Water Cycle Research Articles

Efeito da autocicatrização no fechamento de fissuras decorrentes do ataque por sulfato em cimento Portland, supersulfatado e álcali ativado

Abstract Self-healing consists of closing cracks and recovering the watertight properties of cement-based materials and can occur by hydration of the materials of the mixture (autogenous) or by materials added to the mixture for this purpose (autonomous). This study consists of the use of stimulated self-healing as a way of mitigating sulfate attack (sodium and magnesium), with the evaluation of the influence of crystalline admixture in this process. For that, cycles of sulfate attack and self-healing (by wetting and drying cycles in water) were performed, aiming to evaluate the behavior of mortars with three types of cement: Portland, supersulfated, and alkali-activated. The results showed that self-healing was not sufficient to close cracks due to sulfate attack. This behavior was associated with the hypothesis that the high calcium content of the crystalline admixture reacted with the sulfates and formed expansive products, increasing the attack rate.

A moving target: trade-offs between maximizing carbon and minimizing hydraulic stress for plants in a changing climate.

Observational evidence indicates that tree leaf area may acclimate in response to changes in water availability to alleviate hydraulic stress. However, the underlying mechanisms driving leaf area changes and consequences of different leaf area allocation strategies remain unknown. Here, we use a trait-based hydraulically enabled tree model with two endmember leaf area allocation strategies, aimed at either maximizing carbon gain or moderating hydraulic stress. We examined the impacts of these strategies on future plant stress and productivity. Allocating leaf area to maximize carbon gain increased productivity with high CO2, but systematically increased hydraulic stress. Following an allocation strategy to avoid increased future hydraulic stress missed out on 26% of the potential future net primary productivity in some geographies. Both endmember leaf area allocation strategies resulted in leaf area decreases under future climate scenarios, contrary to Earth system model (ESM) predictions. Leaf area acclimation to avoid increased hydraulic stress (and potentially the risk of accelerated mortality) was possible, but led to reduced carbon gain. Accounting for plant hydraulic effects on canopy acclimation in ESMs could limit or reverse current projections of future increases in leaf area, with consequences for the carbon and water cycles, and surface energy budgets.

Large sensory analysis of vegetables from conventional, organic and no-till practices

There is a growing interest in agriculture to address soil deterioration issues and achieve a sus-tainable agrosystem. Many producers introduce no-till techniques and show significant yields with better ecosystem functioning, as well as improved carbon and water cycles. However, sensory analysis to compare vegetables from these practices are scarce. In this paper, we conduct triangle and hedonic tests over 15 panels of consumers for a total of 915 consumers. Based on statistical hypothesis testing and maximum likelihood estimation, significant statistical differences (p < 0.05 and less) are produced for both triangle tests and hedonic ranking in some specific cases. These results show a trend for sensory preferences of no-till practices.

Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture

Soil moisture is a key variable in the water, energy, and carbon cycles. Mapping sub-surface soil moisture with fine spatial resolution requires integrating downscaling approaches and process-based models. However, the effectiveness of hybrid methods, such as regression kriging (RK), in enhancing soil moisture estimates through process-based parameter predictions remains inconclusive. This study aims to integrate infiltration processes into downscaling models to predict 1-km multi-layer soil moisture, while comparing performance of nonlinear and linear models, and evaluating RK improvements. Random forests (RF) and generalized linear model (GLM) were used to downscale surface soil moisture (0–5 cm) from 36-km Soil Moisture Active Passive satellite products to 1 km across the Qinghai-Tibet Plateau. Next, the soil moisture analytical relationship (SMAR) model was applied to simulate infiltration processes and obtain site-scale parameters. RK variants (RFRK and GLMRK) were applied to jointly predict the spatial distribution of multiple infiltration parameters, which were used in SMAR at 1-km grids to estimate sub-surface soil moisture (5–40 cm). The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2 % to 69.8 %, from 0.09 to 0.03. RF outperformed GLM across all depth intervals, providing higher prediction accuracy (average RMSE, RF: 0.07; GLM: 0.09). Moreover, RK enhanced the Nash-Sutcliffe efficiency coefficient (RFRK: 0.34; GLMRK: 0.28) and coefficient of determination (RFRK: 0.5; GLMRK: 0.38) by 7.7 %–13.3 % and 2.2 %–2.4 %. This study provides a reference for mapping multi-layer soil moisture through the integration of data-driven and knowledge-driven approaches in regional-scale study areas.

