刘洁
, 许振成
LIU Jie, XU Zhen-cheng
中图分类号: P339
文献标识码: A
文章编号: 1000-0690(2015)04-0483-08
通讯作者:
收稿日期: 2014-02-21
修回日期: 2014-05-8
网络出版日期: 2015-04-20
版权声明: 2015 《地理科学》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:
作者简介:
作者简介：刘 洁（1980-）,女,江西南昌人,高级工程师,研究方向为流域环境规划与环境管理。E-mail: shuiji_97@163.com
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摘要
降雨变化对流域水文过程的影响是地理与环境科学领域关注的热点。应用流域水文模型HSPF建立了东江流域的径流模拟模型,并结合日降雨随机模拟模型,分析了降雨的长期变化对流域径流的影响。结果表明,降雨对东江流域的径流影响显著,径流量随降雨量均值与降雨量变差系数的增减而增减;相同的降雨量均值变化幅度条件下,径流增减的变化幅度相近;降雨量变差系数增加对径流的影响大于降雨量变差系数减小的影响,降雨强度的变化是影响径流量的重要因素,降雨量变化剧烈时,产生的径流更多;在同等的变化幅度内,降雨量均值变化对径流量的影响大于降雨量变差系数;由降雨情景变化引起的月径流的变化在6~8月最为明显。
关键词：
Abstract
The impact of rainfall variation on watershed hydrological process is an issue of great deliberation in geographical and environmental sciences. In this article, a comprehensive watershed model—The hydrological simulation program FORTRAN(HSPF) was set up and calibrated for the Dongjiang River basin in China, and was repeatedly run with various rainfall scenarios generated by the daily rainfall stochastic simulation model. The results indicated that: (1) The runoff changed in the same trend either with rise and fall in mean value or variation coefficient of rainfall; (2) In the four given scenarios (mean value of rainfall has increased by 20% and 40%, decreased by 20% and 40%, respectively), change magnitude of runoff was similar under same change magnitude of mean value of rainfall; (3) The average annual runoff raised by 24.93% when the variation coefficient of rainfall raised by 40%, and raised by 10.84% when the variation coefficient of rainfall raised by 20%. However, it fell by 3.84% and 5.35% respectively when the variation coefficient of rainfall fell by 20% and 40%, which presented that the increase of variation coefficient of rainfall has a greater impact on runoff than the decrease of variation coefficient of rainfall does. The intensity of rainfall change is an important factor affecting the runoff, and heavy rainfall has a great effect on the runoff; (4) Change of runoff due to fluctuation of variation coefficient of rainfall gradually decreased, when variation coefficient of rainfall gradually declined; (5) Average annual runoff raised by 33.99% as mean value of rainfall raised by 20%, and raised by 10.84% as variation coefficient of rainfall raised by 20%, which means change of mean value of rainfall has bigger impact on runoff change than variation coefficient of rainfall; (6) Variation of monthly runoff caused by different rainfall scenarios is more obvious in June, July and August than other months.
