| description abstract | Calibration of land surface models improves simulations of surface water and energy fluxes and provides important information for water resources management. However, most calibration studies focus on local sites and/or small catchments because of computational limitations, lack of atmospheric forcing data, and lack of observed water and energy fluxes. Even though a well-established streamflow gauge network exists, its data are not well suited to the calibration of land surface models in cold regions because of large systematic precipitation biases. This study provides a newly developed method to adjust systematic precipitation biases arising from gauge undercatch (e.g., wind blowing, wetting loss, and evaporation loss). The new method estimates model parameter and precipitation errors simultaneously through the use of observed annual streamflow in the northeastern United States. The results show that this method improves streamflow simulations and gives a reasonable estimate for systematic precipitation bias. In addition, the impacts of model parameter errors on the calibration of the Land Dynamics (LaD) model and on the estimation of systematic precipitation biases are investigated in the northeastern United States. | |
