Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework
The absence of a generic modeling framework in hydrology has long been recognized. With our current practice of developing more and more complex models for specific individual situations, there is an increasing emphasis and urgency on this issue. There have been some attempts to provide guidelines f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2012-11-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/16/4119/2012/hess-16-4119-2012.pdf |
| _version_ | 1818115028550680576 |
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| author | B. Sivakumar V. P. Singh |
| author_facet | B. Sivakumar V. P. Singh |
| author_sort | B. Sivakumar |
| collection | DOAJ |
| description | The absence of a generic modeling framework in hydrology has long been recognized. With our current practice of developing more and more complex models for specific individual situations, there is an increasing emphasis and urgency on this issue. There have been some attempts to provide guidelines for a catchment classification framework, but research in this area is still in a state of infancy. To move forward on this classification framework, identification of an appropriate basis and development of a suitable methodology for its representation are vital. The present study argues that hydrologic system complexity is an appropriate basis for this classification framework and nonlinear dynamic concepts constitute a suitable methodology. The study employs a popular nonlinear dynamic method for identification of the level of complexity of streamflow and for its classification. The correlation dimension method, which has its base on data reconstruction and nearest neighbor concepts, is applied to monthly streamflow time series from a large network of 117 gaging stations across 11 states in the western United States (US). The dimensionality of the time series forms the basis for identification of system complexity and, accordingly, streamflows are classified into four major categories: low-dimensional, medium-dimensional, high-dimensional, and unidentifiable. The dimension estimates show some "homogeneity" in flow complexity within certain regions of the western US, but there are also strong exceptions. |
| first_indexed | 2024-12-11T04:00:06Z |
| format | Article |
| id | doaj.art-af72c739024544e08f5262bb05530889 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-11T04:00:06Z |
| publishDate | 2012-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-af72c739024544e08f5262bb055308892022-12-22T01:21:40ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382012-11-0116114119413110.5194/hess-16-4119-2012Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification frameworkB. SivakumarV. P. SinghThe absence of a generic modeling framework in hydrology has long been recognized. With our current practice of developing more and more complex models for specific individual situations, there is an increasing emphasis and urgency on this issue. There have been some attempts to provide guidelines for a catchment classification framework, but research in this area is still in a state of infancy. To move forward on this classification framework, identification of an appropriate basis and development of a suitable methodology for its representation are vital. The present study argues that hydrologic system complexity is an appropriate basis for this classification framework and nonlinear dynamic concepts constitute a suitable methodology. The study employs a popular nonlinear dynamic method for identification of the level of complexity of streamflow and for its classification. The correlation dimension method, which has its base on data reconstruction and nearest neighbor concepts, is applied to monthly streamflow time series from a large network of 117 gaging stations across 11 states in the western United States (US). The dimensionality of the time series forms the basis for identification of system complexity and, accordingly, streamflows are classified into four major categories: low-dimensional, medium-dimensional, high-dimensional, and unidentifiable. The dimension estimates show some "homogeneity" in flow complexity within certain regions of the western US, but there are also strong exceptions.http://www.hydrol-earth-syst-sci.net/16/4119/2012/hess-16-4119-2012.pdf |
| spellingShingle | B. Sivakumar V. P. Singh Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework Hydrology and Earth System Sciences |
| title | Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework |
| title_full | Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework |
| title_fullStr | Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework |
| title_full_unstemmed | Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework |
| title_short | Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework |
| title_sort | hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework |
| url | http://www.hydrol-earth-syst-sci.net/16/4119/2012/hess-16-4119-2012.pdf |
| work_keys_str_mv | AT bsivakumar hydrologicsystemcomplexityandnonlineardynamicconceptsforacatchmentclassificationframework AT vpsingh hydrologicsystemcomplexityandnonlineardynamicconceptsforacatchmentclassificationframework |