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

Abstract

[1] 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.

Community-based early warning systems for flood risk mitigation in Nepal

“…This is a simplification of the more general approach used in previous work. The simplifications enforce a specific hydrological interpretation of the resulting models, which is motivated by the findings presented in Smith et al (2013) (see also Romanowicz et al, 2006Romanowicz et al, , 2008Beven et al, 2011, and the references within).…”

“…In the case of a rainfall input the value of the gain relates to wetness of the catchment, with wetter catchments typically producing more of a response to rainfall. A number of different methods to calculate the gain have proved acceptable representations of a variety of catchments (see for example, Beven et al, 2011;Romanowicz et al, 2006Romanowicz et al, , 2008Leedal et al, 2013). In the work on the Karnali River basin three representations of the gain are used.…”

Towards the provision of site specific flood warnings using wireless sensor networks

“…This section outlines the implementation of a family of parsimonious time series models, termed Data Based Mechanistic (DBM) models, that have been shown to be effective in flood forecasting (Lees et al, 1994;Young, 2002;Romanowicz et al, 2006). Further details and a fuller discussion of DBM modelling can be found in Young (2006) or Young et al (2004).…”

The usability of 250 m resolution data from the UK Meteorological Office Unified Model as input data for a hydrological model

“…The hydrological forecasts, however were carried out without updation using observed discharges (see e.g. Krzysztofowicz, 1999;Young, 2002;or Romanowicz et al, 2006 for adaptive forecasting algorithms). The forecasting plots in Figures 10-17 are constructed as follows.…”