Protected areas are critical for biodiversity conservation and ecosystem services. In the last few years, there has been growing recognition of the role of indigenous peoples and local communities in the management of government designated protected areas, and thus their perceptions and adaptability were paid much attention. Drawing on a survey of 487 residents in the Qilian Mountain National Park Pilot of Northwestern China, this study used the adaptive analysis framework to study the adaptability of local residents. The main contribution of this paper is to select a typical social-ecological system to study the adaptability of local residents, and using Elinor Ostrom’s Social-Ecological System framework to analyze the adaptability mechanism. The results show that different types of residents had different adaptability to environmental change. People whose income mainly depends on work salary with a small part of herding have the highest level of adaptability, while people whose income mostly comes from farming with a small part of herding have the lowest level. This result is related to people’s living location, as people living in the core zone and buffer zone of the reserve mainly earned from grazing, and people living in the experimental zone and peripheral zone earned mainly from outside work. Moreover, people living in the core zone and buffer zone are mostly elders and ethnic groups, while people in the experimental zone and buffer zone are Han people. To improve management effectiveness and to avoid conflict between local residents and managers, this paper suggests that more attention should be paid to these who have lived for a long time in the core zone and buffer zone. They are the most vulnerable groups and show low adaptability in almost all domains. For the long run, education quality should be improved to decrease the population in the reserve.

Accurate satellite precipitation estimates are vital for understanding global and largescale regional water cycles. Among the many factors influencing satellite precipitation data quality, the detection and accuracy of precipitation products at different atmospheric aerosol concentrations are not well studied. In this study, we investigated the impact of atmospheric aerosols on the accuracy of satellite precipitation products (IMERG) over North China by comparing performance metrics such as Bias, Normalized Root Mean Squared Error, probability of detection and false alarm ratio under different atmospheric aerosol conditions. The results revealed that: (1) IMERG generally exhibits poorer detectability and quantification under pollution condition. (2) Based on the error decomposition, the estimated errors in autumn and winter were dominated by false biases, which are mainly affected by atmospheric aerosols. (3) At the sensor level, the false alarm ratios (FARs) of both infrared (IR) and passive microwave (PMW) sensors show escalating trends as pollutant concentrations increase. The probability of detection (POD) of IR sensors is affected by pollution. Pollution has a significant impact on IR detection capability. Our findings suggest that atmospheric aerosols may impact the accuracy of IMERG precipitation estimates over Northern China and need to be taken into consideration in the IMERG retrieval process and data utilization.