Incorporating changes in land surface temperature improves BESS evapotranspiration estimates under water-deficit conditions: A case study for US Midwest and Great Plains grasslands

Evapotranspiration (ET) is a critical climate and ecosystem variable that interconnects water, energy, and carbon cycles. Breathing Earth System Simulator (BESS) is one of the state-of-the-art biophysical models capable of producing spatio-temporal continuous ET results. However, we found that since the BESS model does not use an explicit constraint on soil moisture (SM), it has a relatively lower performance under drier conditions. Given that changes in land surface temperature (LST) are closely associated with surface water status and sensible heat energy, we hypothesize that integrating LST changes could explicitly add the soil moisture constraints and thus enhance BESS’s ability to estimate ET. Here we used the morning rise rate of LST (Trate) as a proxy of LST change because of the low noise level in Trate as well as Trate’s close relationship with daily mean sensible heat. To test the hypothesis, this study first assessed whether the performance of BESS ET can be explained by the LST change, targeting grassland sites of the AmeriFlux network in the US Midwest and Great Plains. Specifically, the ET deviation (i.e., the difference between BESS-modeled ET and field-measured ET) and Trate deviation, as well as their relationships, were investigated under different conditions of precipitation, SM, and vapor pressure deficit (VPD) at the AmeriFlux sites. Results indicated that BESS ET exhibited consistently higher performance under well-watered conditions than water-deficit conditions. Also, the deviations of ET and Trate became more negatively correlated under water-deficit conditions. Leveraging the empirical relationship between ET and Trate deviations, this study developed a new way to calibrate BESS ET based on Trate calculated from LST diurnal observations, particularly under soil or atmospheric water-deficit conditions. After calibrating BESS ET, the statistical indicators between the calibrated ET and the ground measurements showed meaningful improvements relative to those before calibration. Specifically, in the Midwest (Great Plains), R2 increased from 0.42 to 0.51 (from 0.45 to 0.46), and RMSE and absolute bias decreased by 12% and 42% (11% and 45%), respectively. This study highlights that the morning rise rate of LST can effectively constrain the ET models that have no SM constraints under water-deficit conditions.

Wastewater treatment plants circular performance models evaluation: Portugal case-study

Population growth, economic growth, and changes in societal habits have led to significant changes in resource consumption. Therefore, it's crucial to accelerate the “reduce, reuse, recycle, and recover” of resources to ensure the balance of ecosystems, and water is surely one of the most fundamental resources. The acceleration of this approach in the water cycle makes sense only if we combine a circular economy (CE) transition with a sustainable perspective. In this context, more rational usage of water resources (which are under pressure) and more sustainable wastewater practices are expected to be a way towards the CE in the water and wastewater sector. This study provides a description and evaluation of existing frameworks that can be used to measure and assess the level of circularity of the wastewater treatment plant (WWTP). The treatment of urban wastewater requires new concepts of management and operation for the adaptation of existing plants, which lack robustness and flexibility, to face these new challenges and requirements because we can no longer continue to look at the WWTP only as treatment units, but as wastewater resource recovery facilities. This transformation must be transposed according to a matrix that allows the assessment to describe the current situation, analyse the problem, identify vulnerabilities and opportunities, identify, and evaluate measures, and identify and evaluate strategies. Considering that decision-makers face profound uncertainties such as climate change, population growth, population needs, innovative technologies, economic developments, ecosystem preservation and the impacts of human and natural activities.

