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Summary: Optimizing water allocation is a crucial strategy for water planning in large basins, such as the Yellow River basin. The transforming water supply pattern, triggered by inter-basin water transfer, urges the optimization and refinement of water allocation. Here, we predict the future water demand for different sectors at the provincial level and carry out future water allocation based on a multi-objective allocation model. The results imply that “demand increases in midstream and water deficits in downstream” would persist by 2050, and the demand structure highlights potential sectoral rivalry for water resources in each province. Water transfer will be the “antidote” to water disputes and will account for 19.8% and 34.5% of the water demand in 2030 and 2050, respectively. This study provides a basis for the policymaking of water management and establishes the future allocation pattern from water resources to sectors in each province in the Yellow River basin.

Abstract Soil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large‐scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in‐situ geological measurements. Fortunately, recent advancements in satellite technology provide a unique opportunity to constrain the soil texture data sets by introducing observed information at large spatial scales. Here, two major soil texture baseline data sets (Global Soil Data sets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite‐estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon (SOC) content increases globally especially over regions with dense vegetation cover. The optimized soil texture data sets are then used to run simulations in one example LSM, that is, Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite‐optimized soil texture maps is improved at both grid and in‐situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation‐oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation of Earth System Models (ESMs).

Famine still exists in this world, hindering the achievement of Sustainable Development Goal 2: zero hunger. The history and the mechanisms of famine have been broadly studied; however, few studies have focused on quantitative and long-term analyses of the general characteristics of famine. This study analyzes the factors influencing famine and estimates its risks. We developed a historical famine database and estimated the probability and intensity of famine through regression analysis. Herein, we identified that less food production and underdevelopment are related to famine occurrence, and famine tends to be more intense in less urbanized and dryer areas. By extrapolating the regression with future scenarios, we revealed that famine would be less likely to happen in the future mainly due to GDP growth, although conflict would be the key factor of future famine risk. This study is one of the first steps in the quantitative analysis of long-term famine risk.

Measurement(s) Precipitation Technology Type(s) Weather station Sample Characteristic - Environment Climate system Sample Characteristic - Location Japan

Variation in global water scarcity is strongly dependent on the spatial distribution of population and economic conditions, according to a quantitative assessment of combined physical and economic water scarcity under multiple future emissions scenarios.

Significant regional disparities exist in the time left to prepare for unprecedented drought and how much we can buy time depending on climate scenarios. Specific regions pass this timing by the middle of 21st century even with stringent mitigation.

Abstract Floods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding.

Phuket, the study area of this work with 33 sandy beaches, provides about 15% of the nation’s gross domestic product from the tourism industry. Many factors cause shoreline changes affecting beach areas, such as seasonal erosion and rising sea levels. In this study, shoreline position was the key parameter for evaluating shoreline changes. The CoastSat open-source software was selected to analyze the shoreline changes using a publicly available satellite imagery API. The future shoreline recession was projected using the Bruun rule integrated with field observation data and sea level rise scenarios from Coupled Model Intercomparison Project Phase 6 (CMIP6). The result indicated that eight of the study site’s locations were under mild erosion from 2013 to 2021. The average shoreline change varied between −4.10 and 5.47 m/year. The projection of future beach loss due to sea level rise found that 20 beaches and 32 beaches will be lost entirely under SSP1 2.6 and SSP5 8.5, respectively. Beach morphology is influenced by human-induced activities, such as coastal urbanization and the development of structures along the coast. This study discusses the effectiveness of the present coastal protection structure with data from field observations and suggests possible future management strategies.

