Zhang Yifan, Pang Jianzhuang, Xu Hang, Leng Manman, Zhang Zhiqiang
Jixian National Forest Ecosystem Observation and Research Station, CNERN, Beijing Forestry University, Beijing, 100083, PR China; National Key Laboratory of High Efficiency Forest Production, College of Soil and Water Conservation, Beijing Forestry University, Beijing, 100083, PR China; Key Laboratory of Soil and Water Conservation and Desertification Combating, State Forestry and Grassland Administration, China, Beijing, 100083, PR China.
Jining Water Conservancy Development Center, Jining, 272000, Shandong Province, PR China.
Environ Res. 2024 Jun 15;251(Pt 2):118730. doi: 10.1016/j.envres.2024.118730. Epub 2024 Mar 15.
The Budyko framework, widely used to quantify the watershed hydrological response to the watershed characteristics and climate variabilities, is continuously refined to overcome the disadvantages of steady state assumption. However, dynamic variations in vegetations and climate variables are not fully integrated including coverages and precipitation regimes of intensity, frequency, and duration. To address this, we developed an innovative approach for determining the parameter ω in the Budyko framework to quantify the hydrological effects of vegetation restoration in a mesoscale watershed located in northern China. We found that fractional vegetation coverage (FVC), heavy precipitation amount (95pTOT), and the number of precipitation days (R01mm) are significant variables for estimating ω to improve the predictive capability of the watershed response. This extended time-varying Budyko framework can rigorously capture the temporal variations and underlying mechanisms of interactions between vegetation dynamic and precipitation regime partitioning precipitation (P) to R. Under the Budyko-Fu framework, compared to constant ω (ω‾) or ω that only considers FVC (ω) or precipitation regimes (ω) for simulating R, using ω that integrated FVC and precipitation regimes (ω) can improve Nash-Sutcliffe efficiency coefficient (NSE) by 24.81%, while reduced the root mean squared error (RMSE) and relative error (RE) by 64.08% and 65.77%, respectively. Although the increase in climatic dryness (PET/P) resulted in decreased R, the increase in FVC has also a significant contribution to this decrease due to vegetation restoration. We highlight that decrease precipitation intensity (95pTOT) and frequency (R01mm) amplified the hydrological effects of vegetation restoration, causing a 79.09∼100.31% increase in R compared to the independent impact of changes in FVC. We conclude that the extended time-varying Budyko framework by precipitation regime is more rigorous for quantifying the hydrological effects of ecological restoration under climate change and providing more reliable approach for adaptive watershed management.
布迪科框架被广泛用于量化流域水文对流域特征和气候变异性的响应，为克服稳态假设的缺点，该框架不断得到完善。然而，植被和气候变量的动态变化，包括强度、频率和持续时间的覆盖范围和降水模式，并未得到充分整合。为解决这一问题，我们开发了一种创新方法来确定布迪科框架中的参数ω，以量化中国北方一个中尺度流域植被恢复的水文效应。我们发现，植被覆盖度分数（FVC）、强降水量（95pTOT）和降水天数（R01mm）是估算ω以提高流域响应预测能力的重要变量。这个扩展的时变布迪科框架能够严格捕捉植被动态与降水模式之间相互作用的时间变化和潜在机制，将降水（P）分配为径流（R）。在布迪科-傅框架下，与使用恒定的ω（ω‾）或仅考虑FVC（ω）或降水模式（ω）来模拟R相比，使用整合了FVC和降水模式的ω（ω）可以将纳什-萨特克利夫效率系数（NSE）提高24.81%，同时将均方根误差（RMSE）和相对误差（RE）分别降低64.08%和65.77%。尽管气候干燥度（PET/P）的增加导致径流减少，但由于植被恢复，FVC的增加对这种减少也有显著贡献。我们强调，降水强度（95pTOT）和频率（R01mm）的降低放大了植被恢复的水文效应，与FVC变化的独立影响相比，径流增加了79.09%至100.31%。我们得出结论，基于降水模式扩展的时变布迪科框架在量化气候变化下生态恢复的水文效应方面更为严格，为适应性流域管理提供了更可靠的方法。
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