Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa.

Groundwater in sub-Saharan Africa supports livelihoods and poverty alleviation1,2, maintains vital ecosystems, and strongly influences terrestrial water and energy budgets3. Yet the hydrological processes that govern groundwater recharge and sustainability-and their sensitivity to climatic variability-are poorly constrained4,5. Given the absence of firm observational constraints, it remains to be seen whether model-based projections of decreased water resources in dry parts of the region4 are justified. Here we show, through analysis of multidecadal groundwater hydrographs across sub-Saharan Africa, that levels of aridity dictate the predominant recharge processes, whereas local hydrogeology influences the type and sensitivity of precipitation-recharge relationships. Recharge in some humid locations varies by as little as five per cent (by coefficient of variation) across a wide range of annual precipitation values. Other regions, by contrast, show roughly linear precipitation-recharge relationships, with precipitation thresholds (of roughly ten millimetres or less per day) governing the initiation of recharge. These thresholds tend to rise as aridity increases, and recharge in drylands is more episodic and increasingly dominated by focused recharge through losses from ephemeral overland flows. Extreme annual recharge is commonly associated with intense rainfall and flooding events, themselves often driven by large-scale climate controls. Intense precipitation, even during years of lower overall precipitation, produces some of the largest years of recharge in some dry subtropical locations. Our results therefore challenge the 'high certainty' consensus regarding decreasing water resources4 in such regions of sub-Saharan Africa. The potential resilience of groundwater to climate variability in many areas that is revealed by these precipitation-recharge relationships is essential for informing reliable predictions of climate-change impacts and adaptation strategies.

Lost Rivers: Tokyo's Sewage Problem in the High-Growth Period, 1953-73.

Early modern Tokyo was a city of water with rivers and canals crisscrossing the city and connecting its commercial centers. However, modern Tokyo's rivers have disappeared-filled in, or converted into concrete-lined sewers. This article explores what happened to these waterways during Japan's period of rapid economic growth. It focuses on the 1961 policy decision by city planners and water engineers that resulted in the rivers-to-sewers transition in the lead-up to the 1964 Tokyo Olympics. The process of making this policy sheds light on the interface of the long-term urban industrial pollution and the short-term pressures of urban clean up before the 1964 Olympics. Contributing an envirotech perspective on industrial waste management during Japan's high-speed economic growth period, this article brings to focus a rush to pave with concrete Japan's return on the international scene, as part of the showcasing recovery from the political and economic catastrophe of World War II.

Decontamination Reduces Radiation Anxiety and Improves Subjective Well-Being after the Fukushima Accident.

Since the 2011 Fukushima accident, rebuilding society to protect against anxiety and improve feelings of well-being has grown in importance. A questionnaire was carried out among residents of Marumori Town, Igu County, Miyagi Prefecture, to evaluate the effects of radiation-related countermeasures implemented by the town to reduce residents' anxiety and improve their subjective well-being (response rate: 31%; valid responses: n = 174). Further, to propose effective countermeasures regarding town planning for the improvement in subjective well-being, we analyzed associations between residents' sense of attachment to the town and subjective well-being, and then identified primary factors behind their sense of attachment. Marumori Town, located about 50 km to the northwest of the Daiichi Nuclear Power Station, is a good site for evaluating the effects of countermeasures taken by the town itself to fight anxiety, because there have been limited surveys and professional medical support, and mitigation efforts were primarily initiated by the town. The results suggested that decontamination evaluations were associated with a reduction in anxiety after the accident, which contributed to an increase in residents' subjective well-being. On the other hand, the evaluation of items related to human interactions in the community and the natural environment was found as a primary factor of sense of attachment toward the town, which contributed to an increase of residents' subjective well-being. This is the first study to quantitatively measure the effects of radiation-related countermeasures on reducing anxiety and to propose an effective policy approach for improving subjective well-being.

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands.

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).

Understanding the LCA and ISO water footprint: A response to Hoekstra (2016) "A critique on the water-scarcity weighted water footprint in LCA".

