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This paper presents the results of the 2022 Groundwater Time Series Modelling Challenge, where 15 teams from different institutes applied various data-driven models to simulate hydraulic-head time series at four monitoring wells. Three of the wells were located in Europe and one was located in the USA in different hydrogeological settings in temperate, continental, or subarctic climates. Participants were provided with approximately 15 years of measured heads at (almost) regular time intervals and daily measurements of weather data starting some 10 years prior to the first head measurements and extending around 5 years after the last head measurement. The participants were asked to simulate the measured heads (the calibration period), to provide a prediction for around 5 years after the last measurement (the validation period for which weather data were provided but not head measurements), and to include an uncertainty estimate. Three different groups of models were identified among the submissions: lumped-parameter models (three teams), machine learning models (four teams), and deep learning models (eight teams). Lumped-parameter models apply relatively simple response functions with few parameters, while the artificial intelligence models used models of varying complexity, generally with more parameters and more input, including input engineered from the provided data (e.g. multi-day averages). The models were evaluated on their performance in simulating the heads in the calibration period and in predicting the heads in the validation period. Different metrics were used to assess performance, including metrics for average relative fit, average absolute fit, fit of extreme (high or low) heads, and the coverage of the uncertainty interval. For all wells, reasonable performance was obtained by at least one team from each of the three groups. However, the performance was not consistent across submissions within each group, which imp

Climate change and human activity are leading to water scarcity in southwestern Europe. Groundwater use is thought to be unsustainable in the region, yet regional assessments using measured data are missing. Here, we evaluate long-term trends and drivers of groundwater levels and found a more complex situation. Historical data (1960–2020) from 12,398 wells in Portugal, Spain, France, and Italy showed 20% with rising groundwater levels, 68% were stable, and only 12% were declining. Rising wells in temperate climates were due to increased precipitation. Recovering wells in semi-arid regions were attributed to improved groundwater management. Stable wells are concentrated in temperate climates with year-round high precipitation. Declining wells in semi-arid regions are primarily located near agricultural areas and experience prolonged summer soil moisture loss, whereas in temperate regions, the decline is associated with large urban areas. Systematic groundwater monitoring and data sharing are essential for sustainable and science-based water resources management.

Enhanced and effective hydrological monitoring plays a crucial role in understanding water-related processes in a rapidly changing world. Within this context, image-based river monitoring has been shown to significantly enhance data collection, improve analysis and accuracy, and support effective and timely decision making. The integration of remote and proximal sensing technologies with citizen science and artificial intelligence may revolutionize monitoring practices. Therefore, it is crucial to evaluate the quality of current research and ongoing initiatives to envision the potential trajectories for research activities within this specific field. The evolution of monitoring strategies is progressing in multiple directions that should converge to build a critical mass around relevant challenges to find innovative solutions that overcome limitations of traditional approaches. The present study reviews examples and good practices of enhanced hydrological monitoring in different applications, reflecting on the strengths and limitations of new approaches.

Enhanced and effective hydrological monitoring plays a crucial role in understanding water-related processes in a rapidly changing world. Within this context, image-based river monitoring has been shown to significantly enhance data collection, improve analysis and accuracy, and support effective and timely decision making. The integration of remote and proximal sensing technologies with citizen science and artificial intelligence may revolutionize monitoring practices. Therefore, it is crucial to evaluate the quality of current research and ongoing initiatives to envision the potential trajectories for research activities within this specific field. The evolution of monitoring strategies is progressing in multiple directions that should converge to build a critical mass around relevant challenges to find innovative solutions that overcome limitations of traditional approaches. The present study reviews examples and good practices of enhanced hydrological monitoring in different applications, reflecting on the strengths and limitations of new approaches., Water Resources

Enhanced and effective hydrological monitoring plays a crucial role in understanding water-related processes in a rapidly changing world. Within this context, image-based river monitoring has shown to significantly enhance data collection, improve analysis and accuracy, and support effective and timely decision-making. The integration of remote and proximal sensing technologies, with citizen science, and artificial intelligence may revolutionize monitoring practices. Therefore, it is crucial to quantify the quality of current research and ongoing initiatives to envision the potential trajectories for research activities within this specific field. The evolution of monitoring strategies is progressing in multiple directions that should converge to build critical mass around relevant challenges to meet the need for innovative solutions to overcome limitations of traditional approaches. The present study reviews showcases and good practices of enhanced hydrological monitoring in different applications, reflecting the strengths and limitations of new approaches.

