MODEL DESCRIPTION, DATA, AND STUDY AREA 1 Model Description

The model has been extensively discussed in detail in the literature (Liang, 1994, 1996a, 1996b, 1999; Nijssen et al., 1997; Abdulla et al., 1996; Lohmann et al., 1998a, 1998b; Cherkauer and Lettenmaier, 1999; Cherkauer, 2001; and Schnur and Lettenmaier, 1997) and hence a very brief outline of the model is only provided.

The current implementation of the model consists of three soil layers; a top layer of 10 cm thickness and two bottom layers around 30 cm and 100 cm thickness each. We have utilized a predetermined thickness for each of the soil layers. The top layer characterizes dynamic behavior of soil column response to precipitation events and the bottom layer represents storm-soil moisture behavior responding to precipitation events only after the top two layers are wetted. The last layer (100 cm thick) responds to the long-seasonal time scales and displays greater inertia than the top layers. One of the main distinguishing features of the model is its subgrid variability of soil moisture (Zhao et al., 1980;

Wood et al., 1992). The model incorporates the various surface conditions to be described by n = 1, 2, 3…N types of vegetation as well the (N+1)^th type, corresponding to the bare soil type. Their different Leaf Area Index (LAI), canopy resistance, root fraction depth, and distinct soil moisture characteristics define each surface during each time step. Also all the calculations of infiltration, base flow, and runoff are carried out for each of the (N+1) land cover types. Figure 5.1a depicts the various components of the hydrological cycle in the model.

The amount of infiltration is controlled by a variable infiltration curve, which is based on the available moisture content of the top two layers. The water that cannot infiltrate is removed from a grid cell as runoff. The infiltrating moisture fills the top layer and then infiltrates into the lower layers. The bottom layer loses water by both transpiration and base flow generated using an empirical relationship based on the soil moisture of the bottom layer (Liang et al., 1994). The model is then implemented using a grid mesh for the entire basin.

Evaporation, runoff, and base flow are predicted independently for each grid cell. Streamflow is then generated at specified locations by routing runoff and base flow from each grid cell using the linearized Saint-Venant method as discussed by Lohmann et al. (1998a, 1998b, 1996).

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The water balance can be described by the following set of Equations 5.1, 5.2 and 5.3, volumetric soil moisture content for the layers; Qb is the baseflow; Q1,2 and Q2, 3

are the exchanging fluxes between layers 1,2 and 2,3, respectively; P is the precipitation; R is the surface runoff; E is the bare soil evaporation; T is the transpiration; and α1, α2, and α3 are the fraction of transpiration from layers 1, 2, and 3, respectively.

The surface temperature for each hourly time step is initialized from the ground heat flux and is iteratively determined by solving for the energy balance and by minimizing the residuals. Due to the combined solution of water and energy budgets by the model, the surface temperature estimates are dependant on the soil and vegetation types and also on the external forcings such as precipitation and air temperatures.

2.2 Model Parameters and Input

The meteorological, soil, and vegetation data and terrain characteristics, used as input to the model, are described below.

2.2.1 Soil and Vegetation Parameters

Soil data for the continental United States are obtained from Penn State’s Earth System Science Center’s State Soil Geographic Database (STATSGO) data (STATSGO, 1994; Miller and White, 1998) at 30 arc second resolution. For each of the available layers, most of the parameters including saturated hydraulic conductivity, porosity, soil moisture at field capacity, and wilting point are obtained based on the soil texture classes (Rawls et al., 1993). The STATSGO dataset consists of georeferenced digital map data along with attribute data to include the texture classes — percentage of sand, silt, and clay.

The compiled soil maps were created using the USGS 1° × 2° topographic quadrangles (1:250,000 scale) as base maps, which were then merged on a state basis. The approximate minimum map unit area delineated in the STATSGO database is about 625 hectares (1544 acres). The number of delineations on each of the quadrangles is around 100 to 200, but also with values ranging to 400.

The data were obtained (STATSGO, 1994; Miller and White, 1998) at 1/8°

spatial resolution and the parameters were also aggregated to 1° pixels. The top

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11 layers of data, of about 1.5 m total depth (depths below are not available for the majority of regions), are aggregated to 3 layers – 0 to 10 cm, 10 to 40 cm, and 40 to 140 cm, respectively. The limitations in availability of field data for soil texture classes below 1.5 m depth limits the appropriate characterization of depth to bedrock. The vegetation classification data were obtained for the Land Data Assimilation Scheme (LDAS) domain (scaled up to a 1/8° spatial resolution) based on the University of Maryland (UMD) classification system (Hansen et al., 2000). The LAI were obtained from Myneni et al. (1997) and the fraction of vegetation cover within each 1/8° pixel as described by Maurer et al.

(2001a, 2001b). The classification has 13 major classes and the data are static, i.e., time invariant. Terrain characteristics include the Digital Elevation model (DEM), stream-network and basin boundaries based on Pfafstetter codes (Pfafstetter, 1989) were extracted from the U.S. Geological Survey’s (USGS) EROS Data Center HYDRO1K Data at 30 arc second resolution (Verdin and Verdin, 1999).

2.2.2 Model Forcings

Meteorological data include daily precipitation, air temperature, wind speed, humidity, and incoming short-wave and long-wave radiation.