The characteristics and changes of the natural social binary water cycle in the Upper Yellow River Basin under the influence of climate change and human activities: A review

The characteristics and changes of the natural social binary water cycle in the Upper Yellow River Basin under the influence of climate change and human activities: A review

Processable Coordination Polymer Inks for Highly Conductive and Robust Coatings.

The unique properties and processability of conducting and semiconducting organic materials have fascinated scientists since their discovery. Of this broad class of materials, conductive coordination polymers are of immense recent interest due to their innate modularity and tunability. However, these materials are typically generated as powders and, in some cases, single crystals which significantly limits possible processing and many applications. Herein, we report a method that enables solution-phase processing of a previously reported highly conductive coordination polymer, NiTTFtt (TTFtt = tetrathiafulvalenetetrathiolate), into thin films and conductive textiles. Thin films of NiTTFtt show record-breaking conductivity for a coordination polymer and unusual physical behavior that sheds light on the transport mechanisms in this glassy metallic material. Textiles coated with NiTTFtt are conductive and durable to air, water, washing, acid, base, and mechanical cycles. The properties and processability of NiTTFtt reported here make it notable among coordination polymers and conducting organic materials more broadly.

Drivers of global irrigation expansion: the role of discrete global grid choice

Abstract. Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude–latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify the main drivers of historical global irrigation expansion using, among other data, outputs from the global dynamic vegetation model Lund-Potsdam-Jena managed Land (LPJml). Irrigation is critical for food security amidst growing populations, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and the prediction of future trends. We compare predictive accuracy, simulated irrigation patterns, and identification of irrigation drivers between the two grid systems. Using the ISEA3H geodesic grid system increases the predictive accuracy by up to 28 % compared to the longitude–latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. Gross domestic product (GDP) per capita also shows some influence. We conclude that the geodesic discrete global grid system significantly affects predicted irrigation patterns and identification of drivers and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

Leaching behavior of arsenic in coal under the action of mine water

Arsenic, a potentially harmful element in coal, has a detrimental effect on the quality of groundwater and the water cycle. There is a significant safety risk to the drinking water and overall health of residents living near mining areas. It is of great significance to explore the leaching pattern of arsenic in coal under the influence of mining and identify the main controlling factors for the prevention and control of groundwater pollution in mining areas. In this paper, we selected long flame coal and non-caking coal from Wulanmulun Mine, along with four types of water samples from the mine area, as the research subjects. Our aim was to investigate the leaching mechanism of arsenic in groundwater under the influence of coal-water interaction in the mine area. To achieve this, we conducted indoor batch coal-water leaching simulation experiments. The results show that: 1) The four leaching solutions, which correspond to four groundwater types at the coal mine production site, exhibit similar trends in the presence of arsenic ions after water-coal interaction. The leaching process can be divided into three distinct zones: a rapid descent zone, a dissolution shock zone, and a dissolution linear stable zone. The initial decline rate of arsenic content in different water samples is proportional to the initial concentration of arsenic in the water samples. 2) The dissolution of arsenic has a strong negative correlation with DO, and the lower the oxygen content in the solution, the higher the arsenic ion content. There is a relatively strong negative correlation with pH value. It has a strong positive correlation with the leaching of Mg, K, V, Cr, and Fe, and a relatively small positive correlation with the leaching of Mn and Cu. The leaching degree of Mg, K, V, Cr and Fe can be used as a sign to explore the leaching or content change of arsenic in actual mining environment. 3) Most arsenic in coal occurs in pyrite in the form of isomorphism or solid solution, and the dissolution of arsenic is related to pyrite content and its oxidative dissolution behavior. The higher the pyrite content, the greater its oxidation degree, and the higher the arsenic content in the solution. 4) Clay minerals, predominantly kaolinite, found in different coal samples demonstrate varying capacities for arsenic adsorption in the leaching solution. The results of this experiment indicate that the adsorption capacity of clay minerals in long flame coal is considerably higher than that observed in non-caking coal, leading to a substantial decrease in the concentration of arsenic ions in the solution. This reduction is associated with a notable decline in the arsenic concentration within the groundwater associated with the coal samples collected on-site. The results suggest that the leaching of arsenic from the coal samples will not lead to arsenic contamination in four distinct categories of groundwater. This finding holds considerable importance for the management and remediation of water pollution in mining contexts.

