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The influence of moisture content on substrate thermal conductivity at different temperatures was investigated for four different commercially available substrates for green roofs. In the unfrozen state, as moisture content increased, thermal conductivity increased linearly. In the phase transition zone between +5 and −10 °C, as temperature decreased, thermal conductivity increased sharply during the transition from water to ice. When the substrate was frozen, thermal conductivity varied exponentially with substrate moisture content prior to freezing. Power functions were found between thermal conductivity and temperature. Two equally sized, green roof test cells were constructed and tested to compare various roof configurations including a bare roof, varying media thickness for a green roof, and vegetation. The results show that compared with the bare roof, there is a 75% reduction in the interior temperature’s amplitude for the green roof with 150 mm thick substrate. When a sedum mat was added, there was a 20% reduction in the amplitude of the inner temperature as compared with the cell without a sedum mat.
Bohan Shao; Caterina Valeo; Phalguni Mukhopadhyaya; Jianxun He. Influence of Temperature and Moisture Content on Thermal Performance of Green Roof Media. Energies 2021, 14, 2421 .
AMA StyleBohan Shao, Caterina Valeo, Phalguni Mukhopadhyaya, Jianxun He. Influence of Temperature and Moisture Content on Thermal Performance of Green Roof Media. Energies. 2021; 14 (9):2421.
Chicago/Turabian StyleBohan Shao; Caterina Valeo; Phalguni Mukhopadhyaya; Jianxun He. 2021. "Influence of Temperature and Moisture Content on Thermal Performance of Green Roof Media." Energies 14, no. 9: 2421.
On a global scale, urbanization and climate change are two powerful forces that are reshaping ecosystems and their inhabitants
Caterina Valeo; Jianxun He; Kasiapillai Kasiviswanathan. Urbanization under a Changing Climate–Impacts on Hydrology. Water 2021, 13, 393 .
AMA StyleCaterina Valeo, Jianxun He, Kasiapillai Kasiviswanathan. Urbanization under a Changing Climate–Impacts on Hydrology. Water. 2021; 13 (4):393.
Chicago/Turabian StyleCaterina Valeo; Jianxun He; Kasiapillai Kasiviswanathan. 2021. "Urbanization under a Changing Climate–Impacts on Hydrology." Water 13, no. 4: 393.
The presence of the nonstationarity in flow datasets has challenged the flood hazard assessment. Nonstationary tools and evaluation metrics have been proposed to deal with the nonstationarity and guide the infrastructure design and mitigation measures. To date, the examination of how the flood hazards are affected by the nonstationarity is still very limited. This paper thus examined the association between the flood hazards and the nonstationary patterns and degrees of the underlying datasets. The Particle Filter, which allows for assessing the uncertainty of the point estimates, was adopted to conduct the nonstationary flood frequency analysis (NS-FFA) for subsequently estimating the flood hazards in three real study cases. The results suggested that the optimal and top NS-FFA models selected according to the fitting efficiency in general align with the pattern of nonstationarity, although they might not always be superior in terms of uncertainty. Moreover, the results demonstrated the association and the sensitivity of the flood hazards to the perceived patterns and degrees of nonstationarity. In particular, the variations of the flood hazards intensified with the increase in the degree of nonstationarity, which should be assessed in a more elaborate manner, i.e., considering multiple statistical moments. These advocate the potential of using the nonstationarity characteristics as a proxy for evaluating the evolutions of the flood hazards.
Cuauhtémoc Tonatiuh Vidrio-Sahagún; Jianxun He. Flood Hazard Estimation under Nonstationarity Using the Particle Filter. Geosciences 2020, 11, 13 .
AMA StyleCuauhtémoc Tonatiuh Vidrio-Sahagún, Jianxun He. Flood Hazard Estimation under Nonstationarity Using the Particle Filter. Geosciences. 2020; 11 (1):13.
Chicago/Turabian StyleCuauhtémoc Tonatiuh Vidrio-Sahagún; Jianxun He. 2020. "Flood Hazard Estimation under Nonstationarity Using the Particle Filter." Geosciences 11, no. 1: 13.
