Earth Water Research Articles

INOVASI PRODUKSI PACKAGING MANAJEMEN USAHA DAN DIGITAL MARKETING BERBASIS ENVIRONMENTAL SUSTAINABILITY BAGI PETANI GARAM JONO

The national need for salt increases every year along with population growth. Jono Village, Tawangharjo District, Grobogan, Central Java is known as a center for salt production. The source of the earth's spring water in the form of belik, is used by residents to make salt. Although far from the sea, the water from the source tastes salty and warm. Salt production has been around for hundreds of years. In 1960, there were hundreds of salt farmers, now there are dozens left. This requires attention from various parties. The purpose of the community service program in the Jono Village Salt Farmer Group is to provide solutions based on situational analysis of the needs, challenges, or problems that exist in the community and carry out activities that can empower the community. Specifically, with service with salt group partners, it is hoped that it can increase the production of salt, and provide more modern packaging for the salt product. This service can improve the marketing of the salt, it can be sold in modern stores, but it can only enter traditional markets. The results of the study show that Jono salt is tastier and tastier. In terms of belik water before being made into salt, the water feels warm and contains lots of minerals so it is good for health, both for skin diseases and other diseases. The method of implementing the service is carried out by conducting program socialization by the service team to Jono's salt farmer partners, with stages of training and mentoring of the service program. The training and mentoring of the program include increasing salt production with the tunnel method, packaging training using machine packaging technology, and business management mentoring and marketing mentoring. Furthermore, the application of technology in the form of a tunnel method for the salt drying process, packaging technology using a salt packaging machine, mentoring and evaluation as well as program sustainability.

Spatiotemporal variations and driving factors of evapotranspiration in the Yunnan-Guizhou Plateau from 2003 to 2020

ABSTRACT Evapotranspiration (ET) is vital for the Earth's energy and water balance, particularly influenced by global climate change. The Yunnan-Guizhou Plateau (YGP), characterized by abundant water resources and intricate terrain, has been a subject of study. However, previous research often overlooked intra-annual climate variations in ET. This study employed high-spatiotemporal resolution ET data from 2003 to 2020 to quantitatively analyze the spatiotemporal characteristics of ET on the YGP. The annual ET showed an increasing trend of 0.18 mm/year, with monthly ET increases in January, March, November, and December, mainly influenced by vegetation transpiration, which accounts for 56% of ET. Breakpoints in ET trends and seasonal components occurred in January 2007 and June 2018. The geodetector model assessed the impact of 15 driving factors on ET, with net radiation and vegetation index playing dominant roles with q-values of 0.29 and 0.24. Factor impacts varied seasonally, with greater influence in the dry season (q-value of 0.53 for net radiation in January) and lesser in the rainy season (q-value of 0.08 in August). Pearson correlation analysis indicated that different driving factors influenced ET in different months. These findings enhance understanding of plateau ET responses to climate change mechanisms.

Reconstructing Tibetan Plateau lake bathymetry using ICESat-2 photon-counting laser altimetry

Lake bathymetry is important for quantifying and characterizing underwater morphology and its geophysical state, which is critical for hydrological and ecological studies. Due primarily to the harsh environment of the Tibetan Plateau, there is a severe lack of lake bathymetry measurements, limiting the accurate estimation of total lake volumes and their evolutions. Here, we propose a novel lake bathymetry reconstruction by combining ICESat-2/ATLAS (Advanced Topography Laser Altimetry System) data with a numerical model. An improved grid-based photon noise removal method is used to address the photon signal buried in the background noise during the local daytime. The developed model was validated for seven lakes on the Tibetan Plateau and showed good agreement between simulated and measured lake volumes, with an average absolute percentage error of 8.0 % for maximum water depth and 19.7 % for lake volume simulations. The model was then utilized to estimate the water volume of other lakes by combining it with the self-affine theory. The lake depths obtained from ICESat-2/ATLAS show good agreement (RMSE = 0.69 m; rRMSE = 10.3 %) with available in-situ measurements for lakes with depths <16.5 m, demonstrating the potential of ICESat-2/ATLAS for improved reconstruction of the bathymetry of clear water inland lakes. Our study reveals for the first time, that the Tibetan Plateau has an estimated total lake water volume of 1043.69 ± 341.31 km3 for 33,477 lakes (>0.01 km2) in 2022. Over 70 % (∼734.8 km3) of the lake water storage is concentrated in the Inner Plateau, with the Yellow River basin accounting for 10.9 % (∼113.9 km3), followed by the Indus River basin with 7.2 % (∼75.1 km3). Our study provides a robust method for estimating total lake volumes where in-situ measurements are scarce and can be extended to other clear water lakes, thus contributing to more accurate global assessments and towards comprehensive quantification of Earth's surface water resources distribution.

