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1961—2019年江西省降雨侵蚀力时空变化分析

  • 郑潇1

    机 构：

    1. 南京信息工程大学遥感与测绘工程学院,南京， 210044

    ×
  • ，白淑英1

    机 构：

    1. 南京信息工程大学遥感与测绘工程学院,南京， 210044

    ×
  • ，高吉喜2

    机 构：

    2. 生态环境部南京环境科学研究所,南京， 210042

    ×

1. 南京信息工程大学遥感与测绘工程学院,南京， 210044；
2. 生态环境部南京环境科学研究所,南京， 210042；

Spatial-temporal variation of rainfall erosivity in Jiangxi province from 1961 to 2019

  • ZHENG Xiao1

    Affiliation：

    1. School of Remote Sensing & Geomatics Engineering,Nanjing University of Information Science & Technology,Nanjing 210044

    ×
  • ，BAI Shuying1

    Affiliation：

    1. School of Remote Sensing & Geomatics Engineering,Nanjing University of Information Science & Technology,Nanjing 210044

    ×
  • ，GAO Jixi2

    Affiliation：

    2. Nanjing Institute of Environmental Sciences,Ministry of Ecology and Environment,Nanjing 210042

    ×

1. School of Remote Sensing & Geomatics Engineering,Nanjing University of Information Science & Technology,Nanjing 210044；
2. Nanjing Institute of Environmental Sciences,Ministry of Ecology and Environment,Nanjing 210042；

作者简介:

郑潇，男，硕士生，研究方向为洪涝灾害风险评估.zx759174597@gmail.com

通讯作者:

白淑英，女，博士，副教授，主要研究方向为遥感和GIS技术在土地利用/覆被变化、生态环境、气象灾害等方面应用.759174597@qq.com

中图分类号:S157.1

文献标识码:A

DOI:10.13878/j.cnki.jnuist.20220309002

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目录contents

    摘要

    降雨侵蚀力(R)与降雨量、降雨历时、降雨强度、降雨动能有关,反映了降雨特性对土壤侵蚀的影响,是影响土壤侵蚀的重要因素之一．本文利用1961—2019年江西省25个气象站点的逐日降雨量数据,基于降雨侵蚀力模型,通过Mann-Kendall相关检验、小波分析和Kriging空间插值方法,分析江西省降雨侵蚀力空间分布及变化趋势．结果表明:江西省年均降雨量和降雨侵蚀力从赣南到赣北逐渐增加,同时降雨空间分布和降雨侵蚀力空间分布具有相似性;春季和夏季降雨侵蚀力在3000~6000 MJ·mm·hm-2·h-1·a-1,且春季降雨侵蚀力最大值高于夏季,而秋冬季降雨侵蚀力明显小于春夏季;江西省年降雨侵蚀力平均最大值分布在赣北区域,其次为赣中,赣南最小,侵蚀力由北向南逐渐递减．研究表明江西省因降雨导致侵蚀力不断增加,尤其以春夏季更为剧烈,其空间分布存在明显差异．

    Abstract

    As one of the important factors affecting soil erosion,rainfall erosivity (R) is related to rainfall characteristics of amount,duration,intensity,and kinetic energy.Using the daily rainfall data of 25 meteorological stations in Jiangxi province from 1961 to 2019,we analyzed the spatial distribution and variation trends of rainfall erosivity in Jiangxi province via rainfall erosivity model,as well as Mann-Kendall correlation test,wavelet analysis and Kriging spatial interpolation.The results showed that the annual average rainfall and rainfall erosivity varied similarly in spatial distribution,which were gradually increased from southern Jiangxi to northern Jiangxi;the rainfall erosivity in spring and summer ranged from 3000 to 6000 MJ·mm·hm-2·h-1·a-1,which was significantly higher than that in autumn and winter,with the maximum rainfall erosivity occurring in spring;the average annual rainfall erosivity in Jiangxi decreased from north to south,which maximized in northern Jiangxi and followed by central Jiangxi then southern Jiangxi.The research shows that the rainfall-induced soil erosion was increasing in Jiangxi province,especially in spring and summer,and there were obvious differences in their spatial distribution.