Groundwater is a strategic water resource in water-stressed regions such as the Mediterranean region. It used to meet the increasing domestic water demand and food production, maintaining ecosystem integrity and buffering climate change impacts. However, groundwater information that relies mainly on in-situ data observations remains fragmented and lacking standardization in the Mediterranean region due to the lack of systematic monitoring and data-sharing policy. PRIMA Foundation, launched in 2018 as a European Commission funding program, has targeted groundwater as a priority topic in its two first calls of 2018 and 2019. Sustain-COAST and InTheMED are two PRIMA-funded projects aiming for sustainable groundwater management in the Mediterranean region, adopting innovative but complementary approaches. Among their specific goals, Sustain-COAST and InTheMED projects have jointly developed a joint effort to collect groundwater-level data from around the Mediterranean. Over 14,000 time series of historical groundwater level data have been collected from different countries and have been harmonized into a common format. The resulting groundwater database has opened new horizons and perspectives for groundwater assessment that were previously invisible. In this contribution, we present and explore five new directions that have resulted from the groundwater database of the Mediterranean region: 1.  Trend analysis and groundwater patterns clustering and their controlling drivers, 2. Regional groundwater level estimates combining different global groundwater models and regional in-situ data, 3. A methodological framework using Gravity Recovery and Climate Experiment (GRACE) satellite data to retrieve groundwater storage changes, 4. Water policy timeline, harmonization and pathways for innovative governance, and 5. Lesson learned from “success stories” of groundwater trend reversal and their transfer capabilities. This contribution will shed light on the power of dat

Improving river monitoring techniques is critical given the concomitant impact of climate change, population growth, and pollution over the last years. Turbidity is one of the most significant metrics for water quality characteristics. In river basins, high turbidity values can be indicative of both organic and inorganic materials. Turbidity is often used as a proxy for transport of suspended particles and associated fluxes of hydrophobic pollutants in a wide range of hydrological conditions. However, it is demanding to estimate suspended sediment yields in rivers because of the high variability along stream of suspended sediment concentrations. Traditional methods, such as gravimetric analysis, are time-consuming, expensive, often discontinuous in space and time and influenced by human errors or instrumental limitations.Remote sensing techniques are a suitable alternative to point measurements. Satellite remote sensing allows to study the spatial and temporal variations of water status parameters, but it is limited by the spatial and temporal resolution of the satellites considered. Low range systems can help increase the resolution of the imagery used for this purpose. In particular, the use of optical cameras can significantly reduce the monitoring cost and exponentially increase the information on water bodies health and hydrological dynamics, offering a large amount of data distributed in time and space. Nonetheless, all optical sensing methods are strongly affected by many environmental constraints (light, good optical transmission, visibility, etc.), which make them currently not always suitable for regular long-term monitoring of turbidity in rivers. The main goal of the monitoring procedure identified in this work is to avoid all these constraints, by processing the camera image to use it as a real measurement data. In this work, an image processing procedure has been identified by exploiting the water surface reflectance properties to estimate wat

Successful modelling of the groundwater level variations in hydrogeological systems in complex formations considerably depends on spatial and temporal data availability and knowledge of the boundary conditions. Geostatistics plays an important role in model-related data analysis and preparation, but has specific limitations when the aquifer system is inhomogeneous. This study combines geostatistics with machine learning approaches to solve problems in complex aquifer systems. Herein, the emphasis is given to cases where the available dataset is large and randomly distributed in the different aquifer types of the hydrogeological system. Self-Organizing Maps can be applied to identify locally similar input data, to substitute the usually uncertain correlation length of the variogram model that estimates the correlated neighborhood, and then by means of Transgaussian Kriging to estimate the bias corrected spatial distribution of groundwater level. The proposed methodology was tested on a large dataset of groundwater level data in a complex hydrogeological area. The obtained results have shown a significant improvement compared to the ones obtained by classical geostatistical approaches.

