Correction of Global Precipitation Products for Orographic Effects Jennifer C. Adam1, Dennis P. Lettenmaier1, and Eric F. Wood2 1. Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 98195 2. Department of Civil Engineering, Princeton University, Princeton, NJ, 08544 84th AMS Annual Meeting (January, 2004) Seattle, Washington 3 4 Ratio/Correction Band Relation Interpolation of Ratios to Ungauged Basins ABSTRACT Underestimation of precipitation in topographically complex regions is a problem with most gauge-based gridded precipitation data sets. Gauge locations tend to be in or near population centers, which usually lie at low elevations relative to the surrounding region. For example, past modeling studies have found that simulated mean annual Columbia River streamflows using gridded precipitation based on Global Precipitation Climatology Center (GPCC) precipitation products is about one-third of the observed discharge. In an attempt to develop a globally consistent correction for the underestimation of gridded precipitation in mountainous regions, we used a hydrologic water balance approach. The precipitation in orographically-influenced drainage basins was adjusted using a combination of water balance and variations of the Budyko ET/P vs. PET/P curve. The method is similar to other methods in which streamflow measurements are distributed back onto the watershed and a water balance is performed to determine “true” precipitation; but instead of relying on a modeled runoff ratio, evaporation is estimated using the ET/P vs. PET/P curves. This approach requires annual time-series of hundreds of historical discharge records world-wide which were obtained from the Global Runoff Data Center (GRDC) and the Global River Discharge Database (RivDIS v1.1). The correction ratios from each of the basins were interpolated and used to scale monthly precipitation from an existing monthly global data set (1979 through 1999, ½˚ resolution), following application of adjustments for precipitation catch deficiencies. Spatial variability of the correction ratio across each of the gauged basins was constructed by developing a relationship between the correction ratio and the 5min correction bands (discussed in box 1). PRISM (Daly et al., 1994) for the US was used to determine the form of this relation by assuming Correction ratios were interpolated from 5min grid cells in gauged basins to grid cells in the rest of the correction domain using the 5min gridded data of slope types (the correction ratios should be affected by the type of slope, e.g. if the grid is on an upslope or downslope, where the rain shadow occurs). An inverse-distance weighting scheme was used in which the correction ratios were interpolated from grids with the same slope type and the same correction band. A radius of 500 km was used for the interpolation, but this radius was increased if the minimum of 10 data points was not met. The 5min interpolated data was aggregated to ½ ˚ (as shown for North and South America to the right). that the variability of PRISM precipitation with elevation is correct. 33 basins in the US were used for this regression. A quadratic expression was used in which two constraints were imposed (see box to left). The parameter, A, was found to have a slight dependency on Rave, and therefore is calculated as a function of Rave. Equation Constraints: 1. r=1 for band=1 2. Rave is conserved From PRISM: Annual dominant wind direction at ½˚ resolution was determined by using the NCEP/NCAR Reanalysis (Kalnay et al., 1996) daily meridional and zonal windspeeds. Upslope Downslope Cross-Wind 1 Definition of Correction Domain San Joaquim (Vernalis, CA) Area = 35,058 km2 Rave = 1.29 A = 0.026 B = -0.022 C = 0.995 2 3 4 5 6 7 Correction Band The correction domain was defined as all ½˚ slopes (aggregated from 5min) greater than a threshold of 6 m/km, the approximate slope above which the Willmott and Matsuura (2001) data differs by more than 10% from PRISM (Daly et al., 1994). Basins Selected for North and South America Using the ½˚ dominant wind direction data (above), and the 5min DEM by TerrainBase (Row et al., 1995), slope type at a resolution of 5min was determined by finding the direction of steepest slope from the DEM (over a scale of approximately 50 km), and comparing to the dominant windspeed of the overlying ½˚ grid cell. All 5min cells within the correction domain were assigned to a correction band ranging from 2 (lowest elevations) to 7 (highest elevations). The bands were assigned by determining the maximum and minimum elevations within a specified radius of the cell and evenly dividing the elevations between minimum and maximum into the six correction bands. 5 Correction Band Application of Corrections to Precipitation Climatologies 2 3 4 5 6 7 Before Orographic Correction After Orographic Correction The gridded ½˚ correction ratios (shown in box 4, above) were applied (via multiplication) to the annual and monthly climatologies of the dataset described by Adam and Lettenmaier (2003). This is a dataset in which monthly climatological corrections for gauge-catch deficiencies are applied to the monthly ½˚ time-series (1979-1999) of Willmott and Matsuura (2001). Images of the climatologies before and after application of the corrections are shown to the left for North and South America. 2 Determination of Average Ratios for Selected Basins In an attempt to develop a globally consistent correction for the underestimation of gridded precipitation in mountainous regions, an approach is used in which streamflow measurements are distributed back onto the watershed and a water balance is performed for that watershed (equation 1). Because evaporation is also an unknown, a second equation is needed. We used the ET/P vs. PET/P curves, originally introduced by Budyko (1974). The equation of Sankarasubramanian and Vogel (2000) was applied because it also takes into account the soil moisture storage capacity (equation 2). A basin-average correction ratio (Rave) was determined by dividing the “true” precipitation for that basin (calculated from equations 1 and 2) by the precipitation described in Adam and Lettenmaier (2003). Increase in Precipitation North America South America Entire Continent 5.3 % 1.1 % Correction Domain 20.5 % 8.0 % Before Orographic Correction After Orographic Correction The percent increase in precipitation due to the application of the correction ratios was computed for both continents. Because a greater portion of North America is affected by orographic processes, the increase is greater over North America. Also, percent increases were calculated for each elevation band and for each month, for both continents. North America South America In this figure, streamflow stations are overlaid onto the correction domain. Data sources include: RivDIS v1.1, GRDC, and HCDN (United States only). Where = Aridity Index Where = Soil Moisture Storage Index = Soil Moisture Storage Capacity Equations 1 2 PET was calculated at ½˚ for each year between 1979 and 1999 using the Hargreaves and Samani (1982) method. Energy Limited Moisture CONCLUDING REMARKS This approach (using water balance and the ET/P vs. PET/P curves) was implemented over North and South America and found to have realistic results. Work is currently underway to implement this approach over the rest of the global land domain. More work is needed to evaluate the results. For example, how well is the spatial variability of PRISM reconstructed for the US, for both the gauged and ungauged basins? This evaluation is also currently underway. The authors would like to thank Liz Clark (UW graduate student) for her contributions to this poster. Note: See the author for a list of references. The ET/P vs. PET/P curves of Budyko (1974) and Sankarasubramanian and Vogel (2000). The curves are semi-empirical: the limits reflect physical constraints, but the curves are developed from observations. The ½˚ dataset of Dunne and Willmott (2000) was used for maximum soil moisture storage capacity.