兴安落叶松天然林不同分化等级林木树干液流对综合环境因子的响应

[目的]以大兴安岭北部典型寒温带针叶林优势建群树种兴安落叶松为对象,分析不同分化等级林木树干液流对环境因子的综合响应,构建不同分化等级林木树干液流模型。[方法]利用热扩散式液流监测系统和通量塔的梯度气象系统连续监测树干液流及环境因子的变化。[结果]表明:1)观测期间,优势木具有较强的蒸腾能力,其平均液流密度分别为中等木和被压木的1.9倍、2.5倍。总体上,分化程度越高的林木日树干液流持续时间越长,液流密度峰值出现时间越早,液流密度的峰值也越高。2)利用主成分分析将降雨、净辐射、空气温度、空气湿度、风速、土壤温度、土壤含水量和水汽压亏缺降维为蒸发需求因子(EDI)、土壤水热因子和降水因子。EDI(与净辐射、温湿度、水汽压亏缺显著相关)是影响该地区林木树干液流的关键环境要素,其携带环境数据信息量的45%;土壤水热因子和降水因子分别携带20%和13%。3)各分化等级林木树干液流密度对EDI呈顺时针时滞,对净辐射和水汽压亏缺则分别呈逆时针、顺时针时滞,且EDI的时滞效应明显较小。不同分化等级林木液流密度对EDI和水汽压亏缺的时滞表现一致,对净辐射的时滞则以优势木最小。4)各分化等级林木树干液流密度对EDI的响应均符合"S"型模型,即液流升高到阈值后,不再随蒸发需求的增加而增大。模型中,中等木(0.458)和被压木(0.457)的过渡斜率略高于优势木(0.443),表明优势木树干液流对环境因子的敏感性略低。该模型对不同分化等级林木液流密度的模拟精度均在90%以上,考虑EDI的时滞效应或引入土壤水热因子、降水因子对模型精度的影响较小。[结论]兴安落叶松树干液流对综合环境因子存在较强的响应性,且在不同分化等级间存在差异;利用"S"模型和综合环境因子可有效估算不同分化等级兴安落叶松的树干液流。

Response of Tree Sap Flow of Larix gmelinii with Various Differentiation Classes to Multiple Environmental Factors

[Objective] Selected Larix gmelinii which is the dominant and constructive species of typical boreal forest in northern Great Hinggan Mountains as a research object. Analyzing sap flow in response to multiple environmental factors and building sap flow model through various tree differentiation classes. [Methods] Using Granier''s thermal dissipation probe method and gradient meteorological observation system of eddy covariance tower to continuous monitor the change of sap flow and environment factors. [Results] The results showed that:1) During the observation period, dominant trees have strong transpiration capacity. The average sap flow density of dominant trees are 1.9 times and 2.5 times comparing to intermediate trees and suppressed trees, respectively. In general, trees with the higher differentiation class have longer duration of daily sap flow, also the peak value of sap flow density appears earlier and have higher peak value. 2) eight environmental factors (precipitation, net radiation, air temperature, air relative humidity, wind speed, soil temperature, soil water content and vapor pressure deficit) could be divided into evaporative demand index (EDI), soil hydrothermal index and precipitation index by principal components analysis. The evaporative demand index (significant correlated with net radiation, air temperature, air relative humidity and vapor pressure deficit) which can explain 45% information of environmental dataset is the key factor influence the sap flow in research area. Soil hydrothermal index and precipitation index can explain 20% and 13%, respectively. 3) Sap flow density presents asymmetrical response to environmental factors through various differentiation classes, which shows a clockwise delayed time lag with EDI, anticlockwise delayed time lag with net radiation and clockwise with vapor pressure deficit, while the time lag with EDI is smallest. Various differentiation classes have the same level of time lag with EDI and vapor pressure deficit, while the time lag of the net radiations smaller in the dominant than that in the intermediate and suppressed. 4) The sap flow density and EDI present a sigmoid function model through various differentiation classes, when the sap flow reaches an asymptote where higher evaporative demand could not cause sap flow to increase further. In this model, the transition slope of the intermediate (0.457) and suppressed (0.458) are greater than the dominant (0.443), which means the dominant is less sensitive to environmental factors. This model can explain on average 90% of the simulation precision in sap flow density in the dominant, intermediate and suppressed trees, respectively. Considering time lag effect of EDI or inserting soil hydrothermal index and precipitation index cannot promote the precision of sap flow model. [Conclusions] The sap flow of Larix gmelinii has a strong response to multiple environment factors, and these relationships have differences among different tree differentiation classes. By using the sigmoid function model and multiple environment factors, the sap flow of Larix gmelinii with various differentiation classes could be estimated effectively.