S. Karimi, A. Nazemi, A. Sadraddini, T. Xu, S. Bateni, A. F. Fard
Leaf Area Index (LAI) is an important structural feature of our ecosystem as it affects energy, carbon, and water exchanges between the land surface and overlying atmosphere. Global scale LAI datasets have been obtained by regression, heuristic data driven, and radiative transfer models using remotely sensed land surface reflectance data. However, the estimation of LAI from remotely sensed data is limited only to clear sky conditions. Also, it is problematic to estimate LAI in forests by using conventional remote sensing image analysis of multi-spectral data. Due to the above-mentioned shortcomings of estimating LAI from remotely sensed data, this study obtained LAI from meteorological data using the Gene Expression Programming (GEP) technique. The new approach was tested in different forest sites with broad-leaf and needle-leaf trees in USA. The results showed that the GEP technique can accurately estimate LAI from meteorological data in different forest sites.
叶面积指数(Leaf Area Index, LAI)是影响地表与上覆大气之间能量、碳和水交换的重要结构特征。利用遥感地表反射率数据，通过回归、启发式数据驱动和辐射传输模型获得了全球尺度的LAI数据集。然而，利用遥感数据估算LAI仅限于晴空条件。此外，采用传统的多光谱遥感影像分析方法估算森林LAI也存在问题。针对上述遥感数据估算LAI的不足，本研究采用基因表达编程(Gene Expression Programming, GEP)技术从气象数据中获取LAI。在美国不同的阔叶树和针叶树样地对新方法进行了试验。结果表明，GEP技术能较准确地估算不同立地气象资料的LAI。
{"title":"Estimation of Forest Leaf Area Index Using Meteorological Data: Assessment of Heuristic Models","authors":"S. Karimi, A. Nazemi, A. Sadraddini, T. Xu, S. Bateni, A. F. Fard","doi":"10.3808/jei.202000430","DOIUrl":"https://doi.org/10.3808/jei.202000430","url":null,"abstract":"Leaf Area Index (LAI) is an important structural feature of our ecosystem as it affects energy, carbon, and water exchanges between the land surface and overlying atmosphere. Global scale LAI datasets have been obtained by regression, heuristic data driven, and radiative transfer models using remotely sensed land surface reflectance data. However, the estimation of LAI from remotely sensed data is limited only to clear sky conditions. Also, it is problematic to estimate LAI in forests by using conventional remote sensing image analysis of multi-spectral data. Due to the above-mentioned shortcomings of estimating LAI from remotely sensed data, this study obtained LAI from meteorological data using the Gene Expression Programming (GEP) technique. The new approach was tested in different forest sites with broad-leaf and needle-leaf trees in USA. The results showed that the GEP technique can accurately estimate LAI from meteorological data in different forest sites.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86804896","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 this paper the proposed constrained gravitational search algorithm (CGSA) is extended and used to solve multi-reservoir operation optimization problem. Tow constrained versions of GSA named partially constrained GSA (PCGSA) and fully constrained GSA (FCGSA) are outlined to solve this optimization problem. In the PCGSA, the problem constraints are partially satisfied, however, in the FCGSA, all the problem constraints are implicitly satisfied by providing the search space for each agent which contains only feasible solution and hence leading to smaller search space for each agent. These proposed constrained versions of GSA are very useful when they are applied to solve large scale multi-reservoir operation optimization problem. The constrained versions of GSA are formulated here for both possible variables of the problem means considering water release or storage volumes as the decision variables of the problem and therefore first and second formulations of these algorithms are proposed. The proposed algorithms are used to solve the well-known four and ten reservoir operation optimization problems and the results are presented and compared with those of original form of the GSA and any available results in the literature. The results indicate the superiority of the proposed algorithms and especially FCGSA over existing methods to optimally solve large scale multi-reservoir operation optimization problem.