Climate zone classification promotes our understanding of the climate and provides a framework for analyzing a range of environmental and socioeconomic data and phenomena. The Köppen–Geiger classification system is the most widely used climate classification scheme. In this study, we compared the climate zones objectively defined using data-driven methods with Köppen–Geiger rule-based classification. Cluster analysis was used to objectively delineate the world’s climatic regions. We applied three clustering algorithms—k-means, ISODATA, and unsupervised random forest classification—to a dataset comprising 10 climatic variables and elevation; we then compared the obtained results with those from the Köppen–Geiger classification system. Results from both the systems were similar for some climatic regions, especially extreme temperature ones such as the tropics, deserts, and polar regions. Data-driven classification identified novel climatic regions that the Köppen–Geiger classification could not. Refinements to the Köppen–Geiger classification, such as precipitation-based subdivisions to existing Köppen–Geiger climate classes like tropical rainforest (Af) and warm summer continental (Dfb), have been suggested based on clustering results. Climatic regions objectively defined by data-driven methods can further the current understanding of climate divisions. On the other hand, rule-based systems, such as the Köppen–Geiger classification, have an advantage in characterizing individual climates. In conclusion, these two approaches can complement each other to form a more objective climate classification system, wherein finer details can be provided by data-driven classification and supported by the intuitive structure of rule-based classification.

What will be the aggregated cost of climate change in achieving the Paris Agreement, including mitigation, adaptation, and residual impacts? Several studies estimated the aggregated cost but did not always consider the critical issues. Some do not address non-market values such as biodiversity and human health, and most do not address differentiating discount rates. In this study, we estimate the aggregated cost of climate change using an integrated assessment model linked with detailed-process-based climate impact models and different discount rates for market and non-market values. The analysis reveals that a climate policy with minimal aggregated cost is sensitive to socioeconomic scenarios and the way discount rates are applied. The results elucidate that a lower discount rate to non-market value—that is, a higher estimate of future value—makes the aggregated cost of achieving the Paris Agreement economically reasonable.

A more objective assessment of the freshwater scarcity risk at a watershed basis scale contributes to the achievement of the Sustainable Development Goals (SDGs), in which water plays a key role in issues like safe and affordable drinking water, water-related disasters, forest management, biodiversity and ecosystems. Within the SDGs, water scarcity is assessed primarily by using the ratio of withdrawal to availability (WTA). This index has been developed to evaluate the average annual conditions on a broad scale, such as country or large basin. However, there are insufficient cases for assessing specific and detailed risk conditions by using this original WTA concept, due to seasonal and interannual variations of the river discharge and withdrawal volumes. For example, infrastructure (e.g. large dams) and its operation, can drastically change the available water and demand volumes at temporal scales finer than annual. Because recently many daily and monthly records of river discharge and water use have become publicly available, it is possible to conduct water scarcity risk assessments on a monthly basis. By employing these datasets, in this study, we propose to compare volumes of available water and demand by using a Sectoral and Statistical Demand to Availability (SS-DTA) index. The novelty of this index is marked not only by the use of monthly volumes of available and required water but also by how environmental water requirements are included and the statistical relevance of the evaluation. Usually, to determine the available water resources, existing methods deduct from the river discharge the volumes that are required to preserve the surrounding ecosystems. Our proposed method considers the environmental flow requirements as one of the water-demand sectors allowing a simultaneous evaluation of water use that is not possible with existing methods. We applied this method to three representative river basins, two located in Japan (the Tone and the Arakawa River basins) and the Brantas River basin in Indonesia. The results showed the variations of water scarcity risk level through the river operation in each river basin. In the Tone River basin, it was succeeded in a drought year to decrease its risk level from 1 to 0, and to transport sufficient volume of water to the Arakawa River basin through an artificial channel to meet the demands at the same time. In the Brantas River basin, the river operation changed the SS-DTA score from 3 to 2, which means the water scarcity risk level was alleviated, while the main impact of the operation was found in high-water season to decrease the flood risk. Because the number, type, and priority of water demands can be adjusted based on the local situation and all the timeseries data of each demand sector are visualized in parallel, the result includes not only the whole risk level of the river basin but also the practical and specific degree of the risk with demand sector information.

