X. Lin, H. Dong, G. Lin, Y. Li, Q. Yang, Y. Liao, A. Luo, B. Liang, Z. Yang, Y. Hao
{"title":"Spatial-Temporal Distribution of Disability-Adjusted Life-Years of Lung Cancer Attributable to Ambient PM2.5 in Guangzhou, China, 2010 ~ 2013: A Population-Based Study","authors":"X. Lin, H. Dong, G. Lin, Y. Li, Q. Yang, Y. Liao, A. Luo, B. Liang, Z. Yang, Y. Hao","doi":"10.3808/jei.202100452","DOIUrl":"https://doi.org/10.3808/jei.202100452","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75868925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When studying a phenomenon on the earth surface, such as natural disaster, water pollution and land use, the data in some geographic units may be insufficient. Most interpolation models cannot estimate missing data because they rely on continuous assumptions, however most geospatial data is not continuous. In this article, we develop an information diffusion technique, called self-learning discrete regression (SLDR), to infer the missing data of the gap units. To show how to use the suggested model, a virtual case based on flood experience in China is studied, where flood losses of the gap units are inferred with background data: population, per-capita GDP and relative exposure of the unit to flood. To the case, a comparison shows that SLDR is obviously superior to geographically weighted regression (GWR) and the back propagation neural network (BP network), reducing the error about 60% and 33%, respectively. To substantiate the special case arguments, ten simulation experiments are done with pure random seed numbers. The statistical average results show that the validity of GWR for filling gap units is doubtful, and SLDR is more accurate than BP network.
{"title":"Geospatial Information Diffusion Based on Self-Learning Discrete Regression","authors":"C. F. Huang","doi":"10.3808/jei.202000439","DOIUrl":"https://doi.org/10.3808/jei.202000439","url":null,"abstract":"When studying a phenomenon on the earth surface, such as natural disaster, water pollution and land use, the data in some geographic units may be insufficient. Most interpolation models cannot estimate missing data because they rely on continuous assumptions, however most geospatial data is not continuous. In this article, we develop an information diffusion technique, called self-learning discrete regression (SLDR), to infer the missing data of the gap units. To show how to use the suggested model, a virtual case based on flood experience in China is studied, where flood losses of the gap units are inferred with background data: population, per-capita GDP and relative exposure of the unit to flood. To the case, a comparison shows that SLDR is obviously superior to geographically weighted regression (GWR) and the back propagation neural network (BP network), reducing the error about 60% and 33%, respectively. To substantiate the special case arguments, ten simulation experiments are done with pure random seed numbers. The statistical average results show that the validity of GWR for filling gap units is doubtful, and SLDR is more accurate than BP network.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138530664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In South Korea, the endangered Eurasian otter (Lutra lutra) populations have been recovered throughout the country. To examine the status of otter populations, we monitored spraint densities at 250 ~ 355 sites annually from 2014 to 2017 in the Nakdong River basin. The diffusion kernel method was applied to both binary and continuous spraint data. Two geographical popula - tions were identified: northern and southern populations. The northern population continuously increased over a broad area from north to south during the study period. In contrast, the southern population narrowly dispersed, limited by its location in an industrial area. The spatial self-organizing map (Geo-SOM) revealed associations between spraint densities and environmental factors by correlating the geographic locations of the sampling sites. Both populations were negatively affected by anthropogenic factors, including proximi - ty to factories and human population density. However, cumulative association of all environmental factors, including landscape, food sources, and anthropogenic factors, were noted in 2016 after which otter populations fully recovered. Population development stabilized while exhibiting an overall high association with environmental factors. The results of the diffusion kernel method and data variation according to the Geo-SOM consistently presented substantial change in population dispersal (i.e. the merge of two subpopulations, and complete associations between spraint and environmental factors). The combination of the diffusion kernel method and Geo-SOM was effective in portraying temporal changes in population states in association with environmental factors based on spra int data in the last phase of full recovery.
