Salinity Trends Research Articles

Representation of Dense Shelf Water formation by global oceanic reanalyses

This study evaluates the representation of Dense Shelf Water (DSW) formation in the Southern Ocean by two global oceanic reanalyses: GLORYS2v4 and ORAS5. We found that GLORYS2v4 generally represents larger areas and more consistent temporal patterns of DSW compared to ORAS5. This latter reanalysis significantly underestimates the extent and frequency of DSW formation. Although both reanalyses show good statistical agreement with the World Ocean Circulation Experiment data set, with a root mean squared error of approximately 0.7, a mean absolute percentage error of 1.16% and an r value of 0.99, ORAS5 fails to reproduce the expected sea-surface salinity trend, exhibiting a negative trend across most of the Southern Ocean. This may lead to a weak representation of the water-mass formation processes in the region, thereby affecting its ability to satisfactorily represent the Meridional Overturning Circulation and the global thermohaline circulation. These discrepancies are attributed to the nudging process used for the sea-surface temperature in the ORAS5 model. This study highlights the importance of oceanic reanalyses to validate climate modelling results, in particular in high-latitude regions, where observations are scarce and crucial for understanding global climatic changes.

An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost

Soil salinization is a serious land degradation phenomenon, posing a severe threat to regional agricultural resource utilization and sustainable development. It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. To compare the performance of different machine-learning models, this study conducted a soil sampling experiment on saline soils along the south bank of the Yellow River in Dalate Banner. The experiment lasted two years (2022 and 2023) during the spring bare soil period, collecting 304 soil samples. The soil salinity was estimated with the multi-source remote sensing satellite data by combining the extreme gradient boosting model (XGBoost), Optuna hyper-parameter optimization, and Shapley addition (SHAP) interpretable model. Correlation analysis and continuous variable projection were employed to identify key inversion factors. The regression effects of partial least squares regression (PLSR), geographically weighted regression (GWR), long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost) were compared. The optimal model was selected to estimate soil salinity in the study area from 2019 to 2023. The results showed that the XGBoost model fitted optimally, the test set had high R2 (0.76) and the ratio of performance to deviation (2.05), and the estimation results were consistent with the measured salinity values. SHAP analysis revealed that the salinity index and topographic factors were the primary inversion factors. Notably, the same inversion factor influenced varying soil salinity estimates at different locations. The saline soils of the study area in 2019 and 2023 were 65% and 44%, respectively, and the overall trend of soil salinization decreased. From the viewpoint of spatial distribution, the degree of soil salinization showed a gradually increasing trend from south to north, and it was most serious on the side near the Yellow River. This study is of great significance for the quantitative estimation of salinized soil in the irrigated area on the south bank of the Yellow River, the prevention and control of soil salinization, and the sustainable development of agriculture.

Advancements and Perspective in the Quantitative Assessment of Soil Salinity Utilizing Remote Sensing and Machine Learning Algorithms: A Review

Soil salinization is a significant global ecological issue that leads to soil degradation and is recognized as one of the primary factors hindering the sustainable development of irrigated farmlands and deserts. The integration of remote sensing (RS) and machine learning algorithms is increasingly employed to deliver cost-effective, time-efficient, spatially resolved, accurately mapped, and uncertainty-quantified soil salinity information. We reviewed articles published between January 2016 and December 2023 on remote sensing-based soil salinity prediction and synthesized the latest research advancements in terms of innovation points, data, methodologies, variable importance, global soil salinity trends, current challenges, and potential future research directions. Our observations indicate that the innovations in this field focus on detection depth, iterations of data conversion methods, and the application of newly developed sensors. Statistical analysis reveals that Landsat is the most frequently utilized sensor in these studies. Furthermore, the application of deep learning algorithms remains underexplored. The ranking of soil salinity prediction accuracy across the various study areas is as follows: lake wetland (R2 = 0.81) > oasis (R2 = 0.76) > coastal zone (R2 = 0.74) > farmland (R2 = 0.71). We also examined the relationship between metadata and prediction accuracy: (1) Validation accuracy, sample size, number of variables, and mean sample salinity exhibited some correlation with modeling accuracy, while sampling depth, variable type, sampling time, and maximum salinity did not influence modeling accuracy. (2) Across a broad range of scales, large sample sizes may lead to error accumulation, which is associated with the geographic diversity of the study area. (3) The inclusion of additional environmental variables does not necessarily enhance modeling accuracy. (4) Modeling accuracy improves when the mean salinity of the study area exceeds 30 dS/m. Topography, vegetation, and temperature are relatively significant environmental covariates. Over the past 30 years, the global area affected by soil salinity has been increasing. To further enhance prediction accuracy, we provide several suggestions for the challenges and directions for future research. While remote sensing is not the sole solution, it provides unique advantages for soil salinity-related studies at both regional and global scales.

