Yosuke Yamashiki

Initial flux of sediment-associated radiocesium to the ocean from the largest river impacted by Fukushima Daiichi Nuclear Power Plant

This study aimed to quantify the flux of radiocesium in the Abukuma Basin (5,172 km2), the largest river system affected by fallout from the Fukushima Daiichi Nuclear Power Plant (FDNPP) event. In the period from 10 August 2011 to 11 May 2012 an estimated 84 to 92% of the total radiocesium transported in the basins fluvial system was carried in particulate form. During this monitoring period Typhoon Roke (September 2011) was observed to induce a significant and temporally punctuated redistribution of radiocesium. The storm-mobilised radiocesium was an estimated 6.18 Terabecquerels corresponding to 61.4% of the total load delivered to the coastal zone during the observation period. The total flux of radiocesium into the Pacific Ocean estimated at the outlet station (basin area 5,172 km2) was 5.34 TBq for 137Cs, and 4.74 TBq for 134Cs, corresponding to 1.13% of the total estimated radiocesium fallout over the basin catchment (890 TBq). This was equivalent to the estimated amount of direct leakage from FDNPP to the ocean during June 2011 to September 2012 of 17 TBq and the Level 3 Scale Leakage on 21August 2013 (24 TBq).

Assessment of global nitrogen pollution in rivers using an integrated biogeochemical modeling framework

This study has analyzed the global nitrogen loading of rivers resulting from atmospheric deposition, direct discharge, and nitrogenous compounds generated by residential, industrial, and agricultural sources. Fertilizer use, population distribution, land cover, and social census data were used in this study. A terrestrial nitrogen cycle model with a 24-h time step and 0.5° spatial resolution was developed to estimate nitrogen leaching from soil layers in farmlands, grasslands, and natural lands. The N-cycle in this model includes the major processes of nitrogen fixation, nitrification, denitrification, immobilization, mineralization, leaching, and nitrogen absorption by vegetation. The previously developed Total Runoff Integrating Pathways network was used to analyze nitrogen transport from natural and anthropogenic sources through river channels, as well as the collecting and routing of nitrogen to river mouths by runoff. Model performance was evaluated through nutrient data measured at 61 locations in several major world river basins. The dissolved inorganic nitrogen concentrations calculated by the model agreed well with the observed data and demonstrate the reliability of the proposed model. The results indicate that nitrogen loading in most global rivers is proportional to the size of the river basin. Reduced nitrate leaching was predicted for basins with low population density, such as those at high latitudes or in arid regions. Nitrate concentration becomes especially high in tropical humid river basins, densely populated basins, and basins with extensive agricultural activity. On a global scale, agriculture has a significant impact on the distribution of nitrogenous compound pollution. The map of nitrate distribution indicates that serious nitrogen pollution (nitrate concentration: 10-50 mg N/L) has occurred in areas with significant agricultural activities and small precipitation surpluses. Analysis of the model uncertainty also suggests that the nitrate export in most rivers is sensitive to the amount of nitrogen leaching from agricultural lands.

Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data

Application of Random forest approach for crop classification.The use of EVI obtained from time-series Landsat 7 ETM+ imagery as predictor variables.Effects of number of predictor variables, decision trees and training data on classification accuracy.Random forest as an appropriate method to classify the upland field crops within a homogeneous region. Crop classification of homogeneous landscapes and phenology is a common requirement to estimate land cover mapping, monitoring, and land use categories accurately. In recent missions, classification methods using medium or high spatial resolution data, which are multi-temporal with multiple frequencies, have become more attractive. A new mode of incorporating spatial and temporal dependence in a homogeneous region was tried using the Random Forest (RF) classifier for crop classification. A time-series of medium spatial resolution enhanced vegetation index (EVI) and its summary statistics obtained from Landsat 7 Enhanced Thematic Mapper Plus (Landsat 7 ETM+) were used to develop a new technique for crop type classification. Eight classes were studied: alfalfa, asparagus, avocado, cotton, grape, maize, mango, and tomato. Evaluation was based on several criteria: sensitivity to training dataset size, the number of variables, and mapping accuracy. Results showed that the training dataset size strongly affects the classifier accuracy, but if the training data increase, the rate of improvement decreases. The RF algorithm yielded overall accuracy of 81% and a Kappa statistic of 0.70, indicating high model performance. Additionally, the variable importance measures demonstrated that the mode and sum of EVI had extremely important variables for crop class separability. RF had computationally good performance. They can be enhanced by choosing an appropriate classifier for multiple statistics and time-series of Landsat imagery. It might be more economical to use no-cost imaging for crop classification using open-source software.

Spatial and temporal trends in estimates of nutrient and suspended sediment loads in the Ishikari River, Japan, 1985 to 2010.

