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气候变化和人类活动对中国地表水文过程影响定量研究

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刘剑宇¹^,²^,, 张强³^,⁴^,, 陈喜⁵, 顾西辉¹^,²

1. 中山大学水资源与环境系,广州 510275

2. 中山大学华南地区水循环与水安全广东省普通高校重点实验室,广州 510275

3. 北京师范大学地表过程与资源生态国家重点实验室,北京 100875

4. 北京师范大学减灾与应急管理研究院,北京 100875

5. 河海大学水文水资源学院,南京 210098

Quantitative evaluations of human- and climate-inducedimpacts on hydrological processes of China

LIUJianyu¹^,²^,, ZHANGQiang³^,⁴^,, CHENXi⁵, GUXihui¹^,²

1. Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China

2. Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou 510275, China

3. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China

4. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China

5. School of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

通讯作者:通讯作者：张强(1974-), 男, 山东沂水人, 博士, 教授, 博士生导师, 主要从事气象水文学研究。E-mail: zhangq68@bnu.edu.cn
收稿日期:2016-07-9
修回日期:2016-10-22
网络出版日期:2016-11-25
版权声明:2016《地理学报》编辑部本文是开放获取期刊文献，在以下情况下可以自由使用：学术研究、学术交流、科研教学等，但不允许用于商业目的.
基金资助:

国家****科学基金项目(51425903)

国家自然科学基金重大项目(51190091)

安徽省自然科学基金项目(1508085MD65)

作者简介:
-->作者简介：刘剑宇(1991-), 男, 江西丰城人, 博士生, 主要从事气象水文学研究。E-mail: liujianyu68@163.com

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摘要
利用中国372个水文站月径流数据（1960-2000年）及41个水文站年径流数据（2001-2014年）,采用基于Budyko假设的水热耦合平衡方程,构建气候变化和人类活动对径流变化影响定量评估模型,在Penman-Monteith潜在蒸发分析基础上,进一步分析气象因子对径流变化的弹性系数,量化气候变化和人类活动对径流变化的影响。结果表明：① 中国北方地区流域径流变化对各气象因子弹性系数明显大于中国南方地区。就全国而言,径流变化对各因子的弹性系数为：降水>土地利用/土地覆盖变化（LUCC）>相对湿度>太阳辐射>最高气温>风速>最低气温;② 1980-2000年,气候变化总体上有利于增加中国年径流量,而降水对年径流量增加的贡献最为显著;③ 1980-2000年,中国南方流域中,气候变化对年径流变化的影响以增加作用为主,而北方流域,以减少年径流作用为主。对中国大多数流域径流变化而言,人类活动的影响主要以减少年径流量为主。2001-2014年,气候变化以减少径流量为主,人类活动对径流变化的影响程度明显增强,气候变化与人类活动对径流变化的贡献率分别为53.5%、46.5%。该研究对气候变化与人类活动影响下,中国水资源规划管理、防灾减灾及保障水资源安全具有重要理论与现实意义。

关键词：径流变化;Budyko假设;弹性系数;气候变化;人类活动;中国
Abstract
Based on monthly streamflow data from 372 stations covering the period 1960-2000 and the monthly streamflow data from 41 stations covering the period 2001-2014 across China, human- and climate-induced impacts on hydrological processes were quantified for 10 river basins in China based on development of Budyko-based coupled water-energy balance model. Penman-Monteith potential evapotranspiration model was used to analyze evapotranspiration processes. Besides, elasticity coefficient was also quantified for the impacts of meteorological variables on streamflow changes. The results indicated that: (1) Compared to southern China, streamflow changes are more sensitive to climate changes and human activities in northern China. Generally, relative humidity changes have positive impacts on streamflow changes. However, the maximum temperature, minimum temperature, solar radiation, wind speed and LUCC changes tend to go against streamflow changes. The elasticity coefficients of streamflow changes for meteorological variables are: precipitation > LUCC > relative humidity > solar radiation > maximum temperature > wind speed > minimum temperature; (2) Climate changes during 1980-2000 generally help to increase annual streamflow, and the increase of streamflow by precipitation changes is most evident, and the increase of streamflow depth reaches 12.1 mm. However, impacts of meteorological variables on streamflow changes are shifting from one river basin to another, e.g. the maximum temperature and relative humidity help to increase streamflow in northern China but decrease streamflow magnitude in southern China; (3) In general, human activities tend to decrease streamflow. Changes of streamflow in the Yangtze, Songhua, Northwest, and Southeast river basins are 78.7%, 76.9%, 65.7%, and 84.2%, respectively, which can be attributed to climate changes. However, human activities play a dominant role in modifications of streamflow changes, such as Pearl River basin, Huaihe River basin, Haihe River basin, Yellow River basin, Liaohe River basin and southwest river basins, with fractional contribution being 59.4%, 77.3%, 66.2%, 69.7%, 75.3%, and 70.4%, respectively. Generally, the fraction of human activities and climate changes to streamflow changes in the river basins across China can reach 71.0% and 29.0% respectively in river basins, where climate changes play a dominant role in streamflow changes. The results of this study can be helpful to human mitigation to climate changes in terms of water resources management.

Keywords：streamflow changes;elasticity coefficient;Budyko hypothesis;climate changes;human activities;China

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刘剑宇, 张强, 陈喜, 顾西辉. 气候变化和人类活动对中国地表水文过程影响定量研究[J]. , 2016, 71(11): 1875-1885 https://doi.org/10.11821/dlxb201611001
LIU Jianyu, ZHANG Qiang, CHEN Xi, GU Xihui. Quantitative evaluations of human- and climate-inducedimpacts on hydrological processes of China[J]. 地理学报, 2016, 71(11): 1875-1885 https://doi.org/10.11821/dlxb201611001

1 引言

近年来,变化环境下流域水循环及水资源演变研究已成为国内外水科学领域的研究热点,气候变化和人类活动作为变化环境的重要组成部分,其带来的水文效应受到广泛关注^[1-3]。目前,在评估气候变化和人类活动对径流变化影响方面,主要有两类方法：基于水文模拟的方法和基于Budyko假设的水量平衡方法^[4]。前者的优点是水文模型有一定机理性解释,且从日到年等不同时间尺度上,模型模拟有显著优势。但模型结构和参数的不确定性及流域内地形、土壤、植被和气候之间关系的复杂性等,影响了模型响应范围以及模型变异性^[5]。此外,模型模拟对数据质与量的要求较高,分布式模型尤其如此^[6],而并非所有流域均有如此完备的数据。基于Budyko水热耦合平衡理论的水量平衡法较传统的数理统计经验法具有明显物理意义,且计算过程相对简单,参数较易获取,在年及多年时间尺度上,是一种理想的分析方法^[7],已被广泛应用于流域径流变化归因研究^[8-9]。
在流域径流变化特征归因分析方面,在具体流域尺度上已有较多研究。许多****对中国各大江河流域径流变化进行了归因分析,如长江流域^[10],长江流域的支流岷江流域^[11]以及鄱阳湖流域^[12]。所用的研究方法较多,有统计方法,如线性回归法^[10]、双累积曲线法^[11]等。也有部分研究综合运用统计方法、水文模型模拟以及基于Budyko假设的灵敏度分析法等^[12]。相关研究在黄河流域^[13-14]、海河流域^[9]、西北地区^[15]等也有较多开展。上述研究对于理解具体流域径流变化成因具有重要意义。然而,上述研究运用的方法不同,对比时段不同,难以进行大空间尺度对比研究。事实上,已有少量在全国尺度探讨气候变化对径流变化影响的研究,Yang等^[16]基于Budyko假设的水热耦合平衡方程,针对中国210个子流域,评估气候变化（降水、蒸发）对径流变化率的影响。而已有研究主要针对的是气候变化的影响,对于人类活动对径流变化的影响,并未开展定量研究,缺乏径流对人类活动响应的系统研究。同时,在运用基于Budyko假设的水热耦合平衡方程开展相关研究时,考虑不同气象因子对径流变化影响的尚少^[17],如太阳辐射、气温、相对湿度等的影响。已有诸多研究表明^[18-19],FAO修正的Penman-Monteith模型适用于不同气候类型区潜在蒸散发量计算及气候变化对水循环影响研究。因此,可以尝试以修正的Penman-Monteith蒸发来推导各气象因子对径流的弹性系数,进一步量化蒸发因子（最高气温、最低气温、太阳辐射、风速和相对湿度）对径流变化的影响。
基于目前径流变化归因研究现状,结合中国气候变化与人类活动影响下水资源时空特征、机理及归因研究的实际需求,针对中国水资源10大流域片区372个水文站点的月径流数据,基于Budyko假设的水热耦合平衡方程,系统地量化气候变化与人类活动对中国各流域径流变化影响,并结合FAO修正的Penman-Monteith模型,进一步推求太阳辐射、最高气温、最低气温、风速、相对湿度5个蒸发因子对径流变化的弹性系数,量化各蒸发因子对径流变化的影响。该研究对全面而深入探讨变化环境下水循环过程及水资源演变机理,理解气候变化和人类活动对中国各大流域径流演变相对贡献具有重要理论意义,对于中国水资源规划管理,防灾减灾及保障水资源安全具有重要现实意义。