A dataset of soil water storage of typical ecosystems in arid and semi-arid areas in China, 2005-2017

Soil water is one of the important components of the earth's water resources, and also the main source of water directly consumed by vegetation and crops. Therefore, the storage, recharge, consumption, renewal and balance of soil water are of great significance to agriculture, animal husbandry, forestry, ecological environment and water resources balance, especially for the arid and semi-arid region of China. We collect and sort out the long-term observation data of soil moisture from eight ecosystem observation and research stations of the Chinese Ecosystem Research Network (CERN) located in the arid and semi-arid region of China during 2005 - 2017, and generates the corresponding soil water storage dataset through quality control, spatial and temporal normalization, statistical calculation, in order to provide long-term data support for understanding distribution and change of soil water storage in typical ecosystems in arid and semi-arid areas of China.

Multi-scale assessment of a cosmic-ray neutron probe observation of soil moisture for surface layer applications in a mountainous forest environment

Soil moisture is critical to the Earth's water cycle as it influences water exchange between the land, atmosphere, and oceans. This study assessed the potential of a cosmic-ray neutron probe (CRNP) to monitor soil moisture dynamics in a forested mountain region, which is critical for water resource management and ecosystem health. Accurate soil moisture monitoring is essential for effectively managing forested mountainous areas, but various limitations exist in such environments. To overcome the complexities of soil moisture monitoring, an observatory consisting of a CRNP, automatic weather sensors, and dataloggers was established. Fifteen capacitance sensor (CS) stations were placed strategically within the CRNP's footprint to ensure strict evaluation. Although each CS measurement revealed the heterogeneity of the experimental site, temporal stability analysis determined the representative soil moisture. The validation of the CRNP and the average results of the 15 surface CS sensors revealed that they were strongly correlated (R = 0.94). It means that the CRNP can describe the dynamics of soil moisture that fluctuate according to physical properties, underscoring its reliability in estimating intermediate scale soil moisture. This correlation remained robust despite differences in rainfall patterns over three years. These findings indicate that the CRNP may be a valuable tool for monitoring soil moisture in a challenging environment. Additionally, the evaluation of the Advanced Microwave Scanning Radiometer-2 (AMSR2), Advanced Scatterometer (ASCAT), and Global Land Data Assimilation System (GLDAS) demonstrated the usefulness of CRNP in supplying reference data. The AMSR2 showed an inadequate temporal pattern (R = 0.35), and the ASCAT achieved the highest correlation agreement (R = 0.67) with the CRNP. The GLDAS also showed close agreement, although with some discrepancies (R = 0.52). The results of this study support the utilization of CRNP and provide insight into establishing and expanding a soil moisture network.

