How Potential Evapotranspiration Regulates the Response of Canopy Transpiration to Soil Moisture and Leaf Area Index of the Boreal Larch Forest in China

Transpiration is a critical component of the hydrological cycle in the terrestrial forest ecosystem. However, how potential evapotranspiration regulates the response of canopy transpiration to soil moisture and leaf area index of the boreal larch forest in China has rarely been evaluated. The present study was conducted in the larch (Larix gmelinii (Rupr.) Rupr.) forest, which is a typical boreal forest in China. The canopy transpiration was measured using sap flow techniques from May to September in 2021 and simultaneously observing the meteorological variables, leaf area index (LAI) and soil moisture (SWC). The results showed that there were significant differences in canopy transpiration of Larix gmelinii among the months. The correlation and regression analysis indicated that canopy transpiration was mainly influenced by potential evapotranspiration (PET), while the effect of soil moisture on canopy transpiration was lowest compared with other environmental factors. Furthermore, our results revealed that the effect of PET on canopy transpiration was not regulated by soil moisture when soil moisture exceeded 0.2 cm3 cm−3. More importantly, under the condition of sufficient soil moisture, it was demonstrated that the response of canopy transpiration to leaf area index was limited when PET exceeded 9 mm/day. These results provide valuable implications for supporting forest management and water resource utilization in the boreal forest ecosystem under the context of global warming

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions