气候及土地利用变化对大通河源区水文要素空间分布的影响

doi:10.13866/j.azr.2023.03.05

干旱区研究 ›› 2023, Vol. 40 ›› Issue (3): 381-391.doi: 10.13866/j.azr.2023.03.05 cstr: 32277.14.AZR.20230305

气候及土地利用变化对大通河源区水文要素空间分布的影响

赵美亮^1,²(),曹广超^1,^2,³(),赵青林^1,²,曹生奎^1,^2,³

1.青海师范大学地理科学学院，青海省自然地理与环境过程重点实验室，青海西宁 810008
2.青海师范大学，青藏高原地表过程与生态保育教育部重点实验室，青海西宁 810008
3.青海省人民政府-北京师范大学高原科学与可持续发展研究院，青海西宁 810008

收稿日期:2022-09-01 修回日期:2022-12-02 出版日期:2023-03-15 发布日期:2023-03-31
作者简介:赵美亮（1995-），男，博士研究生，研究方向为环境地表过程与生态响应. E-mail: zhaomeiliang2014@163.com
基金资助:
青海省科技厅重大专项(2021-SF-A7-1);青海省创新平台建设专项青海省自然地理与环境过程重点实验室(2020-ZJ-Y06)

Effects of climate and land use change on the spatial distribution of hydrological factors in the source region of Datong River

ZHAO Meiliang^1,²(),CAO Guangchao^1,^2,³(),ZHAO Qinglin^1,²,CAO Shengkui^1,^2,³

1. Qinghai Province Key Laboratory of Physical Geography and Environmental Process, College of Geographical Science, Qinghai Normal University, Xining 810008, Qinghai, China
2. Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Ministry of Education, Qinghai Normal University, Xining 810008, Qinghai, China
3. Academy of Plateau Science and Sustainability People’s Government of Qinghai Province & Beijing Normals University, Xining 810008, Qinghai, China

Received:2022-09-01 Revised:2022-12-02 Published:2023-03-15 Online:2023-03-31

摘要/Abstract

摘要：

水资源短缺已成为人类社会主要的资源环境问题，气候和土地利用变化使得水文要素的演变呈现出复杂性与不确定性，探讨变化情景下水文要素空间分布特征对区域经济社会可持续发展具有重要的理论和现实意义。基于1960—2019年大通河源区气象水文数据，通过模型模拟及情景分割方法定量分析了气候和土地利用变化情景下水文要素空间分布特征。结果表明：（1） SWAT模型经过校准与验证后，模型决定系数、纳什系数和百分比偏差（PBIAS）均达到模型要求，其在率定期内分别为0.81%、0.79%和-0.8%，在验证期内分别为0.81%、0.75%和15.8%，表明模型在大通河源区具有较好的适用性。（2）大通河源区各水文要素空间异质性明显，不能用单一的水文要素代表整体的空间分布情况；降水、潜在蒸散发与土壤含水量随海拔的升高而降低，地表径流与产水量随海拔的升高而增加。（3） 3种情景下各水文要素的空间分布大体一致，产水量的空间分布受土地利用变化情景的影响比较大。气候变化情景使得实际蒸散发和土壤含水量呈现下降趋势，地表径流和产水量呈现增加趋势，土地利用变化情景下各水文要素的变化与之相反。

关键词: 水文要素, 空间分布, SWAT模型, 大通河

Abstract:

Water shortage has become a major resource and environmental challenge worldwide. Climate and land use change have made the evolution of current hydrological factors complex and uncertain. Exploring the spatial distribution characteristics of hydrological factors under dynamic scenarios is of vital theoretical and practical significance for the sustainable development of regional economy and society. The meteorological and hydrological data of the Datong River source region from 1960 to 2019 were used in this study to quantitatively analyze the spatial distribution characteristics of hydrological elements under climate and land use change scenarios based on model simulation and scenario segmentation. The results showed that: (1) After calibration and verification of the SWAT model, the coefficient of determination, Nash coefficient, and percentage bias (PBIAS) all met the model requirements of 0.81%, 0.79%, and -0.8% in the rate period, and 0.81%, 0.75%, and 15.8% in the validation period, respectively, which indicated that the model had good applicability in the headwaters of the Chase River. (2) Obvious spatial heterogeneity of hydrological elements was detected in the headwater area of Datong River, and a single hydrological element could not represent the overall spatial distribution. Precipitation, potential evapotranspiration, and soil water content decreased with the increase in altitude, while surface runoff and water yield increased with the increase in altitude. (3) The spatial distribution of hydrological factors under the three scenarios were generally consistent, while the spatial distribution of water yield was greatly affected by the land use change. Under the climate change scenario, the actual evapotranspiration and soil water content showed a downward trend, while the surface runoff and water yield showed an upward trend. Under the land use change scenario, the changes of hydrological elements were contrary to these observations.