2 研究区域和数据

本文搜集了中国372个水文站点1960-2000年月径流数据（图1）,并收集其中41个主要河流代表水文站2001-2014年径流数据,径流数据来源于水利部数据中心。径流数据缺测率小于1%,缺测值采用前后7年滑动平均进行插值。同时收集了中国气象局1960-2014年的815个气象站的常规观测数据（图1）,每个站点包括日降水量、日平均气温等12个气象指标。全国共分为10大流域片区（图1）：珠江流域（PR）、长江流域（YZR）、淮河流域（HuR）、海河流域（HR）、黄河流域（YR）、辽河流域（LR）、松花江流域（SHR）、东南诸河（SER）、西南诸河（SWR）、西北诸河（NWR）。

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图1中国气象、水文站点和主要流域片区分布
-->Fig. 1Locations of meteorological and hydrological stations in China
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基于美国地质调查局1 km空间分别率高程数据,提取中国10大流域片区372水文站点对应集水范围（子流域）（图1）。采用反距离权重法对降水（P）、太阳辐射（R_n）、最高气温（T_max）、最低气温（T_min）、风速（U₂）、相对湿度（RH）进行空间插值。各气象要素插值到100 m×100 m的网格上,再利用ArcGIS中Zonal Histogram工具提取每个子流域相应气象要素面平均值。

3 研究方法

3.1 各因子弹性系数分解

Budyko^[20]认为在较长时间尺度上,流域蒸发量是流域降水和径流的函数。Yang等^[21]基于Budyko假设,推导出流域水热耦合平衡方程,表达式如下：

\begin{matrix} E = \frac{P E_{0}}{(P^{n} + E_{0}^{n})^{1 / n}} \end{matrix}

（1）
式中：E为多年平均实际蒸发;P为多年平均降水;E₀为多年平均潜在蒸发;n为下垫面特征参数（LUCC）。根据流域多年平均的水量平衡方程,R=P-E,可得R=f (P, E₀, n)。采用实验误差法,n以0.001为增量从0到10试算,取使方程误差最小的值作为相应流域的下垫面参数n的值。
Roderick等^[22]根据蒸发皿蒸发公式导出气象因子对蒸发皿蒸发贡献率的微分方程。由于FAO修正的Penman-Monteith模型^[23]适用于不同气候类型区潜在蒸散发量计算及气候变化对水循环的影响研究^[18-19],因此本文基于修正的Penman-Monteith公式,分解各蒸发因子变化对潜在蒸发变化的全微分方程：

\begin{matrix} d E_{0} \approx \frac{? E_{0}}{? R_{n}} d R_{n} + \frac{? E_{0}}{? T} d T_{\max} + \frac{? E_{0}}{? T} d T_{\min} + \frac{? E_{0}}{? U_{2}} d U_{2} + \frac{? E_{0}}{? RH} dRH \end{matrix}

（2）
结合Budyko水热耦合平衡方程,导出降水（P）、LUCC（n）、太阳辐射（R_n）、最高气温（T_max）、最低气温（T_min）、风速（U₂）和相对湿度（RH）对径流变化的全微分方程：

\begin{matrix} \frac{dR}{R} = ε_{P} \frac{dP}{P} + ε_{n} \frac{dn}{n} + ε_{Rn} \frac{d R_{n}}{R_{n}} + ε_{T_{\max}} \frac{d T_{\max}}{T_{\max}} + ε_{T_{\min}} \frac{d T_{\min}}{T_{\min}} + ε_{U_{2}} \frac{d U_{2}}{U_{2}} + ε_{RH} \frac{dRH}{RH} \end{matrix}

（3）
式中：

\begin{matrix} ε_{P} = \frac{? f}{? P} \frac{P}{R} ， ε_{E_{0}} = \frac{? f}{? E_{0}} \frac{E_{0}}{R} ， ε_{n} = \frac{? f}{? n} \frac{n}{R} ， ε_{R_{n}} = ε_{E_{0}} \frac{R_{n}}{E_{0}} \frac{? E_{0}}{? R_{n}} ， ε_{T_{\max}} = ε_{E_{0}} \frac{T_{\max}}{E_{0}} \frac{? E_{0}}{? T_{\max}} \end{matrix}

\begin{matrix} ε_{T_{\min}} = ε_{E_{0}} \frac{T_{\min}}{E_{0}} \frac{? E_{0}}{? T_{\min}} ， ε_{U_{2}} = ε_{E_{0}} \frac{U_{2}}{E_{0}} \frac{? E_{0}}{? U_{2}} ， ε_{RH} = ε_{E_{0}} \frac{RH}{E_{0}} \frac{? E_{0}}{? RH} \end{matrix}

。

\begin{matrix} ε_{P} \end{matrix}

、

\begin{matrix} ε_{E_{0}} \end{matrix}

、

\begin{matrix} ε_{n} \end{matrix}

、

\begin{matrix} ε_{R_{n}} \end{matrix}

、

\begin{matrix} ε_{T_{\max}} \end{matrix}

、

\begin{matrix} ε_{T_{\min}} \end{matrix}

、

\begin{matrix} ε_{U_{2}} \end{matrix}

、

\begin{matrix} ε_{RH} \end{matrix}

分别是降水、潜在蒸发、LUCC、太阳辐射、最高气温、最低气温、风速、相对湿度对径流变化的弹性系数。无量纲,便于径流变化对不同因子敏感度的对比。

3.2 各因子对径流变化的相对贡献率

根据公式（3）,可得各因子对径流变化的影响量公式：

\begin{matrix} ? R_{x} = ε_{x} \frac{R}{x} ? x \end{matrix}

（4）
式中：R为多年平均年径流量;x为径流变化的某一影响因子,包括降水、LUCC、太阳辐射、最高气温、最低气温、风速和相对湿度;ε_x为各因子对径流变化的弹性系数;?R_x为相应因子对径流变化的影响量。
Tan等^[24]采用基于Budyko假设的水热耦合平衡方程对加拿大径流变化进行归因分析,认为下垫面参数n的变化对径流的影响可以表征为人类活动的影响,其影响量主要受下垫面变化、水库建设、土地利用及社会经济发展状况（人口、GDP）等人类活动的影响。气候变化通过改变降水、气温、相对湿度等气象因子对径流变化产生影响,降水、太阳辐射、最高气温、最低气温、风速和相对湿度对径流变化影响量之和,即为气候变化对径流变化的影响量。气候变化和人类活动对径流变化影响的相对贡献率可用下式表示：

\begin{matrix} δ R_{clim} = \frac{? R_{clim}}{?} \times 100 % ， δ R_{hum} = \frac{? R_{hum}}{?} \times 100 % \end{matrix}

（5）
式中：?R_clim、?R_hum分别为气候变化影响量与人类活动影响量;?为气候变化和人类活动影响量的绝对值之和;

\begin{matrix} δ R_{clim} \end{matrix}

、

\begin{matrix} δ R_{hum} \end{matrix}

分别为气候变化和人类活动对径流变化的相对贡献率（%）。

3.3 径流序列趋势突变检验

选用国际气象组织推荐的Mann-Kendall检测年径流序列趋势特征^[25],为去除时间序列自相关性,采用修正的Mann-Kendall检测法^[26]。不同突变点检测结果可能存在差异,本文选用多个突变点检测方法进行综合判定。Killick等^[27]于2014年开发“changepoint”包,提出基于似然函数框架的AMOC检验法具有较大灵活性,可以克服序列正态分布假设。Villarini^[28]认为Pettitt检验对异常值不敏感的特点适合运用于突变点检验。刘剑宇等^[29]对比8种常用突变检验方法,认为有序聚类检验能有效检测出径流变化突变点。因此,本文采用AMOC检验、有序聚类检验、Pettitt检验3种方法对年径流序列进行突变检验,取多数方法检验一致且非处在序列两端的变异点作为最终突变点。