Evaluation of the effects of Argo data quality control on global ocean data assimilation systems

A series of observing system experiments (OSEs) were conducted in order to evaluate the effects of Argo data quality control (QC), by using the three global ocean data assimilation systems. During the experimental period between 2015 and 2020, some Argo floats are affected by the abrupt salinity drifts, which caused spurious increasing trend of the global mean salinity in the reanalyses using the observations with only real-time QC applied. The spurious trend is mitigated by applying the gray list provided by the Argo Global Data Assembly Centres (GDAC), and further reduced by assimilating the delayed-mode Argo data of the Argo GDAC instead of the real-time Argo data. These impacts of the Argo QC are generally consistent among the three ocean data assimilation systems. Further investigations in the JMA’s system show that errors in the analyzed salinity with respect to the delayed-mode Argo data are smaller in the OSE with more rigorous QC, and the spatiotemporal variations in the sea-surface dynamic height are reproduced better. Additionally, QC impacts on the analyzed temperatures are shown not to directly reflect the difference in temperature observations among OSEs, and may be affected by difference in the salinity observations among OSEs through the cross-covariance relationship in the data-assimilation systems.

Influence of the Southern Hemisphere Supergyre on Antarctic Intermediate Water Properties in CMIP6 Models

AbstractThe supergyre in the Southern Hemisphere is thought to connect the Atlantic, Indian, and Pacific subtropical gyres together. The aim of the study is to investigate whether the supergyre is identifiable in the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and in the Estimating the Circulation and Climate of the Ocean (ECCO) reanalysis and to evaluate the influence of the supergyre on the properties of Antarctic Intermediate Water (AAIW), the dominant water mass at intermediate depths in the Southern Hemisphere. CMIP6 models and ECCO are in agreement at the surface with supergyres connected across all basins but present some differences at depth in both position and strength. AAIW core properties (temperature and salinity) present a high degree of similarity across basins within the supergyre but not outside of it. By the end of the century, the supergyre reduces in size and intensifies at intermediate depths, and the AAIW core depth warms in all basins and freshens in the Pacific although no clear trend in salinity can be found in the Atlantic and Indian basins in the SSP5‐8.5 scenario. The high degree of similarity across basins within the supergyre is maintained in the future scenario. The results suggest that by connecting the basins together at intermediate depth, the supergyre plays a key role in circulating and homogenizing the AAIW core properties. Our results emphasize the role of the supergyre in circulating water masses at the surface and intermediate depths in CMIP6 models and hence its importance to the global circulation.

Long‐Term Changes in Salinity in the South China Sea Due To Anthropogenic Forcing

