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Previous Precipitation Modeling Studies in the Susquehanna Watershed
Previous modeling studies attempted to predict Chesapeake Bay salinity using a variety of methods for different objectives (see Gibson and Najjar, 2000). For example, Wang (1983) accurately captured bay salinity gradients during a single low discharge August using a two-dimensional branching salinity model. Johnson et al. (1991) developed a hydrodynamic model incorporating many variables, most of which are not available for calibration and verification over long time series (i.e. wind, bay turbulence). Multiple regression (Wang et al., 1992) and neural networks (Desilets et al., 1992) have been used to model bay salinity, but do not incorporate freshwater discharge and are useful mainly over short timescales. Citing the need to incorporate Susquehanna River discharge, the primary forcing factor in upper bay salinity, into a modeling scheme, Gibson and Najjar (2000) developed an autoregressive statistical model, which performs a regression of a variable against earlier values of itself, to reconstruct instrumental SusquehWednesday, November 16, 2005 12:52 PMd with a climate-forced hydrological model under a future climate scenario of a doubling of pre-industrial atmospheric CO 2 concentrations to project possible future salinity changes (Jenkins and Barron, 1997; Najjar 1999; see also Crane and Hewitson, 1998, Yarnal et al., 2000). Depending on the region of the bay, Gibson and Najjar's model captures up to 93 % of salinity variability with the best results in the mid bay, where the influence of short-term discharge (northern bay) and oceanic currents (southern bay) were dampened.
Efforts to model regional precipitation variability has proven useful mainly for estimating streamflow during extreme, short-term meteorological and hydrological events. Bindlish and Barros (2000) used a downscaling of mesoscale meteorological models to examine orographic effects on rainfall, and to disaggregate rainfall fields between atmospheric and hydrological models. Yarnal et al. (2000) simulated the Susquehanna River basin hydrological response to atmospheric forcing using a linked high-resolution meteorological model and coupled hydrological models (see also Yu et al. 1999; Yu 2000). These models have resolution of precipitation and hydrological model fields that simulate local (1 to several km) and short-term (i.e. storm) events and may not be appropriate for the large spatial and temporal scales of the present study.
Links to online salinity, discharge and precipitation data sources
Chesapeake Bay Program (CBP) 1985-2001 calibration salinity data: http://www.chesapeakebay.net/data/data_desc.cfm?DB=CBP_WQDB
Chesapeake Bay Institute 1950-1979 verification salinity data (sites 834F, 834G, 847F, 848C, 848E, 850D, 851D, 857C, 858B): http://www.chesapeakebay.net/data/data_desc.cfm?DB=CBI_WQDB
U.S. Geological Survey Streamgage Data: http://waterdata.usgs.gov/nwis/ sw
National Climate Data Center monthly precipitation: http://lwf.ncdc.noaa.gov/oa/climate/onlineprod/drought/xmgrg3.html
Homogeneity investigation of instrumental data
To determine if non-climatic human activity, such as logging or monitoring methods, influenced instrumental salinity or discharge, the homogeneity of data was evaluated through double-mass analysis, a tool designed to evaluate time-series data for a variable from two distinct records (Kohler, 1949). Plotting annual monthly mean CB4.1C salinity values against those from CBP station CB4.1E, just to the east, reveals a strong linear relationship ( r 2 = 1) and suggests little statistical alteration in 1985-2001 salinity data. The accuracy of mean CBI salinity data is supported by a similar strong correlation ( r 2 = 0.99) with annual monthly mean salinity from 5 neighboring sites (834E, 843F, 845F, 854C, 855C).
