Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

Abstract

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km2 located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.

Impact of Urban Growth and Changes in Land Use on River Flood Hazard in Villahermosa, Tabasco (Mexico)

“…The performance of the software Iber could not be validated in the study area because there are no available historical data to do so. However, the model has been extensively validated and applied in previous studies related to river inundation, tidal currents in estuaries, and rainfall-runoff modeling [21][22][23][24][25][26][27], showing its ability to represent 2D free surface shallow flows and river inundation processes. The 2D shallow water equations solved by the software Iber are a high-fidelity physically-based model, and they were assumed to be the best representation of the inundation process for the purposes of this work.…”

Modeling Land Surface Fluxes from Uncertain Rainfall: A Case Study in the Sahel with Field-Driven Stochastic Rainfields

“…The interpolation consists of either Eulerian or Lagrangian kriging, depending on their respective performance as assessed for each event through a cross-validation scheme [69]. The conditional simulation scheme from Vischel et al [46] has been used by others [70] to evaluate the effects of rainfall uncertainty on the performance of calibrated rainfall-runoff models. Figure 1c displays the spatial distribution of time-aggregated ensemble rainfall over the study period, as maps of the ensemble mean and standard deviation.…”

Effects of Rainfall Spatial Distribution on the Relationship between Rainfall Spatiotemporal Resolution and Runoff Prediction Accuracy

“…Advances in measurement and computational techniques have enabled us to consider additional physical processes for accurate flood forecasting. As knowledge regarding rainfall-runoff processes has developed and the related technologies and models have continued to advance, significant research efforts have focused on the accuracy and uncertainty of model input data because the accuracy of rainfall-runoff modeling relies heavily on input data [1][2][3]. Among the many types of input data, rainfall data is not only one of the most critical factors affecting the accuracy of runoff prediction [4], but also one of the factors that produce the highest degree of uncertainty [5].…”

“…Therefore, many studies have been conducted to reduce the uncertainty originating from rainfall data. For example, Fraga et al [2] recently investigated the effects of rainfall data uncertainty on the performance of hydrological models in a small basin (~10 km 2 ).…”