Correction of Global Precipitation Products for Orographic Effects

Correction of Global Precipitation Products for Orographic Effects Jennifer C. Adam 1, Elizabeth A. Clark 1, Dennis P. Lettenmaier 1, and Eric F. Wood 2 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 8 th International Conference on Precipitation (August, 2004) Vancouver, British Columbia, Canada 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 worldwide which were obtained from the Global Runoff Data Center (GRDC) and the Global River Discharge Database (Riv. DIS v 1. 1). The correction ratios from each of the gauged basins were interpolated to the rest of the orographic domain using dominant wind direction and fine-scale elevation information. These ratios were applied to an existing global precipitation data set (1979 through 1999, 0. 5˚ resolution), following application of adjustments for precipitation catch deficiencies. 1 2 Determination of Average Ratios for Selected Basins In this figure, streamflow stations are overlaid onto the correction domain. Data sources include: Riv. DIS v 1. 1, GRDC, and HCDN (United States only). 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, discussed in depth 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). Moisture Limited Definition of Correction Domain The correction domain was defined as all 0. 5˚ slopes (aggregated from 5 min) 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). Energy Limited Equations 1 2 Where = Aridity Index Where = Soil Moisture Storage Index Correction Band 2 3 4 5 6 7 All 5 min 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. 3 Ratio/Correction Band Relation San Joaquin (Vernalis, CA) Area = 35, 058 km 2 Spatial variability of the correction ratio across each of the gauged basins was constructed by developing a relationship between the correction ratio and the 5 min 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 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). The parameter, A, Equation was found to have a slight dependency on Rave, and Constraints: therefore is 1. r=1 for band=1 calculated as a 2. Rave is conserved function of Rave. From PRISM: Rave = 1. 29 A = 0. 026 B = -0. 022 C = 0. 995 2 3 4 5 6 7 Correction Band 5 4 The ET/P vs. PET/P curves of Budyko (1974) and Sankarasubramanian and Vogel (2000). The curves are semiempirical: the limits reflect physical constraints, but the curves are developed from observations. PET was calculated at 0. 5˚ for each year between 1979 and 1999 using the Droogers and Allen (2002) method. The 0. 5˚ dataset of Dunne and Willmott (2000) was used for maximum soil moisture storage capacity. Interpolation of Ratios to Ungauged Basins Annual dominant wind direction at 0. 5˚ resolution was determined by using the NCEP/NCAR Reanalysis (Kalnay et al. 1996) daily meridional and zonal wind speeds. Using the 0. 5˚ dominant wind direction data (below), and a 5 min DEM, slope type at a resolution of 5 min was determined by finding the direction of steepest slope from the DEM (over a scale of approximately 50 km), and comparing to the dominant wind direction of the overlying 0. 5˚ grid cell. Correction ratios were interpolated from 5 min grid cells in gauged basins to grid cells in the Upslope Downslope rest of the Cross-Wind correction domain using the 5 min 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 simple 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 5 min interpolated correction ratios were aggregated to 0. 5˚ (as shown in Box 5 for the globe). Application of Correction Ratios The gridded 0. 5˚ correction ratios (left) were applied (via multiplication) to the annual climatology of the dataset described by Adam and Lettenmaier (2003). Before This is a dataset in which monthly Orographic climatological corrections for gauge Correction -catch deficiencies are applied to the monthly 0. 5˚ time-series (19791999) of Willmott and Matsuura (2001). There are large isolated 5 areas of low correction ratios in most of Africa, Europe, and Eastern Asia (and a few smaller areas After elsewhere) which we suspect Orographic Correction may be due to the use of un-naturalized streamflow. It is reasonable to expect correction ratios less than one on the lee side of major divides, but large isolated areas of low correction ratios suggest a limitation in the method. A simple solution is to truncate correction ratios that are less than one to one. We note that orographic corrections in Europe are not as important as in other continents because the distribution of precipitation stations with elevation matches more closely the hypsometric curve. (See figure at the bottom left which shows the difference between the percent of stations and the percent of area for each elevation band; i. e. the differences are lowest in Europe. ) The percent increase in precipitation due to the Africa All Corr. Africa application of the correction ratios was computed for each continent and Corr. > 1 globally (See table: blue values are for all corrections; red values are for Australia Asia corrections greater than one only). Australia Europe N. America S. America Continent Precipitation Increase: Entire Continent Correction Domain Only Africa -3. 7% / 0. 9% -19. 5% / 4. 5% Australia -2. 2% / 2. 4% -7. 3% / 8. 1% Eurasia 6. 8% / 10. 4% 13. 9% / 21. 2% North America 5. 6% / 6. 4% 19. 9% / 22. 8% South America 1. 8% / 3. 3% 13. 2% / 24. 0% Global 3. 0% / 5. 8% 9. 9% / 19. 0% = Soil Moisture Storage Capacity Eurasia N. America S. America Globe 6 Comparison to PRISM over the Contiguous USA PRISM climatology is an independent estimate of precipitation magnitude and provides a comparison for our corrected precipitation data. We compared the percent increase in precipitation due to our orographic corrections to the percent increase inferred by PRISM (Daly et al. 1994) by using the Willmott and Matsuura (2001) 0. 5˚ data as the base-line precipitation (the PRISMInferred climatology was first Corrections aggregated to 0. 5˚). Adam et al. Corrections Percent Increase PRISM Adam et al. Entire USA 3. 2 % 6. 0 % Inside Corr. Domain 20. 9 % 28. 4 % Outside Corr. Domain 1. 4 % 0. 0 % CONCLUDING REMARKS • This work satisfies a need for global gridded precipitation data that account for orographic effects (Nijssen et al. 2001). • This approach (using water balance and a variation of the Budyko ET/P vs. PET/P curve) was implemented over the globe and found to have realistic results in most places. • Some continents (e. g. Africa, Europe, and Asia) have large isolated areas of low correction ratios which we suspect to be due to the use of un-naturalized streamflow. Therefore, as a simple solution, we recommend applying only correction ratios that are greater than one. • PRISM precipitation (Daly et al. 1994) is an independent estimate of precipitation magnitude (we used PRISM to aid in constructing the spatial variability of the correction ratios). Although our corrections result in somewhat higher precipitation values, these magnitudes are comparable and realistic. Note: See the author for a list of references.