环境因子对不同种类人工乔木林分蒸腾耗水的影响

doi:10.13866/j.azr.2023.08.12

摘要/Abstract

摘要：

探究干旱区不同人工乔木林分蒸腾量对环境因子响应的程度，对矿区移植人工植被选择及维系生态环境提供理论依据。本研究利用EMS81 Sap flow meter 茎流监测系统和Watch Dog土壤水分传感器对锡林浩特胜利东二号矿区内人工白杨和油松的树干液流及相应根系附近土壤水分进行监测，结合当地国家气象站气象数据，分析白杨和油松的林分蒸腾量变化差异，并对不同月份白杨和油松林分蒸腾量及各环境因子进行逐步回归建模。结果表明：（1）自然条件下，干旱区不同月份白杨和油松林分蒸腾量变化具有明显差异，除5月上旬白杨和油松林分蒸腾量变化趋势相近外，其余时间白杨林分蒸腾量变化较油松更剧烈；（2）在土壤水分变化较小或无明显变化时期，油松根系附近土壤深度30 cm、50 cm、70 cm、90 cm处的土壤含水量明显大于同深度白杨根系附近土壤含水量，根系土壤持水性能较白杨更好；（3）白杨林分蒸腾量与根系不同深度处的土壤水分变化最大值在5月、7月、9月具有相关性，油松林分蒸腾量与根系不同深度处的土壤水分变化最大值在6月、8月具有相关性，两者林分蒸腾量变化受土壤水分变化影响的程度是不同的；（4）不同时间区间逐步回归模型进入因子数量与贡献率具有一定差异，按月逐步回归能更好进行对林分蒸腾量的拟合。

关键词: 人工乔木, 林分蒸腾, 环境因子, 土壤水分, 逐步回归

Abstract:

This study aims to explore the transpiration response of different artificial arbor stands to environmental factors in arid areas and provide a theoretical basis for selecting transplanted artificial vegetation and maintaining the ecological environment in mining areas. In this study, the EMS81 Sap flow meter stemflow monitoring system and Watch Dog soil moisture sensor were used to monitor trunk sap flow in artificial Populus tomentosa and Pinus tabulaeformis stands in the Shengli East No. 2 mining area of Xilinhot, as well as the soil moisture near their respective roots. Meteorological data from the local national meteorological station were incorporated to analyze the variations in transpiration between the Populus tomentosa and Pinus tabulaeformis stands. Stepwise regression modeling was employed to assess the transpiration of Populus alba and Pinus tabulaeformis stands across different months and various environmental factors. During the first ten days of May, Populus tomentosa and Pinus tabulaeformis exhibited similar change trends in daily transpiration. However, for the remaining period, Populus tomentosa exhibited a more intense change in daily transpiration than Pinus tabulaeformis. During the periods with no significant changes in soil moisture, the soil moisture content near the roots of Pinus tabulaeformis at depths of 30 cm, 50 cm, 70 cm, and 90 cm was significantly higher than that near the roots of Populus alba at the same depths. The maximum transpiration value of poplar stands and the corresponding soil water changes at different root depths were correlated in May, July, and September, while those of Chinese pine stands were correlated in June and August. The number of entry factors and the contribution rate of stepwise regression models varied across different time intervals. Under natural conditions, the changes in transpiration of Populus tomentosa and Pinus tabulaeformis stands in arid areas differ significantly between months and are affected to varying degrees by changes in soil moisture. Moreover, the soil water holding capacity of Pinus tabulaeformis roots was better than that of Populus tomentosa roots during periods of minimal or insignificant changes in soil water. Monthly stepwise regression provides an improved fit for stand transpiration.

Key words: artificial trees, stand transpiration, environmental factors, soil moisture, stepwise regression

李健男, 史海滨, 苗庆丰, 珊丹, 荣浩, 温雅琴. 环境因子对不同种类人工乔木林分蒸腾耗水的影响[J]. 干旱区研究, 2023, 40(8): 1312-1321.

LI Jiannan, SHI Haibin, MIAO Qingfeng, SHAN Dan, RONG Hao, WEN Yaqin. Effect of environmental factors on the transpiration water consumption of various artificial arbor stands[J]. Arid Zone Research, 2023, 40(8): 1312-1321.

图/表 7

表1

图1

图2

表2

TY、TS与各环境因子相关性"

环境因子	$T Y$		$T S$
环境因子	相关性	显著性（双侧）	相关性	显著性（双侧）
$R s$	0.251^**	0.002	0.213^**	0.009
$R H$	-0.080	0.332	-0.129	0.12
$T m e a n$	0.437^**	0	0.392^**	0
$u 6$	-	-	0.032	0.701
$u 9$	-0.530	0.525	-	-
$P$	-0.202^*	0.014	-0.090	0.277
$θ Y 5$	0.380^**	0	-	-
$θ Y 15$	0.449^**	0	-	-
$θ Y 30$	0.412^**	0	-	-
$θ Y 50$	0.428^**	0	-	-
$θ Y 70$	0.438^**	0	-	-
$θ Y 90$	0.446^**	0	-	-
$θ S 5$	-	-	0.256^**	0.002
$θ S 15$	-	-	0.358^**	0
$θ S 30$	-	-	0.337^**	0
$θ S 50$	-	-	0.419^**	0
$θ S 70$	-	-	0.407^**	0
$θ S 90$	-	-	0.500^**	0

