Katsuaki Komai

Extreme flood events deliver large terrestrial carbon pulses into coastal waters, where physical, thermodynamic, and biological signals become entangled and are difficult to isolate using conventional methods. We present a 3D numerical decoupling framework that integrates two inseparable components—a CR-NBR dual-run strategy isolating biological from physical signals, and explicit vertical resolution resolving their layer-specific expression across the halocline—applied via Delft3D-WAQ across a complete annual cycle (2018) in the Yatsushiro Sea, Japan. The extreme July 2018 flood triggered a two-stage biological response: riverine POM suppressed phytoplankton growth through light attenuation while fueling heterotrophic mineralization, driving inner-estuary surface pCO2 above 800 µatm—well above the NBR conservative mixing ceiling of ~485 µatm. As turbidity cleared, a delayed autotrophic bloom at the mid-bay generated a temporally bounded biological carbon sink (Ibio = -13.80 mmol C m⁻2; 20 July–30 September). The 3D structure revealed pronounced benthic-pelagic decoupling: haline stratification trapped respiration-derived CO2 within the bottom boundary layer while the surface maintained biological drawdown—a divergence that surface-only observations and 2D depth-averaged models cannot resolve. On the annual scale, the biological pump acted as a net carbon source amplifier at all stations (Ibio: +23.64 to +54.73 mmol C m⁻2), yet all stations remained net CO2 sinks, indicating that biological processes eroded rather than reversed the thermodynamic carbon uptake capacity. Neither the decoupling strategy alone nor 3D resolution alone could achieve these results; their integration provides a transferable framework for quantifying biological pump responses to extreme flood forcing in stratified river-dominated coastal systems.

In this study, we investigated how porewater salinity, temperature, ionic strength, and nutrient behavior vary with depth in the intertidal flats of Lake Komuke, a coastal lagoon in northern Japan. A central feature of this work is the use of nutrient activity and activity coefficients—thermodynamic parameters that more directly represent ion mobility—rather than concentrations alone. Statistical analyses showed that salinity exhibited clear depth-dependent variation and was the primary factor associated with changes in nutrient behavior, whereas temperature showed minimal variation and no detectable effect. Physicochemical modeling using the Pitzer approach demonstrated that increases in salinity and ionic strength with depth led to reductions in the activity coefficients of NO₃⁻, NH₄⁺, and PO₄³⁻, with PO₄³⁻ showing the greatest sensitivity due to its trivalent charge. Nutrient activities displayed contrasting vertical patterns: NO₃⁻ and NH₄⁺ tended to increase with depth, whereas PO₄³⁻ exhibited a peak at −20 cm followed by lower values at deeper, more saline layers. These results indicate that subsurface nutrient mobility in coastal tidal flats is shaped primarily by ionic strength-driven non-ideal behavior and associated geochemical gradients. The findings provide baseline information for understanding nutrient dynamics in brackish sediments and support the improved assessment of subsurface biogeochemical processes in intertidal ecosystems.

This study presents a unique approach for characterizing ion distribution within the Kushiro River catchment basin, which is characterized by exceptionally high dissolved ion concentrations. principal component analysis, Mann–Whitney U test, and neural network modeling were employed to analyze data from 11 distinct locations in two different seasons. The 11 sampling locations were subsequently classified into five distinct groups to facilitate precise analysis of the ion distribution using neural networks. Two principal components were also employed to visualize and interpret our dataset. Compositional similarities and seasonal variations in ion distribution were identified, as well as the key variability patterns, thereby revealing underlying correlations among the dissolved ions. Our findings highlighted that Group 1, encompassing a caldera lake, exhibits the highest dissolved ion concentrations. This observation may be attributed to the geological characteristics of the underlying rock formation. Furthermore, a significant correlation was observed between the major dissolved ions present in the catchment basin, as evidenced by positive correlation coefficients. Conversely, nitrate ions exhibited a negative correlation with F⁻, Cl⁻, and Na⁺ ions. This comprehensive analytical framework offers a robust and insightful tool for determining ion distribution within catchment basins with significant implications for environmental monitoring and sustainable resource management.

Soil salinity, soil moisture, and nutrient loss significantly reduce agricultural productivity and economic benefits in the semi-arid regions of Ethiopia. However, knowledge of how to mitigate these risks remains limited. This study examined the combined effects of salinity (EC), soil moisture (Sm), and nitrate (N) on the yield and profitability of banana, cotton, and maize using field-based and satellite data with seven machine learning algorithms. Our results showed that a higher EC level reduced crop yields, whereas sufficient Sm and N improved productivity and income. Among the models, Random Forest (RF) performed the best, achieving high accuracy (e.g., R² = 0.998 for cotton, 0.869 for banana, and 0.793 for maize). SHapley Additive exPlanations (SHAP) analysis further identified EC as the most critical determinant, highlighting the priority of salinity mitigation, alongside water and nutrient management. These findings provide farmers and decision-makers with practical insights into how to sustain crop productivity, improve livelihoods, and strengthen food security in semi-arid regions.