The Nile River is the primary water source for populous and hyper-arid Egypt. However, it suffers from the backfilling of its extensive network of waterways and increasing encroachments along its banks. The Nile’s waterways are among the world’s oldest and largest water irrigation systems. As an integral component of the Nile water system, they are vital in sustaining agriculture, inhabitants’ quality of life in urban and rural settings, and various ecosystem services. Therefore, raising public awareness and a sense of connectivity with the conservation of these waterways is one of the primary mitigation strategies for addressing the dire water deficit in Egypt resulting from rapid population growth and flow alteration associated with increased upstream damming. The large-scale degradation of the waterways system, observed since the mid-1980s, is perplexing, as Egyptians were historically more oriented to preserving the Nile ecosystem and the interrelated landscape for thousands of years. To assess the root cause of this phenomenon, we review the waterways’ public conservation campaigns and explore the grounds for their inefficiencies, failures, and subsequent impacts on transboundary water management. To achieve this objective, we compiled several research publications, local technical reports, and ground observations. Our results show that negligence toward the waterways accelerated during the nation’s transition from water sufficiency in the 1970s (1400 m3/capita/year) to the onset of scarcity in the 1980s (<1000 m3/capita/year). The water authorities’ delay of 15 years in implementing conservation campaigns, which were unhurriedly initiated in the mid-1990s, along with unsustainable landscape transformations, led to nationwide water misuse and sanitary degradation of the Nile’s infrastructure. Considering the findings of our review, we provide recommendations for improving the efficiency of waterways conservation campaigns. Our

Water quality is an essential component of the Sustainable Development Goals (SDGs), specifically highlighted in SDG 6, which focuses on „Ensuring availability and sustainable management of water and sanitation for all.“ While the title may suggest a primary emphasis on drinking water and sanitation, SDG 6 encompasses a much broader scope, including the preservation of aquatic ecosystems and the maintenance of ambient water quality.Maintaining and restoring good ambient water quality is essential for the integrity of aquatic ecosystems on the one hand, while on the other hand aquatic ecosystems provide services to society such as natural purification of water and the storage of freshwater in the landscape. But water quality hazards are present all over the world: Increasing pollution of freshwater as a result of rapid economic growth and urbanization in developing countries, and sustained, chronic pollution including long-term legacies in developed countries.In 2019 a report entitled „Quality unknown“ by the Worldbank concluded that the water quality status is largely invisible and unknown because of the lack of information on water quality.In the 2021 progress update on SDG 6.3.2, water quality could not be assessed for about three billion people due the lack of water quality data. It is obvious that there is a need to improve water quality information.The GlobeWQ project was designed to address the global challenge of improving water quality information. It was conducted from October 1st, 2019 to December 31st, 2022 funded by the German Federal Ministry of Education and Research (BMBF).At a global scale, GlobeWQ has further developed and applied the WorldQual water quality model, providing information on concentration of fecal coliform bacteria, biological oxygen demand and total dissolved solids for rivers worldwide. A data-driven approach was applied to estimate global nitrate concentrations.In addition to global information products, GlobeWQ has also developed

Successful modelling of the groundwater level variations in hydrogeological systems in complex formations considerably depends on spatial and temporal data availability and knowledge of the boundary conditions. Geostatistics plays an important role in model-related data analysis and preparation, but has specific limitations when the aquifer system is inhomogeneous. This study combines geostatistics with machine learning approaches to solve problems in complex aquifer systems. Herein, the emphasis is given to cases where the available dataset is large and randomly distributed in the different aquifer types of the hydrogeological system. Self-Organizing Maps can be applied to identify locally similar input data, to substitute the usually uncertain correlation length of the variogram model that estimates the correlated neighborhood, and then by means of Transgaussian Kriging to estimate the bias corrected spatial distribution of groundwater level. The proposed methodology was tested on a large dataset of groundwater level data in a complex hydrogeological area. The obtained results have shown a significant improvement compared to the ones obtained by classical geostatistical approaches., Water Resources