Nutrients and suspended sediment in surface water play important roles in aquatic ecosystems and contribute strongly to water quality with implication for drinking water resources, human and environmental health. Estimating loads of nutrients (nitrogen and phosphorus) and suspended sediment (SS) is complicated because of infrequent monitoring data, retransformation bias, data censoring, and non-normality. To obtain reliable unbiased estimates, the Maintenance of Variance-Extension type 3 (MOVE. 3) and the regression model Load Estimator (LOADEST) were applied to develop regression equations and to estimate total nitrogen (TN), total phosphorus (TP) and SS loads at five sites on the Ishikari River, Japan, from 1985 to 2010. Coefficients of determination (R(2)) for the best-fit regression models for loads of TN, TP, and SS for the five sites ranged from 71.86% to 90.94%, suggesting the model for all three constituents successfully simulated the variability in constituent loads at all studied sites. Estimated monthly average loads at Yishikarikakou-bashi were larger than at the other sites, with TN, TP, and SS loads ranging from 8.52×10(3) to 2.00×10(5) kg/day (Apr. 1999), 3.96×10(2) to 5.23×10(4) kg/ day (Apr. 1999), and 9.21×10(4) to 9.25×10(7) kg/day (Sep. 2001), respectively. Because of variation in river discharge, the estimated seasonal loads fluctuated widely over the period 1985 to 2010, with the greatest loads occurring in spring and the smallest loads occurring in winter. Estimated loads of TN, TP, and especially SS showed decreasing trends during the study period. Accurate load estimation is a necessary goal of water quality monitoring efforts and the methods described here provide essential information for effectively managing water resources.

IOD and ENSO impacts on the extreme stream-flows of Citarum river in Indonesia

Extreme stream-flow events of Citarum River are derived from the daily stream-flows at the Nanjung gauge station. Those events are identified based on their persistently extreme flows for 6 or more days during boreal fall when the seasonal mean stream-flow starts peaking-up from the lowest seasonal flows of June–August. Most of the extreme events of high-streamflows were related to La Niña conditions of tropical Pacific. A few of them were also associated with the negative phases of IOD and the newly identified El Niño Modoki. Unlike the cases of extreme high streamflows, extreme low streamflow events are seen to be associated with the positive IODs. Nevertheless, it was also found that the low-stream-flow events related to positive IOD events were also associated with El Niño events except for one independent event of 1977. Because the occurrence season coincides the peak season of IOD, not only the picked extreme events are seen to fall under the IOD seasons but also there exists a statistically significant correlation of 0.51 between the seasonal IOD index and the seasonal streamflows. There also exists a significant lag correlation when IOD of June–August season leads the streamflows of September–November. A significant but lower correlation coefficient (0.39) is also found between the seasonal streamflow and El Niño for September–November season only.

3D unmanned aerial vehicle radiation mapping for assessing contaminant distribution and mobility

Abstract Following the events of March 2011 at the Fukushima Daiichi Nuclear Power Plant, significant quantities of radioactive material were released into the local and wider global environment. At five years since the incident, much expense is being currently devoted to the remediation of a large portion of eastern Japan contaminated primarily by radiocesium, yet further significant expenditure will be required over the succeeding decades to complete this clean-up. People displaced from their homes by the incident are now increasingly keen to return, making it more important than ever to provide accurate quantification and representation of any residual radiological contamination. Presented here is the use of an unmanned aerial vehicle equipped with a laser rangefinder unit to generate a three dimensional point-cloud of an area onto which a radiation contamination map, also obtained concurrently via the unmanned aerial platform, can be rendered. An exemplar site of an un-remediated farm consisting of multiple stepped rice paddy fields with a dedicated irrigation system was used for this work. The results obtained show that heightened radiological contamination exists around the site within the drainage network where material is observed to have collected, having been transported by transient water runoff events. These results obtained in May 2014 suggest that a proportion of the fallout material is highly mobile within the natural environment and is likely to be transported further through the system over the succeeding years.

Future projection of radiocesium flux to the ocean from the largest river impacted by Fukushima Daiichi Nuclear Power Plant

Following the initial fall out from Fukushima Dai-ichi Nuclear Power Plant (FDNPP), a significant amount of radiocesium has been discharged from Abukuma River into the Pacific Ocean. This study attempted to numerically simulate the flux of radiocesium into Abukuma River by developing the multiple compartment model which incorporate the transport process of the radionuclide from the ground surface of the catchment area into the river, a process called wash off. The results from the model show that the sub-basins with a high percentage of forest area release the radionuclides at lower rate compared to the other sub-basins. In addition the results show that the model could predict the seasonal pattern of the observed data. Despite the overestimation observed between the modeled data and the observed data, the values of R2 obtained from 137Cs and 134Cs of 0.98 and 0.97 respectively demonstrate the accuracy of the model. Prediction of the discharge from the basin area for 100 years after the accident shows that, the flux of radiocesium into the Pacific Ocean is still relatively high with an order of magnitude of 109 bq.month−1 while the total accumulation of the discharge is 111 TBq for 137Cs and 44 TBq for 134Cs.