Continental freshwater discharge influences sea surface salinity variability near world’s megadeltas

Sea surface salinity ( SSS ) is a key parameter in the thermohaline circulation of global oceans. Near the megadeltas, inland streamflow through large catchments plays a crucial role in mediating salinity. While some regional studies have investigated how SSS is impacted through water cycle and climate components, a global scale quantification of inland streamflow contribution on SSS variability is lacking. Here, we utilized remote sensing and observation-driven datasets to quantify the statistical associations between SSS and streamflow ( Q basin ) at 48 megadeltas worldwide. This study uncovers a robust negative association between Q basin and SSS , with correlation coefficients R less than - 0.6 for seasonal data found in 26 of the 48 megadeltas, and less than - 0.4 for deseasoned data in 21 megadeltas. The anticorrelation relationship is more pronounced in large deltas, particularly near tropical climates and in river-influenced deltas. The study also underscores the significant roles of climate, morphological, and anthropogenic stratification in impacting the natural influence of freshwater discharge on SSS . By highlighting the interconnected impacts of alterations in terrestrial water cycle upstream and SSS , this work contributes to enhancing our understanding of global ocean and climate circulation patterns and in tackling environmental issues pertaining to marine ecosystems.

A Framework for Separating Climate and Anthropogenic Contributions to Evapotranspiration Changes in Natural to Agricultural Regions of Watersheds Based on Machine Learning

Evapotranspiration is a crucial component of the water cycle and is significantly influenced by climate change and human activities. Agricultural expansion, as a major aspect of human activity, together with climate change, profoundly affects regional ET variations. This study proposes a quantification framework to assess the impacts of climate change (ETm) and agricultural development (ETh) on regional ET variations based on the Random Forest algorithm. The framework was applied in a large-scale agricultural expansion area in China, specifically, the Songhua River Basin. Meteorological, topographic, and ET remote sensing data for the years of 1980 and 2015 were selected. The Random Forest model effectively simulates ET in the natural areas (i.e., forest, grassland, marshland, and saline-alkali land) in the Songhua River Basin, with R2 values of around 0.99. The quantification results showed that climate change has altered ET by −8.9 to 24.9 mm and −3.4 to 29.7 mm, respectively, in the natural areas converted to irrigated and rainfed agricultural areas. Deducting the impact of climate change on the ET variation, the development of irrigated and rainfed agriculture resulted in increases of 2.9 mm to 55.9 mm and 0.9 mm to 53.4 mm in ET, respectively, compared to natural vegetation types. Finally, the Self-Organizing Map method was employed to explore the spatial heterogeneity of ETh and ETm. In the natural–agriculture areas, ETm is primarily influenced by moisture conditions. When moisture levels are adequate, energy conditions become the predominant factor. ETh is intricately linked not only to meteorological conditions but also to the types of original vegetation. This study provides theoretical support for quantifying the effects of climate change and farmland development on ET, and the findings have important implications for water resource management, productivity enhancement, and environmental protection as climate change and agricultural expansion persist.

Future response of ecosystem water use efficiency to CO2 effects in the Yellow River Basin, China