  • 0 引言

  • 降雨是引起土壤侵蚀的动力因子之一，降雨侵蚀力常作为衡量降雨导致土壤侵蚀的潜在能力[1]，其时空特征研究对于区域水土保持规划、灾害控制[2]以及生态敏感性指标评价具有重要指导意义[3]．当前全球气候变化大背景下，极端气候事件发生的概率明显增多[4]，在某种程度上增加了土壤侵蚀的风险[5]，带来的风险包括洪涝灾害[6]等造成重大损失．降雨侵蚀力R值作为表征降雨对土壤潜在侵蚀能力的特征值，自20世纪50年代中后期提出并应用于通用土壤流失方程（USLE）以来，逐渐成为目前研究土壤水蚀对降雨变化响应的最佳选择[7]．分析降雨侵蚀力时空变化规律，以及对后续土壤侵蚀力评估和水土整治等过程有利于采取针对性措施制止或减缓降雨侵蚀．

  • 降雨侵蚀力的计算是由Wischmeier等[8]提出的，它利用次降雨动能和降雨强度的乘积来计算，其优势在于计算精度高，但数据要求也高，需要长序列连续性降雨，处理过程繁琐、耗时．国外学者将降雨侵蚀与农作物、径流等结合并分析降雨侵蚀造成的影响．Johannsen 等[9]通过降雨侵蚀力研究奥地利不同农业生产区对土壤侵蚀的影响; Anderson等[10]研究降雨径流对沟壑的侵蚀过程，并得出沟壑、降雨和植被之间的相互关系; Azari等[11]针对气候变化对河流土壤资源和泥沙负荷的关系，利用回归方程得出降雨量增加会导致泥沙负荷提高的结论．国内学者利用日、月、年降雨量计算降雨侵蚀力模型[12-14]，且计算方式得到广泛应用[15]．温天福等[16]利用驻日降雨资料采用TFPW-MK法分析流域降雨侵蚀变化规律，指出侵蚀力增加会引发水土流失风险加剧; 周琳等[17]基于日降雨侵蚀模型分析了降雨侵蚀与山地、平原之间的相关性，以及降雨侵蚀与四季变化之间大小关系; 索笑颖等[18]根据时间和空间侵蚀突变点分析了侵蚀季节变化和地形变化; 曾瑜等[19]基于树立统计及空间插值技术研究了降雨侵蚀力分配特征，给出了降雨侵蚀力整体分布格局．

  • 江西属于典型的中部平原、四周环山区域，赣南地区多以山地和丘陵为主[20]，并且全省分布较广的第四纪红壤，土壤质地黏重、透水性差极易造成水力侵蚀[21]，是全国水土流失最严重的省份之一．本文基于日降雨气象资料，利用降雨侵蚀模型，结合克里金（Kriging）插值、Mann-Kendall突变性检验、小波分析等方法研究1961—2019年江西省降雨侵蚀力时空演替特征及变化规律，以期为后期水土流失监测与防治、环境保护等提供技术支撑．

  • 1 研究区域概况

  • 江西省位于中国东南部，在长江中下游南岸，地处中亚热带，季风气候显著，四季化分明．江西省的主要河流为赣江、抚河、信江、饶河和修水，流域面积达到16.22万km2，这5大水系又以鄱阳湖为中心，经鄱阳湖集纳后流向长江．鄱阳湖是中国最大的淡水湖，赣江流域面积79 173 km2，是长江最重要的支流之一．江西境内水热条件差异较大，多年平均气温自北向南依次增高，南北温差约3℃; 年降水量1 300~2 100 mm，地区分布上是北多南少，东多西少，且降雨的年际变化很大，空间分布差异十分明显，多雨与少雨年份相差也较大，大约50%的蒸发发生在7月中旬到11月，气温与降雨的变化是导致江西旱涝频发的原因之一．