{"title":"Spatial Distribution Patterns of Eurasian Otter (Lutra Lutra) in Association with Environmental Factors Unravelled by Machine Learning and Diffusion Kernel Method","authors":"S. Hong, T. Chon, G. Joo","doi":"10.3808/jei.202000443","DOIUrl":"https://doi.org/10.3808/jei.202000443","url":null,"abstract":"In South Korea, the endangered Eurasian otter (Lutra lutra) populations have been recovered throughout the country. To examine the status of otter populations, we monitored spraint densities at 250 ~ 355 sites annually from 2014 to 2017 in the Nakdong River basin. The diffusion kernel method was applied to both binary and continuous spraint data. Two geographical popula - tions were identified: northern and southern populations. The northern population continuously increased over a broad area from north to south during the study period. In contrast, the southern population narrowly dispersed, limited by its location in an industrial area. The spatial self-organizing map (Geo-SOM) revealed associations between spraint densities and environmental factors by correlating the geographic locations of the sampling sites. Both populations were negatively affected by anthropogenic factors, including proximi - ty to factories and human population density. However, cumulative association of all environmental factors, including landscape, food sources, and anthropogenic factors, were noted in 2016 after which otter populations fully recovered. Population development stabilized while exhibiting an overall high association with environmental factors. The results of the diffusion kernel method and data variation according to the Geo-SOM consistently presented substantial change in population dispersal (i.e. the merge of two subpopulations, and complete associations between spraint and environmental factors). The combination of the diffusion kernel method and Geo-SOM was effective in portraying temporal changes in population states in association with environmental factors based on spra int data in the last phase of full recovery.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80532549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Considering the multiple uncertainties in agricultural water resources management systems, this paper established an agricultural water optimal allocation model under uncertainty for Shijin irrigation district (ID). Uncertainties of four parameters, in- cluding precipitation, available groundwater, purchase prices of crops and crop cultivated area, were fully considered. Agricultural wa- ter allocation schemes were obtained based on the distribution characteristics simulation of the four parameters using Monte Carlo sim- ulation technique. In order to thoroughly analyze the results, the relationship between system benefits and water amounts was shown using 3D diagram. The optimized results show that total water use amount of 2016 ([217.460, 218.017] × 106 m3 for surface water irri- gation and [51.765, 66.266] × 106 m3 for groundwater irrigation) remains fairly static compared with the average level from 2003 to 2013, and irrigation water allocated to winter wheat is considerably larger than that to maize. The significant drop of the purchase price of maize has an apparent effect on water allocation. For winter wheat, surface water allocation of 2016 increases from 129.445 × 106 to 174.905 × 106 m3, and groundwater allocation increases from 24.511×106 m3 to 35.379 × 106 m3. For maize, surface water allocation of 2016 decreases from 88.329 × 106 to 42.846 × 106 m3, and groundwater allocation decreases from 34.733 × 106 to 23.865 × 106 m3. Water allocation amounts for the five subareas of Shijin ID are 54.326 × 106, 31.187 × 106, 51.899 × 106, 39.311 × 106, and 33.779 × 106 m3 respectively during the irrigation period of winter wheat, and are 16.693 × 106, 8.677 × 106, 16.151 × 106, 14.004×106, and 10.752 × 106 m3 during the irrigation period of maize. Moreover, cumulative probability distribution functions of surface water and ground- water allocation amounts for winter wheat and maize were obtained. Further, the linear relations between the difference in purchase price and the difference in water allocation of winter wheat and maize were obtained as well. These results will help decision makers learn detailed water distribution information and thus help make comprehensive irrigation schemes under uncertainty in future.
