Anti-scourability of Soils and Its Influencing Factors under Typical Ecological Restoration Models in Dry-Hot Valley Regions

YAN Jian-mei; LU Yang; WANG Yi-feng; YANG Xiao-lan; JIN Ke; WAN Dan; HU Yue

doi:10.11988/ckyyb.20240738

Journal of Changjiang River Scientific Research Institute >

2025 , Vol. 42 >Issue 10: 80 - 87

DOI: https://doi.org/10.11988/ckyyb.20240738

Soil And Water Conservation And Ecological Restoration

Anti-scourability of Soils and Its Influencing Factors under Typical Ecological Restoration Models in Dry-Hot Valley Regions

YAN Jian-mei ,
LU Yang ,
WANG Yi-feng ,
YANG Xiao-lan ,
JIN Ke ,
WAN Dan ,
HU Yue

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Chongqing Branch, Changjiang River Scientific Research Institute, Chongqing 400026, China

Received date: 2024-07-12

Revised date: 2024-12-12

Online published: 2025-01-23

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Abstract

[Objective] Dry-hot valley regions are characterized by fragile ecological environment and severe soil erosion. Clarifying the differences in soil anti-scourability and the influencing factors under different ecological restoration models is crucial for revealing the soil anti-scourability mechanism and optimizing ecological restoration measures in this region. [Methods] The drawdown zone of Wudongde Hydropower Station in the dry-hot valley of the Jinsha River was selected as the study area. Three typical ecological restoration models were designed, namely tree forest land, slope-to-terrace + farmland, and shrub-grassland. Undisturbed soil samples were collected from three soil layers (0-20 cm, 20-40 cm, and 40-60 cm), and anti-scourability tests were conducted. The anti-scourability indices and runoff sediment amounts of each soil layer under different models at different scouring times were measured. Physicochemical indicators such as soil bulk density, water content, and particle composition were determined through laboratory experiments, and the influencing factors of soil anti-scourability were identified through correlation analysis. [Results] (1) Different ecological restoration models and soil layer depths significantly affected soil anti-scourability. The anti-scourability indice of tree forest land was the largest (0.566 L/g), followed by slope-to-terrace+farmland (0.501 L/g) and shrub-grassland (0.428 L/g). The total runoff sediment yield exhibited an opposite trend, with the shrub-grassland showing the highest value, 28.03% higher than that of the tree forest land. As soil depth increased, the anti-scourability indices of all three ecological restoration models demonstrated significant declining trend. For all three models, the runoff sediment concentration dropped rapidly during the initial scouring period (0-6 min) and stabilized after 6-10 minutes, while the anti-scourability indices exhibited regular temporal patterns that could be fitted by either quadratic or power functions(R²> 0.92 in all cases).(2) Significant differences in soil particle composition and particle fractal dimension were observed among the three models, reflecting variations in soil structural stability. Coarse silt (35.82%-46.10%, mean 41.85%) dominated the particle composition, while coarse sand (0-1.93%, mean 0.42%) was the least abundant fraction. The contents of clay, coarse clay, and fine silt followed the order of shrub-grassland > slope-to-terrace + farmland > tree forest land, whereas fine sand and coarse sand showed the opposite trend, with the highest values in the tree forest land and the lowest in the shrub-grassland. The soil particle fractal dimension ranged from 2.540 to 2.648, with the shrub-grassland exhibiting the highest values, followed by slope-to-terrace + farmland and then tree forest land (all differences significant), indicating that the tree forest land’s soil structure is more stable, which is favorable for soil anti-scourability. (3) Anti-scourability index exhibited significant correlations with multiple physicochemical indicators. Negative correlations were observed between anti-scourability index and soil bulk density, fine clay, coarse clay, fine silt, coarse silt, cumulative eroded soil mass, and fractal dimension, whereas positive correlations were found with fine sand and coarse sand contents. Water content showed a positive correlation with anti-scourability index during the early scouring stage (1-6 min) but turned negative in the later stage (7-10 min). Among these factors, fine clay content and particle fractal dimension demonstrated the most pronounced influences on the anti-scourability index. [Conclusions] Among the three typical ecological restoration models in dry-hot valley regions, tree forest model effectively enhances soil anti-scourability, followed by slope-to-terrace + farmland model. Soil particle composition and structural characteristics significantly affect anti-scourability performance, with fine clay content and particle fractal dimension being the dominant factors. For future ecological restoration measures in drawdown zones, it is recommended to prioritize tree-dominated and tree-shrub-grass composite configuration, tailored to regional topography and soil structural features, to improve soil anti-scourability.