4 结果分析

为便于对比气候变化和人类活动对中国不同流域片区径流变化贡献率,本文拟采用统一的时间点分割径流序列。一般以流域水库平均建成时间^[30]或各站点径流序列平均突变时间作为整个研究区水文站点径流序列的分割点^[24]。图2a为中国10大流域475座大水库建成时间,可以看出大多数流域片区（除SWR以外）,水库建成时间50%或75%分位数分布在1980年或1980年之前,全国大型水库平均建成时间为1972年。图2b为中国372个水文站点径流序列突变点检测结果,各流域片区径流序列突变时间的50%分位数多数分布在1980年之前,所有水文站径流序列平均突变时间为1980年。从经济发展方面来看,1980年中国刚进行改革开放,工农业生产以及满足工农业发展的水利工程建设开始迅速发展,许多流域年径流量变化表现出明显减少趋势^[31]。综上考虑,本文采用1980年为径流序列分割点,将径流时间序列分割为时段1（1960-1979年）和时段2（1980- 2000年）。

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图2中国10大流域片区水库建成时间及年径流序列突变时间
-->Fig. 2Construction time of large reservoirs and change-points of annual runoff series in 10 river basins in China
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4.1 径流对各气象因子和LUCC的弹性系数

弹性系数绝对值大小反应流域径流变化对该影响因子变化的敏感程度^[9]。从图3可知,不同流域径流变化对各因子的敏感程度存在明显差异。松花江、辽河、海河、黄河、淮河流域的大多数子流域径流对降水的弹性系数在1.62~4.84之间（平均为2.24）,表明这些流域降水量增加10%将导致径流量平均增加22.4%。而长江流域、珠江流域、西南诸河、西北诸河、东南诸河的大部分地区降水弹性系数在1.05~1.61之间（平均为1.57）。北方大部分子流域径流对LUCC弹性系数在-1.46~-5.07之间（平均为-1.58）,表明LUCC参数n增加10%径流量将减少14.6%~50.7%。径流变化对降水、相对湿度的弹性系数均为正（图3a、3d）,表明降水、相对湿度对径流变化有正向驱动作用。径流变化对最高温度、最低气温、太阳辐射、风速和LUCC的弹性系数为负（图3b、3c、3e、3f、3g）,表明这些因子对径流变化有负驱动作用。径流变化对最低气温的弹性系数在西北、东北部分地区表现异常,这是由这些区域多年平均最低气温低于零度所致。相对湿度的增加,流域蒸散发减少,进而使得产汇流损失减少,径流增加;相反,太阳辐射的增强,气温的升高以及风速的增加,使得流域蒸散发增加,进而导致径流量减少。下垫面参数n为表征植被、土壤等流域下垫面特征的参数^[21],参数n增大,流域植被覆盖面积增加,植被保持水土功能增强,从而导致径流减少。

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图3中国各流域径流变化对各影响因子的弹性系数空间分布
-->Fig. 3Elasticity of annual runoff to annual precipitation, maximum air temperature , minimum air temperature, relative humidity, net radiation, wind speed, and landscape parameter n over the 372 catchments across China
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径流变化对降水和LUCC较其他因子更为敏感,其中辽河流域径流变化对降水最为敏感,西北诸河对LUCC最为敏感。总体而言,北方地区径流变化对各因子的弹性系数明显大于南方地区,表明气候相对干燥地区径流变化对气候变化和LUCC更为敏感。径流对各因子的敏感度为：降水>LUCC>相对湿度>太阳辐射>最高气温>风速>最低气温。Yang等^[17]结合基于Budyko假设的水热耦合平衡方程和1948 Penman蒸发,评估不同气象因子对黄河、海河流域径流变化的影响,研究结果表明径流变化对降水、气温、太阳辐射、风速和相对湿度的平均弹性系数分别为1.6~3.9、-0.02~-0.11、-0.3~-1.9、-0.1~-0.8和0.2~1.9,与本文研究结果基本一致（图3）。然而还存在一定差异,这是由于对比时段存在差别,此外蒸发因子对径流的弹性系数分解也是基于不同的潜在蒸发模型。另外,该文中气温的弹性系数为气温变化1℃径流变化的百分数,与本文定义有所差别,故气温的弹性系数差别较大。

4.2 各因子对径流变化的影响

将全国各站点模拟径流变化量（

\begin{matrix} ? R_{P} \end{matrix}

\begin{matrix} ? R_{R_{n}} \end{matrix}

\begin{matrix} ? R_{T_{\max}} \end{matrix}

\begin{matrix} ? R_{T_{\min}} \end{matrix}

\begin{matrix} ? R_{U_{2}} \end{matrix}

\begin{matrix} ? R_{RH} \end{matrix}

）与实测径流变化量作比较（图4）,模拟径流变化量与实测径流变化量拟合程度较高,两者的相关系数为0.998,模拟径流变化量与实测径流变化量平均相差0.56 mm,平均误差率为6.24%。因此,基于Bydyko假设的水热耦合平衡方程适合运用于本文研究。

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图4模拟径流与实测径流对比(红色实线为1:1直线)
-->Fig. 4Comparison between the modeled streamflow and observed streamflow change (the red line is a 1:1 straight line)
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各因子变化对径流变化影响存在较大的空间差异性（图5）。降水变化增加松花江流域、长江流域中下游、珠江流域东部、东南诸河、西南诸河、西北诸河大多数子流域年径流量,尤其在长江流域下游地区降水大幅度增加径流量,部分站点年径流深增加超过81 mm（图5a）。在辽河流域南部、海河流域、黄河流域、淮河流域、珠江流域中西部,降水量变化减少大多数水文站点年径流量,其中黄河流域、海河流域、长江流域中部及珠江流域中部地区降水减少径流深达40 mm以上。最高气温（图5b）增加南方大多数地区站点年径流量,对北方地区径流变化基本上表现为减少作用。最低气温（图5c）除对中部地区少量子流域径流有增加作用外,在其他区域的径流均表现为减少作用。相对湿度（图5d）对径流深影响量空间变异性较大,相对湿度主要增加长江流域中下游径流量,部分站点增加径流深2.0~7.1 mm。太阳辐射（图5e）对径流变化影响较小,少量增加北方地区年径流。风速变化（图5f）增加全国绝大多数子流域年径流量。图5g为LUCC对径流深影响量空间分布图,从图5g中可以看出LUCC对径流影响较大,且空间变异性明显,在松花江流域东部、海河流域、黄河流域、淮河流域、长江流域西北部、西北诸河、西南诸河的大多数子流域LUCC均表现为减少径流作用,尤其是海河流域及黄河流域中下游地区的部分子流域,LUCC减少年径流深超过50 mm。就全国而言,各因子对径流变化影响量的绝对值大小依次为：降水>LUCC>风速>最低气温>最高气温>相对湿度>太阳辐射。

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图5各因子变化对中国372水文站径流变化影响量空间分布
-->Fig. 5Contributions of each factor to changes in streamflow for 372 catchments across China
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4.3 气候变化和人类活动对径流变化影响定量分解研究

为进一步分析径流变化的主导因素,图6给出气候变化影响量（

\begin{matrix} ? R_{P} \end{matrix}

\begin{matrix} ? R_{R_{n}} \end{matrix}

\begin{matrix} ? R_{T_{\max}} \end{matrix}

\begin{matrix} ? R_{T_{\min}} \end{matrix}

\begin{matrix} ? R_{U_{2}} \end{matrix}

\begin{matrix} ? R_{RH} \end{matrix}

）和人类活动影响量（LUCC影响量?R_n）的相对大小对比。从图6a可以看出,南方地区和西北地区大多数站点径流变化以气候变化为主导因素;对于北方地区,部分子流域以人类活动为主导因素,包括松花江流域东部、海河流域、辽河流域中部、黄河流域的大多数子流域,另一部分则以气候变化为主导,如松花江流域绝大多数站点径流变化以气候变化为主导。该时期气候在南方地区（珠江、长江、松花江、东南诸河、西南诸河流域）主要表现为增加径流作用,在北方地区（海河、淮河、黄河、辽河流域）主要表现为减少径流作用。人类活动以减少径流作用为主,除珠江流域、东南诸河、长江流域外,其他流域片区人类活动均减少径流,尤其是黄河流域,人类活动平均减少径流深19.4 mm,这主要受该时期黄河流域大规模的生态修复工程的影响^[13]。相对而言,长江流域、松花江流域、西北诸河、东南诸河以气候变化为主导,气候变化贡献率分别为78.7%、76.9%、65.7%、84.2%;珠江流域、淮河流域、海河流域、黄河流域、辽河流域、西南诸河以人类活动影响占主导,人类活动贡献率分别为59.4%、77.3%、66.2%、69.7%、75.3%、70.4%。就全国径流变化而言,气候变化和人类活动主导水文站点数量相当,分别为192、180站,气候变化影响量占主导地位,气候变化和人类活动对径流变化贡献率分别为71.0%、29.0%。