Keywords：
气候变化与土地利用变化对流域水文过程的影响是当前地理与环境科学领域关注的热点。在大中尺度流域,降雨对径流过程影响显著[1~5],一方面降雨是流域水资源的最直接来源,降雨过程直接影响水资源的产生和汇聚;另一方面降雨作为水文模型的重要输入之一,必将影响到流域的水资源量计算。气候变化的水文响应研究一般采用两种方式,一种是采用气候模式,另一种则是直接设定不同的气候情景,输入到水文模型中进行模拟。气候模式与水文模型的空间尺度存在着差异性,涉及到气候模式的气象参数预测结果的空间分辨率的降尺度问题;假定不同的气候变化情景,作为水文模型的输入,是近年来相关研究中较常用到的方法,但设定气候变化情景的随意性较大,未结合流域的长期气候特征研究,并且,气候变化情景下气象参数如何降时间尺度,以符合水文模型对气象输入数据的时间分辨率的要求,也是需要解决的问题之一。
因此本文在参考其它研究成果[6~12]的基础上,采用了基于降雨随机模拟模型的情景分析方法。利用国外较成熟的半分布式水文模型HSPF建立了东江流域的产汇流模型,与日降雨随机模拟模型耦合,研究流域径流对降雨变化的响应。HSPF模型在中国的水资源与环境研究领域的应用成果还不多,本文以位于南方湿润地区的中尺度流域—东江流域为例,对该模型在中国南方地区应用的适用性进行了进一步的探讨;同时,基于流域降雨长期变化特征研究的流域日降雨随机模拟模型可生成不同降雨情景下符合流域特征的日降雨数据,与HSPF水文模型耦合,所采用的研究方法可为建立气候变化对中尺度流域水资源长期影响的模拟分析方法提供参考。研究结果可为中国南方湿润地区水资源对气候变化的响应研究以及东江流域的水资源规划与管理提供支持。
东江是珠江流域的主要河流之一,发源于江西省寻乌县桠髻钵,上游称寻乌水,南流入广东境内,至龙川合河坝安远水后称东江。东江干流由东北向西南流,河道长度至石龙为520 km,至狮子洋为562 km,石龙以上流域总面积27 040 km2。
采用的地形数据是由国家基础地理信息中心提供的1:25万的海拔等值线矢量数据栅格化后形成的东江流域地形栅格数据;土壤数据根据1:25万广东土壤类型图对东江流域部分进行矢量化;土地利用/覆被数据来自于两期 Landsat MSS/TM/ETM+ 遥感影像经过校准和解译完成的资料,成像时间分别为 1980 年代初期（1982年、1983年与1984年）和 2000年（1999年、2000年与2001年）;气象数据采用1980~2010年降雨、最高最低气温等气象因子日值数据,来源于中国气象数据共享服务网提供的中国地面气候资料日值数据集（位于东江流域及附近的寻乌、连平、东源、惠阳与深圳5个气象站点的降雨与气温数据）和中国地面降水日值0.5°×0.5°格点数据集;博罗水文站径流量数据（1980~2005年）来自广东省水文局。
2.1.1 HSPF模型简介
HSPF模型是由美国环境保护署开发的水文模型,用于较大流域范围内自然和人工条件下的水系中水文水质过程的连续模拟[13~22],可模拟研究气候和土地利用的长期变化对流域水文水质时空变异的影响,适用于空间差异较大的流域的研究,至今已发展到HSPF12.0,并内嵌于美国环保署开发的基于GIS技术的整合式平台BASINS系统中。
2.1.2 数据预处理
1） 气象数据的预处理。
降雨的空间分辨率的变化对径流模拟结果有很重要的影响[13,23~27]。为了降低气象数据的空间分辨率对东江流域水文模型建模的影响,采用中国地面降水日值0.5°×0.5°格点数据集中研究区域的降雨格点数据,作为气象观测站点数据的补充。利用BASIN4.0系统中WDMUtil工具编辑生成模型运行必需的东江流域气象数据的时间序列文件（WDM文件）。
2） 下垫面数据的预处理。
将东江流域土地利用类型划分为7种：耕地、园地、林地、草地、建设用地、水域与未利用地。为了充分考虑流域的空间异质性,利用ArcGIS工具将土地利用数据与土壤分布、坡度数据进行叠置得到新的下垫面类型数据。下垫面类型对应HSPF模型中的水文过程率定参数。
3） 子流域和水文响应单元的划分。
HSPF的空间异质性主要考虑每个离散单元的降雨、土壤类型、土地利用类型等。土壤类型、土地利用类型、坡度的空间异质性通过叠加下垫面类型数据体现,气象数据的空间异质性则通过流域再分割来实现。在子流域划分的基础上,根据气象数据的空间分布,进行水文响应单元的分割处理。东江流域水文响应单元的气象站点分配根据泰森多边形方法进行分割。
2.1.3 模型模拟效果评估的基本方法
采用标准偏差法（Dv）[28]与NASH-Sutcliff相关系数法（ENS）[29]评估模型的模拟效果。理想情况下,模型中的ENS接近于1,Dv接近于0。根据Donigian[30,31]的标准,认为Dv在观测值的10%范围内的模拟已经达到了“非常好”的标准。ENS值在0~1之间,并认为0.9以上为甲等,0.7~0.9为乙等,0.5~0.7为丙等[31]。
式中：
2.1.4 模型率定的基本过程
使用1980~2005年东江流域博罗水文站月平均天然径流量资料进行径流的率定与校验,其中1980~1983年数据用于模型预热,1984~1992年数据用于模型率定,1993~2005年数据用于模型验证。与之对应,1980~1992年的径流模拟过程采用1980年~1992年的气象数据、1980年下垫面数据作为模型的输入数据;1993~2005年的径流模拟过程采用1993~2005年的气象数据、2000年下垫面数据作为模型的输入数据。采用以下基本步骤进行水文过程模拟的参数调整[32]：① 年度总径流量偏差纠正;② 季节总径流量偏差纠正;③ 高流量与低流量部分偏差纠正;④ 流量曲线形状调整。
2.2.1 流域降雨现状变化特征分析
