基于SOM的流域分类和无资料区径流模拟

无资料区的径流模拟问题是国内外水文研究的难点之一。基于相似流域的参数移植法是常用的解决方法之一,但如何判断相似流域是制约此类方法发展的难点。本文以滇池流域为例,采用自组织映射神经网络(SOM)和层次聚类分析(HCA)联合模式,选取16个流域物理特征为指标进行子流域分类,以确定相似流域。运用无分层的K-means分类的SOM法将整个滇池流域划分为7类具有水文属性的子流域组,分类情景与HCA基本一致,两者实现相互验证。采用HBV水文模型模拟子流域径流过程,并选择部分子流域进行组内参数移植交叉检验。结果显示,HBV模型可较好的模拟滇池流域径流过程;此外,子流域交叉检验结果优良,表明同组内参数可以相互移植。本文不仅为解决滇池流域无资料问题提供了可靠手段,而且由于SOM实现了高维流域特征可视化展示,有助于管理者全面、深入的把握滇池流域水文属性的空间分布特征,为进行水资源管理提供指导。

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

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.