Intraseasonal variability

In the evaluation of the spatial and temporal variations of the surface energy and water budgets, we focus next on radiation fluxes, root zone soil moisture, evapotranspiration, and rainfall-runoff response within the Elizabeth, Enterprise, and Dailey watersheds at the monthly timescale.

The surface radiation budget in the LHM-3D relies on incoming short-wave and incoming long-wave radiation forcing from RegCM2 outputs, while the outgoing long-wave radiation is calculated on the basis of the simulated surface temperature. The surface fluxes including sensible, latent, and ground heat fluxes simulated by the hydrologic model must balance the net radiation. In the LHM-3D, the surface energy balance is captured virtually without discrepancy with respect to the regional model output, which indicates that energy fluxes in the LHM-3D off-line simulations are consistent with those produced in real-time RegCM2, including the surface albedo parameterization.

Figure 20.10 shows monthly values of basin averaged root zone moisture content (Figure 20.10(a)), evaporative fraction (the ratio of evapotranspiration to precipitation, Figure 20.10(b)), and runoff coefficient (the ratio of runoff to precipitation, Figure 20.10(c)) from April through August in 1988. Evapotranspiration substantially increases everywhere in June in response to rapidly increasing air temperature and LAI, thus consistent with the governing role of vegetation with respect to evapotranspiration as discussed earlier. At Dailey, in particular, evapotranspiration during June (greening phase) is almost twice the rainfall. During this period, the excess water needed to sustain the development of the canopy of the deciduous trees comes from the water stored in the deep root layer. However, there is also a simultaneous increase in the runoff coefficient that is explained by the increase in surface runoff production. In 1993, on the other hand, evapotranspiration increases significantly during the summer, especially at Dailey, because soil water is not a limiting factor (not shown).

Simulated soil water content across the Monongahela River basin exhibit strong seasonality. Monthly spatial distributions of root layer soil moisture at 1-km resolution are displayed in Figure 20.11 for 1988. In the spring, the root layer remains relatively wet across the entire

April

April

June

July

August

June

Figure 20.11 Simulated spatial distribution of monthly volumetric soil water content (9) in the root layer from April through August of 1988

river basin, especially at low elevations. In July, it dries progressively as a result of decreasing rainfall and increasing evapotranspiration. Once again, effects of spatial variability in topography, hydrogeology, and land cover on the evolution of soil moisture can be detected from these figures. Soil moisture levels are generally lower at high elevations where gradients are steep and the land surface is forested than they are at low elevations where gradients are relatively mild and the land surface is covered by short vegetation with shallow roots. In July of 1993, for example, soil moisture content is as low as 0.18 m3/m3 at high altitudes in the southern and eastern parts, and as high as 0.42 m3/m3 at low altitudes in the northern and western parts (not shown). In addition, the root zone layer at low elevations is generally wetter in the 1993 period as compared to the same zone in 1988 period as expected. However, soil moisture levels at high elevations are not significantly different in both years; that is, summertime soil water stress during drought is only anomalous at low elevations along the river valleys, in the areas typically used to grow crops. In this, the model captures the essence of the visual appearance of the landscape during drought in the region - brown fields of grass and shrubs sprinkled with green spots where trees are present.

Finally, the high correlation between simulated (1 km resolution) and observed streamflow hydrographs at

(01 April—31 August 1988)

(01 April—31 August 1988)

(a) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

(09 April—31 August 1993)

is a

(09 April—31 August 1993)

(b) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time (7-day period)

Figure 20.12 Comparison of the observed and simulated [1-km resolution] 7-day period low flows at Elizabeth between April and August: (a) 1988; and (b) 1993

(b) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time (7-day period)

Figure 20.12 Comparison of the observed and simulated [1-km resolution] 7-day period low flows at Elizabeth between April and August: (a) 1988; and (b) 1993

Rowlesburg and Parsons suggests that the model can be an effective tool in the prediction of low flow and floods into the Lake Lynn Reservoir on the Cheat River. The same is true with regard to the Tygart River Lake on the Tygart River based on the simulations at Philipi, Belington, Elkins, and Dailey. The utility of model prognostics for water resources management is illustrated in Figures 20.12(a) and (b) for 1988 and 1993, respectively, via a comparison of the statistics of observed and simulated seven-day period low flows at Enterprise and Elizabeth, respectively. As shown in the figures, the statistics of simulated low flows are generally in agreement with observations, especially during the summer season, when low-flow management is most critical.

20.4 DISCUSSION

Hydrologic model simulations of warm season hydrology in the Monongahela River Basin were evaluated both at the basin and subbasin with an eye on elucidating the dynamics of the water cycle under extreme conditions: drought in 1988 and flooding in 1993. For each case study, the model was implemented at two spatial resolutions: a fine resolution based on the mix of ancillary data available (1 km), and a much coarser resolution conditioned on the model's ability to preserve the terrain envelope and the stream network (5 km). Model simulations were evaluated according to a protocol of increasing complexity: (i) sensitivity to rainfall forcing as described by the streamflow response at the outlet of specific catchments; (ii) model structural stability at different resolutions as described by the physical mechanisms controlling rainfall-runoff response; and (iii) spatial and temporal variability of the coupled water and energy budgets.

First, we showed that because of landscape and river network complexity, even small differences in precipitation fields can have a significant impact depending on the physical processes that control rainfallrunoff response. By conducting simulations at different resolutions, we were able to explain how changes in the spatial resolution translate into nonlinear changes in the simulated hydrologic processes: runoff production (blue water) is controlled by hydraulic head gradients at 5 km, while infiltration capacity is the dominant control at 1 km resolution. Moreover, vegetation governs the spatial and temporal variability of root zone soil moisture (green water), especially during the greening season in June. Through evapotranspiration, vegetation also has an important role in runoff production, a role that is magnified at 5 km resolution because it acts to smooth further the steepness of hydraulic head gradients, thus reducing subsurface flow in the unsaturated zone, and consequently stream recharge. Although the streamflow simulations did not agree with observations as well for the 1988 drought as for the 1993 floods, the model was able to capture the broad space-time patterns of water and energy fluxes at both resolutions. Finally, the 1 km simulation is superior not only in matching the observed hydrographs (and associated statistics) but also with respect to the representation of hydrological processes within the basin as a whole. This implies that the model is structurally stable at 1 km resolution in the Monongahela River basin.

This research substantiates importance and necessity to our working assumptions: (i) the utility of hydrologic models for predictive studies hinges on the quality of the precipitation forcing, especially in regions of complex terrain where the space-time characteristics of rainfall combine with those of the landscape to establish highly nonlinear hydrologic regimes; and (ii) model structural stability is an essential condition of predictive ability. Furthermore, at a time when the policy paradigm in water resources has shifted toward an integrated view of land-water management and the water cycle (''a land-use decision is a water decision'', Falkenmark 2001), hydrologic modeling studies such as this provide an optimistic basis for quantitative impact assessments informed by science.

20.5 ACKNOWLEDGMENTS

This work was funded in part by a NASA Grant NAGW-5254 and NOAA GCIP Grant NA86GP0058 with the second author. We thank Drs Gregory Jenkins and Christopher Duffy for their comments and suggestions.

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