Agricultural irrigation has significantly reshaped the land surface energy and water balance. Previous studies have well investigated its cooling effect on air temperature ( T _air ). However, its effect on increasing air humidity which can intensify the humid heat was often overlooked, particularly over urban areas with high population density, high T _air , and consequently greater exposure to moist heat stress. In this study, using state-of-the-art reanalysis data at a high spatial resolution (∼9 km), we evaluated how changes in area equipped for irrigation (AEI) around a city affect urban moist heat stress (UMHS) in more than 1000 cities in China and India. In addition to T _air , wet-bulb temperature ( T _WB ) and wet-bulb globe temperature ( T _WBG ), which consider humidity and are closer to the perceived temperature, were assessed. We found that although AEI expansion lowers urban T _air , irrigation increases T _WB and T _WBG due to increased air humidity, thereby exacerbating the UMHS. This ‘warming’ effect of irrigation is more evident in northern India where AEI has expanded significantly in recent decades, and is prominent in the pre-monsoon and post-monsoon seasons, when precipitation and air humidity are low. However, this effect is not evident in China due to the lower intensity of AEI expansion and differing climatic conditions. Overall, this study highlights the side-effect of irrigation on regional climate, providing crucial information for better understanding urban heat stress and for future city planning.

Competitions and disputes between various human water sectors and environmental flow of the river are exacerbated due to the rapid growth of the economy in Yellow River basin as well as the limited supply of available water resources in recent decades. It is necessary to implement rational and effective management and allocation to alleviate the pressure of water shortage. In order to promote economic development and maintain the ecological balance of the river, both the water allocation to the river environmental system and different human needs should be of concern when making the allocation polices. This study developed a water allocation model based on Nash–Harsanyi bargaining game theory for optimal water resources allocation among agricultural, industrial, domestic, public, and urban ecological water (watering for urban green space) sectors while ensuring the environmental flow requirements of lower reaches. A comprehensive economic evaluation framework is built to assess the economic benefits of different water uses that were taken as the basis of water allocation model. The annual environmental base flow is 7.50 billion m3 in the lower reaches of Yellow River. Moreover, the optimal annual allocations for agricultural, industrial, domestic, public, and urban ecological water use sectors are estimated as 33.7, 6.42, 3.96, 1.75 and 2.68 billion m3, respectively.

Thailand plays a central economic and policy-making role in Southeast Asia. Although climate change adaptation is being mainstreamed in Thailand, a well-organized overview of the impacts of climate change and potential adaptation measures has been unavailable to date. Here we present a comprehensive review of climate-change impact studies that focused on the Thai water sector, based on a literature review of six sub-sectors: riverine hydrology, sediment erosion, coastal erosion, forest hydrology, agricultural hydrology, and urban hydrology. Our review examined the long-term availability of observational data, historical changes, projected changes in key variables, and the availability of economic assessments and their implications for adaptation actions. Although some basic hydrometeorological variables have been well monitored, specific historical changes due to climate change have seldom been detected. Furthermore, although numerous future projections have been proposed, the likely changes due to climate change remain unclear due to a general lack of systematic multi-model and multi-scenario assessments and limited spatiotemporal coverage of the study area. Several gaps in the research were identified, and ten research recommendations are presented. While the information contained herein contributes to state-of-the-art knowledge on the impact of climate change on the water sector in Thailand, it will also benefit other countries on the Indochina Peninsula with a similar climate.

[This corrects the article DOI: 10.1371/journal.pone.0165594.].

In the wake of the 2011 Fukushima Daiichi Nuclear Power Station accident, to facilitate evidence-based risk communication we need to understand radiation risk perception and the effectiveness of risk-comparison information. We measured and characterized perceptions of dread risks and unknown risks regarding dietary radionuclides in residents of Fukushima, Tokyo, and Osaka to identify the primary factors among location, evacuation experience, gender, age, employment status, absence/presence of spouse, children and grandchildren, educational background, humanities/science courses, smoking habits, and various types of trustworthy information sources. We then evaluated the effects of these factors and risk-comparison information on multiple outcomes, including subjective and objective understanding, perceived magnitude of risk, perceived accuracy of information, backlash against information, and risk acceptance. We also assessed how risk-comparison information affected these multiple outcomes for people with high risk perception. Online questionnaires were completed by people (n = 9249) aged from 20 to 69 years in the three prefectures approximately 5 years after the accident. We gave each participant one of 15 combinations of numerical risk data and risk-comparison information, including information on standards, smoking-associated risk, and cancer risk, in accordance with Covello's guidelines. Dread-risk perception among Fukushima residents with no experience of evacuation was much lower than that in Osaka residents, whereas evacuees had strikingly higher dread-risk perception, irrespective of whether their evacuation had been compulsory or voluntary. We identified location (distance from the nuclear power station), evacuation experience, and trust of central government as primary factors. Location (including evacuation experience) and trust of central government were significantly associated with the multiple outcomes above. Only information on "cancer risk from radiation and smoking risk" enhanced both subjective and objective understanding without diminishing trust in all participants and in the high dread-risk perception group; use of other risk-comparison information could lead the public to overestimate risk.

