| 摘 要： |
Modeling vegetation phenology is crucial to assessing how climate change impacts carbon cycles in terrestrial ecosystems. The process-based biogeochemical model Biome-BGCMuSo is widely used for simulating carbon and water storages and fluxes of grassland ecosystems. However, the lack of accurate phenological information, such as the start of the growing season (SOS), impedes better simulations of the biogeochemical processes in the Tibetan Plateau (TP). Here, based on the snow-free satellite-derived SOS and the end of the growing season (EOS) in the TP during 1982-2018, we calibrated and validated three phenological models for SOS (i.e., the Biome-BGC phenological (BBGC) model, the heatsum growing season index (HSGSI) model, and the alpine meadow prognostic phenological (AMPP) model) and five phenological models for EOS (i.e., BBGC, HSGSI, AMPP, the low temperature and photoperiod multiplicative model induced by photoperiod (TPMP) and temperature (TPMT)) using particle swarm optimization (PSO) algorithm. For SOS, all three phenological models with calibrated parameters performed similarly and all captured the change in SOS well along gradients of aridity. The performance of BBGC, HSGSI, and AMPP models were largely improved with the calibration. The AMPP model simulated SOS with the lowest estimation errors with the mean absolute error (MAE) of 18.67 days and the Kling Gupta efficiency (KGE) of 0.47 in validation. For EOS, the calibrated HSGSI and AMPP models, with mean MAEs of 9.85 and 9.29 days, respectively, captured the change in EOS well along the gradients of aridity and performed better than other models. The calibration significantly improved the simulation performance of all five models. Therefore, the phenological models can be calibrated and validated at a large scale with snow-free satellite derived phenological data. Our study recommends that calibration and validation for the phenological model play a vital role in accurately simulating SOS and EOS in the regional carbon cycle simulation. |
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