Key words： dry-hot valley; ecological restoration model; soil anti-scourability; particle composition; influencing factor

Cite this article

YAN Jian-mei , LU Yang , WANG Yi-feng , YANG Xiao-lan , JIN Ke , WAN Dan , HU Yue . Anti-scourability of Soils and Its Influencing Factors under Typical Ecological Restoration Models in Dry-Hot Valley Regions[J]. Journal of Changjiang River Scientific Research Institute, 2025 , 42(10) : 80 -87 . DOI: 10.11988/ckyyb.20240738

0 引言

金沙江干热河谷位于我国西南大断裂带分布区,气候干热,水热矛盾突出,地质条件复杂,生态环境脆弱。该地区水能资源丰富,近年来建成了多座大型水电站,这些水电站在建成运行后形成了垂直落差较大的消落带^[1-2]。库水位周期性涨落、降雨径流冲刷和波浪淘蚀等导致消落带土壤侵蚀非常剧烈,严重影响库区生态环境的可持续发展,通过水土保持工程、植物和生物等生态修复措施,可对消落带的土壤侵蚀起到一定改善作用^[3]。

土壤抗冲性指土壤抵抗外力机械破坏并被迫移动的能力,抗冲指数是反映土壤抗侵蚀能力的重要指标^[4]。土壤抗冲性的强弱取决于土粒间、微结构间的胶结作用及土壤结构体抵抗径流分散、冲刷的能力^[5],不同的生态修复措施通过改变土壤理化特性、土壤内部结构从而改善土壤抗冲性能。

近年来,众多学者对土壤抗冲性及其影响因素进行了大量的研究。例如,资如毅等^[6]和韦小茶等^[7]对喀斯特土壤抗冲性进行了研究,得出天然草地抗冲性最优,林地次之,农地最差;阔叶林的土壤抗冲性能最强,针叶林次之,灌草地最弱,土壤抗冲性与土壤相对重度、砂粒含量、水稳性团聚体含量呈极显著正相关,与总孔隙度、黏粒含量呈显著负相关。沙小燕等^[8]和郭明明等^[9]系统研究了黄土高塬沟壑区抗冲性,得出草地抗冲性优于裸地,土壤抗冲性与水稳性团聚体(>0.25 mm)呈极显著正相关关系(P<0.01)。侯春镁等^[10]在滇中红壤区研究发现,土壤抗冲性与>0.25 mm粒级的团聚体有着极显著或显著的相关关系。李菊艳等^[11]通过研究天山北坡草地土壤抗冲性,发现土壤抗冲性由强到弱依次为禁牧草场、季节性放牧草场、自由放牧草场、裸地。屈东旭等^[12]对科尔沁沙地研究得出土壤抗冲性由强到弱依次为乔木林、灌木林、草地、农地。王雅琼等^[13]对祁连山区典型草地生态系统的土壤抗冲性研究成果表明植被覆盖度与根系密度对抗冲性的影响最为突出。徐文秀等^[14]研究三峡库区消落带不同草本植物根系对土壤抗冲性的影响,揭示了扁穗牛鞭草草地土壤抗冲指数最大,其次为苍耳、狗牙根草地。