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图6气候变化和人类活动对中国各大流域径流变化贡献率相对大小对比空间分布
-->Fig. 6The spatial distributions of relative role between direct human and climate factors to changes in streamflow
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为探讨气候变化和人类活动对近期主要河流径流变化的影响,图6b给出了2001-2014年径流变化（相对于1960-1979年）的主导因素。尽管所搜集到的包含该时段的径流数据仅有41站点,但这些站点均为主要河流代表性水文站,各站点平均控制流域面积为19.31 km²,一定程度上能代表流域的整体情况。1980-2000年主要河流代表性水文站径流变化主要受气候变化影响,41站有33站以气候变化为主导（图6b）。2001-2014年,41站中有26站径流变化主要是由人类活动引起,其中的22站是由1980-2000年以气候变化为主导的站点转变而来,如黄河干流站点（唐乃亥站除外）、辽河干流控制性站点铁岭站、珠江流域的西江和北江控制性站点石角和博罗站、长江干流控制性站点大通站等。该时期气候变化以减少径流作用为主,尤其是珠江流域、长江流域,气候变化平均分别减少径流深62.3 mm、11.6 mm。相对而言,珠江流域、淮河流域、松花江流域、西北诸河流域以气候变化影响为主,气候变化影响量分别为92.0%、67.8%、68.4%、72.2%;长江流域、黄河流域、辽河流域、东南诸河以人类活动影响为主,人类活动影响量分别为60.9%、83.1%、58.9%、84.8%。就全国径流变化而言,气候变化和人类活动的贡献率分别为53.5%、46.5%。对比两个时期径流变化的归因结果可见,2001-2014年人类活动对径流影响程度大幅增加,说明日益加剧的人类活动对流域水循环和水资源演变产生了更大的影响,人类活动对径流变化的影响不容忽视。

5 结论

本文系统评估了气候变化和人类活动对中国10大流域片区372个水文站点径流变化的影响,基于FAO-PM公式推导出最高气温、最低气温、相对湿度、风速、相对湿度5个蒸发因子对径流变化的弹性系数计算公式,量化气候变化（降水、太阳辐射、最高气温、最低气温、风速、相对湿度）和人类活动（LUCC）对径流变化的贡献率,主要得出以下结论：
（1）气候相对干燥的北方地区流域径流变化对各气象因子和下垫面因子弹性系数明显大于相对湿润的南方地区,北方地区径流变化对气候变化和人类活动较南方地区更为敏感。降水、相对湿度对径流变化有正向驱动作用,最高温度、最低温度、太阳辐射、风速和LUCC变化对径流变化有负向驱动作用。就全国而言,径流对各因子的敏感度为：降水>LUCC>相对湿度>太阳辐射>最高气温>风速>最低气温。
（2）气候变化通过改变降水、气温、相对湿度等气象因子对径流变化产生影响。1980-2000年,降水变化总体上增加中国河流径流量,平均增加径流深12.1 mm。风速变化总体增加各大流域片区径流量,最低气温变化总体减少各大流域片区径流量。最高气温和相对湿度变化对北方流域片区以增加径流作用为主,对南方流域片区以减少径流为主。太阳辐射变化对径流变化影响相对较小。就全国而言,各气象因子对径流变化影响量绝对值大小依次为：降水>风速>最低气温>最高气温>相对湿度>太阳辐射。
（3）1980-2000年,气候变化在南方流域片区主要表现为增加径流作用,在北方流域片区主要表现为减少径流作用,人类活动以减少径流为主,对径流变化的贡献率为29.0%。2001-2014年,气候变化以减少径流作用为主,人类活动影响程度大幅增加,气候变化和人类活动对径流变化的贡献率分别为53.5%、46.5%。
The authors have declared that no competing interests exist.

