1,721,046 research outputs found
REMOTE SENSING DATA ASSIMILATION IN WATER QUALITY NUMERICAL MODELS FOR SIMULATION OF WATER COLUMN TEMPERATURE
Get PDFIndiana University-Purdue University Indianapolis (IUPUI)Numerical models are important tools for simulating processes within complex natural systems, such as hydrodynamics and water quality processes within a water body. From decision makers’ perspectives, such models also serve as useful tools for predicting the impacts of water quality problems or develop early warning systems. However, accuracy of a numerical model developed for a specific site is dependent on multiple model parameters and variables whose values are attained via calibration processes and/or expert knowledge. Real time variations in the actual aquatic system at a site necessitate continuous monitoring of the system so that model parameters and variables are regularly updated to reflect accurate conditions. Multiple sources of observations can help adjust the model better by providing benefits of individual monitoring technology within the model updating process. For example, remote sensing data provide a spatially dense dataset of model variables at the surface of a water body, while in-situ monitoring technologies can provide data at multiple depths and at more frequent time intervals than remote sensing technologies. This research aims to present an overview of an integrated modeling and data assimilation framework that combines three-dimensional numerical model with multiple sources of observations to simulate water column temperature in a eutrophic reservoir in central Indiana. A variational data assimilation approach is investigated for incorporating spatially continuous remote sensing observations and spatially discrete in-situ observations to change initial conditions of the numerical model. This research addresses the challenge of improving the model performance by combining water temperature from multi-spectral remote sensing analysis and in-situ measurements. Results of the approach on a eutrophic reservoir in Central Indiana show that with four images of multi-spectral remote sensing data assimilated, the model results oscillate more from the in-situ measurements during the data assimilation period. For validation, the data assimilation has negative impacts on the root mean square error. According to quantitative analysis, more significant water temperature stratification leads to larger deviations. Sampling depth differences for remote sensing technology, in-situ measurements and model output are considered as possible error source
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Assessment of Multi-year Hydrologic Performance of Bioretention Facility using Real-Time Sensors
Get PDFBioretention facilities are emerging as a popular way to deal with stormwater runoff in urban environments and address its concerns. Real-time sensors installed in bioretention facilities aid in understanding the performance and feasibility of such facilities over time. A bioretention facility in Corvallis, Oregon was monitored for a study period of 3 years using real-time sensors deployed to measure flows in and out of the facility along with soil moisture content in the bioretention cells. Flow was monitored using Steven’s SDX sensor, and Decagon’s CTD sensor. The soil moisture content was monitored using Steven’s Hydra Probes. Overall, results have indicated that performance of CTD sensors was significantly better than SDX sensors in both inlets and outlets. There was large variability in captured runoff ranging from high 94% water treatment to as low as 6% treatment. The bioretention cells were able to maintain peak flow reduction ratio of 0.87, 0.82, 0.85 and 0.76 for fall, winter, spring and summer respectively throughout the study period. Mean peak delays was 44, 19, 150 and 55mins for fall, winter, spring and summer respectively. Soil moisture reading were dependent on the longitudinal distance away from the inlet. The mean values increased from 2015 to 2017, however the variability and standard deviation decreased. Decay rates during drying periods had high variability in 2015 but showed stability after that year.
Lead Distribution in Urban Soils: Relationship Between Lead Sources and Children's Blood Lead Levels
No full textIndiana University-Purdue University Indianapolis (IUPUI
Prediction of Spatial-Temporal Distribution of Algal Metabolites in Eagle Creek Reservoir, Indianapolis, IN
No full textIndiana University-Purdue University Indianapolis (IUPUI)In this research, Environmental Fluid Dynamic Code (EFDC) and Adaptive- Networkbased
Fuzzy Inference System Models (ANFIS) were developed and implemented to
determine the spatial-temporal distribution of cyanobacterial metabolites: 2-MIB and
geosmin, in Eagle Creek Reservoir, IN. The research is based on the current need for
understanding algae dynamics and developing prediction methods for algal taste and odor
release events.
In this research the methodology for prediction of 2-MIB and geosmin production was
explored. The approach incorporated a combination of numerical and heuristic modeling
to show its capabilities in prediction of cyanobacteria metabolites. The reservoir’s
variable data measured at monitoring stations and consisting of chemical/physical and
biological parameters with the addition of calculated mixing conditions within the
reservoir were used to train and validate the models. The Adaptive – Network based
Fuzzy Inference System performed satisfactorily in predicting the metabolites, in spite of
multiple model constraints. The predictions followed the generally observed trends of
algal metabolites during the three seasons over three years (2008-2010). The randomly
selected data pairs for geosmin for validation achieved coefficient of determination of
0.78, while 2-MIB validation was not accepted due to large differences between two
observations and their model prediction. Although, these ANFIS results were accepted,
the further application of the ANFIS model coupled with the numerical models to predict
spatio-temporal distribution of metabolites showed serious limitations, due to numerical
model calibration errors. The EFDC-ANFIS model over-predicted Pseudanabaena spp.
biovolumes for selected stations. The predicted value was 18,386,540 mm3/m3, while
observed values were 942,478 mm3/m3. The model simulating Planktothrix agardhii gave
negative biovolumes, which were assumed to represent zero values observed at the
station. The taste and odor metabolite, geosmin, was under-predicted as the predicted
v
concentration was 3.43 ng/L in comparison to observed value of 11.35 ng/l. The 2-MIB
model did not validate during EFDC to ANFIS model evaluation.