前人关于抗冲性的研究主要集中在黄土高原、滇中红壤、喀斯特等区域,关于消落带的抗冲性研究仅限于三峡库区,而干热河谷区水库消落带生态修复的土壤抗冲性还鲜有研究。然而,干热河谷区典型生态修复模式对土壤抗侵蚀性的影响研究,对于科学评价土壤水土保持能力具有重要意义。因此,本文以乌东德水电站消落带典型生态修复模式土壤为研究对象,通过测定土壤冲刷累积土量、机械组成、相对重度、含水量等指标,深入研究土壤抗冲性及影响因素,以期为后续干热河谷水库消落带生态修复治理提供理论依据。

1 材料与方法

1.1 研究区概况

乌东德水电站位于云南省禄劝县和四川省会东县交界处,地处金沙江干热河谷(25°40'N—28°15'N,100°30'E—103°30'E)影响区。库区消落带生态修复试验区位于云南省楚雄彝族自治州武定县己衣镇新民村西北部近金沙江右岸,处于库区中上游宽缓河谷地带的一级阶地上,沿岸线长3 km,海拔范围975~952 m。试验区属南亚热带低纬度高原季风气候,炎热干燥,水热矛盾突出,年均温度19~22 ℃,相对湿度60%左右,年均降水量630~800 mm,年均蒸发量2 050~3 650 mm。试验区岸坡属于冲洪积土质岸坡,土壤类型为黄棕壤,以残坡积砂壤土为主,夹杂少量碎石^[2],土壤的区域和垂直分布特征较明显。植物物种主要包括乔木中山杉(Taxodium‘Zhongshanshan’)、池杉(Taxodium ascendens)、乌桕(Triadica sebifera)、枫杨(Pterocarya stenoptera)、桑树(Morus alba)、构树(Broussonetia papyrifera)、银合欢(Leucaena leucocephala)、灌木小桐子(Jatropha curcas)、中华蚊母树(Distylium chinense)、小梾木(Cornus quinquenervis)、秋华柳(Salix variegates)、花椒(Zanthoxylum bungeanum),以及草本狗牙根(Cynodon dactylon)、苍耳(Xanthium sibiricum)、狗尾草(Setaria viridis)等^[2]。

本文选取有代表性的3种典型消落带生态修复模式作为研究对象,分别是乔木林地、坡改梯+农耕地、灌草地。乔木林生态修复模式主要植物种类有中山杉、池杉、构树、桑树、狗牙根、中华蚊母、小梾木、小桐子;坡改梯+农耕地生态修复模式为浆砌石坡改梯+农作物,农作物为玉米;灌草地生态修复模式主要植物种类为桑树、小梾木、秋华柳、小桐子、中华蚊母、狗牙根、香蒲、皇竹草。样地基本情况见表1。

表1 不同生态修复模式的基本情况

Table 1 Basic information of different ecological restoration models

生态修复模式	坡度/(°)	枯落物覆盖度/%	植被盖度/%
乔木林地	15	70	80
坡改梯+农耕地	0	—	70
灌草地	15	50	70

1.2 样品采集

分别在乔木林地、坡改梯+农耕地、灌草地3种典型消落带生态修复样地内钻取3个60 cm深的土壤剖面,乔木林地采样点植被以中山杉和池杉为主, 灌草地采样点植被以狗牙根为主。采样前去除地上部植物及枯枝落叶物,使用原状土冲刷水槽配套的方形环刀(长×宽×高:20 cm×10 cm×10 cm)采集原状土样。自上而下取表层(0~20 cm)、中层(20~40 cm)和下层(40~60 cm)原状土样,每层土样取平行样。另外,每层采集约500 g土样密封保存至聚乙烯采样袋,实验室测定理化性状指标。

1.3 指标测定

土壤相对重度采用环刀法;抗冲性采用改进的原状土冲刷水槽法测定,冲刷水槽长1.80 m、宽0.1 m。冲刷前,土样底部衬以滤纸置于水中浸泡约24 h;饱和后,取出土样静置,除去重力水。冲刷坡度设定为20°以模拟陡坡,出水流量为1.5 L/min。待水流稳定后,将土样装入土样室,使土样上表面和槽底面齐平,然后放水冲刷。产流后开始取样,每1 min收集1次径流泥沙样,完成冲刷之后,通过铝盒自各个水盆取样,随后静置去除上清液,仅剩下泥水样,并移至烘箱调至105 ℃,温度持续烘干,再测定泥沙质量。颗粒分析采用MS3000型激光粒度分析仪进行分析。

