The spatial structure of urban-rural system and influence pattern in Shiyang River Basin

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 577 ◽  
Author(s):  
Lizhen Wang ◽  
Yong Zhao ◽  
Yuefei Huang ◽  
Jianhua Wang ◽  
Haihong Li ◽  
...  

Water-rights trade has proved to be an effective method for coping with water shortages through the transfer of water resources between users. The water allocation system is classified into two categories based on information transparency and water rights transaction goals: administered system (AS) and market-based system (MS). A multi-agent and multi-objective optimal allocation model, built on a complex adaptive system, was introduced to direct the distribution of water resources under an AS in the Shiyang River Basin; it was compared with a market-based water rights transaction model using the bulletin-board approach. Ideal economic agent equations played a dominant role in both models. The government and different water users were conceptualized as agents with different behaviors and goals in water allocation. The impact of water-saving cost on optimal water allocation was also considered. The results showed that an agent’s water-saving behavior was incentivized by high transaction prices in the water market. Under the MS, the highest bid in the quotation set had a dominant influence on how trade was conducted. A higher transaction price will, thus, result in a better benefit ratio, and a lower one will result in inactivity in terms of water rights trade. This will significantly impact the economic benefit to the basin.


2020 ◽  
Vol 12 (7) ◽  
pp. 2869
Author(s):  
Xiling Zhang ◽  
Yusheng Kong ◽  
Xuhui Ding

To promote the high-quality development of the Yellow River Basin, the total amount and intensity of agricultural water must be controlled. Further speaking, an urbanization development system should be established that is compatible with water resources and the water environment. We adopted the stochastic frontier analysis model to measure the agricultural water utilization efficiency of the Yellow River Basin from 2007 to 2017. We also adopted the dynamic panel difference generalized method of moments (GMM) and system GMM models to verify the driving factors, in which population urbanization, economic urbanization, and equilibrium urbanization levels were selected as the key variables. The results show that the overall efficiency of agricultural water utilization maintained a steady upward trend during the research period. The spatial differentiation was generally characterized by higher efficiency levels in the eastern region and lower levels in the western region. The variation coefficient of water utilization efficiency showed a downward trend in general, which indicates a space spillover effect. Agricultural water utilization efficiency continued to converge from 2007 to 2017, and the upper reaches area converged relatively more quickly. Regarding the influencing factors, the population urbanization, economic urbanization, balanced urbanization, crop planting ratio, and rice planting ratio had negative effects on agricultural water utilization efficiency. Urbanization did not positively affect agricultural water use efficiency as the related theories, so urbanization quality and urban–rural integration should be paid more attention. However, technology innovation was significantly positive in agricultural water utilization efficiency. The influencing factors of per capita water availability and annual precipitation did not pass the significance test. Therefore, the government should vigorously promote the development of high-quality new-type urbanization, scientifically formulate the scale and speed of urbanization, strengthen the urban, rural, and industrial integration, and promote the adjustment of planting structures and agricultural deep processing.


2021 ◽  
Vol 932 (1) ◽  
pp. 012011
Author(s):  
Y Wang

Abstract The Shiyang River basin is a typical inland arid region and one of the most fragile and sensitive areas of terrestrial ecosystems in China, and it is important to understand its ecological changes in a timely and accurate manner. This article selects the Shiyang River basin forest as the research area and uses Google Earth Engine (GEE) to evaluate and monitor the ecological environment quality of the Shiyang River basin from 1990 to 2020. The geographical detector model (GDM) was also used to analyse the sensitivity of the forest ecological environment to three natural factors: elevation, temperature and altitude. The results showed that the ecological quality of the natural forest is significantly better than that of the man-made forest area, and the ecological quality grade is higher. The forest change area RSEI has a large annual variation in ecological quality and is vulnerable to external factors. Among the influencing natural factors, the sensitive factors of precipitation and altitude are both greater than 84%. The temperature sensitivity of natural forests is stronger than that of man-made forests, ranging from 66% to 92% overall.


Author(s):  
Xuelei Zhang ◽  
Weihua Xiao ◽  
Yicheng Wang ◽  
Yan Wang ◽  
Miaoye Kang ◽  
...  

Abstract This paper focuses on determining the spatial and temporal characteristics of the sensitivity coefficients (SCs) between potential evapotranspiration (ET0) and key climatic factors across the Shiyang River Basin (SYRB) from 1981 to 2015. Penman–Monteith equation and a sensitivity analysis were used to calculate ET0 and the SCs for key climatic factors. Sen's slope was used to analyze the observed series. According to the results, the sensitivity significances were in the order of relative humidity (RH) > net solar radiation (NSR) > wind speed (WS) > maximum air temperature (Tmax) > minimum air temperature (Tmin). The SCs for the RH and NSR were larger in the upper mountainous region, while the other three coefficients were larger in the middle and lower reaches. All five climatic factors for the ET0 SCs showed increasing trends in the mountainous region, and the Tmax, WS and RH SCs increased in the middle and lower reaches. Over the past 35 years, the change in ET0 was dominated by the air temperature (T), RH and NSR, and the increase in ET0 during the studied period was mainly due to the increases in T and NSR.


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