Accurately identifying and predicting droughts can provide local managers with a basis for decision-making. The Xinyang region is prone to droughts and floods, which have a large impact on local agriculture and socio-economics. This paper employs precipitation data from the Xinyang region to provide a scientific basis for drought and flood control measures in this region. The data are first treated with standardized precipitation indices (SPIs) on three-month, six-month, and nine-month time scales. Subsequently, a Morlet wavelet analysis is performed for each of the three time scales analyzed for the SPI. The results show multiple time scales of drought and flood disasters in the Xinyang region. The cycles of drought and flood disasters in the Xinyang region show different fluctuations on different SPI scales. The SPI time series reflect a strong fluctuation period of 17a for drought and flood disasters in the Xinyang region. An analysis of the variance of the wavelet coefficients showed that the first main cycle of drought and flood disasters in the Xinyang region is 7a, and the second and third sub-cycles are 4a and 13a, respectively. We conclude that floods are more frequent than droughts in Xinyang and are more likely to occur from 2017 to 2021, with a subsequent shift to droughts. Local managers should put drought prevention measures in place to deal with droughts after 2021.

To achieve carbon peaking and carbon neutrality in China, photovoltaic (PV) power generation has become increasingly important for promoting a low-carbon transition. The central and western desert areas of China have been identified as major areas for the construction of large PV bases. Remote sensing technology has been used to map the spatial distribution and development status of PV power stations quickly and accurately in ecologically fragile areas, as well as assess the ecological and environmental impact of their construction. However, current remote sensing monitoring of PV power stations focuses mainly on mapping and time series analysis to measure their development process and assess the environmental conditions on a large scale over a long period of time. Therefore, we constructed a random forest model based on image spectral and texture features and mapped 2022 PV power stations in the junction area of Hobq Desert, Ulan Buh Desert, Tengger Desert, and Mu Us Sands in China. Following that, we identified the construction time of the PV power stations by identifying the turning points of the normalized construction land index (NDBI) time series from 1990–2022 using the LandTrendr method. To assess the ecological impact of PV power stations, we used the NDVI to measure the change in vegetation condition before and after the construction of PV power stations and constructed NDVI changes for PV power stations constructed in different years. The results showed that this mapping method achieved an overall classification accuracy of 96.65% and a Kappa coefficient of 0.92. The root mean square error (RMSE) for construction year identification was less than 0.5, and the number of new PV power stations increased significantly after 2010, reaching a total area of 14.52 km² by 2016, which is consistent with the trend driven by national and regional development plans. Furthermore, the study found that the vegetation cover level could be restored to the average level before construction within 5–6 years and continued to increase after that. These findings may help government policymakers and practitioners make decisions on PV power station planning and ecological environment protection, thus contributing promptly to the achievement of China’s dual carbon goals.

The reuse of reclaimed water (RW) for irrigation and recharge is a promising approach to mitigate water scarcity and promote efficient water resource management. Nevertheless, there is increasing concern regarding the ecological and environmental impacts of soil and groundwater contamination resulting from the irrigation and recharge of RW. To understand the current state of research on the ecological risk of soil and groundwater during RW reuse, a bibliometric analysis of 1109 publications throughout 2002-2021 in the Web of Science Core Collection (WoSCC) database about the research topic was conducted. Through the use of HistCite Pro and Citespace, this study analyzed and visualized the number of publications, authors, citation frequency, keywords, institutions, journals, and countries where these publications have appeared. The results of this research showed an ongoing rise in the total number of publications during the 20 years prior. China was identified as the most prominent country in terms of production in this field, while the Chinese Academy of Sciences showed the highest level of productivity among institutions. Water has been identified as the most productive journal, while Li YK was identified as the most productive author. This current research focuses on the migration and transformation of RW contaminants in the soil-groundwater system and their ecological effects on soil and plants, whereas future research will concentrate on the risk assessment and health effects of emerging contaminants (ECs). The analysis presented in this study provides insights into research orientations, historical trends, and current research hotspots in the field of study. The aforementioned findings will offer valuable insights for formulating development strategies pertaining to RW reuse. Additionally, they will serve as guidance for decision-making processes within governmental management organizations

Ordos Plateau is one of the primary sources of sediment in the Yellow River, and changes in regional soil erosion directly affect the ecological status of the lower reaches of the Yellow River. Many recent studies have been published using remote sensing (RS) and geographic information systems (GIS) to evaluate soil erosion. In this study, much satellite remote sensing data in the Google Earth Engine (GEE) can better track soil erosion protection, which is significant in guiding the ecological protection and restoration of the Ordos Plateau and the Yellow River basin. In this study, we used GEE to observe the changes in soil erosion in the Ordos area from 2013 to 2021. The Theil–Sen procedure and Mann–Kendall significance test methods were used to evaluate the trend of land erosion in the Ordos area from 2013 to 2021. Based on GEE, the RUSLE is applied to evaluate soil erosion and analyze the changing trend. As a result, (1) we found that the annual change of soil and water loss in the Ordos Plateau showed three stages: 2013–2015, 2016–2018, and 2018–2021. After 2018, soil loss decreased from 14 × 10¹⁷ Mg in 2018 to 4 × 10¹⁷ Mg in 2021, which indicates that the environmental restoration project started in 2018 has achieved encouraging results. (2) The results showed that 40.9% of the regional soil erosion trend showed a significant decline, and 45.7% of the regional soil erosion trend showed a slight decline. Only 13.3% of the regional soil erosion is on the rise. (3) The test results of different land use types show that 87.3% of soil erosion occurs in bare and cultivated land. Because the terrain of Ordos is relatively flat, 95.39–96.17% of soil erosion occurs in areas with a slope of 0 to 5. (4) The reliability of the RUSLE model based on the GEE platform is proved by regression model verification of observation data and model prediction results. (5) GEE’s cloud-based features can provide data and scripts to users in developing countries which lack sufficient observation data or the necessary computing resources to develop these data. The results show that GEE has robust analysis and processing ability, can analyze a large amount of data, and can provide efficient digital products for soil erosion research.

The integrated governance reform of water resources, water environment, and water ecology (Three Water Integration, TWI) is essential for addressing complex ecological risks in river basins. However, existing studies lack an integrated digital-intelligent platform to support basin-wide assessment. This study develops a framework that integrates a digital-intelligent platform with network analysis to quantify governance evolution and propose county-level strategies. We apply the framework to the Upper Yellow River Basin (2000–2024) using multi-source data. We construct a “Sustainable Water Cube Space” network, identifying four stable indicator clusters: socio-economy, water resources, water environment, and water ecology. We also identify 18 key “bridge” indicators based on network centrality. Network stability tests (Jaccard similarity = 0.76) and multi-temporal topological evolution analysis reveal a governance paradigm shift: the focus transitioned from economy-driven to ecology-economy-coordinated development from 2000 to 2022, We also observe that economic growth has decoupled from environmental degradation, with the correlation between these indices dropping from 0.72 to 0.34. Attribution analysis identifies pollution control (42%) and ecological restoration (35%) as the primary drivers of improvement. Finally, we propose differentiated governance strategies for eight counties based on their local characteristics. This study provides a replicable, data-driven framework for quantifying TWI governance evolution and informing county-differentiated strategies at the watershed scale.