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被引期刊影响因子

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[4]	Wang X.Advances in separating effects of climate variability and human activity on stream discharge: An overview . Advances in Water Resources, 2014, 71: 209-218.https://doi.org/10.1016/j.advwatres.2014.06.007 URL Magsci [本文引用: 1]摘要 Separating effects of climate change (Delta Q(c)) and human activity (Delta Q(h)) on stream discharge at the watershed scale is needed for developing adaptive measures to climate change. However, information is scarce in existing literature regarding whether such separating is feasible and whether reliable results can be produced. The objectives of this overview were to: (1) compare currently-used methods; (2) assess assumptions and issues of the methods; and (3) present a generic framework that overcomes possible issues. Based on the overview of fifteen recent representative studies, two methods can be used to estimate absolute magnitudes of Delta Q(c) and Delta Q(h), while another method can be used to distinguish relative magnitudes of Delta Q(c) versus Delta Q(h) only. Because the methods' fundamental assumptions about baseline versus altered period, water storage change and deep groundwater loss, precipitation-runoff relationship, hysteresis influence of human activity, and record of time series can seldom be satisfied for many watersheds, it is more realistic and practical to distinguish relative effects than to estimate absolute magnitudes of Delta Q(c) and Delta Q(h). Moreover, a generic framework was presented for gauged watersheds with negligible groundwater loss, aiming to avoid misuse of the methods in practice. (C) 2014 Elsevier Ltd. All rights reserved.
[5]	Sivapalan M.Prediction in ungauged basins: A grand challenge for theoretical hydrology . Hydrological Processes, 2003, 17(15): 3163-3170.https://doi.org/10.1002/hyp.5155 URL [本文引用: 1]摘要 First page of article
[6]	Yang Dawen, Li Chong, Ni Guangheng, et al.Application of distributed hydrological model to the Yellow River Basin . Acta Geographica Sinica, 2004, 59(1): 143-154.Magsci [本文引用: 1]摘要 <p>流域的水资源规划和管理都离不开水资源的定量化评估。而准确评估流域的水资源量，尤其是在大流域，必须明晰不同气候、地形、土地利用等自然条件下的水文循环过程。同时农业灌溉及水库调节等人工的直接取水和调控使水文过程变得更为复杂。仅依靠气象及水文观测数据，已很难拟合出单一的降雨-径流关系来模拟和预测流域水资源的时空分布。这时就需要一种新型水文模拟手段，它可以利用地理信息来描述流域的空间不均一性，并基于物理控制方程来描述水文过程，这就是分布式水文模型。作者介绍了这种模型及其在黄河流域的应用。</p> [杨大文, 李翀, 倪广恒, 等. 分布式水文模型在黄河流域的应用 . 地理学报, 2004, 59(1): 143-154.]Magsci [本文引用: 1]摘要 <p>流域的水资源规划和管理都离不开水资源的定量化评估。而准确评估流域的水资源量，尤其是在大流域，必须明晰不同气候、地形、土地利用等自然条件下的水文循环过程。同时农业灌溉及水库调节等人工的直接取水和调控使水文过程变得更为复杂。仅依靠气象及水文观测数据，已很难拟合出单一的降雨-径流关系来模拟和预测流域水资源的时空分布。这时就需要一种新型水文模拟手段，它可以利用地理信息来描述流域的空间不均一性，并基于物理控制方程来描述水文过程，这就是分布式水文模型。作者介绍了这种模型及其在黄河流域的应用。</p>
[7]	Dooge J C I. Sensitivity of runoff to climate change: A Hortonian approach . Bulletin of the American Meteorological Society, 1992, 73(12): 2013-2024. [本文引用: 1]
[8]	Zheng H, Zhang L, Zhu R, et al.Responses of streamflow to climate and land surface change in the headwaters of the Yellow River Basin . Water Resources Research, 2009, 45(7): 641-648.https://doi.org/10.1029/2007WR006665 URL [本文引用: 1]摘要 The headwater catchments of the Yellow River Basin are of great importance for the whole basin in terms of water resources, and streamflow from these catchments has decreased in the last decades. The concept of climate elasticity was used to assess the impacts of climate and land surface change on the streamflow. Results show that for the period 1960-2000 the elasticity of streamflow in relation to precipitation and potential evapotranspiration are 2.10 and -1.04, respectively, indicating that streamflow is more sensitive to precipitation than to potential evapotranspiration. However, land use change played a more important role than climate in reducing streamflow in the 1990s. It is estimated that land use change is responsible for more than 70% of the streamflow reduction in the 1990s, while climate change contributed to less than 30% of the reduction. The precipitation elasticity appears to have an inverse relationship with the runoff coefficient but a positive relationship with the aridity index, showing that the drier the catchment, the more sensitive the streamflow with respect to precipitation change. Copyright 2009 by the American Geophysical Union.
[9]	Xu X, Yang D, Yang H, et al.Attribution analysis based on the Budyko hypothesis for detecting the dominant cause of runoff decline in Haihe basin . Journal of Hydrology, 2014, 510: 530-540.https://doi.org/10.1016/j.jhydrol.2013.12.052 URL Magsci [本文引用: 3]摘要 Catchment hydrological processes have been greatly influenced by the intensive variability in land use/cover, precipitation and air temperature due to climate change and local human activities. It is desired to understand catchment hydrological response to these changes. Observations show that annual runoff had a significant decreasing trend during the past 50 years (1956-2005) in Haihe basin of northern China. In order to detect the major cause for this runoff decline, we first theoretically derived the elasticity of runoff from the Choudhury-Yang equation that is a water-energy balance equation based on the Budyko hypothesis. The elasticity of runoff was calculated in 33 selected mountainous catchments in Haihe basin based on their climate condition (represented by the aridity index, E-0/P) and landscape condition (represented by the parameter, n). We analyzed the breakpoint of the annual runoff of the 33 catchments over the past 50 years and split the whole study period into two sub-periods at the breakpoint (period 1: before the breakpoint; period 2: after the breakpoint). Then we attributed the runoff change between the two sub-periods to the impacts of climate variability and land use/cover change. The change of climate is represented by changes in precipitation (P) and potential evaporation (E-0) and the change of land use/cover is represented by the parameter n in Choudhury-Yang equation. The change of annual runoff from period-1 to period-2 was the catchment hydrological response to the change of precipitation, potential evaporation and land use/cover (represented as Delta P, Delta E-0 and Delta n), and we calculated the runoff change based on the elasticities of runoff. For the 33 catchments, the mean annual runoff decreased by 43.0 mm from the period-1 (91.4 mm) to period-2 (48.4 mm). Impacts of climate variation and land use/cover change were accountable for the runoff decrease by 26.9% and 73.1% on average, respectively. Impact of climate variation mainly came from the decrease in precipitation, and impact of land use/cover change mainly came from the vegetation increase. Vegetation increase was mainly due to the reforestation during the soil-water conservation practice during the past 30 years and also partially due to climate variability especially the temperature increase. This methodology can also be used to predict the runoff change in these catchments without direct influence of local human activities under the future climate scenario based on the climate elasticity of runoff estimated from the historical hydroclimatic data. (C) 2014 Elsevier B.V. All rights reserved.
[10]	Zhao Y, Zou X, Gao J, et al.Quantifying the anthropogenic and climatic contributions to changes in water discharge and sediment load into the sea: A case study of the Yangtze River, China . Science of the Total Environment, 2015, 536: 803-812.https://doi.org/10.1016/j.scitotenv.2015.07.119 URLPMID:26254080 [本文引用: 2]摘要 Based on data from the Datong hydrological station and 147 meteorological stations, the influences of climate change and human activities on temporal changes in water discharge and sediment load were examined in the Yangtze River basin from 1953 to 2010. The Mann–Kendall test, abrupt change test (Mann–Kendall and cumulative anomaly test), and Morlet wavelet method were employed to analyze the water discharge and sediment load data measured at the Datong hydrological station. The results indicated that the annual mean precipitation and water discharge exhibited decreasing trends of 61020.006402mm/1002yr and 61021.4102×0210 8 02m 3 /yr, respectively, and that the water sediment load showed a significant decreasing trend of 610246.502×0210 6 02t/yr. Meanwhile, an abrupt change in the water discharge occurred in 2003. The sediment load also exhibited an abrupt change in 1985. From 1970 to 2010, the climate change and human activities contributed 72% and 28%, respectively, to the water discharge reduction. The human-induced decrease in the sediment load was 914.0302×0210 6 02t/yr during the 1970s and 3301.7902×0210 6 02t/yr during the 2000s. The contribution from human activities also increased from 71% to 92%, especially in the 1990s, when the value increased to 92%. Climate change and human activities contributed 14% and 86%, respectively, to the sediment load reduction. Inter-annual variations in water discharge and sediment load were affected by climate oscillations and human activities. The effect of human activities on the sediment load was considerably greater than those on water discharge in the Yangtze River basin.
