Validation and Intercomparison of Different Updating Procedures for Real-Time Forecasting

The paper presents a classification and a review of the updating procedures currently used in real-time flood forecasting modelling. On the basis of results from the WMO project ‘Simulated Real-Time Intercomparion of Hydrological Models’, comprising more than 10 commonly used hydrological models and a variety of different updating procedures, an analysis of the relative importance of updating procedures and hydrological simulation models is provided. In particular, an intercomparison is made beteween two models (NAMS11/MIKE11 and NAMKAL) consisting of the same hydrological model (NAM conceptual rainfall-runoff) but containing different routing modules (linear reservoirs versus hydraulic routing) and different updating procedures (error prediction versus state variable updating based on an extended Kalman filter). A main conclusion is that updating procedures significantly improve the performances of hydrological models for short-range forecasting. Furthermore, there are no clear conclusions regarding which type of updating procedure performs the better. However. intercomparison of the NAMS11 and NAMKAL models indicates that the extended Kalman filter is marginally better than an error prediction model in cases where the basic hydrological model simulation is good. Finally, it is concluded that the basic simulation is very essential for accurate forecasts, and that the better the basic simulations are the better the updating routines in general function. This puts emphasis on the importance of thoroughly calibrating and validating the hydrological simulation models before applying them together with updating routines in operational real-time forecasting.

[1]  R. K. Kachroo.  River flow forecasting. Part 1. A discussion of the principles , 1992 .

[2]  Angela Lundberg,et al.  Combination of a Conceptual Model and an Autoregressive Error Model for Improving Short Time Forecasting , 1982 .

[3]  Application of hydrological models for flood forecasting and flood control in India and Bangladesh , 1988 .

[4]  Vijay P. Singh,et al.  The Snowmelt Runoff Model (SRM). , 1995 .

[5]  Jens Christian Refsgaard,et al.  Terminology, Modelling Protocol And Classification of Hydrological Model Codes , 1990 .

[6]  Vijay P. Singh,et al.  The NWS River Forecast System - catchment modeling. , 1995 .

[7]  Kieran M. O'Connor,et al.  A reappraisal of the Kalman filtering technique, as applied in river flow forecasting , 1994 .

[8]  Dara Entekhabi,et al.  Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations , 1994, IEEE Trans. Geosci. Remote. Sens..

[9]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[10]  S. Bergström.  OPERATIONAL HYDROLOGICAL FORECASTING BY CONCEPTUAL MODELS IN SWEDEN , 1981 .

[11]  Arnold W. Heemink,et al.  Data assimilation for non‐linear tidal models , 1990 .

[12]  D. Vidal-Madjar,et al.  Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY region , 1994 .

[13]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[14]  John C. Rodda.  Whither world water , 1995 .

[15]  Walter T. Sittner,et al.  Improvement of hydrologic simulation by utilizing observed discharge as an indirect input : (Computed Hydrograph Adjustment Technique-- CHAT) , 1979 .

[16]  Vijay P. Singh,et al.  MIKE 11 - a generalized river modelling package. , 1995 .

[17]  Jens Christian Refsgaard,et al.  Estimation of Catchment Rainfall Uncertainty and its Influence on Runoff Prediction , 1988 .

[18]  J. C. Refsgaard,et al.  Application of the Kalman filter to real time operation and to uncertainty analyses in hydrological modelling , 1985 .