基于CMIP6气候变化下塔里木河流域天然植被生态需水预估

doi:10.13866/j.azr.2025.10.10

Abstract

Abstract:

To adapt to or mitigate the effects of climate change on the water resource demands of terrestrial ecosystems, it is crucial to determine the vegetation ecological water requirements (EWR) under changing climate conditions for maintaining the stability of terrestrial ecosystems. This study used CMIP6 climate model data and integrated the RF, XGBoost, ANN, and LSTM models along with meteorological and remote sensing factors to design various input combinations. Furthermore, an in-depth analysis of each model’s performance was performed under different input configurations for EWR prediction in the Tarim River Basin. Based on this analysis, the optimal model was selected to predict future EWR, and the SHAP method was applied to quantify the contributions of meteorological and remote sensing factors to EWR changes. The results were as follows: (1) All four machine learning models were suitable for evaluating EWR in the study area, with the XGBoost model demonstrating the best overall performance. Temperature and net radiation were the primary meteorological factors influencing the model’s predictions, and the incorporation of remote sensing factors further enhanced the model’s performance. (2) The average EWR under the historical, SSP2-4.5, and SSP5-8.5 scenarios was 498.58×10⁸ m³, 548.81×10⁸ m³, and 570.28×10⁸ m³, respectively, showing an overall upward trend in future EWR. April and May were the periods with the most significant increases in water demand. (3) Spatially, the EWR in the study area under all three scenarios exhibited a distribution pattern of higher values in the north and lower values in the south. However, under the SSP5-8.5 scenario, the changes in EWR were more pronounced, and the north-south differences became significantly smaller. (4) The main factors influencing EWR changes were leaf area index, net radiation, and minimum temperature, with considerable impact of terrain variation. In plain areas, the leaf area index was the dominant factor driving EWR changes, whereas in mountainous regions, EWR was influenced by a combination of leaf area index, net radiation, and minimum temperature.

Key words: ecological water requirement, XGBoost, CMIP6, machine learning, Tarim River Basin

HUANG Mianting, MU Zhenxia, YANG Rongqin, ZHAO Shikang, LI Zilong. Estimation of ecological water requirements for natural vegetation in the Tarim River Basin under climate change using CMIP6[J].Arid Zone Research, 2025, 42(10): 1860-1875.

Figures/Tables 16

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数据集	分辨率		数据来源
数据集	空间	时间	数据来源
实测气象数据	-	1 d	中国气象数据网（http://data.cma.cn/）
MOD13A2	1 km×1 km	16 d	美国国家航空航天局（https://search.earthdata.nasa.gov/search）
MOD15A2H	500 m×500 m	8 d	美国国家航空航天局（https://search.earthdata.nasa.gov/search）
中国多时期土地利用遥感监测数据集	30 m×30 m	5 a	中国科学院资源环境科学与数据中心（https://www.resdc.cn/）
面向陆面过程模型的中国土壤水文数据集	1 km×1 km	-	青藏高原科学数据中心（https://data.tpdc.ac.cn）
中国1 km土壤湿度日尺度数据集	1 km×1 km	1 d	青藏高原科学数据中心（https://data.tpdc.ac.cn）
BCC-CSM2-MR模式数据	100 km×100 m	月	世界气候研究计划（https://esgf-node.ipsl.upmc.fr/search/cmip6-ipsl/）

输入组合	P	R_n	T_min	T_max	sfcWind	RH	Ps	mrso	LAI
S1			√	√
S2		√	√	√
S3	√	√	√	√	√	√	√
S4		√	√	√				√	√
S5		√	√	√	√	√		√	√
S6	√	√	√	√	√			√	√
S7		√	√	√	√	√	√	√	√
S8	√	√	√	√	√	√	√	√	√

气象要素	单位	校正前		校正后
气象要素	单位	绝对偏差范围	偏差绝对值均值	绝对偏差范围	偏差绝对值均值
P	mm·d^-1	-0.44~6.37	1.89	-0.62~0.81	0.22
R_n	MJ·m^-2·d^-1	-5.28~-1.23	3.01	-0.89~1.03	0.38
T_min	℃	-19.65~0.10	9.56	-5.50~2.49	1.78
T_max	℃	-25.84~1.30	12.06	-8.73~3.14	2.83
sfcWind	m·s^-1	-1.79~0.39	0.62	-0.58~0.34	0.21
Ps	kPa	-26.43~-0.07	12.11	-8.94~2.18	2.68
RH	%	-17.55~49.87	29.35	-13.02~20.94	7.51
mrso	%	-16.69~24.47	9.84	-4.87~5.80	2.01
LAI	-	-0.22~0.16	0.07	-0.03~0.04	0.01

Estimation of ecological water requirements for natural vegetation in the Tarim River Basin under climate change using CMIP6

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输入组合	P	R_n	T_min	T_max	sfcWind	RH	Ps	mrso	LAI
S1			√	√
S2		√	√	√
S3	√	√	√	√	√	√	√
S4		√	√	√				√	√
S5		√	√	√	√	√		√	√
S6	√	√	√	√	√			√	√
S7		√	√	√	√	√	√	√	√
S8	√	√	√	√	√	√	√	√	√

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输入组合	P	R_n	T_min	T_max	sfcWind	RH	Ps	mrso	LAI
S1			√	√
S2		√	√	√
S3	√	√	√	√	√	√	√
S4		√	√	√				√	√
S5		√	√	√	√	√		√	√
S6	√	√	√	√	√			√	√
S7		√	√	√	√	√	√	√	√
S8	√	√	√	√	√	√	√	√	√

输入组合	P	R_n	T_min	T_max	sfcWind	RH	Ps	mrso	LAI
S1			√	√
S2		√	√	√
S3	√	√	√	√	√	√	√
S4		√	√	√				√	√
S5		√	√	√	√	√		√	√
S6	√	√	√	√	√			√	√
S7		√	√	√	√	√	√	√	√
S8	√	√	√	√	√	√	√	√	√