I am a Postdoctoral Fellow at The Center for Sustainability at Saint Louis University and a Ph.D. in Water Resources and Environmental Engineering. My research is focused on understanding the hydrological impact of climate change and anthropogenic disturbances on the terrestrial water cycle. Feel free to contact me to discuss your research and possible opportunities for collaboration. In addition to this website, you can follow me on Twitter @Ehsanima for updates.
A large-scale, high-resolution, fully coupled hydrological/reservoir/hydroelectricity model is us... more A large-scale, high-resolution, fully coupled hydrological/reservoir/hydroelectricity model is used to investigate the impacts of climate change on hydroelectricity generation and hydropower potential of non-powered dams across the Northeast United States megaregion with 11,037 dams and 375 hydroelectric power plants. The model is calibrated and validated using the U.S. Department of Energy records. Annual hydroelectricity generation in the region is 41 Terawatt-hours (Twh). Our estimate of the hydropower potential of non-powered dams adds up to 350 Twh. West Virginia, Virginia, Pennsylvania, and New York have significant potential for generating more hydroelectricity from already existing dams. On the other hand, this potential virtually does not exist for Rhode Island and Delaware and is small for New Jersey and Vermont. Climate change may reduce annual hydropower potential from non-powered dams by up to 13% and reduce current annual hydroelectricity generation by up to 8% annuall...
ABSTRACT http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E As part of the development of the Nort... more ABSTRACT http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E As part of the development of the Northeast Regional Earth System Model (NE-RESM), we are developing an integrated hydrologic modeling framework that incorporates various aspects of the coupled human-hydrologic system, from supply to demand, into a single framework. This framework provides temporally and spatially explicit information on the regional water system under different scenarios of climate, population, and land use/ land cover change. To support these efforts, we have incorporated two new modules into the Water Balance Model (WBM) that simulate reservoir operation and calculate the hydropower production from dams. Dams and reservoirs are key characteristics of the modern hydrologic system, with a particular impact on natural stream flow, sediment flux, thermal characteristics and biogeochemical fluxes of rivers. In the Northeast Region of the United States alone, the National Inventory of Dams (NID) lists over 12000 reservoirs. Depending on site-specific characteristics of the dam, its watershed and its intended purpose, each will have a specific optimum operating rule. Since no comprehensive dataset of these operating rules exists, we have to simulate them. We developed an Artificial Neural Network and used data from 35 dams for calibration. Approximately 60 % of the dataset was dedicated to training process, 20 % to validation and the other 20 % used for testing. If (t) represents current time, we use three sets of inputs; inflow [It , It-1 , It-2], release in previous months [Rt-1 , Rt-2] and month (Mt), to calculate release from reservoir (Rt). We also used a simple continuity equation for reservoirs by considering the maximum (from NID) and minimum reservoir storage to improve the model's performance in extreme wet and dry events. Using results from WBM we show how reservoirs regionally alter discharge in streams of Northeast United States compared to their natural state. We also are assessing the effects of climate change on water availability and how our reservoir module responds to climate change. We have extended our efforts in modeling reservoir behaviors in the context of hydropower production across the region. By using the results from the reservoir operation module in our framework we calculate the hydraulic head of reservoirs, which allows us to calculate individual and collective hydropower production in contemporary and future time frames. We show how climate change could affect electricity production from hydropower in the future and estimate electricity production potential by converting currently non-powered dams to dams with electricity generators. We also analyze the impact of producing more electricity by building new dams. http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E
The Gulf Coast watersheds in the United States contain some of the highest levels of biodiversity... more The Gulf Coast watersheds in the United States contain some of the highest levels of biodiversity of all freshwater systems in North America. Developing environmental management policies to protect and preserve these ecosystems makes the study of the impacts of projected climate change on the future hydrologic cycle crucial. We used the Soil and Water Assessment Tool (SWAT) to estimate the potential hydrologic changes for the mid-21st century (2050s) and the late 21st century (2080s) in the Mobile River, Apalachicola River, and Suwannee River watersheds in the Gulf Coast region of the United States. These estimates are based on downscaled future climate projections from 20 global circulation models (GCMs) under two representative concentration pathways (RCPs 4.5 and 8.5). SWAT models were calibrated and validated using the multi-algorithm, genetically adaptive multi-objective (AMALGAM) technique in a high-performance computing (HPC) cluster. For the Gulf Coast watersheds, the climate is projected to be warmer and wetter. Projected changes in climatic variables are likely to bring large changes in both annual and seasonal hydrologic processes within these watersheds. We found substantial decreases in mean annual streamflow under RCP8.5 during the 2080s, with up to a 13.0% decrease projected for the Suwannee River watershed compared to the present day. Summer streamflow is projected to be substantially lower during the 2080s, with up to a 25.1% decrease projected for the Suwannee River watershed, during a time of high demand of water resources for agricultural, industrial, and ecosystem services. These hydrologic projections are expected to help in making better-informed decisions for future water resources and ecosystem management in the Gulf Coast region.
