Catchment classification and regionalisation with Self-Organising Maps (SOMs) and flexible model structures.

该项目的主要研究目标是流域分类，以期区划。三种不同的聚类方法作为集水区分类的基础：自组织地图（SOMs）、基准概念模型和SUPERFLEX建模框架。我们将根据径流行为以及水文气候和地理特征对集水区进行分类。该项目将使用来自德国西部约100个流域的数据。接下来，对不同的分类方法进行比较，并根据最佳的分类方法对径流和模型参数进行分区，以检验其分区的适用性。集水区分类和区域化将用于深入了解中尺度河流流域的功能：揭示相似性和差异，解释其水文行为，并提供有意义的数据支持的假设。此外，我们将开发我们的方法的自动化程序，并将它们组装成一个工具箱，可以被广泛的用户群体使用。本研究将努力采用双重方法：首先，从区域化的角度实施新颖的聚类方法；其次，激发对集水区分类和区域化的持续和高度相关的讨论。

The major research objective of the project is catchment classification with a view to regionalization. Three different clustering methods serve as a basis for catchment classification: Self Organising Maps (SOMs), a benchmark conceptual model and the SUPERFLEX modelling framework. We will classify catchments by runoff behaviour and by hydroclimatic and physiographic properties. The project will use data from approximately 100 catchments in western Germany. Next, the classification methods will be compared and based on the best classification method we will regionalize runoff and model parameters to test its suitability for regionalization.Catchment classification as well as regionalization will be used to gain insight into the functioning of meso-scale river basins: to uncover resemblances and discrepancies, to interpret their hydrological behaviour and to provide meaningful data-backed-up hypothesis. Furthermore, we will develop auto-mated procedures of our methods and assemble them into a toolbox, which can be used by a wide group of users. This study will strive for a dual approach: firstly, to implement novel clustering methods with a view to regionalization and secondly, to stimulate the ongoing and highly relevant discussion on catchment classification and regionalisation.