表2

表3

T Y、 T S与各层土壤水分每日变化最大值相关性"

月份	土壤深度 /cm	$T Y$		$T S$
月份	土壤深度 /cm	相关性	显著性（双侧）	相关性	显著性（双侧）
5	5	0.206	0.267	-0.105	0.574
	15	0.675^**	0.000	0.182	0.327
	30	-0.084	0.653	0.037	0.845
	50	-0.039	0.836	0.051	0.784
	70	0.086	0.647	0.155	0.404
	90	-0.518^**	0.003	0.009	0.962
6	5	-0.088	0.643	-0.426^*	0.019
	15	-0.070	0.712	-0.240	0.200
	30	-0.068	0.720	-0.137	0.471
	50	-0.057	0.766	-0.064	0.736
	70	-0.059	0.756	-0.056	0.767
	90	-0.045	0.813	-0.084	0.660
7	5	-0.479^**	0.006	-0.198	0.286
	15	-0.490^**	0.005	-0.046	0.807
	30	-0.421^*	0.018	0.347	0.056
	50	-0.426^*	0.017	0.047	0.803
	70	-0.344	0.058	0.247	0.180
	90	-0.247	0.181	0.339	0.062
8	5	-0.066	0.725	-0.193	0.298
	15	0.026	0.888	0.162	0.384
	30	0.137	0.462	0.404^*	0.024
	50	0.141	0.449	0.054	0.775
	70	-0.065	0.730	-0.097	0.604
	90	-0.029	0.875	-0.050	0.790
9	5	-0.147	0.484	-0.141	0.503
	15	-0.131	0.531	-0.265	0.200
	30	-0.159	0.448	-0.044	0.835
	50	-0.654^**	0.000	0.235	0.259
	70	-0.401^*	0.047	-0.077	0.714
	90	0.686^**	0.000	0.321	0.117

表3

表4

各月TY、TS与各环境因子多元逐步线性回归模型"

月份	因变量	进入顺序	进入因子	逐步回归方程	R²
5	$T Y$	1	$T m e a n$	$T Y$ =1.228+0.05T_mean	0.288
	$T S$	1	$T m e a n$	$T S$ =0.923+0.014T_mean	0.255
		2	$θ Y$	$T S$ =0.298+0.016T_mean+8.337θ_Y	0.414
6	$T Y$	1	$R H$	$T Y$ =3.582-0.019RH	0.393
		2	$θ Y$	$T Y$ =2.969-0.019RH+3.943θ_Y	0.520
	$T S$	1	$R H$	$T S$ =1.83-0.011RH	0.314
		2	$θ S$	$T S$ =1.518-0.01RH+3.534θ_S	0.446
		3	$R s$	$T S$ =0.331-0.006RH+4.07θ_S+0.4R_s	0.564
7	$T Y$	1	$R s$	$T Y$ =0.754+0.069R_s	0.398
		2	$θ Y$	$T Y$ =0.485+0.074R_s+1.855θ_Y	0.479
	$T S$	1	$θ S$	$T S$ =0.746+6.388θ_S	0.193
8	$T Y$	1	$P$	$T Y$ =2.639-0.069P	0.320
		2	$T m e a n$	$T Y$ =1.86-0.058P+0.048T_mean	0.476
		3	$θ Y$	$T Y$ =1.972-0.058P+0.048T_mean-3.193θ_Y	0.550
	$T S$	1	$T m e a n$	$T S$ =0.101+0.066T_mean	0.725
9	$T Y$	1	$θ Y$	$T Y$ =5.49-73.964θ_Y	0.188
		2	$T m e a n$	$T Y$ =5.199-84.678θ_Y+0.052T_mean	0.408
		3	$R s$	$T Y$ =8.082-130.646θ_Y+0.067T_mean-0.055R_s	0.516
	$T S$	1	$T m e a n$	$T S$ =0.768+0.028T_mean	0.363
		2	$R H$	$T S$ =1.004+0.027T_mean-0.003RH	0.486
		3	$P$	$T S$ =1.209+0.023T_mean-0.006RH-0.01P	0.595

表4

表5

研究期内TY、TS与各环境因子多元逐步线性回归模型"

因变量	进入顺序	进入因子	逐步回归方程	R²
$T Y$	1	$T m e a n$	$T Y$ =1.51+0.042T_mean	0.191
	2	$θ Y$	$T Y$ =0.581+0.045T_mean+11.558θ_Y	0.335
	3	$P$	$T Y$ =0.637+0.045T_mean+11.147θ_Y-0.013P	0.366
$T S$	1	$θ S$	$T S$ =0.789+5.667θ_S	0.171
	2	$T m e a n$	$T S$ =0.421+6.212θ_S+0.019T_mean	0.358
	3	$θ S y$	$T S$ =0.387+7.131θ_S+0.018T_mean-6.699θ_Sy	0.382
	4	$R H$	$T S$ =0.387+7.131θ_S+0.018T_mean-6.699θ_Sy-0.002RH	0.408

表5

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编号	高度/m	胸径/cm	树皮和韧皮部厚度/cm	距地面1.5 m处周长/cm	冠幅
Y1	8.8	18.94	0.5	59.5	2.32 m×2.13 m
S1	3.2	12.01	0.4	37.7	1.39 m×1.68 m
Y2	8.3	17.52	0.45	55	2.08 m×1.97 m
S2	3.8	13.47	0.4	42.3	1.43 m×1.73 m