{"title":"Extension of the Constrained Gravitational Search Algorithm for Solving Multi-Reservoir Operation Optimization Problem","authors":"R. Moeini, M. Soltani-nezhad","doi":"10.3808/jei.202000434","DOIUrl":"https://doi.org/10.3808/jei.202000434","url":null,"abstract":"In this paper the proposed constrained gravitational search algorithm (CGSA) is extended and used to solve multi-reservoir operation optimization problem. Tow constrained versions of GSA named partially constrained GSA (PCGSA) and fully constrained GSA (FCGSA) are outlined to solve this optimization problem. In the PCGSA, the problem constraints are partially satisfied, however, in the FCGSA, all the problem constraints are implicitly satisfied by providing the search space for each agent which contains only feasible solution and hence leading to smaller search space for each agent. These proposed constrained versions of GSA are very useful when they are applied to solve large scale multi-reservoir operation optimization problem. The constrained versions of GSA are formulated here for both possible variables of the problem means considering water release or storage volumes as the decision variables of the problem and therefore first and second formulations of these algorithms are proposed. The proposed algorithms are used to solve the well-known four and ten reservoir operation optimization problems and the results are presented and compared with those of original form of the GSA and any available results in the literature. The results indicate the superiority of the proposed algorithms and especially FCGSA over existing methods to optimally solve large scale multi-reservoir operation optimization problem.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86041466","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}
Changes in runoff and sediment transport in the Middle Reach of the Huai River have been studied by using 58 years of field data. The runoff yield from the Huai River watershed mainly occurs in the sub-watershed of the river. At the downstream Wujiadu station, the difference in total drainage area between the south and the north branches of the river is 43% while the difference in runoff yield is only 9%. Sediment yield mainly comes from the headwaters in the northern region with the upstream of the Huai River playing a secondary role. The data demonstrate that there has been little change in long-term average annual runoff in the Middle Reach of the Huai River (MRHR) but there has been a dramatic decrease in average annual sediment transport. This decrease in sediment transport in the Huai River has resulted in changes in the geomorphology of the Middle Reach of the Huai River with time. Further analysis indicates that both the main channel and the floodplain of the estuary of Hongzehu Lake have a tendency towards the deposition of sediment. A trend and regression analysis is used in the compilation of field data, calculations, and analysis.
{"title":"Variation of Runoff and Sediment Transport in the Huai River – A Case Study","authors":"B. Yu, Anhui, P. Wu, J. Sui, J. Ni, T. Whitcombe","doi":"10.3808/jei.202000429","DOIUrl":"https://doi.org/10.3808/jei.202000429","url":null,"abstract":"Changes in runoff and sediment transport in the Middle Reach of the Huai River have been studied by using 58 years of field data. The runoff yield from the Huai River watershed mainly occurs in the sub-watershed of the river. At the downstream Wujiadu station, the difference in total drainage area between the south and the north branches of the river is 43% while the difference in runoff yield is only 9%. Sediment yield mainly comes from the headwaters in the northern region with the upstream of the Huai River playing a secondary role. The data demonstrate that there has been little change in long-term average annual runoff in the Middle Reach of the Huai River (MRHR) but there has been a dramatic decrease in average annual sediment transport. This decrease in sediment transport in the Huai River has resulted in changes in the geomorphology of the Middle Reach of the Huai River with time. Further analysis indicates that both the main channel and the floodplain of the estuary of Hongzehu Lake have a tendency towards the deposition of sediment. A trend and regression analysis is used in the compilation of field data, calculations, and analysis.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82897574","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}
Restricted by conventional energy resources and environmental space, the sustainable development of urban power sector faces enormous challenges. Renewable energy generation and carbon capture and storage (CCS) are attractive technologies for reducing conventional energy resource consumption and improving CO2 emission mitigation. Considering the limitation of expensive investment cost on their wide application, a stochastic optimization model for the optimal design and operation strategy of regional electric power system is proposed to achieve conventional resource-consumption reduction and CO2 emission mitigation under cost-risk control. The hybrid method integrates interval two-stage stochastic programming with downside risk theory. It can not only effectively deal with the complex uncertainties expressed as discrete intervals and probability distribution, but also help decision-makers make cost-risk tradeoff under predetermined budget. The proposed model is applied in the electric power system planning of Zhejiang Province, an economically developed area with limited fossil energy resources. The influences of different resource and environmental policies on the investment portfolio and power system operation are analyzed and discussed under various scenarios. The results indicated that different policies would lead to different generation technology portfolios. The aggressive CO2 emission reduction policy could stimulate the development of CCS technology, and the electric power system would still heavily rely on coal resource, while the tough coal-consumption control policy could directly promote regional renewable energy development and electric power structure adjustment.
