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

使用自组织地图 (SOM) 和灵活的模型结构进行流域分类和区域化。

基本信息

批准号：
221805597
负责人：
Professor Dr.-Ing. Markus Casper
金额：
--
依托单位：
Fach Physische Geographie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2012
资助国家：
德国
起止时间：
2011-12-31 至 2016-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/221805597?language=en
关键词：
Catchment classification regionalisation Self Organising

项目摘要

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.

该项目的主要研究目标是流域分类，以便进行区域化。三种不同的聚类方法作为流域分类的基础：自组织地图（SOM），基准概念模型和SUPERFLEX建模框架。我们将根据径流特性和水文气候及地文特性对集水区进行分类。该项目将使用来自德国西部约100个集水区的数据。接下来，我们将比较各种分类方法，并根据最佳分类方法对径流和模型参数进行区域化，以测试其是否适合区域化。集水区分类和区域化将用于深入了解中尺度流域的功能：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，它可以被广泛的用户群使用。这项研究将努力采取双重方法：首先，实施新的聚类方法，以区域化，其次，刺激正在进行的和高度相关的讨论流域分类和区域化。