Representing surface and subsurface hydrology at hyperresolution for Earth system models: Development of a hybrid 3-D approach

Wednesday, 17 December 2014
Pieter Hazenberg1, Patrick D Broxton1, Michael Brunke1, David J Gochis2, David M Lawrence2, Guo-Yue Niu1, Jon D Pelletier1, Peter A A Troch1 and Xubin Zeng1, (1)University of Arizona, Tucson, AZ, United States, (2)National Center for Atmospheric Research, Boulder, CO, United States
Traditionally, LSMs for use in Earth system models (ESMs) only account for one-dimensional (1-D) vertical hydrological processes at very coarse resolutions (~100 km). Recently, there has been interest in simulating lateral exchange of surface and subsurface water, as the grid resolution of ESMs increases (currently towards ~20 km) due to advances in computational power.

In the current work, we present a new physically-based hydrological model capable of simulating lateral flow at a hyperresolution (1 km pixels) coupled with the vertical soil column of the Community Land Model (CLM), which has a much coarser resolution. Our new approach also uses sub-pixel topographic information to represent small scale lateral-flow processes. As input to our model, we use 1 km data of required surface and subsurface information, such depth to bedrock, hillslope width functions, fractional coverage of hillslopes/wetlands/riparian zones within each 1 km pixel, and a river network. Such data are largely derived from higher-resolution (30 m) topographic data, and will be made available to the community as global products.

We have tested our model against measurements over a well instrumented, artificial hillslope at the University of Arizona’s Biosphere 2 and found that both our approach and a full 3-D physically-based hydrological model can realistically simulate hydrological states and fluxes. However, computationally, our approach is 2 – 3 orders of magnitude faster than the latter. We are currently testing it for an ESM grid box that covers much of southeastern New York State, which includes the headwaters of the Delaware River, the Susquehana River, and parts of the Mohawk River Basin. Our approach improves upon the 1-D hydrological representation in CLM by representing the subgrid topographic and geomorphological variability in the grid box. The impact of representing such subgrid variability on surface water and energy fluxes will be discussed in our presentation.