Reducing waiting times is crucial for ports to be efficient and competitive. Important causes of waiting times are cascading interactions between realistic hydrodynamics, accessibility policies, vessel-priority rules, and detailed berth availability. The main challenges are determining the cause of waiting and finding rational solutions to reduce waiting time. In this study, we focus on the role of the design depth of a channel on the waiting times. We quantify the performance of channel depth for a representative fleet rather than the common approach of a single normative design vessel. The study relies on a mesoscale agent-based discrete-event model that can take processed Automatic Identification System and hydrodynamic data as its main input. The presented method’s validity is assessed by hindcasting one year of observed anchorage area laytimes for a liquid bulk terminal in the Port of Rotterdam. The hindcast demonstrates that the method predicts the causes of 73.4% of the non-excessive laytimes of vessels, thereby correctly modelling 60.7% of the vessels-of-call. Following a recent deepening of the access channel, cascading waiting times due to tidal restrictions were found to be limited. Nonetheless, the importance of our approach is demonstrated by testing alternative maintained bed level designs, revealing the method’s potential to support rational decision-making in coastal zones.@en

Salt intrusion is a growing problem in many deltas around the world. During periods with low river discharges, salinity upstream in a delta increases and affects freshwater availability, ecology, and other delta functions. For example, in the Rhine-Meuse estuary (the Netherlands), brackish water can reach drinking water intakes about 40 km from the estuary mouth during droughts. Salt intrusion is likely to become more severe in the context of climate change, as a result of sea level rise and a lower river discharge during droughts. The challenges with salt intrusion for the Netherlands are addressed in the Salti Solutions research program. Within this program the Delta Management Game offers an interactive environment where policy-making stakeholders can experience salt intrusion management and experiment with adaptation and mitigation strategies in the Rhine-Meuse estuary. As a serious game, the goal is for players to “learn by taking actions and by experiencing their effects through feedback mechanisms that are deliberately built into and around the game” (Mayer, 2009, p. 825). A particular design challenge for serious games is simplifying the environmental system and sufficiently representing the relevant physics, while offering exploratory and experimentation through (near-)instant, interactive feedback. The physical module for salt intrusion in the Delta Management Game should be able to deal with, among others, changes in bathymetry (e.g. depth or width of waterways, adding a sill) of the estuary in the game, while offering relatively quick feedback. @en

The planning and construction of offshore wind parks reduces the margin for error of nearby shipping activities. During the last couple of years, several incidents on the North Sea have raised the attention for the risk of ship-ship and ship-infrastructure collisions. Although often not the primary cause, environmental conditions play an important role in these incidents, and prevention and intervention measures, like the placement of emergency response vessels, are often deployed considering metocean conditions. To improve the design of risk-reducing measures, we need to understand better how vessels behave under varying environmental conditions. It is important to gain insight into risk patterns at system scale while retaining the ability to explain how these patterns are linked to underlying mechanisms. For this purpose we propose creating a so-called ‘event’ table, that couples ship behaviour data from Automatic Identification System (AIS) data to environmental data. The structure of the ‘event’ table, whereby events are defined as vessels sailing within a specific section of the system, allows appending analysis results and other data sources to the events. We show how the ‘event’ table adds important new perspectives to the analysis of nautical safety at sea.@en

PIANC Task Group 234 concludes that the “path to decarbonization of inland waterway transport is different for different corridors and in different countries”. This calls for an approach that can consider largescale differences as well as local influences when evaluating the emissions of inland vessels. This paper demonstrates the use of a so-called “event table” that allows corridor scale estimations of inland shipping emissions, while retaining the ability to identify the most important source mechanisms that produce these emissions. We considered three corridors in the Netherlands: Antwerp-Rotterdam, Antwerp-Duisburg and Rotterdam-Duisburg. Using the event table and four “pivoting perspectives”, we quantify large-scale emission patterns and investigate underlying mechanisms for these three corridors. Our study shows that despite their close vicinity, different mechanisms are responsible for observed emission peaks on these corridors. On the Antwerp-Rotterdam corridor, the most important contributions to emissions are slowly sailing vessels near the two locks that are on this route. It is furthermore shown that deeper fairway sections contribute to significantly lower emissions locally. On the Rotterdam-Duisburg corridor, we show that river currents significantly influence the emissions of vessels per travelled distance unit. The Antwerp-Duisburg corridor contains a combination of these factors.
@en