Water footprinting has emerged as an important approach to assess water use related effects from consumption of goods and services. Assessment methods are proposed by two different communities, the Water Footprint Network (WFN) and the Life Cycle Assessment (LCA) community. The proposed methods are broadly similar and encompass both the computation of water use and its impacts, but differ in communication of a water footprint result. In this paper, we explain the role and goal of LCA and ISO-compatible water footprinting and resolve the six issues raised by Hoekstra (2016) in "A critique on the water-scarcity weighted water footprint in LCA". By clarifying the concerns, we identify both the overlapping goals in the WFN and LCA water footprint assessments and discrepancies between them. The main differing perspective between the WFN and LCA-based approach seems to relate to the fact that LCA aims to account for environmental impacts, while the WFN aims to account for water productivity of global fresh water as a limited resource. We conclude that there is potential to use synergies in research for the two approaches and highlight the need for proper declaration of the methods applied.

Estimating the collapse of aggregated fine soil structure in a mountainous forested catchment.

This paper describes the relationship of forest soil dryness and antecedent rainfall with suspended sediment (SS) yield due to extreme rainfall events and how this relationship affects the survival of forest plants. Several phenomena contribute to this relationship: increasing evaporation (amount of water vapour discharged from soil) due to increasing air temperature, decreasing moisture content in the soil, the collapse of aggregates of fine soil particles, and the resulting effects on forest plants. To clarify the relationships among climate variation, the collapse of soil particle aggregates, and rainfall-runoff processes, a numerical model was developed to reproduce such aggregate collapse in detail. The validity of the numerical model was confirmed by its application to the granitic mountainous catchment of the Nagara River basin in Japan and by comparison with observational data. The simulation suggests that important problems, such as the collapse of forest plants in response to decreases in soil moisture content and antecedent rainfall, will arise if air temperature continues to increase.

Testing the hypothesis on the relationship between aerodynamic roughness length and albedo using vegetation structure parameters.

Surface albedo (α) and aerodynamic roughness length (z(0)), which partition surface net radiation into energy fluxes, are critical land surface properties for biosphere-atmosphere interactions and climate variability. Previous studies suggested that canopy structure parameters influence both α and z(0); however, no field data have been reported to quantify their relationships. Here, we hypothesize that a functional relationship between α and z(0) exists for a vegetated surface, since both land surface parameters can be conceptually related to the characteristics of canopy structure. We test this hypothesis by using the observed data collected from 50 site-years of field measurements from sites worldwide covering various vegetated surfaces. On the basis of these data, a negative linear relationship between α and log(z(0)) was found, which is related to the canopy structural parameter. We believe that our finding is a big step toward the estimation of z(0) with high accuracy. This can be used, for example, in the parameterization of land properties and the observation of z(0) using satellite remote sensing.

A 0.01-degree gridded precipitation dataset for Japan, 1926-2020.

We developed a 0.01-degree gridded precipitation dataset of Japan based on historical observation datasets covering 1926 to 2020. Historical observations conducted by the Japan Meteorological Agency and other Japanese bureaucratic agencies were spatially interpolated using the inverse distance weighting method at daily and hourly temporal resolutions. Optimal parameterization for our interpolation process was selected by comparing interpolated results of various parameter combinations with precipitation observation conducted by the University of Tokyo Forests. We conducted cross-validation for over 1,000 stations with sufficient data throughout our data period and verified our product can reproduce the temporal variability of local precipitation. The strong points of our precipitation dataset are its high spatiotemporal resolution and the abundance of point precipitation source data. We expect our dataset to be highly relevant to various future studies as it can serve multiple purposes such as forcing data for hydrological models or a database for analyzing the characteristics of historical rainfall events.

The timing of unprecedented hydrological drought under climate change.