{"title":"Monte Carlo-Based Agricultural Water Management under Uncertainty: A Case Study of Shijin Irrigation District, China","authors":"G. Yang, M. Li, P. Guo","doi":"10.3808/JEI.202000441","DOIUrl":"https://doi.org/10.3808/JEI.202000441","url":null,"abstract":"Considering the multiple uncertainties in agricultural water resources management systems, this paper established an agricultural water optimal allocation model under uncertainty for Shijin irrigation district (ID). Uncertainties of four parameters, in- cluding precipitation, available groundwater, purchase prices of crops and crop cultivated area, were fully considered. Agricultural wa- ter allocation schemes were obtained based on the distribution characteristics simulation of the four parameters using Monte Carlo sim- ulation technique. In order to thoroughly analyze the results, the relationship between system benefits and water amounts was shown using 3D diagram. The optimized results show that total water use amount of 2016 ([217.460, 218.017] × 106 m3 for surface water irri- gation and [51.765, 66.266] × 106 m3 for groundwater irrigation) remains fairly static compared with the average level from 2003 to 2013, and irrigation water allocated to winter wheat is considerably larger than that to maize. The significant drop of the purchase price of maize has an apparent effect on water allocation. For winter wheat, surface water allocation of 2016 increases from 129.445 × 106 to 174.905 × 106 m3, and groundwater allocation increases from 24.511×106 m3 to 35.379 × 106 m3. For maize, surface water allocation of 2016 decreases from 88.329 × 106 to 42.846 × 106 m3, and groundwater allocation decreases from 34.733 × 106 to 23.865 × 106 m3. Water allocation amounts for the five subareas of Shijin ID are 54.326 × 106, 31.187 × 106, 51.899 × 106, 39.311 × 106, and 33.779 × 106 m3 respectively during the irrigation period of winter wheat, and are 16.693 × 106, 8.677 × 106, 16.151 × 106, 14.004×106, and 10.752 × 106 m3 during the irrigation period of maize. Moreover, cumulative probability distribution functions of surface water and ground- water allocation amounts for winter wheat and maize were obtained. Further, the linear relations between the difference in purchase price and the difference in water allocation of winter wheat and maize were obtained as well. These results will help decision makers learn detailed water distribution information and thus help make comprehensive irrigation schemes under uncertainty in future.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90439715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate streamflow estimation remains a great challenge although diverse modeling techniques have been developed during recent decades. In contrast to the process based models, the empirical data driven methods are easy to operate, require low computing capacity and yield fairly accurate outcomes, among which the state space (STATE) approach takes use of the temporal structures inherent in streamflow series and serves as a feasible solution for streamflow estimation. Yet this method has rarely been applied, neither its comparison with other methods. The objective was to compare the performance of an autoregressive STATE approach to the traditional multiple linear regression and artificial neural network in simulating annual streamflow series of 15 catchments located in the Loess Plateau of China. Annual data of streamflow (Q), precipitation (P) and potential evapotranspiration (PET) during 1961 ~ 2013 were collected. The results show that STATE was generally the most accurate method for Q estimation, explaining almost 90% of the total variance averaged over all the 15 catchments. The estimation of streamflow relied on its own of the previous year for most catchments. Besides, the impacts of P and PET on the temporal distribution of streamflow were almost equal. Missing data were estimated using the STATE method, which allowed inter annual trend analysis of the streamflow. Significant downward trends were manifested at all the 15 catchments during the study period and the corresponding slopes ranged from 0.24 to 1.71 mm/y. These findings hold important implications for hydrological modelling and management in China’s Loess Plateau and other arid and semi-arid regions
{"title":"Comparing the Performance of an Autoregressive State-Space Approach to the Linear Regression and Artificial Neural Network for Streamflow Estimation","authors":"Y. Yang, T. Huang, Y. Shi, O. Wendroth, Bo Liu","doi":"10.3808/jei.202000440","DOIUrl":"https://doi.org/10.3808/jei.202000440","url":null,"abstract":"Accurate streamflow estimation remains a great challenge although diverse modeling techniques have been developed during recent decades. In contrast to the process based models, the empirical data driven methods are easy to operate, require low computing capacity and yield fairly accurate outcomes, among which the state space (STATE) approach takes use of the temporal structures inherent in streamflow series and serves as a feasible solution for streamflow estimation. Yet this method has rarely been applied, neither its comparison with other methods. The objective was to compare the performance of an autoregressive STATE approach to the traditional multiple linear regression and artificial neural network in simulating annual streamflow series of 15 catchments located in the Loess Plateau of China. Annual data of streamflow (Q), precipitation (P) and potential evapotranspiration (PET) during 1961 ~ 2013 were collected. The results show that STATE was generally the most accurate method for Q estimation, explaining almost 90% of the total variance averaged over all the 15 