在时间序列趋势分析中,Mann-Kendall检验法是被世界气象组织推荐并被广泛使用的非参数检验方法,该方法不需要样本遵从一定的分布,也不受少数异常值的干扰,适用于水文、气象等非正态分布的时间序列数据的分析。Mann-Kendall检验法的统计分析步骤详见文献[33] [35]。
2.2.2 建立日降雨模拟模型采用的数学方法
1） 随机理论
年降雨序列可看作是天气转化情况和降雨量两者随机遭遇组合而成,经过统计试验法随机选择天气转化情况和降雨量,并将两者的遭遇随机组合在一起,模拟出年降雨序列[6,36]。
2） Pearson-Ⅲ型分布
Pearson-Ⅲ型分布曲线是Pearson曲线族中的一种,较常应用于气象水文频率计算。分布函数参数包括Cv,Cs,
3） 马尔科夫链
马尔科夫概型分析是以时间序列内部概率分布结构为出发点,应用多元时间序列分析和马尔科夫过程的理论从实测时间序列中总结出随机过程的概率规律[37]。
2.2.3 东江流域日降雨模拟模型的建立过程
东江流域内降雨年内变化特征较一致,参考谢平等[6]的研究方法,用泰森多边形面积权重法计算得到的面平均雨量作为研究对象。采用东江流域1986~2010年间的降雨数据进行现状条件下降雨的模拟。
1） 计算有雨转移概率
以每月降雨数据作为统计样本,将1986~2010年多年的降雨系列分为12个样本进行研究。根据马尔科夫链理论,某天的降雨情况只与前一天的降雨情况有关,将每个降雨样本又分为晴天到雨天、雨天到雨天样本,分别计算晴天到雨天、雨天到雨天的转移概率[6]：
上式中,P01表示第i-1天晴天第i天雨天的概率,P11表示第i-1天雨天第i天雨天的概率,D01表示晴天转雨天的天数,D11表示雨天转雨天的天数,D0表示晴天总数,D1表示雨天总数。
2） 生成天气状况
利用一阶马尔科夫有雨转移概率和随机变量的概率分布来模拟生成未来的天气情况。假定降雨概率满足0-1随机均匀分布函数,将随机生成的随机数p与该月的有雨转移概率比较,生成天气状况,重复生成p,依次进行后一天的模拟[6]。
3） 日降雨量的随机模拟
根据随机生成的天气情况,若为晴天,则日降雨量为0,否则为雨天,随机生成一个随机数p,作为降雨量发生的概率。雨日降雨量样本序列符合Pearson-Ⅲ型分布,根据每月雨日降雨量样本序列计算日降雨量分布函数参数（均值
利用日降雨模拟模型生成降雨变化情景下的降雨日值数据序列,建立HSPF模型运行必需的气象数据的WDM时间序列文件。以2000年东江流域下垫面数据、2001~2010年气象数据为基准数据,在降雨变化情景模拟的基础上,应用构建的东江流域HSPF水文模型模拟径流过程,分析降雨变化对流域径流的影响。
月径流过程在模型校正期和验证期都取得了较好的模拟精度（表1,图1~图3）。在径流校正和验证中Dv分别为0.53%和5%,小于10%;ENS分别为0.93和0.89（大于0.7）。
表1 博罗站径流模拟结果
Table 1 The simulation results of runoff at Boluo hydrologic station
| 时段 | 年均径流量观测值(104m3) | 年均径流量模拟值(104m3) | Dv(%) | ENS |
|---|---|---|---|---|
| 校正期(1984~1992年） | 2.227E+06 | 2.239E+06 | 0.527 | 0.926 |
| 验证期(1993~2005年） | 2.368E+06 | 2.250E+06 | 4.996 | 0.894 |
图1 1980~1992年博罗水文站径流模拟校准结果（a）与相关分析（b）
Fig.1 Runoff simulation calibration results (a) and correlation analysis(b) at Boluo hydrologic station in 1980-1992
图2 1993~2005年博罗水文站径流模拟验证结果（a）与相关分析（b）
Fig. 2 Runoff simulation validation results (a) and correlation analysis (b) at Boluo hydrologic station in 1993-2005
图3 博罗水文站1984~1992年（a）与1993~2005年（b）多年平均月径流过程
Fig.3 Average monthly runoff process in 1984-1992(a) and in 1993-2005(b)at Boluo hydrologic station
1980~2010年东江流域寻乌、连平、东源、深圳这4个气象站点的年降雨量呈下降趋势,只有惠阳站呈略上升趋势（表2）。显著性检验结果表明：除东源站数据通过了90%的显著性检验外,其它4个站点的年降雨量变化趋势不明显。利用Mann—Kendall突变检验分析东江流域年降雨量的变化特征,结果显示：寻乌、东源和深圳站年降雨量变化在1984年左右可能存在突变。寻乌站突变点在1983年和1983年开始寻乌站降水量有下降趋势;东源站突变点在1985年,从1985年开始东源降水量有减少趋势;深圳站突变点也在1983年,从1983年开始深圳降水量出现减少趋势,从1994年开始降雨量有小幅上升趋势。使用Mann-Kendall检验方法进一步统计分析东江流域月降雨量的年际变化特征,结果表明,总体上东江流域5个站点的月降水量的年际变化特征较为一致。
表2 东江流域年降雨量的Mann-Kendall检验值
Table 2 Mann-Kendall test value of annual and seasonal rainfall at representative stations in the Dongjiang River Basin
