Model Structure Estimation and Correction Through Data Assimilation

The main philosophy underlying this research is that a model should constitute a representation of both what we know and what we do not know about the structure and behavior of a system. In other words it should summarize, as far as possible, both our degree of certainty and degree of uncertainty, so that it facilitates statements about prediction uncertainty arising from model structural uncertainty. Based on this philosophy, the following issues were explored in the dissertation: Identification of a hydrologic system model based on assumption about perceptual and conceptual models structure only, without strong additional assumptions about its mathematical structure Development of a novel data assimilation method for extraction of mathematical relationships between modeled variables using a Bayesian probabilistic framework as an alternative to up-scaling of governing equations Evaluation of the uncertainty in predicted system response arising from three uncertainty types: o uncertainty caused by initial conditions, o uncertainty caused by inputs, o uncertainty caused by mathematical structure Merging of theory and data to identify a system as an alternative to parameter calibration and state-updating approaches Possibility of correcting existing models and including descriptions of uncertainty about their mapping relationships using the proposed method Investigation of a simple hydrological conceptual mass balance model with two-dimensional input, one-dimensional state and two-dimensional output at watershed scale and different temporal scales using the metho

A comparison of rainfall-runoff modelling approaches for estimating impacts of rural land management on flood flows

There is a requirement for predictive tools to assist in land management and flood risk planning, and a variety of tools have been proposed recently. We compare four tools developed under various UK research programmes. The strengths and limitations of the tools are reviewed, model performances on historic data are assessed, and the methods are applied to estimating flood flows of 5- and 10-year return periods, and flow peaks under both recent land management conditions and speculative scenarios (grazing intensification and tree planting), using the Pontbren catchment, UK as a case study. Overall, the models agree on the direction of change, so that heavy grazing increases, and afforestation and tree strips decrease the flood flows. However, the estimated effects vary significantly between methods. It is concluded that method selection needs to carefully consider the type and scale of land management scenario being examined, and the sources of data available to support the modelling. Using an ensemble of suitable models is proposed as a useful way to represent a multi-expert opinion and to characterise the structural error associated with a single model

Improving parameter priors for data-scarce estimation problems

Runoff prediction in ungauged catchments is a recurrent problem in hydrology. Conceptual models are usually calibrated by defining a feasible parameter range and then conditioning parameter sets on observed system responses, e.g., streamflow. In ungauged catchments, several studies condition models on regionalized response signatures, such as runoff ratio or base flow index, using a Bayesian procedure. In this technical note, the Model Parameter Estimation Experiment (MOPEX) data set is used to explore the impact on model performance of assumptions made about the prior distribution. In particular, the common assumption of uniform prior on parameters is shown to be unsuitable. This is because the uniform prior on parameters maps onto skewed response signature priors that can counteract the valuable information gained from the regionalization. To address this issue, we test a methodological development based on an initial transformation of the uniform prior on parameters into a prior that maps to a uniform response signature distribution. We demonstrate that this method contributes to improved estimation of the response signatures.</p

Modelling the hydrological impacts of rural land use change

The potential role of rural land use in mitigating flood risk and protecting water supplies continues to be of great interest to regulators and planners. The ability of hydrologists to quantify the impact of rural land use change on the water cycle is however limited and we are not able to provide consistently reliable evidence to support planning and policy decisions. This shortcoming stems mainly from lack of data, but also from lack of modelling methods and tools. Numerous research projects over the last few years have been attempting to address the underlying challenges. This paper describes these challenges, significant areas of progress and modelling innovations, and proposes priorities for further research. The paper is organised into five inter-related subtopics: (1) evidence-based modelling; (2) upscaling to maximise the use of process knowledge and physics-based models; (3) representing hydrological connectivity in models; (4) uncertainty analysis; and (5) integrated catchment modelling for ecosystem service management. It is concluded that there is room for further advances in hydrological data analysis, sensitivity and uncertainty analysis methods and modelling frameworks, but progress will also depend on continuing and strengthened commitment to long-term monitoring and inter-disciplinarity in defining and delivering land use impacts research

The role of rating curve uncertainty in real-time flood forecasting

Data assimilation has been widely tested for flood forecasting, although its use in operational systems is mainly limited to a simple statistical error correction. This can be due to the complexity involved in making more advanced formal assumptions about the nature of the model and measurement errors. Recent advances in the definition of rating curve uncertainty allow estimating a flow measurement error that includes both aleatory and epistemic uncertainties more explicitly and rigorously than in the current practice. The aim of this study is to understand the effect such a more rigorous definition of the flow measurement error has on real-time data assimilation and forecasting. This study, therefore, develops a comprehensive probabilistic framework that considers the uncertainty in model forcing data, model structure, and flow observations. Three common data assimilation techniques are evaluated: (1) Autoregressive error correction, (2) Ensemble Kalman Filter, and (3) Regularized Particle Filter, and applied to two locations in the flood-prone Oria catchment in the Basque Country, northern Spain. The results show that, although there is a better match between the uncertain forecasted and uncertain true flows, there is a low sensitivity for the threshold exceedances used to issue flood warnings. This suggests that a standard flow measurement error model, with a spread set to a fixed flow fraction, represents a reasonable trade-off between complexity and realism. Standard models are therefore recommended for operational flood forecasting for sites with well-defined stage-discharge curves that are based on a large range of flow observations