Purpose: Maintenance dredging can often hinder port operations resulting in waiting times for seagoing vessels. The purpose of this paper is to investigate the dynamics between maintenance dredging activities and seagoing vessels, specifically focusing on how waiting times can be reduced. Then, the role of selecting different maintenance dredging strategies in reducing these waiting times is outlined. Methods: The study analyzes historical automatic identification system (AIS) data to identify the interaction between maintenance dredging and seagoing vessels and quantify the hindrance periods for the Mississippihaven case study in the Port of Rotterdam, the Netherlands. The trajectories of the vessels are analyzed in a simple case to show how the vessels interact and how the waiting times are quantified. The interactions are checked with the Port of Rotterdam for different port calls to ensure that maintenance dredging was the reason for these delays. Results: By analyzing the AIS data analysis of vessels in a given time window, the dredgers for maintenance work can be identified and their activities within or near the terminal can be determined. In addition, the waiting time of the seagoing vessel caused by the maintenance dredging is quantified at the terminal entrance. Conclusion: The study discusses how the maintenance dredging operations could be improved by adjusting the loading and sailing phases of maintenance dredging and provides some theoretical and managerial insights. Alternative port maintenance strategies to minimize the waiting time caused by the hindrance are also discussed.

@en

In the 2015 Paris agreement, countries committed to implementing measures to reduce greenhouse gas emissions to limit global warming. For the maritime industry specifically, the International Maritime Organization (IMO) has proposed measures for energy efficiency of vessels and candidate measures regarding fuel choice and speed optimisation. This article aims to contribute to the latter by showing how logistical simulations can be used to optimise fleet operations. We will illustrate this in the form of a conceptual case using one cutter and a range of barge fleets. Running simulations with all possible fleets, we will demonstrate the value of extra energy-based alternatives to challenge the fastest, cheapest and most flexible alternatives.@en

Estuarine salt intrusion causes problems with freshwater availability in many deltas. Water managers require timely and accurate forecasts to be able to mitigate and adapt to salt intrusion. Data-driven models derived with machine learning are ideally suited for this, as they can mimic complex non-linear systems and are computationally efficient. We set up a long short-term memory (LSTM) model to forecast salt intrusion in the Rhine-Meuse delta, the Netherlands. Inputs for this model are chloride concentrations, water levels, discharges and wind speed, measured at nine locations. It forecasts daily minimum, mean and maximum chloride concentrations up to 7 d ahead at Krimpen aan den IJssel, an important location for freshwater provision. The model forecasts baseline concentrations and peak timing well but peak height is underestimated, a problem that becomes worse with increasing lead time. Between lead times of 1 and 7 d, forecast precision declines from 0.9 to 0.7 and forecast recall declines from 0.7 to 0.5 on average. Given these results, we aim to extend the model to other locations in the delta. We expect that a similar setup can work in other deltas, especially those with a similar or simpler channel network.

@en

Transdisciplinary research requires improved knowledge exchange between science and practice. Such improvements include diversifying and scaling up knowledge accessing and sharing through online platforms. We conducted twenty interviews informed by behavioral science methods to clarify the aim, components, and participants' perspectives on the usefulness of the proposed components for an envisioned platform. Participants were members of a Dutch community of practice for river studies and a research programme into integrated and collaborative management. The proposed concept included storylines, data repositories, user profiles, interactive visualisations, and collaborative sessions. Interview results include drivers and barriers from prospective users that we translated into requirements to increase the potential adoption and effective use of online platforms with similar components. From the experiences with implementing these requirements, we provide recommendations for enabling primary drivers: (i) Combining online and offline interactions to provide various options for knowledge exchange between disciplines and organisations. (ii) Sharing the content and application of the research with a non-scientific audience. (iii) Reusing existing online platforms as much as possible without restricting any to improve the reuse of research methods and results. We further provide recommendations to overcome the main barriers: (i) Partnering with various communities to extend knowledge exchange. (ii) Following a participatory approach to improve the design and content while considering the time and resources that such a process entails. (iii) Providing flexible options to contribute and tailor overviews of available knowledge in different ways according to prospective users' roles in practice. (iv) Purposefully facilitating online interactions according to the transdisciplinary process-intended attributes.@en