A large-scale, high-resolution, fully coupled hydrological/reservoir/hydroelectricity model is us... more A large-scale, high-resolution, fully coupled hydrological/reservoir/hydroelectricity model is used to investigate the impacts of climate change on hydroelectricity generation and hydropower potential of non-powered dams across the Northeast United States megaregion with 11,037 dams and 375 hydroelectric power plants. The model is calibrated and validated using the U.S. Department of Energy records. Annual hydroelectricity generation in the region is 41 Terawatt-hours (Twh). Our estimate of the hydropower potential of non-powered dams adds up to 350 Twh. West Virginia, Virginia, Pennsylvania, and New York have significant potential for generating more hydroelectricity from already existing dams. On the other hand, this potential virtually does not exist for Rhode Island and Delaware and is small for New Jersey and Vermont. Climate change may reduce annual hydropower potential from non-powered dams by up to 13% and reduce current annual hydroelectricity generation by up to 8% annually. Increased rainfall in winters and earlier snowmelt in springs result in an increase in regional water availability in December through March. In other months, reduced precipitation and increased potential evapotranspiration rates combined with reduced recharge from the shift in spring snowmelt and smaller snowpack result in a decrease in availability of water and thus hydroelectricity generation. This changes call for the recalibration of dam operations and may raise conflict of interests in multipurpose dams.
Observed changes in precipitation patterns, rising surface temperature, increases in frequency an... more Observed changes in precipitation patterns, rising surface temperature, increases in frequency and intensity of floods and droughts, widespread melting of ice, and reduced snow cover are some of the documented hydro-logic changes associated with global climate change. Climate change is therefore expected to affect the water supply-demand balance in the Northeast United States and challenge existing water management strategies. The hydrological implications of future climate will affect the design capacity and operating characteristics of dams. The vulnerability of water resources systems to floods and droughts will increase, and the trade-offs between reservoir releases to maintain flood control storage, drought resilience, ecological flow, human water demand, and energy production should be reconsidered. We used a Neural Networks based General Reservoir Operation Scheme to estimate the implications of climate change for dams on a regional scale. This dynamic daily reservoir module automatically adapts to changes in climate and readjusts the operation of dams based on water storage level, timing, and magnitude of incoming flows. Our findings suggest that the importance of dams in providing water security in the region will increase. We create an indicator of the Effective Degree of Regulation (EDR) by dams on water resources and show that it is expected to increase, particularly during drier months of year, simply as a consequence of projected climate change. The results also indicate that increasing the size and number of dams, in addition to modifying their operations, may become necessary to offset the vulnerabilities of water resources systems to future climate uncertainties. This is the case even without considering the likely increase in future water demand, especially in the most densely populated regions of the Northeast.