{"title":"A Stochastic Optimization Model for Carbon-Emission Reduction Investment and Sustainable Energy Planning under Cost-Risk Control","authors":"L. Ji, G. Huang, D. Niu, Y. Cai, J. Yin","doi":"10.3808/jei.202000428","DOIUrl":"https://doi.org/10.3808/jei.202000428","url":null,"abstract":"Restricted by conventional energy resources and environmental space, the sustainable development of urban power sector faces enormous challenges. Renewable energy generation and carbon capture and storage (CCS) are attractive technologies for reducing conventional energy resource consumption and improving CO2 emission mitigation. Considering the limitation of expensive investment cost on their wide application, a stochastic optimization model for the optimal design and operation strategy of regional electric power system is proposed to achieve conventional resource-consumption reduction and CO2 emission mitigation under cost-risk control. The hybrid method integrates interval two-stage stochastic programming with downside risk theory. It can not only effectively deal with the complex uncertainties expressed as discrete intervals and probability distribution, but also help decision-makers make cost-risk tradeoff under predetermined budget. The proposed model is applied in the electric power system planning of Zhejiang Province, an economically developed area with limited fossil energy resources. The influences of different resource and environmental policies on the investment portfolio and power system operation are analyzed and discussed under various scenarios. The results indicated that different policies would lead to different generation technology portfolios. The aggressive CO2 emission reduction policy could stimulate the development of CCS technology, and the electric power system would still heavily rely on coal resource, while the tough coal-consumption control policy could directly promote regional renewable energy development and electric power structure adjustment.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73115189","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}
D. Stampoulis, H. G. Damavandi, D. Boscovic, J. Sabo
The spatial distribution, magnitude and timing of precipitation events are being altered globally, often leading to extreme hydrologic conditions with serious implications to ecosystem services, water, food and energy security, as well as the welfare of billions of people. Motivated by the pressing need to understand, from a hydro-ecological perspective, how the dynamic nature of the hydrologic cycle will impact the environment in water-stressed regions, we implemented a novel approach that predicts precipitation spatio-temporal trends over the drought-burdened region of East Africa, based on other major hydrological components, such as vegetation water content (VWC), soil moisture (SM) and surface temperature (ST). The spatial patterns and characteristics of the inter-relations among the four aforementioned hydrologic variables were investigated over regions of East Africa characterized by different vegetation types and for various precipitation intensity rates during 2003-2011. To this end, we analyzed multi-year satellite microwave remote sensing observations of SM, ST, and VWC (derived from Naval Research Laboratory's WindSat radiometer) as well as their response to precipitation patterns (derived from NASA's TRMM 3B42 V7). We categorized precipitation into four bins (ranges) of intensity and trained five different state-of-the-art machine learning models for each of these categories. The models were then applied to predict the spatiotemporal precipitation dynamics over this complex region. Specifically, the Random Forest and Linear Regression models outperformed the others with the normalized mean absolute error being less than 27% for all of the categories. The characteristics of the predicted precipitation were in turn used to classify vegetation regimes in East Africa. Our results indicate significant discrepancies in the performance of the models with varying values in the predicting skill as well as their ability to accurately classify vegetation into different types. Our predictive models were able to forecast the three vegetation regimes, i.e., Forest/Woody Savanna, Savanna/Grasslands and Shrubland, with precision rate of at least 81% for all of the aforementioned precipitation bins.
