Abstract
Watershed-scale annual evapotranspiration (ET) is routinely estimated by a simplified water balance as the difference in catchment precipitation (P) and stream discharge (Q). With recent developments in ET estimation by the calibration-free generalized complementary relationship, the water balance equation is employed to estimate watershed/basin P at an annual scale as ET + Q on the United States (US) Geological Survey’s Hydrologic Unit Code (HUC) 2- and 6-level watersheds over the 1979–2015 period. On the HUC2 level, mean annual PRISM P was estimated with a correlation coefficient (R) of 0.99, relative bias (RB) of zero, root-mean-squared-error (RMSE) of 54 mm yr−1, ratio of standard deviations (RS) of 1.08, and Nash–Sutcliffe efficiency (NSE) of 0.98. On the HUC6 level, R, RS, and NSE hardly changed, RB remained zero, while RMSE increased to 90 mm yr−1. Even the long-term linear trend values were found to be fairly consistent between observed and estimated values with R = 0.97 (0.81), RMSE = 0.63 (1.63) mm yr−1, RS = 0.99 (1.05), NSE = 0.92 (0.59) on the HUC2 and HUC6 (in parentheses) levels. This calibration-free water-balance method demonstrates that annual watershed precipitation can be estimated with an acceptable accuracy from standard atmospheric/radiation and stream discharge data.
Highlights
Precipitation constitutes spatially and temporally the most highly variable atmospheric parameter [1]
Alternative ways of precipitation observations present ever broader coverage and spatiotemporal resolution [2,11]; they cannot substitute for long-term records of precipitation that are crucial in understanding the ongoing climate change [11] with its multifaceted consequences, such as encountered in water resources management
It can be stated that annual precipitation rates can be estimated at least with an acceptable accuracy (sample means of R = 86 ± 8 (82 ± 10)%, relative bias (RB) = −3 ± 11 (−1 ± 15)%, RMSE = 77 ± 24 (115 ± 59) mm yr−1, ratio of standard deviations (RS) = 83 ± 14 (82 ± 20)%, and median
Summary
Precipitation constitutes spatially and temporally the most highly variable atmospheric parameter [1]. Alternative ways of precipitation observations (i.e., radar and satellite-based) present ever broader coverage and spatiotemporal resolution [2,11]; they cannot substitute for long-term records (mostly rain gauge-based) of precipitation that are crucial in understanding (and gauging the extent of) the ongoing climate change [11] with its multifaceted consequences, such as encountered in water resources management Due to its crucial role in these areas and its pivotal position in the global hydrological cycle in general [17], any approach that can help with its estimation in the lack of measured values on any spatial and temporal scale must be highly valuable (see the rapid expansion of satellite-based precipitation estimation products in e.g., [11]), especially so if it can yield the same long-term temporal coverage as rain gauges do
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