore, multiple water quality parameters are detected in our water quality monitoring system, such as PH, conductivity, temperature, dissolved oxygen, turbidity, etc. The final water quality monitoring decisions are made by fusing various types of water quality indexes using the Dempster-Shafer evidence theory. Finally, experimental results prove that the proposed method can detect pollutants in water with higher accuracy by effectively fusing various types of water quality indexes.
Flood disaster is one of the natural disasters which cause the most serious economic losses, the most casualties, and the greatest social impact. Flood frequency analysis is very important for reducing flood disaster. In this paper, based on the flood data of Manas River and tools of Box–Cox and Johnson normal transformation, the nonparametric statistical method for flood frequency analysis is studied in order to analyze the adaptability between it and the rivers in arid region of north-western China. The calculation result of the fitness index is divided into two parts: high flood discharge and low flood discharge. One of the two evaluation indexes has an advantage in fitting, and the number of advantages of the three methods in each part has been counted. After analysis, for the flood peak discharge frequency of rivers in arid region of north-western China, the frequency curve of Johnson transformation fits best with empirical data. The high flood discharge advantage is 6, and the low flood discharge is 4. For the flood volume frequency of rivers in arid region of north-western China, Box–Cox transform fits well with empirical data at the high flood discharge frequency curve, and its advantage is 12; Johnson transformation has a better fit between the low flood discharge frequency curve and empirical data, and its advantage is 12. Therefore, it is the way of improving the precision of flood frequency analysis to use the method of P-III distribution and normal transformation comprehensively.
Measuring evapotranspiration (ET) components in cotton fields under mulched drip irrigation is needed to improve water use efficiency and promote the development of water-saving agriculture. In this study, an Eddy Covariance (EC) system was used to observe the water-carbon fluxes of cotton fields under mulched drip irrigation in an arid region during two years (2021–2022). The Underlying Water Use Efficiency (uWUE) method was used to partition the ET into transpiration (T) and evaporation (E) in order to reveal the changing characteristics of ET and its components in cotton fields under mulched drip irrigation and analyze the effects of environmental factors on each component. The results showed that the diurnal variation of ET was the same as gross primary productivity (GPP), and their course of change showed a bimodal curve at budding, blooming, and boll stages. The relationship of T at different growth stages was the same as ET, which is blooming and boll stage > budding stage > boll opening stage > seedling stage. ET and its components were mainly affected by temperature (Tair) and net radiation (Rn). This study can provide a theoretical and practical basis for the application of uWUE in cotton fields under mulched drip irrigation and a scientific basis for the rational allocation of water resources and the formulation of a scientific water-saving irrigation system for farmland in an arid region.
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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