1.4 数据分析处理

土壤的抗冲能力用抗冲指数AS表示,即冲失1 g干土所需水量,计算式为

(1)

A S t = Q T W L D S t 。

式中:AS_t为t时刻的抗冲指数(L/g),t=1,2,…,10 min;Q为冲刷流量(L/min);T为冲刷历时;WLDS_t为t时刻冲失的干土质量(g)。

土壤分形维数的推导采用王国梁等^[15]提出的土壤颗粒体积分形维数计算方法,具体计算式如下:

(2)

d i d m a x 3 - D = V (δ d ¯ i) V T,

(3)

l g V (δ d ¯ i) V T = (3 - D) l g d ¯ i d ¯ m a x 。

式中:

d i

为第i级粒径;

d ¯ i

为某级粒径平均值;

V (δ d ¯ i)

表示粒径小于

d ¯ i

的颗粒体积;VT为颗粒总体积;

d m a x

为最大粒径;

d ¯ m a x

为最大粒径平均值;D为土壤颗粒分形维数。

利用Excel2018进行数据的整理和制图,使用SPSS22.0进行回归性分析。

2 结果与分析

2.1 土壤抗冲性

随冲刷时间延长,3种典型生态修复模式径流泥沙量均在1~2 min内呈急剧下降趋势,乔木林地和坡改梯+农耕地在2~6 min呈缓慢下降趋势,6~10 min趋于稳定,灌草地在2~8 min呈缓慢下降趋势,8~10 min趋于稳定,乔木林地和坡改梯+农耕地能在较短时间内趋于稳定,灌草地稳定时间明显滞后(图1)。

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图1 径流泥沙量动态变化

Fig.1 Dynamic changes of runoff sediment volume

从径流泥沙总量来看,3种生态修复模式差异较大,在不同土层径流泥沙总量及平均值均呈现乔木林地<坡改梯+农耕地<灌草地的规律,坡改梯+农耕地(103.300 g)比乔木林地(94.272 g)多9.57%,灌草地(120.696 g)比乔木林地(94.272 g)多28.03%。

3种典型生态修复模式土壤抗冲指数在0~10 min内均随冲刷时间的变化总体呈上升趋势,表明随冲刷时间的延长,土壤抗冲性逐渐增强,其中5~10 min内速度较快(图2)。通过最小二乘法对不同生态修复区土壤抗冲指数与时间的关系进行拟合,得出方程AS=at²+bt+c(R²=0.922~0.989)或AS=atⁿ(R²=0.946~0.982)(a、b、c、n为常数,AS为平均土壤抗冲指数),拟合较好(表2)。

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图2 抗冲指数动态变化

Fig.2 Dynamic changes of anti-scourability index

表2 不同生态修复模式下不同时刻的抗冲指数

Table 2 Anti-scourability indices of different ecological restoration models at different time points

冲刷历时/min	不同深度抗冲指数/(L·g^-1)
	0~20 cm			20~40 cm			40~60 cm
	乔木林地	坡改梯+农耕地	灌草地	乔木林地	坡改梯+农耕地	灌草地	乔木林地	坡改梯+农耕地	灌草地
1	0.028	0.028	0.027	0.028	0.027	0.027	0.032	0.025	0.021
2	0.131	0.114	0.103	0.124	0.104	0.099	0.119	0.107	0.095
3	0.221	0.155	0.146	0.205	0.134	0.137	0.201	0.185	0.127
4	0.278	0.235	0.166	0.258	0.224	0.165	0.236	0.209	0.159
5	0.325	0.254	0.227	0.321	0.250	0.241	0.303	0.270	0.222
6	0.734	0.521	0.324	0.664	0.488	0.286	0.434	0.467	0.341
7	0.857	0.658	0.582	0.726	0.588	0.559	0.472	0.500	0.380
8	0.985	0.763	0.714	0.839	0.758	0.562	0.563	0.513	0.486
9	1.203	1.163	1.034	1.127	1.111	0.919	0.917	0.746	0.627
10	1.722	1.695	1.645	1.522	1.471	1.455	1.402	1.282	0.964
平均值	0.648	0.559	0.497	0.582	0.515	0.445	0.468	0.430	0.342
径流泥沙总量/g	93.040	97.335	108.86	92.900	102.780	113.794	96.875	109.785	139.435