| 时间 | 寻 乌 | 连平 | 东源 | 惠阳 | 深圳 |
|---|---|---|---|---|---|
| 全年 | -0.442 | -0.714 | -1.428* | 0.204 | -0.068 |
| 春 | -2.176** | -1.632* | -2.55*** | -1.428* | -0.85 |
| 夏 | 2.108** | 2.312** | 0.799 | 1.564* | 0.748 |
| 秋 | -1.326* | -0.408 | -0.969 | 0.136 | -0.204 |
| 冬 | -0.272 | -0.476 | -0.238 | -0.102 | -0.136 |
在降雨现状特征分析的基础上建立东江流域的日降雨随机模拟模型。东江流域1986~2010年有雨转移概率计算结果见表3;日降雨随机模拟结果如表4所示,模拟结果显示各月降雨天数与实测资料最大相差2 d,全年相差13 d,各月模拟值与实测值月均降雨量相对误差都在15%以内,相对误差的绝对值最小0.33%,最大14.1%。降雨模拟序列与实测序列较吻合,模拟结果具有良好的精度,基于马尔科夫链与Pearson-Ⅲ型分布的日降雨随机模拟模型适合应用在东江流域的降雨变化的情景模拟及其所带来的影响的分析中。
表3 东江流域1986~2010年有雨转移概率计算结果
Table 3 Monthly probability of rainy day transition in the Dongjiang River Basin in 1986-2010
| 月份 | 晴天到雨天 | 雨天到雨天 |
|---|---|---|
| 1 | 0.2034 | 0.6711 |
| 2 | 0.3100 | 0.7427 |
| 3 | 0.3859 | 0.8333 |
| 4 | 0.4400 | 0.8400 |
| 5 | 0.4489 | 0.8664 |
| 6 | 0.5905 | 0.9116 |
| 7 | 0.4578 | 0.8736 |
| 8 | 0.6028 | 0.8596 |
| 9 | 0.3651 | 0.8112 |
| 10 | 0.1572 | 0.6579 |
| 11 | 0.1515 | 0.6577 |
| 12 | 0.1387 | 0.6545 |
表4 日降雨随机模拟结果（1986~2010年多年平均）
Table 4 Results of daily rainfall stochastic simulation of every month (average of years 1986-2010)
| 月份 | 实测月均降雨天数 (d) | 模拟月均降雨天数 (d) | 相差天数 (d) | 实测月均降雨量 (mm) | 模拟月均降雨量 (mm) | 绝对差值 (mm) | 相对误差 (%) |
|---|---|---|---|---|---|---|---|
| 1 | 11 | 12 | 1 | 48.23 | 46.02 | -2.20 | -4.57 |
| 2 | 15 | 14 | -1 | 76.28 | 73.91 | -2.37 | -3.11 |
| 3 | 21 | 20 | -1 | 139.01 | 119.41 | -19.60 | -14.10 |
| 4 | 22 | 20 | -2 | 209.12 | 181.49 | -27.63 | -13.21 |
| 5 | 23 | 22 | -1 | 246.21 | 236.07 | -10.14 | -4.12 |
| 6 | 26 | 24 | -2 | 344.98 | 343.76 | -1.22 | -0.35 |
| 7 | 24 | 23 | -1 | 221.93 | 222.67 | 0.74 | 0.33 |
| 8 | 25 | 24 | -1 | 229.39 | 217.55 | -11.84 | -5.16 |
| 9 | 19 | 18 | -1 | 147.19 | 141.03 | -6.15 | -4.18 |
| 10 | 10 | 8 | -2 | 39.18 | 40.98 | 1.80 | 4.59 |
| 11 | 9 | 7 | -2 | 32.90 | 36.96 | 4.05 | 12.32 |
| 12 | 8 | 8 | 0 | 39.84 | 38.44 | -1.41 | -3.53 |
| 合计 | 213 | 200 | -13 | 1774.28 | 1698.3 | -75.97 | -4.28 |
3.3.1 对年径流的影响
各降雨情景下,年径流的变化情况如表5和表6所示。情景一：降雨量均值
表5 降雨均值变化情景下的年径流模拟结果
Table 5 Simulated rate of annual runoff variation under different mean value of rainfall
| 情景一 | 情景二 | 情景三 | 情景四 | |
|---|---|---|---|---|
| 年均径流量 变化率(%) | 68.90 | 33.99 | -32.60 | -63.02 |
表6 降雨量Cv变化情景下的年径流模拟结果
Table 6 Simulated rate of annual runoff variation under different variation coefficient of rainfall
| 情景五 | 情景六 | 情景七 | 情景八 | |
|---|---|---|---|---|
| 年均径流量 变化率(%) | 24.93 | 10.84 | -3.84 | -5.35 |
