Effects of heatwave features on machine-learning-based heat-related ambulance calls prediction models in Japan.

Researchers agree that there is substantial evidence of an increasing trend in both the frequency and duration of extreme temperature events. Increasing extreme temperature events will place more pressure on public health and emergency medical resources, and societies will need to find effective and reliable solutions to adapt to hotter summers. This study developed an effective method to predict the number of daily heat-related ambulance calls. Both national- and regional-level models were developed to evaluate the performance of machine-learning-based methods on heat-related ambulance call prediction. The national model showed a high prediction accuracy and can be applied over most regions, while the regional model showed extremely high prediction accuracy in each corresponding region and reliable accuracy in special cases. We found that the introduction of heatwave features, including accumulated heat stress, heat acclimatization, and optimal temperature, significantly improved prediction accuracy. The adjusted coefficient of determination (adjusted R2) of the national model improved from 0.9061 to 0.9659 by including these features, and the adjusted R2 of the regional model also improved from 0.9102 to 0.9860. Furthermore, we used five bias-corrected global climate models (GCMs) to forecast the total number of summer heat-related ambulance calls under three different future climate scenarios nationally and regionally. Our analysis demonstrated that, at the end of the 21st century, the total number of heat-related ambulance calls in Japan will reach approximately 250,000 per year (nearly four times the current amount) under SSP-5.85. Our results suggest that disaster management agencies can use this highly accurate model to forecast potential high emergency medical resource burden caused by extreme heat events, allowing them to raise and improve public awareness and prepare countermeasures in advance. The method proposed in Japan in this paper can be applied to other countries that have relevant data and weather information systems.

Why apple orchards are shifting to the higher altitudes of the Himalayas?

Apple cultivation is one of the most important sources of livelihood in Indian side of the Himalayas. The present study focuses on the apple orchards of Himachal Pradesh, a state within the Himalayan Mountains, a major apple producers of India. In the study, it is found that the optimum apple growing conditions in the region have been consistently shifting and farmers are shifting their orchards to the higher altitudes. For example, orchards have shifted to 1500-2500 meters in the 2000s compared to the cultivated elevation of 1200-1500 meters during 1980s. As of 2014, apples are being cultivated at an elevation of more than 3500 meters, for example, the newly developed orchards of Leo village in upper Kinnaur and Keylong area of Lahul and Spiti districts. Chilling hours for different districts are calculated. The trend of temperature during the growth period, winter session and annual rainfall have been analysed using Mann-Kendall and Sen's slope test. Data catalogued from different time periods indicates that the northward shift (towards higher altitude) is due to changes in chilling hours, total annual rainfall and mean surface temperature during the apple growing season. The mean surface temperature in all the districts has increased by almost 0.5°C during last 2000-2014. These changes are directly related to global warming. While the changing climate is reducing the apple production in low altitudinal regions of the state, it is creating new opportunities for apple cultivation in higher altitudes as conditions are getting more favourable for apple growth in those higher regions. The associated socio-economic changes are posing new societal issues for the local farmers.

Comparison of gridded precipitation datasets for rainfall-runoff and inundation modeling in the Mekong River Basin.

Precipitation, as a primary hydrological variable in the water cycle plays an important role in hydrological modeling. The reliability of hydrological modeling is highly related to the quality of precipitation data. Accurate long-term gauged precipitation in the Mekong River Basin, however, is limited. Therefore, the main objective of this study is to assess the performances of various gridded precipitation datasets in rainfall-runoff and flood-inundation modeling of the whole basin. Firstly, the performance of the Rainfall-Runoff-Inundation (RRI) model in this basin was evaluated using the gauged rainfall. The calibration (2000-2003) and validation (2004-2007) results indicated that the RRI model had acceptable performance in the Mekong River Basin. In addition, five gridded precipitation datasets including APHRODITE, GPCC, PERSIANN-CDR, GSMaP (RNL), and TRMM (3B42V7) from 2000 to 2007 were applied as the input to the calibrated model. The results of the simulated river discharge indicated that TRMM, GPCC, and APHRODITE performed better than other datasets. The statistical index of the annual maximum inundated area indicated similar conclusions. Thus, APHRODITE, TRMM, and GPCC precipitation datasets were considered suitable for rainfall-runoff and flood inundation modeling in the Mekong River Basin. This study provides useful guidance for the application of gridded precipitation in hydrological modeling in the Mekong River basin.

Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions.

Flash floods have long been common in Asian cities, with recent increases in urbanization and extreme rainfall driving increasingly severe and frequent events. Floods in urban areas cause significant damage to infrastructure, communities and the environment. Numerical modelling of flood inundation offers detailed information necessary for managing flood risk in such contexts. This study presents a calibrated flood inundation model using referenced photos, an assessment of the influence of four extreme rainfall events on water depth and inundation area in the Hanoi central area. Four types of historical and extreme rainfall were input into the inundation model. The modeled results for a 2008 flood event with 9 referenced stations resulted in an R2 of 0.6 compared to observations. The water depth at the different locations was simulated under the four extreme rainfall types. The flood inundation under the Probable Maximum Precipitation presents the highest risk in terms of water depth and inundation area. These results provide insights into managing flood risk, designing flood prevention measures, and appropriately locating pump stations.

Impact of forest maintenance on water shortages: Hydrologic modeling and effects of climate change.

