4
Conclusions
The workshop presentations and discussions confirmed the importance of developing an understanding to the limits of hydrologic prediction. Discussions during the workshop and written contributions by the participants resulted in defining milestones of progress in advancing predictability research and understanding limits-to-prediction in the hydrologic sciences. These milestones are valuable for any research initiative because they define the important and critical research directions. Additionally, they will allow the development of a timeframe for progress by the research program. In addition, such objectives are benchmarks for tracking the maturation of a research area, the vision for advancement of the area, and the metrics for progress.
While it is recognized that USGCRP agencies have focused research activities on forecasting and prediction, the workshop participants indicated that USGCRP agencies should establish programs to investigate the limits to predictability of the wider range of hydrologic variables. For example, current programs tend to focus on meteorological prediction, while understanding the limits-of-prediction for groundwater contaminant transport or ecosystem dynamics have received less attention. Yet these systems are of critical importance to the nation. In fact, the NRC report on environmental grand challenges (NRC, 2001b) included improved hydrologic forecasting among five priorities in environmental science.
The workshop identified the need for furthering the understanding between predictability and sub-grid-scale processes. Recent research suggests that increased resolution of distributed hydrological models has not necessarily lead to improved predictions due to the fact that the lack of understanding and modeling of subgrid scale processes is not compensated by improved resolution data sets. The increased availability of high-resolution data sets (e.g., data from space-borne sensors) allows for research programs that address the relationship among distributed data sets, modeling hydrological processes across a range of spatial and temporal scales, and predictability.
The improved availability of data holds the promise of improved predictions, regardless of the concerns about understanding small-scale processes raised above. Workshop deliberations pointed to research aimed at determining how the data can be best utilized to maximize the predictability from models. Data assimilation, where observations are merged with models, is well developed in the meteorology community. Research into data assimilation in the other areas of hydrologic and environmental sciences may be used to demonstrate how models can have synergy with measurements and to evaluate the predictability benefits from such approaches.
Multi-agency joint projects need to be devised to maximize the return for the resource investment and to engage a larger cross-section of the research and user communities. The fundamental issues regarding predictability and predictions are not restricted to a few variables such as precipitation or air temperature, but are pervasive across hydrologic and environmental sciences. One of the important issues identified both at the workshop and in numerous previous NRC reports is the need to reverse the degradation of existing monitoring systems where it can be demonstrated that the collection of consistent measurements and observations can lead to improved predictions of operational importance.
The key to success in research programs on predictability in hydrologic systems and in operational prediction programs is to develop strong linkages between research institutions and operational activities. Neither can fully realize their potential without recognizing their mutual synergies.
The workshop findings are consistent with those from an earlier COHS report on USGCRP (NRC, 1999) and the USGCRP Water Cycle Initiative Science Plan (Hornberger et al., 2001). These reports collectively define needed research that potentially has wide-spread and deep impacts on society by the incorporation of improved understanding of predictability into the operational arena.
In conclusion, discussions during the workshop and written contributions by the participants resulted in the definition of five research challenges and associated milestones, as presented in the previous section, that mark the path towards progress in advancing predictability research and understanding limits-to-prediction in the hydrologic sciences. The definition of such milestones is valuable for a research initiative because these milestones describe potential priority areas for research. More importantly, they define, in specific terms, where the community wants to see itself at different times along this path. Such milestones also provide benchmarks for tracking the maturation of a research area by identifying a vision for advancement and ensuring that the community has metrics for progress.