基于日降雨量随机模拟模型和东江流域HSPF水文模型的模拟结果显示在其它条件不变时,当降雨量均值增加20%时,年均径流总量增加33.99%,当降雨量均值减小20%时,年均径流总量减少32.60%,降雨对径流的影响显著;在其它条件不变时,年径流量随着降雨均值的增加而增加,随降雨均值的减小而减小;在降雨量均值20%的变化范围内,东江流域年径流量增减的幅度相近,降雨量均值增加40%所带来的径流的增加幅度稍大于降雨量均值减小40%所带来的径流的减小幅度。
降雨量变差系数的情景模拟结果表明,年径流量随降雨量变差系数值的增加而增加,随降雨量变差系数值的减小而减小;降雨量变差系数值表明降雨量的变化程度,模拟结果表明,降雨量变差系数值增加对年径流的影响大于变差系数减小的影响;随着降雨量变差系数值的减小,年均径流量的变化幅度逐渐减小;基于日降雨量随机模拟模型和东江流域HSPF水文模型的模拟结果显示在其它条件不变时,当降雨量变差系数值增加20%时,年均径流总量增加10.84%,当降雨量变差系数值减小20%时,年均径流总量减少3.84%,当降雨量变化剧烈时,产生的径流更多。
3.3.2 对月径流的影响
各降雨情景下,月径流的变化情况如图4和图5所示。月径流量随降雨量均值的增加而增加,随降雨量均值的减小而减少;随降雨量变差系数的增加而增加,随降雨量变差系数的减小而减少;降雨均值变化对月径流的影响大于降雨量变差系数的影响。
图4 降雨均值变化情景下的多年平均月径流模拟结果（博罗）
Fig.4 Simulated average monthly runoff under different mean value of rainfall at Boluo hydrologic station
图5 降雨Cv变化情景下的多年平均月径流模拟结果（博罗）
Fig.5 Simulated average monthly runoff under different variation coefficient of rainfall at Boluo hydrologic station
与年径流的变化情况相似,当降雨均值增大与减小的幅度一样时,月径流的变化幅度相近;降雨量变差系数增大对月径流量的影响大于降雨量变差系数减小的影响,仍然体现了降雨量的不均匀对径流量的影响,降雨量的不均匀变化程度的加大会造成径流量的增加,体现了大暴雨对径流量的影响。
从径流量的月分布变化来看,降雨量均值与降雨量变差系数的变化对月径流的影响在丰水期明显大于枯水期,由降雨情景变化引起的月径流的变化在6~8月最为明显。
从东江流域不同降雨情景的年径流量和月径流量的变化分析可知,降雨对东江流域的径流影响显著。径流量随降雨量均值的增减而增减,随降雨量变差系数的增减而增减;在设定的变化情景下,相同的降雨量均值变化幅度条件下,径流的增减量的变化幅度相近,降雨量变差系数增加对径流的影响大于降雨量变差系数减小的影响,说明降雨强度的变化也是影响径流量的重要因素,大暴雨对径流量的影响较大;在同等的变化幅度内,降雨量均值变化对径流量的影响大于降雨量变差系数;随着降雨量变差系数的逐渐减小,所带来的径流量的变化幅度逐渐减小;由降雨情景变化引起的月径流的变化在6~8月最为明显。
The authors have declared that no competing interests exist.
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SummaryQuantifying relationships between stream water quality and catchment land uses is a major goal of many water quality monitoring programs. This is a challenging task that is rarely achieved through simple analysis of raw data alone. Multiple regression analysis provides one approach, which despite significant limitations, can be successful when very large data sets are available and only annual estimates are required. However, regression techniques have limited application to sub-annual data sets. We present a new method for isolating the water quality responses of different land uses from monitoring data through hydrological model calibration, using a process of simultaneous calibration at several monitoring sites. In addition to model parameters, model algorithm complexity and the number of land-attribute groups are also used as calibration 'parameters'. This helps increase model parameter uniqueness and model predictive certainty. We applied