Water footprint assessments contribute to a better understanding of potential environmental impacts related to water and have become essential in water management. The methodologies for characterizing such assessments, however, usually fail to reflect temporal and spatial variations at local scales. In this paper, we employ four widely-used characterization factors, which were originally developed with global estimates of water demand and availability, to evaluate the impact that inter-basin transfer (IBT) of water has on water risk assessments and, consequently, on the evaluation of the soundness of water cycle. The study was conducted for two major river basins in Japan, where diversion channels were built to move water from the Tone river basin to the Arakawa river basin. Considering IBT, the available water in the Arakawa river basin increases a 45%, reducing the characterization factors a 44% on average and denoting their tendency to overestimate the risk in this basin, while the Tone river basin increased the characterization factors a 28% on average by IBT. Moreover, with a simple example we show how ambiguity in the definition of some characterization factors may cause significant changes in the result of the assessments. Finally, we concluded that local water footprint characterization can be more helpful in local assessment of water resources if the results are unanimous, Targetable, Replicable, Ameliorable, Comparable, and Engageable (uTRACE).

Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models’ ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.

After the 2011 accident at the Fukushima Daiichi nuclear power plant, nursing-home residents and staff were evacuated voluntarily from damaged areas to avoid radiation exposure. Unfortunately, the evacuation resulted in increased mortalities among nursing home residents. We assessed the risk trade-off between evacuation and radiation for 191 residents and 184 staff at three nursing homes by using the same detriment indicator, namely loss of life expectancy (LLE), under four scenarios, i.e. "rapid evacuation (in accordance with the actual situation; i.e. evacuation on 22 March)," "deliberate evacuation (i.e. evacuation on 20 June)," "20-mSv exposure," and "100-mSv exposure." The LLE from evacuation-related mortality among nursing home residents was assessed with survival probability data from nursing homes in the city of Minamisoma and the city of Soma. The LLE from radiation mortality was calculated from the estimated age-specific mortality rates from leukemia and all solid cancers based on the additional effective doses and the survival probabilities. The total LLE of residents due to evacuation-related risks in rapid evacuation was 11,000 persons-d-much higher than the total LLEs of residents and staff due to radiation in the other scenarios (27, 1100, and 5800 persons-d for deliberate evacuation, 20 mSv-exposure, and 100 mSv-exposure, respectively). The latitude for reducing evacuation risks among nursing home residents is surprisingly large. Evacuation regulation and planning should therefore be well balanced with the trade-offs against radiation risks. This is the first quantitative assessment of the risk trade-off between radiation exposure and evacuation after a nuclear power plant accident.