  • 2 数据与研究方法

  • 2.1 数据来源

  • 降雨量是表征气候特征的最基本要素．江西省1961—2019年25个站点的降雨量数据来源于中国气象数据网（http://data.cam.cn），站点分布均匀（图1），数据可靠．利用Kriging空间插值方法可以推算出该区域气候要素情况，该方法在我国降雨量研究上有很好的拟合优度[22]．本文采用Kriging空间插值方法生成年均降雨量和降雨侵蚀力栅格数据．

  • 2.2 降雨侵蚀力（R）

  • 降雨侵蚀力（R）用以评估降雨量对地表土壤侵蚀的潜在能力．根据所获取的降雨资料采用不同的公式计算侵蚀力，其中逐日降雨数据计算公式如下:

  • Rr=αPrβα=21.586β-7.182β=0.836+17.144Pd12-1+24.455Py12-1
    (1)

  • 式中：Rr表示日的降雨侵蚀度力（MJ·mm·hm-2·h-1·a-1）; Pr表示第r日的日降雨量（mm）; α，β为计算降雨侵蚀力统计系数，无量纲; Pd12表示日降雨量≥12 mm的日降雨量（mm）; Py12表示日降雨量≥12 mm的年降雨量（mm）．获得逐日降雨侵蚀度力后，可累加得多日、月、年降雨侵蚀度力．

  • Rn=∑i=112 Ri,Ri=0.0037Pi2.1603,i=1,2,⋯,12,
    (2)

  • 式中：Ri表示第i月降雨侵蚀度力（MJ·mm·hm-2·h-1·a-1）; Pi 表示第i月的降雨量（mm）．获得月降雨侵蚀度力后，可累加得Rn 年降雨侵蚀力．

  • 逐年降雨数据计算公式如下:

  • Rn=0.053Pn1.655,
    (3)

  • 式中：Rn表示年降雨侵蚀度力（MJ·mm·hm-2·h-1·a-1）; Pn 表示年降雨量（mm）．

  • 图1 江西省气象站点分布

  • Fig.1 Distribution of meteorological stations in Jiangxi province

  • 2.3 Mann-Kendall相关检验

  • 众多趋势分析法中，由于Mann-Kendall法属于非参数检验，不受样本值、分布类型等的影响被广泛应用[23]．在一个时间序列X中，Mann-Kendall趋势检验的统计公式如下:

  • S=∑i=1n-1 ∑j=i+1n sgn⁡xj-xi
    (4)

  • 式中：xj为时间序列的第j个数据值; n为数据样本的长度; sgn是符号函数，定义如下:

  • sgn⁡(θ)=1,θ>0,0,θ=0,-1,θ<0.
    (5)

  • 当观测量n≥8时，统计量S大致服从正态分布，其均值为0，方差

  • V(S)=n(n-1)(2n+5)-∑i=1n ti(i-1)(2i+5)18.
    (6)

  • 再通过标准化统计量，计算公式如下:

  • Zc=S-1V(S), S>0,0, S=0,S+1V(S), S<0,
    (7)

  • Zc服从标准正太分布，当Zc的绝对值大于等于1.28、1.64、2.32时，表示分别通过了置信度为90%、95%、99%的显著性检验．

  • 2.4 小波分析

  • 小波分析可用于分析包含非平稳幂次的时间序列[24]．为进一步获得年降雨侵蚀力变化时间尺度特征，将时间序列进行Morlet小波变换，公式如下:

  • ψ0(η)=π-1/4eiω0ηe-ηη2/2,
    (8)

  • 其中η表示时间，ω0是无量纲频率．对于给定的能量有限信号f（t）∈L2（R），其离散小波变换和连续小波变换形式[25]分别是

  • Wf(a,b)=1|a|Δt∑k=1N f(kΔt)ψ-kΔt-ba,
    (9)
  • Wf(a,b)=1|a|∫R ψ-t-badt,
    (10)

  • 式中:a和b表示频域参数，a反映小波的周期长度，b反映时间上的平移长度; Wf（a，b）对于离散小波是小子波，对于连续小波是小波变换系数[26]; ψ-（t）为基小波函数．最后对小波系数的平方值在b域上进行积分，得到小波方差式:

  • Var⁡(a)=∫-∞+∞ Wf(a,b)2db
    (11)

  • 小波方差图体现的是尺度a的变化过程，它能确定波动的能量强度和时间尺度分布．因此，小波方差图可用来确定一个水文序列中存在的主要时间尺度[27]，即主周期．

  • 3 结果分析

  • 3.1 降雨量与降雨侵蚀力时空分析

  • 3.1.1 江西省降雨量和降雨侵蚀力年际分析

  • 据江西省25个站点求出一元线性参数a=4.179、b=1 561可知（图2），江西省58 a间降雨趋势整体向上，其中1973年、1975年、1997年、1998年、2002年、2010年、2012年、2015年和2016年降雨量超过2 000 mm; 在1961—1990年，有14 a降雨量低于趋势线水平; 在1990—2019年，有15 a降雨量低于趋势线水平．由图2可知，58 a间降雨量呈现一定的周期性变化．通过图3可知，多年平均降雨侵蚀力的空间存在一定差异，各站点的变化范围在8 661.33~14 732.02 MJ·mm·hm-2·h-1·a-1，其中降雨侵蚀力最大的是庐山站点、最小的是赣州站点．

  • 为了进一步探究年降雨量和降雨侵蚀力的空间分布，利用Arcgis软件对获取数据的站点进行Kriging插值运算，结果如图4和图5所示．从总体上看，降雨空间分布（图4）和侵蚀力空间分布（图5）具有正相关性，即降雨侵蚀力随着降雨量的增加而增加．将江西省分为赣北、赣中、赣南三个大区域，赣南区域站点为龙南、赣州、遂川、宁都、井冈山、莲花和吉安，年均降雨量为1 578.89 mm，降雨侵蚀力为10 465.42 MJ·mm·hm-2·h-1·a-1; 赣中区域站点为修水、宜春、宜丰、广昌、永丰、武安、靖安、南昌、南城和樟树，年均降雨量为1 624.53 mm，降雨侵蚀力为10 929.41 MJ·mm·hm-2·h-1·a-1; 赣北区域站点为波阳、玉山、贵溪、上饶、德兴、景德镇和庐山，年均降雨量为1 802.41 mm，降雨侵蚀力为12 987.93 MJ·mm·hm-2·h-1·a-1．年均降雨量和降雨侵蚀力从赣南到赣北逐渐增加，同时降雨空间分布和降雨侵蚀力空间分布具有相似性，但降雨侵蚀力的平均增幅比降雨量大．

  • 图2 江西省年际降雨趋势变化

  • Fig.2 Changes of interannual rainfall trends in Jiangxi province

  • 图3 江西省各站点1961—2019年降雨侵蚀力

  • Fig.3 Rainfall erosivity at each station from 1961 to 2019 in Jiangxi province

  • 图4 江西省年平均降雨量空间分布

  • Fig.4 Spatial distribution of average annual rainfall in Jiangxi province

  • 图5 江西省年降雨侵蚀力空间分布

  • Fig.5 Spatial distribution of annual rainfall erosivity in Jiangxi province

  • 江西省的降雨侵蚀力和降雨量空间分布变化与该地区的地形地貌有很大关系，高程对气象要素的影响是主要的，复杂山地环境则会影响气象要素的地形空间尺度和分布格局[28]．赣南多数为丘陵地带，赣中、北平原地带相较多[29]，庐山海拔较高，加上赣江和鄱阳湖分布庐山四周，湿润气流受到地形阻挡，易形成长时间降雨．因此，庐山降雨侵蚀力比其他地区大，从赣北向赣南呈依次递减的空间分布特征．

