Based on the meteorological data from 46 stations in the Hai River Basin (HRB) from 1961–2010, the annual and seasonal variation of reference evapotranspiration was analyzed. The sensitivity coefficients combined with the detrend method were used to discuss the dominant factor affecting the reference evapotranspiration (ET_o). The obtained results indicate that the annual reference evapotranspiration is dominated by the decreasing trends at the confidence level of 95% in the southern and eastern parts of the HRB. The sensitivity order of climatic variables to ET_o from strong to weak is: relativity humidity, temperature, shortwave radiation and wind speed, respectively. However, comprehensively considering the sensitivity and its variation strength, the detrend analysis indicates that the decreasing trends of ET_o in eastern and southern HRB may be caused mainly by the decreasing wind speed and shortwave radiation. As for the relationship between human activity and the trend of ET_o, we found that ET_o decreased more significantly on the plains than in the mountains. By contrast, the population density increased more considerably from 2000 to 2010 on the plains than in the mountains. Therefore, in this paper, the correlation of the spatial variation pattern between ET_o and population was further analyzed. The spatial correlation coefficient between population and the trend of ET_o is −0.132, while the spatial correlation coefficient between the trend of ET_o and elevation, temperature, shortwave radiation and wind speed is 0.667, 0.668, 0.749 and 0.416, respectively. This suggests that human activity has a certain influence on the spatial variation of ET_o, while natural factors play a decisive role in the spatial variation of reference evapotranspiration in this area.

In this study, housing prices data for residential quarters from the period 2001–2012 were used and Global Differentiation Index (GDI) was established to measure the overall differentiation trend in housing prices in Yangzhou City, eastern China. Then the influence of the natural landscape and environment on prices of global housing market and housing submarkets was evaluated by the hedonic price model. The results are shown as follows. (1) There have been increasing gaps among housing prices since 2001. In this period, the differentiation trend has shown an upward fluctuation, which has been coupled with the annual growth rate of housing prices. (2) The spatial distribution of residential quarters of homogenous prices has changed from clustered in 2001 into dispersed in 2012. (3) Natural landscape and environmental externalities clearly influence spatial differentiation of housing prices. (4) In different housing submarkets, the influence of natural landscape and environmental externalities are varied. Natural landscape characteristics have significant impact on housing prices of ordinary commercial houses and indemnificatory houses, while the impact of environmental characteristics have obvious influence on housing prices of cottages and villas.

With the increasing deployment of unmanned systems in maritime patrol, coastal monitoring, and environmental mapping, achieving effective UAV-USV collaboration in dynamic environments remains challenging. This paper proposes an edge-intelligence-driven collaborative control framework that integrates unified data modeling, multi-objective task scheduling, lightweight fault-tolerant middleware, and multi-sensor fusion. A Weighted Kalman Filter combines UAV imaging and USV sonar data to enhance perception accuracy, while NSGA-II optimizes task allocation considering completion time, energy consumption, and sensing reliability. The framework was validated through representative maritime scenarios, including patrol and coastal sediment mapping, on a virtual simulation platform. Results show improved task efficiency, energy utilization, communication latency, and robustness compared with single-platform and centralized scheduling approaches. The proposed method provides a balanced optimization of execution efficiency, energy consumption, data accuracy, and resilience, offering a reliable solution for large-scale maritime applications.