The importance of representing the spatial structure of rainfall accurately has been emphasized in various hydrological studies. It has also been widely acknowledged that there is a need to account for uncertainty in rainfall input. Common approaches focus on accounting for either point measurement or sampling uncertainty in rainfall estimation. We present a method that jointly considers three sources of uncertainty affecting the space-time mapping of rainfall: point measurement, sampling and neighborhood uncertainty. To our knowledge, neighborhood uncertainty has not been included in any prior rainfall uncertainty analysis. We generated an ensemble of 400 realizations of daily rainfall fields at a 2 km × 2 km spatial resolution for a catchment in Western Denmark (1055 km 2 ). At the core of our method is the sequential Gaussian simulation (SGS) technique. Results indicate that our approach is able to reproduce key statistical features of the rainfall distribution. We examined the impact of different spatial (grid and catchment) and temporal supports (one day, one month, 5-year period) on the overall uncertainty. We also quantified the effect of each uncertainty source on rainfall field uncertainty. Finally, we compared our simulation results with those of a parallel expert elicitation study. We found that the expert elicitation uncertainty for average catchment rainfall in a 5-year period was considerably larger than quantified in our study (CV of 1.1% vs. 5%). An even larger discrepancy was found for the 5-year average of gauge rainfall, where expert elicitation resulted in a value that was an order of magnitude higher (CV of 0.2% vs. 2%). Possible reasons for this gap are discussed.
- Neighborhood uncertainty
- Rain gauge
- Rainfall uncertainty
- Sequential Gaussian simulation
- Spatial and temporal support effects