Abstract. Ecosystem water use efficiency (WUE) is pivotal for understanding carbon–water cycle interplay. Current research seldom addresses how WUE might change under future elevated CO2 concentrations, limiting our understanding of regional ecohydrological effects. We present a land–atmosphere attribution framework for WUE in the Yellow River basin (YRB), integrating the Budyko model with global climate models (GCMs) to quantify the impacts of climate and underlying surface changes induced by CO2. Additionally, we further quantitatively decoupled the direct and secondary impacts of CO2 radiative and biogeochemical effects. Attribution results indicate that WUE in the YRB is projected to increase by 0.36–0.84 gC kg−1H2O in the future, with climate change being the predominant factor (relative contribution rate of 77.9 %–101.4 %). However, as carbon emissions intensify, the relative importance of land surface changes becomes increasingly important (respective contribution rates of −1.4 %, 14.9 %, 16.9 %, and 22.1 % in SSP126, SSP245, SSP370, and SSP585). Typically, WUE is considered a reflection of an ecosystem's adaptability to water stress. Thus, we analyzed the response of WUE under different scenarios and periods and various drought conditions. The results show a distinct “two-stage” response pattern of WUE to drought in the YRB, where WUE increases under moderate–severe drought conditions but decreases as drought intensifies across most areas. Furthermore, GCM projections suggest that plant adaptability to water stress may improve under higher-carbon-emission scenarios. Our findings enhance the understanding of regional ecohydrological processes and provide insights for future predictions of drought impacts on terrestrial ecosystems.

Divergent Drivers of Declining Urban Vegetation Productivity and Transpiration During Heatwaves

AbstractHeatwave events, characterized by high vapor pressure deficit (VPD) and low soil moisture (SM), considerably disrupt the regional carbon and water cycle. However, research pertaining to the impact of heatwaves on vegetation productivity (VPu) and vegetation transpiration (VTu), along with their underlying drivers, particularly in urban areas, remains limited. This study investigates the response of VPu and VTu to heatwave events across 895 global cities from 1990 to 2022. The analysis reveals a notable upward trend in the average heatwave frequency, intensity, and duration across the global cities. Heatwave events demonstrate a detrimental impact on VPu and VTu, resulting in an average decrease of 28% and 26%, respectively, during heatwave occurrences. The attribution analysis reveals divergent driving factors for the decline in VPu and VTu during heatwaves. The decrease in VPu is primarily influenced by SM, contributing 60% to the downward trend of VPu during heatwaves. Notably, VPu displays a sharp downward trend when SM falls below 0.38 m3/m3. In contrast, the primary driver of VTu decline is VPD, contributing more than 66% to the downward trend of VTu during heatwave events. VTu exhibits a significant downward trend when VPD exceeds 1.35 kPa. The results of this study show the important effects of increasingly frequent heatwaves on vegetation transpiration and productivity, and that can be used as quantitative factor to be evaluated when investigating policy measures for the resilience of urban areas.

Improving time upscaling of instantaneous evapotranspiration based on machine learning models

ABSTRACT Evapotranspiration (ET) plays a crucial role in the global water and energy cycle. Upscaling instantaneous ET ( E T i ) to daily ET ( E T d ) is vital for thermal-based ET estimation. Conventional methods – such as the constant evaporative fraction method (ConEF), radiation-based method, and evaporative ratio method – often overlook environmental factors, leading to biased estimates of E T d from E T i . To resolve this issue, this study aimed to assess four machine learning (ML) algorithms—XGBoost, LightGBM, AdaBoost, and Random Forest—to integrate meteorological and remote sensing data for upscaling E T i across 88 global flux sites. Each ML model was tested with eight different variable combinations. Results indicated that XGBoost exhibited the best performance, with a root mean square error (RMSE) generally below 13 W m − 2 in estimating E T d from E T i . The best variable combination simultaneously considers evaporative fraction, available energy, meteorology factors, remote sensing albedo, normalized vegetation index, and leaf area index. Using this combination, the XGBoost model achieved an R 2 = 0.88 and an RMSE = 12.33 W m − 2 , outperforming the ConEF method ( R 2 = 0.71 and RMSE = 18.86 W m − 2 ) and its expansions. These findings support the application of ML models in ET upscaling, enabling ET estimation across large spatiotemporal scales.

Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China

Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land–atmosphere energy balance, and water cycle processes. However, current data products use different classification methods, resulting in significant classification inconsistency and triggering serious disagreements among related studies. Here, we compared four mainstream land cover products in China, namely GLC_FCS30, CLCD, Globeland30, and CNLUCC. The result shows that only 50.34% of the classification results were consistent across the four datasets. The differences between pairs of datasets ranged from 21.10% to 37.53%. Importantly, most inconsistency occurs in transitional zones among land cover types sensitive to climate change and human activities. Based on the accuracy evaluation, CLCD is the most accurate land cover product, with an overall accuracy reaching 86.98 ± 0.76%, followed by CNLUCC (81.38 ± 0.87%) and GLC_FCS30 (77.83 ± 0.80%). Globeland30 had the lowest accuracy (75.24 ± 0.91%), primarily due to misclassification between croplands and forests. Misclassification diagnoses revealed that vegetation-related spectral confusion among land cover types contributed significantly to misclassifications, followed by slope, cloud cover, and landscape fragmentation, which affected satellite observation angles, data availability, and mixed pixels. Automated classification methods using the random forest algorithm can perform better than those that depend on traditional human–machine interactive interpretation or object-based approaches. However, their classification accuracy depends more on selecting training samples and feature variables.

Returning to Integrated Landscape Management as an Approach to Counteract Land Degradation in Small Mediterranean Islands: The Case Study of Stromboli (Southern Tyrrhenian Sea, Italy)

The small Mediterranean islands, unique geographical places where coastlines and mountains converge due to volcanic genesis, are among the most threatened environments on Earth. Their marginality, which has historically led to their use as places of detention and punishment, coupled with the extreme climate and rugged geomorphology shaped by terracing practices, has resulted in the loss of systematic land management. This loss stems from the abandonment of cropland in favor of alternative activities and migrations, impacting essential ecosystem services such as the water cycle, soil fertility, and the cultural landscape. The need to counteract the land degradation in these vulnerable areas has been acknowledged for some Mediterranean small islands, including the UNESCO heritage site of Stromboli in the Aeolian Islands, Sicily, Italy—an especially captivating location due to its active volcano. The agricultural abandonment on terraces, intensively cultivated with olives groves and vineyards until the mid-20th century, has rendered the area highly fragile and susceptible to risks such as fires and soil erosion, particularly as a consequence of extreme weather events, as proven in 2022, which saw a destructive fire followed by storms. To mitigate the negative effects of hydrogeological disruptions, the implementation of integrated landscape management—managing ecosystems at the landscape level—has been proposed. Specifically, an agroforestry intervention, coupled with the restoration of dry stone walls, the shaping of soil slopes by recovering the traditional ecological knowledge (TEK), and the design of water-collecting devices incorporated with the traditional hydraulic knowledge, may be proposed as a strategic approach to minimize the soil erosion risks, adapt to climate change, and extensively restore the use of traditional agrobiodiversity to support the local economy and tourism. A pilot intervention by local stakeholders based on these principles is described as an emblematic agrobiodiversity-based landscape design project in a vulnerable area, aiming at the preservation of the cultural landscapes of the small Mediterranean islands.

Clouds and precipitation in the initial phase of marine cold-air outbreaks as observed by airborne remote sensing

Abstract. Marine cold-air outbreaks (MCAOs) strongly affect the Arctic water cycle and, thus, climate through large-scale air mass transformations. The description of air mass transformations is still challenging, partly because previous observations do not resolve fine scales, particularly for the initial development of an MCAO, and due to a lack of information about the thermodynamical evolution starting over sea ice and continuing over open ocean and associated cloud microphysical properties. Therefore, we focus on the crucial initial development within the first 200 km over open water for two case studies in April 2022 during the HALO-(AC)3 campaign (named after the High Altitude and Long Range Research Aircraft and Transregional Collaborative Research Centre ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes and Feedback Mechanisms (AC)3). The two events, just 3 d apart, belong to a particularly long-lasting MCAO and occurred under relatively similar thermodynamic conditions. Even though both events were stronger than the climatological 75th percentile of that period, the first event was characterized by colder air masses from the central Arctic which led to an MCAO index twice as high compared to that of the second event. The evolution and structure were assessed by flight legs crossing the Fram Strait multiple times at the same location, sampling perpendicularly to the cloud streets. Airborne remote sensing and in situ measurements were used to build statistical descriptions of the boundary layer, dynamics, clouds, and precipitation. For this purpose, we established a novel approach based solely on radar reflectivity measurements to detect roll circulation that forms cloud streets. The two cases exhibit different properties of clouds, riming, and roll circulations, though the width of the roll circulation is similar. For the stronger event, cloud tops are higher; more liquid-topped clouds exist; the liquid water path, mean radar reflectivity, precipitation rate, and precipitation occurrence have increased; and riming is active. The variability in rime mass has the same horizontal scale as the roll circulation, implying the importance of roll circulation on cloud microphysics and precipitation. Boundary layer and cloud properties evolve with distance over open water, as seen by, e.g., cloud top height rising. In general, cloud streets form after traveling 15 km over open water. After 20 km, this formation enhances cloud cover to just below 100 %. After around 30 km, precipitation forms, though for the weaker event, the development of precipitation is shifted to larger distances. Within our analysis, we developed statistical descriptions of various parameters (i) within the roll circulation and (ii) as a function of distance over open water. These detailed cloud metrics are particularly well suited for the evaluation of cloud-resolving models close to the sea ice edge to evaluate their representation of dynamics and microphysics.

