Introduction
The field of Data Assimilation is concerned with the philosophy, theory and practice of how to merge quantitative, fuzzy and/or qualitative data with models. At the Unversity of Arizona we investigate these issues in the context of land surface hydrologic systems -- including
rainfall-runoff models for streamflow forecasting in gauged and ungaged watersheds, hydro-chemical models for simulation of solute and nutrient balances, and soil-vegetation-atmosphere transfer schemes for offline and coupled global/local simulation of energy-water-nutrient interactions. We are also interested in the application of new data sources (including remotely sensed data -- NEXRAD, NASA-EOS, etc.) to hydrology and climate studies, and in the development and application of Artificial Neural Network methods for inferring complex relationships from input-output data.
The Data Assimilation Group has made contributions to the theory and practice of model calibration (including multi-criteria and Bayesian methods for assimilating information from data), global optimization, parameter sensitivity analysis, and artificial neural networks. We are also keenly interested in the application of emerging and futuristic technologies to hydrologic science, including for example distributed and embedded sensor networks, parallel processing computational tools, and multi-satellite sensors. We hold the view that the future of hydrologic science requires a much stronger collaboration between systems scientists and experimental and field hydrologists.
Our longstanding collaboration (over 20 years) with the Hydrologic Laboratory of the US National Weather Service (HL-NWS) involves supporting the cost-effective and timely infusion of cutting edge science into weather and water forecast products, with particular attention to automated calibration procedures in support of the expert-manual approaches used by NWS hydrologists, the development of spatially distributed models for predicting river height, flood potential, and inundation extent (for both wet and semi-arid regions), and the use of probabilistic approaches for quantifying uncertainty.
Our work with the National Center for Environmental Prediction (NCEP) involves the development and testing of multi-criteria methods for assimilating measurement data into improved Land surface model structures and better estimates for soil and vegetation parameters, with a view to understanding and improving the simulation of coupled land-atmosphere interactions relevant to numerical weather and climate prediction.
Our work with the Hydrometeorology and Precipitation Estimation Group at the University of California, Irvine, (lately from HWR-UA) involves the development of ANN and satellite remote sensing methods appropriate for hydrological applications including flood forecasting and precipitation estimation.
Our work with SAHRA involves designing, coordinating and contributing to the development of a multi-level, multi-resolution (fine, medium, coarse) “overlapping coordination” strategy for building watershed models that bridge the physical and behavioral sciences, with particular attention to the economics of water and the needs of public policy.
Our Philosophy is to encourage questioning of conventional scientific wisdom and methods, to examine and relax underlying assumptions about what is and/or how to do things, to draw freely from ideas developed in other disciplines, and to think “outside the box”. We try to remember that “facts” are really just ideas about the world that we all “agree to agree” on (value of staying flexible), that when things do not happen as expected is when we can break new ground and learn something new (problems are opportunities), and that to stay true to our scientific heritage we must treat ideas not as personal possessions, but as gifts to be shared freely with whomsoever is interested, and in this way we can truly contribute to society (share ideas freely).
Department of Hydrology and Water Resources, Harshbarger Building, The University of Arizona, Tucson, AZ 85721-0011