Droughts that exceed the magnitudes of historical variation ranges could occur increasingly frequently under future climate conditions. However, the time of the emergence of unprecedented drought conditions under climate change has rarely been examined. Here, using multimodel hydrological simulations, we investigate the changes in the frequency of hydrological drought (defined as abnormally low river discharge) under high and low greenhouse gas concentration scenarios and existing water resource management measures and estimate the time of the first emergence of unprecedented regional drought conditions centered on the low-flow season. The times are detected for several subcontinental-scale regions, and three regions, namely, Southwestern South America, Mediterranean Europe, and Northern Africa, exhibit particularly robust results under the high-emission scenario. These three regions are expected to confront unprecedented conditions within the next 30 years with a high likelihood regardless of the emission scenarios. In addition, the results obtained herein demonstrate the benefits of the lower-emission pathway in reducing the likelihood of emergence. The Paris Agreement goals are shown to be effective in reducing the likelihood to the unlikely level in most regions. However, appropriate and prior adaptation measures are considered indispensable when facing unprecedented drought conditions. The results of this study underscore the importance of improving drought preparedness within the considered time horizons.

Spatial and temporal variation in nutrient parameters in stream water in a rural-urban catchment, Shikoku, Japan: effects of land cover and human impact.

Seasonal and spatial variations in major ion chemistry and isotope composition in the rural-urban catchment of the Shigenobu River were monitored to determine the influences of agricultural and urban sewage systems on water quality. Temporal patterns of biochemical oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP), and suspended sediment (SS) were examined at four sites in the rural-urban catchment. Urban land cover, incorporating the effects of increased population, domestic water use, and industrial wastewater, was positively associated with increases in water pollution and was included as an important explanatory variable for the variations in all water quality parameters. Significant trends were found in each parameter. BOD concentrations ranged widely, and were high in urban regions, due to the presence of a waste water treatment plant. TN and SS showed various trends, but did not vary widely, unlike TP. TP concentrations varied greatly, with high concentrations in cultivated areas, due to fertilizer use. Local water quality management or geology could further explain some of the variations in water quality. Non-point-source pollution exhibited strong positive spatial autocorrelation, indicating that incorporating spatial dimensions into water quality assessment enhances our understanding of spatial patterns of water quality. Data from the Ministry of Land Infrastructure and Transport (MLIT) and Environment Ministry (EM) were used to investigate trends in land management. Stepwise regression analysis was used to test the correlation between specific management practises and substance concentrations in surface water and sediment. MLIT and EM data for 1981-2003 showed an increase in TN, TP, and SS concentrations in surface water. High levels of fertilizer in dormant sprays and domestic water use were associated with high pesticide concentrations in water and sediment. This paper presents a novel method of studying the environmental impact of various agricultural management practises and recommends a management strategy that combines the use of reduced-risk pesticides with irrigation and non-irrigation periods in paddy fields.

Estimating monthly total nitrogen concentration in streams by using artificial neural network.

Artificial Neural Network (ANN) is a flexible and popular tool for predicting the non-linear behavior in the environmental system. Here, the feed-forward ANN model was used to investigate the relationship among the land use, fertilizer, and hydrometerological conditions in 59 river basins over Japan and then applied to estimate the monthly river total nitrogen concentration (TNC). It was shown by the sensitivity analysis, that precipitation, temperature, river discharge, forest area and urban area have high relationships with TNC. The ANN structure having eight inputs and one hidden layer with seven nodes gives the best estimate of TNC. The 1:1 scatter plots of predicted versus measured TNC were closely aligned and provided coefficients of errors of 0.98 and 0.93 for ANNs calibration and validation, respectively. From the results obtained, the ANN model gave satisfactory predictions of stream TNC and appears to be a useful tool for prediction of TNC in Japanese streams. It indicates that the ANN model was able to provide accurate estimates of nitrogen concentration in streams. Its application to such environmental data will encourage further studies on prediction of stream TNC in ungauged rivers and provide a useful tool for water resource and environment managers to obtain a quick preliminary assessment of TNC variations.

Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019.

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.

Modelling sewer sediment deposition, erosion, and transport processes to predict acute influent and reduce combined sewer overflows and CO(2) emissions.