Comparison of the Distribution of Evapotranspiration on Shady and Sunny Slopes in Southwest China

Evapotranspiration (ET) plays a significant role in the surface water cycle, particularly within the unique geographical context of Southwest China. The region’s different topography, driven by mountain uplift and variations in slope direction, alters regional hydrothermal conditions, thereby affecting local ecoclimatic patterns. ET characteristics, shaped by slope orientation, can also serve as important indicators of climate variability in the study area. While most existing ET research on shady and sunny slopes has been conducted at the point scale, this study employed Global Land Surface Satellite (GLASS) ET products to estimate the average ET for shady and sunny slopes across five provinces in Southwest China between 2003 and 2018. The driving factors behind the variation in ET across different regions were also explored. Key results include the following: (1) Annual ET in Southwest China ranges between 200 mm and 800 mm, with Tibet exhibiting the lowest values and Yunnan the highest. (2) ET decreases gradually with increasing altitude in the altitude range of 0 m to 5000 m. The ET is higher on the sunny slopes than on the shady slopes. Notably, when the altitude is higher than 5000 m, ET on shady slopes in Tibet is greater than that on sunny slopes as the altitude increases. (3) ET initially increases with slope inclination before decreasing. Notably, in areas with slopes exceeding 35° in Yunnan, the ET value is found to be significantly higher on shady slopes compared to sunny slopes. (4) The effects of soil moisture, the Normalized Difference Vegetation Index, relative humidity, and land surface temperature on ET are more substantial on shady slopes than sunny slopes, whereas air temperature has a stronger impact on ET on sunny slopes. These results provide valuable data for research on regional climate change and contribute to strategies for ecological and environmental protection.

Tritium determination in natural water samples in Fukushima from 2022 to 2023 using an ultra-low-level tritium counting system.

The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident created large stockpiles of tritium containing cooling water, which is to be gradually released into the Pacific Ocean, gaining attention from surrounding countries, environmental groups, and local residents concerned with the possibility of increasing tritium concentrations in the water and food cycle. Establishing baseline concentration levels and monitoring tritium immission values are important for assuring public safety, providing data for scientific research and risk communication. Tritium concentrations in the environment are very low; therefore, tritium measurements require enrichment in order to estimate the radiation exposure from drinking water intake and provide information on the water cycle. Natural water samples were collected at Tomioka Town located south of the FDNPP. Samples were distilled, enriched by electrolysis, and re-distilled using an improved SPE method to preconcentrate tritium to measureable levels. Tritium concentrations were determined by a low-background liquid scintillation counter. The observed tritium concentrations were relatively low, rainwater had a mean and SD value of 0.40±0.13Bq/L, and freshwater samples showed similar concentrations, while brackish coastal water samples were below 0.13±0.02Bq/L. The observed tritium concentrations in this study are considered safe as effective doses based on annual drinking water intake; however, continous monitoring is necessary to assure public safety.