[11]	Zhang M, Wei X, Sun P, et al.The effect of forest harvesting and climatic variability on runoff in a large watershed: The case study in the Upper Minjiang River of Yangtze River basin . Journal of Hydrology, 2012, 464: 1-11.https://doi.org/10.1016/j.jhydrol.2012.05.050 URL Magsci [本文引用: 2]摘要 Forest disturbance (or land cover change) and climatic variability are commonly recognized as two major drivers interactively influencing hydrology in forested watersheds. However, separating their relative contributions to hydrology is rarely examined, particularly in large watersheds (>1000 km(2)). This study used a large watershed, the Upper Zagunao River watershed, situated in the upper reach of the Minjiang River, the Yangtze River basin, China as an example to demonstrate how the effects of forest harvesting and climatic variability on hydrology can be quantitatively separated. Long-term data on climate, hydrology and forest harvesting history are available from 1953 to 1996. Time series cross-correlation analysis and non-parametric tests were performed first to identify possible responses of annual and seasonal runoff to forest harvesting, and to determine breakpoints of runoff change over its long-term time series. Then, modified double mass curve of accumulated annual effective precipitation (the residual of precipitation and evapotranspiration) and accumulated annual runoff was used to quantify the relative contributions of forest harvesting and climatic variability to annual runoff variation. Our analysis showed that the breakpoint of significant annual runoff change occurred in1969, about 10 yrs after the intensive harvesting period of 1955-1962, suggesting the delayed hydrological response in the studied large watershed. Over the period of 1970-1996, the average annual runoff increment attributed to forest harvesting was 38 mm/yr, while the annual runoff variation attributed to climatic variability was -38.3 mm/yr, clearly demonstrating that forest harvesting and climatic variability had offsetting effects on annual runoff. Our results also disclosed that the positive effect of forest harvesting on runoff decreased with forest recovery and eventually diminished about 20 yrs after intensive harvesting period. (C) 2012 Elsevier B.V. All rights reserved.
[12]	Zhang Q, Liu J, Singh V P, et al.Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China . Hydrological Processes, 2016.https://doi.org/10.1002/hyp.10814 URL [本文引用: 2]摘要 Abstract Variations in streamflows of five tributaries of the Poyang Lake basin, China, due to the influence of human activities and climate change were evaluated using the Australia Water Balance Model (AWBM) and multivariate regression. Results indicated that multiple regression models were appropriate with precipitation, potential evapotranspiration of the current month, and precipitation of the last month as explanatory variables. The NASH coefficient for the AWBM model was larger than 0.842, indicating satisfactory simulation of streamflow of the Poyang Lake basin. Comparison indicated that the sensitivity method could not exclude the benchmark-period human influence, and the human influence on streamflow changes was overestimated. Generally, contributions of human activities and climate change to streamflow changes were 73.2% and 26.8%, respectively. However, human- and climate-induced influences on streamflow were different in different river basins. Specifically, climate change was found to be the major driving factor for the increase of streamflow within the Rao, Xin and Gan River basins; however, human activity was the principal driving factor for the increase of streamflow of the Xiu River basin and also for the decrease of streamflow of the Fu River basin. Meanwhile, impacts of human activities and climate change on streamflow variations were distinctly different at different temporal scales. At the annual time scale, the increase of streamflow was largely due to climate change and human activities during the 1970s-1990s and the decrease of streamflow during the 2000s. At the seasonal scale, Climate change was the main factor behind the increase of streamflow in the spring and summer season. Human activities increase the streamflow in autumn and winter, but decrease the streamflow in spring. At the monthly scale, different influences of climate change and human activities were detected. Climate change was the main factor behind the decrease of streamflow during May to June and human activities behind the decrease of streamflow during February to May. Results of this study can provide a theoretical basis for basin-scale water resources management under the influence of climate change and human activities. This article is protected by copyright. All rights reserved.
[13]	Liang W, Bai D, Wang F, et al.Quantifying the impacts of climate change and ecological restoration on streamflow changes based on a Budyko hydrological model in China's Loess Plateau . Water Resources Research, 2015, 51(8): 6500-6519.https://doi.org/10.1002/2014WR016589 URL [本文引用: 2]摘要 Understanding hydrological effects of ecological restoration (ER) is fundamental to develop effective measures guiding future ER and to adapt climate change in China's Loess Plateau (LP). Streamflow (Q) is an important indicator of hydrological processes that represents the combined effects of climatic and land surface conditions. Here 14 catchments located in the LP were chosen to explore the Q response to different driving factors during the period 1961-2009 by using elasticity and decomposition methods based on the Budyko framework. Our results show that (1) annual Q exhibited a decreasing trend in all catchments (-0.30 similar to -1.71 mm yr(-2)), with an average reduction of -0.87 mm yr(-2). The runoff coefficients in flood season and nonflood season were both decreasing between two periods divided by the changing point in annual Q series; (2) the precipitation (P) and potential evapotranspiration (E-0) elasticity of Q are 2.75 and -1.75, respectively, indicating that Q is more sensitive to changes in P than that in E-0; (3) the two methods consistently demonstrated that, on average, ER (62%) contributing to Q reduction was much larger than that of climate change (38%). In addition, parameter n that entails catchment characteristics in the Budyko framework showed positive correlation with the relative area of ER measures in all catchments (eight of them are statistically significant with p< 0.05). These findings highlight the importance of ER measures on modifying the hydrological partitioning in the region. However, ER actions over the sloping parts of the landscape weakened the impact of those in channels (i.e., check-dams) on Q, especially after the implementation of the Grain-for-Green project in 1999.
[14]	Sun Weiguo, Cheng Bingyan, Li Rong.Multitime scale correlations between runoff and regional climate variations in the source region of the Yellow River . Acta Geographica Sinica, 2009, 64(1): 117-127.Magsci [本文引用: 1]摘要 <p>采用交叉小波变换方法，分析了黄河源区实测径流量与区域降水量、蒸发量以及最高、最低气温之间的时频域统计特征，讨论了黄河源区径流与区域气候变化之间的多时间尺度相关。结果表明，黄河源区径流和区域气候变化具有多时间尺度结构，两者都存在准2a、4a、 6~8a、12~14a 和20a 以上尺度的显著变化周期，不同尺度周期振荡能量的强弱和时域分布的位相差异是两者相关不稳定和存在时延相关的重要原因。径流与区域降水量之间正相关振荡的凝聚性最强，区域降水量对径流变化起主控作用，前期降水异常对后期径流变化具有持续性影响。径流变化与区域蒸发量存在显著负相关振荡，年际尺度相关存在不稳定和时延现象。年代际尺度上径流与最高气温的负相关比其与最低气温的正相关凝聚性更强，最高气温升高对增大流域蒸发量导致径流补给的减少作用大于最低气温升高引起冰雪融水补给的增大作用；两者年际尺度相关不稳定，径流对气温变化的响应时间不同。分析认为，区域降水量是黄河源区径流变化的主导因子，最高气温是重要因子；在区域降水量逐年减小的背景下，气温升高进一步加剧了径流量的减小。区域蒸发量和最低气温变化对径流量也有不同程度的影响，气候因子的综合作用是黄河源区径流变化的根本原因。</p> [孙卫国, 程炳岩, 李荣. 黄河源区径流量与区域气候变化的多时间尺度相关 . 地理学报, 2009, 64(1): 117-127.]Magsci [本文引用: 1]摘要 <p>采用交叉小波变换方法，分析了黄河源区实测径流量与区域降水量、蒸发量以及最高、最低气温之间的时频域统计特征，讨论了黄河源区径流与区域气候变化之间的多时间尺度相关。结果表明，黄河源区径流和区域气候变化具有多时间尺度结构，两者都存在准2a、4a、 6~8a、12~14a 和20a 以上尺度的显著变化周期，不同尺度周期振荡能量的强弱和时域分布的位相差异是两者相关不稳定和存在时延相关的重要原因。径流与区域降水量之间正相关振荡的凝聚性最强，区域降水量对径流变化起主控作用，前期降水异常对后期径流变化具有持续性影响。径流变化与区域蒸发量存在显著负相关振荡，年际尺度相关存在不稳定和时延现象。年代际尺度上径流与最高气温的负相关比其与最低气温的正相关凝聚性更强，最高气温升高对增大流域蒸发量导致径流补给的减少作用大于最低气温升高引起冰雪融水补给的增大作用；两者年际尺度相关不稳定，径流对气温变化的响应时间不同。分析认为，区域降水量是黄河源区径流变化的主导因子，最高气温是重要因子；在区域降水量逐年减小的背景下，气温升高进一步加剧了径流量的减小。区域蒸发量和最低气温变化对径流量也有不同程度的影响，气候因子的综合作用是黄河源区径流变化的根本原因。</p>