A velocity meter was designed and built in order to meet market needs for an affordable instrument that measures the range of velocity magnitudes and direction experienced in medium- to large-sized water bodies. The velocity meter consists of a graduated plate with an injector protruding from the center and a camera held downward above the plate. Once the Dye Injection Velocity (DIV) meter is in the flow, dye is injected and the camera records the dye fluid transport. The recorded video is analyzed to determine the local flow velocity and direction. The DIV was calibrated for a range of velocities between 0.0094 m/s and 0.1566 m/s using particle image velocimetry (PIV) in a flow visualization flume. The accuracy of the instrument was found to be +6.3% and −9.8% of full scale. The coefficient of determination of the calibration curve was equal to 98%. Once calibrated, the DIV was deployed to the Inverness Stormwater pond in Calgary, Canada, for validation tests against an Acoustic Doppler Velocity (ADV) meter. During the validation tests, both flow velocity magnitude and direction were measured at several spatial points. The velocity magnitude results showed good agreement and the Mann-Whitney test showed no statistically significant difference (p-value > 0.05). At two spatial points, the differences between direction data were significant, which could be caused by the random errors involved in the validation test. However, the averaged data showed good agreement.
Farzam Allafchi; Caterina Valeo; Angus Chu; Jianxun He; Waltfred Lee; Peter Oshkai; Norman Neumann. A Velocity Meter for Quantifying Advection Velocity Vectors in Large Water Bodies. Sensors 2020, 20, 7204 .
AMA StyleFarzam Allafchi, Caterina Valeo, Angus Chu, Jianxun He, Waltfred Lee, Peter Oshkai, Norman Neumann. A Velocity Meter for Quantifying Advection Velocity Vectors in Large Water Bodies. Sensors. 2020; 20 (24):7204.
Chicago/Turabian StyleFarzam Allafchi; Caterina Valeo; Angus Chu; Jianxun He; Waltfred Lee; Peter Oshkai; Norman Neumann. 2020. "A Velocity Meter for Quantifying Advection Velocity Vectors in Large Water Bodies." Sensors 20, no. 24: 7204.
In most of the Indian cities, around half of the urban water requirement is fulfilled by groundwater. Recently, seasonal urban droughts have been frequently witnessed globally, which adds more stress to groundwater systems. Excessive pumping and increasing demands in several Indian cities impose a high risk of running out of groundwater storage, which could potentially affect millions of lives in the future. In this paper, groundwater level changes have been comprehensively assessed for seven densely populated and rapidly growing secondary cities across India. Several statistical analyses were performed to detect the trends and non-stationarity in the groundwater level (GWL). Also, the influence of rainfall and land use/land cover changes (LULC) on the GWL was explored. The results suggest that overall, the groundwater level was found to vary between ±10 cm/year in the majority of the wells. Further, the non-stationarity analysis revealed a high impact of rainfall and LULC due to climate variability and anthropogenic activities respectively on the GWL change dynamics. Statistical correlation analysis showed evidence supporting that climate variability could potentially be a major component affecting the rainfall and groundwater recharge relationship. Additionally, from the LULC analysis, a decrease in the green cover area (R = 0.93) was found to have a higher correlation with decreasing groundwater level than that of urban area growth across seven rapidly developing cities.
Aadhityaa Mohanavelu; K. S. Kasiviswanathan; S. Mohanasundaram; Idhayachandhiran Ilampooranan; Jianxun He; Santosh M. Pingale; B.-S. Soundharajan; M. M. Diwan Mohaideen. Trends and Non-Stationarity in Groundwater Level Changes in Rapidly Developing Indian Cities. Water 2020, 12, 3209 .
AMA StyleAadhityaa Mohanavelu, K. S. Kasiviswanathan, S. Mohanasundaram, Idhayachandhiran Ilampooranan, Jianxun He, Santosh M. Pingale, B.-S. Soundharajan, M. M. Diwan Mohaideen. Trends and Non-Stationarity in Groundwater Level Changes in Rapidly Developing Indian Cities. Water. 2020; 12 (11):3209.