ABSTRACT http://adsabs.harvard.edu/abs/2012AGUFMEP34A..02E Accurate estimates of water supply and... more ABSTRACT http://adsabs.harvard.edu/abs/2012AGUFMEP34A..02E Accurate estimates of water supply and demand are crucial elements in water resources management and modeling. As part of our NSF-funded EaSM effort to build a Northeast Regional Earth System Model (NE-RESM) as a framework to improve our understanding and capacity to forecast the implications of planning decisions on the region's environment, ecosystem services, energy and economic systems through the 21st century, we are producing a high resolution map (3' x 3' lat/long) of estimated water supply and use for the north east region of United States. Focusing on water demand, results from this study enables us to quantify how demand sources affect the hydrology and thermal-chemical water pollution across the region. In an attempt to generate this 3-minute resolution map in which each grid cell has a specific estimated monthly domestic, agriculture, thermoelectric and industrial water use. Estimated Use of Water in the United States in 2005 (Kenny et al., 2009) is being coupled to high resolution land cover and land use, irrigation, power plant and population data sets. In addition to water demands, we tried to improve estimates of water supply from the WBM model by improving the way it controls discharge from reservoirs. Reservoirs are key characteristics of the modern hydrologic system, with a particular impact on altering the natural stream flow, thermal characteristics, and biogeochemical fluxes of rivers. Depending on dam characteristics, watershed characteristics and the purpose of building a dam, each reservoir has a specific optimum operating rule. It means that literally 84,000 dams in the National Inventory of Dams potentially follow 84,000 different sets of rules for storing and releasing water which must somehow be accounted for in our modeling exercise. In reality, there is no comprehensive observational dataset depicting these operating rules. Thus, we will simulate these rules. Our perspective is not to find the optimum operating rule per se but to find composite behaviors that are consistent with the nominal use of each reservoir and their impacts on observed stream gage behaviors. We decided to use Artificial Neural Networks (ANN) in this context. We see as an important advantage of ANN, its ability to detect complex nonlinear relations between input and output data, which makes it a valuable tool for time series prediction and fitness approximation. High quality, measured parameters are available throughout the United States; thus, we are able to use measured data to train and test ANN and operate reservoirs in a way that simulates real world reservoirs' behavior more accurately. By using economics, population, land cover and climate change estimates for 21st century, we are seeking to forecast in a systematic manner all major facets of future water supply and use in north east United States which will enable us to identify locations prone to water stress due to urban and domestic or agriculture and irrigation water demand, quantify change in chemical and thermal pollution of rivers and availability of water for power generation. http://adsabs.harvard.edu/abs/2012AGUFMEP34A..02E
ABSTRACT http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E As part of the development of the Nort... more ABSTRACT http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E As part of the development of the Northeast Regional Earth System Model (NE-RESM), we are developing an integrated hydrologic modeling framework that incorporates various aspects of the coupled human-hydrologic system, from supply to demand, into a single framework. This framework provides temporally and spatially explicit information on the regional water system under different scenarios of climate, population, and land use/ land cover change. To support these efforts, we have incorporated two new modules into the Water Balance Model (WBM) that simulate reservoir operation and calculate the hydropower production from dams. Dams and reservoirs are key characteristics of the modern hydrologic system, with a particular impact on natural stream flow, sediment flux, thermal characteristics and biogeochemical fluxes of rivers. In the Northeast Region of the United States alone, the National Inventory of Dams (NID) lists over 12000 reservoirs. Depending on site-specific characteristics of the dam, its watershed and its intended purpose, each will have a specific optimum operating rule. Since no comprehensive dataset of these operating rules exists, we have to simulate them. We developed an Artificial Neural Network and used data from 35 dams for calibration. Approximately 60 % of the dataset was dedicated to training process, 20 % to validation and the other 20 % used for testing. If (t) represents current time, we use three sets of inputs; inflow [It , It-1 , It-2], release in previous months [Rt-1 , Rt-2] and month (Mt), to calculate release from reservoir (Rt). We also used a simple continuity equation for reservoirs by considering the maximum (from NID) and minimum reservoir storage to improve the model's performance in extreme wet and dry events. Using results from WBM we show how reservoirs regionally alter discharge in streams of Northeast United States compared to their natural state. We also are assessing the effects of climate change on water availability and how our reservoir module responds to climate change. We have extended our efforts in modeling reservoir behaviors in the context of hydropower production across the region. By using the results from the reservoir operation module in our framework we calculate the hydraulic head of reservoirs, which allows us to calculate individual and collective hydropower production in contemporary and future time frames. We show how climate change could affect electricity production from hydropower in the future and estimate electricity production potential by converting currently non-powered dams to dams with electricity generators. We also analyze the impact of producing more electricity by building new dams. http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E
Stochastic Environmental Research and Risk Assessment, Aug 2016
Construction of dams and the resulting water impoundments are one of the most common engineering ... more Construction of dams and the resulting water impoundments are one of the most common engineering procedures implemented on river systems globally; yet simulating reservoir operation at the regional and global scales remains a challenge in human–earth system interactions studies. Developing a general reservoir operating scheme suitable for use in large-scale hydrological models can improve our understanding of the broad impacts of dams operation. Here we present a novel use of artificial neural networks to map the general input/output relationships in actual operating rules of real world dams. We developed a new general reservoir operation scheme (GROS) which may be added to daily hydrologic routing models for simulating the releases from dams, in regional and global-scale studies. We show the advantage of our model in distinguishing between dams with various storage capacities by demonstrating how it modifies the reservoir operation in respond to changes in capacity of dams. Embedding GROS in a water balance model, we analyze the hydrological impact of dam size as well as their distribution pattern within a drainage basin and conclude that for large-scale studies it is generally acceptable to aggregate the capacity of smaller dams and instead model a hypothetical larger dam with the same total storage capacity; however we suggest limiting the aggregation area to HUC 8 sub-basins (approximately equal to the area of a 60 km or a 30 arc minute grid cell) to avoid exaggerated results.
The most complex issues threatening water distribution networks are deliberate injection of chemi... more The most complex issues threatening water distribution networks are deliberate injection of chemical or biological contaminants into distribution water networks. The main problem in using contaminant sensors in drinking water distribution systems is the high purchase, installation and maintenance prices for this kind of sensors. In addition, we need to provide society with maximum health protection; contamination must be detected as fast as possible and all possible threats need to be covered. In this study we took into consideration two main objectives: (1) Maximizing Detection Likelihood and (2) Minimizing Expected Time to Detection. A new formulation that combines these two objectives into a single formulation is introduced and then we use Genetic Algorithm for solving the problem. As second approach, NSGA II is used for solving the sensor problem considering both objectives separately and at the same time. An example network is used to demonstrate the functionality of the proposed methods.
A large-scale, high-resolution, fully coupled hydrological/reservoir/hydroelectricity model is us... more A large-scale, high-resolution, fully coupled hydrological/reservoir/hydroelectricity model is used to investigate the impacts of climate change on hydroelectricity generation and hydropower potential of non-powered dams across the Northeast United States megaregion with 11,037 dams and 375 hydroelectric power plants. The model is calibrated and validated using the U.S. Department of Energy records. Annual hydroelectricity generation in the region is 41 Terawatt-hours (Twh). Our estimate of the hydropower potential of non-powered dams adds up to 350 Twh. West Virginia, Virginia, Pennsylvania, and New York have significant potential for generating more hydroelectricity from already existing dams. On the other hand, this potential virtually does not exist for Rhode Island and Delaware and is small for New Jersey and Vermont. Climate change may reduce annual hydropower potential from non-powered dams by up to 13% and reduce current annual hydroelectricity generation by up to 8% annuall...
