Abstract
Abstract. 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.
Highlights
Large-scale hydrological modelling has become a focal point in hydrological research in recent years and is of fundamental importance for understanding continental and global water balances, impacts of climate and land-use changes, and for water-resources management (e.g. Jung et al, 2012; Li et al, 2012; Mulligan, 2012; Werth and Guntner, 2010)
Of the 7763 stations available in the Global Runoff Data Centre (GRDC) data archive, 245 stations were excluded from the study because of insufficient metadata records, i.e. missing coordinates or basin areas
The larger scatter observed for STN-30p and DRT compared to DDM30 can likely be explained by the extensive manual corrections of the flow network (35 % of all cells) performed on the latter (Doll and Lehner, 2002)
Summary
Large-scale hydrological modelling has become a focal point in hydrological research in recent years and is of fundamental importance for understanding continental and global water balances, impacts of climate and land-use changes, and for water-resources management (e.g. Jung et al, 2012; Li et al, 2012; Mulligan, 2012; Werth and Guntner, 2010). Hydrological modelling and analysis of large spatial domains is severely constrained by data availability and quality (Arnell, 1999a; Decharme and Douville, 2006; Doll and Siebert, 2002; Fekete et al, 2004; Guntner, 2008; Hunger and Doll, 2008; Peel et al, 2010; Widen-Nilsson et al, 2009). Several previous studies have emphasised the importance of uncertainties and errors associated with input and evaluation data for robust hydrological inference The possibility that data uncertainties may even render combinations of model input and evaluation data disinformative has only recently been discussed (Beven and Westerberg, 2011; Beven et al, 2011). Disinformative data in a hydrological context are data that are physically inconsistent and misleading for model inference and hydrological analyses
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