Chicago/Turabian StyleAadhityaa Mohanavelu; K. S. Kasiviswanathan; S. Mohanasundaram; Idhayachandhiran Ilampooranan; Jianxun He; Santosh M. Pingale; B.-S. Soundharajan; M. M. Diwan Mohaideen. 2020. "Trends and Non-Stationarity in Groundwater Level Changes in Rapidly Developing Indian Cities." Water 12, no. 11: 3209.
The use of the nonstationary hydrological frequency analysis (HFA) has been prompted when nonstationarity is diagnosed in hydrometeorological data. However, the inconclusive identification of the physical process(es) and driver(s) behind the nonstationarity challenges the identification of an appropriate model structure, and consequently might hinder its reliable implementation. To date, no solid consensus on whether the nonstationary HFA is always superior to the stationary HFA has been reached. Therefore, this paper aimed to advance the understanding of the stationary and nonstationary HFAs under nonstationary scenarios by illustratively comparing their performance in real applications, and examining the effects of the nonstationarity on the stationary HFA through a simulation study, especially from the perspective of the uncertainty. The investigation of the effects of the nonstationarity on the stationary HFA was conducted in two fundamental nonstationary scenarios, namely temporal trends in the mean and variance, in which the degree of nonstationarity was quantifiable and known a priori. The HFAs were conducted using the Particle Filter, a Bayesian filtering technique which was recently employed in the stationary HFA and was further extended for the nonstationary HFA in this paper. The illustrative comparison did not demonstrate a consistent superiority of either HFA approach in terms of both fitting efficiency and uncertainty. This result thus implied that the stationarity HFA could outperform the nonstationary HFA in some cases. Besides, the simulation investigation of the stationary HFA revealed that the increase of nonstationarity degree would lead to the deterioration in the analysis accuracy and the elevation of uncertainty. The uncertainty in the stationary HFA was found to primarily originate from the nonstationarity, while the distribution selection would be the other but secondary source of uncertainty. The results from both the illustrative comparison and the simulation investigation suggested that whether the stationary or nonstationary HFA outperforms the other could be associated with the degree and pattern of nonstationarity. Therefore, it is recommended to consider them when developing a strategic framework to select an appropriate approach to deal with nonstationary hydrometeorological data.
Cuauhtémoc Tonatiuh Vidrio-Sahagún; Jianxun He; K.S. Kasiviswanathan; Subhamoy Sen. Stationary hydrological frequency analysis coupled with uncertainty assessment under nonstationary scenarios. Journal of Hydrology 2020, 598, 125725 .
AMA StyleCuauhtémoc Tonatiuh Vidrio-Sahagún, Jianxun He, K.S. Kasiviswanathan, Subhamoy Sen. Stationary hydrological frequency analysis coupled with uncertainty assessment under nonstationary scenarios. Journal of Hydrology. 2020; 598 ():125725.
Chicago/Turabian StyleCuauhtémoc Tonatiuh Vidrio-Sahagún; Jianxun He; K.S. Kasiviswanathan; Subhamoy Sen. 2020. "Stationary hydrological frequency analysis coupled with uncertainty assessment under nonstationary scenarios." Journal of Hydrology 598, no. : 125725.
Despite the benefits of green roofs in managing stormwater quality, green roofs especially at their early age might leach nutrients. Research in this regard is still very limited. Therefore, this paper conducted both the laboratory and field observations to characterize and model the leaching of nutrients including nitrogen (N) and phosphorus (P) and to examine the discrepancy in knowledge produced from these two settings. The experiment revealed that the higher the initial nutrient contents of media were, the higher the degree of nutrient leaching was. The nutrient leaching from both the laboratory cells and the field green roof declined temporally, which was largely explained by the cumulative inflow. The semi-physically based nutrient leaching model generally captured the nutrient leaching from both the laboratory cells (R2 in the range of 0.87–0.98) and the field green roof (R2 in the range of 0.28–0.86). The mass balance analysis for the laboratory cells demonstrated that the masses of nutrients leached in outflow were 85–112% of the nutrients reduced in media in general (except P of two laboratory cells). The analysis and modeling results supported that media was the primary source for nutrients leached and the pattern of nutrient leaching was consistent with wash-off being the dominant process. The results also revealed the difference in the P leaching between the laboratory cells and the field green roof. Apart from the wash-off, other chemical and biological processes and/or nutrient sources might play non-negligible roles on the P leaching of the field green roof, implied by the relatively low performance of the models (R2 of approximately 0.30 in both the regression analysis and the nutrient leaching model). The difference observed between the laboratory experiment and the field observation also calls into attention when translating knowledge derived from laboratory experiments into real practice.
