西大洋水库流域径流变化特征及其量化归因

Runoff variation characteristics and quantitative attribution analysis in the Xidayang Reservoir basin

摘要

为探明气候变化与人类活动对山区水库流域径流变化的相对贡献和作用机制，以河北省西大洋水库流域为研究对象，基于1970−2022年水文气象数据，结合趋势检验、突变点交叉验证及Budyko模型，系统开展流域径流演变的趋势特征与定量归因分析，并揭示径流变化的周期结构及其未来趋势。结果表明：近53 a流域径流深显著下降，幅度高达54.74%，并在1992年发生突变；而同期降水显著上升（+7.53%），反映气候与径流的非线性关系。基于4种典型Budyko模型的弹性系数分析结果一致，人类活动对径流减少的贡献率为125.66%～130.05%，其中下垫面变化的敏感性最强，达352.38%。流域城镇化进程、水库调蓄、水土保持等人类活动作用显著。当前处于42 a主周期的高波动末端，未来极端降水事件发生频率增加。研究结果为山区典型水库流域水资源管理和生态补水决策提供方法支撑与理论依据。

Abstract

In recent years, global warming coupled with intense human activity has disturbed the basin's precipitation and runoff patterns, resulting in frequent severe floods and droughts. Urban expansion, farmland reclamation, cascade power stations, and dense pipe network construction have dramatically changed the land use pattern, resulting in a sharp rise in impervious rate, a surge in water consumption, and a sharp decline in soil infiltration and river recharge capacity. As a result, the runoff and spatial and temporal distribution have sharply declined. The dry season extends longer, while the flood peak rapidly increases, leading to worsening water scarcity, ecological degradation, and flood risks. Based on the multi-source hydrometeorological records (daily runoff, precipitation, temperature, and evapotranspiration) from 1970 to 2022, the trend analysis is used to evaluate the changing behavior of these variables from several partial perspectives. A range of mutation techniques are used to identify the significant mutation points of runoff and associated meteorological factors, and the mutation points are cross-validated. The relative contributions of high-intensity human activities (land use, reservoir regulation, and storage) and climate fluctuations (precipitation, potential evapotranspiration) to runoff change were quantitatively separated, and the evolution trend and mutation characteristics of runoff were methodically described. The decadal and multidecadal cycle structure of the runoff is revealed when combined with the wavelet power spectrum, and the potential continuous attenuation scenario of runoff in the future is predicted. This provides a scientific foundation for the sustainable management of regional water resources and water ecological restoration. It was found that the runoff depth in basin 53 a decreased significantly, with a range of up to 54.74%, and that a sudden change occurred in 1992. The runoff variation caused by precipitation change was about 10.22 to 11.66 mm, corresponding to a contribution rate of −21.80% to −25.19%. Potential evapotranspiration had a low contribution rate (−3.87% to −4.86%). Runoff decreased by 58.93 mm to 62.62 mm as a result of human activity-induced changes in the underlying surface, with a corresponding contribution rate of more than 125%. The elastic coefficient analysis results based on four typical Budyko models were consistent, and the contribution rate of human activities to runoff reduction was 125.66% to 130.05%, and the sensitivity of underlying surface change was the strongest, up to 352.38%. It demonstrates that urbanization, reservoir regulation, soil and water conservation, and other human activities have a significant impact on the basin. The frequency of extreme precipitation events will rise in the future because it is currently at the high fluctuation end of the 42 a main cycle. Based on the long series of hydrological and meteorological data of the Western Ocean Reservoir basin from 1970 to 2022, this study systematically analyzed the variation trend, abrupt characteristics, periodic structure of runoff evolution, and the attribution mechanism between climate and human activities. The primary findings were as follows: from 1970 to 2022, there was a notable decline in the annual runoff depth, with an abrupt point occurring around 1992. Although precipitation increased significantly and evapotranspiration tended to be stable, the runoff depth decreased by more than 50%, indicating that there was a non-linear relationship between climate factors and runoff change. The sensitivity of underlying surface parameters was the highest, and the contribution rate of human activities to runoff variation was 125.66% to 130.05%, which was significantly higher than that of climate factors (−30.05% to −25.66%). This suggests that human intervention, such as changing land use patterns, scheduling reservoirs, and conserving soil and water were the primary causes of the decline in runoff yield capacity. The wavelet cycle analysis identified 42 dominant cycles and 24 sub-cycles. The current runoff in the basin was in a strong fluctuation stage of periodic oscillation, and the isoline was not closed, indicating that the frequency and intensity of extreme hydrological events may increase in the future period.

