Indiana 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.

Indiana 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).

Indiana 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.