Abstract: The chlorophyll-a (Chla) products of seven processors developed for the Medium Resolution Imaging Spectrometer (MERIS) sensor were evaluated. The seven processors, based on a neural network and band height, were assessed over an optically complex water body with Chla concentrations of 8.10–187.40 mg∙m−3 using 10-year MERIS archival data. These processors were adopted for the Ocean and Land Color Instrument (OLCI) sensor. Results indicated that the four processors of band height (i.e. the Maximum Chlorophyll Index (MCI_L1); and Fluorescence Line Height (FLH_L1)); neural network (i.e. Eutrophic Lake (EUL); and Case 2 Regional (C2R)) possessed reasonable retrieval accuracy with root mean square error (R2) in the range of 0.42–0.65. However, these processors underestimated the retrieved Chla > 100 mg∙m−3, reflecting the limitation of the band height processors to eliminate the influence of non-phytoplankton matter and highlighting the need to train the neural network for highly turbid waters. MCI_L1 outperformed other processors during the calibration and validation stages (R2 = 0.65, Root mean square error (RMSE) = 22.18 mg∙m−3, the mean absolute relative error (MARE) = 36.88%). In contrast, the results from the Boreal Lake (BOL) and Free University of Berlin (FUB) processors demonstrated their inadequacy to accurately retrieve Chla concentration > 50 mg∙m−3, mainly due to the limitation of the training datasets that resulted in a high MARE for BOL (56.20%) and FUB (57.00%). Mapping the spatial distribution of Chla concentrations across Lake Kasumigaura using the seven processors showed that all processors—except for the BOL and FUB—were able to accurately capture the Chla distribution for moderate and high Chla concentrations. In addition, MCI_L1 and C2R processors were evaluated over 10-years of monthly measured Chla as they demonstrated the best retrieval accuracy from both groups (i.e. band height and neural network, respectively). The retrieved Chla of MCI_L1 was more accurate at tracking seasonal and annual variation in Chla than C2R, with only slight overestimation occurring during the springtime.

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3).

Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms’ indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R2 = 0.692, root mean square error (RMSE) = 21.4 mg m−3), Lake Kasumigaura (R2 = 0.866, RMSE = 11.3 mg m−3), and MERIS data over Lake Kasumigaura (R2 = 0.57, RMSE = 36.5 mg m−3). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura.

Although water is rarely traded over long distances by itself, the total weight of the water consumed to produce traded commodities exceeds the weight of any other commodity traded in the world. This concept is known as virtual water trade. Although space-/time-/commodity-based quantification has been conducted extensively, the underlying causes of this peculiar feature have thus far received little exploration. Here, we use estimates of water consumption from a global hydrological model and statistical data related to food trade to elucidate three facts that explain the fundamental nature of virtual water trade with respect to alleviating water scarcity. First, we quantitatively illustrate the unique position of water among commodities based on its unit price and quantity of sales. Water has an extremely low unit price, and a tremendous volume of water is consumed per person each day. Second, we show that rich but water-scarce countries tend to reduce local water consumption by importing virtual water. Third, we demonstrate that nations characterized by net virtual water exports have higher water resources and income per capita and that no countries fall below a certain threshold with respect to both GDP and water resources. These points suggest that the virtual water trade is explained by economic characteristics of water and that sustainable development depends on promoting the co-development of poverty alleviation and water resource development.

The radionuclides released from the Fukushima Daiichi nuclear power plant in 2011 pose a health risk. In this study, we estimated the 1st-year average doses resulting from the intake of iodine 131 (131I) and cesium 134 and 137 (134Cs and 137Cs) in drinking water and food ingested by citizens of Fukushima City (∼50 km from the nuclear power plant; outside the evacuation zone), Tokyo (∼230 km), and Osaka (∼580 km) after the accident. For citizens in Fukushima City, we considered two scenarios: Case 1, citizens consumed vegetables bought from markets; Case 2, citizens consumed vegetables grown locally (conservative scenario). The estimated effective doses of 134Cs and 137Cs agreed well with those estimated through market basket and food-duplicate surveys. The average thyroid equivalent doses due to ingestion of 131I for adults were 840 µSv (Case 1) and 2700 µSv (Case 2) in Fukushima City, 370 µSv in Tokyo, and 16 µSv in Osaka. The average effective doses due to 134Cs and 137Cs were 19, 120, 6.1, and 1.9 µSv, respectively. The doses estimated in this study were much lower than values reported by the World Health Organization and the United Nations Scientific Committee on the Effects of Atomic Radiation, whose assessments lacked validation and full consideration of regional trade in foods, highlighting the importance of including regional trade. The 95th percentile effective doses were 2-3 times the average values. Lifetime attributable risks (LARs) of thyroid cancers due to ingestion were 1.7-37×10-6 (Case 1) and5.6-79×10-6 (Case 2) in Fukushima City, 0.73-13×10-6 in Tokyo, and 0.04-0.49×10- 6 in Osaka. The contributions of LARs of thyroid cancers due to ingestion were 5.4%-11% of all exposure (Case 1) and 11%-25% (Case 2) in Fukushima City [corrected].