Precipitation and air temperature data were obtained at daily time intervals from Cooperative Summary of the Day data, National Climatic Data Center (NCDC), Asheville, North Carolina (National Weather Service, 1987; Reek et al., 1992) for the period 1950 to 1999, to include all the measurement stations within the study area. These observations have undergone quality control with internal consistency checks, comparisons against climatological limits, serial checks, and evaluations against surrounding stations. The point measurements were gridded using an interpolation routine SYMAP (Shepard, 1984) at 1/8° spatial resolution. Similarly the subdaily (hourly) precipitation data for the energy balance forcings were obtained from the NCDC Hourly Precipitation dataset (National Weather Service, 1987). The raw data were then gridded to the model resolution of a 1/8° grid cell (approximately 140 km²). Short-wave radiation, long-wave radiation, and vapor pressure are internally parameterized in the model from air temperature using methods described by Thornton and Running (1999), Bras et al. (1990), and Kimball et al. (1997). The model preprocessing includes lapsing the temperature data to the mean grid elevation at a rate of -6.5K/km and the gridded air temperatures are disaggregated to a time step of 1 hour by assuming that the minimum temperature occurred at dawn and the maximum temperature occurred at two-thirds of the interval between dawn and dusk (Maurer et al., 2002; Rhoads et al., 2001). The daily average 10 m wind speed data were obtained from the National Centers for Environmental Prediction (NCEP). Reanalysis output was provided by the National Oceanic and Atmospheric Administration (NOAA)-Cooperative Institute for Research in Environmental Sciences (CIRES) Climate Diagnostics Center, Boulder, Colorado, USA (Kalnay et al., 1996).

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Figure 5.1 (a) Simple VIC-3L model schematic (redrawn from Lettenmaier, 2003). (b) Upper Mississippi River basin, major rivers, and the discharge measurement stations.

2.3 Illinois Soil Moisture Measurements

In situ observations of soil moisture at 17 Illinois Climate Network stations were initiated in 1981 by the Illinois Water Survey (Hollinger and Isard, 1994;

Robock et al., 2000). Direct gravimetric measurements for each site were used to calibrate the Troxler Neutron Depth Probe. Soil moisture measurements are measured at the top 10 cm and then for every 20 cm layer to a depth of 2 m. All observational sites are grasslands. Observations are made at each site at least twice each month (at the middle and end of the month) during the warm season (March through September) and only once a month (the last week of the month)

(a)

(b)

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during cold seasons (October through February). The observations of the layers were aggregated to match VIC-3L model simulation layer thickness.

2.4 TOVS Surface Temperature Data

TIROS Operational Vertical Sounder (TOVS) missions have flown on polar orbiting satellites TIROS-N, NOAA-6 through NOAA-12, and NOAA-14 from late 1978 to the present. Depending on the satellite, TOVS has equatorial, at nadir, daily overpass times of either 0230 and 1430 local time (LT), or 0730 and 1930 LT. The exact times for each pixel varies with distance from the equator and up to ± 1 hour for off-nadir observations. TOVS is composed of HIRS2 (High Resolution Infrared Sounder), MSU (Microwave Sounding Unit), and SSU (Stratospheric Sounding Unit). Susskind et al. (1984) used an algorithm that calculates surface temperature using a 6-hour forecast of atmospheric temperature and moisture profiles produced by a 4° × 5° version of the Goddard Earth Observation System data-assimilation system. An iterative relaxation of atmospheric conditions is carried out on the basis of the difference between the modeled and observed clear sky radiances until convergence is obtained or the retrieval is rejected. Retrievals can be made with up to approximately 80% cloud coverage and are gridded on a 1° × 1° spatial grid, and for this reason the estimated surface temperature should be considered an average of the cloud-free areas of the scene. Lakshmi and Susskind (2001a) conducted direct comparisons of TOVS land surface temperatures to field observations collected during the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), the Boreal Ecosystem-Atmosphere Study (BOREAS), and the Hydrologic-Atmospheric Pilot Experiment (HAPEX). Standard deviations of TOVS surface temperatures with ground observations were 4 to 5 degrees and a bias of nearly zero for the duration of the experiments, which were 1 year (HAPEX), 2.5 years (FIFE), and 3 years (BOREAS).

2.5 USGS Measured Streamflow Data

The daily streamflow measurements were obtained from USGS Water Resources data. The two stations chosen were the basin outlet of the Mississippi River at Grafton, IL and the Illinois River at Valley City, IL, for the period of 50 years (1950 to 1999) (U.S. Geological Survey, 1995). The stream discharge data are provisional and may have errors due to instrument malfunctions and/or physical changes at the measurement site. Figure 5.1b depicts the location of the two stations in the Upper Mississippi River basin.

2.6 Study Area: Upper Mississippi River Basin

The Mississippi River basin is made up of five major subbasins – the Upper Mississippi River basin, the Missouri River basin, the Red River basin, the Arkansas River basin, and the Ohio River basin. The area of study, the Upper Mississippi River basin, is shown in Figure 5.1b. The major rivers in the basin are the Mississippi River, the Illinois River and the Wisconsin River. The basin

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encompasses parts of Illinois, Iowa, Indiana, Minnesota, Missouri, and Wisconsin and the basin area is approximately 443,475 km². The average elevation of the basin is 310 m, with highest elevations around the northern regions of about 600 m; and the southern region has an elevation of around 130 m. The soil is primarily sandy/sandy loam. The most predominant vegetation type in the southern part of the basin is cropland (UMD class 11) and grasslands (UMD class 10), and predominantly deciduous broadleaf forests (UMD class 4) in the northern part of the basin. The basin receives an average annual precipitation of about 800 mm (averaged over the period 1950 to 1999).

3. RESULTS AND ANALYSIS