关键词

径流变化 / 量化归因分析 / 人类活动 / 西大洋水库 / Budyko模型

Key words

runoff change / quantitative attribution analysis / human activity / Xidayang Reservoir / Budyko model

参考文献

[1] 张娜, 李波, 刘子慧, 等. 南水北调京津冀豫受水区水资源供需新形势及对策[J]. 南水北调与水利科技（中英文）, 2023, 21(6): 1223-1234. DOI: 10.13476/j.cnki.nsbdqk.2023.0121.

[2] 丁启慧, 贾美蓉, 丁相毅, 等. 青海湖流域主要河流的生态需水[J]. 南水北调与水利科技（中英文）, 2024, 22(3): 575-584. DOI: 10.13476/j.cnki.nsbdqk.2024.0059.

[3] 冯胜航, 王党伟, 秦蕾蕾, 等. 金沙江流域径流变化特征及成因[J]. 南水北调与水利科技（中英文）, 2023, 21(2): 248-257. DOI: 10.13476/j.cnki.nsbdqk.2023.0025.

[4] 张青青, 钟徳钰, 贾宝真. 黄河中游水沙变化归因分析[J]. 南水北调与水利科技（中英文）, 2022, 20(5): 976-987. DOI: 10.13476/j.cnki.nsbdqk.2022.0097.

[5] Samaneh A, Niloufar B Y, Mohammad K, et al. Understanding Uranium distribution: A systematic review and meta-analysis in the context of drinking water resources[J]. Results in Engineering, 2024, 22: 102152. DOI: 10.1016/j.rineng.2024.102152.

[6] 黄静, 李想, 沈延青, 等. 黄河上游径流年际变化规律及丰枯统计特征[J]. 水利水电技术（中英文）, 2025, 56(7）: 165-176. DOI: 10.13928/j.cnki.wrahe.2025.07.012.

[7] 胡广录, 陶虎, 焦娇, 等. 黑河中游正义峡径流变化趋势及归因分析[J]. 干旱区研究, 2023, 40(9): 1414-1424. DOI: 10.13866/j.azr.2023.09.05.

[8] 黄斌斌, 郝成元, 李若男, 等. 气候变化及人类活动对地表径流改变的贡献率及其量化方法研究进展[J]. 自然资源学报, 2018, 33(5): 899-910. DOI: 10.11849/zrzyxb.20170261.

[9] Huang Tingting, Liu Yu, Wu Zhiyong, et al. Quantitative analysis of runoff alteration based on the Budyko model with time-varying underlying surface parameters for the Wuding River basin, Loess Plateau[J]. Ecological Indicators, 2024, 158: 111377. DOI: 10.1016/j.ecolind.2023.111377.

[10] 杨倩, 刘登峰, 孟宪萌, 等. 环境变化对汉江上游径流影响的定量分析[J]. 水力发电学报, 2019, 38(12): 73-84. DOI: 10.11660/slfdxb.20191208.

[11] 彭涛, 梅子祎, 董晓华, 等. 基于Budyko假设的汉江流域径流变化归因[J]. 南水北调与水利科技（中英文）, 2021, 19(6): 1114-1124. DOI: 10.13476/j.cnki.nsbdqk.2021.0114.

