A. H. Weerts

Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models

[1] Sequential importance resampling (SIR) filter, residual resampling filter (RR), and an ensemble Kalman (EnKF) filter that can handle dynamic nonlinear/non-Gaussian models are compared to correct erroneous model inputs and to obtain a rainfall-runoff update with a conceptual rainfall-runoff model HBV-96 for flood forecasting purposes. EnKF performs best with a low number of ensemble members. The RR filter performs best at intermediate and high number of particles, although differences are small. With all filters the rainfall error could be estimated during a synthetic experiment when the soil is not too dry and the measurement error on the discharge is not dominant. The temperature error could only be estimated when the temperature is close to 0� C. When applying these methods to a real case, good results are obtained. For low flows, EnKF outperforms both particle filters, because it is less sensitive to misspecification of the model and uncertainties. These methods are feasible and easy to implement in real flood forecasting systems. Further research on the assumptions on model uncertainties and measurement uncertainties is recommended.

Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system

Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive framework for Bayesian revision has been recently developed and applied to operational flood forecasting with deterministic weather forecasts. The Bayesian revision yields a posterior density, conditional on all information available to the forecaster at the onset of a forecast run. This conditional density objectively quantifies the uncertainty. Here the Bayesian approach is generalized for use with ensemble weather predictions. An end-to-end application of a Bayesian postprocessor for ensemble streamflow forecasts in the river Rhine forecasting system is presented. A verification of the postprocessor shows good performance when compared in terms of the ranked probability skill score to non-Bayesian uncertainty assessment, such as ranking threshold exceedance probabilities for members of a streamflow ensemble prediction. In this context it is also addressed how the proposed Bayesian processor can serve in supporting rational decision making for flood warning under conditions of uncertainty.

Information content of time domain reflectometry waveforms

The possibility to link model parameters with soil properties is obstructed if identification problems in inverse modeling of time domain reflectometry (TDR) waveforms occur. Therefore multiple objective functions, each associated with one model parameter, are sought for identification of probe (resistance and length) and Debye (static permittivity, high-frequency permittivity, relaxation frequency, and bulk electrical conductivity) parameters in TDR waveforms. An objective function could be defined for all six model parameters. Some of the defined objective functions correspond with theoretical and experimental observations of earlier work. Other new insights have been obtained. For instance, it is possible to obtain estimates of high-frequency permittivity even when the reflection of the end of the probe has vanished because of high salinity. Application of the found objective functions on measured TDR waveforms yielded parameter values that compared well with values obtained from several references. However, identification of a parameter is only possible if its true value lies within the frequency bandwidth of TDR.

Application of generic data assimilation tools (DATools) for flood forecasting purposes

This paper describes the generic data assimilation software tool DATools. DATools can be used as standalone or within Delft-FEWS. DATools is completely configurable via XML configuration. DATools is built up of three components: a Filter, a Stochastic Modeler, and a Stochastic Observer. Configuration of all these three parts is explained in detail. At the moment two data assimilation filters are available within DATools: (1) ensemble Kalman Filter and (2) the residual resampling filter. Results of a twin experiment with both filters with DATtools show similar results as a previous study performed with custom implementations. It is also shown that DATools can function inside Delft-FEWS software used for operational flood forecasting. Applying EnKF to a 1D hydrodynamic SOBEK-RE model of the river Rhine within the operational system FEWS-NL Rhine and Meuse improves the forecasts at the Lobith gaugin station and downstream of Lobith. DATools has been coupled with the HBV-96, SOBEK, and REW models and will be coupled to MODFLOW, Delft-3D, and the geotechnical model MSetlle in the near future. Uncertainty analysis with this tool is also possible and calibration will be added later this year.