The availability of supporting bunker infrastructure for zero-emission energy sources will be key to accommodate zero-emission inland waterway transport (IWT). However, it remains unclear which (mix of) zero-emission energy sources to prepare for, and how to plan the bunker infrastructure in relative positions and required capacity at corridor scale. To provide insight into the positioning and dimensions of bunkering infrastructure we propose a bottom-up energy consumption method combined with agent based network simulation. In the method, we first produce a two-way traffic energy consumption map, aggregated from the energy footprint of individual vessels on the transport network. Next we investigate the potential sailing range of the vessels on the network if they would sail the same routes, but with alternative energy carriers. Based on the sailing range of the vessels for different energy carriers, the maximum inter-distance between refuelling points can be estimated. By aggregating the energy consumptions of all the vessels on the network, we can estimate the required capacity of a given refuelling point. To demonstrate the basic functionality we implement the method to four representative corridor scale inland shipping examples using zero-emission energy sources including hydrogen, batteries, e-NH3, e-methanol and e-LNG. The application in this paper is limited to four abstract cases. A recommended next step is to apply this approach to a more realistic network.@en

Small and medium-sized reservoirs play an important role in water systems that need to cope with climate variability and various other man-made and natural challenges. Although reservoirs and dams are criticized for their negative social and environmental impacts by reducing natural flow variability and obstructing river connections, they are also recognized as important for social and economic development and climate change adaptation. Multiple studies map large dams and analyze the dynamics of water stored in the reservoirs behind these dams, but very few studies focus on small and medium-sized reservoirs on a global scale. In this research, we use multi-annual multi-sensor satellite data, combined with cloud analytics, to monitor the state of small (10–100 ha) to medium-sized (> 100 ha, excluding 479 large ones) artificial water reservoirs globally for the first time. These reservoirs are of crucial importance to the well-being of many societies, but regular monitoring records of their water dynamics are mostly missing. We combine the results of multiple studies to identify 71,208 small to medium-sized reservoirs, followed by reconstructing surface water area changes from satellite data using a novel method introduced in this study. The dataset is validated using 768 daily in-situ water level and storage measurements (r2 > 0.7 for 67% of the reservoirs used for the validation) demonstrating that the surface water area dynamics can be used as a proxy for water storage dynamics in many cases. Our analysis shows that for small reservoirs, the inter-annual and intra-annual variability is much higher than for medium-sized reservoirs worldwide. This implies that the communities reliant on small reservoirs are more vulnerable to climate extremes, both short-term (within seasons) and longer-term (across seasons). Our findings show that the long-term inter-annual and intra-annual changes in these reservoirs are not equally distributed geographically. Through several cases, we demonstrate that this technology can help monitor water scarcity conditions and emerging food insecurity, and facilitate transboundary cooperation. It has the potential to provide operational information on conditions in ungauged or upstream riparian countries that do not share such data with neighboring countries. This may help to create a more level playing field in water resource information globally.

@en

Point cloud is made up of a multitude of three-dimensional (3D) points with one or more attributes attached. Point cloud is the third data paradigm in addition to the well-established object (vector) and gridded (raster) representations, since point cloud data can be directly collected, computed, stored, and analyzed without converting to other types. Modern ways of data acquisition, including laser scanning from airborne, mobile, or static platforms, multi-beam echo-sounding, and dense image matching from photos, generate millions to trillions of 3D points with attached attributes. If the collection is carried out in different periods, one of the essential attributes is precisely time, allowing spatiotemporal analysis to be performed. Its use is widespread in some fields such as metrology and quality inspection, virtual reality, indoor/outdoor navigation, object detection, vegetation monitoring, building modeling, cultural heritage, and diverse visualization applications. There are some examples in fields related to hydroinformatics, mainly related to terrain modeling. Due to its nature of big data, over the past decades, a series of developments have been carried out in the different processing chains for the optimal use of point cloud. This research seeks to introduce the various point cloud developments from which the hydroinformatics community and research could benefit. A review of recent advances is made, mainly including the analysis and visualization of point cloud for dealing with water-related problems. Potential areas of application and development in hydroinformatics are identified. These include, for example, the topics of coastal monitoring, coastal erosion, shallow water assessment, ice sheet change analysis, sea-level rise assessment, monitoring of levels in water bodies, crop and vegetation monitoring, analysis of the effects of groundwater depletion, detail tracing of basins and channels, analysis of floods with detailed terrain models, and drought monitoring in crops and forests. The challenges to overcome and ongoing developments regarding point cloud application in hydroinformatics are also discussed.@en

The river Rhine is one of Europe’s busiest waterways and is part of the Rhine-Alpine corridor. In 2018 the river experienced a severe low discharge extreme. This impacted the river’s transport capacity for a period of several months, causing shortages of source materials and fuels in regions far in-land. Historically, prolonged droughts of this magnitude are not uncommon. Concerns have been raised, however, that climate change may further increase their frequency and severity. Additionally the increased proportion of larger vessels in the overall fleet composition has made the supply of cargo via the river Rhine more vulnerable to reduced water depths. A better understanding of the risks and effects of sustained low water levels for Inland Waterway Transport network performance is therefore essential to enable sensible mitigation. An integral model that explicitly links the state of the river to supply chain performance at the scale of corridors, however, appears to be not yet available. This paper suggests a novel method to explicitly include the cascading effects of low discharge events (and mitigating measures) in climate risk assessments of waterborne supply chain performance, at system level. It is shown that its implementation can describe cascading effects and climate risks for fleet management and terminal operation.@en

