Analysis of the Spatial-temporal Variation and Driving Factors of Water Yield in Qilian Mountain National Park

WAN Zhigang; DING Wenguang; PU Xiaoting; WU Yansong; WANG Yiyue

doi:10.13870/j.cnki.stbcxb.2023.06.021

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Analysis of the Spatial-temporal Variation and Driving Factors of Water Yield in Qilian Mountain National Park

更新时间：2025-03-12

- Analysis of the Spatial-temporal Variation and Driving Factors of Water Yield in Qilian Mountain National Park
- Vol. 37, Issue 6, Pages: 161-169(2023)
- 作者机构：
  
  1. 兰州大学资源环境学院,兰州,730000
  2. 兰州大学西部环境教育部重点实验室,兰州,730000
- 作者简介：
- 基金信息：
- DOI：10.13870/j.cnki.stbcxb.2023.06.021
  CLC：
- Published：2023
- 稿件说明：
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WAN Zhigang, DING Wenguang, PU Xiaoting, et al. Analysis of the Spatial-temporal Variation and Driving Factors of Water Yield in Qilian Mountain National Park[J]. 2023, 37(6): 161-169.
DOI：

WAN Zhigang, DING Wenguang, PU Xiaoting, et al. Analysis of the Spatial-temporal Variation and Driving Factors of Water Yield in Qilian Mountain National Park[J]. 2023, 37(6): 161-169. DOI： 10.13870/j.cnki.stbcxb.2023.06.021.

摘要

为准确表征祁连山国家公园产水量时空差异性

探究产水量影响因素和驱动机制。基于降水量、参考作物蒸散量、土壤深度、根系深度和土地利用类型等参数

利用InVEST模型可视化祁连山国家公园1990-2018年产水量时空格局

使用地理探测器并结合情景模拟方法

评估和量化气候、土地利用、植被、地形等因子对祁连山国家公园产水量空间异质性和产水量变化的影响。结果表明:(1)1990-2018年研究区产水量以0.56×108 m3/a的速度显著增加

在空间上呈自西向东递增趋势;(2)耕地和林地产水深度最大

未利用土地和草地是研究区产水量的主要贡献者

且产水深度高值区位于3 200~4 600 m高海拔处;(3)降水量是产水量空间异质性的第一驱动因素

与实际蒸散量以及土地利用类型的交互作用占主导地位;(4)1990-2018年

气候变化是产水量变化的主要驱动因子

对总产水量影响为正向作用

其次为土地利用变化

主要影响产水量变化的空间分布

对总产水量影响不大。研究成果可进一步为祁连山国家公园水资源管理和可持续发展提供基础理论依据。

Abstract

To accurately characterize the spatial and temporal heterogeneity of water yield in Qilian Mountains National Park and explore the influencing factors and driving mechanisms affecting water yield. The InVEST model was used to visualise the spatial and temporal pattern of water yield in the Qilian Mountains National Park from 1990 to 2018 based on the parameters of precipitation

evapotranspiration of reference crops

soil depth

root depth

and land use type

using a geo-detector and combined with a scenario simulation methods to assess and quantify the effects of climate

land use

vegetation

terrain

and other factors on the spatial heterogeneity and changes in water yield in the Qilian Mountains National Park. The results showed that: (1) From 1990 to 2018

the water yield in the study area increased significantly with a trend of 0.56×108 m3/a

with a spatial increasing trend from west to east. (2) Farmland and Forestland had the largest water yield depth

and Unused land and Grassland were the main contributors to water yield in the study area. The high-value water yield depth areas were located at high altitudes between 3 200 m to 4 600 m. (3) Precipitation was the first driver of spatial heterogeneity of water yield

and the interaction with actual evapotranspiration and land use type dominates. (4) From 1990 to 2018

climate change was the main driver of changes in water yield

with a positive effect on total water yield

followed by land use change

which mainly affects the spatial distribution of water yield change but had little effect on total water yield. The research results can further provide the basic theoretical for the water resources management and sustainable development of Qilian Mountain National Park.

