基于势能信息熵的黄土小流域地貌演化特征

doi:10.12118/j.issn.1000-6060.2022.217

摘要/Abstract

摘要：

黄土高原流域地貌系统的地貌演化特征十分复杂，尚有诸多科学问题有待进一步深入研究。以往研究大多集中在流域地貌演化的侵蚀和发育特征等某一方面，缺乏从流域地貌系统及其势能信息熵的视角深入剖析野外多岩土层黄土小流域地貌演化特征的研究。为此，基于系统论的观点和方法，构建多岩土层黄土小流域地貌系统及其势能信息熵的数学模型，并以辛店沟小流域为例，对其地貌演化特征进行研究。结果表明：（1）构建的野外多岩土层黄土小流域地貌系统的概念模型及其势能信息熵的数学模型能够有效对辛店沟小流域进行数值模拟。（2）以黄土侵蚀作用为主的辛店沟小流域从2000—2019年的地貌演化过程是其势能信息熵的熵减过程和黄土地貌不断侵蚀的过程。（3）辛店沟小流域的势能信息熵能较好地反映该小流域的地貌演化阶段和地貌侵蚀过程。

关键词: 数字高程模型（DEM）, 数字地形分析, 势能信息熵, 流域地貌系统, 辛店沟小流域

Abstract:

Research on the evolution characteristics of loess erosion and the development of watershed geomorphic systems in the Loess Plateau of China is a hotspot in the study of loess landforms, and many related scientific issues still need to be studied. Most previous studies have focused on the erosion and development characteristics of watershed geomorphology, but there is a lack of research on indepth analysis of the geomorphic evolution characteristics of loess small watersheds with multiple rock and soil layers in the field from the perspective of watershed geomorphology system and its potential energy information entropy (PEIE). Therefore, based on the viewpoint and method of system theory, mathematical models of the geomorphic system and its PEIE of a small loess watershed with multiple rock and soil layers were built. Taking the Xindiangou small watershed at Suide County, Shaanxi Province, China as the research sample area, the digital elevation model data of 5 periods from 2000 to 2019 were used to investigate the entropy change law of the PEIE and the characteristics of landform evolution in the watershed. The results show that from 2000 to 2019, the geomorphic evolution process dominated by loess erosion in the Xindiangou small watershed is the process of entropy reduction of its PEIE and continuous erosion of the loess landform. Combined with the entropy change of PEIE in the Xindiangou small watershed and the indoor small watershed, it is speculated that the watershed geomorphic system will form a W-shaped PEIE change curve in its complete geomorphic evolution process, and its PEIE can better indicate the watershed geomorphic development demarcation points in its infancy, maturity, and old age. The research results also confirm the effectiveness of the conceptual model of the loess watershed geomorphic system and its mathematical model of PEIE in the numerical simulation of the Xindiangou small watershed. This model is an extension of the existing mathematical model of PEIE of watershed geomorphic systems with homogeneous single loess layers, which can be used in heterogeneous multiple rock and soil layer loess watershed geomorphic systems and has wider applicability. The research results provide ideas for further research on the geomorphic formation mechanism and evolution law of the Xindiangou small watershed in the future and guide soil and water conservation, ecological restoration, and regional sustainable development of the Xindiangou small watershed, which has important theoretical significance and good application prospects.

Key words: digital elevation model (DEM), digital terrain analysis, potential energy information entropy, watershed geomorphic system, Xindiangou small watershed

赵卫东, 王淑琴, 田剑, 季斌, 马雷. 基于势能信息熵的黄土小流域地貌演化特征[J]. 干旱区地理, 2023, 46(1): 65-75.

ZHAO Weidong, WANG Shuqin, TIAN Jian, JI Bin, MA Lei. Geomorphic evolution characteristics of small loess watersheds based on potential energy information entropy[J]. Arid Land Geography, 2023, 46(1): 65-75.

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DEM数据	年份	分辨率/m	数据来源
第1期	2000	90.0	SRTM V3（https://www.usgs.gov/）
第2期	2007	12.5	ALOS（https://search.asf.alaska.edu/#/）
第3期	2009	30.0	ASTER GDEM（http://www.gscloud.cn/search）
第4期	2015	90.0	TanDEM-X（http://tandemx-90m.dlr.de/）
第5期	2019	8.0	91卫图（https://www.91wemap.com/）

序号	点号	高程/m	采样深度/cm	黄土密度/g·cm^-3
1	1_1	889	26	1.22
2	1_2	981	35	1.20
3	1_3	983	35	1.20
4	1_4	918	28	1.30
5	1_5	929	25	1.28
6	1_6	857	19	1.28
7	1_7	970	21	1.42
8	1_8	1015	24	1.25
9	1_9	824	20	1.37
10	1_10	781	10	1.52
11	1_11	888	13	1.38
12	1_12	899	16	1.33
13	2_1	856	12	1.28
14	2_2	869	15	1.35
15	2_3	938	17	1.33
16	2_4	921	13	1.42
17	2_5	879	20	1.42
18	2_6	944	24	1.33
19	2_7	867	19	1.33
20	2_8	931	14	1.23
21	2_9	955	14	1.37
22	2_10	948	19	1.33
23	2_11	926	12	1.30
24	2_12	919	18	1.35
25	2_13	955	17	1.32
26	2_14	968	19	1.35
27	2_15	969	24	1.30

DEM数据	地貌演化年份	平均高程/m	最小高程/m	最大高程/m	HI
第1期	2000	930	820	1026	0.534
第2期	2007	903	789	1009	0.518
第3期	2009	924	814	1039	0.489
第4期	2015	904	791	1023	0.487
第5期	2019	935	820	1057	0.485