Investigating the effects of surface water recharge on groundwater quality using hydrochemistry and ANFIS model: A case study Minia Governorate, Egypt.

Monitoring and assessing groundwater quality and quantity lays the basis for sustainable management. Therefore, this research aims to investigate various factors that affect groundwater quality, emphasizing its distance to the primary source of recharge, the Nile River. To this end, two separate study areas have been considered, including the West and West-West of Minia, Egypt, located around 30 and 80 km from the Nile River. The chosen areas rely on the same aquifer as groundwater source (Eocene aquifer). Groundwater quality has been assessed in the two studied regions to investigate the difference in quality parameters due to the river's distance. The power of machine learning to associate different variables and generate beneficial relationships has been utilized to mitigate the cost consumed in chemical analysis and alleviate the calculation complexity. Two adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the water quality index (WQI) and the irrigation water quality index (IWQI) using EC and the distance to the river. The findings of the assessment of groundwater quality revealed that the groundwater in the west of Minia exhibits suitability for agricultural utilization and partially meets the criteria for potable drinking water. Conversely, the findings strongly recommend the implementation of treatment processes for groundwater sourced from the West-West of Minia before its usage for various purposes. These outcomes underscore the significant influence of surface water recharge on the overall quality of groundwater. Also, the results revealed the uncertainty of using sodium adsorption ratio (SAR), Sodium Percentage (Na%), and Permeability Index (PI) techniques in assessing groundwater for irrigation and recommended using IWQI. The developed ANFIS models depicted perfect accuracy during the training and validation stages, reporting a coefficient of correlation (R) equal to 0.97 and 0.99 in the case of WQI and 0.96 and 0.98 in the case of IWQI. The research findings could incentivize decision-makers to monitor, manage, and sustain groundwater.

Preliminary quantitative assessment of the multidimensional impact of the COVID-19 pandemic on Sustainable Development Goals.

The intersectoral impacts of the COVID-19 pandemic on humanity raises concerns about its implications for sustainable development. Here, we examine a global quantitative impact of COVID-19 pandemic on Sustainable Development Goals (SDGs) across all 17 goals using 65 proxy indicators across 72 countries collected from April 2020 to February 2021. Our data-driven analysis indicated that adverse impacts of the pandemic have been particularly concerned on gender equality (Goal 5), affordable and clean energy (Goal 7), decent work and economic growth (Goal 8), sustainable cities and communities (Goal 11), and responsible consumption and production (Goal 12) with global scores estimated to be -0.38, -0.21, -0.28, -0.22 and -0.16, respectively. Country income level was a variable that strongly differentiates the responses to the pandemic (e.g., lower incomes had 14 negative goals compared to 11 and 4 negative goals assigned to middle- and high-income countries, respectively). However, Goals 5 and 8 were highly impacted worldwide regardless of income status. Furthermore, countries that had already higher performance in SDGs were less impacted by the pandemic, highlighting the importance of progress on the SDGs in increasing societal resilience to pandemics. The findings provide insights into the reinforcement of recovery policies (e.g., protecting vulnerable groups and transitioning to a green economy) and a basis for a quantitative discussion on the sectors to be prioritized.

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.

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.

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.

Response of vegetation to submergence along Jingjiang Reach of the Yangtze River.

This work explores the changes in vegetation coverage and submergence time of floodplains along the middle and lower reaches of the Yangtze River (i.e., the Jingjiang River) and the relations between them. As the Three Gorges Dam has been operating for more than 10 years, the original vegetative environment has been greatly altered in this region. The two main aspects of these changes were discovered by analyzing year-end image data from remote sensing satellites using a dimidiate pixel model, based on the normalized difference vegetation index, and by calculating water level and topographic data over a distance of 360 km from 2003-2015. Given that the channels had adjusted laterally, thus exhibiting deeper and broader geometries due to the Three Gorges Dam, 11 floodplains were classified into three groups with distinctive features. The evidence shows that, the floodplains with high elevation have formed steady vegetation areas and could hardly be affected by runoff and usually occupied by humans. The low elevation group has not met the minimal threshold of submerging time for vegetation growth, and no plants were observed so far. Based on the facts summed up from the floodplains with variable elevation, days needed to spot vegetation ranges from 70 to 120 days which happened typically near 2006 and between 2008 and 2010, respectively, and a negative correlation was detected between submergence time and vegetation coverage within a certain range. Thus, floods optimized by the Three Gorges Dam have directly influenced plant growth in the floodplains and may also affect our ability to manage certain types of large floods. Our conclusions may provide a basis for establishing flood criteria to manage the floodplain vegetation and evaluating possible increases in resistance caused by high-flow flooding when these floodplains are submerged.