Understanding of solids deposition, erosion, and transport processes in sewer systems has improved considerably in the past decade. This has provided guidance for controlling sewer solids and associated acute pollutants to protect the environment and improve the operation of wastewater systems. Although measures to decrease combined sewer overflow (CSO) events have reduced the amount of discharged pollution, overflows continue to occur during rainy weather in combined sewer systems. The solution lies in the amount of water allotted to various processes in an effluent treatment system, in impact evaluation of water quality and prediction technology, and in stressing the importance of developing a control technology. Extremely contaminated inflow has been a serious research subject, especially in connection with the influence of rainy weather on nitrogen and organic matter removal efficiency in wastewater treatment plants (WWTP). An intensive investigation of an extremely polluted inflow load to WWTP during rainy weather was conducted in the city of Matsuyama, the region used for the present research on total suspended solid (TSS) concentration. Since the inflow during rainy weather can be as much as 400 times that in dry weather, almost all sewers are unsettled and overflowing when a rain event is more than moderate. Another concern is the energy consumed by wastewater treatment; this problem has become important from the viewpoint of reducing CO(2) emissions and overall costs. Therefore, while establishing a prediction technology for the inflow water quality characteristics of a sewage disposal plant is an important priority, the development of a management/control method for an effluent treatment system that minimises energy consumption and CO(2) emissions due to water disposal is also a pressing research topic with regards to the quality of treated water. The procedure to improve water quality must make use of not only water quality and biotic criteria, but also modelling systems to enable the user to link the effect of changes in urban sewage systems with specific quality, energy consumption, CO(2) emission, and ecological improvements of the receiving water.

Modelling the catchment-scale environmental impacts of wastewater treatment in an urban sewage system for CO2 emission assessment.

Water shortages and water pollution are a global problem. Increases in population can have further acute effects on water cycles and on the availability of water resources. Thus, wastewater management plays an important role in mitigating negative impacts on natural ecosystems and human environments and is an important area of research. In this study, we modelled catchment-scale hydrology, including water balances, rainfall, contamination, and urban wastewater treatment. The entire water resource system of a basin, including a forest catchment and an urban city area, was evaluated synthetically from a spatial distribution perspective with respect to water quantity and quality; the Life Cycle Assessment (LCA) technique was applied to optimize wastewater treatment management with the aim of improving water quality and reducing CO2 emissions. A numerical model was developed to predict the water cycle and contamination in the catchment and city; the effect of a wastewater treatment system on the urban region was evaluated; pollution loads were evaluated quantitatively; and the effects of excluding rainwater from the treatment system during flooding and of urban rainwater control on water quality were examined. Analysis indicated that controlling the amount of rainwater inflow to a wastewater treatment plant (WWTP) in an urban area with a combined sewer system has a large impact on reducing CO2 emissions because of the load reduction on the urban sewage system.

Estimation of total nitrogen transport and retention during flow in a catchment using a mass balance model incorporating the effects of land cover distribution and human activity information.

The load of total nitrogen (TN) in stream water was surveyed in the Nagara River Basin (2,000 km(2)), central Japan. Multivariate analysis placed the TN data in an environmental and social context, relating TN to land use conditions such as geologic features, population density, and percentage of the population using the sewer system. Multivariate analysis was used to examine relationships among the land use distribution with and without human activity and the amount of pollution effluent from waste water treatment plants (WWTP). The pollution load in stream water is related to characteristics of the land cover in the river basin, so the influence of land use on the pollutant load was investigated. However, key factors affecting the pollutant load are human activities associated with the land use. In this study, a relationship between pollutant load, land use, and human activity is developed. Land use was estimated from Landsat data using ISODATA clustering. The distribution of the land cover factors was related to human activities, i.e. population density, agricultural production, industrial wastewater discharge, percentage of sewered population, and stock breeding in the catchment. Multivariate analysis related the TN data to land use and human activities. However, the types of land use were found to be insufficient to evaluate the TN, which appeared to be largely governed by other human-related factors such as industrial wastewater discharge, agricultural production, population density, and livestock density. Socioeconomic data, were obtained from government agencies. The results indicate that the TN load outflow characteristics of the study catchment were affected not only by outside human activity, but also largely by the various human activities in the small drainage basin. Industrial waste water contributed as much to the pollution load outflow as did human activity. This is shown quantitatively in that land use information collected at the same time as that collected on human activities provides effective baseline data. The model proposed here is suitable for evaluating best management practices.

