High-resolution global topographic index values for use in large-scale hydrological modelling

T R Marthews,B Lehner,S J Dadson,N Gedney,S Abele

doi:10.5194/hess-19-91-2015

T R Marthews, B Lehner + Show 3 more

Open Access

https://doi.org/10.5194/hess-19-91-2015

Copy DOI

Abstract

Abstract. Modelling land surface water flow is of critical importance for simulating land surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL (TOPography-based hydrological MODEL), and the most important parameter of this model is the well-known topographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically conditioned HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) data using the GA2 algorithm (GRIDATB 2). At 15 arcsec resolution, these layers are 4 times finer than the resolution of the previously best-available topographic index layers, the compound topographic index of HYDRO1k (CTI). For the largest river catchments occurring on each continent we found that, in comparison with CTI our revised values were up to 20% lower in, e.g. the Amazon. We found the highest catchment means were for the Murray–Darling and Nelson–Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. For the majority of large catchments, however, the spread of our new GA2 index values is very similar to those of CTI, yet with more spatial variability apparent at fine scale. We believe these new index layers represent greatly improved global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

Highlights

IntroductionLand surface models (LSMs) are widely used for predicting the effects of global climate change on vegetation development, runoff and inundation, evapotranspiration rates and land surface temperature (Gerten et al, 2004; Prentice et al, 2007; Clark and Gedney, 2008; Dadson and Bell, 2010; Dadson et al, 2010, 2011; Wainwright and Mulligan, 2013; IPCC, 2013)
The simulation of hydrological dynamics within Land surface models (LSMs) remains relatively simplified because these models are usually run at coarse spatial resolution and the physics they follow is based predominantly on approximations of processes that occur at much finer spatial scales (Ducharne, 2009; Wainwright and Mulligan, 2013)
The algorithm required for calculating this index is relatively simple (Appendix A), but it has not previously been applied to generate a global data layer at very high spatial resolution because (1) the index must be calculated from harmonised topographic information, which only became available in the 2000s and (2) LSMs have only recently become sophisticated enough to make use of such a high-quality layer (Prentice et al, 2007)

Summary

Introduction

Land surface models (LSMs) are widely used for predicting the effects of global climate change on vegetation development, runoff and inundation, evapotranspiration rates and land surface temperature (Gerten et al, 2004; Prentice et al, 2007; Clark and Gedney, 2008; Dadson and Bell, 2010; Dadson et al, 2010, 2011; Wainwright and Mulligan, 2013; IPCC, 2013). A growing body of work has lately emerged using LSMs to produce large-area projections of current and future water resources for use in applications related to climate change impact assessments (Gedney and Cox, 2003; Gerten et al, 2004; Falloon and Betts, 2010; Wood et al, 2012; Zulkafli et al, 2013; Harding et al, 2013)

Methods

Results

Discussion

Conclusion