Musa Akther; Jianxun He; Angus Chu; Bert van Duin. Nutrient leaching behavior of green roofs: Laboratory and field investigations. Science of The Total Environment 2020, 754, 141841 .
AMA StyleMusa Akther, Jianxun He, Angus Chu, Bert van Duin. Nutrient leaching behavior of green roofs: Laboratory and field investigations. Science of The Total Environment. 2020; 754 ():141841.
Chicago/Turabian StyleMusa Akther; Jianxun He; Angus Chu; Bert van Duin. 2020. "Nutrient leaching behavior of green roofs: Laboratory and field investigations." Science of The Total Environment 754, no. : 141841.
The COVID-19 pandemic has created a global crisis and the governments are fighting rigorously to control the spread by imposing intervention measures and increasing the medical facilities. In order to tackle the crisis effectively we need to know the trajectories of number of the people infected (i.e. confirmed cases). Such information is crucial to government agencies for developing effective preparedness plans and strategies. We used a statistical modeling approach – extreme value distributions (EVDs) for projecting the future confirmed cases on a global scale. Using the 69 days data (from January 22, 2020 to March 30, 2020), the EVDs model predicted the number of confirmed cases from March 31, 2020 to April 9, 2020 (validation period) with an absolute percentage error < 15 % and then projected the number of confirmed cases until the end of June 2020. Also, we have quantified the uncertainty in the future projections due to the delay in reporting of the confirmed cases on a global scale. Based on the projections, we found that total confirmed cases would reach around 11.4 million globally by the end of June 2020.The USA may have 2.9 million number of confirmed cases followed by Spain-1.52 million and Italy-1.28 million.
M. Aadhityaa; K. S. Kasiviswanathan; Idhayachandhiran Ilampooranan; B. Soundharajan; M. Balamurugan; Jianxun He. A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions. 2020, 1 .
AMA StyleM. Aadhityaa, K. S. Kasiviswanathan, Idhayachandhiran Ilampooranan, B. Soundharajan, M. Balamurugan, Jianxun He. A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions. . 2020; ():1.
Chicago/Turabian StyleM. Aadhityaa; K. S. Kasiviswanathan; Idhayachandhiran Ilampooranan; B. Soundharajan; M. Balamurugan; Jianxun He. 2020. "A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions." , no. : 1.
Despite the benefits of green roofs in managing urban stormwater quantity and quality, a number of studies have demonstrated that green roofs can pose negative impacts on the urban environment due to chemical leaching, in particular in their early age. Besides design variables such as growing media composition and depth, the roof age and hydro-meteorological variables are also expected to affect or govern the temporal evolution of chemical leaching. To characterize the chemical leaching behavior of green roofs and explore possible modeling approaches, a full-scale extensive green roof and a reference roof, which are located in a cold and semi-arid climate region, were monitored in both rain and snowmelt events in 2015–2018. The temporal evolution of chemical leaching was examined at both intra- and inter-annual time scales. The roles of hydro-meteorological variables including the growing media temperature (GMT), moisture condition (antecedent moisture condition (AMC) for rain events, and moisture condition (MC) for snowmelt), event rainfall amount (Ra), and cumulative inflow/precipitation amount on chemical leaching were investigated. The field observations demonstrated the leaching of nutrients (i.e., both nitrogen (N) and phosphorous (P)) and conductivity from the green roof, whereas the roof acted as a sink for metals (Zn, Cu, and Pb). The leaching of N appeared to cease, whereas P leaching was still ongoing at the end of the study period. Although the degree of nutrient leaching was not significantly different between the rain and snowmelt events, the nutrient leaching in rain events appeared to be relatively higher in the spring than in the summer and fall. Furthermore, the leaching of nutrients was found to decline annually, but at different rates for different nutrients. Among the investigated hydro-meteorological variables, the cumulative inflow was identified influencing the temporal evolution of chemical leaching considerably. In addition, the results from two modeling approaches, namely multiple linear regression modeling and semi-physically based modeling adapted from the pollutant wash-off concept for urban stormwater runoff, confirmed the critical role of the cumulative inflow on chemical leaching. Besides the cumulative inflow, the GMT was the other important explanatory variable for P leaching. The results suggested that chemical leaching from green roofs in semi-arid regions, where precipitation and moisture level are low, could persist longer but at a lower degree compared to that in mild and temperate climate regions.