[12] 郑文婷, 韩京成, 吴颖菊, 等. 城市水资源联合调配的综合效益评估: 以北京市为例[J]. 南水北调与水利科技（中英文）, 2024, 22(2): 237-246. DOI: 10.13476/j.cnki.nsbdqk.2024.0025.

[13] 郭楠楠, 李丹丹, 陈曦, 等. 春秋两季西大洋水库生态修复示范区浮游植物群落结构特征[J]. 大连海洋大学学报, 2020, 35(3): 417-423. DOI: 10.16535/j.cnki.dlhyxb.2019-235.

[14] Zeng Hang, Sun Xun, Lall U, et al. Nonstationary extreme flood/rainfall frequency analysis informed by large-scale oceanic fields for Xidayang Reservoir in north China[J]. International Journal of Climatology, 2017, 37(10): 3810-3820. DOI: 10.1002/joc.4955.

[15] 王庆明, 姜珊, 李森, 等. 大清河流域山区径流量衰减影响因素[J]. 南水北调与水利科技（中英文）, 2021, 19(4): 669-679. DOI: 10.13476/j.cnki.nsbdqk.2021.0070.

[16] 张金萍, 张鑫, 肖宏林. 西大洋水库唐河支流丰枯演化特征研究[J]. 水利水电技术, 2019, 50(7): 64-69. DOI: 10.13928/j.cnki.wrahe.2019.07.008.

[17] 杨盈, 陈贺, 于世伟, 等. 基于改进调度图的西大洋水库综合调度研究[J]. 水力发电学报, 2012, 31(4): 139-144.

[18] 李秋梅, 杨建英, 吴海龙, 等. 大清河流域水系分形及连通性研究[J]. 武汉大学学报（工学版）, 2023, 56(3): 296-303. DOI: 10.14188/j.1671-8844.2023-03-005.

[19] 耿念姿, 李若男, 郑华. 地表水资源供给服务供需关系对节水政策的响应：以大清河流域为例[J]. 生态学报, 2023, 43(12): 4813-4822. DOI: 10.5846/stxb202204010823.

[20] 孙美平, 张磊, 姚晓军, 等. 1954−2016年疏勒河上游径流变化特征及影响因素[J]. 冰川冻土, 2022, 44(2): 657-666. DOI: 10.7522/j.issn.1000-0240.2022.0067.

[21] Xu Xiangyu, Yang Dawen, Yang Hanbo, et al. Attribution analysis based on the Budyko hypothesis for detecting the dominant cause of runoff decline in Haihe basin[J]. Journal of Hydrology, 2014, 510: 530-540. DOI: 10.1016/j.jhydrol.2013.12.052.

[22] Zhang Lingyu, Dawes W R, Walker G R. Response of mean annual evapotranspiration to vegetation changes at catchment scale[J]. Water Resources Research, 2001, 37(3): 701-708. DOI: 10.1029/2000WR900325.

[23] 傅抱璞. 论陆面蒸发的计算[J]. 大气科学, 1981, 5(1): 25-33. DOI: 10.3878/j.issn.1006-9895.1981.01.03.

[24] Wang Dingbao, Tang Yin. A one-parameter Budyko model for water balance captures emergent behavior in Darwinian hydrologic models[J]. Geophysical Research Letters, 2014, 41(13): 4569-4577. DOI: 10.1002/2014GL060509.

[25] Bai Xuelian, Zhao Wenzhi. Impacts of climate change and anthropogenic stressors on runoff variations in major river basins in China since 1950[J]. Science of The Total Environment, 2023, 898(000): 13. DOI: 10.1016/j.scitotenv.2023.165349.

[26] Guan Xiaoxiang, Zhang Jianyun, Bao Zhenxin, et al. Past variations and future projection of runoff in typical basins in 10 water zones, China[J]. Science of The Total Environment, 2021, 798(9): 149277. DOI: 10.1016/j.scitotenv.2021.149277.