AbstractChanges in ocean salinity are essential for the stratification of the upper ocean and the regional marine ecosystem. In this study, 10 sets of large ensemble experiments and multi‐model ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) are used to investigate the effect of anthropogenic forcing on upper ocean salinity in the South China Sea (SCS). In most models, surface salinity increases during the historical period due to external forcing. Using the salinity budget, we find that a decrease in freshwater flux, particularly precipitation, is responsible for the increase in salinity, although horizontal advection also contributes to the change. Single forcing experiments reveal that the change in salinity in the SCS is mainly attributed to anthropogenic forcing, while the influence of natural forcing is relatively small. Anthropogenic aerosols (AAs) can decrease the dynamic and thermal components of precipitation, resulting in a considerable increase in salinity. In contrast, anthropogenic greenhouse gases (GHGs) have less effect on long‐term trend in SCS salinity because the GHG forcing leads to an increased thermal response of precipitation but a decreased dynamic response. Additionally, we use the Community Earth System Model version 1 (CESM1) to evaluate the role of different aerosol emission sources in modulating the salinity change in the SCS. The experimental results show that aerosol emissions from Asia dominate the salinity change in the SCS by changing the local Hadley circulation. In contrast, the contribution of aerosol emissions from North America and Europe (NAEU) is much smaller.

Monitoring Salinity in Inner Mongolian Lakes Based on Sentinel-2 Images and Machine Learning

Salinity is an essential parameter for evaluating water quality and plays a crucial role in maintaining the stability of lake ecosystems, particularly in arid and semi-arid climates. Salinity responds to changes in climate and human activity, with significant impacts on water quality and ecosystem services. In this study, Sentinel-2A/B Multi-Spectral Instrument (MSI) images and quasi-synchronous field data were utilized to estimate lake salinity using machine learning approaches (i.e., XGB, CNN, DNN, and RFR). Atmospheric correction for MSI images was tested using six processors (ACOLITE, C2RCC, POLYMER, MUMM, iCOR, and Sen2Cor). The most accurate model and atmospheric correction method were found to be the extreme gradient boosting tree combined with the ACOLITE correction algorithm. These were used to develop a salinity model (N = 70, mean absolute percentage error = 9.95%) and applied to eight lakes in Inner Mongolia from 2016 to 2024. Seasonal and interannual variations were explored, along with an examination of potential drivers of salinity changes over time. Average salinities in the autumn and spring were higher than in the summer. The highest salinities were observed in the lake centers and tended to be consistent and homogeneous. Interannual trends in salinity were evident in several lakes, influenced by evaporation and precipitation. Climate factors were the primary drivers of interannual salinity trends in most lakes.

Amplified vertical salinity contrasts in the northwestern tropical Pacific under ocean warming

Analyses of vertically layered structures in ocean salinity present a recently amplified vertical contrast in the northwestern tropical Pacific, which has been attributed to the reversal of the long-term linear trend in salinity within the upper ocean from 1960 to 2023. Based on data obtained from both observations (Argo and WOD) and reanalysis (EN4), salinity trends shifted from freshening (− 0.04 psu/40 yr) to salinification (+ 0.04 psu/60 yr) in the near-surface (above 24.6 σθ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\sigma }_{\ heta }$$\\end{document} surface), while subsurface (below 25 σθ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\sigma }_{\ heta }$$\\end{document} surface) freshening further strengthened from – 0.03 psu/40 yr to – 0.1 psu/60 yr after 2000. The near-surface salinification can be partly explained by atmospheric forcing related to global warming. The anomalous cyclonic wind-induced Ekman suction and wind-driven horizontal salt transport were favorable for increased salinity in upper layers. Nevertheless, the oceanic dynamic forces governed the vertical salinity structure. Under a warming climate, heat influx and warm water accumulation due to diabatic effects play a deterministic role in isopycnal deepening. The changes in salinity evoked by isopycnal changes were investigated from two perspectives: entrainment at the bottom of 24.6 σθ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\sigma }_{\ heta }$$\\end{document} layer and heaving variabilities for the upper layers above 24.6 σθ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\sigma }_{\ heta }$$\\end{document}, were primary factors in near-surface salinification. However, the relative significance of heaving variabilities decreased with depth and the major controlling factor became contingent on spiciness variabilities. It is suggested that, owing to a northward migration of the outcrop line, subduction along the path of the geostrophic streamline from the ventilation region, where freshened spiciness anomalies can be injected into subduction surfaces, tended to dominate the significant freshening trend at subsurface isopycnals.

