Mohammad Abouali

Residential plumbing is critical for the health and safety of populations worldwide. A case study was con- ducted to understand fixture water use, drinking water quality and their possible link, in a newly plumbed residential green building. Water use and water quality were monitored at four in-building locations from September 2015 through December 2015. Once the home was fully inhabited average water stagnation pe- riods were shortest at the 2nd floor hot fixture (90 percentile of 0.6e1.2… Show more

Residential plumbing is critical for the health and safety of populations worldwide. A case study was con- ducted to understand fixture water use, drinking water quality and their possible link, in a newly plumbed residential green building. Water use and water quality were monitored at four in-building locations from September 2015 through December 2015. Once the home was fully inhabited average water stagnation pe- riods were shortest at the 2nd floor hot fixture (90 percentile of 0.6e1.2 h). The maximum water stagnation time was 72.0 h. Bacteria and organic carbon levels increased inside the plumbing system compared to the municipal tap water entering the building. A greater amount of bacteria was detected in hot water samples (6 e74,002 gene copy number/mL) compared to cold water (2e597 gene copy number/mL). This suggested that hot water plumbing promoted greater microbial growth. The basement fixture brass needle valve may have caused maximum Zn (5.9 mg/L), Fe (4.1 mg/L), and Pb (23 mg/L) levels compared to other fixture water samples (Zn \le 2.1 mg/L, Fe \le 0.5 mg/L and Pb \le 8 mug/L). At the basement fixture, where the least amount of water use events occurred (cold: 60e105, hot: 21e69 event/month) compared to the other fixtures in the building (cold: 145e856, hot: 326e2230 event/month), greater organic carbon, bacteria, and heavy metal levels were detected. Different fixture use patterns resulted in disparate water quality within a single-family home. The greatest drinking water quality changes were detected at the least frequently used fixture. Show less

Excessive nutrient use in agricultural practices is a major cause of water quality degradation around the world, which results in eutrophication of the freshwater systems. Among the nutrients, phosphorus enrichment has recently drawn considerable attention due to major environmental issues such as Lake Erie and Chesapeake Bay eutrophication. One approach for mitigating the impacts of excessive nutrients on water resources is the implementation of wetlands. However, proper site selection for… Show more

Excessive nutrient use in agricultural practices is a major cause of water quality degradation around the world, which results in eutrophication of the freshwater systems. Among the nutrients, phosphorus enrichment has recently drawn considerable attention due to major environmental issues such as Lake Erie and Chesapeake Bay eutrophication. One approach for mitigating the impacts of excessive nutrients on water resources is the implementation of wetlands. However, proper site selection for wetland implementation is the key for effective water quality management at the watershed scale, which is the goal of this study. In this regard, three conventional and two pseudo-random targeting methods were considered. A watershed model called the Soil and Water Assessment Tool (SWAT) was coupled with another model called System for Urban Stormwater Treatment and Analysis IntegratioN (SUSTAIN) to simulate the impacts of wetland implementation scenarios in the Saginaw River watershed, located in Michigan. The inter-group similarities of the targeting strategies were investigated and it was shown that the level of similarity increases as the target area increases (0.54–0.86). In general, the conventional targeting method based on phosphorus load generated per unit area at the subwatershed scale had the highest average reduction among all the scenarios (44.46 t/year). However, when considering the total area of implemented wetlands, the conventional method based on long-term impacts of wetland implementation showed the highest amount of phosphorus reduction (36.44 t/year). Show less

Among livestock systems, grazing is likely to be most impacted by climate change because of its dependency to feed quality and availability. In order to reduce the impact of climate change on grazing livestock systems, adaptation measures should be implemented. The goal of this study is to identify the best pasture composition for a representative grazing dairy farm in Michigan in order to reduce the impacts of climate change on production. In order to achieve the goal of this study, three… Show more