The influence of plant water stress on vegetation–atmosphere exchanges: implications for ozone modelling

Abstract. Evapotranspiration is important for Earth's water and energy cycles as it strongly affects air temperature, cloud cover, and precipitation. Leaf stomata are the conduit of transpiration, and their opening is sensitive to weather and climate conditions. This feedback can exacerbate heat waves and can play a role in their spatiotemporal propagation. Sustained high temperatures strongly favour high ozone levels, with significant negative impacts on air quality and thus on human health. Our study evaluates the process representation of evapotranspiration in the atmospheric chemistry–climate European Centre for Medium-Range Weather Forecasts – Hamburg(ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry model. Different water stress parameterizations are implemented in a stomatal model based on CO2 assimilation. The stress factors depend on either soil moisture or leaf water potential, which act on photosynthetic activity, and mesophyll and stomatal conductance. The new functionalities reduce the initial overestimation of evapotranspiration in the model globally by more than an order of magnitude, which is most important in the Southern Hemisphere. The intensity of simulated warm spells over continents is significantly improved. For ozone, we find that a realistic model representation of plant water stress suppresses uptake by vegetation and enhances photochemical production in the troposphere. These effects lead to an overall increase in simulated ground-level ozone, which is most pronounced in the Southern Hemisphere over the continents. More sophisticated land surface models with multi-layer soil schemes could address the uncertainties in representing plant dynamics representation due to too-shallow roots. In regions with low evaporative loss, the representation of precipitation remains the largest uncertainty.

Boosting Photocatalytic Hydrogen Production by MOF-Derived Metal Oxide Heterojunctions with a 10.0 % Apparent Quantum Yield.

Photocatalytic hydrogen production offers an alternative pathway to establish a sustainable energy economy, utilizing the Earth's natural sunlight and water resources to address environmental concerns associated with fossil fuel combustion. While numerous photoactive materials exhibit high potential for generating hydrogen from water, the synergy achieved by combining two different materials with complementary properties in the form of heterojunctions can significantly enhance the rate of hydrogen production and quantum efficiency. Our study describes the design and generation of the metal-organic framework-derived (MOF) metal oxide heterojunction herein referred to as RTTA, composed of RuO2/N,S-TiO2. The RuO2/N,S-TiO2 is generated through the pyrolysis of MOFs, Ru-HKUST-1, and the amino-functionalized MIL-125-NH2 in the presence of thiourea. Among the various RTTA materials tested, RTTA-1, characterized by the lowest RuO2 content, exhibited the highest hydrogen evolution rate, producing 10,761 μmol ⋅ hr-1 ⋅ g-1 of hydrogen with an apparent quantum yield of 10.0 % in pure water containing glycerol. In addition to RTTA-1, we generated two other MOF-derived metal oxide heterojunctions, namely ZTTA-1 (ZnO/N,S-TiO2) and ITTA-1 (In2O3/N,S-TiO2). These heterojunctions were tested for their photocatalytic activity, leading to apparent quantum yields of 0.7 % and 0.3 %, respectively. The remarkable photocatalytic activity observed in RTTA-1 is thought to be attributed to the synergistic effects arising from the combination of metallic properties inherent in the metal oxides, complemented by the presence of suitable band alignment, porosity, and surface properties inherited from the parent MOFs. These properties enhance electron transfer and restrict hole movement. The photocatalytic efficiency of RTTA-1 was further demonstrated in actual water samples, producing hydrogen with a rate of 8,190 μmol ⋅ hr-1 ⋅ g-1 in tap water, and 6,390 μmol ⋅ hr-1 ⋅ g-1 in river water.

Inverse Isotope Kinetic Effect of the Charge Transfer Reactions of Ar+ with H2O and D2O.

Hydrogen isotopic effect, as the key to revealing the origin of Earth's water, arises from the H/D mass difference and quantum dynamics at the transition state of reaction. The ion-molecule charge-exchange reaction between water (H2O/D2O) and argon ion (Ar+) proceeds spontaneously and promptly, where there is no transition-state or intermediate complex. In this energetically resonant process, we find an inverse kinetic isotope effect (KIE) leading to the higher charge transfer rate for D2O, by the velocity map imaging measurements of H2O+/D2O+ products. Using the average dipole orientation capture model, we estimate the orientation angles of C2v axis of H2O/D2O relative to the Ar+ approaching direction and attribute to the difference of stereodynamics. According to the long-distance Landau-Zener charge transfer model, this inverse KIE could be also attributed to the density-of-state difference of molecular bending motion between H2O+ and D2O+ around the resonant charge transfer.