ABSTRACT http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E As part of the development of the Nort... more ABSTRACT http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E As part of the development of the Northeast Regional Earth System Model (NE-RESM), we are developing an integrated hydrologic modeling framework that incorporates various aspects of the coupled human-hydrologic system, from supply to demand, into a single framework. This framework provides temporally and spatially explicit information on the regional water system under different scenarios of climate, population, and land use/ land cover change. To support these efforts, we have incorporated two new modules into the Water Balance Model (WBM) that simulate reservoir operation and calculate the hydropower production from dams. Dams and reservoirs are key characteristics of the modern hydrologic system, with a particular impact on natural stream flow, sediment flux, thermal characteristics and biogeochemical fluxes of rivers. In the Northeast Region of the United States alone, the National Inventory of Dams (NID) lists over 12000 reservoirs. Depending on site-specific characteristics of the dam, its watershed and its intended purpose, each will have a specific optimum operating rule. Since no comprehensive dataset of these operating rules exists, we have to simulate them. We developed an Artificial Neural Network and used data from 35 dams for calibration. Approximately 60 % of the dataset was dedicated to training process, 20 % to validation and the other 20 % used for testing. If (t) represents current time, we use three sets of inputs; inflow [It , It-1 , It-2], release in previous months [Rt-1 , Rt-2] and month (Mt), to calculate release from reservoir (Rt). We also used a simple continuity equation for reservoirs by considering the maximum (from NID) and minimum reservoir storage to improve the model's performance in extreme wet and dry events. Using results from WBM we show how reservoirs regionally alter discharge in streams of Northeast United States compared to their natural state. We also are assessing the effects of climate change on water availability and how our reservoir module responds to climate change. We have extended our efforts in modeling reservoir behaviors in the context of hydropower production across the region. By using the results from the reservoir operation module in our framework we calculate the hydraulic head of reservoirs, which allows us to calculate individual and collective hydropower production in contemporary and future time frames. We show how climate change could affect electricity production from hydropower in the future and estimate electricity production potential by converting currently non-powered dams to dams with electricity generators. We also analyze the impact of producing more electricity by building new dams. http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E
The Gulf Coast watersheds in the United States contain some of the highest levels of biodiversity... more The Gulf Coast watersheds in the United States contain some of the highest levels of biodiversity of all freshwater systems in North America. Developing environmental management policies to protect and preserve these ecosystems makes the study of the impacts of projected climate change on the future hydrologic cycle crucial. We used the Soil and Water Assessment Tool (SWAT) to estimate the potential hydrologic changes for the mid-21st century (2050s) and the late 21st century (2080s) in the Mobile River, Apalachicola River, and Suwannee River watersheds in the Gulf Coast region of the United States. These estimates are based on downscaled future climate projections from 20 global circulation models (GCMs) under two representative concentration pathways (RCPs 4.5 and 8.5). SWAT models were calibrated and validated using the multi-algorithm, genetically adaptive multi-objective (AMALGAM) technique in a high-performance computing (HPC) cluster. For the Gulf Coast watersheds, the climate is projected to be warmer and wetter. Projected changes in climatic variables are likely to bring large changes in both annual and seasonal hydrologic processes within these watersheds. We found substantial decreases in mean annual streamflow under RCP8.5 during the 2080s, with up to a 13.0% decrease projected for the Suwannee River watershed compared to the present day. Summer streamflow is projected to be substantially lower during the 2080s, with up to a 25.1% decrease projected for the Suwannee River watershed, during a time of high demand of water resources for agricultural, industrial, and ecosystem services. These hydrologic projections are expected to help in making better-informed decisions for future water resources and ecosystem management in the Gulf Coast region.
A large-scale, high-resolution, fully coupled hydrological/reservoir/hydroelectricity model is us... more A large-scale, high-resolution, fully coupled hydrological/reservoir/hydroelectricity model is used to investigate the impacts of climate change on hydroelectricity generation and hydropower potential of non-powered dams across the Northeast United States megaregion with 11,037 dams and 375 hydroelectric power plants. The model is calibrated and validated using the U.S. Department of Energy records. Annual hydroelectricity generation in the region is 41 Terawatt-hours (Twh). Our estimate of the hydropower potential of non-powered dams adds up to 350 Twh. West Virginia, Virginia, Pennsylvania, and New York have significant potential for generating more hydroelectricity from already existing dams. On the other hand, this potential virtually does not exist for Rhode Island and Delaware and is small for New Jersey and Vermont. Climate change may reduce annual hydropower potential from non-powered dams by up to 13% and reduce current annual hydroelectricity generation by up to 8% annually. Increased rainfall in winters and earlier snowmelt in springs result in an increase in regional water availability in December through March. In other months, reduced precipitation and increased potential evapotranspiration rates combined with reduced recharge from the shift in spring snowmelt and smaller snowpack result in a decrease in availability of water and thus hydroelectricity generation. This changes call for the recalibration of dam operations and may raise conflict of interests in multipurpose dams.
