The two study basins are a subset of a number of study basins of the Distributed Model Inter-comparison Project – Phase 2 (DMIP2) (Smith et al., 2009). The Blue River basin is located in Blue in south-central Oklahoma near the Texas border, and the Illinois River basin is located in the north-east at the border of Oklahoma and Arkansas, and draining to the US Geological Survey flow station at Tahlequah. As shown in Figure 2.1, the Blue River basin is narrow and long with a drainage area of 1233 km2, while the Illinois River basin is leaf shaped with a drainage area of 2484 km2. The elevation range in the Blue River basin is 157-403m, and in the Illinois River basin it is 207-599m, making them somewhat hilly.
Oklahoma is located in a temperate climate region and experiences occasional extremes of temperature and precipitation typical of a continental climate. Much of the state is characterized by frequent interactions between cold and warm air masses, producing severe weather, including tornadoes and thunderstorms. There is considerable spatial variability of climate in the state, transitioning from subtropical in the east where the Illinois River basin is located to semi-arid in the high plains of the Panhandle. The Blue River basin is located in the middle of this climate range. During the study period (09/30/1995 to 10/01/2006), mean annual precipitation (P) in the Blue River basin was 1034mm, and in the Illinois River basin it was 1140mm. The mean annual free water evaporation (Ep) in the Blue River basin is about 1345mm, and in the Illinois River basin
it is about 1066mm. Based on this, the aridity index, Ep/P, is 1.3 for Blue and 0.93 for
Illinois, and the spatial variability of the aridity index is relatively small within both watersheds. In this sense climate in the Blue River basin is slightly more arid than in the
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Illinois River basin, which is consistent with estimated annual watershed scale runoff coefficient values, i.e., 0.19 in the Blue River basin and 0.27 in the Illinois River basin.
The dominant land cover type in the Blue River basin is woody-savanna, and in Illinois it is deciduous broadleaf forest, according to the 2001 National Landcover Data (NLCD) based on International Geosphere-Biosphere Program (IGBP) classification system (Eidenshink and Faundeen, 1994). According to the State Soil Geographic (STATSGO) dataset, soil texture in the Blue River basin mainly consists of clay soil (45.0%) and loam soil (16.3%), and in Illinois it mainly consists of silty clay (48.0%), silt loam (25.9%) and silty clay loam (22.1%). It is well known that any soil type consists of three common components: clay, silt and sand. Soil texture classification is based on the relative volumetric percentage of these components. The percentage of clay, or clay content, in the Blue River basin is much higher than that in the Illinois River basin, as shown in Figure 2.2(a). Figure 2.2(a) shows the clay content values averaged within the top 40cm of the soil column within each REW, based on the STATSGO soil database. The high clay content in the Blue River basin is also reported in Smith et al. (2004). Figure 2.2(b) presents the land surface slopes estimated from the DEM for each of the REWs in both basins. On average, it is clear that the Illinois River basin is somewhat steeper than Blue, although there is considerable spatial variability in the slopes within both watersheds. Moreover,, in the Blue River basin, there are complex interactions between surface water and groundwater due to an underlying Karst geological formation. As shown in Figure 2.2(c), groundwater from the Arbuckle-Simpson aquifer and Edwards-Trinity aquifer discharges to the upper reaches of the Blue River system, and contributes to the baseflow there. (see fact sheet put out by the Oklahoma Water Resources Board, 2003; Fabian, 2008). Here we view baseflow as the complementary part of overland flow, i.e., the low flow part which comes from both unconfined and confined aquifers. In addition, the Illinois River basin is occupied by porous limestone overlain by cherty soils (Peters and Easton, 1996), contributing to efficient subsurface drainage (Smith et al., 2004).
21 2.2.2. Data Collection
Most of the data used in this work were provided by the Office of Hydrologic Development, NOAA/NWS, as a contribution to the DMIP2 project (Smith et al., 2009). Only a brief description is provided here.
DEM data, spatial resolution 30x30m
Hourly multi-sensor (NEXRAD radar and gauge) precipitation data, spatial resolution 4kmx4km
Monthly mean daily free water surface evaporation
North American Regional Reanalysis (NARR) potential evaporation data, spatial resolution 32kmx32km, temporal resolution 3-hour ( interpolated to hourly resolution)
Hourly Observed USGS streamflow data
STATSGO soil data, spatial resolution 1kmx1km
2001 NLCD land cover map, spatial resolution 30mx30m
MODIS/Terra LAI (Leaf Area Index) data, spatial resolution 1kmx1km, temporal resolution 8-day ( interpolated to daily data)
For all time series data, the record period is 10/01/1995 to 09/30/2006. For more
details refer to Smith et al. (2009) and http://www.nws.noaa.gov/oh/hrl/dmip/2/data_link.html.
The annual potential evaporation obtained from the NARR dataset, which has been estimated on the basis of the Penman equation, is much higher than that estimated from the monthly mean free water surface evaporation. This is the case for both the Blue River and Illinois River basins. In order to be consistent, we rescaled the hourly potential
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evaporation series extracted from NARR dataset so that the resulting hourly potential evaporation series (hereafter noted as scaled potential evaporation) has the same seasonal variability as the free water surface evaporation, i.e., the monthly mean values are the same. This scaled potential evaporation series is used throughout this chapter. It is beyond the scope of this work to make a judgment on the appropriateness of the two sources of potential evaporation data, i.e., the original NARR data and the free water surface evaporation, although it is admitted these differences could contribute to some differences in the model predictions of water balance.