catchments. The estimation of streamflow relied on its own of the previous year for most catchments. Besides, the impacts of P and PET on the temporal distribution of streamflow were almost equal. Missing data were estimated using the STATE method, which allowed inter annual trend analysis of the streamflow. Significant downward trends were manifested at all the 15 catchments during the study period and the corresponding slopes ranged from 0.24 to 1.71 mm/y. These findings hold important implications for hydrological modelling and management in China’s Loess Plateau and other arid and semi-arid regions","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79057152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the largest hydropower project in China, the impact of the Three Gorges Project (TGP) on the ecological environment has aroused public attention and has been studied from various aspects. After the TGP was operated, seasonably adjusted runof f by the Three Gorge Reservoir (TGR) might cause great changes of hydrological regime in the lower reaches of the Yangtze River, especially the estuarine areas where seawater intrusion is strong and salt-water environment is complicated. The salt-water dynamics in this region will be greatly influenced in response to the operation of TGP. In order to study the impact of the project on salt -water dynamics and soil salinity, a field experiment has been carried out in the Yangtze River Estuary (YRE). This paper seeks to investigate the influence of the TGR on salt-water dynamics and soil salinity in YRE through the analysis of the relationship between the impact factors of salt - water dynamics in the estuary area. Results showed that the estuarine runoff was closely related to the impoundment process, and the estuarine runoff was one of the most important factors that influenced salinity dynamics of water and soil in the estuary. Results showed that discharge of the TGR was affected by its water storage speed, and runoff at Datong station in the estuary was significantly correlated with the reservoir discharge. The reservoir discharge affected Datong runoff and the lag time of influence of the TGR on Datong discharge is about 8 to 9 days. Runoff at Datong station influenced the river water level in the estuary, and impact of the river runoff on the river water level was delayed around 13 days. The lag time of the influence of the impoundment process of the TGR on the estuarine water level is about 20 days. The discharge decrease of the reservoir reached 3600 m3/s during the impoundment period in 2011, and it would cause about 3500 m3/s flow decline at Datong station with 8 days lag. It will result in 15.6 cm drop in the water level and 4.26 mS/cm increase in river water electrical conductivity (EC) in the estuary, which indicated the increase risk of seawater intrusion intensity in the estuary. Significantly positive correlation between river water salinity and ground-water salinity was also observed, and influence of the river water salinity on groundwater salinity increased gradually from the river bank to the inland. Significant correlation existed between groundwater salinity and soil salinity in different soil layers, with the closest relation in the deep layers. Monitoring results showed that salinity of river water, groundwater and soil under impoundment process performed higher than that of non-impoundment process, and it could be inferred that the impoundment process has a certain impact on the salt-water dynamics and soil salinity in the estuary. In summary, soil and groundwater salinity were affected by the TGR, and risk of soil salinization increased.
{"title":"Impact Study of Impoundment of the Three Gorges Reservoir on Salt-Water Dynamics and Soil Salinity in the Yangtze River Estuary","authors":"W. Xie, J. S. Yang, R. Yao, X. P. Wang","doi":"10.3808/jei.202000432","DOIUrl":"https://doi.org/10.3808/jei.202000432","url":null,"abstract":"As the largest hydropower project in China, the impact of the Three Gorges Project (TGP) on the ecological environment has aroused public attention and has been studied from various aspects. After the TGP was operated, seasonably adjusted runof f by the Three Gorge Reservoir (TGR) might cause great changes of hydrological regime in the lower reaches of the Yangtze River, especially the estuarine areas where seawater intrusion is strong and salt-water environment is complicated. The salt-water dynamics in this region will be greatly influenced in response to the operation of TGP. In order to study the impact of the project on salt -water dynamics and soil salinity, a field experiment has been carried out in the Yangtze River Estuary (YRE). This paper seeks to investigate the influence of the TGR on salt-water dynamics and soil salinity in YRE through the analysis of the relationship between the impact factors of salt - water dynamics in the estuary area. Results showed that the estuarine runoff was closely related to the impoundment process, and the estuarine runoff was one of the most important factors that influenced salinity dynamics of water and soil in the estuary. Results showed that discharge of the TGR was affected by its water storage speed, and runoff at Datong station in the estuary was significantly correlated with the reservoir discharge. The reservoir discharge affected Datong runoff and the lag time of influence of the TGR on Datong discharge is about 8 to 9 days. Runoff at Datong station influenced the river water level in the estuary, and impact of the river runoff on the river water level was delayed around 13 days. The lag time of the influence of the impoundment process of the TGR on the estuarine water level is about 20 days. The