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argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not "re-invent the water wheel" but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science. With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: 10.5281/zenodo.5119389,), we are putting our money where our mouth is and providing the hydrological community with a "FAIR by design" (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science. The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple "hello world" where only a hydrograph is generated to a complex coupling of models in different languages. In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk , last access: 28 June 2022)​​​​​​​. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science. [ABSTRACT FROM AUTHOR]

Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components and their past evolution as well as potential future development under various scenarios. While GHMs have been part of the hydrologist's toolbox for several decades, the models are continuously being developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance, and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge; however, they can – at least to some extent – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similarly to MPI-HM and thus conclude the successful transition from MPI-HM to HydroPy. [ABSTRACT FROM AUTHOR]

Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components as well as their past evolution and potential future development under various scenarios. While GHMs are a part of the Hydrologist's toolbox since several decades, the models are continuously developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max-Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge, however they can – at least to some part – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similar to MPI-HM and, thus, conclude the successful transition from MPI-HM to HydroPy. [ABSTRACT FROM AUTHOR]

Highlights • A model is proposed to evaluate runoff and AET of green roofs with a storage layer. • Daily ET and event runoff of green roofs decrease as ADP increases. • A storage layer enlarges the ability of green roofs to increase AET and reduce runoff. • The effects are pronounced under high temperature and rainy weather. • The effects of an additional storage layer have seasonal and yearly variations. Abstract Reduction in runoff and increase in actual evapotranspiration (AET) are two well-known benefits of green roofs. However, few studies have been carried out to investigate how green roofs affect runoff and AET when there is interaction between them. Such interaction is most prominent when green roofs have an additional storage layer. In this study, a modified hydrological model for green roofs is proposed to explicitly describe water retention and release in different layers and during wet/dry periods, which can reflect the interaction between runoff and AET under alternating dry and wet conditions. After calibration and validation with experimental data, the model was used to evaluate the combined effects of runoff reduction and increase in AET for green roofs under moist sub-tropical weather condition (such as Shenzhen, China). The results indicate that the daily AET and event runoff of green roofs decrease with increasing antecedent dry period (ADP); an additional storage layer can increase the ability of green roofs in reducing runoff and increasing AET. Generally, the effect is more pronounce under weather conditions with higher temperature, more rainfall amount and longer ADP. Such results provide hydrologists and engineers with a new and effective tool to simulate and understand the performance of green roofs by considering water retention and release in the layers. [ABSTRACT FROM AUTHOR]

This paper highlights mistakes that have helped shape my professional career. I use examples of personal, as well as technical failures to explore some guiding principles that I have found useful when navigating my professional journey. It is only the rarest of technical gurus who can afford to ignore the limitations of their personal interactions: to be impactful, the rest of us need to give as much, if not more, attention to soft skills than we do to our technical ones. I thus discuss the qualities that I think are needed to leverage technical strengths, and I outline some professional behaviours that I think are required to be effective. I also outline some high level technical themes that I have found important as a hydrologist. These cover the challenges of extrapolation, the analysis of joint interactions in compound events, the differentiation between hydrologic change and variability, and the value of separately considering different sources of uncertainty. [ABSTRACT FROM AUTHOR]

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large-scale, high-resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high-throughput computing (HTC) and cloud computing provide an ideal resource for large-scale modeling because they are cost-effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI-WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national-scale modeling. [ABSTRACT FROM AUTHOR]

In this study, we review the ways in which water has recently been conceptualized by both natural and social scientists as either hydro-social or socio-hydrological. We do this in order to discuss whether and how they can be compatible, in order to enable dialogue across disciplines that seek to address the ecological and social challenges related to the complex human/water interactions. Through our review, we document the emergence of these specific terminologies, identify how these terms-and the conceptualizations they represent-relate to each other, and suggest what opportunities there are for building further interdisciplinary approaches to understanding water and society. Specifically, we review the recent rise in socio-hydrology amongst natural scientists/hydrologists to put this in discussion with a much longer tradition in social sciences of seeing water as both natural and social. We identify what the paradigms are in both conceptualizations in order to assess what their respective focus is, and what they omit. Our purpose is not to judge competing claims. Rather we want to assess the knowledge claims made in both paradigms: what can we learn when we employ these different approaches, what different rationales for action do they suggest, and what scope exists for collaboration. We conclude that there is scope in combining both approaches without a need to antagonistically question their respective fundamental assumptions, and playing to the strengths of each: the rich case study narratives produced by hydrosocial research can be the basis for the conceptual and quantitative modeling of socio-hydrology. WIREs Water 2017, 4:e1196. doi: 10.1002/wat2.1196 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human-natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors. [ABSTRACT FROM AUTHOR]

Global Satellite Mapping of Precipitation (GSMaP) products, as important satellite-based precipitation products (SPPs) of Global Precipitation Measurement (GPM) mission, have provided hydrologists with critical precipitation data sources for hydrological applications in gauge-sparse or ungauged basins. This study statistically and hydrologically evaluated the latest GPM-era GSMaP SPPs in real-, near-real- and post-real-time versions at daily and hourly temporal scales in the sparsely gauged Yellow River source region (YRSR) in China. It includes the five latest GSMaP SPPs, namely, gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-NRT-Gauge), and real-time product (GSMaP-Now). The statistical assessment showed that among all five GSMaP SPPs, GSMaP-Gauge presented the best overall performance in daily and hourly precipitation detections in YRSR, followed by GSMaP-Now. GSMaP-NRT-Gauge was ranked the third, whereas GSMaP-MVK and GSMaP-NRT had relatively inferior performance. Given that GSMaP-Gauge demonstrated the best quality among all evaluated GSMaP SPPs, GSMaP-Gauge displayed the best hydrological feasibility in daily streamflow simulation. Both GSMaP-MVK and GSMaP-NRT presented inferior hydrological capability, with a considerable overestimation of the total streamflow. In contrast, GSMaP-Now and GSMaP-NRT-Gauge displayed basically acceptable hydrological performance in daily discharge simulations. In terms of hourly flood simulations, the performance of GSMaP-Gauge slightly worsened but was comparable to the rain-gauge-based precipitation data set. Following GSMaP-Gauge, GSMaP-Now and GSMaP-NRT-Gauge obtained certain predictability of flood events. In general, GSMaP-MVK and GSMaP-NRT barely had hydrological utility for flood-event simulations. [ABSTRACT FROM AUTHOR]

• A continuous hydrologic model is tested using as input a long observed rainfall time series at 10-min resolution. • The proposed continuous model is tailored for small and ungauged basin applications. • The test results confirm that continuous models can be an appealing alternative to the event-based approach. The design hydrograph estimation in small and ungauged basins represents one of the most common practices and, yet, a challenging open research topic for hydrologists. When discharge observations are not available, the practitioner is compelled to apply empirical approaches. The rational formula is slowly disappearing, while event-based approaches are more and more widespread. A step forward is represented by continuous models that have the potential to address the major drawbacks of event-based approaches. In this work we applied a continuous model specifically designed for ungauged basins (COSMO4SUB) and tested its use in conditions where typically the rational formula and the event-based approaches are applied. Results confirm that the continuous modelling is suitable for rapid and effective design simulations supporting flood hazard modelling and mapping studies. [ABSTRACT FROM AUTHOR]