Observed changes in precipitation patterns, rising surface temperature, increases in frequency an... more Observed changes in precipitation patterns, rising surface temperature, increases in frequency and intensity of floods and droughts, widespread melting of ice, and reduced snow cover are some of the documented hydro-logic changes associated with global climate change. Climate change is therefore expected to affect the water supply-demand balance in the Northeast United States and challenge existing water management strategies. The hydrological implications of future climate will affect the design capacity and operating characteristics of dams. The vulnerability of water resources systems to floods and droughts will increase, and the trade-offs between reservoir releases to maintain flood control storage, drought resilience, ecological flow, human water demand, and energy production should be reconsidered. We used a Neural Networks based General Reservoir Operation Scheme to estimate the implications of climate change for dams on a regional scale. This dynamic daily reservoir module automatically adapts to changes in climate and readjusts the operation of dams based on water storage level, timing, and magnitude of incoming flows. Our findings suggest that the importance of dams in providing water security in the region will increase. We create an indicator of the Effective Degree of Regulation (EDR) by dams on water resources and show that it is expected to increase, particularly during drier months of year, simply as a consequence of projected climate change. The results also indicate that increasing the size and number of dams, in addition to modifying their operations, may become necessary to offset the vulnerabilities of water resources systems to future climate uncertainties. This is the case even without considering the likely increase in future water demand, especially in the most densely populated regions of the Northeast.
ABSTRACT http://adsabs.harvard.edu/abs/2012AGUFMEP34A..02E Accurate estimates of water supply and... more ABSTRACT http://adsabs.harvard.edu/abs/2012AGUFMEP34A..02E Accurate estimates of water supply and demand are crucial elements in water resources management and modeling. As part of our NSF-funded EaSM effort to build a Northeast Regional Earth System Model (NE-RESM) as a framework to improve our understanding and capacity to forecast the implications of planning decisions on the region's environment, ecosystem services, energy and economic systems through the 21st century, we are producing a high resolution map (3' x 3' lat/long) of estimated water supply and use for the north east region of United States. Focusing on water demand, results from this study enables us to quantify how demand sources affect the hydrology and thermal-chemical water pollution across the region. In an attempt to generate this 3-minute resolution map in which each grid cell has a specific estimated monthly domestic, agriculture, thermoelectric and industrial water use. Estimated Use of Water in the United States in 2005 (Kenny et al., 2009) is being coupled to high resolution land cover and land use, irrigation, power plant and population data sets. In addition to water demands, we tried to improve estimates of water supply from the WBM model by improving the way it controls discharge from reservoirs. Reservoirs are key characteristics of the modern hydrologic system, with a particular impact on altering the natural stream flow, thermal characteristics, and biogeochemical fluxes of rivers. Depending on dam characteristics, watershed characteristics and the purpose of building a dam, each reservoir has a specific optimum operating rule. It means that literally 84,000 dams in the National Inventory of Dams potentially follow 84,000 different sets of rules for storing and releasing water which must somehow be accounted for in our modeling exercise. In reality, there is no comprehensive observational dataset depicting these operating rules. Thus, we will simulate these rules. Our perspective is not to find the optimum operating rule per se but to find composite behaviors that are consistent with the nominal use of each reservoir and their impacts on observed stream gage behaviors. We decided to use Artificial Neural Networks (ANN) in this context. We see as an important advantage of ANN, its ability to detect complex nonlinear relations between input and output data, which makes it a valuable tool for time series prediction and fitness approximation. High quality, measured parameters are available throughout the United States; thus, we are able to use measured data to train and test ANN and operate reservoirs in a way that simulates real world reservoirs' behavior more accurately. By using economics, population, land cover and climate change estimates for 21st century, we are seeking to forecast in a systematic manner all major facets of future water supply and use in north east United States which will enable us to identify locations prone to water stress due to urban and domestic or agriculture and irrigation water demand, quantify change in chemical and thermal pollution of rivers and availability of water for power generation. http://adsabs.harvard.edu/abs/2012AGUFMEP34A..02E