Historical Trends in Ocean Heat, Carbon, Salinity, and Oxygen Simulations: Impact of a Changing Ocean Transport

AbstractExamination of historical simulations from CMIP6 models shows substantial pre‐industrial to present‐day changes in ocean heat (ΔH), salinity (ΔS), oxygen (ΔO2), dissolved inorganic carbon (ΔDIC), chlorofluorocarbon‐12 (ΔCFC12), and sulfur hexafluoride (ΔSF6). The spatial structure of the changes and the consistency among models differ among tracers: ΔDIC, ΔCFC12, and ΔSF6 all are largest near the surface, are positive throughout the thermocline with weak changes below, and there is good agreement among the models. In contrast, the largest ΔH, ΔS, and ΔO2 are not necessarily at the surface, their sign varies within the thermocline, and there are large differences among models. These differences between the two groups of tracers are linked to climate‐driven changes in the ocean transport, with this tracer “redistribution” playing a significant role in changes in ΔH, ΔS, and ΔO2 but not the other tracers. The spatial structure, and differences between models, of changes in age tracers are consistent with ΔH, ΔS, and ΔO2, supporting the hypothesis that redistribution plays a major role for these tracers. Further, the impact of the vertical displacement of isopycnals (heave) plays a major role in the differing impact of redistribution between the two groups, with this process causing insignificant changes to ΔDIC, ΔCFC12, and ΔSF6 due to their weak spatial gradients. A similar multi‐tracer analysis of observations could provide insights into the relative role of the addition and redistribution of tracers in the ocean.

Hydrochemical Characteristics and Variation in Karst Groundwater in the Baiquan Spring Area of Xingtai Over the Last 30 Years

To elucidate the variation patterns in the hydrochemical characteristics of karst groundwater in the Baiquan Spring area of Xingtai over the past 30 years, an integrated approach utilizing mathematical statistics, Piper trilinear diagrams, Gibbs diagrams, and ion ratio analyses was employed. Comparative analysis was conducted on 62 sets of karst water samples collected during the dry seasons of 1991 and 2020. The findings indicated that the groundwater in the spring area was generally weakly alkaline with a low salinization degree, predominantly characterized by Ca2+ and HCO3- as the dominant ions. Compared to that in 1991, the alkalinity of groundwater in 2020 had intensified, with a general increase in the concentration of various indicators. The hydrochemical types had shifted from the relatively concentrated HCO3-Ca and HCO3-Ca·Mg types to a broader spectrum of types, gradually exhibiting a salinization trend. The distribution characteristics of exceeding components such as TDS, Na++K+, SO42-, and F- showed significant spatial differences. Rock weathering played a pivotal role in the changes observed in the hydrochemical components of groundwater, with enhanced cation exchange and evaporation processes further influencing the hydrochemical characteristics and their spatial distribution.

Trend of soil salinization in Africa and implications for agro-chemical use in semi-arid croplands

Soil salinization is a gradual degradation process that begins as a minor problem and grows to become a significant economic loss if no control action is taken. It progressively alters the soil environment which eventually negatively affects plants and organism that were not originally adapted for saline conditions. Soil salinization arises from diverse sources such as side-effects of long-term use of agro-chemicals, saline parent rocks, periodic inundation of soil with saline water, etc. In Africa, soil salinization has not been adequately documented particularly in the croplands. The objective of this study was to identify trends of cropland salinization in Africa and how its relationship with long-term land use practices affected the soil environment. The study analysed soil salinization between 1965 and 2020 using measured electrical conductivity (EC), spatial modelling with environmental covariates, and national statistics on cropland expansion and application of mineral fertilizers, herbicides, and pesticides. The results showed increasing trends of EC in Africa due to climatic and land use drivers. Increasing trends of EC, which evidenced salinization, was found in 31 million hectares of topsoils and 18 million hectares of subsoils. About 2 million hectares of croplands were depicted with salinization and >25 million hectares at the risk of salinization in the arid and semi-arid areas. The study also found statistical relationships between semi-arid cropland salinization and trends of agro-chemical use and cropland sizes. There were significant (p < 0.001) positive correlations between semi-arid cropland salinization and trends of cropland expansion and applied nitrogenous fertilizers. It found that increasing trend of applied mineral nitrogenous fertilizers could double the odds of salinization in semi-arid croplands while cropland expansion could increase the odds of semi-arid cropland salinization by >10 %. These findings present ground-breaking baseline information for future works on sustainable land-use practices that can control cropland soil salinization in Africa.

