Comment on “Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water” by Eric F. Wood et al.

Abstract

[1] Wood et al. [2011, hereafter W2011] promote the development of hyperresolution global land surface models ("1 km2 or finer, say to 100 m at continental scale," p. 2) as the "Grand Challenge" for the hydrological community. Grand Challenges are useful in several ways: as a way of exciting the hydrological community; as a way of encouraging the community to work together for a common aim; as a way of persuading funding agencies to devote resources into a particular area. We certainly endorse the objective of creating a global effort for improving our ability to predict and monitor land surface hydrology. However, we believe that the Grand Challenge as laid out in W2011 is incomplete in some important ways. [2] In particular it neglects to mention that there are fundamental issues of lack of knowledge which remain in catchment hydrological modeling including: lack of knowledge about input and boundary conditions; lack of knowledge in the representation of process; and inadequate knowledge about the errors in observational data. Applying hydrological models to the continental and global scale has demonstrated that this lack of knowledge has a significant effect on predictive capability [e.g., Widén-Nilsson et al., 2007; Sperna Weiland et al., 2010; Fischer et al., 2011; Prudhomme et al., 2011]. Over much of the globe we cannot close the water balance on the basis of the observations available without allowing for significant uncertainty in the observed variables. Changing the scale of implementation of hydrological models does not, in itself, resolve the issues arising from fundamental lack of knowledge. Indeed, it is not clear how these issues will be resolved in the future, even when SWOT and other high resolution satellite data might be available, and even in the various hydrological and critical zone observatory catchments that are being developed around the world. Remote sensing images require an interpretative model to provide useful (if uncertain) information for hydrological applications; while the unknown nature of the detailed characteristics of experimental catchments still makes it difficult to define adequate process representations. Very often, as in the case of discharges interpreted from sensing of water levels, or soil moisture interpreted from surface emissivity, the information will be associated with significant uncertainty. [3] These lack of knowledge (or epistemic uncertainty) issues are not recognized in W2011 and we would argue that explicit consideration of epistemic uncertainty is absolutely fundamental to a successful scientific hydrology [e.g., Pappenberger and Beven, 2006]. Furthermore we would argue resolution is not the only problem. We can agree of course that finer resolution models would allow for "much better representation of the effects of spatial heterogeneity in topography, soils, and vegetation on hydrological dynamics at large scales" (W2011, p. 2) but the heterogeneities that are important are not only those that can be identified from high resolution remote sensing (or other spatial data). They are also not only those that are resolved at scales of 100 m. There will still be subgrid heterogeneities at the sub-100 m scale that are not properly resolved by using homogeneous soil and vegetation properties at that scale (as is all too evident in current practice of applying hydrological models at small catchment scales). Therefore there will still be a need for some more realistic subgrid-scale parameterization of the processes. [4] The problem is much more than a need to "develop … fine-scale process parameterizations" (W2011, p. 8). Even the physics tells us this. A heterogeneous unsaturated zone will not average linearly in its parameters even if the Darcy-Richards equation was the correct process equation [Beven, 1989, 2001]. But it is not (except in some rather exceptional circumstances). Normally preferential flows of some type will have an important effect on the flux of water to deeper layers and therefore on the water balance partitioning [Beven and Germann, 1982; Uhlenbrook, 2006; Jarvis, 2007; Allaire et al., 2009]. This has been shown to be the case even in the constructed Chicken Creek catchment [Holländer et al., 2009] and will also be true for hyperresolved hydrological models of all the basins that make up the continents and the global land surface. [5] We would consequently argue that regardless of scale, the main challenge is not hyperresolution but instead it is much more one of an appropriate scale-dependent subgrid parameterisation that recognizes the epistemic uncertainties in knowing and representing the characteristics of a grid element, and the nonlinearity and hysteresis in its dynamics [Beven, 2006a]. This is true at current global modeling scales, but it will still be true at 100 m. The visualization of Figure 1 in W2011 only looks more impressive because of the resolution of the graphics. There is absolutely no reason why the more detailed