Impacts of Uncertain Flow Data on Rainfall-Runoff Model Calibration

1. Estimation of PDF of Flow Uncertainty: The “Uncertain Rating Curve” • By specifying uncertainty in validation data we give our models a ‘fair hearing’ • Model structure/parameterisations are not forced to compensate for poor data 1. Stage/Velocity measurement errors • Method: • Individual stage/discharge gaugings are grouped into coherent sets between large flood events, representing more stable phases in bed evolution • Each set is used to construct rating curves by random sampling from the PDF of true stage/discharge surrounding each gauging point. • If a rating curve fits all points in the set within the error bounds, it is retained (Figure 1) • All rating curves are collated to give the Uncertain Rating Curve (Figure 2) Results: Shape of discharge uncertainty bounds is reflected in model discharge predictions 2. Rating Curve interpolation or extrapolation errors • Calibration Method: Markov Chain Monte Carlo • The model is a simplification of nature and does not include all processes occurring in the catchment. Hence different model parameter sets may give equally good (or bad) predictions. • MCMC is used to sample the parameter space, sampling more frequently where model performs well • Model performance measure is based on the Discharge PDF taken directly from the Uncertain Rating Curve • Sample sets give median prediction + confidence intervals Gauging location at Barnett’s Bank, Wairau River: note mobile bed 3. Cross-section change due to vegetation growth or bed movement • Further work: • How does use of uncertain flow data change parameter distributions? • How does this change our understanding of catchment processes? Figure 1: Rating Curves are retained if they pass within error bounds of each point in the gauging set Figure 2: All rating curves are collated to give the Uncertain Rating Curve, shown here using flow quantiles The Ultimate Aim: Results: Stage data is transformed to uncertain discharge data: • Our ultimate aim is to quantify total error affecting hydrological models and predictions, by explicitly recognising errors in input data, model structure, model parameters and validation data. • This will allow us to provide unbiased model predictions, and is vital to enable us to learn more about sources of model uncertainty and methods to reduce uncertainty. • This paper quantifies one error source, errors in discharge measurements, and hence provides one step towards this goal. Impacts of Uncertain Flow Data on Rainfall-Runoff Model Calibration and Discharge Predictions in a Mobile-Bed River H43D-1030 Hilary McMillan1, Jim Freer2, Florian Pappenberger3, Tobias Krueger4 and Martyn Clark1 Contact: h.mcmillan@niwa.co.nz 1 National Institute of Water and Atmospheric Research Ltd. (NIWA), New Zealand. 2 School of Geographical Sciences, University of Bristol, UK 3 European Centre for Medium-Range Weather Forecasts, Reading, UK 4 University of East Anglia, UK Why Uncertainty in Flow Data is Important Case Study Results ... Rainfall and Flow series are needed to calibrate hydrological models: Impacts on Model Calibration and Discharge Predictions Before: After: A Solution? • TopNet Model • Water balance model of sub-basins • + kinematic network routing model • 7 parameters per sub-basin • Soil and vegetation parameters from catchment maps • Other parameters set at default constant value Catchment processes Incorrect flow data Model structure/parameterisations are forced to compensate for poor data Incorrect model with weaker predictive power Network routing What Causes the Uncertainty in Flow Data? Our data is usually stagedata transformed to flow via a rating curve Significant Errors can occur: Hydrological Model Discharge (m3/s) Discharge (m3/s) Case Study: Wairau River, New Zealand Stage (m) Stage (m) • Discharge information is required to calibrate hydrological models for flood warning and water resource applications • All three sources of rating curve error (above) are present Previous rating curves show spread but are not a surrogate for uncertainty