For users of hydrological models, the suitability of models can depend on how well their simulated outputs align with observed discharge. This study emphasizes the crucial role of factoring in discharge observation uncertainty when assessing the performance of hydrological models. We introduce an ad hoc approach, implemented through the eWaterCycle platform, to evaluate the significance of differences in model performance while considering the uncertainty associated with discharge observations. The analysis of the results encompasses 299 catchments from the Catchment Attributes and MEteorology for Large-sample Studies Great Britain (CAMELS-GB) large-sample catchment dataset, addressing three practical use cases for model users. These use cases involve assessing the impact of additional calibration on model performance using discharge observations, conducting conventional model comparisons, and examining how the variations in discharge simulations resulting from model structural differences compare with the uncertainties inherent in discharge observations.

Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations.

Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users.

Terrestrial microwave links are increasingly being used to estimate path-averaged precipitation by determining the attenuation caused by rainfall along the link path, mostly with commercial microwave links from cellular telecommunication networks. However, the temporal resolution of these rainfall estimates and the method to derive them are often determined by the temporal sampling strategy that is employed by the mobile network operators. Currently, the links are most often sampled at a temporal resolution of 15 min with a recording of the minimum and maximum values, while more recently, a form of instantaneous sampling with possible intervals up to 1 s has also been set up. For rainfall research purposes, often high temporal resolutions in combination with averaged values are preferred. However, it is uncertain how these various temporal sampling strategies affect the estimated rainfall intensity. Here we aim to understand how temporal sampling strategies affect the measured rainfall intensities using microwave links. To do so, we use data from three collocated microwave links, two 38 GHz and one 26 GHz, sampled at 20 Hz and covering a 2.2 km path over the city of Wageningen, the Netherlands. We aggregate the microwave link power levels to multiple time intervals (1 s to 60 min) and use a mean, instantaneous, and minimum and maximum value to characterize the signal. Based on the aggregated data, we compute rainfall intensities and compare these with 20 Hz rainfall estimates, such that we isolate errors and uncertainties caused by the sampling strategies from instrumental effects, such as different biases between instruments and representativeness errors. In general, our results show that for all sampling strategies, an increase in sampling time interval reduces the performance of the rainfall estimates, which especially holds for the instantaneous sampling strategy. Even the mean sampling strategy, which generally performs best of all strategies, is sensitive to this reduction in temporal resolution and could lead to significant underestimations. This sensitivity of the mean sampling to the temporal resolution seems to be largely affected by the non-linear relation between attenuation and rainfall. The min–max sampling strategy is mostly prone to minor underestimations or large overestimations of the path-averaged rainfall intensities. Moreover, our results, including a comparison with theoretical events, show that the attenuation due to wet antennas not only affects the comparison between the rainfall estimates obtained with a microwave link and another reference instrument but also has a significant influence on the performance of the rainfall retrieval algorithm, especially for devices with relatively long duration of the wet-antenna attenuation combined with the longer sampling time intervals. Overall, this study demonstrates the effect a selected sampling strategy can have on rainfall intensity estimates using (commercial) microwave links.

Hutton et al. (2016) 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 (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), 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.

Open, accessible, reusable, and reproducible hydrologic research can have a significant positive impact on the scientific community and broader society. While more individuals and organizations within the hydrology community are embracing open science practices, technical (e.g., limited coding experience), resource (e.g., open access fees), and social (e.g., fear of weaknesses being exposed or ideas being scooped) challenges remain. Furthermore, there are a growing number of constantly evolving open science tools, resources, and initiatives that can be overwhelming. These challenges and the ever-evolving nature of the open science landscape may seem insurmountable for hydrologists interested in pursuing open science. Therefore, we propose the general “Open Hydrology Principles” to guide individual and community progress toward open science for research and education and the “Open Hydrology Practical Guide” to improve the accessibility of currently available tools and approaches. We aim to inform and empower hydrologists as they transition to open, accessible, reusable, and reproducible research. We discuss the benefits as well as common open science challenges and how hydrologists can overcome them. The Open Hydrology Principles and Open Hydrology Practical Guide reflect our knowledge of the current state of open hydrology; we recognize that recommendations and suggestions will evolve and expand with emerging open science infrastructures, workflows, and research experiences. Therefore, we encourage hydrologists all over the globe to join in and help advance open science by contributing to the living version of this document and by sharing open hydrology resources in the community-supported repository (https://open-hydrology.github.io, last access: 1 February 2022).

Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (>50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins.

Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling-related challenges that remain unsolved. To the user, in light of model result interpretation, finer-resolution output might imply an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the form of varying spatial resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances are derived at three spatial resolutions, namely 3 km, 1 km, and 200 m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (Kling–Gupta efficiency (KGE) score) of the three model instances resulted in only a statistical difference between the 3 km and 200 m streamflow estimates. However, an assessment of sampling uncertainty shows high uncertainties surrounding the KGE score throughout the domain. This makes the conclusion based on the statistical testing inconclusive. The results do indicate strong locality in the differences between model instances expressed by differences in KGE scores of on average 0.22 with values larger than 0.5. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help to further understand the challenges that need to be resolved for hyper-resolution hydrological modelling.

The reproducibility of computational hydrology is gaining attention among hydrologists. Reproducibility requires open and reusable code and data, allowing users to verify results and process new datasets. The creation of input files for global hydrological models (GHMs) requires complex high-resolution gridded dataset processing, limiting the model’s reproducibility to groups with advanced programming skills. GlobWat is one of these GHMs, which was developed by the Food and Agriculture Organization (FAO) to assess irrigation water use. Although the GlobWat code and sample input data are available, the methods for pre-processing model inputs are not available. Here, we present a set of open-source Python and YAML scripts within the Earth System Model Evaluation Tool (ESMValTool) that provide a formalized technique for developing and processing GlobWat model weather inputs. We demonstrate the use of these scripts with the ERA5 and ERA-Interim datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). To demonstrate the advantage of using these scripts, we ran the GlobWat model for 30 years for the entire world. The focus of the evaluation was on the Urmia Lake Basin in Iran. The validation of the model against the observed discharge in this basin showed that the combination of ERA5 and the De Bruin reference evaporation method yields the best GlobWat performance. Moreover, the scripts allowed us to examine the causes behind the differences in model outcomes.

Anthropogenic macrolitter (>0.5 cm) in rivers is of increasing concern. It has been found to have an adverse effect on riverine ecosystem health, and the livelihoods of the communities depending on and living next to these ecosystems. Yet, little is known on how macrolitter reaches and propagates through these ecosystems. A better understanding of macrolitter transport dynamics is key in developing effective reduction, preventive, and cleanup measures. In this study, we analyzed a novel dataset of citizen science riverbank macrolitter observations in the Dutch Rhine-Meuse delta, spanning two years of observations on over 200 unique locations, with the litter categorized into 111 item categories according to the river-OSPAR protocol. With the use of regression models, we analyzed how much of the variation in the observations can be explained by hydrometeorology, observer bias, and location, and how much can instead be explained by temporal trends and seasonality. The results show that observation bias is very low, with only a few exceptions, in contrast with the total variance in the observations. Additionally, the models show that precipitation, wind speed, and river flow are all important explanatory variables in litter abundance variability. However, the total number of items that can significantly be explained by the regression models is 19% and only six item categories display an R2 above 0.4. This suggests that a very substantial part of the variability in macrolitter abundance is a product of chance, caused by unaccounted (and often fundamentally unknowable) stochastic processes, rather than being driven by the deterministic processes studied in our analyses. The implications of these findings are that for modeling macrolitter movement through rivers effectively, a probabilistic approach and a strong uncertainty analysis are fundamental. In turn, point observations of macrolitter need to be planned to capture short-term variability.

