The new Scientific Decade 2013–2022 of IAHS, entitled “Panta Rhei—Everything Flows”, is dedicated to research activities on change in hydrology and society. The purpose of Panta Rhei is to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems. The practical aim is to improve our capability to make predictions of water resources dynamics to support sustainable societal development in a changing environment. The concept implies a focus on hydrological systems as a changing interface between environment and society, whose dynamics are essential to determine water security, human safety and development, and to set priorities for environmental management. The Scientific Decade 2013–2022 will devise innovative theoretical blueprints for the representation of processes including change and will focus on advanced monitoring and data analysis techniques. Interdisciplinarity will be sought by increased efforts to connect with the socio-economic sciences and geosciences in general. This paper presents a summary of the Science Plan of Panta Rhei, its targets, research questions and expected outcomes. Editor Z.W. Kundzewicz Citation Montanari, A., Young, G., Savenije, H.H.G., Hughes, D., Wagener, T., Ren, L.L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S.J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and Belyaev, V., 2013. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrological Sciences Journal. 58 (6) 1256–1275.

Hydrologic data are at the core of our understanding of physical hydrologic processes, our simulation models and forecasts of water resources and hazards, and our monitoring of water quantity and quality. However, hydrologic data are subject to multiple sources of uncertainty that can introduce bias and error into our analyses and decision‐making if not properly accounted for. In this article, we summarize five categories of data uncertainty: measurement uncertainty, derived data uncertainty, interpolation uncertainty, scaling uncertainty, and data management uncertainty. Hydrologic data uncertainty magnitudes are typically in the range 10–40%. To quantify data uncertainty, hydrologists should first construct a perceptual model of uncertainty that itemizes uncertainty sources. The magnitude of each source can then be estimated using replicates (repeated, nested or subsampled measurements), or information from the literature (in‐depth uncertainty results from experimental catchments, colocated gauges or method comparisons). Multiple uncertainty sources can be combined using Monte Carlo methods to determine total uncertainty. Data uncertainty analysis improves hydrologic process understanding by enabling robust hypothesis testing and identification of spatial and temporal patterns that relate to true process differences rather than data uncertainty. By quantifying uncertainty in data used for input or evaluation of hydrologic models, we can prevent parameter bias, exclude disinformative data, and enhance model performance evaluation. In water management applications, quantifying data uncertainty can lead to robust risk analysis, reduced costs, and transparent results that improve the trust of the public and water managers.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods Engineering Water > Planning Water