A two-step downscaling method for high-scale super-resolution of daily temperature - a case study of Wei River Basin, China.

Climate data with high spatial and temporal resolution were of great significance for regional environmental management, such as for early response to possible predicted local climate changes and extreme weather. However, the current downscaling targets for CMIP6 climate simulations were mostly medium-resolution (MR) reanalysis data, which were still coarse for local analysis. A two-step downscaling method was proposed for 100 × resolution enhancements of general circulation model (GCM) daily temperature data in this study. First, the historical GCM outputs were 10 × downscaled to a set of dynamically predictable MR data using a deep convolutional neural network (CNN), which included both encode-decode structure and long-short skip connections. Then, using high-resolution (HR) topographic data and MR climate data as auxiliary data, the GCM data were super-resolved to a series of images with spatial resolution of 1 km. A one-step downscaling analysis combined only with HR topographic data was performed as comparison. Seven evaluation metrics were selected to evaluate the prediction accuracy, and the results showed that the overall performance of two-step downscaling method was better than one-step downscaling method. Higher Nash-Sutcliffe efficiency (NSE) and lower mean absolute relative error (MARE) indicated that the two-step method performed better prediction of peak and low values. It was further confirmed by accuracy evaluation on the 10% max and 10% min values of the testing dataset. The introduction of dynamically predictable MR data could provide effective detailed information during the downscaling process and improve the prediction accuracies. Finally, the projected data of four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) during 2015-2050 were downscaled to the study area. The complex temporal and spatial variations indicated that there were great differences in temperature changes in a basin, and differentiated management measures should be proposed in advance.

Perceived Teacher Enthusiasm and Professional Commitment: The Mediating Role of Boredom and Learning Engagement.

Introduction: Some students in current society do not pursue careers related to their majors after graduation, which may be the result of low professional commitment of college students, and the teaching enthusiasm of college teachers presented in the classroom may influence students' professional commitment. This study considered the effect of teacher enthusiasm on students' emotional state of boredom during class and its effect on students' engagement in learning. This correlational study aims to explore the relationship between perceived teacher enthusiasm and professional commitment as mediated by class-related boredom and learning engagement. Methods: This study is a correlational design and adopts regression analysis. The respondents were college students (n=358; 68% female, 22% male) of different grades and majors from universities in Wenzhou, China. Questionnaires about perceived teacher enthusiasm, professional commitment, class-related boredom and learning engagement were adopted to measure the study variables. Results: The results reveal that although there is no significant direct influence between perceived teacher enthusiasm and professional commitment, perceived teacher enthusiasm affects students' professional commitment through students' class-related boredom and learning engagement, and there is an indirect and statistically significant correlation between them. Conclusion: This study provides insight into the facilitative effect of teachers' increased enthusiasm on students' professional commitment and how this facilitative effect is triggered through the mediating role of class related boredom and learning engagement. Future research should explore the theoretical and teaching significance and how to guide and enhance students' professional commitment.