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### 基于SOM的流域分类和无资料区径流模拟

1. 1. 北京大学 环境科学与工程学院,水沙科学教育部重点实验室,北京 100871
2. 北京大学 城市与环境学院,地表过程分析与模拟教育部重点实验室,北京 100871
• 出版日期:2014-08-25 发布日期:2014-08-25
• 作者简介:

作者简介:伊璇(1989-),女,山东泰安人,博士生,主要研究方向为水资源管理与水污染控制,E-mail:xuanyi.eva@gmail.com

• 基金资助:
国家水体污染控制与治理科技重大专项项目(2013ZX07102-006)

### Classification and runoff simulation of data-scarce basins based on self-organizing maps

Xuan YI1(), Feng ZHOU2, Xinyu WANG1, Yonghui YANG1, Huaicheng GUO1()

1. 1. Laboratory of Water and Sediment Sciences, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
2. Laboratory for Earth Surface Process, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
• Online:2014-08-25 Published:2014-08-25

Abstract:

Runoff prediction in ungauged basins (PUB) is one of the difficult research areas in hydrological studies. Parameter replacement using data from similar basins is one of the common methods in dealing with the PUB problem. When basins are similar in physical properties, their hydrological behaviors are assumed to be also similar and thus the hydrological model parameters can be transferred from the donor basin to the target basin. However, it is hard to determine whether a donor basin is indeed similar to a target basin and therefore it is not always clear whether the parameters can be transferred between the basins. Existing research often focus on river basin PUB problem, with inadequate attention on lake basins that contain a number of river streams. This study addresses the PUB question using Lake Dianchi Basin as an example. Lake Dianchi Basin has a complicated river network as well as serious PUB problems. Self-organizing maps (SOM) and hierarchical clustering analysis (HCA) were jointly used to identify analogy basins based on 16 physical attributes, including area, length, slope, drainage density, Ke, mean elevation, average precipitation, six land use types and three soil types. SOM method with K-means cluster was applied to classify similar sub-basins into distinct groups and Davis-Bouldin index was used to determine the optimal group numbers. After 1000 iterations the 43 sub-basins were classified into seven groups (I-VII). This SOM-based classification result is the same as the result of HCA except for two sub-basins. Among the seven groups, group I, IV, and VII contains most of the sub-basins and the other four groups contain no more than three sub-basins each. Different groups have different characteristics and the classification result provides a guidance for local management of the lake basin. For instance, group I is located in high elevation area where the density of streams and infiltration rate of the soil are both low therefore the area is flood-prone, thus the local government should pay more attention on flood control in such area. HBV model was used to simulate the runoff process and for sub-basins where the simulation went well, their parameters were used in the cross-basin test. The cross-basins test was applied to test whether or not hydrological model parameters could be transferred between two sub-basins in the same group. Six stations in three groups were selected as examples and sub-basins in each two sub-basin pair are from the same group. The result shows that the HBV model performs well in the runoff simulation of Lake Dianchi Basin (R2≥0.718, NSE≥=0.495). The cross-basin test result is also very promising (R2≥0.654 and NSE≥0.472) — it proves that ungauged sub-basins could borrow the model parameters of gauged basins in the same group. Thus, this research provides a solution for solving the PUB problem in the Lake Dianchi Basin. This research provides a basis for solving the problem of lack of data for runoff modeling for the basin. Meanwhile, SOM visualizes multi-dimensional properties of the basin, which is useful for practitioners in water resource management to comprehensively understand the spatial distribution of hydrological characteristics of Lake Dianchi Basin.

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