discharge decrease of the reservoir reached 3600 m3/s during the impoundment period in 2011, and it would cause about 3500 m3/s flow decline at Datong station with 8 days lag. It will result in 15.6 cm drop in the water level and 4.26 mS/cm increase in river water electrical conductivity (EC) in the estuary, which indicated the increase risk of seawater intrusion intensity in the estuary. Significantly positive correlation between river water salinity and ground-water salinity was also observed, and influence of the river water salinity on groundwater salinity increased gradually from the river bank to the inland. Significant correlation existed between groundwater salinity and soil salinity in different soil layers, with the closest relation in the deep layers. Monitoring results showed that salinity of river water, groundwater and soil under impoundment process performed higher than that of non-impoundment process, and it could be inferred that the impoundment process has a certain impact on the salt-water dynamics and soil salinity in the estuary. In summary, soil and groundwater salinity were affected by the TGR, and risk of soil salinization increased.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89537998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ambient air PM2.5 is one of the major pollutants linked to respiratory and lung diseases in the Yangtze River Delta (YRD), which is China’s leading economic region and one of the top economic regions worldwide. The main objectives of this work is to compare the accuracy of some widely-used techniques to characterize and predict the space-time distribution of ground-level PM2.5 in the YRD, and to propose a synthesis of techniques that can yield better results than previous techniques. First, a land-use regression (LUR) model is implemented using the relevant data bases (such as air quality, aerosol optical depth, AOD, Modern -Era Retrospective analysis for Research and Applications, MERRA, meteorological monitoring, road networks information, longitude, latitude, elevation and land-use data). Then, the synthesis of the LUR and the Bayesian maximum entropy (BME) techniques is proposed and implemented, for the first time, in the study of PM2.5 concentrations over the YRD region. It was found that the combined (integrated) BME-LUR technique generated PM2.5 concentration estimates showing a 28.34% improvement in accuracy (R2 indicator) compared to the standard LUR technique, and a 12.53% improvement compared to the mainstream geostatistical Kriging technique.
{"title":"Space-Time Ground-Level PM2.5 Distribution at the Yangtze River Delta: A Comparison of Kriging, LUR, and Combined BME-LUR Techniques","authors":"L. Xiao, G. Christakos, J. He, Y. Lang","doi":"10.3808/jei.202000438","DOIUrl":"https://doi.org/10.3808/jei.202000438","url":null,"abstract":"Ambient air PM2.5 is one of the major pollutants linked to respiratory and lung diseases in the Yangtze River Delta (YRD), which is China’s leading economic region and one of the top economic regions worldwide. The main objectives of this work is to compare the accuracy of some widely-used techniques to characterize and predict the space-time distribution of ground-level PM2.5 in the YRD, and to propose a synthesis of techniques that can yield better results than previous techniques. First, a land-use regression (LUR) model is implemented using the relevant data bases (such as air quality, aerosol optical depth, AOD, Modern -Era Retrospective analysis for Research and Applications, MERRA, meteorological monitoring, road networks information, longitude, latitude, elevation and land-use data). Then, the synthesis of the LUR and the Bayesian maximum entropy (BME) techniques is proposed and implemented, for the first time, in the study of PM2.5 concentrations over the YRD region. It was found that the combined (integrated) BME-LUR technique generated PM2.5 concentration estimates showing a 28.34% improvement in accuracy (R2 indicator) compared to the standard LUR technique, and a 12.53% improvement compared to the mainstream geostatistical Kriging technique.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80172356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Best Management Practices (BMPs) are commonly adopted to ameliorate the quality of runoff and reduce the frequency and intensity of flash floods in urban areas. To date, many of the BMP studies are conducted using coarse resolution data. However, the accuracy of such studies may be compromised due to the shortcomings inherent in the input data; as such, the evaluation of the BMP cost-effectiveness may not be accurate. The objective of this paper is to demonstrate the improvements of higher resolution images over coarse resolution data in BMP analyses. An unmanned aerial vehicle (UAV) was used to collect a more detailed and accurate picture of the digital surface model and digital elevation model. Landsat 8 multi-spectral imagery was classified by object-oriented classification to generate a land use/land cover map. The method used in this study provided more detailed and accurate information of the physical conditions of the study area, an improved subwatershed delineation, a more comprehensive list of the suitable locations for BMPs, and a more reliable estimate of the cost-effectiveness of the BMP ensembles than that generated using coarse resolution data. Using the fine resolution data, this study further determined the utility of the selected BMP ensembles under a changed future climate regime and identified the best BMP and BMP ensemble in reducing urban surface runoff. This method can be especially useful in areas without quality topography and land use data.
