We present a novel, fine-resolution temperature profiling method based on Distributed Temperature Sensing (DTS) that is adaptable, reproducible, and fully FAIR. Accurate probing of near-surface temperature gradients requires sub-centimeter resolution, particularly in environments with short vegetation such as grass, where strong insulating properties promote steep gradients. Conventional DTS systems provide spatial resolutions of approximately 25 cm along fiber optic cables that can span several kilometers. By compacting such cables into a helical coil supported by a laser-cut frame, the Fine Resolution Adaptable Distributed Temperature Sensing (FRADTS) method attains vertical resolution and accuracy at the millimeter scale. The frame design is generated by a parametric script that outputs laser-cutting files, enabling users to assemble coil structures from sheet material with identical or easily adapted geometries. We demonstrate the method in both laboratory tests and a field campaign at the CESAR atmospheric observatory in Cabauw, the Netherlands, where a prototype coil captured high-quality vertical temperature profiles within the lowest meter above the soil, including a 10 cm grass layer. A resolution of 1.3 mm was attained and verified, and the influence of environmental factors such as solar radiation and precipitation on measurement biases was mapped and quantified. Despite minor artifacts, the method proved robust and effective, providing high-quality profiles under a wide range of weather conditions. As the method is modular and parametric, it can easily be applied in other research, potentially extending its application to other fields.

Scintillometers are used to estimate path-integrated evaporation and sensible heat fluxes. Commercial microwave links (CMLs), such as are used in cellular telecommunication networks, are similar line-of-sight instruments that also measure signal intensity of microwave signals, just like microwave scintillometers do. However, CMLs are not designed to capture scintillation fluctuations. Here, we investigate if and under what conditions CMLs can be used to obtain the structure parameter of the refractive index, Cnn, which would be a first step in computing turbulent heat fluxes with CMLs using scintillation theory. We use data from three collocated microwave links installed over an 856 m path at the Ruisdael Observatory near Cabauw, the Netherlands. Two of these links are 38 GHz CMLs formerly employed in telecom networks in the Netherlands, a Nokia Flexihopper and an Ericsson MiniLink. We compare Cnn estimates obtained from the received signal intensity of these links, sampled at 20 Hz, with those obtained from measurements of a 160 GHz microwave scintillometer (RPG-MWSC) sampled at 1 kHz and with those of an eddy-covariance system. After comparison of the unprocessed Cnn, we rejected the Ericsson MiniLink because its 0.5 dB power quantization (i.e. the discretization of the signal intensity) was found to be too coarse to be applied as a scintillometer. Based on the power spectra of the Nokia Flexihopper and the microwave scintillometer, we propose two methods to correct for the white noise present in the signal of the Nokia Flexihopper: (1) we apply a high-pass filter and subtract a low quantile of the resulting variances of the Nokia Flexihopper and (2) we correct for the noise by comparing with a microwave scintillometer (MWS) and select the parts of the power spectra where the Nokia Flexihopper behaves in correspondence with scintillation theory, also considering different crosswind conditions, and correct for the underrepresented part of the scintillation spectrum based on theoretical scintillation spectra. The comparison and noise determination with the microwave scintillometer provide the best-possible Cnn estimates for the Nokia Flexihopper, although this method is not feasible in operational settings for CMLs. Both of our proposed methods show an improvement in Cnn estimates in comparison to uncorrected estimates, albeit with larger uncertainty than when using the reference instruments. Our study illustrates the potential for using CMLs as scintillometers but also outlines some major drawbacks, most of which are related to unfavourable design choices made for CMLs. If these were overcome, given their global coverage, there is potential for CMLs to perform large-scale evaporation monitoring.

As the spatial coverage of evaporation observations is limited, we propose a novel, opportunistic method to estimate evaporation in which we consider commercial microwave links (CMLs), such as used in cellular telecommunication networks, in combination with scintillometry. Scintillometers are dedicated instruments to measure path-integrated latent and sensible heat fluxes, transmitting electromagnetic radiation that is diffracted by turbulent eddies between transmitter and receiver, causing the so-called scintillation effect. CMLs are line-of-sight devices that transmit electromagnetic radiation at similar frequencies as microwave scintillometers. However, CMLs and their sampling strategies are designed to ensure a continuously functioning wireless communication network rather than to capture the scintillation effect. Here, we estimate 30 min latent heat fluxes and daily evaporation using a former CML. To do so, we use data of a 38 GHz Nokia CML (formerly part of a telecom network) installed over an 856 m path at the Ruisdael Observatory near Cabauw, the Netherlands. We compare our results with estimates from an optical and microwave scintillometer setup, as well as an EC system. To obtain the flux estimates using the CML, we apply the two-wavelength method, in combination with the optical scintillometer, as well as a standalone energy-balance method (EBM), requiring net radiation estimates. For comparison, we also consider the free-convection limit of Monin-Obukhov similarity theory (MOST), instead of the complete scaling. An advantage of this approach is that it does not require horizontal wind speed measurements, which are more difficult to obtain in complex environments. For the net radiation estimates, we use in-situ measured radiation and data products provided by the Satellite Application Facility on Land Surface Analysis (LSA SAF) of EUMETSAT. Considering both turbulent heat fluxes, the two-wavelength method outperforms the EBM. The standalone EBM shows a reasonable performance, but also a large dependence on the quality of the net radiation estimates. When aggregating our 30 min latent heat fluxes to daily evaporation estimates, the relative performance of the methods remains comparable to that at 30 min intervals. These daily evaporation estimates could also be useful for catchment hydrological applications. Application of the free-convection scaling instead of the complete MOST scaling results in a comparable performance for all methods.

