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Human activities in a transborder watershed are complex under the influence of domestic policies, international relations, and global events. Understanding the forces driving human activity change is important for the development of transborder watershed. In this study, we used global historical land cover data, the hemeroby index model, and synthesized major historical events to analyze how human activity intensity changed in the Heilongjiang River (Amur River in Russia) watershed (HLRW). The results showed that there was a strong spatial heterogeneity in the variation of human activity intensity in the HLRW over the past century (1900—2016). On the Chinese side, the human activity intensity change shifted from the plain areas for agricultural reclamation to the mountainous areas for timber extraction. On the Russian side, human activity intensity changes mostly concentrated along the Trans-Siberian Railway and the Baikal-Amur Mainline. Localized variation of human activity intensity tended to respond to regional events while regionalized variation tends to reflect national policy change or broad international events. The similarities and differences between China and Russia in policies and positions in international events resulted in synchronous and asynchronous changes in human activity intensity. Meanwhile, policy shifts were often confined by the natural features of the watershed. These results reveal the historical origins and fundamental connotations of watershed development and contribute to formulating regional management policies that coordinate population, economic, social, and environmental activities. [ABSTRACT FROM AUTHOR]

BACKGROUND: Drought affects the characteristics of water use during crop production and so quantitatively evaluating the impacts is important. However, it remains unclear how crop water use responds to drought. To address this gap, water footprint (WF) and standardized precipitation evapotranspiration index (SPEI) were calculated by remote sensing approaches to assess the effects of drought on crop water use. Rainfed maize is the most important crop in Jilin Province, and its growth and water use are more susceptible to drought. The present study explored not only the impact of growing season drought on the maize WF values in Jilin Province, but also the response of WF values to drought at different time scales. RESULTS: Spatially, 72.94% of the WFblue pixels showed a non‐significant increase, and the WFgreen in 68% pixels decreased significantly, being mainly concentrated in the middle region. Furthermore, the pixels affected by monthly time scale drought were mainly in the middle region, whereas the pixels affected by annual time scale drought were mainly in the western region. CONCLUSION: Drought not only affected on the source and structure of agricultural water consumption, but also had different effects on WF values at different time scale. These effects had obvious spatial differences. The present study systematically explored the effects of drought on the WF values for rainfed maize in different climate regions and a consideration of these effects could provide valuable information on rainfed maize growth and the agricultural water use response to a changing climate. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The identification of groundwater contamination sources (IGCSs) is an important requirement for the remediation and treatment of groundwater contamination. The data assimilation methods such as ensemble Kalman filter (EnKF) and ensemble smoother (ES) have been applied to IGCSs in recent years and obtained good identification results. The unscented kalman filter (UKF) is also a data assimilation method with the potential to simultaneously identify hydraulic conductivity and GCSs. However, when UKF is applied to identify hydraulic conductivity and GCSs, it is necessary to use the observed data at different times separately, which increases the complexity of the update process and this may result in low identification accuracy. ES is a variant of EnKF that updates the system parameters with all observed data in all time periods, which makes ES faster and easier to implement than EnKF. Therefore, inspired by the ES, an unscented kalman smoother (UKS) based on UKF was proposed for simultaneously identifying the hydraulic conductivity and GCSs in this study. The UKS can use the data observed in all time periods simultaneously, while it is also simpler to operate and the calculation speed is faster. Present studies have shown that ES can solve IGCS problems. Thus, ES was also applied to identify the hydraulic conductivity and GCSs in this study, and its identification performance was compared with UKS. In contrast to previous applications of ES to IGCSs, both UKS and ES were set up with stop iteration conditions instead of only performing one update process, and thus both methods applied multiple update processes. The results showed that compared with ES, the identification results obtained by UKS were characterized by greater stability, higher accuracy, and the iterative process required less iteration process and computational time. [ABSTRACT FROM AUTHOR]