On Modeling Dry Deposition of Long-Lived and Chemically Reactive Species over Heterogeneous Terrain

An explicit multi-layer subgrid-scheme was developed for ameso-/-scale model to consider subgrid-scale surface heterogeneity, dry deposition, biogenic and anthropogenic emission of trace gases. Since dry deposition measurements of highly reactive trace species are scarce we try to evaluate this scheme by heuristic principles. The results of simulations conducted for a 5×5 km² resolution with and without thisscheme are evaluated by using results of a model run with 1×1 km²resolution, which is taken as a `grand thruth' and which has the same resolution as the subgrid. The explict multi-layer subgrid scheme provides a similar distribution of dry deposition fluxes as the much more computationally expensive simulation with the 1×1 km² resolution.Dry deposition fluxes determined from observations give evidence that the explicit multi-layer subgrid scheme which does not require a constant flux approximation for a layer of several decameters leads to an improvement in determining the exchange between the atmosphere and the ground.Results of simulations with a microscale model show that the inhomogeneity at forest edges leads to an increase of the turbulent transports of up to a factor 4 compared to horizontally homogeneous terrain, which is assumed to be the conditions of the subgrid cells (and which is usually the assumption for the entire grid cell in mesoscale models). Inhomogeneity inside an extended stand of trees causes an overall increase of 5–10% withhigh local extremes, i.e. such an inhomogeneity results to an underestimation of dry deposition in meso-/-scale models. The effects are most pronounced for a wind direction perpendicular to the forest edge.     

Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.