### A Basis for Achieving Economic, Societal and Environmental Goals in Illinois

Xin-Zhong LiangDepartment of Atmospheric Sciences and Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois

The regional Climate-Weather Research and Forecasting model (CWRF) has built in a Conjunctive Surface-Subsurface Process model (CSSP) to predict soil temperature/moisture distributions, terrestrial hydrology variations, and land-atmosphere flux exchanges. The CSSP, rooted in the Common Land Model (CoLM) with a few updates from the Community Land Model version 3.5 (CLM3.5), incorporates significant advances in representing hydrology processes with realistic surface (soil and vegetation) characteristics. These include dynamic surface albedo based on satellite retrievals, subgrid soil moisture variability of topographic controls, surface-subsurface flow interactions, and bedrock constraint on water table depths. As a result, the CSSP is superior to both CoLM and CLM3.5 in representing seasonal and interannual variations of rooting zone soil moisture over Illinois.

1. Choi, H.I. and X.-Z. Liang, 2009: Improved Terrestrial Hydrologic Representation in Mesoscale Land Surface Models. *J. of Hydrometeorology*, doi: 10.1175/2010JHM1221.1

This study addresses several deficiencies in the existing formulations for terrestrial hydrologic processes in the Common Land Model (CLM) and presents improved solutions, focusing on runoff prediction. In particular, we have: (1) incorporated a realistic geographic distribution of bedrock depth to improve estimates of the actual soil water capacity; (2) replaced an equilibrium approximation with a dynamic prediction of the water table to produce more reasonable variations of the saturated zone depth; (3) used an exponential decay function with soil depth for the saturated hydraulic conductivity, to consider the effect of macropores near the ground surface; (4) formulated an effective hydraulic conductivity of the liquid part at the frozen soil interface and imposing a maximum surface infiltration limit to eliminate numerically generated negative or excessive soil moisture solution; and (5) examined an additional contribution to subsurface runoff from saturation lateral runoff or baseflow controlled by topography. To assess the performance of these modifications, runoff results from a set of offline simulations are validated at a catchment-scaled study domain around the Ohio Valley region. Together, these new schemes enable the CLM to well capture the major characteristics of the observed total runoff variations. The improvement is especially significant at peak discharges under high flow conditions.

**Figure.**a) Plot of spatial distribution of the 30-km terrain elevation (in meters), b) Plot of spatial distribution of the 30-km USGS land cover type, and c) Vertically exaggerated schematic of 11-layer model structure with 30-km DEM and bedrock profiles.

2. Liang, X.-Z., H.I. Choi, K.E. Kunkel, Y. Dai, E. Joseph, J.X.L. Wang, P. Kumar, 2005: Surface Boundary Conditions for Mesoscale Regional Climate Models. *Earth Interactions*, 9 (18).

This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (land cover category, and static fractional vegetation cover and varying leaf-plusstem-area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe.

**Figure.**The geographic distributions of the Oct 2002 mean MODIS SST differences (°C) from (a) RTG and (b) OI and the frequency distributions of the differences at (c) raw data pixels and (d) 30-km RCM grids, and (e) the mean and absolute differences of MODIS, RTG and OI SSTs from the hourly observations at 69 buoy stations [dot marks in (b)] uniformly distributed over U.S. coastal oceans and the Great Lakes in Jan, Apr, Jul, and Oct.

3. Choi, H.I., P. Kumar, and X.-Z. Liang, 2007: Three-dimensional volume-averaged soil moisture transport model with
a scalable parameterization of subgrid topographic variability. *Water Resour. Res.*, 43, W04414, doi:10.1029/2006WR005134.

Subgrid variability of subsurface moisture flux transport is strongly influenced by the local variation of topographic attributes, such as elevation, slope, and curvature. A three dimensional volume-averaged soil moisture transport (VAST) model is developed to incorporate these effects using the volume-averaged Richards equation. The small perturbation approach is used to decompose the equation into mean and fluctuation, which are then averaged over the model grid box. This formulation explicitly incorporates the variability of moisture flux due to subgrid variation of topographic attributes. The model is independent of scale, but the parameters need to be estimated at the model scale. It is demonstrated that the flux contribution from the subgrid variability can be comparable to that of mean flux, particularly under drier moisture conditions. This formulation can be substituted for subsurface moisture transport schemes in most existing land surface models.

**Figure.**Basic scheme for modeling the subgrid topographic control on soil moisture dynamics. (a) Illustration of subgrid variability of elevation in a 6 _ 6 km2 grid box characterized by 30 m DEM data. (b) Corresponding subgrid variability of slopes and curvatures in two principal directions derived using the 30 m data. (c) Schematic to arrive at a grid-averaged equation using the small-perturbation approach. The VAST equation is a grid-scale-averaged form of the three-dimensional Richards equation of a differential element (dxdydz*). Soil is modeled as vertically homogeneous only for each layer (Zk-1 < z* < Zk). However, in a column when we have two or more horizontal layers with distinct properties stacked on top of another, the column is nonhomogeneous, and we use appropriate layer-weighted average functions for each interfacial layer and develop a numerical scheme for the solution.