The proposed approach and developed methodology could be used for future applications
if the limitations are appropriately addressed
Analysis of Mercury Concentrations in Indiana Soil to Evaluate Patterns of Long-Term Atmospheric Mercury Deposition
No full textIndiana University-Purdue University Indianapolis (IUPUI)Mercury (Hg) has proven to be a risk to the public, mainly through the consumption of fish. Because of this, many fish consumption advisories have been issued in Indiana. Although much is known about the global cycle of mercury, little is known about how local and regional emission sources of mercury impact local and regional mercury cycling. This study’s objective was to determine the scope of mercury concentration in central Indiana by using a broad grid of soil mercury measurements. Sampling was designed to capture the net retained mercury content in soils, and to determine whether spatial patterns in exist in soil mercury contents that could be related to emission sources of mercury and post-emission transport patterns from wind. Results from this study revealed significant differences in mercury concentrations for soils in central Indiana. The core of the study area, concentrated in the urban area of Indianapolis, exhibited soil mercury contents that were 20 times higher than values in the outskirts of the study area. The spatial pattern resembled a bulls-eye shape centered on Indianapolis, and with comparison to the reported Hg emission from local sources, including a coal-fired power plant, indicates a strong regional deposition signal linked to those emission sources but marked by wind-driven transport to the northeast. This effect of local emission sources
resulting in local deposition indicates that limiting mercury emissions will have a net beneficial impact on local environmental quality and human health
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Optimizing Placement of Weather Stations: Defining and Testing a Meaningful Objective for Multi-Variable Weather Monitoring Networks
Get PDFAs the number of weather stations declines globally, ensuring that meteorological monitoring networks efficiently and effectively monitor weather variables becomes increasingly important. Multi-variable weather sensors are becoming more widely available and make monitoring weather phenomena more economical. However, when seeking an optimal spatial distribution of these stations, choosing an objective function that weights these variables in a way that is relevant to a majority of the data consumers poses a significant challenge. We propose the use of physically based combinations of parameters, and illustrate this approach with an objective function based on a model of evapotranspiration (ET) that can combine precipitation, temperature, relative humidity, solar radiation, wind speed, barometric pressure, and soil moisture capacity data in a meaningful way. We calculate this objective using a gridded weather data set. We assess the suitability of different network design techniques to be employed with gridded data sets and proposed an optimization method that allows us to minimize the mean squared interpolation error for our objective. Our network design method is able to reduce interpolation error by about 10% when compared to a maximally spaced monitoring network or a network designed based solely on expert recommendations. Our network design is further improved by the inclusion of expert recommended sites in our set of candidate locations
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Houston, We have a Flooding Problem: An Analysis on the potential role of green roofs in Houston, Texas's stormwater infrastructure
No full textBetween 2015 and 2017, Harris County, Texas, experienced an unusually high number of significant precipitation events, including three 500-year precipitation events and separate 100-year storms. On average, the county experiences a disastrous flood event every two years, leading to a combined billions of dollars in damages. This study aimed to evaluate the efficacy of green roofs as a green stormwater infrastructure solution across Harris County to alleviate flood hazards. This study estimated the maximum retention capacity, assuming universal adoption of green roofs, by first determining the available roof surface area across the county using the City of Houston data in ArcGIS Pro. I designed Houston’s Optimized Green Roof (HOGR), an intensive green roof with a depth of 121 centimeters, utilizing extensive research on effective green roofs and Harris County’s climate to maximize stormwater retention. The HOGR design has a storage volume of 62%. This retention variable was utilized to calculate runoff using the Soil Conservation Services (SCS) Runoff Curve Number Method, calculated for a wide range of 24-hour rainfall data. Findings show that impervious surfaces experienced 0.1-2.1% retention, while green roofs retained 7.9-88.0% of runoff, performing best with smaller amounts of rainfall. The reduced stormwater runoff from rooftops decreases stormwater entering roadways and bayous, delaying peak flow rates, and decreasing flooding. This project estimates the maximum efficacy of widespread implementation of green stormwater infrastructure, such as green roofs, to mitigate flood hazards and promote resiliency for the future
Coupled biogeochemical cycles in riparian zones with contrasting hydrogeomorphic characteristics in the US Midwest
