A challenge to managing water resources is characterizing the scale-dependent heterogeneity created by the interactions among hydrological, ecological and anthropological processes. It is often difficult to collect sufficient empirical data over the range of scales required to construct mathematical models that facilitate robust bottom-up descriptions or predictions. An alternative is identifying emergent properties of complex systems, whose components self-organize into novel structures or processes via their collective interactions with each other and the environment. A new level of organization and complexity emerges that cannot be predicted from or attributed to the components alone. Emergence offers a number of perspectives from which to interpret, if not predict, the behavior of complex water resource systems. One of these is entropy, which maximizes the options for system components to alter their interactions and, thus, permits variability and adaptability. At the scale of watersheds, increased entropy is pertinent because of its relationship to information (as probability functions), which is transmitted through connected components of a watershed in a manner such that the accrued information gives rise to emergent properties. Hence, analyzing the behaviors of a system according to emergence introduces the possibility of evaluating the information content via its interconnected components. Connectivity then assumes an integral role in a hydrologic system’s response to natural or anthropogenic disturbances (e.g., climate change, land use). Replacing the details of multi-scale heterogeneity and causal mechanisms with the functions that watersheds perform allows processes such as stream flow rate/duration and flood frequency to be construed as emergent spatiotemporal patterns. A reductionist or bottom-up approach to assessing the behavior of aquatic systems shifts to a functional or top-down approach that does not depend upon an understanding of all the physical, chemical or biological mechanisms involved. This latter approach could supplement conventional water resource descriptions and predictions via more comprehensively characterizing watershed or aquatic ecosystem functions.

Data quality is only one of many uncertainties involved in our attempt to understand, model and predict complex nonlinear systems and to identify the properties emerging from them. Perhaps not surprisingly, less than half of published scientific studies can be successfully reproduced, with the earth sciences occupying an intermediate position among the major disciplines. The uncertainty of data quality is only one factor that contributes to the relatively poor reproducibility of research studies and the resulting hypotheses. Potentially more important factors include unsuitably low thresholds for assessing statistical relevance, inappropriate data manipulation, inadequate research design, and outright fraud. Data quality for earth science data can be communicated via measurement errors (e.g., instrument accuracy, technician practices) and addressed by employing measurement quality codes, whereas the other aforementioned factors can be identified or controlled through rigor, methods selection and research competency. A more difficult source of uncertainty includes biases, assumptions, and environmental or social influences that affect the judgment and perceptions of scientists themselves. The difficulty is that many of these factors operate beyond one’s awareness or control, residing within automatic processes of the human brain. From continually seeking, interpreting and projecting patterns (spatial and temporal) to confabulating answers and relying on heuristics, we are largely at the mercy of what the brain has evolved to do, which is not necessarily to accurately perceive the natural world. Peer reviews, precision instruments, mathematical abstractions and digital computers certainly assist us in probing nature; nevertheless, we ultimately perceive the world as we are, rather than as it is. Consequently, a scientifically “objective” view of nature is being questioned in research ranging from quantum mechanics to human consciousness. To what extent are the peculiarities of human brain responsible for the uncertainty and nuances that we believe exist in nature? Are scientists fooled by a brain that constructs, rather than simply observes, the world around us? If so, do we have the tools to deal with this kind of uncertainty?