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Multi-model ensemble approaches for simulation of evapotranspiration of karst agroforestry ecosystems

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  • Zhang, Rongfei
  • Xu, Xianli
  • Guo, Jingsong
  • Sheng, Zhuping

Abstract

Water shortages frequently occur in karst areas, and there is an urgent need to quantify water fluxes to provide information for sustainable management of water resources. Thus, a variety of models have been developed to simulate the water balance process, including actual evapotranspiration (ETc), which is a key variable for linking water and energy cycles. However, high heterogeneity of the land surface makes it hard to get precise complete set of parameters for single model, and a single model often has uncertainties in simulating evapotranspiration in karst regions. Therefore, this study integrated three well-known individual models (Penman-Monteith, PM; Priestley and Taylor, PT; and Shuttleworth-Wallace, SW) with two multi-model ensemble approaches (Bayesian model averaging, BMA; and simple model average, SA) to enhance ETc modeling in a subtropical humid karst catchment. Results show that: 1) The individual models show different strengths for different ecosystems, which could be attributed to differences in the underlying landscape surface characteristics; 2) individual models exhibited seasonal uncertainties. For example, simulated ETc (ETs) by the PM and PT model was lower than ETo (observed ETc) during November-March but higher than during April-October for forest-grass mixed and grass ecosystems; 3) Two multi-model ensemble approaches (R2 ≥ 0.85) performed better than any individual model (R2 ≤ 0.85) most likely because multi-model ensemble approaches reduce model uncertainties by weakening the bias of individual models.

Suggested Citation

  • Zhang, Rongfei & Xu, Xianli & Guo, Jingsong & Sheng, Zhuping, 2022. "Multi-model ensemble approaches for simulation of evapotranspiration of karst agroforestry ecosystems," Agricultural Water Management, Elsevier, vol. 273(C).
    • Handle: RePEc:eee:agiwat:v:273:y:2022:i:c:s0378377422004164
    DOI: 10.1016/j.agwat.2022.107869
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    References listed on IDEAS

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