[27] Zhang Yu, Wu Xiufeng, Wu Shiqiang, et al. A framework for methodological options to assess climatic and anthropogenic influences on streamflow[J]. Frontiers in Environmental Science, 2021, 9: 765227. DOI: 10.3389/fenvs.2021.765227.

[28] Wang Manlin, Zhang Yu, Lu Yan, et al. Attribution analysis of streamflow changes based on large-scale hydrological modeling with uncertainties[J]. Water Resources Management, 2023, 37(2): 713-730. DOI: 10.1007/s11269-022-03396-7.

[29] 钱维宏, 蒋宁, 杜钧. 中国东部7类暴雨异常环流型[J]. 气象, 2016, 42(6): 12. DOI: 10.7519/j.issn.1000-0526.2016.06.003.

[30] 尹露莹, 张荣华, 智海. 热带太平洋淡水通量年际变率对ENSO的作用机制及数值模拟研究[J]. 海洋与湖沼, 2024, 55(6): 1350-1367. DOI: 10.11693/hyhz20240600132.

[31] 梁苏洁, 丁一汇, 赵南, 等. 近50年中国大陆冬季气温和区域环流的年代际变化研究[J]. 大气科学, 2014, 38(5): 974-992. DOI: 10.3878/j.issn.1006-9895.1401.13234.

[32] 张芳华, 杨舒楠, 胡艺, 等. “23·7”华北特大暴雨过程的水汽特征[J]. 气象, 2023, 49(12): 1421-1434. DOI: 10.7519/j.issn.1000-0526.2023.103003.

[33] 胡文浩, 张晓婧, 陈雅杰, 等. 坝上地区不同年代退耕还林生境的草本层植物多样性及影响因子[J]. 生态学报, 2021, 41(3): 1116-1126. DOI: 10.5846/stxb201912042631.

[34] Zhang Yu, Wang Manlin, Chen Juan, et al. Multi-scale attribution analysis for assessing effects of changing environment on runoff: Case study of the Upstream Yangtze River in China[J]. Journal of Water and Climate Change, 2021, 12(2): 627-646. DOI: 10.2166/wcc.2020.155.

[35] 陈峻琳, 温天福, 李国芳, 等. 土地利用变化和水库调控对洪水过程的影响: 以赣江上游平江流域为例[J]. 湖泊科学, 2025, 37(2): 612-626. DOI: 10.18307/2025.0242.

[36] 丁楠, 李义博, 陶福禄. 过去30年气候和土地利用变化对海河流域径流、泥沙及氮磷流失的影响[J]. 自然资源学报, 2024, 39(7): 1720-1734. DOI: 10.31497/zrzyxb.20240713.

[37] 李天毅, 戴宁, 丛佩娟, 等. “23·7”京津冀地区暴雨洪水与土壤侵蚀调查: 以河北省邢台市临城县为例[J]. 水土保持通报, 2024, 44(2): 155-166. DOI: 10.13961/j.cnki.stbctb.2024.02.017.

[38] Javed M, Didovets I, Boehner J H S. Attributing historical streamflow changes in the Jhelum River basin to climate change[J]. Climatic Change, 2023, 176(11): 161. DOI: 10.1007/s10584-023-03628-8.

[39] 李秀, 郎琪, 雷坤, 等. 基于Budyko假设的永定河流域上游不同支流径流变化归因分析[J]. 水资源与水工程学报, 2021, 32(6): 94-101. DOI: 10.11705/j.issn.1672-643X.2021.06.13.

[40] 周金玉, 张璇, 许杨, 等. 基于Budyko假设的滦河流域上游径流变化归因识别[J]. 南水北调与水利科技（中英文）, 2020, 18(3): 15-30. DOI: 10.13476/j.cnki.nsbdqk.2020.0047.

基金资助

国家自然科学基金项目(U2443201)

国家自然科学基金项目(U21A2004)

河北省省级水利科技计划项目(HBSL2025-01)