Using different hydrological variables to assess the impacts of atmospheric forcing errors on optimization and uncertainty analysis of the CHASM surface model at a cold catchment

Abstract

Estimation of parameters for land‐surface models, along with their corresponding uncertainties, relies on the input data for the atmospheric forcing variables including atmospheric pressure, temperature, humidity, wind speed, precipitation, and incoming shortwave and longwave radiation. Most studies assume that forcing data are accurate and contain no random or systematic observational errors. In practice, there are indeed systematic errors in precipitation measurements, especially for snowfall, due to wind‐caused undercatch. Incoming shortwave and longwave radiation fluxes are often not directly measured, but estimated from empirical formulations. Uncertainties in these forcing data may substantially affect optimization and uncertainty estimates of land surface models. In this study, we used 18‐year forcing and calibration data as well as information about the uncertainties in the forcing variables at Valdai, Russia, to study the impacts of forcing errors on selection of optimal model parameters and their uncertainty estimates when three different hydrological variables were used for calibration. The results show that forcing errors have few effects on the selection of optimal model parameter sets when monthly evapotranspiration and runoff are calibrated. However, forcing errors do introduce significant effects on the selection of optimal model parameters when daily snow water equivalent is calibrated. Forcing errors also significantly affect uncertainty estimates of the land surface model parameters. In addition, constraints of forcing errors are different when different hydrological variables are calibrated. All three hydrological variables constrain the incoming longwave radiation error well, and the snow water equivalent and runoff constrain winter snowfall errors well. However, all three hydrological variables cannot constrain the incoming solar radiation error well. We highlight in this study that runoff is shown to be a good observable to use for calibration, the reason being that it integrates multiple hydrological processes; and the results support the theory that typical rain/snow gauges have 10–20% undercatch.