Hydrology faces critical challenges in reproducibility, accessibility and collaboration, limiting progress and innovation. This article introduces 'moving research down the academic career scale' (MRDTACS): the idea that work should be reproducible by someone at an earlier career stage and in less time than the original work. We advocate for research tools and methods to be accessible to students and early career researchers. By embedding open and findable, accessible, interoperable, reusable (FAIR) principles, modular tool design and user-friendly interfaces, we can lower barriers to reproducibility and foster equitable participation in hydrological research. Herein, we highlight practical strategies to empower researchers at all levels to build on existing work, reducing time spent overcoming technical challenges and enabling a deeper focus on innovation. When existing technologies and tools do not meet hydrology's advancing needs and innovation is needed, we use eWaterCycle to illustrate how we have practically implemented open and FAIR principles to support MRDTACS. This approach advances equity and inclusivity while strengthening collaboration across academic and professional communities. By prioritizing reproducibility and transparency, we can create a more resilient and effective hydrological science field equipped to tackle urgent global challenges. This article is part of the discussion meeting issue 'Hydrology in the 21st century: challenges in science, to policy and practice'.

Environmental models and software are essential tools for understanding the complex interactions of the natural world. They empower us to foresee potential futures, unravel intricate trends and expand our scientific knowledge, ensuring we make informed decisions for a sustainable future. This includes environmental emissions and exposure models, which tell us how chemicals and other potential pollutants enter, move around and behave in the environment. To achieve accurate, efficient, collaborative and integrative insights into this pollution and its sources, it is imperative that our models and software keep pace with scientific and technological advances, the increasing availability of data and a heightened importance on assessing complex, interconnected systems under a changing climate. But often our models follow outmoded programming paradigms and technological setups that makes this difficult. They are often monolithic codebases rather than flexible, standalone modules, making them difficult to adapt to emerging risks or integrate with other models to predict, e.g., the societal drivers and One Health impacts of pollution. Furthermore, modelling efforts often overlook ethical and sustainability issues, like the carbon footprint of running complex simulations or the societal impacts of using model predictions to inform policy. These considerations framed a workshop that took place in October 2024, bringing together interdisciplinary environmental modellers from around the world to discuss the question: what does the next generation of environmental models look like?
The focus was interactive sessions, where participants discussed this question by referering to six pillars: Software engineering and collaborative platforms; Interdisciplinary learning; Cloud-based and exascale computing; Citizen science; Artificial intelligence, and; Big data and better monitoring. In this presentation, we reflect on the outcomes, placing them in the context of emissions and exposure modelling.
The workshop was part of broader efforts to build an international community of practice around environmental modelling. A priority identified is that training, education and knowledge transfer are vital to ensuring that we empower the next generation of environmental modellers, as well as the models themselves, and we hope this community will provide a space to enable this.

East Africa relies heavily on satellite-based rainfall estimates due to the lack of in situ data. However, satellite rainfall products often perform poorly in this region. In this study, data from the Trans-African Hydrometeorological Observatory (TAHMO) were used to build a regional rainfall product in East Africa based on the Soil Moisture to Rain (SM2Rain) algorithm. Subsequently, this regional product was merged with a reanalysis product (ERA5) and two microwave (MW)/infrared (IR)-based rainfall products (IMERG and CHIRPS) based on the Statistical Uncertainty Analysis-Based Precipitation Merging (SUPER) framework. Within this framework, merging weights are derived from the error variances of the rainfall products determined from quadruple collocation on a pixel-to-pixel basis. The merged and individual products are evaluated using data from individual TAHMO stations. We tested SUPER with various interproduct dependency assumptions and found that, in the best-performing configuration, IMERG contributed the most to the merged product, followed by CHIRPS, ERA5, and SM2Rain. SM2Rain showed performance comparable to other rainfall products but is more useful for detecting the offset of the rainy season in drier climates and less reliable under wet conditions. The findings indicated that the merged product outperforms the individual products in most performance metrics. Additionally, we demonstrated the importance of comparing satellite and ground-measured precipitation time series, alongside evaluating performance metrics. The ultimate goal of this study is to develop a workflow to enhance the accuracy of rainfall measurements in East Africa by leveraging information from TAHMO data and different existing products, contributing to the improvement of satellite-based rainfall estimates in East Africa.

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.

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.

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).

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.

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.

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.

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.

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.

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 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.

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.

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.