Musa Akther; Jianxun He; Angus Chu; Bert van Duin. Chemical leaching behaviour of a full-scale green roof in a cold and semi-arid climate. Ecological Engineering 2020, 147, 105768 .
AMA StyleMusa Akther, Jianxun He, Angus Chu, Bert van Duin. Chemical leaching behaviour of a full-scale green roof in a cold and semi-arid climate. Ecological Engineering. 2020; 147 ():105768.
Chicago/Turabian StyleMusa Akther; Jianxun He; Angus Chu; Bert van Duin. 2020. "Chemical leaching behaviour of a full-scale green roof in a cold and semi-arid climate." Ecological Engineering 147, no. : 105768.
Bioretention systems have gained considerable popularity as a more natural approach to stormwater management in urban environments. The choice of bioretention media is frequently cited as one of the critical design parameters with the ultimate impact on the performance of the system. The goal of this review is to highlight data that challenge the importance of media as being the dominant design parameter and argue that the long-term performance is shaped by the interactions between media and the living components of a bioretention system, especially vegetation. Some of the key interactions are related to the impact of plant roots on media pore structure, which has implications on infiltration, storage capacity, and treatment. Another relevant interaction pertains to evapotranspiration and the associated impacts on the water balance and the water quality performance of bioretention systems. The impacts of vegetation on the media are highlighted and actual, as well as potential, impacts of plant-media interactions on bioretention performance are presented.
Anton Skorobogatov; Jianxun He; Angus Chu; Caterina Valeo; Bert van Duin. The impact of media, plants and their interactions on bioretention performance: A review. Science of The Total Environment 2020, 715, 136918 .
AMA StyleAnton Skorobogatov, Jianxun He, Angus Chu, Caterina Valeo, Bert van Duin. The impact of media, plants and their interactions on bioretention performance: A review. Science of The Total Environment. 2020; 715 ():136918.
Chicago/Turabian StyleAnton Skorobogatov; Jianxun He; Angus Chu; Caterina Valeo; Bert van Duin. 2020. "The impact of media, plants and their interactions on bioretention performance: A review." Science of The Total Environment 715, no. : 136918.
The influence of climatic variables and land use on fecal coliform (FC) levels in stormwater collected from outfalls throughout southern Vancouver Island between 1995 and 2011 are examined through statistical analyses, Fourier analysis, Multiple Linear Regression (LR) and Multivariate Logistic Regression (MLR). Kendall’s τ-b demonstrated that FC levels were significantly and positively correlated with the amount of residential area within a drainage catchment generating the runoff, and that FC levels were location dependent. Climatic variables of temperature and antecedent dry period length were significantly and positively correlated with FC levels at both the sampling location level and across the region overall. Precipitation and flowrates were negatively correlated with FC levels. Fourier analysis showed that monthly FC levels shared the same 12 month cycle (peaking in July) as precipitation and temperature. MLR modelling was applied by aggregating the LogFC data by order of magnitude. The MLR model shows that the data are subject to different influences depending on the season and as well, the month of the year. The land use and climate analyses suggest that future climate change impact studies attempted on nearshore bacterial water quality should be conducted at the urban catchment scale.
Kaifeng Xu; Caterina Valeo; Jianxun He; Zhiying Xu. Climate and Land Use Influences on Bacteria Levels in Stormwater. Water 2019, 11, 2451 .
AMA StyleKaifeng Xu, Caterina Valeo, Jianxun He, Zhiying Xu. Climate and Land Use Influences on Bacteria Levels in Stormwater. Water. 2019; 11 (12):2451.
