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Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting

Advances in
Data-Based Approaches
for Hydrologic Modeling
and Forecasting

 

Bellie Sivakumar
The University of New South Wales, Sydney, Australia
and
University of California, Davis, USA
Ronny Berndtsson
Lund University, Sweden

 

 

PREFACE
The last few decades have witnessed an enormous growth in hydrologic
modeling and forecasting. Many factors have contributed to this growth
sheer necessity, pure curiosity, and others. Population explosion and its
associated effects (e.g. increase in water demands, degradation in water
quality, increase in human and economic impacts of floods and droughts)
have certainly necessitated better understanding, modeling, and
forecasting of hydrologic systems and processes and increased funding
for hydrologic teaching, research, and practice. Technological
developments (e.g. invention of powerful computers, remote sensors,
geographic information systems, worldwide web and networking
facilities) and methodological advances (e.g. novel concepts, data
analysis tools, pattern recognition techniques) have largely facilitated
extensive data collection, better data sharing, formulation of
sophisticated mathematical methods, and development of complex
hydrologic models, which have led to new directions in hydrology. The
widespread availability of technology, hydrologic data, and analysis tools
have also aroused a certain level of curiosity in studying hydrologic
systems, both by trained engineers/scientists and by others. All these
have brought about a whole different dimension to hydrologic teaching,
research, and practice. There is no doubt that we today have a far greater
ability to mimic real hydrologic systems and processes and possess a
much better understanding of almost all of their salient properties as well
as finer details (e.g. determinism, stochasticity, linearity, nonlinearity,
complexity, scale, thresholds, sensitivity to initial conditions) when
compared to not so long ago.

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