{"title":"Using Satellite Remote Sensing and Machine Learning Techniques Towards Precipitation Prediction and Vegetation Classification","authors":"D. Stampoulis, H. G. Damavandi, D. Boscovic, J. Sabo","doi":"10.3808/jei.202000427","DOIUrl":"https://doi.org/10.3808/jei.202000427","url":null,"abstract":"The spatial distribution, magnitude and timing of precipitation events are being altered globally, often leading to extreme hydrologic conditions with serious implications to ecosystem services, water, food and energy security, as well as the welfare of billions of people. Motivated by the pressing need to understand, from a hydro-ecological perspective, how the dynamic nature of the hydrologic cycle will impact the environment in water-stressed regions, we implemented a novel approach that predicts precipitation spatio-temporal trends over the drought-burdened region of East Africa, based on other major hydrological components, such as vegetation water content (VWC), soil moisture (SM) and surface temperature (ST). The spatial patterns and characteristics of the inter-relations among the four aforementioned hydrologic variables were investigated over regions of East Africa characterized by different vegetation types and for various precipitation intensity rates during 2003-2011. To this end, we analyzed multi-year satellite microwave remote sensing observations of SM, ST, and VWC (derived from Naval Research Laboratory's WindSat radiometer) as well as their response to precipitation patterns (derived from NASA's TRMM 3B42 V7). We categorized precipitation into four bins (ranges) of intensity and trained five different state-of-the-art machine learning models for each of these categories. The models were then applied to predict the spatiotemporal precipitation dynamics over this complex region. Specifically, the Random Forest and Linear Regression models outperformed the others with the normalized mean absolute error being less than 27% for all of the categories. The characteristics of the predicted precipitation were in turn used to classify vegetation regimes in East Africa. Our results indicate significant discrepancies in the performance of the models with varying values in the predicting skill as well as their ability to accurately classify vegetation into different types. Our predictive models were able to forecast the three vegetation regimes, i.e., Forest/Woody Savanna, Savanna/Grasslands and Shrubland, with precision rate of at least 81% for all of the aforementioned precipitation bins.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75181551","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}
{"title":"Detecting Spatiotemporal Features of Phosphorus Concentrations Using MODIS Images: A Case Study of Hongze Lake, China","authors":"C. Lin, J. Xiong, K. Xue, R. Ma, Z. Cao","doi":"10.3808/jei.202000445","DOIUrl":"https://doi.org/10.3808/jei.202000445","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90843462","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}
Simulation methods have become an important tool to reveal radionuclide migration during accidental radionuclide releases and predict influences of accidents on the marine environment. The instantaneous point source model is a useful method to simulate the large-scale radionuclide diffusion in marine areas. However, the simulation accuracy of this method requires improvement as it didn’t take radionuclide decay into account. In this study, an improved instantaneous point source model considering radionuclide decay was proposed on the basis of the original model. Furthermore, the instantaneous point source model and the improved version were used to simulate the concentrations of 131I and 137Cs following the Fukushima Dai-ichi nuclear power plant accident. The results showed that the relative error of 131I concentrations decreased from 136.03% to 37.59% when using the improved model; and improvements in relative errors for 137Cs concentrations were not apparent as the simualtion period was much shorter than its half-life period. Therefore, the improved model can accurately simulate the diffusion process for radionuclides following an accident and provides an efficient decision support tool for risk assessment managers and for use in safety guarantees of nuclear power plants during siting and operational phases.