The original version of this Article contained an error in the Data Availability section where “All new data and code generated in this research are available under the terms of Creative Commons BY 4.0 (for the data) and Apache 2.0 (for the code) licenses. Datasets and supplementary materials generated during this study are accessible from the TODO: upload and add Zenodo link here. The source code used to produce datasets is accessible from: https://github.com/global-water-watch/research-reservoir-water-dynamics. For more information about this research and to access the demo app visit: https://globalwaterwatch.earth.” now reads: “All new data and code generated in this research are available under the terms of Creative Commons BY 4.0 (for the data) and Apache 2.0 (for the code) licenses. Datasets and supplementary materials generated during this study are accessible from the supplementary materials document below. The source code used to produce datasets is accessible from: https://github.com/global-water-watch/research-reservoir-water-dynamics. For more information about this research and to access the demo app visit: https://globalwaterwatch.earth.” The original Article has been corrected.

@en

Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines. Analyzing more than 37,000 transects around the globe, we find that traditional forecast methods altogether with some of the evaluated probabilistic Machine Learning (ML) time-series forecast algorithms, outperform Ordinary Least Squares (OLS) predictions for the majority of the sites. When forecasting seven years ahead, we find that these algorithms generate better predictions than OLS for 54% of the transect sites, producing forecasts with, on average, 29% smaller Mean Squared Error (MSE). Importantly, this advantage is shown to exist over all considered forecast horizons, i.e., from 1 up to 11 years. Although the ML algorithms do not produce significantly better predictions than traditional time-series forecast methods, some proved to be significantly more efficient in terms of computation time. We further provide insight in how these ML algorithms can be improved so that they can be expected to outperform not only OLS regression, but also the traditional time-series forecast methods. These forecasting algorithms can be used by coastal engineers, managers, and scientists to generate future shoreline prediction at a global level and derive conclusions thereof.

@en

Sand nourishment is widely adopted as an effective soft approach to provide long-term coastal safety, protect the ecology environment, and promote tourism and recreation. With the increase in frequency and expenses in beach nourishment worldwide, an adequate prediction of morphology evolution is greatly desired for coastline management. Based on detailed monitoring data of the mega-nourishment Sand Engine, this article integrates a convolutional neural network (CNN) and long short-term memory (LSTM) to predict the nourishment morphology evolution. The historical surveyed data are transformed into sequence grids, which are input into the CNN to obtain the spatial features of beach nourishment. The CNN is constructed by performing the convolutional and pooling operation on the historical data, which can extract actual spatial features and reduce network complexity. The output of the CNN is input to LSTM to learn the temporal relationship to predict future nourishment terrain using past time-series features. Finally, the LSTM output is decoded by the fully connected layer to obtain the prediction result. The complex spatiotemporal correlations among the input data are identified through effective training of the proposed model. The major contribution of this article is to propose a data-driven model that combines CNN and LSTM for the morphology evolution prediction of beach nourishment, and validate the effectiveness of the proposed model by comparing with the performances of other popular methods in predicting the nourishment changes.@en

Fluvial flood events are a major threat to people and infrastructure. Typically, flood hazard is driven by hydrologic or river routing and floodplain flow processes. Since they are often simulated by different models, coupling these models may be a viable way to increase the integration of different physical drivers of simulated inundation estimates. To facilitate coupling different models and integrating across flood hazard processes, we here present GLOFRIM 2.0, a globally applicable framework for integrated hydrologic-hydrodynamic modelling. We then tested the hypothesis that smart model coupling can advance inundation modelling in the Amazon and Ganges basins. By means of GLOFRIM, we coupled the global hydrologic model PCR-GLOBWB with the hydrodynamic models CaMa-Flood and LISFLOOD-FP. Results show that replacing the kinematic wave approximation of the hydrologic model with the local inertia equation of CaMa-Flood greatly enhances accuracy of peak discharge simulations as expressed by an increase in the Nash-Sutcliffe efficiency (NSE) from 0.48 to 0.71. Flood maps obtained with LISFLOOD-FP improved representation of observed flood extent (critical success index C = 0:46), compared to downscaled products of PCR-GLOBWB and CaMa-Flood (C = 0:30 and C = 0:25, respectively). Results confirm that model coupling can indeed be a viable way forward towards more integrated flood simulations. However, results also suggest that the accuracy of coupled models still largely depends on the model forcing. Hence, further efforts must be undertaken to improve the magnitude and timing of simulated runoff. In addition, flood risk is, particularly in delta areas, driven by coastal processes. A more holistic representation of flood processes in delta areas, for example by incorporating a tide and surge model, must therefore be a next development step of GLOFRIM, making even more physically robust estimates possible for adequate flood risk management practices.