A new type of rainfall sensor (the intervalometer), which counts the arrival of raindrops at a piezo electric element, is implemented during the Tanzanian monsoon season alongside tipping bucket rain gauges and an impact disdrometer. The aim is to test the validity of the Poisson hypothesis underlying the estimation of rainfall rates using an experimentally determined raindrop size distribution parameterisation based on Marshall and Palmer (1948)'s exponential one. These parameterisations are defined independently of the scale of observation and therefore implicitly assume that rainfall is a homogeneous Poisson process. The results show that 28.3 % of the total intervalometer observed rainfall patches can reasonably be considered Poisson distributed and that the main reasons for Poisson deviations of the remaining 71.7 % are non-compliance with the stationarity criterion (45.9 %), the presence of correlations between drop counts (7.0 %), particularly at higher arrival rates (ρa>500 m−2s−1), and failing a χ2 goodness-of-fit test for a Poisson distribution (17.7 %). Our results show that whilst the Poisson hypothesis is likely not strictly true for rainfall that contributes most to the total rainfall amount, it is quite useful in practice and may hold under certain rainfall conditions. The parameterisation that uses an experimentally determined power law relation between N0 and rainfall rate results in the best estimates of rainfall amount compared to co-located tipping bucket measurements. Despite the non-compliance with the Poisson hypothesis, estimates of total rainfall amount over the entire observational period derived from disdrometer drop counts are within 4 % of co-located tipping bucket measurements. Intervalometer estimates of total rainfall amount overestimate the co-located tipping bucket measurement by 12 %. The intervalometer principle shows potential for use as a rainfall measurement instrument.

In het eerstejaars vak Design Engineering voor fysici van Technische Natuurkunde Delft bouwen groepjes studenten voor hun eindopdracht een demonstratieproef die een natuurkundig fenomeen laat zien. Het belangrijkste leerdoel is het in de praktijk leren brengen van natuurkundige kennis via een aangeleerd, gestructureerd ontwerpproces. Veel van de activiteiten uit het vak zijn bruikbaar in en interessant voor het vo.

Particle filters are non-Gaussian filters, which means that the assumption that the error distribution of the ensemble should be Gaussian is unnecessary. Like the ensemble Kalman filter, particle filters are based on the Monte Carlo approximation to represent the distribution of model states. It requires a substantial number of particles to approximate the probability density function of states in high-dimensional models, which is prohibitive for real applications. In order to overcome problems with high dimensionality, localization was applied in an Ensemble-type data assimilation system. This study combines the localization in LETKF (Local Ensemble Transformation Kalman Filter) with particle filters and proposes a new local particle filter with the model state space correction using Gamma test theory for high-dimensional models. A series of tests with various parameter settings, including different the numbers of particles, observation intervals, localization scale, inflation factors, and observation operators, were used to evaluate the performance of this new method using a Lorenz model with 40 variables. Besides, the proposed filter was applied in the Lorenz model with 1,000 variables to evaluate its performance in the model with higher dimensions. The results show that this approach can deal with the issue of dimensionality, which otherwise leads to the collapse of the particle filters in high-dimensional systems. The local particle filter is stable and has considerable potential for complex higher-dimensional models.

Forests play a key part in the water cycle, so both planting and removing forests can affect streamflow. In a recent Article1, Evaristo and McDonnell used a gradient-boosted-tree model to conclude that streamflow response to forest removal is predominantly controlled by the potential water storage in the landscape, and that removing the world’s forests would contribute an additional 34,098 km3 yr−1 to streamflow worldwide, nearly doubling global river flow. Here we report several problems with Evaristo and McDonnell’s1 database, their model, and the extrapolation of their results to the continental and global scale. The main results of the paper1 remain unsubstantiated, because they rely on a database with multiple errors and a model that fails validation tests.

Teaching a hands- and minds-on course, in which feedback is essential in order to learn, is difficult, especially in times of COVID-19 where student progression cannot be monitored directly. During the lockdown period, the workshops of an undergraduate Design Engineering course had to be transferred to the home situation, which required a redesign of this course by the staff. It also provided new opportunities for students to adapt to this situation, which required extra creativity and problem-solving skills. The adapted workshops revealed conditions that enhance maker education. However, providing timely feedback required a substantial amount of time not anticipated for. We also report that short instruction videos seem to work much better than longer lectures or tedious materials. As we practice what we preach, we will evaluate the course and apply our design knowledge acquired over the years.

Drifters that track their position are important tools in studying the hydrodynamic behavior of rivers. Drifters that can be tracked in real time have so far been rather expensive. Recently, due to the rise of the open-hardware revolution and the associated Arduino ecosystem, both GPS receivers and cellular modems have become available at lower prices to "tinkering scientists", i.e., scientists that like to build their own measurement devices as much as is possible. This article serves two goals. Firstly, we provide detailed instructions on how to build a low-power GPS drifter with local storage and cellular model that we tested in a fieldwork on the confluence of the Chindwin and Ayeyarwady rivers in Myanmar. The device was designed from easily connected off-the-shelf components, allowing construction without a background in electrical engineering. The instructions allow fellow geoscientists to recreate the device. Secondly, we set the following question: has the open-hardware revolution progressed to the point that a low-power GPS drifter that wirelessly transmits its position can be made from open-hardware components by most geoscientists?. We feel this question is relevant and timely as more low-cost open-hardware devices are promoted, but in practice applicability is often restricted to the "tinkering engineer". We argue that because of the plug-and-play nature of the components geoscientists should be able to construct these type of devices. However, to get such devices to operate at low power levels that fieldwork often demands requires detailed (micro)electrical expertise.