Among livestock systems, grazing is likely to be most impacted by climate change because of its dependency to feed quality and availability. In order to reduce the impact of climate change on grazing livestock systems, adaptation measures should be implemented. The goal of this study is to identify the best pasture composition for a representative grazing dairy farm in Michigan in order to reduce the impacts of climate change on production. In order to achieve the goal of this study, three objectives were sought: (1) identify the best pasture composition, (2) assess economic and resource use impacts of pasture compositions under future climate scenarios, and (3) evaluate the resiliency of pasture compositions. A representative farm was developed based on a livestock practices survey and incorporated into the Integrated Farm System Model (IFSM). For the pasture compositions, four cool-season grass species and two legumes were evaluated under both current and future climate scenarios. The effectiveness of adaptation measures based on economic and resource use criteria was evaluated. Overall, the pasture composition with 50% perennial ryegrass (Lolium multiflorum) and 50% red clover (Trifolium pratense) was identified as the best. In addition, the increase in precipitation and temperature of the most intensive climate scenario could significantly improve farm net return per cow (Bos taurus) and whole farm profit while no significant impact was observed on resource use criteria. Finally, the overall sensitivity assessment showed that the most resilient pasture composition under future climate scenarios was ryegrass with red clover and the least resilient was orchardgrass (Dactylis glomerata) with white clover (Trifolium repens). Show less

Droughts are known as the world's costliest natural disasters impacting a variety of sectors. Despite their wide range of impacts, no universal drought definition has been defined. The goal of this study is to define a universal drought index that considers drought impacts on meteorological, agricultural, hydrological, and stream health categories. Additionally, predictive drought models are developed to capture both categorical (meteorological, hydrological, and agricultural) and overall… Show more

Droughts are known as the world's costliest natural disasters impacting a variety of sectors. Despite their wide range of impacts, no universal drought definition has been defined. The goal of this study is to define a universal drought index that considers drought impacts on meteorological, agricultural, hydrological, and stream health categories. Additionally, predictive drought models are developed to capture both categorical (meteorological, hydrological, and agricultural) and overall impacts of drought. In order to achieve these goals, thirteen commonly used drought indices were aggregated to develop a universal drought index named MASH. The thirteen drought indices consist of four drought indices from each meteorological, hydrological, and agricultural categories, and one from the stream health category. Cluster analysis was performed to find the three closest indices in each category. Then the closest drought indices were averaged in each category to create the categorical drought score. Finally, the categorical drought scores were simply averaged to develop the MASH drought index. In order to develop predictive drought models for each category and MASH, the ReliefF algorithm was used to rank 90 variables and select the best variable set. Using the best variable set, the adaptive neuro-fuzzy inference system (ANFIS) was used to develop drought predictive models and their accuracy was examined using the 10-fold cross validation technique. The models' predictabilities ranged from R2 = 0.75 for MASH to R2 = 0.98 for the hydrological drought model. The results of this study can help managers to better position resources to cope with drought by reducing drought impacts on different sectors. Show less

Aquatic ecosystems are impacted by climate change. However, the level of impacts, especially on macroinvertebrates, are not well known, which is the goal of this study. Here, three biotic indices were used. The Saginaw River Basin, the largest watershed in Michigan was selected for this study. Since no know modeling package exists to evaluate the impacts of climate change on these biotic indices, a coupled modeling approach was proposed. First, the Soil and Water Assessment Tool (SWAT) was… Show more

Aquatic ecosystems are impacted by climate change. However, the level of impacts, especially on macroinvertebrates, are not well known, which is the goal of this study. Here, three biotic indices were used. The Saginaw River Basin, the largest watershed in Michigan was selected for this study. Since no know modeling package exists to evaluate the impacts of climate change on these biotic indices, a coupled modeling approach was proposed. First, the Soil and Water Assessment Tool (SWAT) was setup to estimate streamflow and water quality for all stream segments. Next, the results from the SWAT model in combination with long-term biological monitoring data were used to develop biotic indices predictive models using a two-phase approach. An assemblage of 42 climate scenarios (2040–2060) were obtained from the National Aeronautics and Space Administration, which are biased corrected and statistically downscaled. The dataset provides two extreme Representative Concentration Pathways (RCPs) to account for best (RCP 4.5) and worst (RCP 8.5) case scenarios. The climate data were incorporated in SWAT and biotic indices predictive models to understand stream health conditions in the watershed. Results showed that the majority of streams have a probability of declining in values of biotic indices while streams that currently have excellent conditions are the most vulnerable streams to the climate change. This modeling approach provides a new mean to link more strongly biota indicators with flow regime indicators, which is of prime interest for the implementation of ecohydrology principles. Show less

Highlights
• A new drought index that focus on the impacts to stream health was introduced.
• The ReliefF algorithm was used to select the variables used for drought model.
• The drought model was successfully tested on forty-seven climate scenarios.
• Majority of study area will experience higher drought probability under future climate.