Double-mass analysis of streamflow is hindered by the lack of a second long discharge record to compare with the Harrisburg data, but we obtained an independent estimate of discharge through a linear regression of instrumental precipitation and annual monthly mean discharge ( r 2 = 0.57) with the equation:
d t = 285.714 * p t - 1232.771
where d t is annual monthly mean discharge (m 3 /s) and p t is precipitation (cm). Using the discharge record from this regression, double-mass analysis indicates early data may be erroneous, and the linear relationship seen from 1931 to 2001 becomes significantly steeper prior to 1931. The deviation is likely caused by the precipitation record due to a change in the method of calculating state divisional precipitation prior to 1931. Current values are computed through a complex average of all representative stations within a division, but insufficient data made this impossible from 1895-1930, and values were instead derived from regressions of statewide averages (Guttman and Quayle, 1996). A 1900-1987 spatially averaged Susquehanna precipitation record from the National Ocean and Atmospheric Administration Climatological Baseline Station Data Over Land (CBSDOL) (Najjar, 1999) exhibits no deviation ( r 2 = 0.99) and is likely a more accurate precipitation record. Because CBSDOL data is highly correlated ( r 2 = 0.98) with the 1931-1987 NCDC record, our ultimate precipitation record is a composite of 1900-1987 CBSDOL, and 1987-2001 NCDC data.
Bindlish, R. and Barros, A. P. 2000: Disaggregation of rainfall for one-way coupling of atmospheric and hydrological models in regions of complex terrain. Global and Planetary Change 25 , 111-32.
Crane, R. G. and Hewitson, B. C. 1998: Doubled CO2 precipitation changes for the susquehanna basin: Down-scaling from the genesis general circulation model. International Journal of Climatology 18 , 65-76.
Desilets, L., Golden, B., Wang, Q. W. and Kumar, R. 1992: Predicting Salinity in the Chesapeake Bay Using Backpropagation. Computers & Operations Research 19 , 277-85.
Gibson, J. R. and Najjar, R. G. 2000: The response of Chesapeake Bay salinity to climate-induced changes in streamflow. Limnology and Oceanography 45 , 1764-72.
Guttman, N. B. and Quayle, R. G. 1996: A historical perspective of US climate divisions. Bulletin of the American Meteorological Society 77 , 293-303.
Jenkins, G. S. and Barron, E. J. 1997: Global climate model and coupled regional climate model simulations over the eastern United States: GENESIS and RegCM2 simulations. Global and Planetary Change 15 , 3-32.
Johnson, B. H., Heath, R. E., Hsieh, B. B., Kim, K. W. and Butler, H. L. 1991 In Technical Report HL-91-20 US Army Engineer Waterways Experiment Station, Vicksburg, MS.
Kohler, M. A. 1949: On the use of double-mass analysis for testing the consistency of meteorological records and for making required adjustments. Bulletin of the American Meteorological Society 30 , 188-89.
Najjar, R. G. 1999: The water balance of the Susquehanna River Basin and its response to climate change. Journal of Hydrology 219 , 7-19.
Wang, D. 1983: Two-dimentional branching salt intrusion model. Journal of Waterway, Port, Coastal and Ocean Research 109 , 103-14.
Wang, Q., Golden, B. L., Wasil, E. A. and DiNardo, G. 1992: Modeling salinity dynamics in the Chesapeake Bay. American Journal of Mathematical and Management Sciences 12 ,
Yarnal, B., Lakhtakia, M. N., Yu, Z., White, R. A., Pollard, D., Miller, D. A. and Lapenta, W. M. 2000: A linked meteorological and hydrological model system: the Susquehanna River Basin Experiment (SRBEX). Global and Planetary Change 25 , 149-61.
Yu, Z., Lakhtakia, M. N., Yarnal, B., White, R. A., Miller, D. A., Frakes, B., Barron, E. J., Duffy, C. and Schwartz, F. W. 1999: Simulating the river-basin response to atmospheric forcing by linking a mesoscale meteorological model and hydrologic model system. Journal of Hydrology 218 , 72-91.Yu, Z. 2000: Assessing the response of subgrid hydrologic processes to atmospheric forcing with a hydrologic model system. Global and Planetary Change 25 , 1-17.
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