Urbanization has been responsible for the loss of cropland worldwide, especially in China. Since this trend is expected to continue in the near future, China has implemented the strictest cropland protection policies in the world, to guarantee its national food security. However, the negative impact of cropland protection policies on ecosystem services has always been ignored. In this paper, we used LANDSCAPE (Land System Cellular Automata model for Potential Effects) model to assess the ecological lands loss under different scenarios in Wuhan, China during S2010–2020. Our scenarios differ in whether or not the cropland protection policy is imposed. Then, the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model was used to calculate the amount of nutrient export under two different scenarios and to analyze the mechanism of impact of Cropland Balance Policy on water purification. Results show that the scenarios with strict cropland protection (CP) will lead to more losses of ecological lands compared with scenarios without cropland protection (NCP). Besides, the nitrogen export in the CP scenario is average 8.6% higher than the NCP scenario, which indicates that the Cropland Balance Policy has a negative impact on water purification. The nitrogen export is transported mainly by subsurface, which is 1.73 times higher than the surface averaged over the two scenarios. Accordingly, this study proposed that reasonable land use planning, and lowering the nutrient delivery ratio would be more beneficial to the ecosystem service of water purification.

A cropland system is one of the most sensitive socio-ecological systems to climate change, such as drought and flood. Facing frequent extreme weather events worldwide, how to improve cropland system resilience to climate change (CSRCC) and thus ensure food production has been concerned. Although a small number of studies have attempted to evaluate CSRCC through single or multiple indicators, few studies have considered the perspective of the three basic capacities of resilience (i.e., robustness, adaptability, and transformability), which could ignore the dynamic characteristics of cropland system resilience against shocks within a certain period. Therefore, this study first constructs an evaluation index system from the three capacities of system resilience. Then, taking Hubei province, China, as a case and comprehensively using the methods of Delphi, AHP, and TOPSIS to assess the spatio-temporal characteristics of CSRCC at the municipal scale from 2011 to 2018. On this basis, the regional disparities of CSRCC are analyzed by using the Theil coefficient. The results show that the CSRCC of Hubei province fluctuates on a downward trend, with the lowest in 2017 and the highest in 2013. Most municipalities have witnessed a pattern of fluctuated decline, except for a few ones in the plains, such as Wuhan and Jingmen. Generally, municipalities in the plains have greater scores, while some municipalities in the southern and eastern hilly regions show higher adaptability and transformability. In addition, adaptability contributes the least to the CSRCC at the municipal scale. At last, indicator selection against different research objects, influencing mechanism of CSRCC, and policy implications are discussed. This study is expected to provide a reference for the practice in sustainable management and utilization of cropland systems.

With the rapid development of urbanization and social economy, the utilization and protection of land have become one of the great social problems globally

Forecasting the industrial water demand accurately is crucial for sustainable water resource management. This study investigates industrial water demand forecasting by case-based reasoning (CBR) in an arid area, with a case study of Zhangye, China. We constructed a case base with 420 original cases of 28 cities in China, extracted six attributes of the industrial water demand, and employed a back propagation neural network (BPN) to weight each attribute, as well as the grey incidence analysis (GIA) to calculate the similarities between target case and original cases. The forecasting values were calculated by weighted similarities. The results show that the industrial water demand of Zhangye in 2030, which is the t arget case, will reach 11.9 million tons. There are 10 original cases which have relatively high similarities to the target case. Furthermore, the case of Yinchuan, 2010, has the largest similarity, followed by Yinchuan, 2009, and Urumqi, 2009. We also made a comparison experiment in which case-based reasoning is more accurate than the grey forecast model and BPN in water demand forecasting. It is expected that the results of this study will provide references to water resources management and planning.