Data-driven modeling for enhanced management of water resources: Problems and solutions

Changing climatic conditions, global warming trends, global population increase, water-related conflicts, and water shortages have resulted in changes in the water cycle, and hydrologic processes which were once thought to be simple are now known to be highly nonlinear. This compels the development of more sophisticated tools for enhanced and intensive water resources management. Data-driven tools have gained in popularity in recent years and have spawned a plethora of applications in water resources. Despite enjoying tremendous success in small-scale studies, there are very few applications to field-scale problems so far because various issues must be understood in order to make data-driven tools more practical to hydrologic applications. In the current research, three problem areas in hydrologic modeling have been identified that limit the applicability of data-driven tools: parameter specification, missing or incomplete data, and data compatibility. Each of these is studied in the present research, and solutions are provided. A new multiobjective calibration procedure in the form of Multiobjective Particle Swarm Optimization (MOPSO) is developed and tested. A solution to the problem of missing data is found through local least square imputation methodology. Furthermore, a downscaling algorithm is developed for the scale reconciliation problem. These tools are examined in various applications such as soil moisture forecasting, streamflow estimation, groundwater level forecasting, and downscaling of remotely sensed soil moisture. The current research only focuses on data-driven tools, and hence all the problems are examined in this same context. At the same time, the tools that are developed might well be appropriate for other modeling applications. This research addresses significant problems in the use of data-driven modeling tools so that they can be more effectively used in water resources management and hydrologic science. The results from the research show that the techniques developed and demonstrated here are sound and can help to remove some of the limitations in the use of data-driven tools, making them more attractive for application in hydrologic sciences.