For more than four decades it has been an important scientific activity to develop mathematical simulation software (model codes) to describe the whole or parts of the hydrological system; describing specific water resources and environmentally oriented processes, or describing management oriented processes, e.g. how water could be best used and allocated. Such model codes can vary in complexity, ranging from simple empirical relationships, e.g. those based on the observed relationship between rainfall and stream flow, to process-oriented descriptions, which attempt to mirror the natural system in a physically-based manner taking into account spatial and temporal variations in catchment characteristics.
Code developments have been driven not only by a desire to gain increasing understanding about the hydrological cycle, but also from the need to quantify the water resources and predict the impact of human activities or the occurrence of short-term or long-term natural events. They have also been developed to investigate how the available land and water resources can be best used for food and energy production, thus realizing the importance of the water-foodenergy nexus. They are also assisting in the protection of vulnerable water resources and ecosystems.
Data quality becomes a major issue. In this situation, it becomes crucial to develop and use appropriate model codes and software to check, validate and handle the data in order to obtain the most benefit and to prepare it for assessments, analysis, simulations and forecasts. Real-time operations place additional requirements on the data stream and the model codes. In a fast-changing world, where climate change also poses huge challenges, efforts need to be made to collect, handle and use data more strategically, assisted by modelling.
Model codes are indispensable analytical tools because they allow water resources professionals to conduct structured analyses of complex phenomena, which often require massive amounts of spatially and temporally varying data. With these tools, it is possible to make more reliable interpolations and extrapolations from existing data measured in the field and, thereby, enhance the information obtained from monitoring programs.