0~20 cm土层,从冲刷起始至结束均呈现乔木林地>坡改梯+农耕地>灌草地的规律,平均值呈现乔木林地(0.648 L/g)>坡改梯+农耕地(0.559 L/g)>灌草地(0.497 L/g)的规律;20~40 cm土层,t=3 min时刻,呈现乔木林地>灌草地>坡改梯+农耕地的规律,其余时刻则呈现乔木林地>坡改梯+农耕地>灌草地的规律,平均值呈现乔木林地(0.582 L/g)>坡改梯+农耕地(0.515 L/g)>灌草地(0.445 L/g)的规律;40~60 cm土层,t=6、7 min时刻,呈现坡改梯+农耕地>乔木林地>灌草地的规律,其余时刻呈现乔木林地>坡改梯+农耕地>灌草地的规律,平均值呈现乔木林地(0.468 L/g)>坡改梯+农耕地(0.430 L/g)>灌草地(0.342 L/g)的规律。所有土层抗冲指数平均值呈现乔木林地(0.566 L/g)>坡改梯+农耕地(0.502 L/g)>灌草地(0.428 L/g)的规律。从土层深度来看,抗冲指数呈现0~20 cm(0.568 L/g)>20~40 cm(0.514 L/g)>40~60 cm(0.413 L/g)的规律。

2.2 土壤颗粒组成及分形维数

3种典型生态修复模式的土壤以粗粉粒含量最高,为35.82%~46.10%(平均值为41.85%),其次是细砂粒,为15.66%~36.89%(平均值为27.30%),细黏粒组成占比为3.09%~5.79%(平均值为4.36%),而粗砂粒的含量最低,为0~1.93%(平均值为0.42%)(表3)。

表3 不同生态修复模式土壤颗粒组成及分形维数

Table 3 Soil particle composition and fractal dimension under different ecological restoration models

土层	分区	不同土壤粒径的质量分数/%						分形维数D
土层	分区	<0.001 (细黏粒)	[0.001,0.005)mm (粗黏粒)	[0.005,0.01)mm (细粉粒)	[0.01,0.05)mm (粗粉粒)	[0.05,0.25)mm (细砂粒)	[0.25,1)mm (粗砂粒)	分形维数D
	乔木林地	3.09a	11.32a	9.79a	39.30a	35.80b	0.70b	2.540a
0~20 cm	坡改梯+农耕地	3.60b	13.59b	11.92b	45.52b	25.35a	0.02a	2.569b
	灌草地	4.37c	13.05b	11.43b	46.02b	25.11a	0.02a	2.588c
	乔木林地	3.31a	12.24a	10.03a	35.82a	36.66c	1.93b	2.551a
20~40 cm	坡改梯+农耕地	3.97b	12.35a	10.79a	46.10c	26.77b	0.02a	2.574b
	灌草地	4.93c	20.63b	16.60b	42.17b	15.66a	0.00a	2.626c
	乔木林地	4.37a	11.97a	9.53a	36.26a	36.89c	0.98b	2.580a
40~60 cm	坡改梯+农耕地	4.19a	12.09a	10.82b	46.08b	26.79b	0.02a	2.626a
	灌草地	5.79b	22.27b	17.62c	38.10a	16.20a	0.02a	2.648b
	乔木林地	3.59	11.84	9.78	37.13	36.45	1.20	2.557
平均值	坡改梯+农耕地	4.18	12.50	11.01	46.07	26.22	0.02	2.596
	灌草地	4.77	18.83	15.38	41.93	19.07	0.01	2.614