[15]	Li Baofu, Chen Yaning, Chen Zhongsheng, et al.The effect of climate change during snowmelt period on streamflow in the mountainous areas of Northwest China . Acta Geographica Sinica, 2012, 67(11): 1461-1470.Magsci [本文引用: 1]摘要利用8 个山区气象站1960-2010 年日平均气温、降水和7 个出山口水文站的年径流数据(1960-2008), 统计分析了山区融雪期开始时间、结束时间、天数、温度和降水的变化趋势及其空间差异性, 并定量评估了年径流量对融雪期温度和降水变化的敏感性。结果表明, 近50年来, 山区融雪期平均提前了15.33 天, 延迟了9.19 天;其中, 天山南部山区融雪期提前时间最长, 为20.01 天, 而延迟时间最短, 仅6.81 天;祁连山北部山区融雪期提前时间最短(10.16天), 而延迟时间最长(10.48 天)。这显示山区融雪期提前时间越长, 延迟时间则越短。山区融雪期平均降水量增加了47.3 mm, 平均温度升高了0.857℃;其中天山南部山区降水增量最大, 达65 mm, 昆仑山北部山区降水和温度增量均最小, 分别为25 mm和0.617℃, 而祁连山北部山区温度增量最高(1.05℃)。河流径流量对融雪期气候变化敏感, 降水变化诱发年径流量变化了7.69%, 温度变化使得年径流量改变了14.15%。 [李宝富, 陈亚宁, 陈忠升, 等. 西北干旱区山区融雪期气候变化对径流量的影响 . 地理学报, 2012, 67(11): 1461-1470.]Magsci [本文引用: 1]摘要利用8 个山区气象站1960-2010 年日平均气温、降水和7 个出山口水文站的年径流数据(1960-2008), 统计分析了山区融雪期开始时间、结束时间、天数、温度和降水的变化趋势及其空间差异性, 并定量评估了年径流量对融雪期温度和降水变化的敏感性。结果表明, 近50年来, 山区融雪期平均提前了15.33 天, 延迟了9.19 天;其中, 天山南部山区融雪期提前时间最长, 为20.01 天, 而延迟时间最短, 仅6.81 天;祁连山北部山区融雪期提前时间最短(10.16天), 而延迟时间最长(10.48 天)。这显示山区融雪期提前时间越长, 延迟时间则越短。山区融雪期平均降水量增加了47.3 mm, 平均温度升高了0.857℃;其中天山南部山区降水增量最大, 达65 mm, 昆仑山北部山区降水和温度增量均最小, 分别为25 mm和0.617℃, 而祁连山北部山区温度增量最高(1.05℃)。河流径流量对融雪期气候变化敏感, 降水变化诱发年径流量变化了7.69%, 温度变化使得年径流量改变了14.15%。
[16]	Yang H, Qi J, Xu X, et al.The regional variation in climate elasticity and climate contribution to runoff across China . Journal of Hydrology, 2014, 517: 607-616.https://doi.org/10.1016/j.jhydrol.2014.05.062 URL Magsci [本文引用: 1]摘要 The climate elasticity of runoff is an important indicator that is used to quantify the relationship between changes in runoff and changes in climate variables. It is a function of both climate and catchment characteristics. Recently, Yang and Yang (2011) proposed an analytical derivation of climate elasticity (YY2011), in which a parameter n was used to represent the impact of the catchment characteristics. In China, both climate and catchment characteristics have large spatial variations. To understand the spatial variation of hydrologic response to climate change, this paper divided China into 210 catchments, further calculated the parameter n, and then estimated the climate elasticity and evaluated the contribution of climate change to runoff for each catchment. The results show that n ranges from 0.4 to 3.8 (with a mean of 1.3 and a standard deviation of 0.6), which has a logarithmic relationship with catchment slope; the precipitation elasticity ranges from 1.1 to 4.8 (with a mean of 1.9 and a standard deviation of 0.6), which shows a large regional variation, smaller values (1.1-2.0) mainly appearing in Southern China, the Songhua River basin and the Northwest, and larger values (2.1-4.8) mainly appearing in the Hai River basin, the Liao River basin and the Yellow River basin. In addition, climate contribution to runoff exhibits a large regional variation, the largest positive values (1.1-3.1%/a) occurring in the Northwest, the largest negative values (-1.0 to -0.5%/a) occurring in the Hai River basin and the middle reach of the Yellow River basin. In theory, the YY2011 method is a first-order approximation. The approximation underestimates the precipitation (P) contribution to runoff when P increases and overestimates that when P decreases, and the relative error has a median of similar to 3% and a maximum of similar to 20% when 10% precipitations change in those catchments of China. (C) 2014 Elsevier B.V. All rights reserved.
[17]	Yang H, Yang D.Derivation of climate elasticity of runoff to assess the effects of climate change on annual runoff . Water Resources Research, 2011, 47(7): 197-203.https://doi.org/10.1029/2010WR009287 URL [本文引用: 2]摘要 Climate elasticity of runoff is an important indicator for evaluating the effects of climate change on runoff. Consequently, this paper proposes an analytical derivation of climate elasticity. Based on the mean annual water-energy balance equation, two dimensionless numbers (the elasticities of runoff to precipitation and potential evaporation) were derived. Combining the first-order differential of the Penman equation, the elasticities of runoff to precipitation, net radiation, air temperature, wind speed, and relative humidity were derived to separate the contributions of different climatic variables. The case study was carried out in the Futuo River catchment in the Hai River basin, as well as in 89 catchments of the Hai River and the Yellow River basins of China. Based on the mean annual of climatic variables, the climate elasticity in the Futuo River basin was estimated as follows: precipitation elasticity ?, net radiation elasticity ?, air temperature elasticity ?, wind speed elasticity ?, and relative humidity elasticity ?. In this catchment, precipitation decrease was mainly responsible for runoff decline, and wind speed decline had the second greatest effect on runoff. In the 89 catchments of the Hai River and the Yellow River basins of China, climate elasticity was estimated as follows: ? ranging from 1.6 to 3.9, ? ranging from -1.9 to -0.3, ? ranging from -0.11 to -0.02 C, ? ranging from -0.8 to -0.1, and ? ranging from 0.2 to 1.9. Additional analysis shows that climate elasticity was sensitive to catchment characteristics.
[18]	Liu Changming, Zhang Dan.Temporal and spatial change analysis of the sensitivity of potential evapotranspiration to meteorological influencing factors in China . Acta Geographica Sinica, 2011, 66(5): 579-588.Magsci [本文引用: 2]摘要潜在蒸散发是农田灌溉管理、作物需水量估算、稀缺资料地区水量平衡等研究中的重要参量，分析其对气象因子的敏感性有助于农业水资源优化配置和气候变化对水资源的影响研究。根据中国1960-2007 年的653 个气象台站的常规气象观测资料，采用优化太阳辐射计算的Penman-Monteith 潜在蒸散发计算方法，分析了中国10 大流域片区的潜在蒸散发对最高气温、最低气温、风速、太阳辐射、水汽压的敏感性及其区域分异。研究结果表明：(1) 采用优化后的Penman-Monteith 公式，计算的潜在蒸散发与蒸发皿蒸发量的复相关系数从0.61 提高到了0.75；计算得出的潜在蒸散发在8 个流域片区呈下降趋势，从流域尺度上揭示了“蒸发悖论”在中国的普遍存在。(2) 空间上，海河流域片区、黄河流域片区、淮河流域片区、长江流域片区、珠江流域片区、东南诸河的潜在蒸散发对最高气温最为敏感，松花江流域片区、辽河流域片区和西北诸河对水汽压最为敏感，西南诸河则对太阳辐射最为敏感。全国范围内，潜在蒸散发对气象因子的敏感性为：水汽压>最高气温>太阳辐射>风速>最低气温；且各敏感系数与海拔有一定的线性相关性。(3) 时间尺度上，潜在蒸散发对最高气温和太阳辐射最为敏感的月份是7 月，而对最低气温、风速和水汽压最为敏感的月份是1 月。1960-2007 年之间，潜在蒸散发对最高气温的敏感性呈下降趋势，而对最低气温、风速、太阳辐射和水汽压的敏感性呈上升趋势。 [刘昌明, 张丹. 中国地表潜在蒸散发敏感性的时空变化特征分析 . 地理学报, 2011, 66(5): 579-588.]Magsci [本文引用: 2]摘要潜在蒸散发是农田灌溉管理、作物需水量估算、稀缺资料地区水量平衡等研究中的重要参量，分析其对气象因子的敏感性有助于农业水资源优化配置和气候变化对水资源的影响研究。根据中国1960-2007 年的653 个气象台站的常规气象观测资料，采用优化太阳辐射计算的Penman-Monteith 潜在蒸散发计算方法，分析了中国10 大流域片区的潜在蒸散发对最高气温、最低气温、风速、太阳辐射、水汽压的敏感性及其区域分异。研究结果表明：(1) 采用优化后的Penman-Monteith 公式，计算的潜在蒸散发与蒸发皿蒸发量的复相关系数从0.61 提高到了0.75；计算得出的潜在蒸散发在8 个流域片区呈下降趋势，从流域尺度上揭示了“蒸发悖论”在中国的普遍存在。(2) 空间上，海河流域片区、黄河流域片区、淮河流域片区、长江流域片区、珠江流域片区、东南诸河的潜在蒸散发对最高气温最为敏感，松花江流域片区、辽河流域片区和西北诸河对水汽压最为敏感，西南诸河则对太阳辐射最为敏感。全国范围内，潜在蒸散发对气象因子的敏感性为：水汽压>最高气温>太阳辐射>风速>最低气温；且各敏感系数与海拔有一定的线性相关性。(3) 时间尺度上，潜在蒸散发对最高气温和太阳辐射最为敏感的月份是7 月，而对最低气温、风速和水汽压最为敏感的月份是1 月。1960-2007 年之间，潜在蒸散发对最高气温的敏感性呈下降趋势，而对最低气温、风速、太阳辐射和水汽压的敏感性呈上升趋势。
[19]	Zhu Guofeng, He Yuanqing, Pu Tao, et al.Spatial distribution and temporal trends in potential evaporation over Hengduan Mountains Region from 1960 to 2009 . Acta Geographica Sinica, 2011, 66(7): 905-916.Magsci [本文引用: 2]摘要以横断山区20 个气象站1960-2009 年逐日气象数据为基础,应用1998 年FAO 修正的Penman-Monteith 模型分析了横断山区潜在蒸发量的变化,在ArcGIS 环境下通过样条插值法分析了潜在蒸发量变化的时空分异,并对影响潜在蒸发量变化的气象因素进行了讨论,结果表明：年潜在蒸发量自20 世纪60 年代中期以来呈波动减小趋势,20 世纪80 年代中期之后减小趋势更加明显,2000-2009 年呈增加趋势。潜在蒸发量的年际变化倾向率为-0.17 mm a<sup>-1</sup>,从空间分布来看,北部、中部、南部都呈减少趋势,倾向率由北向南逐渐减小。从季节来看,秋季和冬季潜在蒸发量呈增加趋势,春季和夏季呈减小趋势,春季减小趋势大于夏季,秋季增加趋势大于冬季。气温上升、风速和日照时数的降低是横断山区潜在蒸发量减少的主导因素,风速和日照时数的下降导致春季和夏季潜在蒸发量减小,气温上升导致秋季和冬季潜在蒸发量增加。 [朱国锋, 何元庆, 蒲焘, 等. 1960-2009年横断山区潜在蒸发量时空变化 . 地理学报, 2011, 66(7): 905-916.]Magsci [本文引用: 2]摘要以横断山区20 个气象站1960-2009 年逐日气象数据为基础,应用1998 年FAO 修正的Penman-Monteith 模型分析了横断山区潜在蒸发量的变化,在ArcGIS 环境下通过样条插值法分析了潜在蒸发量变化的时空分异,并对影响潜在蒸发量变化的气象因素进行了讨论,结果表明：年潜在蒸发量自20 世纪60 年代中期以来呈波动减小趋势,20 世纪80 年代中期之后减小趋势更加明显,2000-2009 年呈增加趋势。潜在蒸发量的年际变化倾向率为-0.17 mm a<sup>-1</sup>,从空间分布来看,北部、中部、南部都呈减少趋势,倾向率由北向南逐渐减小。从季节来看,秋季和冬季潜在蒸发量呈增加趋势,春季和夏季呈减小趋势,春季减小趋势大于夏季,秋季增加趋势大于冬季。气温上升、风速和日照时数的降低是横断山区潜在蒸发量减少的主导因素,风速和日照时数的下降导致春季和夏季潜在蒸发量减小,气温上升导致秋季和冬季潜在蒸发量增加。