Chicago/Turabian StyleKaifeng Xu; Caterina Valeo; Jianxun He; Zhiying Xu. 2019. "Climate and Land Use Influences on Bacteria Levels in Stormwater." Water 11, no. 12: 2451.
A hydrological model was integrated with a computational fluid dynamics (CFD) model to determine bacteria levels distributed throughout the Inverness stormwater pond in Calgary, Alberta. The Soil Conservation Service (SCS) curve number model was used as the basis of the hydrological model to generate flow rates from the watershed draining into the pond. These flow rates were then used as input for the CFD model simulations that solved the Reynolds-Averaged Navier-Stokes (RANS) equations with k-ɛ turbulence model. E. coli, the most commonly used fecal indicator bacteria for water quality research, was represented in the model by passive scalars with different decay rates for free bacteria and attached bacteria. Results show good agreement with measured data in each stage of the simulations. The middle of the west wing of the pond was found to be the best spot for extracting water for reuse because it had the lowest level of bacteria both during and after storm events. In addition, only one of the four sediment forebays was found efficient in trapping bacteria.
Farzam Allafchi; Caterina Valeo; Jianxun He; Norman F. Neumann. An Integrated Hydrological-CFD Model for Estimating Bacterial Levels in Stormwater Ponds. Water 2019, 11, 1016 .
AMA StyleFarzam Allafchi, Caterina Valeo, Jianxun He, Norman F. Neumann. An Integrated Hydrological-CFD Model for Estimating Bacterial Levels in Stormwater Ponds. Water. 2019; 11 (5):1016.
Chicago/Turabian StyleFarzam Allafchi; Caterina Valeo; Jianxun He; Norman F. Neumann. 2019. "An Integrated Hydrological-CFD Model for Estimating Bacterial Levels in Stormwater Ponds." Water 11, no. 5: 1016.
This paper presents a single-objective optimization-based perturbation analysis to quantify model prediction uncertainty. A new index named coverage width index (CWI), which combines two commonly used uncertainty indices, the percentage of coverage (POC) and the average width (AW), was proposed to facilitate the optimization. Considering the outperformance of the wavelet neural network (WNN) among various data-driven modeling approaches in hydrogeological modeling, the proposed approach was integrated into WNN (called OPWNN). A case study was conducted to demonstrate the application of OPWNN in groundwater level forecasting at two wells in the Amaravathi River Basin, India. The sensitivity analysis of the effect of initial perturbation range on CWI suggested that uncertainty is sensitive to the selected perturbation range and a small perturbation does not guarantee an acceptable prediction interval (PI). The modeling results demonstrated that the OPWNN can optimize the PI effectively with minimized AW corresponding to an expected high POC. Therefore, this approach can yield more reliable predictions/forecasts for water resources management.
K. S. Kasiviswanathan; Jianxun He; Joo-Hwa Tay; K. P. Sudheer. Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling. Water Resources Management 2018, 33, 229 -243.
AMA StyleK. S. Kasiviswanathan, Jianxun He, Joo-Hwa Tay, K. P. Sudheer. Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling. Water Resources Management. 2018; 33 (1):229-243.
Chicago/Turabian StyleK. S. Kasiviswanathan; Jianxun He; Joo-Hwa Tay; K. P. Sudheer. 2018. "Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling." Water Resources Management 33, no. 1: 229-243.
Many regions have turned to low impact development technologies (LIDs), which are implemented to restore the changes in stormwater runoff that have resulted from urbanization. Green roofs are one typical type of LID. Until now, many studies have validated their roles in managing urban stormwater runoff. However, they have also revealed that the performance of green roofs largely varies with their design configuration, as well as their hydro-climatic exposure. The objectives of this review paper are to statistically synthesize the effects of the influential factors, including design and hydrologic variables, on green roof performance and to explore their effects in different climatic zones. The review’s results confirm the differences in the influential variables and, thus, the performance of green roofs in different climatic zones. These are the barriers to knowledge translation among engineering designers, stormwater managers, and policymakers in different climatic zones when implementing green roofs. Consequently, region- or site-specific studies are necessary to implement green roofs with confidence.
