The sensitivity of catchment runoff models to rainfall data at different spatial scales

mum water levels upstream and downstream of the weir were obtained by surveying the flood marks. The longitudinal section is shown in Fig. 5, with the ground level and flood

For instance, the method can be used to identify the features of catchment morphology which attenuates (or magnify) the effects of rainfall space- time organization. With the use of

Figures 2, 4, 6 and 8 provide companion MLIN scatterplots for the same experiments. Training [A] and testing [B] dataset outputs are provided in each case; BPNN outputs are for

Monthly precipitation values for the Rhine basin according to di ff erent datasets (top) and daily discharge values at Lobith (bottom); model simulation results for the

In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction – SCF) for a distributed rainfall-runoff model to investigate when the

Comparison of (a) the 5 % daily high flows and (b) the 80 % daily low flows simulated with REF data, RAW (NCEP or GCM) data, and the three downscaling methods (applied to NCEP, CNRM

Given this identified challenge, the study has two main aims: (i) to investigate the impact of changes in spatial scale on model outcome for a set of spatial structures; (ii)

In this case, the application of traditional local sensitivity analysis methods (i.e. the examination of changes in the model output due to changes in the parameters values in