Complementary Relationship of evaporation (CR) theory has gained attention in recent years, in part because it relies solely on standard weather data for estimating evaporation, eliminating the need for land surface moisture and resistance information. CR models show varied skill across diverse landscapes and climates, however, differences in model parameterization, calibration, station data, and study locations have precluded the ability to consistently and systematically compare model skill. This study intercompares four widely used CR models, uncalibrated and calibrated, with eddy covariance data from 82 sites worldwide, and assesses how environmental conditions affect model skill and calibration. Eddy covariance data from over 200 FLUXNET and AmeriFLUX stations were quality assured and controlled, filtered, and energy balance closure corrected using open-source software and benchmarking procedures for reproducibility. Systematic intercomparison showed that, overall, the Rescaled Power function (Szilagyi et al., 2022) had the highest skill followed by Sigmoid (Han & Tian, 2018), while Polynomial model (Brutsaert, 2015) generally biased high, and Advection-Aridity (Brutsaert & Stricker, 1979) often produced unrealistic negative values when observed evaporation was low. Calibration greatly improved model skill, and single parameter models yielded similar skill, highlighting the potential to reduce model non-uniqueness. Calibration and model skill rankings were consistent using energy balance closure corrected and uncorrected datasets. Results indicate that environmental conditions such as aridity index, relative humidity, vapor pressure deficit and the ratio between radiative and apparent potential evaporation can explain spatial patterns of CR model parameters, revealing opportunities for future development of improved, calibration-free CR evaporation modeling.

Water supply reliability in the Truckee River basin stands to substantially benefit from forecast-informed reservoir operations(FIRO), especially given expected increases in rain: snow ratios and a transition to earlier runoff under warming climate that current infrastructure and operational rules were not designed for. However, in its position on the lee side of the Sierra Nevada mountains (CA/NV, USA), several unique forecast uncertainties exist that must be considered to mitigate against increased flood risk potential. Both water supply and floods are strongly linked to wintertime atmospheric rivers (AR) but despite improvements in forecasting these events at long lead times, the timing and amount of spillover precipitation onto the lee side remains a key uncertainty. In addition, storm runoff volumes in this basin are highly sensitive to rain-snow elevation, which is also difficult to forecast. Finally, antecedent snowpack and soil conditions have the potential to modulate runoff volumes but factors controlling the strength of these modulations are incompletely understood and monitored. In this study, we assess streamflow forecast skill in the Truckee River to provide a preliminary understanding of potential forecast-related challenges and opportunities for FIRO. To accomplish this, we used an archive of readily available short-range Hydrologic Ensemble Forecast System winter (Oct-Apr) streamflow forecasts for water years 2015-2020 and compared these to observed3-day flows at lead times of 0 to 15 days. We subset the data into AR days, non-AR days and top 10% flow days examined the variance explained between the ensemble median and observed 3-day flows as a function of lead time. We also examined how the observed 3-day flows rank in relation to the ensemble members for each day. We found that forecast accuracy improves considerably starting at a 7-day lead time but tends to be lower for high-flow and AR events relative to non-ARs. We also found the ensembles to have a slight bias toward underprediction and tendency toward under-dispersion (i.e. observed flows were sometimes outside the ensemble range) with this being the case for AR and high flow days for some but not all sites.