  • 3.1.2 江西省降雨侵蚀力季节性分析

  • 除地形的影响外，江西省年均降雨分布和降雨侵蚀力也受到季节的影响．春季（图6 a）和夏季（图6 b）降雨侵蚀力随年际变化波动较大，而秋季（图6 c）和冬季（图6 d）降雨侵蚀力随年际变化波动不大，且春夏季降雨侵蚀力大于秋冬季降雨侵蚀力．为进一步探究年均降雨分布和降雨侵蚀力随季节性的分布特征，对江西省25个站点的降雨侵蚀力进行空间划分，最后利用Arcgis软件进行Kriging空间插值计算．由图7—10可知，春季（图7）和夏季（图8）降雨侵蚀力在3 000~6 000 MJ·mm·hm-2·h-1·a-1，侵蚀力较高地区主要分布在赣北及赣东地区．受到亚热带季风影响，春季和夏季降雨由东部转向北部地区，但春季和夏季降雨侵蚀力分别为2 958.88~5 933.10 MJ·mm·hm-2·h-1·a-1 和3 991.00~4 174.1 MJ·mm·hm-2·h-1·a-1，表明春季降雨在北部集中，夏季降雨分布较为均匀，且春季降雨侵蚀力最大值高于夏季，但夏季降雨侵蚀力最小值高于春季降雨侵蚀力最小值．秋季（图9）和冬季（图10）降雨侵蚀力明显小于春夏季降雨侵蚀力，秋季降雨主要分布在江西西南部（即井冈山和遂川一带），大部分地区降雨侵蚀力较为平稳，冬季降雨分布在赣北地区，且由北向南逐渐递减．赣北地区降雨量较大，主要原因是受到北方冷空气向南方偏移的影响．

  • 3.2 降雨侵蚀力检验分析

  • 本文运用Mann-Kendall突变检验方法对江西省58 a来降雨侵蚀力年际变化趋势进行突变性检验，设上下显著性水平为0.05，红色实线代表UF统计量，蓝色虚线代表UB统计量．通过图11可知:1970年后UF大于0，表明降雨随年际变化有增加趋势，且在1996—2006年超过0.05的显著性水平检验; 1970—1990年存在多次震荡相交，1990年之后逐渐平缓，表明江西省的降雨侵蚀力在1970—1990年发生显著突变．根据Mann-Kendall变异系数和Z值空间插值分析降雨侵蚀力分布情况（图12），各站点变异范围在0.25~0.38之间，各地区变化差异比较明显，以庐山为代表的赣北地区，以德兴、上饶和玉山为代表的赣东北地区和以宁都为代表的赣南地区变异系数相对较大，而赣西南和西北地区变异系数相对较小，主要原因是赣北地区降雨变化不稳定，而赣南和赣西南地区降雨相对稳定，且变异系数基本小于0.3．由降雨侵蚀力统计量Z值得出，除赣州、广昌、波阳和宁都的降雨侵蚀力表现出降低的趋势外，其余各站点都表现出增长趋势．但是，无论降低还是增加的变化，统计量|Z|＜1.96，均未通过显著性检验，侵蚀力均表现为不显著的变化趋势．

  • 图13为1961—2019年江西地区降雨侵蚀力小波分析[30]结果，图中红色代表降雨强度变化显著，越接近蓝色，则表示降雨强度变化稳定．由图13可见，年际降雨量在时间尺度上存在复杂的周期结构变化，在降雨演变的过程中，降雨强度也存在5~10 a、3~6 a的周期变化规律，在1970年后生成6 a左右的震荡周期，其中在5~10 a中存在4次震荡，3~6 a中存在5次震荡．小波系数为正值时，表明降雨强度提高; 小波系数为负值时，表明降雨强度明显减弱．1970—1975年小波系数逐渐增加，降雨强度增大; 1975—1980年小波系数逐渐减小，降雨强度逐渐下降．

  • 图6 江西省春夏秋冬季年际降雨侵蚀力变化趋势

  • Fig.6 Variation trends of interannual rainfall erosivity in Jiangxi province

  • a. spring; b. summer; c. autumn; d. winter

  • 图7 江西省春季年降雨侵蚀力变化趋势

  • Fig.7 Variation trend of annual rainfall erosivity in spring in Jiangxi province