{"title":"Using High Resolution Images from UAV and Satellite Remote Sensing for Best Management Practice Analyses","authors":"B. Yang, Susanna T. Y. Tong, R. Fan","doi":"10.3808/jei.202000433","DOIUrl":"https://doi.org/10.3808/jei.202000433","url":null,"abstract":"Best Management Practices (BMPs) are commonly adopted to ameliorate the quality of runoff and reduce the frequency and intensity of flash floods in urban areas. To date, many of the BMP studies are conducted using coarse resolution data. However, the accuracy of such studies may be compromised due to the shortcomings inherent in the input data; as such, the evaluation of the BMP cost-effectiveness may not be accurate. The objective of this paper is to demonstrate the improvements of higher resolution images over coarse resolution data in BMP analyses. An unmanned aerial vehicle (UAV) was used to collect a more detailed and accurate picture of the digital surface model and digital elevation model. Landsat 8 multi-spectral imagery was classified by object-oriented classification to generate a land use/land cover map. The method used in this study provided more detailed and accurate information of the physical conditions of the study area, an improved subwatershed delineation, a more comprehensive list of the suitable locations for BMPs, and a more reliable estimate of the cost-effectiveness of the BMP ensembles than that generated using coarse resolution data. Using the fine resolution data, this study further determined the utility of the selected BMP ensembles under a changed future climate regime and identified the best BMP and BMP ensemble in reducing urban surface runoff. This method can be especially useful in areas without quality topography and land use data.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89836162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
X. Wen, X. Lei, X. H. Lei, Q. Tan, G. Fang, X. Wang, C. Wang, Z. H. Liu
Most ecological operation charts of hydropower stations have focused on the average ecological benefits over a long period of time, while the possible ecological damage caused by flood or drought is often overlooked or averaged out. This study proposed a new hydropower-ecological operation chart of cascade hydropower stations, in which limited ecological curves were introduced and optimized to alleviate the negative impacts caused by drought or flood events on fish habitat and to maintain the long-term average habitat quality without reducing the power generation. The optimal ecological discharge range at a given ecological conservation target was determined from the weighted usable area-discharge curve using the physical habitat simulation model, and then the upper and lower limited ecological curves were obtained by reverse calculation, which together with the conventional operation chart (COC) formed the ecological operation chart (EOC). The limited ecological curves were further optimized with the goal of reducing the ecological damage frequency in wet and dry extremes, and then incorporated into COC to form the optimized ecological operation chart (OEOC). A case study was performed with Jasajiang (JS) and Madushan (MDS) cascade reservoirs on the Yuan River in southwestern China. The results show that the EOC that takes into account the ecological benefits can reduce the ecological damage frequency compared to the COC, but potentially at the expense of the overall ecological benefit. However, further optimization of limited ecological curves in OEOC makes it possible to obtain higher short-term ecological benefit and lower ecological damage frequency with the loss of lower overall ecological benefit. Specifically, OEOC is helpful to reduce the ecological damage frequency and improve the power generation and overall ecological benefit at an ecological target of 60 ~ 80%. Notably, at an ecological target of 80%, OEOC results in a 4.1% increase in power generation and a 11.25% decrease in ecological damage frequency for JS-MDS cascade reservoirs compared with that of COC, respectively.
