Model-data fusion: Using observations to understand and reduce uncertainty in hydrological models
Date
2011
Authors
Van Dijk, Albert
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Publisher
Modelling and Simulation Society of Australia and New Zealand Inc.
Abstract
Without corroborating observations, hydrological models should not be trusted. Hydrometric observations are the basis for any statistical or empirical hydrological model, and are widely used for 'calibration' - tweaking model parameter values to reduce the disagreement between model and observations. However there are other, less travelled paths to understand, quantify, and reduce hydrological model uncertainty using observations. A wealth of new observation types has become available from in situ sensor networks, airborne data collection and satellite remote sensing. With conventional hydrometric observation networks in decline, using these new observations is not merely of scientific interest but a bare necessity. This paper discusses opportunities and challenges to expand the application of model-data fusion techniques - using observations to make models behave better. Assessing the outcome of future boundary conditions (predictions or hypothetical scenarios) on water resources remains the most common hydrological model application. New observations can be used to improve model inputs (e.g. spatial parameter maps) or be considered in calibration to derive a more robust parameter set. Confronting the model with new observations helps to assess model uncertainty and build our confidence in it, though not usually without introducing some tough questions. (Is past performance actually relevant to uncertainty in future predictions? What about unknown or unquantifiable uncertainty components; are partial uncertainty estimates better than none? How does one determine whether a model is too poor to suit its intended purpose; what is the alternative? What is the uncertainty in the observations?) When looking into the past rather than the future, some of these questions disappear, while new ones emerge. Estimating past (and current) conditions is becoming increasingly important: as the basis of monitoring systems (of drought, flood, water resource generation, availability and use); to initialize forecasting systems; and to inform water resource accounting and situation analysis. The Australian Water Resources Assessment system (AWRA) is such an application, developed to support the Bureau of Meteorology's water information services. Having a predictive model is no longer an objective in this case. Rather, models take on a subservient role to reconcile and interpolate the available observations. In this case, observations are still used before the model is run (in model configuration and calibration), but now can also be applied during the model run (via data assimilation) and/or afterwards (e.g. via bias correction). Available techniques vary from the mathematically near-trivial to the very complicated, but assumptions about error in both model and observations are always critical. The generation of large volumes of relevant observations and derived products (e.g. from time series remote sensing) grows opportunities for model-data fusion, but equally grows the conceptual, mathematical and computational challenges. Several examples are discussed.
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Keywords
Keywords: Airborne data collection; Bias correction; Computational challenges; Data assimilation; Derived products; Forecasting system; Fusion techniques; Hydrological models; In-situ; Model and observation; Model configuration; Model inputs; Model parameters; Mode Data assimilation; Hydrological model; Model-data fusion; Remote sensing; Water resources
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Source
Proceedings of MODSIM 2011 International Congress on Modelling and Simulation
Type
Conference paper
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Open Access
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