the technique to water quality data from the Johnstone River catchment (1602 km ) in north-east Australia, using the HSPF model. The data comprised >4000 samples from over five years of monitoring at 16 sites, which drained sub-catchments of differing land area and proportions of each land use. Monitoring occurred at flow gauging sites during high stream flows, and regularly at all sites during non-event periods. Variables modelled included discharge, suspended sediment, and various forms of nitrogen and phosphorus. The calibration process aimed to maximise both goodness-of-fit and parameter sensitivity. We achieved a substantial simplification of HSPF algorithms without appreciable reduction in goodness-of-fit, by a combination of: fixing parameters, tying parameters, and introducing new, simpler equations. Two key calibration tools were reducing the number of land-use groups (by combining land uses) and tying parameters between the three flow paths modelled (surface flow, interflow and base flow). These in turn removed insensitive parameters, increased the sensitivity of remaining parameters, and increased model predictive power. For calibration of suspended sediment, we found our conceptual model was too complex for the information content of the data, so it was necessary to group certain land uses and simplify algorithms in HSPF. In contrast, for nitrate, our conceptual model was too simple, with successful calibration requiring a finer resolution of land use in the catchment. A major advantage of our technique is that it can isolate land-use effects from stream monitoring data on a modelling time step that is unachievable by regression analysis, with a sensitivity that enables detection of relatively small differences in time and/or space. The technique also enhances confidence in estimates, it identifies 'sensitive' and 'insensitive' parameters and processes, and it provides a robust calibration by reducing the parameter solution space. Further, by using a hydrological model the technique can account for spatial and temporal variation in land use and rainfall. It is also predictive, so it can be used to assess impacts of land-use-change scenarios within the catchment. Additionally, it can provide insights into the type and amount of data required to isolate land-use effects on different water quality parameters, which can be used to enhance the effectiveness and cost-efficiency of the monitoring program design.
