Abstract

Operational hydrologic models are typically calibrated using meteorological inputs derived from retrospective station data that are commonly not available in real-time. Inconsistencies between the calibration and (generally sparser) real-time station datasets can be a source of bias, which we show can be addressed by expressing real-time hydrological model forcings (primarily precipitation) as percentiles for a set of index stations that report both in real-time and during the retrospective calibration period, and using the real-time percentiles to create adjusted precipitation forcings. Although hydrological model precipitation forcings typically are required at time steps of one day or shorter, percentiles can be calculated for longer averaging periods to reduce the percentile estimation errors. We propose an index station percentile method (ISPM) to estimate precipitation at the models input time step using percentiles, relative to a climatological period, for a set of index stations that report in real-time. In general, this approach is most appropriate to situations where the spatial correlation of precipitation is high, such as cold season rainfall in the western U.S. We evaluate the ISPM approach, including performance sensitivity to the choice of percentile-estimation period length, using as a case study the Klamath River Basin, OR. Relative to orographically-adjusted interpolation of the real-time index station values, ISPM gives better estimates of precipitation throughout the basin. We find that ISPM performs best for percentile-estimation periods greater than 10 days, with diminishing returns for averaging periods longer than 30 days. We also evaluated the performance of ISPM for a reduced station scenario, and find that performance is relatively stable, relative to the competing methods, as the number of real-time stations diminishes