Renata J. Romanowicz

Dynamic real-time prediction of flood inundation probabilities

Abstract The Bayesian Generalised Likelihood Uncertainty Estimation (GLUE) methodology, previously used in rainfall-runoff modelling, is applied to the distributed problem of predicting the space and time varying probabilities of inundation of all points on a flood plain. Probability estimates are based on conditioning predictions of Monte Carlo realizations of a distributed quasi-two-dimensional flood routing model using known levels at sites along the reach. The methodology can be applied in the flood forecasting context for which the N-step ahead inundation probability estimates can be updated in real time using telemetered information on water levels. It is also shown that it is possible to condition the Nstep ahead forecasts in real time using the (uncertain) on-line predictions of the downstream water levels at the end of the reach obtained from an adaptive transfer function model calibrated on reach scale inflow and outflow data.

Data assimilation and adaptive forecasting of water levels in the river Severn catchment, United Kingdom

This paper describes data assimilation (DA) and adaptive forecasting techniques for flood forecasting and their application to forecasting water levels at various locations along a 120 km reach of the river Severn, United Kingdom. The methodology exploits the top-down, data-based mechanistic (DBM) approach to the modeling of environmental processes, concentrating on the identification and estimation of those “dominant modes” of dynamic behavior that are most important for flood prediction. In particular, hydrological processes active in the catchment are modeled using the state-dependent parameter (SDP) method of estimating a nonlinear, effective rainfall transformation together with a linear stochastic transfer function (STF) method for characterizing both the effective rainfall–river level behavior and the river level routing processes. The complete model consists of these lumped parameter, linear and nonlinear stochastic, dynamic elements connected in a quasi-distributed manner that represents the physical structure of the catchment. The adaptive forecasting system then utilizes a state-space form of the complete catchment model, including allowance for heteroscedasticity in the errors, as the basis for data assimilation and forecasting using a Kalman filter forecasting engine. Here the predicted model states (water levels) and adaptive parameters are updated recursively in response to input data received in real time from sensors in the catchment. Direct water level forecasting is considered, rather than flow, because this removes the need to transform the level measurement through the rating curve and tends to decrease the forecasting errors.

Comments on generalised likelihood uncertainty estimation

The paper presents an application of the generalised likelihood uncertainty estimation methodology to the problem of estimating the uncertainty of predictions produced by environmental models. The methodology is placed in a wider context of different approaches to inverse modelling and, in particular, a comparison is made with Bayesian estimation techniques based on explicit structural assumptions about model error. Using a simple example of a rainfall-flow model, different evaluation measures and their influence on the prediction uncertainty and credibility intervals are demonstrated.

Partial Analysis Applied to Scale Problems in Surface Moisture Fluxes

Partial analysis is applied to the problem of predicting the moisture fluxes of infiltraton and evaporation at land surfaces. The discussion covers the widely different scales of the soil particle, a soil pedon, a field, a basin and a biome. It is suggested that simplified models can be used at these different scales to provide bounding solutions to the integrated behaviour of land surface fluxes of interest in linking hydrologic models and general circulation climate models.

The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics

Abstract An HBV rainfall–runoff model was applied to test the influence of climatic characteristics on model parameter values. The methodology consisted of the calibration and cross-validation of the HBV model on a series of 5-year periods for four selected catchments (Axe, Kamp, Wieprz and Wimmera). The model parameters were optimized using the SCEM-UA method which allowed for their uncertainty also to be assessed. Nine climatic indices were selected for the analysis of their influence on model parameters, and divided into water-related and temperature-related indices. This allowed the dependence of HBV model parameters on climate characteristics to be explored following their response to climate change conditioned on the catchment’s physical characteristics. The Pearson correlation coefficient and weighted Pearson correlation coefficient were used to test the dependence. Most parameters showed a statistically significant dependence on several climatic indices in all catchments. The study shows that the results of the correlation analysis with and without parametric uncertainty taken into account differ significantly.