[20]	Budyko M I.Climate and Life . San Diego, CA: Academic, 1974. [本文引用: 1]
[21]	Yang H, Yang D, Lei Z, et al.New analytical derivation of the mean annual water-energy balance equation . Water Resources Research, 2008, 44(3).https://doi.org/10.1029/2007WR006135 URL [本文引用: 2]摘要 [1] The coupled water-energy balance on long-term time and catchment scales can be expressed as a set of partial differential equations, and these are proven to have a general solution as E / P = F ( E 0 / P , c ), where c is a parameter. The state-space of ( P , E 0 , E ) is a set of curved faces in P 61 E 0 61 E three-dimensional space, whose projection into E / P 61 E 0 / P two-dimensional space is a Budyko-type curve. The analytical solution to the partial differential equations has been obtained as E = E 0 P /( P n + E 0 n ) 1/ n (parameter n representing catchment characteristics) using dimensional analysis and mathematic reasoning, which is different from that found in a previous study. This analytical solution is a useful theoretical tool to evaluate the effect of climate and land use changes on the hydrologic cycle. Mathematical comparisons between the two analytical equations showed that they were approximately equivalent, and their parameters had a perfectly significant linear correlation relationship, while the small difference may be a result of the assumption about derivatives in the previous study.
[22]	Roderick M L, Rotstayn L D, Farquhar G D, et al.On the attribution of changing pan evaporation . Geophysical Research Letters, 2007, 34(17): 251-270.https://doi.org/10.1029/2007GL031166 URL [本文引用: 1]摘要 Evaporative demand, measured by pan evaporation, has declined in many regions over the last several decades. It is important to understand why. Here we use a generic physical model based on mass and energy balances to attribute pan evaporation changes to changes in radiation, temperature, humidity and wind speed. We tested the approach at 41 Australian sites for the period 1975-2004. Changes in temperature and humidity regimes were generally too small to impact pan evaporation rates. The observed decreases in pan evaporation were mostly due to decreasing wind speed with some regional contributions from decreasing solar irradiance. Decreasing wind speeds of similar magnitude has been reported in the United States, China, the Tibetan Plateau and elsewhere. The pan evaporation record is invaluable in unraveling the aerodynamic and radiative drivers of the hydrologic cycle, and the attribution approach described here can be used for that purpose.
[23]	Allen R G, Pereira L S, Raes D, et al.Crop evapotranspiration: Guidelines for computing crop water requirements-[2] FAO Irrigation and Drainage Paper 56. FAO, Rome, 1998, 300(9): D05109.URL [本文引用: 1]摘要 Page 1. AB.W 1 Crop evapotranspiration - Guidelines for computing crop water requirements -FAO Irrigation and drainage paper 56 By Richard G. Allen Utah State University Logan, Utah,USA Luis S. Pereira Instituto Superior de Agronomia Lisbon, Portugal
[24]	Tan X, Gan T Y.Contribution of human and climate change impacts to changes in streamflow of Canada . Scientific Reports, 2015, 5.https://doi.org/10.1038/srep17767 URL [本文引用: 2]摘要 Climate change exerts great influence on streamflow by changing precipitation, temperature, snowpack and potential evapotranspiration (PET), while human activities in a watershed can directly alter the runoff production and indirectly through affecting climatic variables.
[25]	Chebana F, Ouarda T B M J, Duong T C. Testing for multivariate trends in hydrologic frequency analysis . Journal of Hydrology, 2013, 486: 519-530.https://doi.org/10.1016/j.jhydrol.2013.01.007 URL [本文引用: 1]摘要 Hydrological frequency analysis (HFA) relies on a number of assumptions on the data series, especially independence, homogeneity and stationarity. In the univariate setting, these assumptions are generally checked before the modeling step. During the last decade, multivariate HFA approaches have gained popularity since most hydrological events can be described through a number of dependent characteristics, e.g. peak, volume and duration for floods. However, checking the above assumptions remains neglected in the multivariate HFA literature whereas the focus is directly on the modeling. For a reliable analysis and accurate results, these assumptions should also be checked prior to modeling in the multivariate setting. The present paper attempts to start bridging this gap in the multivariate HFA by highlighting the importance of the testing step and focusing on the review and application of nonparametric tests for monotonic trends. The presented multivariate trend tests are usually developed and employed to treat water quality data. In the present work, two types of multivariate applications are performed, multi-variable for flood attributes and multi-site for different locations. The results indicate that, in both types of applications, the univariate and multivariate tests led to the detection of different trend signals. It is hence recommended to jointly apply univariate and multivariate trend tests in order to capture all existing trend components and guide the user towards the appropriate models.
[26]	Zhang Q, Qi T, Li J, et al.Spatiotemporal variations of pan evaporation in China during 1960-2005: Changing patterns and causes . International Journal of Climatology, 2015, 35(6): 903-912.https://doi.org/10.1002/joc.4025 URL [本文引用: 1]摘要 Not Available
[27]	Killick P.Changepoint: An R package for changepoint analysis . Journal of Statistical Software, 2014, 58(3): 1-19. [本文引用: 1]
[28]	Villarini G, Serinaldi F, Smith J A, et al.On the stationarity of annual flood peaks in the continental United States during the 20th century . Water Resources Research, 2009, 45(8): W08417.https://doi.org/10.1029/2008WR007645 URL [本文引用: 1]摘要 Annual peak discharge records from 50 stations in the continental United States with at least 100 years of record are used to investigate stationarity of flood peaks during the 20th century. We examine temporal trends in flood peaks and abrupt changes in the mean and/or variance of flood peak distributions. Change point analysis for detecting abrupt changes in flood distributions is performed u...
[29]	Liu Jianyu, Zhang Qiang, Gu Xihui.Evaluation of ecological flow with considerations of hydrological alterations in the Poyang Lake basin . Acta Ecologica Sinica, 2015, 35(16): 5477-5485.https://doi.org/10.5846/stxb201404080664 URL Magsci [本文引用: 1]摘要受气候变化和人类活动综合影响,鄱阳湖流域水文状况发生变异。河流生态系统适应了变异前的水文状况,变异后势必会影响当地生态系统。基于此,采用8种变异检测方法对水文变异进行综合诊断,阐明水文变异原因。在此基础上,采用15种概率分布函数分别拟合5站各月变异前日流量序列,最终确定5站点各月最优分布函数及所对应的概率密度最大处的流量,即得河道内生态流量。研究表明:(1)抚河于1962年发生弱变异,赣江、修河于1968年发生中变异,信江、饶河于1991年发生弱变异;(2)变异后,赣江、信江、饶河、修河生态需水满足率平均上升11%,抚河生态需水满足率下降32%;(3)水文变异增加提高生态需水满足率,水利工程建设降低年均生态需水满足率、提高干季生态需水满足率。高森林覆盖率提高干季生态需水满足率,对年均生态需水满足率影响不明显。研究结果为鄱阳湖流域水资源管理及区域水资源规划与配置提供重要科学依据。 [刘剑宇, 张强, 顾西辉. 水文变异条件下鄱阳湖流域的生态流量 . 生态学报, 2015, 35(16): 5477-5485.]https://doi.org/10.5846/stxb201404080664 URL Magsci [本文引用: 1]摘要受气候变化和人类活动综合影响,鄱阳湖流域水文状况发生变异。河流生态系统适应了变异前的水文状况,变异后势必会影响当地生态系统。基于此,采用8种变异检测方法对水文变异进行综合诊断,阐明水文变异原因。在此基础上,采用15种概率分布函数分别拟合5站各月变异前日流量序列,最终确定5站点各月最优分布函数及所对应的概率密度最大处的流量,即得河道内生态流量。研究表明:(1)抚河于1962年发生弱变异,赣江、修河于1968年发生中变异,信江、饶河于1991年发生弱变异;(2)变异后,赣江、信江、饶河、修河生态需水满足率平均上升11%,抚河生态需水满足率下降32%;(3)水文变异增加提高生态需水满足率,水利工程建设降低年均生态需水满足率、提高干季生态需水满足率。高森林覆盖率提高干季生态需水满足率,对年均生态需水满足率影响不明显。研究结果为鄱阳湖流域水资源管理及区域水资源规划与配置提供重要科学依据。
[30]	Poff N L R, Olden J D, Merritt D M, et al. Homogenization of regional river dynamics by dams and global biodiversity implications . Proceedings of the National Academy of Sciences, 2007, 104(14): 5732-5737. [本文引用: 1]
[31]	Zhang Jianyun, Zhang Silong, Wang Jinxing, et al.Study on runoff trends of the six larger basins in China over the past 50 years . Advances in Water Science, 2007(2): 230-234.URL Magsci [本文引用: 1]摘要应用1950年以来的中国六大流域19个重点控制水文站年径流观测资料,采用MK检验方法研究了中国六大江河的年径流量变化情况。结果表明,近50年来中国六大江河的实测径流量均呈下降趋势。其中海河、黄河、辽河、松花江实测径流量下降明显,严重影响了我国社会经济的发展。 [张建云, 章四龙, 王金星, 等. 近50年来中国六大流域年际径流变化趋势研究 . 水科学进展, 2007(2): 230-234.]URL Magsci [本文引用: 1]摘要应用1950年以来的中国六大流域19个重点控制水文站年径流观测资料,采用MK检验方法研究了中国六大江河的年径流量变化情况。结果表明,近50年来中国六大江河的实测径流量均呈下降趋势。其中海河、黄河、辽河、松花江实测径流量下降明显,严重影响了我国社会经济的发展。

气候变化和人类活动对中国地表水文过程影响定量研究

本站小编 Free考研考试/2021-12-29

Quantitative evaluations of human- and climate-inducedimpacts on hydrological processes of China

1 引言

2 研究区域和数据