The Rational Method, while a simple and widely used approach for estimating peak runoff, is primarily suited for small catchments due to its underlying assumptions. One key assumption is that the time of concentration (Tc), the time it takes for water to travel from the farthest point in the catchment to the outlet, equals the rainfall duration. This is often valid for smaller catchments with shorter Tc values.
However, in larger catchments, the assumption of uniform rainfall intensity throughout the storm duration becomes less reliable. Rainfall intensity can vary significantly across different parts of a large catchment, leading to inaccurate runoff estimates. Additionally, the Rational Method’s assumption of constant rainfall intensity over time may not hold true for longer durations, as shorter, more intense bursts within the storm could produce higher peak flows.
Furthermore, the method neglects the effect of temporary storage within the catchment, such as in channels or depressions, which can attenuate peak runoff. In reality, runoff rates vary spatially due to differing soil properties and antecedent conditions. The Bureau of Public Roads (1965) observed that peak discharge can sometimes occur before the entire drainage area contributes. This can happen when a significant portion of the catchment has a very short Tc, allowing for higher rainfall intensity to be applied to that area, resulting in a larger runoff contribution than the entire catchment with a lower intensity due to longer Tc in other parts. This leads to an underestimation of the peak runoff for large catchments when using the Rational Method.
In conclusion, while the Rational Method is a valuable tool for small catchments, its limitations become evident when applied to larger areas. Its simplifying assumptions regarding rainfall intensity, temporal variation, and storage effects can lead to significant errors in peak runoff estimation. Therefore, for large catchments, more sophisticated hydrological models that account for spatial and temporal variability are essential for accurate runoff prediction.