Abstract
Droughts affect many sectors, such as agriculture, economic, social, human health, and ecosystems. Many drought indices have… Show more

Highlights
• A new drought index that focus on the impacts to stream health was introduced.
• The ReliefF algorithm was used to select the variables used for drought model.
• The drought model was successfully tested on forty-seven climate scenarios.
• Majority of study area will experience higher drought probability under future climate.

Abstract
Droughts affect many sectors, such as agriculture, economic, social, human health, and ecosystems. Many drought indices have been developed; yet, none of them quantifies the impacts of drought on stream health. The purpose of this study is to define a new drought index capable of assessing fish vulnerability. To accomplish this, a hydrological model, called the Soil and Water Assessment Tool (SWAT), and the Regional-scale Habitat Suitability model were integrated in order to understand the state of drought within 13,831 stream segments within the Saginaw Bay Watershed. The ReliefF algorithm was used as the variable selection method, and partial least squared regression was used to develop two sets of predictor models capable of determining current and future drought severities. Forty-seven different climate scenarios were used to investigate drought model predictability of future climate scenarios. The results indicated that the best drought model has a high capability for predicting future drought conditions with R2 values ranging from 0.86 to 0.89. In general, the majority of reaches (94%) will experience higher drought probability under future climate scenarios compared to current conditions. The procedure introduced in this study is transferable to other watersheds with regional standards for environmental flow to measure the impacts of drought on stream health.

Keywords
Great Lakes; Stream health; Climate change; Risk; SWAT Show less

The emission of greenhouse gases continues to amplify the impacts of global climate change. This has led to the increased focus on using renewable energy sources, such as biofuels, due to their lower impact on the environment. However, the production of biofuels can still have negative impacts on water resources. This study introduces a new strategy to optimize bioenergy landscapes while improving stream health for the region. To accomplish this, several hydrological models including the Soil… Show more

The emission of greenhouse gases continues to amplify the impacts of global climate change. This has led to the increased focus on using renewable energy sources, such as biofuels, due to their lower impact on the environment. However, the production of biofuels can still have negative impacts on water resources. This study introduces a new strategy to optimize bioenergy landscapes while improving stream health for the region. To accomplish this, several hydrological models including the Soil and Water Assessment Tool, Hydrologic Integrity Tool, and Adaptive Neruro Fuzzy Inference System, were linked to develop stream health predictor models. These models are capable of estimating stream health scores based on the Index of Biological Integrity. The coupling of the aforementioned models was used to guide a genetic algorithm to design watershed-scale bioenergy landscapes. Thirteen bioenergy managements were considered based on the high probability of adaptation by farmers in the study area. Results from two thousand runs identified an optimum bioenergy crops placement that maximized the stream health for the Flint River Watershed in Michigan. The final overall stream health score was 50.93, which was improved from the current stream health score of 48.19. This was shown to be a significant improvement at the 1% significant level. For this final bioenergy landscape the most often used management was miscanthus (27.07%), followed by corn-soybean-rye (19.00%), corn stover-soybean (18.09%), and corn-soybean (16.43%). The technique introduced in this study can be successfully modified for use in different regions and can be used by stakeholders and decision makers to develop bioenergy landscapes that maximize stream health in the area of interest. Show less

This study introduces a new two-phase predictive modelling approach. In the first phase, an initial estimate of the dependent variable along with an estimate of the error associated with those initial predictions is obtained. In the second phase, these initial estimates are combined to develop a new predictive model. Although different modeling methods can be used in each phase, to demonstrate the concept, in this study we tested Partial Least Square Regression (PLSR) and Adaptive Neuro-Fuzzy… Show more

This study introduces a new two-phase predictive modelling approach. In the first phase, an initial estimate of the dependent variable along with an estimate of the error associated with those initial predictions is obtained. In the second phase, these initial estimates are combined to develop a new predictive model. Although different modeling methods can be used in each phase, to demonstrate the concept, in this study we tested Partial Least Square Regression (PLSR) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed approach was evaluated based on monitoring data form the Flint River watershed, located in Michigan, USA to predict four biotic indices based on water quality and quantity indices. The results demonstrate that the two-phase approach that uses PLSR (first phase) and ANFIS (second phase) is superior to common-single-phase approach (R2 for the stream health predictive models increased on average from 0.5 in the first phase to over 0.9 in the second phase). Additionally, the two-phase approach eliminates the need for variable selection, a common pre-processing step, and provides satisfactory results despite the limited number of samples, which makes the approach more reliable, robust, and applicable.