注:a、b、c为统计学显著性标记。同列数据后不同小写字母表示不同处理间差异显著(P<0.05),下同。

0~20 cm土层,细黏粒和细粉粒含量均呈现灌草地>坡改梯+农耕地>乔木林地的规律,粗粉粒和粗黏粒含量呈现坡改梯+农耕地>灌草地>乔木林地的规律,细砂粒和粗砂粒含量均呈现乔木林地>坡改梯+农耕地>灌草地的规律,粗黏粒和细粉粒含量在不同治理模式下差异不显著。20~40 cm土层,细黏粒、粗黏粒和细粉粒含量均呈现灌草地>坡改梯+农耕地>乔木林地的规律,粗粉粒含量呈现坡改梯+农耕地>灌草地>乔木林地的规律,细砂粒和粗砂粒含量均呈现乔木林地>坡改梯+农耕地>灌草地的规律,均呈显著差异。40~60 cm土层,细黏粒含量呈现灌草地>乔木林地>坡改梯+农耕地的规律,粗黏粒和细粉粒含量均呈现灌草地>坡改梯+农耕地>乔木林地的规律,粗粉粒含量呈现坡改梯+农耕地>灌草地>乔木林地的规律,细砂粒和粗砂粒含量呈现乔木林地>坡改梯+农耕地>灌草地的规律,均呈显著差异。从各区平均值分析得出,细黏粒、粗黏粒和细粉粒含量均呈现灌草地>坡改梯+农耕地>乔木林地的规律,粗粉粒含量呈现坡改梯+农耕地>灌草地>乔木林地的规律,细砂粒和粗砂粒含量呈现乔木林地>坡改梯+农耕地>灌草地的规律。

0~20 cm土层,分形维数为2.540~2.588;20~40 cm土层,分形维数为2.551~2.626;40~60 cm土层,分形维数为2.580~2.648;均呈灌草地>坡改梯+农耕地>乔木林地的规律,且呈显著性差异。同一生态修复模式下的土壤,随着土层深度的增加,分形维数逐渐增大(表3)。

2.3 土壤相对重度和含水量

整个土壤剖面自上而下土壤相对重度除在0~20 cm土层呈现灌草地>乔木林地>坡改梯+农耕地的规律以外,其他土层均呈现灌草地>坡改梯+农耕地>乔木林地的特征(表4)。土壤含水量在0~20 cm土层呈现坡改梯+农耕地>乔木林地>灌草地的规律,而20~60 cm土层则呈现乔木林地>坡改梯+农耕地>灌草地的规律,其中0~20 cm和40~60 cm土层呈显著性差异。

表4 不同生态修复模式土壤相对重度和含水量

Table 4 Soil bulk density and water content under different ecological restoration models

土层	分区	土壤相对重度	土壤含水量/%
0~20 cm	乔木林地	1.11a	9.50b
	坡改梯+农耕地	1.06a	9.77a
	灌草地	1.23a	8.38b
	平均值	1.13	9.04
20~40 cm	乔木林地	1.29a	10.44a
	坡改梯+农耕地	1.30a	9.80a
	灌草地	1.32a	8.59a
	平均值	1.30	9.61
40~60 cm	乔木林地	1.34a	13.66b
	坡改梯+农耕地	1.39b	12.13b
	灌草地	1.40b	7.96a
	平均值	1.38	11.25

2.4 土壤性质对抗冲性的影响

通过对不同时刻的抗冲指数与土壤指标进行相关性分析,抗冲指数与土壤相对重度、分形维数、累计冲刷土量、细黏粒含量、粗黏粒含量、细粉粒含量均成负相关关系,与细砂粒和粗砂粒含量呈正相关关系(表5)。具体为,t=1~6 min时抗冲指数与含水量呈正相关,t=7~10 min时抗冲指数与含水量呈负相关;t=1~8 min时抗冲指数与粗粉粒呈负相关,t=9~10 min时抗冲指数与粗粉粒呈正相关;抗冲指数与分形维数和细黏粒呈极显著负相关,与土壤相对重度、粗黏粒含量、细粉粒含量呈显著负相关,与含水量、累计冲刷土量、粗粉粒含量呈不显著负相关关系,与细砂粒含量呈显著正相关关系,与粗砂粒含量呈不显著正相关关系。