Automatic Error Correction of Rainfall-Runoff models in Flood Forecasting Systems

Physical modelling of the dynamics of a catchment area produces simulation models with a limited forecasting accuracy for the discharge of rivers. The discrepancies between the simulation model and the actually observed past discharges can be used as additional information for error correction. With a time series model of the recent past error signal, an improved discharge forecast can be made for the next few days. The best type and order of the time series model can be selected automatically. Adaptive modelling in data assimilation calculates updates of the time series model estimated from the error data of only the last few weeks. The use of variable updated models has advantages in periods with the largest discharges, which are most important in flood forecasting

Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

(1) Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands (n.tangdamrongsub@tudelft.nl), (2) Water Resources Management, Delft University of Technology, Delft, The Netherlands, (3) School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, The United States, (4) Operational Water Management, Deltares, Delft, The Netherlands, (5) Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, The Netherlands

Assessment of flood risk accounting for river system behaviour

Abstract In this paper “river system behaviour” is defined as the complex interaction between river flow and the flooding of flood prone areas. A basic aspect of river system behaviour is that a local dike breach may affect hydraulic loads and hence dike failure probabilities at other locations. Important aspects in river system behaviour are discussed as well as the fact that effects of river system behaviour on flood risk may be both beneficial as well as adverse. This paper presents a conceptual approach to quantify effects of river system behaviour on probabilities of dike breach and flood risk. It was successfully applied to two example river configurations. The results of these examples are discussed. It is concluded that for proper flood risk assessment all relevant failure mechanisms, uncertainties as well as all proposed safety improvement measures are to be jointly taken into account. The conceptual approach enable all this. In the authors’ views, there is a need for developing models that account for effects of river system behaviour on flood risk. Such models can serve as a tool for policy makers in evaluating the effects that (regional) safety improvement measures have on the flood risk in the entire river basin.

Probabilistic Quantitative Precipitation Forecast for Flood Prediction: An Application

Abstract This paper outlines a methodology to produce probabilistic quantitative precipitation forecasts by means of a dedicated uncertainty processor for weather model output. The uncertainty processor is developed as a component of a Bayesian forecasting system for river flow prediction. In this context the quantitative precipitation forecast is envisaged as a mixed binary–continuous predictand. The processor is applied to the quantitative precipitation forecasts and to precipitation observations covering a 5-yr period, whereby the forecasted and observed relative air humidity are used as ancillary meteorological indicators. The application of the processor to the selected dataset highlights a significantly larger skill of the quantitative precipitation forecast in predicting event occurrence rather than event depth and provides an objective quantification of the forecast uncertainty. The methodology applied here remains restricted to small basins, in which spatial variability of precipitation can be co...

Real-Time Geospatial Data Handling and Forecasting: Examples From Delft-FEWS Forecasting Platform/System

Environmental research that involves assessment of climate impact or extreme events like drought, floods, or other risks often requires the combination of geospatial data, distributed models, and weather forecast products. The Delft-FEWS forecasting platform, used by operational flood warning organizations around the world, offers the opportunity to carry out this type of research within a state-of-the-art forecasting environment. This paper shows opportunities Delft-FEWS offers in dealing with geospatial data like satellite measurements, weather, and climate forecasts. It also shows that Delft-FEWS offers the possibility to include distributed hydrological models using an embedded version of PCRASTER. This is illustrated by two case studies that combine different geospatial data, satellite data, and AOGCM climate data, with two different types of models, a groundwater model and a global hydrological model, within the Delft-FEWS system.

Simultaneous measurement of water retention and electrical conductivity in soils: Testing the Mualem‐Friedman Tortuosity Model

A conceptual model that accounts for the influence of pore geometry is needed to obtain soil water conductivity from bulk soil electrical conductivity measurements in unsaturated soils. Mualem and Friedman (1991) proposed such a model, based on the hypothesis that the tortuosity factor affecting bulk soil electrical conductivity is identical to that defined for prediction of soil hydraulic conductivity. Soil water retention curves (model input) and bulk soil electrical conductivity (model output) of two soils were measured simultaneously over a wide range of soil water conductivity using a multistep outflow (MSO) experiment in combination with time domain reflectometry. The model with only one free parameter can describe the measured data. Predictions indicate a large dependence of the model outcome on the value of the fitting parameter. The fitted tortuosity parameters were used for inverse modeling of the water flow during the MSO experiments, obtaining unique estimates of the hydraulic conductivity.