iH2O: Taking a Close Look at Watersheds
iH2O (pronounced “eye-water”) is a user-friendly computational framework that allows watershed scientists, engineers and managers to design and optimize watershed monitoring systems and data collection campaigns based on state-of-the-art simulation and optimization techniques.
Managing our water resources requires accurate model predictions that are based on field data collected in a cost-effective manner. Finsterle GeoConsulting in collaboration with Michael B. Kowalsky Consulting and Lawrence Berkeley National Laboratory are in the process of developing a computer program that reduces the uncertainty in model predictions relevant for watershed management; at the same time it helps significantly reduce costs for data collection.
Data Worth Analysis
iH2O uses advanced numerical simulators to forecast the hydro-ecological response of a watershed to changes in natural conditions or watershed management decisions. Such decisions critically depend on the accuracy with which the flow of water and chemicals through the watershed can be predicted. The contribution of watershed characterization data to reducing prediction uncertainties can be evaluated in a so-called data-worth analysis, which provides transparent, quantitative metrics about a data set’s value for the support of relevant watershed management objectives.
Data-worth analysis as implemented in the iH2O watershed simulation-optimization framework: Uncertainties in model predictions can be reduced by calibrating influential parameters using testing or monitoring data. The data-worth analysis determines he worth of existing or potential data for reducing prediction uncertainties, thus leading to more valuable decision support at reduced watershed characterization costs.
Seeking Testers
iH2O is under development. If you are interested in commenting on its prototype, testing early versions of the software, or integrating your own simulation tool into the iH2O framework, please contact the developers.