Although in this study the proposed two-phase approach is applied to biotic indices, the process can be extended to other natural and physical systems. Show less

Ecologically relevant hydrologic indices are being used for developing environmental flow baselines. EflowStats is an R-package developed by US Geological Survey that calculates 171 ecologically relevant hydrologic indices. Although, EflowStats provides a set of easy to use functions; once it is applied to a large area with thousands of streams, it requires a long processing time. This particularly limits its application in problems that are dealing with optimizations, requiring evaluation of… Show more

Ecologically relevant hydrologic indices are being used for developing environmental flow baselines. EflowStats is an R-package developed by US Geological Survey that calculates 171 ecologically relevant hydrologic indices. Although, EflowStats provides a set of easy to use functions; once it is applied to a large area with thousands of streams, it requires a long processing time. This particularly limits its application in problems that are dealing with optimizations, requiring evaluation of thousands of scenarios. As a result, a new package called MATLAB Hydrological Index Tools (MHIT) was developed, incorporating different technologies, such as vectorization, memoization, and parallelism to considerably reduce the processing time. Both EflowStats and MHIT were tested against a long-term (20 years) daily streamflow data set from the Saginaw River Watershed in Michigan. In summary, MHIT reduces the processing time by 227 times for the study area. Therefore, MHIT is much more suitable for dealing with Big Data. Show less

Authors: Woznicki, S.A., Nejadhashemi, A. P., Abouali, M., Esfahanian, E., Herman, M.R., Hamaamin, Y.A., Zhang, Z.

This paper introduces a new software package, written in MATLAB®, that generates an extended discrete Laplacian (L=DG=∇⋅∇) based on the Castillo-Grone Mimetic difference operators over a general curvilinear grid. The boundary conditions are included in the extended discrete Laplacian Operator, i.e. Dirichlet, Newmann, as well as Robin boundary conditions. The user only needs to provide a curvilinear grid, a desired operator order, and the degree of the interpolating polynomial. The operator is… Show more

This paper introduces a new software package, written in MATLAB®, that generates an extended discrete Laplacian (L=DG=∇⋅∇) based on the Castillo-Grone Mimetic difference operators over a general curvilinear grid. The boundary conditions are included in the extended discrete Laplacian Operator, i.e. Dirichlet, Newmann, as well as Robin boundary conditions. The user only needs to provide a curvilinear grid, a desired operator order, and the degree of the interpolating polynomial. The operator is tested in different condition and its performance is reported. Finally, based on accuracy of the results, amount of required memory, and the computation that is needed, it is concluded that the 4th order operator is the best one Show less

In this paper, the performance of the second- and fourth-order accurate Castillo-Grone Mimetic (CGM) gradient and divergence operator for solving advection equations, in conjunction with the RK3 time discretization scheme, is investigated. In addition, the effect of different interpolation schemes on the CGM operator's stability is also studied numerically. It has been shown that, in all cases, the CGM operators were stable for Courant numbers higher than 1, and in some cases, up to a Courant… Show more

In this paper, the performance of the second- and fourth-order accurate Castillo-Grone Mimetic (CGM) gradient and divergence operator for solving advection equations, in conjunction with the RK3 time discretization scheme, is investigated. In addition, the effect of different interpolation schemes on the CGM operator's stability is also studied numerically. It has been shown that, in all cases, the CGM operators were stable for Courant numbers higher than 1, and in some cases, up to a Courant number of 1.8. Furthermore, in the case of the fourth-order accurate CGM divergence operator and linear interpolation, the amplification factor is calculated analytically using the Von Neumann stability analysis method, and the accuracy and stability of the combined CGM and RK3 scheme is also discussed. Show less