{"title":"Improvement of Instantaneous Point Source Model for Simulating Radionuclide Diffusion in Oceans under Nuclear Power Plant Accidents","authors":"A. Zhai, X. Ding, Y. Zhao, W. Xiao, B. Lu","doi":"10.3808/JEI.201700380","DOIUrl":"https://doi.org/10.3808/JEI.201700380","url":null,"abstract":"Simulation methods have become an important tool to reveal radionuclide migration during accidental radionuclide releases and predict influences of accidents on the marine environment. The instantaneous point source model is a useful method to simulate the large-scale radionuclide diffusion in marine areas. However, the simulation accuracy of this method requires improvement as it didn’t take radionuclide decay into account. In this study, an improved instantaneous point source model considering radionuclide decay was proposed on the basis of the original model. Furthermore, the instantaneous point source model and the improved version were used to simulate the concentrations of 131I and 137Cs following the Fukushima Dai-ichi nuclear power plant accident. The results showed that the relative error of 131I concentrations decreased from 136.03% to 37.59% when using the improved model; and improvements in relative errors for 137Cs concentrations were not apparent as the simualtion period was much shorter than its half-life period. Therefore, the improved model can accurately simulate the diffusion process for radionuclides following an accident and provides an efficient decision support tool for risk assessment managers and for use in safety guarantees of nuclear power plants during siting and operational phases.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76104066","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}
D. Zhang, Q. Lin, H. Yao, Y. R. He, J. Deng, X. X. Zhang, Bellwood Road Dorset P A E Ontario Canada Parks
Due to its versatility, the Soil and Water Assessment Tool (SWAT) has been widely applied to investigate the effects of management activities and climate change on water availability and quality. However, the use of high spatial resolution data and the advantages of SWAT itself have significantly increased the input/output (I/O) demand and thus the runtime of modeling routines that require a large number of iterative simulations. In this study, we proposed a generic scheme to reduce the SWAT runtime by caching the model inputs using the in-memory NoSQL database Redis. Then the SWAT source codes (rev 488) was modified according to this proposed scheme to develop the MA-SWAT (memory accelerated SWAT) model by incorporating a new subroutine known as Fortran_calls_c to retrieve the cached inputs. We then evaluated MA-SWAT with four synthetic hydrological models and five different parallel schemes in a quad-core commodity laptop. The test results showed that when applied with a parallel simulation program, MA-SWAT could achieve a speedup by a factor of 8.4 ~ 10.9 depending on model complexity. Compared with the original SWAT, MA-SWAT significantly improved the computation speed, indicating that the proposed scheme is a desirable method for solving high computational demand problems such as calibration, sensitivity and uncertainty analysis. Moreover, the proposed concept of linking the SWAT model with Redis via the minimalistic C client driver of Redis is a generic method, and it is possible to apply this method to other Fortran-implemented environmental model to alleviate I/O demands.
{"title":"Accelerating SWAT Simulations Using An In-Memory NoSQL Database","authors":"D. Zhang, Q. Lin, H. Yao, Y. R. He, J. Deng, X. X. Zhang, Bellwood Road Dorset P A E Ontario Canada Parks","doi":"10.3808/jei.201900425","DOIUrl":"https://doi.org/10.3808/jei.201900425","url":null,"abstract":"Due to its versatility, the Soil and Water Assessment Tool (SWAT) has been widely applied to investigate the effects of management activities and climate change on water availability and quality. However, the use of high spatial resolution data and the advantages of SWAT itself have significantly increased the input/output (I/O) demand and thus the runtime of modeling routines that require a large number of iterative simulations. In this study, we proposed a generic scheme to reduce the SWAT runtime by caching the model inputs using the in-memory NoSQL database Redis. Then the SWAT source codes (rev 488) was modified according to this proposed scheme to develop the MA-SWAT (memory accelerated SWAT) model by incorporating a new subroutine known as Fortran_calls_c to retrieve the cached inputs. We then evaluated MA-SWAT with four synthetic hydrological models and five different parallel schemes in a quad-core commodity laptop. The test results showed that when applied with a parallel simulation program, MA-SWAT could achieve a speedup by a factor of 8.4 ~ 10.9 depending on model complexity. Compared with the original SWAT, MA-SWAT significantly improved the computation speed, indicating that the proposed scheme is a desirable method for solving high computational demand problems such as calibration, sensitivity and uncertainty analysis. Moreover, the proposed concept of linking the SWAT model with Redis via the minimalistic C client driver of Redis is a generic method, and it is possible to apply this method to other Fortran-implemented environmental model to alleviate I/O demands.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2019-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75893944","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}
Two basin-wide hydrologic-economic optimization models are presented to estimate how much water can be conserved while maintaining at least the same level of economic output. Water consumption is interpreted as either water diverted to consumptive users or water consumed by all users. Two different formulations for representing the two interpretations of water consumption are examined. The characteristics of different users, such as the consumption ratio and productivity, are considered. The models are applied to the South Saskatchewan River Basin (SSRB) in southern Alberta, Canada, where water scarcity is a severe issue. It is found that: a substantial amount of water can be conserved without sacrificing economic output; irrigation is the largest contributor while municipal and industrial (MI) users make a small difference in terms of water conservation; MI users make major economic contribution in order to retain the same level of system-wide aggregated benefits, and thereby overall water productivity can be considerably improved; MI users’ reactions are diversified depending on the specified conservation targets; and overall water conservation may be limited if MI users act independently. The implications of the results can be used to facilitate a better understanding of present water usage and guide policy makers into making informed decision for water demand management.