We quantify the potential impacts of global food production on freshwater availability (water scarcity footprint; WSF) by applying the water unavailability factor (fwua) as a characterization factor and a global water resource model based on life cycle impact assessment (LCIA). Each water source, including rainfall, surface water, and groundwater, has a distinct fwua that is estimated based on the renewability rate of each geographical water cycle. The aggregated consumptive water use level for food production (water footprint inventory; WI) was found to be 4344 km3/year, and the calculated global total WSF was 18,031 km3 H2Oeq/year, when considering the difference in water sources. According to the fwua concept, which is based on the land area required to obtain a unit volume of water from each source, the calculated annual impact can also be represented as 98.5 × 106 km2. This value implies that current agricultural activities requires a land area that is over six times larger than global total cropland. We also present the net import of the WI and WSF, highlighting the importance of quantitative assessments for utilizing global water resources to achieve sustainable water use globally.

The United Nations has reported that the world is off track to achieve water sustainability and this has been exacerbated by climate change. Studies indicate that waterrelated sustainable development goals (SDGs) have significant synergies and trade-offs involving various sectors. We measure the synergies and trade-offs between climate actions (mitigation and adaptation) and five water-related SDG targets based on a global numerical simulation driven by climate change and socioeconomic scenarios. We found that synergies outweigh trade-offs in all regions regarding water-related targets, with an approximately five-fold difference in the globally affected population. The main tradeoff source is increased water use due to economic development in Africa and Central/South America, and increased irrigation water if bio-energy crop production is enhanced in Africa, suggesting that more coordinated policy-making is needed between climate action and water management. [ABSTRACT FROM AUTHOR]

Climate change impacts due to unprecedented rising concentrations of greenhouse gas (GHG) are intensifying and widespread, making extreme climate events more widespread, frequent, and severe. To mitigate the worst consequences of climate warming, herein it is investigated how the global community can collectively achieve a large‐scale, sustained reduction in GHG emissions, and how to effectively move away from a predominantly fossil fuel‐based economy to one dominated by renewable energy? This transition is necessary to achieve the sustainable development goals (SDGs) of United Nations (UN) to ensure resilient and healthy environment for present and future generations, especially the SDG 7 of UN, "Affordable and Clean Energy", set up to achieve global development of modern renewable energy systems. Investment policies and patterns of developed and developing countries in transitioning to energy productions primarily from renewable sources and obstacles such as scale‐up challenges, innovations in new energy systems, policies, financing mechanisms, and implementation strategies are examined. Furthermore, a comprehensive overview of the present global status of hydropower, wind, and solar, the three most significant renewable electricity technologies, as well as their basic operating principles, costs, and potential is conducted. Hydroelectric, wind, and solar power had grown from 3429, 346, and 34 TWh yr−1 in 2010 to 4274, 1598, and 846 TWh yr−1 in 2020, a growth of about 1.25, 4.60, and 24.9 times in a decade, respectively. Strategies to achieve energy systems that are of or near net zero GHG emissions by 2050s through the deployment of renewable energy systems are also investigated. [ABSTRACT FROM AUTHOR]

There is a need to consider people's welfare when formulating policies, where subjective well-being is a proxy for welfare. Although it is important to elucidate the mechanisms underlying the decline in subjective well-being associated with flood experience when making policy, this has not been studied in detail. Therefore, this study sought to clarify the relationship between flood experience and subjective well-being, focusing on anxiety about floods. We conducted an online questionnaire in Tochigi Prefecture, Japan and analyzed the results of 2,630 respondents. Mediation analysis revealed that flood experience does not have a direct effect on subjective well-being (p < 0.05) but exerts a negative effect through anxiety about floods. The same was true when only people with recent flood experience were analyzed. This study suggests methods for restoring subjective well-being to its original level after a flood, such as managing anxiety about floods. [ABSTRACT FROM AUTHOR]