Key words: hydrological elements, spatial distribution, SWAT model, Datong River

赵美亮, 曹广超, 赵青林, 曹生奎. 气候及土地利用变化对大通河源区水文要素空间分布的影响[J]. 干旱区研究, 2023, 40(3): 381-391.

ZHAO Meiliang, CAO Guangchao, ZHAO Qinglin, CAO Shengkui. Effects of climate and land use change on the spatial distribution of hydrological factors in the source region of Datong River[J]. Arid Zone Research, 2023, 40(3): 381-391.

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数据名称	数据来源	数据描述
DEM	地理空间数据云（http://www.gscloud.cn）	数字高程模型，30 m×30 m
土地利用	资源环境科学与数据中心（https://www.resdc.cn）	1980年、2020年土地利用类型图，1000 m×1000 m
土壤数据	联合国粮食及农业组织（FAO）	土地利用空间分布数据，1:1 000 000
气象数据	中国气象数据网（http://data.cma.cn/）	1960—2019年祁连、门源、野牛沟、刚察、托勒气象站气温、降水、日照、风速、湿度观测数据，逐日
水文数据	青海省水文与水资源局	1960—2016年青石嘴水文站径流观测数据，逐日

情景	情景类别	气候数据时段	土地利用数据
S0	基础情景	1962—1980年	1980年
S1	气候变化情景	1981—2019年	1980年
S2	土地利用变化情景	1962—1980年	2020年
S3	协同变化情景	1981—2019年	2020年

时间段	观测平均值/（m³·s^-1)	模拟平均值/（m³·s^-1)	NS	R²	PBIAS/%
率定期	50.56	50.96	0.79	0.81	-0.8
验证期	59.33	49.97	0.75	0.81	15.8

海拔/m	降水/mm	潜在蒸散发/mm	实际蒸散发/mm	土壤含水量/mm	地表径流/mm	产流量/mm	干旱指数	蒸发系数
2884~3427	568.01	737.38	378.89	63.29	84.86	180.41	1.30	0.67
3427~3704	546.87	769.00	368.72	61.73	116.44	188.36	1.41	0.67
3704~3935	535.94	745.05	336.06	52.99	138.11	204.11	1.39	0.63
3935~4177	536.65	744.37	334.63	52.53	136.26	203.01	1.39	0.62
4177~5067	535.81	749.81	332.99	51.92	134.83	203.12	1.40	0.62

		2020年
		草地	耕地	居民区	林地	裸地	其他建设用地	湿地	水域	永久性冰川雪地	总计
1980年	草地	3701.71	4.5	1.62	66.3	52.4	38.92	49.52	64.57	0.07	3979.61
	耕地	0.7	8.87	0.07	0.45	-	-	-	0.07	-	10.16
	居民区	0.13	0.06	0.95	0.04	-	-	-	0.03	-	1.21
	林地	65.32	1.33	0.9	991.19	8.83	0.17	0.64	24.82	-	1093.2
	裸地	116.7	-	-	4.33	1228.33	3.29	9.57	1.67	0.37	1364.26
	其他建设用地	2.13	-	-	0.11	-	1.84	-	-	-	4.08
	湿地	60.55		0.01	0.54	15.84	0.92	1027.49	7.11	-	1112.46
	水域	22.8	0.07	-	5.63	1.48	1.61	6.7	254.88	0.03	293.2
	永久性冰川雪地	-	-	-	-	8.3	-	-	-	4.81	13.11
	总计	3970.04	14.83	3.55	1068.59	1315.18	46.75	1093.92	353.15	5.28	7871.29

气候及土地利用变化对大通河源区水文要素空间分布的影响

Effects of climate and land use change on the spatial distribution of hydrological factors in the source region of Datong River

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参考文献 33

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子流域编码	实际蒸散发/mm	土壤含水量/mm	地表径流/mm	产水量/mm	草地转林地/km²	林地转草地/km²	裸地转草地/km²
4	26.93	11.89	-41.43	-27.81	0.00	0.00	14.61
6	24.04	11.30	-45.95	-28.59	0.00	0.00	2.53
7	38.39	19.11	-76.58	-48.23	0.00	0.00	6.37
36	68.87	5.31	7.78	9.62	0.17	0.24	0.00

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