表5 抗冲指数与各因子的相关分析

Table 5 Correlation analysis between anti-scourability index and various factors

抗冲指数	土壤相对重度	含水量	分形维数	累计冲刷土量	细黏粒含量	粗黏粒含量	细粉粒含量	粗粉粒含量	细砂粒含量	粗砂粒含量
AS₁	-0.371	0.586	-0.678^*	-0.615	-0.580	-0.640	-0.692^*	-0.183	0.699^*	0.469
AS₂	-0.531	0.433	-0.853^**	-0.833^**	-0.846^**	-0.705^*	-0.754^*	-0.440	0.902^**	0.727^*
AS₃	-0.230	0.619	-0.621	-0.767^*	-0.688^*	-0.667^*	-0.725^*	-0.491	0.882^**	0.730^*
AS₄	-0.498	0.466	-0.853^**	-0.84^**	-0.881^**	-0.745^*	-0.779^**	-0.305	0.868^**	0.637
AS₅	-0.234	0.581	-0.709^*	-0.823^**	-0.653	-0.630	-0.702^*	-0.569	0.895^**	0.819^**
AS₆	-0.501	0.237	-0.803^*	-0.714^*	-0.733^*	-0.645	-0.663^*	-0.295	0.769^*	0.621
AS₇	-0.76^*	-0.124	-0.848^**	-0.365	-0.863^**	-0.541	-0.527	-0.047	0.550	0.422
AS₈	-0.768^*	-0.174	-0.910^**	-0.444	-0.881^**	-0.576	-0.566	-0.053	0.582	0.415
AS₉	-0.81^**	-0.075	-0.921^**	-0.404	-0.853^**	-0.615	-0.600	0.960	0.540	0.034
AS₁₀	-0.843^**	-0.044	-0.807^**	-0.249	-0.853^**	-0.624	-0.589	0.250	0.464	0.184
AS	-0.789^*	-0.039	-0.952^**	-0.529	-0.945^**	-0.693^*	-0.687^*	-0.046	0.687^*	0.474

注:*表示P<0.05;**表示P<0.01。

3 讨论

研究结果表明,乔木林地的冲刷含沙量最小,其次是坡改梯+农耕地,灌草地含沙量最大,且趋于稳定时间明显滞后于乔木林地地和坡改梯+农耕地。乔木林地的抗冲指数最高,其次是坡改梯+农耕地,灌草地抗冲指数最低。总体而言,乔木林地抗冲性能最好,其次是坡改梯+农耕地,灌草地最差,这与蒋定生^[16]研究的乔木林地抗冲指数大于农地和灌草地结论保持一致,但与张建军等^[17]的研究中灌木林抗冲指数大于农耕地的结论相反。主要原因是乔木林地是乔灌草综合配置模式,具有可持续发展、稳定性较高的植被结构,植物根系长短、粗细结合,在土体中根系穿插范围广、扎根深,形成完整的网络状态,对土体有固结作用,阻止土颗粒分散,更有利于提高土壤抗冲性^[18-19]。试验区消落带周期性水位涨落对不同植被覆盖类型下的土体结构有一定影响,农耕地是用浆砌石将坡耕地变为了梯平地,改变了微地形,加之玉米作物具有一定的根系缠绕作用,受水位涨落影响较小,而灌草地具有一定坡度,水流冲刷可能会引起土体结构稳定性降低,导致抗冲性较差^[20]。

