TY  - JOUR
A1  - Agarwal, Ankit
A1  - Maheswaran, Rathinasamy
A1  - Kurths, Jürgen
A1  - Khosa, R.
T1  - Wavelet Spectrum and Self-Organizing Maps-Based Approach for Hydrologic Regionalization -a Case Study in the Western United States
JF  - Water Resources Management
N2  - Hydrologic regionalization deals with the investigation of homogeneity in watersheds and provides a classification of watersheds for regional analysis. The classification thus obtained can be used as a basis for mapping data from gauged to ungauged sites and can improve extreme event prediction. This paper proposes a wavelet power spectrum (WPS) coupled with the self-organizing map method for clustering hydrologic catchments. The application of this technique is implemented for gauged catchments. As a test case study, monthly streamflow records observed at 117 selected catchments throughout the western United States from 1951 through 2002. Further, based on WPS of each station, catchments are classified into homogeneous clusters, which provides a representative WPS pattern for the streamflow stations in each cluster.
KW  - Wavelet power spectrum
KW  - Regionalization
KW  - Ungauged catchments
KW  - K-means technique
KW  - Self-organizing map
Y1  - 2016
U6  - https://doi.org/10.1007/s11269-016-1428-1
SN  - 0920-4741
SN  - 1573-1650
VL  - 30
SP  - 4399
EP  - 4413
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Wenz, Leonie
A1  - Willner, Sven N.
A1  - Radebach, Alexander
A1  - Bierkandt, Robert
A1  - Steckel, Jan Christoph
A1  - Levermann, Anders
T1  - Regional and sectoral disaggregation of multi-regional input-output tables - a flexible algorithm
JF  - Economic systems research : journal of the International Input-Output Association
N2  - A common shortcoming of available multi-regional input-output (MRIO) data sets is their lack of regional and sectoral detail required for many research questions (e.g. in the field of disaster impact analysis). We present a simple algorithm to refine MRIO tables regionally and/or sectorally. By the use of proxy data, each MRIO flow in question is disaggregated into the corresponding sub-flows. This downscaling procedure is complemented by an adjustment rule ensuring that the sub-flows match the superordinate flow in sum. The approximation improves along several iteration steps. The algorithm unfolds its strength through the flexible combination of multiple, possibly incomplete proxy data sources. It is also flexible in a sense that any target sector and region resolution can be chosen. As an exemplary case we apply the algorithm to a regional and sectoral refinement of the Eora MRIO database.
KW  - Disaster impact analysis
KW  - Disaggregation
KW  - Global supply chains
KW  - Life cycle assessment
KW  - Regionalization
Y1  - 2015
U6  - https://doi.org/10.1080/09535314.2014.987731
SN  - 0953-5314
SN  - 1469-5758
VL  - 27
IS  - 2
SP  - 194
EP  - 212
PB  - Routledge, Taylor & Francis Group
CY  - Abingdon
ER  -