A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data

This paper presents the methodology for the spatial and temporal interpolation of air quality data. As a practical example, the methodology is applied to the daily nitric oxide NO concentrations measured at 23 stations around Paris. Analysis of the temporal and spatial variability of observations of NO in the Paris area is divided into: (i) time series analysis of AirParif data; and (ii) development of combined spatial and temporal analysis techniques using NO observations from 19 stations. The first part of the paper shows how advanced methods of nonstationary time series analysis can be used to interpolate the data sets of NO concentrations over periods where measurements are missing and to decompose the time series into trend and harmonic components. The results of this analysis applied to 19 stations around Paris are then used in further spatio-temporal analysis of the data. This consists of two steps: (i) preliminary analysis of spatial relations within the data sets; and (ii) the development of a spatio-temporal model for log-transformed NO measurements. The results of the analysis indicate that the simple spatio-temporal model consisting of trend and noise efficiently represents the spatio-temporal variations in the data and it can be applied to predict air pollution variations in time and space at un-sampled locations.

A MATLAB implementation of TOPMODEL

The MATLAB SIMULINK programming language is applied to the TOPMODEL rainfall–runoff model. SIMULINK requires a good recognition of model dynamics, which has been achieved here in a version based on the first TOPMODEL (Beven and Kirkby, 1979). Introducing the topographic index distribution in a vector form allows the generalization and simplification of the SIMULINK structure. The SIMULINK version of TOPMODEL has a very easy to understand graphical representation, which shows, in a straightforward way, all the physical interactions that take place in the model. Moreover, owing to its modular structure it is easy to add new and/or develop old submodels, depending on the available data and the goal of the modelling. In the example given here TOPMODEL was extended by two submodels representing the soil moisture and evaporation distribution in the catchment. Preparation of the data and presentation of the results is done in MATLAB. Discharge predictions and spatial patterns of hydrological response are demonstrated for a separate validation period.

Climate Change Impact on Hydrological Extremes: Preliminary Results from the Polish-Norwegian Project

This paper presents the background, objectives, and preliminary outcomes from the first year of activities of the Polish–Norwegian project CHIHE (Climate Change Impact on Hydrological Extremes). The project aims to estimate the influence of climate changes on extreme river flows (low and high) and to evaluate the impact on the frequency of occurrence of hydrological extremes. Eight “twinned” catchments in Poland and Norway serve as case studies. We present the procedures of the catchment selection applied in Norway and Poland and a database consisting of near-natural ten Polish and eight Norwegian catchments constructed for the purpose of climate impact assessment. Climate projections for selected catchments are described and compared with observations of temperature and precipitation available for the reference period. Future changes based on those projections are analysed and assessed for two periods, the near future (2021–2050) and the far-future (2071–2100). The results indicate increases in precipitation and temperature in the periods and regions studied both in Poland and Norway.

Moments and cumulants of linearized St. Venant equation

Abstract A general closed form expression for the Rth cumulant of the unsteady flow due to an upstream impulse input in a semi-infinite channel is derived by (a) reducing the recurrence relationship between successive cumulants to a recurrence relationship between the set of parameters γ(R, i) characterizing the ratio of successive terms in the series for the Rth cumulant and (b) deriving the closed form equivalent to this recurrence relationship in terms of either nested sums or factorials.

On the choice of calibration periods and objective functions: A practical guide to model parameter identification

Despite the development of new measuring techniques, monitoring systems and advances in computer technology, rainfall-flow modelling is still a challenge. The reasons are multiple and fairly well known. They include the distributed, heterogeneous nature of the environmental variables affecting flow from the catchment. These are precipitation, evapotranspiration and in some seasons and catchments in Poland, snow melt also. This paper presents a review of work done on the calibration and validation of rainfall-runoff modelling, with a focus on the conceptual HBV model. We give a synthesis of the problems and propose a practical guide to the calibration and validation of rainfall-runoff models.