RESUMO
We collected evapotranspiration data of Dajiuhu peatland in Shennongjia from 2016 to 2017 with eddy covariance method and estimated the value of crop coefficient (Kc) using FAO56 Penman-Monteith equation and the linear relationship between actual evapotranspiration (ETa) and referenced evapotranspiration (ET0). We analyzed the characteristics of referenced evapotranspiration and its main influencing factors and calculated the crop coefficient of the wetland dominated by Sphagnum. The results showed that the daily averaged ETa were 1.63 and 1.38 mm·d-1 in 2016 and 2017, the daily averaged ET0 were 1.61 and 1.23 mm·d-1 in 2016 and 2017. Environmental factors influencing ET0 included net radiation, air temperature, vapor pressure deficit, wind speed, and relative humidity. The Kc values for the growing seasons of 2016, 2017, and 2016-2017 were 0.95 (R2 of linear regression between ETa and ET0 was 0.96), 1.03 (R2=0.95), and 0.98 (R2=0.95). The Kc values in 2016, 2017, and 2016-2017 were 0.92 (R2=0.94), 0.95 (R2=0.89), and 0.93 (R2=0.92). Kc was effective in the range of 0.92-1.03 for the wetland dominated by Sphagnum. The identified parameters could be widely used in studies on climate change, ecosystem services, and water management in peatlands.
Assuntos
Ecossistema , Transpiração Vegetal , Produtos Agrícolas , Temperatura , Água , VentoRESUMO
Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problem-level and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed.
