Abstract
Design rainfall is an essential input to a hydrologic model, which is used to estimate design discharge that is needed in the planning and design of many engineering infrastructure projects. Design rainfall estimation is made using recorded rainfall data over many stations in a given region. Uncertainties in design rainfall estimates arise from various sources such as data error, sampling error, regionalization error, model error and error due to climate change. This paper reviews various sources of uncertainties in design rainfall estimation. It has been found that uncertainty in design rainfall estimates are hardly considered in design applications. Uncertainty in design rainfall estimation can be assessed using Monte Carlo simulation and bootstrapping techniques. These techniques require significant computer power, which however is not a problem now a days. The biggest challenge in uncertainty estimation lies in the assessment of the impacts of non-stationarity in the rainfall data on design rainfall estimates. The findings of this paper would be useful to future studies on design rainfall estimation.
| Original language | English |
|---|---|
| Pages (from-to) | 65-75 |
| Number of pages | 11 |
| Journal | Journal of Hydrology and Environment Research |
| Volume | 2 |
| Issue number | 1 |
| Publication status | Published - 2014 |
Keywords
- rain and rainfall
- Monte Carlo method
- bootstrap (statistics)
- measurement uncertainty (statistics)
- climatic changes