Investigating the spatial scaling effect of the non-linear hydrological response to precipitation forcing in a physically based land surface model

Precipitation is the most important component and critical to the study of water and energy cycle. In this study we investigated the propagation of precipitation retrieval uncertainty in the simulation of hydrological variables, such as soil moisture, temperature, runoff, and fluxes, for varying spatial resolution on different vegetation cover. Two remotely sensed rain retrievals were explored (one based on satellite IR-only data and the other one based on ground radar data) and three spatial grid resolutions: 0.25°, 0.5° and 1.0°. This investigation was facilitated by an offline Community Land Model (CLM) which is forced by in situ meteorological data from Oklahoma Mesonet and high-resolution (0.1 o /hourly) rain gauge-calibrated WSR-88D radar (Nexrad) based precipitation fields. In turn, radar rainfall is replaced by the satellite rain estimates at coarser resolution (0.25°, 0.5° and 1°) to determine their impact on model predictions. A fundamental assumption made in this study is that CLM can adequately represent the physical land surface processes. Results show how uncertainty of precipitation measurement affects the spatial variability of model output in various modelling scales. The study provides some information on the uncertainty of hydrological prediction via interaction between the land surface and near atmosphere fluxes in the modelling approach and hopefully it will contribute to water resource redistribution due to climate change in the Korean Peninsula.