River flooding can have severe societal, economic, and environmental consequences. However, limited understanding of the regional differences in flood‐generating mechanisms results in poorly understood historical flood trends and uncertain predictions of future flood conditions. Through systematic data analyses of 420 catchments we expose the primary drivers of flooding across the contiguous United States. This is achieved by exploring which flood‐generating processes control the seasonality and magnitude of maximum annual flows. The regional patterns of seasonality and interannual variabilities of maximum annual flows are, in general, poorly explained by rainfall characteristics alone. For most catchments soil moisture dependent precipitation excess, snowmelt, and rain‐on‐snow events are found to be much better predictors of the flooding responses. The continental‐scale classification of dominant flood‐generating processes we generate here emphasizes the disparity in timing and variability between extreme rainfall and flooding and can assist predictions of flooding and flood risk within the continental U.S.

Reproducibility is a foundational principle in scientific research. Yet in computational hydrology the code and data that actually produces published results are not regularly made available, inhibiting the ability of the community to reproduce and verify previous findings. In order to overcome this problem we recommend that reuseable code and formal workflows, which unambiguously reproduce published scientific results, are made available for the community alongside data, so that we can verify previous findings, and build directly from previous work. In cases where reproducing large‐scale hydrologic studies is computationally very expensive and time‐consuming, new processes are required to ensure scientific rigor. Such changes will strongly improve the transparency of hydrological research, and thus provide a more credible foundation for scientific advancement and policy support.

Abstract. The core component of many hydrological systems, the moisture storage capacity available to vegetation, is impossible to observe directly at the catchment scale and is typically treated as a calibration parameter or obtained from a priori available soil characteristics combined with estimates of rooting depth. Often this parameter is considered to remain constant in time. Using long-term data (30–40 years) from three experimental catchments that underwent significant land cover change, we tested the hypotheses that: (1) the root-zone storage capacity significantly changes after deforestation, (2) changes in the root-zone storage capacity can to a large extent explain post-treatment changes to the hydrological regimes and that (3) a time-dynamic formulation of the root-zone storage can improve the performance of a hydrological model.A recently introduced method to estimate catchment-scale root-zone storage capacities based on climate data (i.e. observed rainfall and an estimate of transpiration) was used to reproduce the temporal evolution of root-zone storage capacity under change. Briefly, the maximum deficit that arises from the difference between cumulative daily precipitation and transpiration can be considered as a proxy for root-zone storage capacity. This value was compared to the value obtained from four different conceptual hydrological models that were calibrated for consecutive 2-year windows.It was found that water-balance-derived root-zone storage capacities were similar to the values obtained from calibration of the hydrological models. A sharp decline in root-zone storage capacity was observed after deforestation, followed by a gradual recovery, for two of the three catchments. Trend analysis suggested hydrological recovery periods between 5 and 13 years after deforestation. In a proof-of-concept analysis, one of the hydrological models was adapted to allow dynamically changing root-zone storage capacities, following the observed changes due to deforestation. Although the overall performance of the modified model did not considerably change, in 51 % of all the evaluated hydrological signatures, considering all three catchments, improvements were observed when adding a time-variant representation of the root-zone storage to the model.In summary, it is shown that root-zone moisture storage capacities can be highly affected by deforestation and climatic influences and that a simple method exclusively based on climate data can not only provide robust, catchment-scale estimates of this critical parameter, but also reflect its time-dynamic behaviour after deforestation.

Abstract. Reproducibility and repeatability of experiments are the fundamental prerequisites that allow researchers to validate results and share hydrological knowledge, experience and expertise in the light of global water management problems. Virtual laboratories offer new opportunities to enable these prerequisites since they allow experimenters to share data, tools and pre-defined experimental procedures (i.e. protocols). Here we present the outcomes of a first collaborative numerical experiment undertaken by five different international research groups in a virtual laboratory to address the key issues of reproducibility and repeatability. Moving from the definition of accurate and detailed experimental protocols, a rainfall–runoff model was independently applied to 15 European catchments by the research groups and model results were collectively examined through a web-based discussion. We found that a detailed modelling protocol was crucial to ensure the comparability and reproducibility of the proposed experiment across groups. Our results suggest that sharing comprehensive and precise protocols and running the experiments within a controlled environment (e.g. virtual laboratory) is as fundamental as sharing data and tools for ensuring experiment repeatability and reproducibility across the broad scientific community and thus advancing hydrology in a more coherent way.

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