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references

Belete M, Deng J S, Wang K, et al. Evaluation of satellite rainfall products for modeling water yield over the source region of Blue Nile Basin[J].Science of the Total Environment,2020,708:e134834.

刘美娟,仲俊涛,王蓓,等.基于InVEST模型的青海湖流域产水功能时空变化及驱动因素分析[J].地理科学,2023,43(3):411-422.

吕乐婷,任甜甜,李赛赛,等.基于InVEST模型的大连市产水量时空变化分析[J].水土保持通报,2019,39(4):144-150,157.

Redhead J W, Stratford C, Sharps K, et al. Empirical validation of the InVEST water yield ecosystem service model at a national scale[J].Science of the Total Environment,2016,569/570:1418-1426.

Sánchez-Canales M, López Benito A, Passuello A, et al. Sensitivity analysis of ecosystem service valuation in a Mediterranean watershed[J].Science of the Total Environment,2012,440:140-153.

Shaw M R, Pendleton L, Cameron D R, et al. The impact of climate change on California's ecosystem services[J].Climatic Change,2011,109(1):465-484.

Guo Q, Yu C, Xu Z, et al. Impacts of climate and land-use changes on water yields: Similarities and differences among typical watersheds distributed throughout China[J].Journal of Hydrology-Regional Studies,2023,45:e17.

黄欣,彭双云,王哲,等.基于地理探测器的云南省生态系统产水服务的空间异质性及驱动因素[J].应用生态学报,2022,33(10):2813-2821.

戴尔阜,王亚慧.横断山区产水服务空间异质性及归因分析[J].地理学报,2020,75(3):607-619.

贾雨凡,王国庆.基于InVEST模型的伊洛河流域水源涵养能力评估[J].水土保持学报,2023,37(3):101-108.

Lang Y Q, Song W, Deng X Z. Projected land use changes impacts on water yields in the karst mountain areas of China[J].Physics and Chemistry of the Earth,2018,104:66-75.

魏培洁,吴明辉,贾映兰,等.基于InVEST模型的疏勒河上游产水量时空变化特征分析[J].生态学报,2022,42(15):6418-6429.

赵亚茹,周俊菊,雷莉,等.基于InVEST模型的石羊河上游产水量驱动因素识别[J].生态学杂志,2019,38(12):3789-3799.

李芳,张金龙,杨环.基于InVEST模型的黑河流域上游1990—2018年产水量模拟[J].高原气象,2022,41(3):698-707.

赵晓涵,张方敏,卢琦,等.未来气候情景内蒙古蒸散和产水量的变化特征[J].水土保持学报,2022,36(4):151-159.

王晓琪,赵雪雁。人类活动对国家公园生态系统服务的影响:以祁连山国家公园为例[J].自然资源学报,2023,38(4):966-982.

王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.

张利利.基于InVEST模型的石羊河上游产水量变化及其对气候与土地利用变化的响应[D].兰州:西北师范大学,2017.

Lang Y Q, Song W, Zhang Y. Responses of the water-yield ecosystem service to climate and land use change in Sancha River Basin, China[J].Physics and Chemistry of the Earth,2017,101:102-111.

薛健,李宗省,冯起,等.1980—2017年祁连山水源涵养量时空变化特征[J].冰川冻土,2022,44(1):1-13.

郑续,魏乐民,郭建军,等.基于地理探测器的干旱区内陆河流域产水量驱动力分析:以疏勒河流域为例[J].干旱区地理,2020,43(6):1477-1485.

Zhang X A, Chen X M, Zhang W S, et al. Impact of land use changes on the surface run off and nutrient load in the Three Gorges Reservoir area, China[J].Sustainability,2022,14(4):e21.

孙琪,徐长春,任正良,等.塔里木河流域产水量时空分布及驱动因素分析[J].灌溉排水学报,2021,40(8):114-122.

Mo W B, Zhao Y L, Yang N, et al. Effects of climate and land use/land cover changes on water yield services in the Dongjiang Lake basin[J].ISPRS International Journal of Geo-Information,2021,10(7):e466.

Chen Y P, Wang K B, Lin Y S, et al. Balancing green and grain trade[J].Nature Geoscience,2015,8(10):739-741.

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