Evolving Subduction Zone Thermal Structure Drives Extensive Forearc Mantle Wedge Hydration

AbstractHydration of the subduction zone forearc mantle wedge influences the downdip distribution of seismicity, the availability of fluids for arc magmatism, and Earth's long term water cycle. Reconstructions of present‐day subduction zone thermal structures using time‐invariant geodynamic models indicate relatively minor hydration, in contrast to many geophysical and geologic observations. We pair a dynamic, time‐evolving thermal model of subduction with phase equilibria modeling to investigate how variations in slab and forearc temperatures from subduction infancy through to maturity contribute to mantle wedge hydration. We find that thermal state during the intermediate period of subduction, as the slab freely descends through the upper mantle, promotes extensive forearc wedge hydration. In contrast, during early subduction the forearc is too hot to stabilize hydrous minerals in the mantle wedge, while during mature subduction, slab dehydration dominantly occurs beyond forearc depths. In our models, maximum wedge hydration during the intermediate phase is 60%–70% and falls to 20%–40% as quasi‐steady state conditions are approached during maturity. Comparison to global forearc H2O capacities reveals that consideration of thermal evolution leads to an order of magnitude increase in estimates for current extents of wedge hydration and provides better agreement with geophysical observations. This suggests that hydration of the forearc mantle wedge represents a potential vast reservoir of H2O, on the order of 3.4–5.9 × 1021 g globally. These results provide novel insights into the subduction zone water cycle, new constraints on the mantle wedge as a fluid reservoir and are useful to better understand geologic processes at plate margins.

Hydrological Research Evolution: A Large Language Model‐Based Analysis of 310,000 Studies Published Globally Between 1980 and 2023

AbstractHydrology plays a crucial role in understanding Earth's intricate water system and addressing water‐related problems, including against the backdrop of ongoing climate change. A retrospective review of the evolution of hydrology up to the current state of research is of great importance for understanding this role. While there have been some quantitative reviews of large numbers of hydrological publications, there still remains a lack of overarching hydrological research assessment, particularly with the focus on hydrological basins as fundamental spatial‐geographic units of hydrological analysis. Large language models, represented by OpenAI's ChatGPT, have demonstrated powerful textual understanding capabilities, making it possible to extract such overarching and basin information from hydrological publications. Here, we considered publications related to hydrology from Web of Science spanning January 1980 to October 2023, and parsed the information from this extensive body of literature by integrating a large language model and geocoding. These techniques enable quantitative analysis of research characteristics across different spatio‐temporal scales, focusing on hotspot topics, collaboration networks, and various basins worldwide. Our study revealed an increase in hydrological research since the 1990 s, with shifts in research priorities from groundwater and nutrients to climate change and ecohydrology. Some basins in North America and Europe have consistently been hotspots for hydrological research. Since the 2010s, there has been a noteworthy increase in interest toward basins in China and South Asia, but attention to many regions with frequent extreme rainfall remains insufficient. Geographical patterns show different preferred research topics for different basins, but climate change has emerged as the most prominent topic across all regions in the last decade. In conclusion, our study provides an effective approach to quantitative analysis of research trends, offering a fresh view on the evolution of hydrology as a research field, its focus on various hydrological basins around the world, and the emergence of overarching and basin‐specific hot topics over time.

Insights into the Crystallinity-Dependent Photochemical Productions of Reactive Oxygen Species from Iron Minerals.