Using remotely sensed imagery to estimate potential annual pollutant loads in river basins.

Land cover changes around river basins have caused serious environmental degradation in global surface water areas, in which the direct monitoring and numerical modeling is inherently difficult. Prediction of pollutant loads is therefore crucial to river environmental management under the impact of climate change and intensified human activities. This research analyzed the relationship between land cover types estimated from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery and the potential annual pollutant loads of river basins in Japan. Then an empirical approach, which estimates annual pollutant loads directly from satellite imagery and hydrological data, was investigated. Six water quality indicators were examined, including total nitrogen (TN), total phosphorus (TP), suspended sediment (SS), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Dissolved Oxygen (DO). The pollutant loads of TN, TP, SS, BOD, COD, and DO were then estimated for 30 river basins in Japan. Results show that the proposed simulation technique can be used to predict the pollutant loads of river basins in Japan. These results may be useful in establishing total maximum annual pollutant loads and developing best management strategies for surface water pollution at river basin scale.

Event-to-event intensification of the hydrologic cycle from 1.5 °C to a 2 °C warmer world.

The Paris agreement was adopted to hold the global average temperature increase to well below 2 °C and pursue efforts to limit it to 1.5 °C. Here, we investigate the event-to-event hydroclimatic intensity, where an event is a pair of adjacent wet and dry spells, under future warming scenarios. According to a set of targeted multi-model large ensemble experiments, event-wise intensification will significantly increase globally for an additional 0.5 °C warming beyond 1.5 °C. In high latitudinal regions of the North American continent and Eurasia, this intensification is likely to involve overwhelming increases in wet spell intensity. Western and Eastern North America will likely experience more intense wet spells with negligible changes of dry spells. For the Mediterranean region, enhancement of dry spells seems to be dominating compared to the decrease in wet spell strength, and this will lead to an overall event-wise intensification. Furthermore, the extreme intensification could be 10 times stronger than the mean intensification. The high damage potential of such drastic changes between flood and drought conditions poses a major challenge to adaptation, and the findings suggest that risks could be substantially reduced by achieving a 1.5 °C target.

Towards the incorporation of tipping elements in global climate risk management: probability and potential impacts of passing a threshold.

Evidence suggests that several elements (i.e., subsystems) of the Earth's climate system could tip into a qualitatively different state due to on-going and future anthropogenically induced climate change. Risks associated with tipping could form a component of critical climate risks, and their consideration should be indispensable in decision-making. However, there is lack of scientific knowledge about the risks associated with tipping elements, inhibiting their incorporation into comprehensive risk assessments of climate change (i.e., assessments of impact, adaptation, and mitigation with uncertainty). Using two major tipping elements (Arctic summer sea-ice loss and Greenland ice-sheet melting) as examples, this study attempted to address this lack of knowledge by conducting several calculations under various policy choices based on target temperature, including (i) the probability of passing a threshold temperature in this century and (ii) the potential impact of passing a threshold temperature on a millennial timescale beyond this century. The first theme of this study [Item (i) above] suggested that probability of exceeding the threshold within this century is 24.8% for the Greenland ice sheet and 2.7% for Arctic summer sea ice under a 1.5 °C temperature goal. However, it should be noted that the estimated probabilities of exceeding the threshold are largely dependent on the specification of the probability density function and key assumptions. With regard to the second theme of this study [Item (ii) above], estimation of the potential global coastal exposure using the estimated sea level exhibited a significant gap between scenarios not exceeding the threshold (1.5 °C target) and those exceeding the threshold.

Correction: Evaluation of Risk Perception and Risk-Comparison Information Regarding Dietary Radionuclides after the 2011 Fukushima Nuclear Power Plant Accident.

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