The use of additional experimental data to educe the prediction uncertainty of models The solution?

Size: px

Start display at page:

Download "The use of additional experimental data to educe the prediction uncertainty of models The solution?"

Stuart Patrick
6 years ago
Views:

1 Workshop ChoP-HM: Change of Paradigms in Hydrological Modelling; October 2003, Thurnau Castle, Germany The use of additional experimental data to educe the prediction uncertainty of models The solution? Stefan Uhlenbrook University of Freiburg, Institute of Hydrology, Germany

2 What do we need for a Change of Paradigms? Complete new insights gained, e.g. by: a new sensor/device? a really new modeling structure?... (Hydrology Einstein?) Maybe we should state the problem clearly, as... the mere formulation of a problem is far more often essential than its solution. (Einstein)

3 Problem: How predictions are done? - Hydrology - Ecology - Socio-economy????? Precip. [mm/h] Niederschlag [mm/h] discharge [mm/h] Abfluss [m³/s] gemessener Abfluss observ ed simulat ed simulierter Abfluss 4 2

4 Predictive Uncertainty: (PUB 2003) Links to Heterogeneity

5 Objectives (1) Multiple-response validation using a process-based model (2) Prediction uncertainty of modeled floods (3) Ways to reduce the uncertainty by incorporation additional field data

6 rugga Basin (Black Forest Mountains, Germany) P = Q + ET : 1750 mm = 1230 mm mm

7 (6) Runoff routing: Kinematic wave The model TAC D (tracer aided catchment model, distributed) Evapotranspiration: Penman-Monteith approach (1) Regionalization of input data: IDW, elevation gradients, topography and rainfall radar (2) Snow melt: Temperature index model (3) Interception: after Hoyningen-Heune (4) Soil water: BETA-function (HBV model) (5) Runoff generation: Designed reservoirs

8 Results Modelling of the main basin and a sub-basin 80 0 Abfluss [m³/s] Zeit observed simulated 25 Niederschlag gemessener Abfluss simulierter Abfluss Dreisam (258 km 2 ) Abfluss [m³/s] Niederschlag [mm] Brugga (40 km 2 ) Zeit

9 Simulation of Dissolved Silica: Snow melt in spring 1996 precipitation [mm/h] discharge [mm/h] simulated runoff observed runoff Silikat Si [mg/l] Si observed Si simulated

10 Using TAC D in an extrapolation mode (2) uncertainty analysis of flood predictions Parameter uncertainy Model uncertainty uncertain predictions Incomplete process understanding Error in observed data Error due to regionalization

11 Results of runoff simulations Statistical measures: R eff and logr eff ummer vents winter vents R eff Oberried R eff St. Wilhelm (40 km 2 ) (15.2 km 2 ) Min Max Min Max Event Event Event Event

12 Results of runoff simulations tatistical measures: R eff (Nash and Sutcliffe 1970) ummer vents winter vents R eff Oberried R eff St. Wilhelm (40 km 2 ) (15.2 km 2 ) Min Max Min Max Event Event Event Event

13 Uncertainty Analysis Generalized Likelihood Uncertainty Estimation (GLUE) probability (-) mean accumulated probability (-) mean 5%-quantile 95%-quantile discharge (m 3 /s) discharge (m 3 /s)

14 Uncertainty of runoff prediction event no. 2, summer event %-Quantil 95 %-Quantil R eff Oberried fuzzy-transf. discharge (m 3 /s) Abfluss [m 3 /s] R eff Oberried / R 2 Silikat R eff Oberried / R eff St.Wilhelm observed gemessener Abfluss runoff

15 Reducing the uncertainty bounds by integrating additional information (1/2) %-Quantil 95 %-Quantil R eff Oberried fuzzy-transf. discharge (m 3 /s) Abfluss [m 3 /s] R eff Oberried / R 2 Silikat R eff Oberried / R eff St.Wilhelm observed gemessener Abfluss runoff

16 discharge (m 3 /s) Reducing the uncertainty bounds by integrating additional information (2/2) 5 Abfluss [m 3 /s] %-Quantil 95 %-Quantil R eff Oberried fuzzy-transf. R eff Oberried / R 2 Silikat R eff Oberried / R eff St.Wilhelm R eff Oberried / R 2 Silikat / R eff St.Wilhelm observed gemessener Abfluss runoff

17 Conclusions from the case study (1) Well-validated model give uncertain predictions (2) Value of additional data to reduce uncertainty can be large, but is not warranted (additional parameters) (3) Spatial and temporal variability of uncertainties

18 Should we wait for the mega-multi-super probe/sensor? Now I understand hydrological processes!

19 Should we wait for a single flash light? (PUB 2003)

20 PUB: Research Targets (PUB 2003)

21 Do you agree???

Runoff-rainfall modelling: Predicting areal precipitation from runoff observations

HydroPredict 212 Session M: Methodology, modelling, prediction and uncertainty 24. 27. September 212, Runoff-rainfall modelling: Predicting areal precipitation from runoff observations Mathew Herrnegger