No abstract

No abstract

No abstract

No abstract

River discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge. However, remote sensing images still have some difficulty generating continuous long-term river discharge. To address this problem, we developed a new method coupling the remote sensing hydrology station method (RSHS) with statistical regression downscaling, using data from optical satellites (Landsat-8, Sentinel-2), radar satellites (Sentinel-1), and un-manned aerial vehicles (UAVs). We applied this method to monitor monthly river discharge for small- and medium-scale rivers from 2016 to 2020 on Yunnan-Guizhou Plateau and evaluated the accuracy of the results. The results show that (1) by applying the newly constructed method, the water body continuity index obtained by Landsat-8 increased by 7% and the average river length percentage in the channel reached 90.7%, a 40% increase; (2) there were only 10 river flow data points, on average, in the 5-year period obtained before this method was applied; after this method was applied, more than 50 river flow data points could be obtained, on average, extending the quantity of data fivefold; in addition, improper extreme values could also be avoided; (3) with better continuity of water body distribution, the images provided steadier river widths. The relative error of daily flow estimation from Landsat-8 images was reduced by 60% and the mean percentage error was reduced by one-fourth. The relative error of the multisource remote sensing composited flow was reduced by 37% with a reduction in the mean percentage error of over a half; (4) in addition, we found that when the threshold difference between water bodies and land in remote sensing images is more than 0.2, the impact of water body recognition error on flow accuracy can be ignored. This method helps to overcome the absence of remote sensing methods for the long-term estimation of flow series in small- and medium-scale rivers, improves the accuracy of remote sensing methods for calculating flow, and provides ideas for regional water resource management and utilization.

No abstract

Blue–green water resources and their transformation play a vital role in crop production and ecosystem services regulation, and their distribution and utilization are affected by human activity, such as policy‐driven green water resources development projects. However, it is unclear how policy‐driven green water resources development projects, such as the Grain for Green Project (GGP) in China, affect eco‐environmental quality and socio‐economic development, that is, human–water co‐evolution. Therefore, considering the GGP in a subtropical ungauged basin in southern China as an example, based on remote sensing hydrological station (RSHS) technology and multisource remote sensing data, this study analyzed the impact of the GGP on human–water co‐evolution from the perspective of blue–green water resources. The results show that: (1) From 2001 to 2020 the GGP transformed more blue water resources into green water resources, resulting in an increase in green water resources of 282.29% compared to that of blue water resources. (2) Due to the initial success of the GGP in 2005, there was a distinct positive correlation between the green water coefficient (GWC) and basin development index (BDI) from 2005 to 2020 (R2 = 0.70, p < 0.05), indicating that the GGP did not cause conflict between the sustainability of socio‐economic development and the health of the ecological environment. (3) In the GGP scenario, in which the cultivated land area maintained 50% of the current decreasing rate, the overall efficiency index maintain a continuously increasing trend at an average rate of 0.001 year−1 from 2021 to 2050, which not only improved the eco‐environmental quality but also met cultivated land demand. This research reveals the role of GGP in promoting the transformation from blue to green water resources, and proves the feasibility of the simultaneous promotion of GGP and socio‐economic development, providing significant insights for the promotion of policy‐driven green water resources development projects and long‐term blue–green water resources planning and management in other countries or regions.

No abstract