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A systematic, seven-step approach to integrated watershed management for sustainability was proposed and applied to upstream watershed of the Anyangcheon in Korea, which experiences streamflow depletion, frequent flood damages, and poor water quality due to rapid urbanization. To understand watershed components and processes, static and dynamic data were collected and synthetic hydrologic cycles generated by HSPF (Hydrologic Simulation Program – FORTRAN) were simulated (STEP 1). To identify and quantify problems within the watershed, three indices (following the pressure–state–response model) were employed: Potential Flood Damage (PFD), Potential Streamflow Depletion (PSD), and Potential Water Quality Deterioration (PWQD). Composite programming, a method of multi-criteria decision-making, was employed to estimate all indices and analytic hierarchy process are introduced to quantify the weighting values of all indicators (STEP 2). The primary goal of managers is to maintain certain minimum levels of water for instreamflow requirement and total maximum daily load (TMDL). Therefore target water quality and, instreamflow requirements (including low flow and fish flow) were specifically set (STEP 3). All possible management alternatives were listed (STEP 4) and a few specific management options which are technically, economically, and environmentally feasible, were selected (STEP 5). The ability of each feasible option to achieve the desired water quantity and quality criteria was analyzed and quantified using the HSPF (STEP 6). Finally, an evaluation index was calculated using each of the proposed alternatives in order to rank the sustainability and priority of alternatives (STEP 7).
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基于全球降水数据估计值的地表径流模拟—以长江上游地区为例 [J].
<p>为了评价在相对较短时间内针对长江上游地区地表水径流所建立模型的模拟效果，以及检验以GCM模型和其他卫星数据所估算的降水数据作为输入数据的可行性，选择分布式水文模型HSPF以及1987年和1988年的ISLSCP降水数据作为输入数据。模型模拟结果表明：从整个长江上游地区看，在校正期内，5天平均流量的Nash–Sutcliffe相关系数 (R<sub>2</sub>) 为0.94；在验证期内，Nash-Sutcliffe相关系数 (R<sub>2</sub>) 为0.95。此外，该模型对长江上游主要支流的5天平均流量的模拟效果也很好，R<sub>2</sub>的值在0.46到0.96之间。例外的情况主要发生在沱江和嘉陵江，模型对2年洪水期的峰值流速的估计值偏低，沱江只有实际值的71%，嘉陵江只有实际值的61%。ISLSCP估计的降水比实际测量的降水频繁且程度要弱，这可能是HSPF不能在所有时间和所有区域都具有较好模拟效果的一个主要原因。</p>
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| [32] |
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| [33] |
A program for the computation of multivariate and partial Mann-Kendall test [M].
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| [34] |
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| [35] |
Mann-Kendall检验方法在降水趋势分析中的应用研究 [J].https://doi.org/10.3969/j.issn.1674-1161.2008.05.013 URL 摘要
采用本溪地区4个气象站1958--2006年间气象资料，应用Mann—Kendall检验方法对降水量的年际变化特征、月际变化特征及趋势进行分析，并进行突变性检验。结果表明：近50a本溪地区降水量随时间呈上升趋势变化；本溪站和草河口站降水量发生突变。分别是1978年、1986年和1976年。
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| [36] |
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