Uncertainty In Rainfall Data Research Articles

Bayesian scrutiny of simple rainfall–runoff models used in forest water management

Summary Simple rainfall–runoff models used in the assessment of land-use change and to support forest water management are subjected to a selection process which scrutinises their veracity and integrity. Veracity, the ability of a model to provide meaningful information is assessed using performance criteria, incorporating: a popular mean square error (MSE) approach; empirical distribution functions and information criteria. Integrity, a model’s plausibility reflected in its ability to extract information from data, is assessed using a Bayesian approach. A delayed rejection, adaptive Metropolis algorithm is used with a generalised likelihood to calibrate the models. Predictive uncertainty is assessed using a split sample procedure which uses high runoff data for calibration and drier data for validation. A simple multiplicative latent variable is used to accommodate input uncertainty in rainfall data, enabling a distinction to be made between uncertainty associated with data, parameters and the models themselves. The study demonstrates: the focus provided by setting model evaluation in a philosophical context; the benefits of using a more meaningful range of performance criteria than MSE-based approaches and the insights into integrity provided by Bayesian analyses. A hyperbolic tangent model is selected as the best of five candidates for its superior veracity and integrity under Australian conditions. Models with extensive application in South Africa, Australia and USA are rejected. Challenges to applying this approach in water management are identified in the pragmatic nature of the sector, its capacity constraints and a tendency of researchers to place confidence in accepted methods at the expense of rigour.

Comparison of spatial rainfall data calculated with a meteorological model and from interpolation of measurements - implications for FRAME modelled wet deposition

This work quantifies the differences between the two sources of spatial information of precipitation on FRAME modelled wet deposition of oxidised sulphur (SOx), nitrogen (NOy) and reduced nitrogen (NHx) for two years in an area of Poland. The model was run twice, first with precipitation dataset calculated with the WRF model (pWRF). The second run was based on interpolation of measured precipitation with kriging (pOK). SOx, NOy and NHx wet deposition calculated with pWRF precipitation gives lower country mean values if compared with pOK. The maximum values are higher for pWRF precipitation. Grid to grid correlation between pOK and pWRF modelled wet deposition is similar for both years, with the lowest values for NHx and the highest for SOx. The model-measurement agreement is better for the pOK FRAME run. The results show large uncertainties related with wet deposition modelling due to uncertainties in rainfall data.

PARAMETER UNCERTAINTY IN RAINFALL‐RUNOFF MODELING FOR POLDER SYSTEM DESIGN1

ABSTRACT: An approach is developed for incorporating the uncertainty of parameters for estimating runoff in the design of polder systems in ungaged watersheds. Monte Carlo Simulation is used to derive a set of realizations of streamflow hydrographs for a given design rainstorm using the U. S. Soil Conservation Service (SCS) unit hydrograph model. The inverse of the SCS curve number, which is a function of the antecedent runoff condition in the SCS model, is the random input in the Monte Carlo Simulation. Monte Carlo realizations of streamfiow hydrographs are used to simulate the performance of a polder flood protection system. From this simulation the probability of occurrence of flood levels for a particular hydraulic design may be used to evaluate its effectiveness. This approach is demonstrated for the Pluit Polder flood protection system for the City of Jakarta, Indonesia. While the results of the application indicate that uncertainty in the antecedent runoff condition is important, the effects of uncertainty in rainfall data, in additional runoff parameters, such as time to peak, in the hydraulic design, and in the rainfall‐runoff model selected should also be considered. Although, the SCS model is limited to agricultural conditions, the approach presented herein may be applied to other flood control systems if appropriate storm runoff models are selected.