The importance of water quantity for domestic and industrial water supply, agriculture, and the economy more broadly has led to the development of many water quantity assessment methods. In this study, surface flow and soil water in the forested upper reaches of the Yoshino River are compared using a distributed hydrological model with Forest Maintenance Module under two scenarios; before and after forest maintenance. We also examine the impact of forest maintenance on these variables during extreme droughts. Results show that surface flow and soil water increased after forest maintenance. In addition, projections of future water resources were estimated using a hydrological model and the output from a 20km mesh Global Climate Model (GCM20). River discharge for the near-future (2015-2039) is similar to that of the present (1979-2003). Estimated river discharge for the future (2075-2099) was found to be substantially more extreme than in the current period, with 12m3/s higher peak discharge in August and 7m3/s lower in July compared to the discharges of the present period. Soil water for the future is estimated to be lower than for the present and near future in May. The methods discussed in this study can be applied in other regions and the results help elucidate the impact of forests and climate change on water resources.

Evaluation of low impact development approach for mitigating flood inundation at a watershed scale in China.

Low impact development (LID) has attracted growing attention as an important approach for urban flood mitigation. Most studies evaluating LID performance for mitigating floods focus on the changes of peak flow and runoff volume. This paper assessed the performance of LID practices for mitigating flood inundation hazards as retrofitting technologies in an urbanized watershed in Nanjing, China. The findings indicate that LID practices are effective for flood inundation mitigation at the watershed scale, and especially for reducing inundated areas with a high flood hazard risk. Various scenarios of LID implementation levels can reduce total inundated areas by 2%-17% and areas with a high flood hazard level by 6%-80%. Permeable pavement shows better performance than rainwater harvesting against mitigating urban waterlogging. The most efficient scenario is combined rainwater harvesting on rooftops with a cistern capacity of 78.5 mm and permeable pavement installed on 75% of non-busy roads and other impervious surfaces. Inundation modeling is an effective approach to obtaining the information necessary to guide decision-making for designing LID practices at watershed scales.

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.

Spatiotemporal evaluation of water quality incidents in Japan between 1996 and 2007.

We present a spatiotemporal evaluation of water quality incidents in Japan considering incident numbers, incident causes, pollutant categories, and pollution effects. Water pollution incidents in first-class river systems almost tripled to about 1487 in the 12 years from 1996 to 2007. In addition, oil makes up the largest proportion of pollutants nationwide (76.61%) and the major source of pollution for each region in Japan. Moreover, every category shows a growth trend, especially since 2005. The main cause of incidents was "Unknown" (43%), followed by "Poor working practice" (24%), and then by "Accident" (10%) and "Other" (10%). In Hokuriku, however, the main cause of incidents was "Poor working practice" (36%), which is greater than "Unknown" (30%). Finally, waterworks (approximately 60%) was the largest of four kinds of water supply infrastructure affected by pollution incidents, followed by simplified waterworks. The population affected by offensive odors and tastes peaked in 1990 and has been decreasing. Overall, the results show the characteristics of incidents from 1996 to 2007, with significant implications for adaptation measures, strategies and policies to reduce water quality incidents.

Statistical analysis and estimation of annual suspended sediments of major rivers in Japan.

We evaluate the spatiotemporal trends of recent suspended sediment conditions in Japanese rivers. Statistical and spatiotemporal trend analysis is conducted on the 92 major rivers in Japan based on water quality monitoring data from 1992 to 2005. The Mann-Kendall non-parametric method was used to investigate the spatial and temporal trends for the suspended sediment indicator. Results show that the mean concentration of suspended sediments in Japanese rivers has generally declined in recent years, although there are still water quality problems at some monitoring sites (Kanto, Chubu, Kinki and Kyushu regions). A positive relationship between observed yearly discharge and suspended sediment load was found. Land use maps with 100 meter spatial resolution were used to apply an empirical model and develop a regression model for estimating annual suspended sediment loads directly from land use and hydrologic data. Rivers were assigned to three groups according to statistical cluster analysis of suspended sediment (SS) concentration. The correlation between the simulation result from the empirical model and the observed data had R(2) values of 0.62 and 0.71 for groups 2 and 3, and the correlation between the simulation result from the regression model and the observed data had R(2) values of 0.48 and 0.34 for groups 2 and 3. Results show that the proposed simulation technique can be used to predict the pollutant loads to river basins in Japan. Results also suggest prioritization methods and strategies that policy-makers can use to address suspended sediment pollution in rivers and water quality management in general.

Spatiotemporal trend analysis of recent river water quality conditions in Japan.

In order to promote pollutant monitoring and preservation of water resources, we evaluate the spatiotemporal trends in recent water quality conditions in Japanese rivers. Trend analysis is conducted on the 92 major rivers in Japan using the available water quality data recorded from 1992 to 2005 and the characteristics of major pollutants in these rivers are analyzed. Spatial and temporal analysis of trends for six water quality indicators is conducted using the Mann Kendall test, a non-parametric statistical method. The indicators analyzed are biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP) and pH. The majority of sampling locations monitoring BOD, COD, TN and TP show trends toward decreasing concentrations over time. Many sampling locations show increasing DO concentrations. Our results show that water quality in Japanese rivers has improved dramatically over the past decade, although there are still problems in some places, most notably in the Hokkaido, Kanto, Kinki and Kyushu regions. The improvements seen in water quality appear to be the result of improved wastewater treatment and other water quality improvement efforts achieved through government initiative.

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.