同一生态修复模式下的土壤,随着土层深度的增加,土壤抗冲性逐渐降低,主要原因是上层土壤在根系的固结作用下,具有更稳定的土壤结构,增强了抗冲性^[21]。

各生态修复模式径流含沙量随时间的延长呈现减少后趋于稳定、抗冲指数呈逐渐增加的规律,这与许航等^[22]、谌芸等^[18]的研究结论一致。在冲刷过程中,因为表层土颗粒较为松散,在很短时间内便被水流冲走,随着时间的延长,下层紧实的土壤具有抵抗水流冲走的能力,径流含沙量就逐渐减少,其抗冲性就随之增加。

3种典型生态修复模式各土壤粒级中,粗粉粒含量最高,在35.82%~46.10%之间(平均值为41.85%),粗砂粒的含量最低,在0~1.93%之间(平均值为0.4%)。细黏粒、粗黏粒、细粉粒含量最高的是坡改梯+农耕地,粗粉粒含量最高的是灌草地,而细砂粒和粗砂粒含量最高的是乔木林地。各生态修复模式下土壤颗粒的分形维数在2.540~2.648之间,均呈灌草地>坡改梯+农耕地>乔木林地的规律,且差异显著。这与董智今等^[23]、郑桂莲等^[24]的研究结论整体一致,但与农地分形维数大于灌草地的结论相反。由于乔木林地地表覆盖物较多,根系发达,具有较好的群落结构,雨滴击溅及人为扰动较少,促进土壤颗粒的胶结,因而粗颗粒含量逐渐增加,分形维数最小^[25]。农耕地在人为扰动下大的土壤颗粒不断被细化,细颗粒含量较高,但农耕地受到浆砌石田坎的阻拦影响,促进了土壤胶结和增加了粗颗粒含量,同时削弱了人为扰动导致的细颗粒增加,导致分形维数相比灌草地更低。

抗冲指数与土壤相对重度呈负相关关系,且随着时间的延长,相关性越来越显著,与Wu等^[26]、王月玲等^[27]研究结论一致,而与资如毅^[6]研究结论相反。抗冲指数与含水量在前6 min呈正相关,随着时间的延长,抗冲指数与含水量呈负相关。以上结果表明在冲刷前期土壤相对重度越小,总孔隙度越小,含水量越大,缓解了径流对土壤的分离作用,导致土壤抗冲性增强。随着时间的推移,含水量对土壤的分离保护作用越来越弱,抗冲性反而因含水量的增大而降低。此外,抗冲指数与细黏粒含量、粗黏粒含量、细粉粒含量、粗粉粒含量、累计冲刷土量呈负相关关系,与细砂粒含量和粗砂粒含量呈正相关关系,这与徐加盼等^[28]、史冬梅等^[29]的研究结论一致,表明细黏粒、粗黏粒、细粉粒、粗粉粒因粒径较小,质量较轻,更容易被径流分散、输送。随着较细颗粒的流失,土质更疏松,更容易受到径流的冲刷,冲刷土量就越多,抗冲性能越低,较粗颗粒含量越高,则更有利于提高抗径流冲刷能力。抗冲指数与分形维数呈负相关关系表明,分形维数越低,土壤粒径分配不均匀,其土壤结构越好,稳定性越好,土壤更不易被径流分散、冲刷,抗冲性能就越强。

4 结论

3种典型生态修复模式径流含沙量随时间的延长呈现减少后趋于稳定、抗冲指数呈逐渐增加的规律。建立了土壤抗冲指数随时间的动态变化方程为AS=at²+bt+c)或AS=atⁿ(a、b、c、n为常数,t=0~10 min)。不同生态修复模式下的抗冲指数乔木林地最高,其次是坡改梯+农耕地,灌草地最低。

不同典型生态修复模式各土壤粒级中,粗粉粒含量最高,粗砂粒的含量最低。坡改梯+农耕地含有更多的细黏粒、粗黏粒和细粉粒的组分,灌草地含有更多的粗粉粒,而乔木林地则含有较多的细砂粒和粗砂粒。各生态修复模式下土壤颗粒的分形维数均呈灌草地>坡改梯+农耕地>乔木林地的规律,且达显著性差异。

影响土壤抗冲指数的众多土壤指标中,土壤组分中细黏粒含量和土壤分形维数是对其作用最为显著。

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