{"title":"Conservation-Targeted Hydrologic-Economic Models for Water Demand Management","authors":"Y. Xiao, L. Fang, K. Hipel","doi":"10.3808/jei.201900418","DOIUrl":"https://doi.org/10.3808/jei.201900418","url":null,"abstract":"Two basin-wide hydrologic-economic optimization models are presented to estimate how much water can be conserved while maintaining at least the same level of economic output. Water consumption is interpreted as either water diverted to consumptive users or water consumed by all users. Two different formulations for representing the two interpretations of water consumption are examined. The characteristics of different users, such as the consumption ratio and productivity, are considered. The models are applied to the South Saskatchewan River Basin (SSRB) in southern Alberta, Canada, where water scarcity is a severe issue. It is found that: a substantial amount of water can be conserved without sacrificing economic output; irrigation is the largest contributor while municipal and industrial (MI) users make a small difference in terms of water conservation; MI users make major economic contribution in order to retain the same level of system-wide aggregated benefits, and thereby overall water productivity can be considerably improved; MI users’ reactions are diversified depending on the specified conservation targets; and overall water conservation may be limited if MI users act independently. The implications of the results can be used to facilitate a better understanding of present water usage and guide policy makers into making informed decision for water demand management.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2019-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76166815","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}
The spatial structure of climatic variables synthesized by a weather generator has considerable impact on the modeling of hydrological variability; however, in most cases, it needs computationally intensive work to reproduce multisite and/or multivariate correlations. This work proposed a two-stage weather generator (TSWG) to preserve intersite and intervariable correlations of daily precipitation, maximum and minimum temperatures. The first stage generates daily precipitation and temperature for each site and for each variable with, but not limited to, the Richardson-type approach. The second stage rebuilds the multisite multivariate correlation using a distribution-free shuffle procedure. The TSWG was applied to a network of 15 stations in the Jing River catchment (Northwest China). It reproduced the statistical parameters and multisite and multivariate correlations well. Furthermore, indirect validation by hydrological modeling showed TSWG outputs could be used satisfactorily for simulating streamflow variability. As a distribution-free method, the correlation reconstruction method can be applied to variables with different probability distributions. The TSWG can efficiently reconstruct the correlation with one optimization for all stations and all variables, which is superior to most current methods operated once for one station pair and one variable. The TSWG provides an option for improved multisite and multivariate weather generation.
{"title":"A Two-Stage Multisite and Multivariate Weather Generator","authors":"Z. Li, J. Li, X. Shi","doi":"10.3808/jei.201900424","DOIUrl":"https://doi.org/10.3808/jei.201900424","url":null,"abstract":"The spatial structure of climatic variables synthesized by a weather generator has considerable impact on the modeling of hydrological variability; however, in most cases, it needs computationally intensive work to reproduce multisite and/or multivariate correlations. This work proposed a two-stage weather generator (TSWG) to preserve intersite and intervariable correlations of daily precipitation, maximum and minimum temperatures. The first stage generates daily precipitation and temperature for each site and for each variable with, but not limited to, the Richardson-type approach. The second stage rebuilds the multisite multivariate correlation using a distribution-free shuffle procedure. The TSWG was applied to a network of 15 stations in the Jing River catchment (Northwest China). It reproduced the statistical parameters and multisite and multivariate correlations well. Furthermore, indirect validation by hydrological modeling showed TSWG outputs could be used satisfactorily for simulating streamflow variability. As a distribution-free method, the correlation reconstruction method can be applied to variables with different probability distributions. The TSWG can efficiently reconstruct the correlation with one optimization for all stations and all variables, which is superior to most current methods operated once for one station pair and one variable. The TSWG provides an option for improved multisite and multivariate weather generation.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2019-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77982996","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}
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