To support a modal shift toward sustainable freight solutions, such as inland waterway transport (IWT), researchers and practitioners require long-term historical data on IWT freight flows. However, such comprehensive time series have been unavailable until now. This study addresses this gap by presenting a harmonized dataset encompassing 50 years (1970–2023) of IWT freight data across Europe, with a focus on the Rhine-Alpine Corridor. The dataset includes transport volumes (in tonnes) and transport performance (in ton-kilometers), classified according to NST-R, NST2007, and CCR nomenclatures. To ensure data continuity and completeness, processing techniques—including imputation and optical character recognition—were applied. The dataset offers valuable insights for researchers, policymakers, and transport planners aiming to comprehend and enhance the role of IWT in Europe’s freight transport landscape.

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.

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.

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.

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.

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.