Iron minerals are widespread in earth's surface water and soil. Recent studies have revealed that under sunlight irradiation, iron minerals are photoactive on producing reactive oxygen species (ROS), a group of key species in regulating elemental cycling, microbe inactivation, and pollutant degradation. In nature, iron minerals exhibit varying crystallinity under different hydrogeological conditions. While crystallinity is a known key parameter determining the overall activity of iron minerals, the impact of iron mineral crystallinity on photochemical ROS production remains unknown. Here, we assessed the photochemical ROS production from ferrihydrites with different degrees of crystallinity. All examined ferrihydrites demonstrated photoactivity under irradiation, resulting in the generation of hydrogen peroxide (H2O2) and hydroxyl radical (•OH). The photochemical ROS production from ferrihydrites increased with decreasing ferrihydrite crystallinity. The crystallinity-dependent photochemical •OH production was primarily attributed to conduction band reduction reactions, with the reduction of O2 by conduction band electrons being the rate-limiting key process. Conversely, the crystallinity of iron minerals had a negligible influence on photon-to-electron conversion efficiency or surface Fenton-like activity. The difference in ROS productions led to a discrepant degradation efficiency of organic pollutants on iron mineral surfaces. Our study provides valuable insights into the crystallinity-dependent ROS productions from iron minerals in natural systems, emphasizing the significance of iron mineral photochemistry in natural sites with abundant lower-crystallinity iron minerals such as wetland water and surface soils.

Spatiotemporal coupling analysis of surface water and groundwater in Tibet based on multi-source sensors

Abstract. Lakes and groundwater are two crucial components of the global terrestrial water cycle, collectively forming a vital network for Earth's water resources. However, in the Tibetan region, the spatiotemporal relationship between lakes and groundwater and their impact on the local hydrological cycle remain inadequately understood. Satellite remote sensing serves as an effective observational tool, enabling comprehensive investigation and analysis of surface lakes and groundwater in Tibet with high spatial resolution. Therefore, this study integrates Landsat and Cryosat-2 satellite data to examine the spatiotemporal patterns of surface lake extents and water levels in Tibet. Additionally, combining Gravity Recovery and Climate Experiment (GRACE) satellite observations with the Global Land Data Assimilation System (GLDAS) model data, we quantitatively analyze the spatiotemporal variations in groundwater storage and its correlation with lake water. The results indicate that: 1) Rivers and lakes in Tibet are mainly located in the central and northwest regions, displaying noticeable intra-annual variations; 2) Substantial lagged relationships exist between groundwater storage and lake water levels and areas, revealing that lakes contribute significantly to groundwater replenishment, especially in the Ngari prefecture and Lhokha prefecture. This study comprehensively utilizes multi-source remote sensing data to dynamically monitor surface and groundwater in Tibet, providing robust support for a better understanding of the interaction between groundwater and surface water.

Photomolecular effect: Visible light interaction with air–water interface

Although water is almost transparent to visible light, we demonstrate that the air-water interface interacts strongly with visible light via what we hypothesize as the photomolecular effect. In this effect, transverse-magnetic polarized photons cleave off water clusters from the air-water interface. We use 14 different experiments to demonstrate the existence of this effect and its dependence on the wavelength, incident angle, and polarization of visible light. We further demonstrate that visible light heats up thin fogs, suggesting that this process can impact weather, climate, and the earth's water cycle and that it provides a mechanism to resolve the long-standing puzzle of larger measured clouds absorption to solar radiation than theory could predict based on bulk water optical constants. Our study suggests that the photomolecular effect should happen widely in nature, from clouds to fogs, ocean to soil surfaces, and plant transpiration and can also lead to applications in energy and clean water.