Get PDFIndiana University-Purdue University Indianapolis (IUPUI)Numerous studies have investigated the fate of pollutants in riparian buffers, but few studies have focused on the control of multiple contaminants simultaneously in riparian zones. To better understand what drives the biogeochemical cycles of multiple contaminants in riparian zones, a 19-month study was conducted in riparian buffers across a range of hydrogeomorphic (HGM) settings in the White River watershed in Indiana. Three research sites [Leary Webber Ditch (LWD), Scott Starling (SS) and White River (WR)] with contrasting hydro-geomorphology were selected. We monitored groundwater table depth, oxidation reduction potential (ORP), dissolved oxygen (DO), dissolved organic carbon (DOC), NO3-, NH4+, soluble reactive phosphorus (SRP), SO42- , total Hg and methylmercury (MeHg). Our results revealed that differences in HGM conditions translated into distinctive site hydrology, but significant differences in site hydrology did not lead to different biogeochemical conditions. Nitrate reduction and sulfate re-oxidation were likely associated with major hydrological events, while sulfate reduction, ammonia and methylmercury production were likely associated with seasonal changes in biogeochemical conditions. Results also suggest that the LWD site was a small sink for nitrate but a source for sulfate and MeHg, the SS site was a small sink for MeHg but had little effect on NO3-, SO42- and SRP, and the WR was an intermediate to a large sink for nitrate, an intermediate sink for SRP, and a small source for MeHg. Land use and point source appears to have played an important role in regulating solute concentrations (NO3-, SRP and THg). Thermodynamic theories probably oversimplify the complex patterns of solute dynamics which, at the sites monitored in the present study, were more strongly impacted by HGM settings, land use, and proximity to a point source
Combining Multivariate Statistical Methods and Spatial Analysis to Characterize Water Quality Conditions in the White River Basin, Indiana, U.S.A.
No full textIndiana University-Purdue University Indianapolis (IUPUI)This research performs a comparative study of techniques for combining spatial data and multivariate statistical methods for characterizing water quality conditions in a river basin. The study has been performed on the White River basin in central Indiana, and uses sixteen physical and chemical water quality parameters collected from 44 different monitoring sites, along with various spatial data related to land use – land cover, soil characteristics, terrain characteristics, eco-regions, etc. Various parameters related to the spatial data were analyzed using ArcHydro tools and were included in the multivariate analysis methods for the purpose of creating classification equations that relate spatial and spatio-temporal attributes of the watershed to water quality data at monitoring stations. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. The linear statistical multivariate method uses a combination of principal component analysis, cluster analysis, and discriminant analysis, whereas the non-linear multivariate method uses a combination of Kohonen Self-Organizing Maps, Cluster Analysis, and Support Vector Machines. The final models were tested with recent and independent data collected from stations in the Eagle Creek watershed, within the White River basin. In 6 out of 20 models the Support Vector Machine more accurately classified the Eagle Creek stations, and in 2 out of 20 models the Linear Discriminant Analysis model achieved better results. Neither the linear or non-linear models had an apparent advantage for the remaining 12 models. This research provides an insight into the variability and uncertainty in the interpretation of the various statistical estimates and statistical models, when water quality monitoring data is combined with spatial data for characterizing general spatial and spatio-temporal trends
Nitrous oxide emission from riparian buffers in agricultural landscapes of Indiana
Get PDFIndiana University-Purdue University Indianapolis (IUPUI)Riparian buffers have well documented capacity to remove nitrate (NO3-) from runoff and subsurface flow paths, but information on field-scale N2O emission from these buffers is lacking. This study monitored N2O fluxes at two agricultural riparian buffers in the White River watershed (Indiana) from December 2009 to May 2011 to assess the impact of landscape and hydrogeomorphologic factors on emission. Soil chemical and biochemical properties were measured and environmental variables (soil temperature and moisture) were monitored in an attempt to identify key drivers of N2O emission. The study sites included a mature riparian forest (WR) and a riparian grass buffer (LWD); adjacent corn fields were also monitored for land-use comparison. With the exception of net N mineralization, most soil properties (particle size, bulk density, pH, denitrification potential, organic carbon, C:N) showed little correlation with N2O emission. Analysis of variance (ANOVA) identified season, land-use (riparian buffer vs. crop field), and site geomorphology as major drivers of N2O emission. At both study sites, N2O emission showed strong seasonal variability; the largest emission peaks in the riparian buffers (up to 1,300 % increase) and crop fields (up to 3,500 % increase) occurred in late spring/early summer as a result of flooding, elevated soil moisture and N-fertilization. Nitrous oxide emission was found to be significantly higher in crop fields than in riparian buffers at both LWD (mean: 1.72 and 0.18 mg N2O-N m-2 d-1) and WR (mean: 0.72 and 1.26 mg N2O-N m-2 d-1, respectively). Significant difference (p=0.02) in N2O emission between the riparian buffers was detected, and this effect was attributed to site geomorphology and the greater potential for flooding at the WR site (no flooding occurred at LWD). More than previously expected, the study results demonstrate that N2O emission in riparian buffers is largely driven by landscape geomorphology and land-stream connection (flood potential)
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