{"title":"Incorporation of Optimal Limited Ecological Curves into the Operation Chart of Cascade Hydropower Systems to Alleviate Ecological Damages in Hydrological Extremes","authors":"X. Wen, X. Lei, X. H. Lei, Q. Tan, G. Fang, X. Wang, C. Wang, Z. H. Liu","doi":"10.3808/jei.202000436","DOIUrl":"https://doi.org/10.3808/jei.202000436","url":null,"abstract":"Most ecological operation charts of hydropower stations have focused on the average ecological benefits over a long period of time, while the possible ecological damage caused by flood or drought is often overlooked or averaged out. This study proposed a new hydropower-ecological operation chart of cascade hydropower stations, in which limited ecological curves were introduced and optimized to alleviate the negative impacts caused by drought or flood events on fish habitat and to maintain the long-term average habitat quality without reducing the power generation. The optimal ecological discharge range at a given ecological conservation target was determined from the weighted usable area-discharge curve using the physical habitat simulation model, and then the upper and lower limited ecological curves were obtained by reverse calculation, which together with the conventional operation chart (COC) formed the ecological operation chart (EOC). The limited ecological curves were further optimized with the goal of reducing the ecological damage frequency in wet and dry extremes, and then incorporated into COC to form the optimized ecological operation chart (OEOC). A case study was performed with Jasajiang (JS) and Madushan (MDS) cascade reservoirs on the Yuan River in southwestern China. The results show that the EOC that takes into account the ecological benefits can reduce the ecological damage frequency compared to the COC, but potentially at the expense of the overall ecological benefit. However, further optimization of limited ecological curves in OEOC makes it possible to obtain higher short-term ecological benefit and lower ecological damage frequency with the loss of lower overall ecological benefit. Specifically, OEOC is helpful to reduce the ecological damage frequency and improve the power generation and overall ecological benefit at an ecological target of 60 ~ 80%. Notably, at an ecological target of 80%, OEOC results in a 4.1% increase in power generation and a 11.25% decrease in ecological damage frequency for JS-MDS cascade reservoirs compared with that of COC, respectively.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90842458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Babamiri, A. Vanaei, X. Guo, P. Wu, Amy Richter, K. T. W. Ng
Understanding the water quality in mountainous rivers is critical for sustainable water resources management. By using the rivers’ self-purification to improve water quality is also the most economical and sustainable way to clean water. In the present study, the QUAL2KW model is applied to investigate the water quality and self -purification capacity in a mountainous river. The Abbasabad River in Iran is used as the study site. The river is divided into two interva ls based on the main purpose of water usage: drinking and agriculture. The model is calibrated and validated using field data from five monitoring stations along the river. Six parame ters, COD, BOD, DO, P-PO4, N-NO3, and N-NH4 are calculated and compared with field data. The Margin of Safety (MOS) is presented and added to the value of each parameter for better water resources protection. The sensitivity analysis is conducted to identify the m ost influential parameters in water quality simulation for mountainous rivers. It is revealed that the parameters of oxidation rate, nitrification rate, and denitrification rate have the maximum influence on water quality simulation for mountainous rivers using QUAL2KW. Additionally, three scenarios are tested for water quality and self-purification. It is found that the river flow rate has a stronger impact for water self - purification in mountainous rivers and the location of point-source pollution has very limited impact.
{"title":"Numerical Simulation of Water Quality and Self-Purification in a Mountainous River Using QUAL2KW","authors":"O. Babamiri, A. Vanaei, X. Guo, P. Wu, Amy Richter, K. T. W. Ng","doi":"10.3808/jei.202000435","DOIUrl":"https://doi.org/10.3808/jei.202000435","url":null,"abstract":"Understanding the water quality in mountainous rivers is critical for sustainable water resources management. By using the rivers’ self-purification to improve water quality is also the most economical and sustainable way to clean water. In the present study, the QUAL2KW model is applied to investigate the water quality and self -purification capacity in a mountainous river. The Abbasabad River in Iran is used as the study site. The river is divided into two interva ls based on the main purpose of water usage: drinking and agriculture. The model is calibrated and validated using field data from five monitoring stations along the river. Six parame ters, COD, BOD, DO, P-PO4, N-NO3, and N-NH4 are calculated and compared with field data. The Margin of Safety (MOS) is presented and added to the value of each parameter for better water resources protection. The sensitivity analysis is conducted to identify the m ost influential parameters in water quality simulation for mountainous rivers. It is revealed that the parameters of oxidation rate, nitrification rate, and denitrification rate have the maximum influence on water quality simulation for mountainous rivers using QUAL2KW. Additionally, three scenarios are tested for water quality and self-purification. It is found that the river flow rate has a stronger impact for water self - purification in mountainous rivers and the location of point-source pollution has very limited impact.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88175615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：