Future changes in soil salinization across Central Asia under CMIP6 forcing scenarios

AbstractSoil salinization is a critical environmental and socio‐economic concern with global implications, and its severity is expected to amplify under changing climate. The impact of climate change on salinization in Central Asia is still not fully understood. This study addresses this gap by employing a digital soil mapping (DSM) framework. Cubist, random forest (RF), and quantile regression forests (QRF) are utilized to project variations in soil surface salinity (0‐10 cm) in Central Asia from 2025 to 2100 under two shared socio‐economic pathways (SSPs): SSP2‐4.5 and SSP5‐8.5. These models are developed using data from 20 global climate models (GCMs) obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results reveal that the RF model exhibits superior predictive capability in estimating soil salinity. RF performed on the calibration set with a coefficient of determination (R2) of 0.86, root mean square error (RMSE) of 9.84 and 9.90 dS m−1, ratio of performance to interquartile distance (RPIQ) of 3.09 and 3.07, and a Nash–Sutcliffe efficiency (NSE) of 0.86. The multi‐GCM ensemble means revealed the potential for varying degrees of salinization in Central Asia, with higher levels predominantly observed in the southeast and southwest of the study area, particularly downstream of the river and in the lakeside areas. Temporal analysis of soil salinity evolution reveals an overall increase in salinity across the region, with more notable changes projected under SSP5‐8.5. Specifically, the projected increase rate in soil salinity for Central Asia was 0.0005 dS m−1/year under SSP2‐4.5 and 0.01 dS m−1/year under SSP5‐8.5. Turkmenistan is notable for possessing the highest regional average of soil salinity, with the exception of a declining trend observed within this area. The remaining regions of Central Asia exhibit an upward trend in average soil salinity, particularly noteworthy under the SSP5‐8.5 scenario, where variations in soil salinity are more obvious. These findings hold significant potential in enhancing our understanding of how Central Asia responds to global change, advances toward sustainable development, and enhances comprehension of the dynamics within the region.

Detection of soil salinity distribution and its change in the Yellow River Delta comparing 2006 and 2022

AbstractThe Yellow River Delta (YRD) has the world's highest land formation rate. However, soil salinization has caused severe land degradation in the region. Understanding the distribution of soil salinity and its variation is essential for saline soil management. This study combined soil salinity sampling data, remote sensing imagery, and layers of geographic environmental factors. Three different models, including Ordinary Kriging (OK), Geographically Weighted Regression (GWR), and Bayesian Maximum Entropy (BME), were used and compared to predict the soil salinity of two soil layers in 2006 and 2022. Then the spatial distribution characteristics and development trends of soil salinity in the YRD were analyzed. The results indicated that (1) The BME model is an optimal salinity prediction model that integrates soft data from multiple sources to perform nonlinear estimates. Compared to the OK and GWR models, the RMSE was reduced by up to 25% and 13%, respectively, and the greatest improvement in R2 was increased from 0.0534 and 0.2718 to 0.5569, respectively. (2) Soil salinity in the YRD shows a spatially increasing trend from the southwestern inland to the northeastern coast. Over the past 16 years, the salinization pattern has become more complex: soil salinization has been mitigated in the central and southern regions, with the average salinity decreasing from 1.03% to 0.86% and the salinization rate decreasing from 99% to 88%; and it has significantly intensified in the northern part of the study area near the old Yellow River channel and the Gubei Reservoir, and in some scattered inland areas. Continuous water and sediment regulation in the Yellow River basin and ecological management of natural reserves can generally alleviate soil salinization, but the risk of soil salinization can be increased by seawater erosion, inappropriate land use, and resource exploitation.