@en

Rising sea levels due to climate change can have severe consequences for coastal populations and ecosystems all around the world. Understanding and projecting sea-level rise is especially important for low-lying countries such as the Netherlands. It is of specific interest for vulnerable ecological and morphodynamic regions, such as the Wadden Sea UNESCO World Heritage region. Here we provide an overview of sea-level projections for the 21st century for the Wadden Sea region and a condensed review of the scientific data, understanding and uncertainties underpinning the projections. The sea-level projections are formulated in the framework of the geological history of the Wadden Sea region and are based on the regional sea-level projections published in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). These IPCC AR5 projections are compared against updates derived from more recent literature and evaluated for the Wadden Sea region. The projections are further put into perspective by including interannual variability based on long-Term tide-gauge records from observing stations at Den Helder and Delfzijl. We consider three climate scenarios, following the Representative Concentration Pathways (RCPs), as defined in IPCC AR5: The RCP2.6 scenario assumes that greenhouse gas (GHG) emissions decline after 2020; the RCP4.5 scenario assumes that GHG emissions peak at 2040 and decline thereafter; and the RCP8.5 scenario represents a continued rise of GHG emissions throughout the 21st century. For RCP8.5, we also evaluate several scenarios from recent literature where the mass loss in Antarctica accelerates at rates exceeding those presented in IPCC AR5. For the Dutch Wadden Sea, the IPCC AR5-based projected sea-level rise is 0.07±0.06m for the RCP4.5 scenario for the period 2018-30 (uncertainties representing 5-95%), with the RCP2.6 and RCP8.5 scenarios projecting 0.01m less and more, respectively. The projected rates of sea-level change in 2030 range between 2.6mma^-1 for the 5th percentile of the RCP2.6 scenario to 9.1mma^-1 for the 95th percentile of the RCP8.5 scenario. For the period 2018-50, the differences between the scenarios increase, with projected changes of 0.16±0.12m for RCP2.6, 0.19±0.11m for RCP4.5 and 0.23±0.12m for RCP8.5. The accompanying rates of change range between 2.3 and 12.4mma^-1 in 2050. The differences between the scenarios amplify for the 2018-2100 period, with projected total changes of 0.41±0.25m for RCP2.6, 0.52±0.27m for RCP4.5 and 0.76±0.36m for RCP8.5. The projections for the RCP8.5 scenario are larger than the high-end projections presented in the 2008 Delta Commission Report (0.74m for 1990-2100) when the differences in time period are considered. The sea-level change rates range from 2.2 to 18.3mma^-1 for the year 2100. We also assess the effect of accelerated ice mass loss on the sea-level projections under the RCP8.5 scenario, as recent literature suggests that there may be a larger contribution from Antarctica than presented in IPCC AR5 (potentially exceeding 1m in 2100). Changes in episodic extreme events, such as storm surges, and periodic (tidal) contributions on (sub-)daily timescales, have not been included in these sea-level projections. However, the potential impacts of these processes on sea-level change rates have been assessed in the report.

@en

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.@en

Coastal zones constitute one of the most heavily populated and developed land zones in the world. Despite the utility and economic benefits that coasts provide, there is no reliable global-scale assessment of historical shoreline change trends. Here, via the use of freely available optical satellite images captured since 1984, in conjunction with sophisticated image interrogation and analysis methods, we present a global-scale assessment of the occurrence of sandy beaches and rates of shoreline change therein. Applying pixel-based supervised classification, we found that 31% of the world’s ice-free shoreline are sandy. The application of an automated shoreline detection method to the sandy shorelines thus identified resulted in a global dataset of shoreline change rates for the 33 year period 1984–2016. Analysis of the satellite derived shoreline data indicates that 24% of the world’s sandy beaches are eroding at rates exceeding 0.5 m/yr, while 28% are accreting and 48% are stable. The majority of the sandy shorelines in marine protected areas are eroding, raising cause for serious concern. @en