The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on "Weather and Climate Science in the Digital Era"at the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Roughly 80ĝ€¯% of the studies presented in the conference session showed the added value of open data and software. These studies included open datasets from disparate sources in their analyses or developed tools and approaches that were made openly available to the research community. Furthermore, shared software is a prerequisite for the studies which presented systems like a model coupling framework or digital collaboration platform. Although these studies showed that sharing code and data is important, the consensus among the participants was that this is not sufficient to achieve open weather and climate science and that there are important issues to address. At the level of technology, the application of the findable, accessible, interoperable, and reusable (FAIR) principles to many datasets used in weather and climate science remains a challenge. This may be due to scalability (in the case of high-resolution climate model data, for example), legal barriers such as those encountered in using weather forecast data, or issues with heterogeneity (for example, when trying to make use of citizen data). In addition, the complexity of current software platforms often limits collaboration between researchers and the optimal use of open science tools and methods. The main challenges we observed, however, were non-technical and impact the practice of science as a whole. There is a need for new roles and responsibilities in the scientific process. People working at the interface of science and digital technology - e.g., data stewards and research software engineers - should collaborate with domain researchers to ensure the optimal use of open science tools and methods. In order to remove legal boundaries on sharing data, non-academic parties such as meteorological institutes should be allowed to act as trusted agents. Besides the creation of these new roles, novel policies regarding open weather and climate science should be developed in an inclusive way in order to engage all stakeholders. Although there is an ongoing debate on open science in the community, the individual aspects are usually discussed in isolation. Our approach in this paper takes the discourse further by focusing on "open science in weather and climate research"as a whole. We consider all aspects of open science and discuss the challenges and opportunities of recent open science developments in data, software, and hardware. We have compiled these into a list of concrete recommendations that could bring us closer to open weather and climate science. We acknowledge that the development of open weather and climate science requires effort to change, but the benefits are large. We have observed these benefits directly in the studies presented in the conference and believe that it leads to much faster progress in understanding our complex world.

In data assimilation(DA), various types of observations can be assimilated. Highly nonlinear observation operators are very common in geoscience, which goes against the linear and Gaussian assumptions of the Kalman filter. Particle filters are promising and possible solutions to non-linear issues in DA because they do not rely on Gaussian assumptions. However, traditional particle filters cannot be applied in high dimensional systems. As a method based on Bayes theory and Monte Carlo approximation, particle filters require numerous particles to represent the distribution of high-dimensional model states, which is called the curse of dimensionality. Because of this, it is prohibitive for realistic DA applications. In this study, a new local particle filter was proposed to deal with both the nonlinearity of observation operators and the dimensionality of particle filters. Localization methods are frequently used in Ensemble-type methods to solve the issues caused by limited ensemble size in high dimensional models. In this research, localization is applied to overcome the curse of dimensionality in particle filters. For the non-linear issue in observation operators, Gaussian process regression (GPR) is used to estimate the uncertainty of non-linear observation operators. At each update, the surrogate is adaptively trained and refined by current observations and model states. When the model states are transferred to the observation space, the surrogate can give the information about estimations and the corresponding uncertainty. A Lorenz model (1996) with 40 variables is used to evaluate the performance of this proposed local particle by conducting a set of experiments with different settings including the number of particles, the impact of localization scales, etc. To test its ability to deal with nonlinear issues, a highly nonlinear observation operator is designed and used in experiments. LETKF and local EAKF are two benchmarks in this research. The results show that the new method has a stable performance with high accuracy and it outperforms the two benchmarks. More importantly, for the non-linear case in this study, the new method only uses 25 particles to achieve a good performance. Although only the Lorenz model is considered in this study, it is highly likely to apply the proposed method to other models.