Sensitivity Analysis in Mean Annual Sediment Yield Modeling with Respect to Rainfall Probability Distribution Functions

Abstract

An accurate estimation of the mean annual sediment yield from basins contributes to optimizing water resources planning and management. More specifically, both reservoir sedimentation and the damage caused to infrastructures fall within its field of application. Through a simple probabilistic combination function implemented in hydrometeorological models, this sediment yield can be estimated on a planning and management scale for ungauged basins. This probabilistic combination methodology requires the use of probability distribution functions to model design storms. Within these functions, SQRT-ET max and log-Pearson type III are currently highlighted in applied hydrology. Although the Gumbel distribution is also relevant, its use has progressively declined, as it has been considered to underestimate precipitation depth and flow discharge for high return periods, compared to the SQRT-ET max and log-Pearson III functions. The quantification of sediment yield through hydrometeorological models will ultimately be affected by the choice of the probability distribution function. The following four different functions were studied: Gumbel type I with a small sample size, Gumbel type I with a large sample size, log-Pearson type III and SQRT-ET max. To illustrate this, the model with these four functions has been applied in the Alto Palmones basin (South Iberian Peninsula). In this paper, it is shown that the application of Gumbel function type I with a small sample size, for the estimation of the mean annual sediment yield, provides values on the conservative side, with respect to the SQRT-ET max and log-Pearson type III functions.

Keywords

• sediment yield
• soil loss
• design storms
• water resources management
• probability distributions