ABSTRACT http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E As part of the development of the Nort... more ABSTRACT http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E As part of the development of the Northeast Regional Earth System Model (NE-RESM), we are developing an integrated hydrologic modeling framework that incorporates various aspects of the coupled human-hydrologic system, from supply to demand, into a single framework. This framework provides temporally and spatially explicit information on the regional water system under different scenarios of climate, population, and land use/ land cover change. To support these efforts, we have incorporated two new modules into the Water Balance Model (WBM) that simulate reservoir operation and calculate the hydropower production from dams. Dams and reservoirs are key characteristics of the modern hydrologic system, with a particular impact on natural stream flow, sediment flux, thermal characteristics and biogeochemical fluxes of rivers. In the Northeast Region of the United States alone, the National Inventory of Dams (NID) lists over 12000 reservoirs. Depending on site-specific characteristics of the dam, its watershed and its intended purpose, each will have a specific optimum operating rule. Since no comprehensive dataset of these operating rules exists, we have to simulate them. We developed an Artificial Neural Network and used data from 35 dams for calibration. Approximately 60 % of the dataset was dedicated to training process, 20 % to validation and the other 20 % used for testing. If (t) represents current time, we use three sets of inputs; inflow [It , It-1 , It-2], release in previous months [Rt-1 , Rt-2] and month (Mt), to calculate release from reservoir (Rt). We also used a simple continuity equation for reservoirs by considering the maximum (from NID) and minimum reservoir storage to improve the model's performance in extreme wet and dry events. Using results from WBM we show how reservoirs regionally alter discharge in streams of Northeast United States compared to their natural state. We also are assessing the effects of climate change on water availability and how our reservoir module responds to climate change. We have extended our efforts in modeling reservoir behaviors in the context of hydropower production across the region. By using the results from the reservoir operation module in our framework we calculate the hydraulic head of reservoirs, which allows us to calculate individual and collective hydropower production in contemporary and future time frames. We show how climate change could affect electricity production from hydropower in the future and estimate electricity production potential by converting currently non-powered dams to dams with electricity generators. We also analyze the impact of producing more electricity by building new dams. http://adsabs.harvard.edu/abs/2013AGUFMGC23F..04E
Stochastic Environmental Research and Risk Assessment, Aug 2016
Construction of dams and the resulting water impoundments are one of the most common engineering ... more Construction of dams and the resulting water impoundments are one of the most common engineering procedures implemented on river systems globally; yet simulating reservoir operation at the regional and global scales remains a challenge in human–earth system interactions studies. Developing a general reservoir operating scheme suitable for use in large-scale hydrological models can improve our understanding of the broad impacts of dams operation. Here we present a novel use of artificial neural networks to map the general input/output relationships in actual operating rules of real world dams. We developed a new general reservoir operation scheme (GROS) which may be added to daily hydrologic routing models for simulating the releases from dams, in regional and global-scale studies. We show the advantage of our model in distinguishing between dams with various storage capacities by demonstrating how it modifies the reservoir operation in respond to changes in capacity of dams. Embedding GROS in a water balance model, we analyze the hydrological impact of dam size as well as their distribution pattern within a drainage basin and conclude that for large-scale studies it is generally acceptable to aggregate the capacity of smaller dams and instead model a hypothetical larger dam with the same total storage capacity; however we suggest limiting the aggregation area to HUC 8 sub-basins (approximately equal to the area of a 60 km or a 30 arc minute grid cell) to avoid exaggerated results.
The most complex issues threatening water distribution networks are deliberate injection of chemi... more The most complex issues threatening water distribution networks are deliberate injection of chemical or biological contaminants into distribution water networks. The main problem in using contaminant sensors in drinking water distribution systems is the high purchase, installation and maintenance prices for this kind of sensors. In addition, we need to provide society with maximum health protection; contamination must be detected as fast as possible and all possible threats need to be covered. In this study we took into consideration two main objectives: (1) Maximizing Detection Likelihood and (2) Minimizing Expected Time to Detection. A new formulation that combines these two objectives into a single formulation is introduced and then we use Genetic Algorithm for solving the problem. As second approach, NSGA II is used for solving the sensor problem considering both objectives separately and at the same time. An example network is used to demonstrate the functionality of the proposed methods.
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Read More: http://ascelibrary.org/doi/abs/10.1061/41203%28425%2932
Read More: http://ascelibrary.org/doi/abs/10.1061/41203%28425%2932