Assessing the Impact of Brackish Water on Soil Salinization with Time-Lapse Inversion of Electromagnetic Induction Data

Over the last decade, electromagnetic induction (EMI) measurements have been increasingly used for investigating soil salinization caused by the use of brackish or saline water as an irrigation source. EMI measurements proved to be a powerful tool for providing spatial information on the investigated soil because of the correlation between the output geophysical parameter, i.e., the electrical conductivity, to soil moisture and salinity. In addition, their non-invasive nature and their capability to collect a high amount of data over broad areas and in a relatively short time makes these measurements attractive for monitoring flow and transport dynamics, which are otherwise undetectable with conventional measurements. In an experimental field, EMI measurements were collected during the growth season of tomatoes and irrigated with three different irrigation strategies. Time-lapse data were collected over three months in order to visualize changes in electrical conductivity associated with soil salinity. A rigorous time-lapse inversion procedure was set for modeling the soil salinization induced by brackish irrigation water. A clear soil response in terms of an increase in electrical conductivity (EC) in the upper soil layer confirmed the reliability of the geophysical tool to predict soil salinization trends.

Seasonal and Inter-annual Variability in the East of the Sea of Marmara Water Masses by Temperature and Salinity Time Series

This study mainly aims to examine the relationship between oceanographic properties and climate change in the conjunction of the Istanbul Strait (IS), specifically in the east of the Sea of Marmara (SoM) via evaluating observational temperature and salinity profiles at six stations with monthly atmospheric pressure and air temperature during the period of 1997-2010. The annual cycle of temperature and salinity of the upper and lower layers in the east of SoM have strong seasonal variation: The upper layer temperature rises during summer while decreases in winter. Conversely, the lower layer temperature is higher in winter compared to spring and summer. Additionally, salinity levels in the upper layer are higher in winter and lower in summer, whereas in the lower layer, the pattern is reversed, with lower salinity in winter and higher salinity in summer. In this context, linear trend analyzes are calculated by using monthly time series of temperature and salinity and compared with the trends of atmospheric conditions and the surrounding seas. The average temperature trend in the east of the SoM is increase of 0.067°C/y in the upper layer and increase of 0.028°C/y in the lower layer during this period. The salinity trend is increase of 0.105 psu/y in the upper layer and increase of 0.012 psu/y in the lower layer. The air temperature trend is increase of 0.086°C/y, and the air pressure trend is decrease of 0.034 mbar/y. These values closely align with those observed in the surrounding seas. The air temperature and upper layer temperatures are consistent with each other in terms of both long-term series and trend values.

Seasonal Salinity Trends in Central and Southern Biscayne Bay, Florida

Seasonal Salinity Trends in Central and Southern Biscayne Bay, Florida

Hydrogeochemical characteristics of shallow groundwater and salinization evaluation in the coastal aquifers of Cangzhou, China