Km‐Scale Simulations of Mesoscale Convective Systems Over South America—A Feature Tracker Intercomparison

AbstractMesoscale convective systems (MCSs) are clusters of thunderstorms that are important in Earth's water and energy cycle. Additionally, they are responsible for extreme events such as large hail, strong winds, and extreme precipitation. Automated object‐based analyses that track MCSs have become popular since they allow us to identify and follow MCSs over their entire life cycle in a Lagrangian framework. This rise in popularity was accompanied by an increasing number of MCS tracking algorithms, however, little is known about how sensitive analyses are concerning the MCS tracker formulation. Here, we assess differences between six MCS tracking algorithms on South American MCS characteristics and evaluate MCSs in kilometer‐scale simulations with observational‐based MCSs over 3 years. All trackers are run with a common set of MCS classification criteria to isolate tracker formulation differences. The tracker formulation substantially impacts MCS characteristics such as frequency, size, duration, and contribution to total precipitation. The evaluation of simulated MCS characteristics is less sensitive to the tracker formulation and all trackers agree that the model can capture MCS characteristics well across different South American climate zones. Dominant sources of uncertainty are the segmentation of cloud systems in space and time and the treatment of how MCSs are linked in time. Our results highlight that comparing MCS analyses that use different tracking algorithms is challenging. We provide general guidelines on how MCS characteristics compare between trackers to facilitate a more robust assessment of MCS statistics in future studies.

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data

Abstract. Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits promising performance across various vegetation coverage types, as validated against in situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square errors (RMSEs) of 0.81 and 0.73 mm d−1, unbiased root-mean-square errors (ubRMSEs) of 1.20 and 1.04 mm d−1, mean absolute errors (MAEs) of 0.81 and 0.73 mm d−1, and Kling–Gupta efficiencies (KGEs) of 0.60 and 0.65 on average at resolutions of 0.1 and 0.25°, respectively. In addition, comparisons indicate that CAMELE can effectively characterize the multiyear linear trend, mean average, and extreme values of ET. However, it exhibits a tendency to overestimate seasonality. In summary, we propose a reliable set of ET data that can aid in understanding the variations in the water cycle and has the potential to serve as a benchmark for various applications. The dataset is publicly available at https://doi.org/10.5281/zenodo.8047038 (Li et al., 2023b).

Machine learning classification of rainfall forecasts using Austin weather data

The paper examines the machine learning classification of rainfall forecasts using Austin weather data. Rain is a natural phenomenon that is essential for the Earth's water cycle. Rain brings benefits to daily lives and also causes disasters, such as floods, which will endanger lives in addition to causing great losses. Due to this, many methods have been studied and experimented with to find a solution to predict rainfall and prevent tragedies from happening. In this research, the Austin weather dataset is applied to make predictions of rainfall through the implementation of machine learning models. The models used to predict rainfall based on the data set were Extreme Gradient Boosting, Support Vector Machine, Long Short-Term Memory, and Random Forest models. 21 variables with 1319 records were present in the dataset, but the variables used for the modelling were 18 variables from the original data, and 1 variable, “Precipitation Sum,” was converted to the variable “Precipitation Range,” which contained the classes “no rain,” “small rain,” “moderate rain,” and “heavy rain” based on specific value ranges. After training and predicting the data on the models, it was shown that Extreme Gradient Boosting gave the best results of 85.17% accuracy, 83.19% F1 score, 85.17% recall score, and 82.14% precision score, and was able to give predictions on all 4 classes of rainfall. This study and the way to implement machine learning models for rainfall prediction have the potential to provide new insights and methodologies for future studies and pave the way for finding a high-accuracy rainfall prediction method to avoid disaster.

Impact and Trends of Climate Change Analysis of Streamflow at Various Gauging Stations in the Jhelum River, Jammu &amp; Kashmir

Abstract: The planet Earth's water resources are involved in the biggest matter movement in the Earth's system, the hydrological cycle, which is infinitely repeated. Given that water is the fundamental component of the planet's life support system, it is critical to comprehend how current and anticipated climate change is affecting water availability and supplies. Stream flow trend analysis is crucial for flood forecasting and hydrologic drought assessment-related research, in addition to the study of water resource management. This study examined the long-term variations in stream flow at several gauging stations in the Jhelum River of Jammu and Kashmir . The Modified Mann-Kendall Test was used to analyze trends (MMKT). Sen's Slope Estimator (SSE) was employed in order to quantify trend. Trend analysis was performed using long-term time series data of the monthly and annual maximum stream flows at 10 gauging stations. The streamflow annual timeseries was not visible. any discernible upward or downward trends at any gauging point. Nonetheless, it was discovered that every noteworthy trend discovered at several gauging sites over the course of several seasons was declining in character. Throughout July, six gauging sites showed tendencies of declining streamflow. However, in addition to helping with regional hydrologic studies, this kind of river water flow research would also be helpful for studying the occurrence of floods and hydrologic droughts.

