2014
  • Non-ICIMOD publication
No Cover Photo

Share

1,139 Views
Generated with Avocode. icon 1 Mask color swatch
0 Downloads

Hydrological Modeling of Large River Basins: How Much Is Enough?

  • Johnston, R.
  • Smakhtin, V.
  • Summary
Hydrological modeling has become an indispensable component of water resources research and management in large river basins. Hydrological models help understand the past and current state of water resources in the basin, and provide a way to explore the implications of management decisions and imposed changes (such as climate change). In large river basins in the developing world, international donors have supported hydrological modeling for water resources management and planning, from two perspectives: to inform decisions relating to national development and poverty alleviation; and to prevent trans-boundary conflicts by promoting equitable allocation and access. Very significant effort and funding has gone into these models. In pursuit of improved accuracy, there is a tendency for each new group working in a basin to develop their own model, or suite of models, resulting in a plethora of hydrological tools for each major basin. The results are published, but model setup files, actual inputs and outputs are hardly ever shared in public domain. Due to limited access to observed data for model calibration, every “new” model is likely to be bound to use the same data and hence have similar deficiencies to previous modeling attempts. This paper explores the question: at what stage do we have enough information to stop modeling and get on with planning and management? How much modeling is enough? In this paper, we review the modeling effort in four major river basins in the developing world: Nile, Mekong, Ganges and Indus. For each basin, we provide an inventory of the main studies published since the year 2000; examine the types, purpose and use of existing models; overlaps and duplicate investments; constraints and gaps in knowledge. We then provide recommendations to assist research modeling groups and funding agencies to improve coordination, reduce repetition and improve the effectiveness of their investments in model development and application.