To investigate the hydrogeochemical characteristics of shallow groundwater and evaluate groundwater salinization state in Cangzhou, China, two sampling campaigns have been conducted. In summer, 33 groundwater samples, 3 seawater samples and 5 river water samples were collected. In winter, 22 groundwater samples were collected. The hydrochemical type was determined by the Piper diagram. Evolution mechanisms of groundwater were analyzed by the Gibbs diagram. The trend of de-salinization or salinization of groundwater was determined by the hydrochemical facies evolution diagram. The groundwater salinization grade was evaluated by both the seawater intrusion groundwater quality index (GQISWI) and the attribute recognition model based on entropy weight (ARMEW). The Piper diagram shows that Na+ is the dominant cation and Cl- is the dominant anion in shallow groundwater, and the groundwater in this area is mainly of Cl-Na type followed by Cl-Ca·Mg type. The evaporation-crystallization process has significant influence on the evolution of saline groundwater. In summer, most brackish groundwater exhibit compositions of the freshening stage with direct cation exchange, and most saline groundwater is mainly at the seawater intrusion stage with reverse cation exchange. In winter, compared to the summer season, more groundwater samples in the middle of study area exhibit intrusion trend, and without obvious Na-HCO3 facies. In summer, values of GQISWI range from 20.47 to 75.38 with an average of 59.31. The GQISWI gradually increases from east to west, denoting the degree of groundwater salinization is alleviated from coast to inland. In winter, values of GQISWI range from 54.47 to 79.09 with an average of 66.00, slightly higher than that in summer. The proportion of no salinization (Grade I), minor salinization (Grade II) and serious salinization (Grade III) of groundwater samples in Cangzhou identified by ARMEW is 3.0%, 21.2% and 75.8% in summer, respectively. In winter, the proportion is 9.1% for Grade II and 90.9% for Grade III. The GQISWI index shows the largest area of Grade II, and ARMEW presents the largest area of Grade III in both seasons. Compared with GQISWI index, ARMEW model gives more conservative evaluation results of groundwater salinization. The results provide useful information on the groundwater salinization status for the local area, and help for the management of groundwater resources in Cangzhou.

Assessing the utility of shellfish sanitation monitoring data for long-term estuarine water quality analysis

Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999–2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to investigate management-driven bias in fecal coliform observations collected in a particular area.

Estimating freshwater flux amplification with ocean tracers via linear response theory

Abstract. Accurate estimation of changes in the global hydrological cycle over the historical record is important for model evaluation and understanding future trends. Freshwater flux trends cannot be accurately measured directly, so quantification of change often relies on ocean salinity trends. However, anthropogenic forcing has also induced ocean transport change, which imprints on salinity. We find that this ocean transport affects the surface salinity of the saltiest regions (the subtropics) while having little impact on the surface salinity in other parts of the globe. We present a method based on linear response theory which accounts for the regional impact of ocean circulation changes while estimating freshwater fluxes from ocean tracers. Testing on data from the Community Earth System Model large ensemble, we find that our method can recover the true amplification of freshwater fluxes, given thresholded statistical significance values for salinity trends. We apply the method to observations and conclude that from 1975–2019, the hydrological cycle has amplified by 5.04±1.27 % per degree Celsius of surface warming.

Evaluating Soil Water–Salt Dynamics under Brackish Water Drip Irrigation in Greenhouses Subjected to Localized Topsoil Compaction

Localized soil compaction in greenhouses resulting from less frequent tillage operations and frequent trampling by farmers inevitably disturbs the continuity and homogeneity of soil’s hydraulic properties, which impacts the precision of greenhouse cultivation regarding water supply and salinity control. However, predicting water–salt dynamics under partly compacted topsoil is difficult because of the interactions between many factors related to soil properties, including irrigation method and water quality, which are especially subjected to varied compaction sizes and positions. Here, two field treatments were conducted in brackish water (3 g L−1) drip-irrigated plots, with the designed soil compaction region (40 cm width and 30 cm depth) adjacent to (T1) and below (T2) the drip lines. The calibrated and validated HYDRUS-2D model was applied to analyze salt exchanges across the vertical and horizontal interfaces between the compacted and non-compacted zones and the associated solute concentration variations within these two zones. The results indicated that the limited horizontal solute flux under T1 enhanced the subsequent downward flux below the drip lines, whereas, under T2, the restricted downward flux with relatively limited improved horizontal salt spreading resulted in more salt retention in the soil profile. Additional scenario simulations considering the vertical and horizontal extension of soil compaction sizes (ranging from 10 × 10 cm to 40 × 40 cm) were also conducted and revealed that, with the same increment in compaction size, the vertical extension of the compacted zone aggravated salt accumulation compared with that of horizontal extension, while the simulated cumulative water and salt downward fluxes were positive in relation to the compaction sizes in both vertical and horizontal directions under T1, but negative under T2. The findings of this study explore the effect of relative positions between drip lines and the soil compaction zone on salt transports under brackish water irrigation and reveal the potential soil salinization trend as extending compaction regions in the vertical or horizontal direction.