This study examined the diffusion of nutrients (NH₄⁺, NO₃⁻, and PO₄³⁻) in the pore water of intertidal sediments in Lake Komuke (located in the northeastern part of Hokkaido on the coast of Okhotsk Sea) across nine stations, at 0 cm to 40 cm depths, from 19 May to 23 August 2015. A comparison was made between the traditional version of Fick’s first law and a modified version to understand nutrient diffusion in the intertidal flat. The novelty of this study lies in establishing a flux correction factor as a function of the activity coefficient in intertidal sediments. The outcomes of the standard and modified versions of Fick’s law exhibited observable physical differences between stations for all nutrients. However, the statistical analysis demonstrated significant differences only for NO₃⁻ and PO₄³⁻ at stations D, E, and F, suggesting that there is no statistically meaningful difference in NH₄⁺ flux. A statistical comparison of 27 pairs of fluxes obtained by the classical and modified versions of Fick’s first law indicated that 88.9% of the differences were not statistically significant between the two methods, and 11.1% were statistically significant. This shows the consistency of the modified method, firstly in reproducing the classical version of Fick’s law, and secondly in improving upon it, in a porous medium. These findings indicate that the modified version of Fick’s first law is an accurate methodology for evaluating nutrient diffusion dynamics in intertidal environments, and is potentially applicable to coastal salt marshes, with practical implications for environmental management and conservation efforts.

Salinity and sodicity severely impact soil health and agricultural productivity by disrupting nutrient and water availability, ultimately threatening food security and ecosystem sustainability. This study examined the spatial and temporal distributions of soil salinity and sodicity under the Sille irrigation scheme in southern Ethiopia. Soil samples collected from two depths (0–30 cm and 30–60 cm) during both the wet and dry seasons revealed significant variations in salinity levels, ranging from nonsaline to very strongly saline. During the dry season, moderately saline areas constituted 43% and 44% of the 0–30 cm and 30–60 cm depths, respectively, with strongly saline areas at 0–30 cm varying (37%–38%) from the wet to dry season. Sodicity also varied significantly, with 39% of the area exhibiting strong sodicity at 30–60 cm depth during the dry season. A strong positive correlation was found between electrical conductivity (EC) and the exchangeable sodium percentage (ESP), as well as soluble sodium. Additionally, the soil water balance analysis indicated increased irrigation and groundwater recharge during dry periods. These findings highlight the need for integrated management strategies, such as selecting salt‐tolerant crops and implementing effective salt‐leaching practices, to increase productivity and ensure the sustainability of irrigated agriculture.

This study investigated the distribution of salinity and sodicity in the irrigated areas of Abaya Chamo. Representative water and soil samples were collected from different soil depths (0–30 cm and 30–60 cm). Sodium absorption ratio (SAR), electrical conductivity (Ec), pH, exchange sodium, magnesium, calcium, and potassium cations, and exchange sodium percentage (ESP) of the sampled sites were analyzed for soil salinity classification and severity analysis. The spatial analysis revealed predominant strongly saline (72%) and sodic (71.1%) conditions in the surface layer (0–30 cm), intensifying very strongly saline (78.7%) and sodic (71.9%) conditions at 30–60 cm depth. Combined saline-sodic conditions dominated both layers, increasing from 91.2% at the surface to 97.1% in the deeper layer, indicating severe agricultural limitations. The results show that soil in the catchments of Lakes Abaya and Chamo suffer from different salinity and sodium content classes. Therefore, appropriate remedial measures based on soil salinity and sodium content should be developed to achieve better reclamation, agricultural production, and land sustainability.

Suspended sediment (SS) has a wide range of negative effects such as increased water turbidity, altered habitat structures, sedimentation, and effects on hydraulic systems and environmental engineering projects. Nevertheless, the methods for accurately determining SS sources on a basin-scale are poorly understood. Herein, we used a simplified neural network analysis (NNA) model to identify the sources of SS in Japan’s Oromushi River Catchment Basin. Fine soil samples were collected from different locations of the catchment basin, processed, and separately analysed using X-ray fluorescence (XRF) and X-ray diffraction (XRD). The sampling stations were grouped according to the type of soil cover, vegetation type and land-use pattern. The geochemical components of each group were fed into the same neural network layer, and a series of equations were applied to estimate the sediment contribution from each group to the downstream side of the river. Samples from the same sampling locations were also analysed by XRD, and the obtained peak intensity values were used as the input in the NNA model. SS mainly originated from agricultural fields, with regions where the ground is covered with volcanic ash identified as the key sources through XRF and XRD analysis, respectively. Therefore, based on the nature of the surface soil cover and the land use pattern in the catchment basin, NNA was found to be a reliable data analytical technique. Moreover, XRD analysis does not incorporate carbon, and also provides detailed information on crystalline phases. The results obtained in this study, therefore, do not depend on seasonal uncertainty due to organic matter.

Aegagropila linnaei is a filamentous green algal species that often forms beautiful spherical shapes called "lake balls" or "Marimo". A. linnaei were once globally distributed around the world, but the population has been declining for several decades. Lake Akan, in Japan, is now the only lake in the world with a colony of giant Marimo (over 20 cm in diameter). Here we show the net growth rate of Marino resulting from photosynthesis and decomposition based on laboratory experiments, MRI analysis, and quantitative element analysis, which show the decomposition rate, the maximum annual Marimo diametric growth rate, and the carbon-to-nitrogen ratio, respectively. We found an explicit dependence of the decomposition rate of Marimo on the cumulative water temperature, with a threshold of 7 °C. MRI analysis showed a high correlation among a Marimo's diameter, surface thickness, and annual diametric growth rate. Moreover, the C/N ratio was high in the exterior side of the surface thickness, indicating that this layer is the main growth area for photosynthesis. These results suggest that the central cavity and the surface thickness represent the change in the growth environment such as water temperature and light intensity. Between the 1980s and the present, Between the 1980s and the present, the cumulative water temperature has increased from about 1250 to about 1600 °C-days. Therefore, the maximum surface thickness has decreased by approximately 1 cm, as estimated by water temperature records and annual diametric growth rates¹⁰. As a measure to preserve preferable conditions for colonies of giant Marimo in the face of global warming, the flow of low-temperature river water into Marimo colonies should be protected.