Effects of climate change on daily minimum and maximum temperatures and cloudiness in the Shikoku region: a statistical downscaling model approach

In this study, we present a detailed analysis of the effect of changes in cloudiness (CLD) between a future period (2071–2099) and the base period (1961–1990) on daily minimum temperature (TMIN) and maximum temperature (TMAX) in the same period for the Shikoku region, Japan. This analysis was performed using climate data obtained with the use of the Statistical DownScaling Model (SDSM). We calibrated the SDSM using the National Center for Environmental Prediction (NCEP) reanalysis dataset for the SDSM input and daily time series of temperature and CLD from 10 surface data points (SDP) in Shikoku. Subsequently, we validated the SDSM outputs, specifically, TMIN, TMAX, and CLD, obtained with the use of the NCEP reanalysis dataset and general circulation model (GCM) data against the SDP. The GCM data used in the validation procedure were those from the Hadley Centre Coupled Model, version 3 (HadCM3) for the Special Report on Emission Scenarios (SRES) A2 and B2 scenarios and from the third generation Coupled Global Climate Model (CGCM3) for the SRES A2 and A1B scenarios. Finally, the validated SDSM was run to study the effect of future changes in CLD on TMIN and TMAX. Our analysis showed that (1) the negative linear fit between changes in TMAX and those in CLD was statistically significant in winter while the relationship between the two changes was not evident in summer, (2) the dependency of future changes in TMAX and TMIN on future changes in CLD were more evident in winter than in other seasons with the present SDSM, (3) the diurnal temperature range (DTR) decreased in the southern part of Shikoku in summer in all the SDSM projections while DTR increased in the northern part of Shikoku in the same season in these projections, (4) the dependencies of changes in DTR on changes in CLD were unclear in summer and winter. Results of the SDSM simulations performed for climate change scenarios such as those from this study contribute to local-scale agricultural and hydrological simulations and development of agricultural and hydrological models.

High-resolution radiation mapping to investigate FDNPP derived contaminant migration.

As of March 2016, five years will have passed since the earthquake and ensuing tsunami that crippled the Fukushima Daiichi Nuclear Power Plant on Japans eastern coast, resulting in the explosive release of significant quantities of radioactive material. Over this period, significant time and resource has been expended on both the study of the contamination as well as its remediation from the affected environments. Presented in this work is a high-spatial resolution foot-based radiation mapping study using gamma-spectrometry at a site in the contaminated Iitate Village; conducted at different times, seventeen months apart. The specific site selected for this work was one in which consistent uniform agriculture was observed across its entire extent. From these surveys, obtained from along the main northwest trending line of the fallout plume, it was possible to determine the rate of reduction in the levels of contamination around the site attributable to the natural decay of the radiocesium, remediation efforts or material transport. Results from the work suggest that neither the natural decay of radiocesium nor its downward migration through the soil horizons were responsible for the decline in measured activity levels across the site, with the mobilisation of contaminant species likely adhered to soil particulate and the subsequent fluvial transport responsible for the measurable reduction in activity. This transport of contaminant via fluvial methods has already well studied implications for the input of contaminant material entering the neighbouring Pacific Ocean, as well as the deposition of material along rivers within previously decontaminated areas.

Analysis of stream water quality and estimation of nutrient load with the aid of Quick Bird remote sensing imagery

Abstract Human activities have created high nutrient surpluses in agricultural lands due to the increasing rate of chemical fertilizer application and the increase in livestock production. To analyse the nutrient characteristics and estimate the nutrient load in streams, we conducted extensive field survey and water quality experiments from 2007 to 2008 in Koise River, a major river of the Lake Kasumigaura watershed, Japan. Water quality indicators of total nitrogen (TN), total phosphorus (TP) and total organic carbon (TOC) were investigated. The nutrient loads of TN, TP and TOC, as well as dissolved total nitrogen, dissolved inorganic nitrogen, dissolved organic nitrogen, particle organic nitrogen, dissolved total phosphorus, dissolved organic carbon and particle organic carbon were also estimated for the Koise River. Seasonal variation of the nutrient concentration from 2007 to 2008 was analysed considering the river discharge variation and agricultural activities. The results showed that the irrigation water from Lake Kasumigaura has the potential ability to decrease the TN concentration and increase the TOC concentration in the Koise River. Significant correlation coefficients between nutrient load and river discharge were found. The monthly pollution loads from different sources were then evaluated based on land cover classification generated from high-resolution Quick Bird remote sensing imagery. This study presents a useful interpretation of water quality data sets with a view to obtaining better information about water quality for more effective management of water resources in river basins. Editor Z.W. Kundzewicz Citation He, B., Oki, K., Wang, Y., Oki, T., Yamashiki, Y., Takara, K., Miura, S., Imai, A., Komatsu, K. and Kawasaki, N., 2012. Analysis of stream water quality and estimation of nutrient load with the aid of Quick Bird remote sensing imagery. Hydrological Sciences Journal, 57 (5), 850–860.