Snow water scarcity induced by record-breaking warm winter in 2020 in Japan.

This study highlights the severity of the low snow water equivalent (SWE) and remarkably high temperatures in 2020 in Japan, where reductions in SWE have significant impacts on society due to its importance for water resources. A continuous 60-year land surface simulation forced by reanalysis data revealed that the low SWE in many river basins in the southern snowy region of mainland Japan are the most severe on record. The impact of the remarkably high temperatures in 2020 on the low SWE was investigated by considering the relationships among SWE, temperature, and precipitation. The main difference between the 2020 case and prior periods of low SWE is the record-breaking high temperatures. Despite the fact that SWE was the lowest in 2020, precipitation was much higher than that in 2019, which was one of the lowest SWE on record pre-2020. The results indicate the possibility that even more serious low-SWE periods will be caused if lower precipitation and higher temperatures occur simultaneously.

Risk implications of long-term global climate goals: overall conclusions of the ICA-RUS project.

We have assessed the risks associated with setting 1.5, 2.0, or 2.5 °C temperature goals and ways to manage them in a systematic manner and discussed their implications. The results suggest that, given the uncertainties in climate sensitivity, "net zero emissions of anthropogenic greenhouse gases in the second half of this century" is a more actionable goal for society than the 2 or 1.5 °C temperature goals themselves. If the climate sensitivity is proven to be relatively high and the temperature goals are not met even when the net zero emission goal is achieved, the options left are: (A) accepting/adapting to a warmer world, (B) boosting mitigation, and (C) climate geoengineering, or any combination of these. This decision should be made based on a deeper discussion of risks associated with each option. We also suggest the need to consider a wider range of policies: not only climate policies, but also broader "sustainability policies", and to envisage more innovative solutions than what integrated assessment models can currently illustrate. Finally, based on a consideration of social aspects of risk decisions, we recommend the establishment of a panel of "intermediate layer" experts, who support decision-making by citizens as well as social and ethical thinking by policy makers.

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.

Contributions of natural and anthropogenic radiative forcing to mass loss of Northern Hemisphere mountain glaciers and quantifying their uncertainties.

Observational evidence indicates that a number of glaciers have lost mass in the past. Given that glaciers are highly impacted by the surrounding climate, human-influenced global warming may be partly responsible for mass loss. However, previous research studies have been limited to analyzing the past several decades, and it remains unclear whether past glacier mass losses are within the range of natural internal climate variability. Here, we apply an optimal fingerprinting technique to observed and reconstructed mass losses as well as multi-model general circulation model (GCM) simulations of mountain glacier mass to detect and attribute past glacier mass changes. An 8,800-year control simulation of glaciers enabled us to evaluate detectability. The results indicate that human-induced increases in greenhouse gases have contributed to the decreased area-weighted average masses of 85 analyzed glaciers. The effect was larger than the mass increase caused by natural forcing, although the contributions of natural and anthropogenic forcing to decreases in mass varied at the local scale. We also showed that the detection of anthropogenic or natural influences could not be fully attributed when natural internal climate variability was taken into account.

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.

Global hydrological cycles and world water resources.

Water is a naturally circulating resource that is constantly recharged. Therefore, even though the stocks of water in natural and artificial reservoirs are helpful to increase the available water resources for human society, the flow of water should be the main focus in water resources assessments. The climate system puts an upper limit on the circulation rate of available renewable freshwater resources (RFWR). Although current global withdrawals are well below the upper limit, more than two billion people live in highly water-stressed areas because of the uneven distribution of RFWR in time and space. Climate change is expected to accelerate water cycles and thereby increase the available RFWR. This would slow down the increase of people living under water stress; however, changes in seasonal patterns and increasing probability of extreme events may offset this effect. Reducing current vulnerability will be the first step to prepare for such anticipated changes.

Regions of strong coupling between soil moisture and precipitation.

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.