Analysis of feedback effects and atmosphere responses when 2-way coupling a hydrological land surface model with a regional climate model – a case study for the Upper-Danube catchment

Abstract

Abstract. Most land surface hydrological models (LSHMs) take land surface processes (e.g. soil-plant-atmosphere interactions, lateral water flows, snow and ice) into detailed spatial account. On the other hand, they usually consider the atmosphere as exogenous driver only, thereby neglecting feedbacks between the land surface and the atmosphere. Regional climate models (RCMs), on the other hand, generally describe land surface processes much coarser but naturally include land-atmosphere interactions. What is the impact on RCMs performance of the differently applied model physics and spatial resolution of LSHMs? In order to investigate this question, this study analyses the impact of replacing the land surface model (LSM) within a RCM by a LSHM. Therefore, a 2-way coupling approach was applied for a full integration of the LSHM PROMET (1×1 km2) and the atmospheric part of the RCM MM5 (45×45 km2). The scaling interface SCALMET is used for down- and upscaling the linear and non-linear fluxes between the model scales. The response of the MM5 atmosphere to the replacement is investigated and validated for temperature and precipitation for a 4 yr period from 1996 to 1999 for the Upper-Danube catchment. By substituting the NOAH-LSM with PROMET, simulated non-bias-corrected near surface air temperature significantly improves for annual, monthly and daily courses, when compared to measurements from 277 meteorological weather stations within the Upper-Danube catchment. The mean annual bias was improved from −0.85 K to −0.13 K. In particular, the improved afternoon heating from May to September is caused by increased sensible heat flux and decreased latent heat flux as well as more incoming solar radiation in the fully coupled PROMET/MM5 in comparison to the NOAH/MM5 simulation. Triggered by the LSM replacement, precipitation overall is reduced, however simulated precipitation amounts are still of high uncertainty, both spatially and temporally. The distribution of precipitation follows the coarse topography representation in MM5, resulting in a spatial shift of maximum precipitation northwards the Alps. Consequently, simulation of river runoff inherits precipitation biases from MM5. However, by comparing the water balance, the bias of annual average runoff was improved from 21.2% (NOAH/MM5) to 4.4% (PROMET/MM5) when compared to measurements at the outlet gauge of the Upper-Danube watershed in Achleiten.