Disinformative data in large-scale hydrological modelling release_xkafeari5fhjrnrxb67jhtoh24

by Anna Kauffeldt, S. Halldin, A. Rodhe, C.-Y. Xu, I. K. Westerberg

Published in Hydrology and Earth System Sciences by Copernicus GmbH.

2013   Volume 17, p2845-2857

Abstract

<strong>Abstract.</strong> Large-scale hydrological modelling has become an important tool for the study of global and regional water resources, climate impacts, and water-resources management. However, modelling efforts over large spatial domains are fraught with problems of data scarcity, uncertainties and inconsistencies between model forcing and evaluation data. Model-independent methods to screen and analyse data for such problems are needed. This study aimed at identifying data inconsistencies in global datasets using a pre-modelling analysis, inconsistencies that can be disinformative for subsequent modelling. The consistency between (i) basin areas for different hydrographic datasets, and (ii) between climate data (precipitation and potential evaporation) and discharge data, was examined in terms of how well basin areas were represented in the flow networks and the possibility of water-balance closure. It was found that (i) most basins could be well represented in both gridded basin delineations and polygon-based ones, but some basins exhibited large area discrepancies between flow-network datasets and archived basin areas, (ii) basins exhibiting too-high runoff coefficients were abundant in areas where precipitation data were likely affected by snow undercatch, and (iii) the occurrence of basins exhibiting losses exceeding the potential-evaporation limit was strongly dependent on the potential-evaporation data, both in terms of numbers and geographical distribution. Some inconsistencies may be resolved by considering sub-grid variability in climate data, surface-dependent potential-evaporation estimates, etc., but further studies are needed to determine the reasons for the inconsistencies found. Our results emphasise the need for pre-modelling data analysis to identify dataset inconsistencies as an important first step in any large-scale study. Applying data-screening methods before modelling should also increase our chances to draw robust conclusions from subsequent model simulations.
In application/xml+jats format

Archived Files and Locations

application/pdf  2.7 MB
file_6zeudw2idzetviayxjpvx3hfou
web.archive.org (webarchive)
www.hydrol-earth-syst-sci.net (web)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2013-07-22
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  1027-5606
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 152e04c7-c5db-427d-826e-4bdce356ea06
API URL: JSON