The Surface Water and Ocean Topography Mission: A Breakthrough in Radar Remote Sensing of the Ocean and Land Surface Water

AbstractThe elevations of water surfaces hold important information on the earth's oceans and land surface waters. Ocean sea surface height is related to the internal change of the ocean's density and mass associated with ocean circulation and its response to climate change. The flow rates of rivers and volume changes of lakes are crucial to freshwater supplies and the hazards of floods and drought resulting from extreme weather and climate events. The Surface Water and Ocean Topography (SWOT) Mission is a new satellite using advanced radar technology to make headway in observing the variability of the elevation of water surfaces globally, providing fundamentally new information previously not available to the study of earth's waters. Here, we provide the first results of SWOT over oceans, rivers, and lakes. We demonstrate the potential of the mission to address science questions in oceanography and hydrology.

Field installation of ion exchange technology for purification of retention reservoirs from nitrogen-based nutrient contamination

N- and P-nutrient contamination is recognised as one of the key factors that reduces the quality of water. In this context, a new water purification installation was set up in the Turawa reservoir (district Opole, Poland). The process involves ion exchange columns operated in twin configuration: once one column filtrates water, the second one regenerates. The installation design is modular, and the devices are placed in a container station. This enables efficient water purification in various locations and further offers scalability. The research focused on the design and optimization of the installation, as well as environmental assessment of how the installation influenced the quality of water. This technology afforded a decrease in the concentrations of NO3− ions, suggesting an apparent selectivity. The removal of NO3− reached 97 % and was maintained for over 100 m3 of purified water. In turn, the removal of PO43− was not effective; however, the collected data allowed the installation design to be adjusted such that the cascade arrangement of ion exchange columns can be considered. The technology was assessed over all four seasons in the two-year test period (2020−2021). This revealed that environmental factors, such as season, air temperature, wind, and relative humidity, influence the operation of the installation. The installation functions optimally in spring with an average air temperature of 11–20 °C, high relative humidity of 90–100 %, and an average wind speed of 2 m/ s. The implementation of the technique could easily broaden the perspectives of Earth's water preservation and sustainability.

Well-controlled preparation of WO3-x/WSe2 sponges by electric field-assisted laser-induced plasma deposition for hydrovoltaic

Hydrovoltaic, which converts the energy of water into electrical energy, is a regenerative energy harvesting method that utilizes the earth's sufficient water resources. Electricity production relies on water molecules interaction with functional materials. Strategies like surface modification, structure engineering, and ion doping can be used to increase output performance. We present an electric field-assisted laser-induced plasma deposition method to prepare WO3-x/WSe2 sponges for water-induced electricity generation. By adjusting the electric field strength, we are able to control the porosity, Se vacancies, and O doping of the sponges. These modifications directly impact the performance of water-induced electricity generation by altering the streaming potential and ion diffusion gradient of sponge structures. The prepared WO3-x/WSe2 sponges can participate in the complete water cycle to achieve the energy harvesting of different forms of water, obtaining droplet-induced voltages of 65.4 mV and moisture-induced voltages of 207.1 mV. This overcomes the limitation of single-stimulation water-induced electricity systems that require specific conditions. When exposed to Sunlight, the photosensitive properties of WO3-x and WSe2 can lead to a current up to 7 times higher than that produced in darkness. Furthermore, this research can be expanded to develop other two-dimensional materials and be utilized in various applications, such as the self-powering of flexible and wearable electronic devices.