基于指数平滑和ARIMA模型的西北地区饱和水汽压差预测

doi:10.13866/j.azr.2021.02.02

Abstract

Abstract:

Vapor pressure deficit (VPD) is a key factor affecting water transport in the soil-plant-atmosphere continuum; in the context of global climate change, predicting VPD has practical significance for vegetation management and risk assessment of meteorological disasters affecting agriculture and forestry in Northwest China. Using VPD data from five provinces (regions) in Northwest China from 1990 to 2019, we analyzed the characteristics of VPD interannual variation and periodic variation using trend and wavelet analyses. The optimal sample step and prediction step were selected; exponential models and autoregressive integrated moving average (ARIMA) models were used to simulate and predict VPD in Northwest China. Among the five provinces, Ningxia had the highest trend slope of VPD [0.036 kPa·(10a)^-1], followed by Xinjiang [0.033 kPa·(10a)^-1]. The annual average VPD in Xinjiang was the highsest at 0.61 kPa, followed by Ningxia, Shaanxi, Gansu, and Qinghai (0.54 kPa, 0.48 kPa, 0.46 kPa, and 0.36 kPa, respectively). Over the past 30 years, the VPD in Northwest China followed an upward trend; Ningxia and Xinjiang had the largest increases in VPD at 0.036 and 0.033 kPa·(10a)^-1, respectively, followed by Gansu [0.026 kPa·(10a)^-1], Qinghai [0.021 kPa·(10a)^-1], and Shaanxi [0.012 kPa·(10a)^-1]. Compared with the exponential model, the root mean square error (RMSE) of the ARIMA model was reduced by 42.3%, the R² was increased by 11.1%, and the Nash-Sutcliffe efficiency coefficient increased by 17.7%. Thus, the VPD prediction accuracy was effectively improved. The VPD in Northwest China is expected to increase by varying degrees; Ningxia and Xinjiang showed the highest VPD growth rates of 9.5% and 8.9%, respectively.

Key words: vapor pressure deficit, exponential smoothing, ARIMA model, prediction, Northwest China

HAN Yonggui,HAN Lei,HUANG Xiaoyu,GAO Yang. Prediction of vapor pressure deficit in Northwest China based on exponential and ARIMA models[J].Arid Zone Research, 2021, 38(2): 303-313.

Figures/Tables 13

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		SN	GS	NX	QH	XJ
原始序列	T	-4.20	-2.87	-3.01	-2.52	-2.05
	1%	-3.45	-3.45	-3.45	-3.45	-3.45
	5%	-2.86	-2.86	-2.86	-2.86	-2.86
	10%	-2.57	-2.57	-2.57	-2.57	-2.57
	AP/%	0.10	4.90	3.50	11.20	26.56
一阶差分	T	-17.68	-15.18	-15.36	-18.36	-22.37
	1%	-2.58	-2.58	-2.58	-2.58	-2.58
	5%	-1.94	-1.94	-1.94	-1.94	-1.94
	10%	-1.62	-1.62	-1.62	-1.62	-1.62
	AP/%	0.00	0.00	0.00	0.00	0.00

省份	模型	调整的R²	AIC
SN	ARIMA(1,1,1)	0.45	-0.32
	ARIMA(2,1,1)	0.46	0.25
	ARIMA(3,1,1)	0.47	0.43
GS	ARIMA(1,1,1)	0.39	-0.78
	ARIMA(2,1,1)	0.45	-0.02
	ARIMA(3,1,1)	0.46	0.22
NX	ARIMA(1,1,1)	0.38	-0.04
	ARIMA(2,1,1)	0.38	0.70
	ARIMA(3,1,1)	0.37	0.92
QH	ARIMA(1,1,1)	0.43	-1.80
	ARIMA(2,1,1)	0.47	-0.99
	ARIMA(3,1,1)	0.47	-0.76
XJ	ARIMA(2,1,1)	0.49	1.81
XJ	ARIMA(3,1,1)	0.50	2.06

省份	指数模型	调整的R²	均方根误差
SN	Holt	0.67	0.130
GS	Holt-Winters	0.70	0.081
NX	Holt-Winters	0.69	0.120
QH	Holt-Winters	0.71	0.046
XJ	Holt-Winters	0.68	0.103

模型	最佳样本步长/a	均方根误差	平均绝对误差
Holt	4	0.086	0.067
Holt-Winters	4	0.076	0.053
ARIMA（3,1,1）	16	0.113	0.079
ARIMA（2,1,1）	16	0.048	0.036
ARIMA（1,1,1）	22	0.130	0.092

模型	模拟步长/a	R²	均方根误差	相对误差/%	绝对误差/kPa	NSE
Holt	3	0.76	0.1809	21.3	0.1196	0.6713
	5	0.75	0.1935	22.3	0.1327	0.6383
	7	0.73	0.1924	22.5	0.1320	0.6233
	9	0.73	0.1910	23.2	0.1347	0.6292
Holt-Winters	3	0.95	0.0939	9.8	0.0584	0.9207
	5	0.96	0.1003	11.7	0.0688	0.9128
	7	0.95	0.1080	12.9	0.0734	0.9013
	9	0.94	0.1154	14.4	0.0802	0.8901
ARIMA（3,1,1）	3	0.97	0.0926	17.1	0.0735	0.9543
	5	0.96	0.0878	15.9	0.0699	0.9566
	7	0.97	0.0854	17.7	0.0697	0.9589
	9	0.96	0.0878	17.4	0.0718	0.9553
ARIMA（2,1,1)	3	0.94	0.0537	9.6	0.0363	0.9096
	5	0.94	0.0512	9.2	0.0363	0.9114
	7	0.94	0.0461	8.7	0.0326	0.9256
	9	0.92	0.0497	8.8	0.0347	0.9154
ARIMA（1,1,1）	3	0.92	0.1111	15.0	0.0852	0.9098
	5	0.93	0.1063	14.9	0.0794	0.9250
	7	0.93	0.1081	14.9	0.0807	0.9243
	9	0.92	0.1024	14.7	0.0806	0.9222

Prediction of vapor pressure deficit in Northwest China based on exponential and ARIMA models

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	SN	尺度/a	GS	尺度/a	NX	尺度/a	QH	尺度/a	XJ	尺度/a
MV/(kPa·mon^-1)	0.55	-	0.50	-	0.63	-	0.38	-	0.90	-
PV-E/(kPa·mon^-1)	0.57	3	0.43	3	0.61	3	0.32	3	0.96	3
PV-A /(kPa·mon^-1)	0.57	7	0.54	7	0.69	5	0.41	7	0.98	7

省份	SN	GS	NX	QH	XJ
MV/(kPa·mon^-1)	0.58	0.53	0.68	0.40	0.95
PV-E/(kPa·mon^-1)	0.57	0.52	0.66	0.39	0.94
PV-A /(kPa·mon^-1)	0.56	0.54	0.68	0.41	0.95

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