Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (COD_Mn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and COD_Mn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R² values of the best inversion models for Chl-a, TN, TP, and COD_Mn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L⁻¹, 0.189 mg L⁻¹, 0.053 mg L⁻¹, and 0.767 mg L⁻¹, and the mean absolute errors were 1.272 μg L⁻¹, 0.632 mg L⁻¹, 0.045 mg L⁻¹, and 0.674 mg L⁻¹, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.

A revegetation program in North China could potentially increase carbon sequestration and mitigate climate change. However, the responses of water yield ecosystem services to climate factors are still unclear among different vegetation types, which is critically important to select appropriate species for revegetation. Based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, we estimated the temporal variations and associated factors in water yield ecosystem services in North China. The result showed that the InVEST model performed well in water yield estimation (R² = 0.93), and thus can be successfully applied across the study area. The total water yield across North China is 6.19 × 10¹⁰ m³/year, with a mean water yield (MWY) of 47.15 mm/year. A large spatial difference in the MWY was found, which is strongly related to temperature, precipitation, and land use types. The responses of the MWY to mean annual precipitation (MAP) are closely tied to temperature conditions in forests and grasslands. The sensitivities of the MWY to climate variables indicated that temperature fluctuation had a positive influence on the forest MWY in humid regions, and the influence of precipitation on grassland water yield was enhanced in warmer regions. We suggest shrub and grass would be more suitable revegetation programs to improve water yield capacity, and that climate warming might increase the water yield of forests and grasslands in humid regions in North China.

Droughts and hot extremes may lead to tremendous impacts on the ecosystem and different sectors of the society. A variety of studies have been conducted on the variability of the individual drought or hot extreme in China. However, the evaluation of compound droughts and hot extremes, which may induce even larger impacts than the individual drought or hot extreme, is still lacking. The aim of this study is to investigate changes in the frequency and spatial extent of compound droughts and hot extremes during summer in China using monthly precipitation and daily temperature data from 1953 to 2012. Results show that a high frequency of compound droughts and hot extremes mostly occur in the regions stretching from northeast to southwest of China. There is an overall increase in the frequency of co-occurrence of droughts and hot extremes across most parts of China with distinct regional patterns. In addition, an increasing trend in the areas covered by compound extremes has been observed, especially after the 1990s. At regional scales, the increase of the frequency and spatial extent of compound extremes has been shown to be most profound in North China (NC), South China (SC), and Southwest China (SWC), while the decrease of compound extremes was found in Central China (CC). These results show the variability of compound droughts and hot extremes and could provide useful insights into the mitigation efforts of extreme events in China.