  • 图8 江西省夏季年降雨侵蚀力变化趋势

  • Fig.8 Variation trend of annual rainfall erosivity in summer in Jiangxi province

  • 图9 江西省秋季年降雨侵蚀力变化趋势

  • Fig.9 Variation trend of annual rainfall erosivity in autumn in Jiangxi province

  • 图10 江西省冬季年降雨侵蚀力变化趋势

  • Fig.10 Variation trend of annual rainfall erosivity in winter in Jiangxi province

  • 4 讨论

  • 降雨对于降雨侵蚀力而言是主要的因素，江西省降雨量与降雨侵蚀力呈现正相关，和夏正兵[31]对黄河流域降雨侵蚀力研究、伊力哈木·伊马木[32]对新疆维吾尔自治区降雨侵蚀力研究结论一致．因此，针对不同降雨资料选择不同的降雨侵蚀力模型，可以得出大致相同的降雨侵蚀力结果．

  • 江西的土壤类型以红壤为主，再加上江西降雨分布不均，超过70%的降雨量发生在4—9月之间[33]，但降雨侵蚀力较强地区分布在赣北一带，其次是赣中，最后是赣南．产生的原因有:1）不同地区所处的季风带不相同; 2）地区高程的大小影响季风的走势，进而造成降雨量的不同．赣北地区降雨侵蚀力在春夏季出现峰值，原因在于地处亚热带季风气候，受大陆季风影响，导致春夏季降雨侵蚀力增强．另外，并非所有的降雨都会带来侵蚀影响，尤其是生态脆弱性区域和气候影响显著地区．

  • 5 结论

  • 本文以江西省为研究对象，从时间和空间上分析了降雨侵蚀力的变化趋势，得到以下结论:

  • 1）江西省年降雨侵蚀力空间分布趋势为由赣北向赣南逐渐递减，年平均降雨量与年平均降雨侵蚀力分布基本一致．随着降雨量的增加，降雨侵蚀力逐渐增大; 随着地貌地形的变化，降雨侵蚀力也有相应改变．

  • 图11 江西省降雨侵蚀力年际Mann-Kendall突变检验

  • Fig.11 Mann-Kendall mutation test of rainfall erosivity in Jiangxi province

  • 图12 江西省降雨侵蚀力变异系数及Z值空间分布

  • Fig.12 Variation coefficient of rainfall erosivity and spatial distribution of Z value in Jiangxi province

  • 2）春季和夏季（3—8月）降雨侵蚀力波动较大，侵蚀力较高地区主要分布在赣北及赣东，且春季降雨侵蚀力的最大值高于夏季降雨侵蚀力的最大值，夏季降雨侵蚀力波动较小，降雨侵蚀分布比夏季降雨侵蚀力均匀; 秋冬季降雨侵蚀力明显小于春夏季降雨侵蚀力，秋季降雨主要分布在江西西南部，大部分地区降雨侵蚀力较为平稳，而冬季降雨分布在赣北地区，主要原因是受到北方冷空气的影响．

  • 3）根据Mann-Kendall突变检验、变异系数和Z值以及小波分析表明，1970年后降雨虽然有增加趋势，但是变化不显著，且变化范围在0.25~0.38之间．通过小波分析可得在1990—1995年UF与UB变化方向一致，且UF在1995年超过0.05显著水平．空间分布上，吉安、樟树站点Z值平均超过1，其余站点小于1，说明吉安和樟树区域年降雨变化较显著，且降雨侵蚀分布赣北＞赣中＞赣南．因此，江西省应该进一步加强对赣北区域进行水土防治工作，减少降雨引起的水土流失危害．

  • 图13 江西省降雨侵蚀力年际小波分析

  • Fig.13 Interannual wavelet analysis of rainfall erosivity in Jiangxi province

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  • 基本信息

    中图分类号: S157.1
    文献标识码: A
    DOI: 10.13878/j.cnki.jnuist.20220309002

    基金信息

    生态环境部“三定”职责项目“全国生态状况遥感调查与评估”(22110499002)；

    引用信息

    稿件历史

    收稿日期: 2022-03-09

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