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Computational hydrological models and simulations are fundamental pieces of the workflow of contemporary hydroscience research, education, and professional engineering activities. In support of hydrological modelling efforts, web-enabled tools for data processing, storage, computation, and visualization have proliferated. Most of these efforts rely on server resources for computation and data tasks and client-side resources for visualization. However, continued advancements of in-browser, client-side compute performance present an opportunity to further leverage client-side resources. Towards this end, we present an operational rainfall-runoff model and simulation engine running entirely on the client side using the JavaScript programming language. To demonstrate potential uses, we also present an easy-to-use in-browser interface designed for hydroscience education. Although the use case presented here is self-contained, the core technologies can extend to leverage multi-core processing on single machines and parallelization capabilities of multiple clients or JavaScript-enabled servers. These possibilities suggest that client-side hydrological simulation can play a central role in a dynamic, interconnected ecosystem of web-ready hydrological tools.

Although prior studies have evaluated the role of sampling errors associated with local and regional methods to estimate peak flow quantiles, the investigation of epistemic errors is more difficult because the underlying properties of the random variable have been prescribed using ad‐hoc characterizations of the regional distributions of peak flows. This study addresses this challenge using representations of regional peak flow distributions derived from a combined framework of stochastic storm transposition, radar rainfall observations, and distributed hydrologic modeling. The authors evaluated four commonly used peak flow quantile estimation methods using synthetic peak flows at 5,000 sites in the Turkey River watershed in Iowa, USA. They first used at‐site flood frequency analysis using the Pearson Type III distribution with L‐moments. The authors then pooled regional information using (1) the index flood method, (2) the quantile regression technique, and (3) the parameter regression. This approach allowed quantification of error components stemming from epistemic assumptions, parameter estimation method, sample size, and, in the regional approaches, the number ofpooledsites. The results demonstrate that the inability to capture the spatial variability of the skewness of the peak flows dominates epistemic error for regional methods. We concluded that, in the study basin, this variability could be partially explained by river network structure and the predominant orientation of the watershed. The general approach used in this study is promising in that it brings new tools and sources of data to the study of the old hydrologic problem of flood frequency analysis.