Musa Akther; Jianxun He; Angus Chu; Jian Huang; Bert Van Duin. A Review of Green Roof Applications for Managing Urban Stormwater in Different Climatic Zones. Sustainability 2018, 10, 2864 .
AMA StyleMusa Akther, Jianxun He, Angus Chu, Jian Huang, Bert Van Duin. A Review of Green Roof Applications for Managing Urban Stormwater in Different Climatic Zones. Sustainability. 2018; 10 (8):2864.
Chicago/Turabian StyleMusa Akther; Jianxun He; Angus Chu; Jian Huang; Bert Van Duin. 2018. "A Review of Green Roof Applications for Managing Urban Stormwater in Different Climatic Zones." Sustainability 10, no. 8: 2864.
Surface waters are prone to the influences from both natural condition and anthropogenic activities. The aim of this paper was to study the impacts of one natural variable, precipitation, and its change posed by a changing climate on water quality of three rivers in Alberta, Canada. Eleven water quality parameters monitored during the time period of 1988–2014 were used to investigate the impact of precipitation. The results showed the significant dependence of most water quality parameters as well as river flow on the cumulative antecedent precipitation. Water quality parameters however had different associations with precipitation; and thus they would respond to climate change qualitatively and quantitatively differently in the rivers and at the stations of each river. In general, some water quality parameters such as turbidity and total phosphorus would increase; whereas other parameters would decrease or show no appreciable change under the projected increase of precipitation under the median climate change scenario for the river basins. On all three rivers, the maximum increase (17.20%) and decrease (−1.53%) were projected for turbidity and chloride, respectively, in the 2050s; while the maximum increase (29.68%) and decrease (−2.45%) were calculated for turbidity and chloride, respectively, in the 2080s. The results imply the need to manage riverine water quality considering precipitation and its change under a changing climate.
Sajjad Rostami; Jianxun He; Quazi K. Hassan. Riverine Water Quality Response to Precipitation and Its Change. Environments 2018, 5, 8 .
AMA StyleSajjad Rostami, Jianxun He, Quazi K. Hassan. Riverine Water Quality Response to Precipitation and Its Change. Environments. 2018; 5 (1):8.
Chicago/Turabian StyleSajjad Rostami; Jianxun He; Quazi K. Hassan. 2018. "Riverine Water Quality Response to Precipitation and Its Change." Environments 5, no. 1: 8.
River flow forecasting is critical for flood forecasting, reservoir operations, and water resources management. However, flow forecasting can be difficult, challenging and time consuming due to the spatial and temporal variability of climatic conditions and watershed characteristics. From a practical point of view, a simple and intuitive approach might be more preferable than a complex modeling approach. In this study, our objective was to develop short-term (i.e., daily) flow forecasting models in the Bow River at the city of Calgary, Alberta, Canada. Here, we evaluated the performance of several regression models, along with a newly proposed “base difference” model, by using antecedent daily river flow values from three gauge stations (i.e., Banff, Seebe, and Calgary). Our analyses revealed that using a multivariable linear regression formulated as a function of upstream gauge stations (i.e., Banff or Seebe) and the station of interest (i.e., Calgary) using antecedent flows demonstrated strong relationships (i.e., having r2 (coefficient of determination) and RMSE (root-mean-square deviation) of approximately 0.93 and 14 m3/s, respectively). As such, we opted to suggest that the use of Banff and Calgary stations in forecasting the flows at Calgary could be considered as it would require a relatively lower number of gauge stations.
Victor B. Veiga; Quazi K. Hassan; Jianxun He. Development of Flow Forecasting Models in the Bow River at Calgary, Alberta, Canada. Water 2014, 7, 99 -115.
AMA StyleVictor B. Veiga, Quazi K. Hassan, Jianxun He. Development of Flow Forecasting Models in the Bow River at Calgary, Alberta, Canada. Water. 2014; 7 (12):99-115.
Chicago/Turabian StyleVictor B. Veiga; Quazi K. Hassan; Jianxun He. 2014. "Development of Flow Forecasting Models in the Bow River at Calgary, Alberta, Canada." Water 7, no. 12: 99-115.