Hydrologic and water-quality characterization and modeling of the Onondaga Lake Basin, Onondaga County, New York

Coon, William F.; Reddy, James E.

doi:10.3133/sir20085013

Cited by 7 publications

(13 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the model development, a number of assumptions and simplifications were made to simplify complex hydrological processes which can contribute to have increased errors in the model output. According to Coon and Reddy [83], errors in a hydrological model can arise from various sources including: (i) errors in precipitation and streamflow data; (ii) limitations in model structure; (iii) errors in model calibration; (iv) inaccurate estimation and interpretation of land-use and land-cover data; and (v) changes in land-use during the simulation period. While developing the model, the precipitation data used were obtained from point-based rainfall gauge stations and then applied uniformly over a specified area around the gauging stations defined by the Thiessen-polygon method.…”

Section: Modelling Errors Limitations and Future Directionsmentioning

confidence: 99%

A Comparison of Continuous and Event-Based Rainfall–Runoff (RR) Modelling Using EPA-SWMM

Hossain

Hewa

Wella-Hewage

2019

Water

View full text Add to dashboard Cite

This study investigates the comparative performance of event-based and continuous simulation modelling of a stormwater management model (EPA-SWMM) in calculating total runoff hydrographs and direct runoff hydrographs. Myponga upstream and Scott Creek catchments in South Australia were selected as the case study catchments and model performance was assessed using a total of 36 streamflow events from the period of 2001 to 2004. Goodness-of-fit of the EPA-SWMM models developed using automatic calibration were assessed using eight goodness-of-fit measures including Nash–Sutcliff efficiency (NSE), NSE of daily high flows (ANSE), Kling–Gupta efficiency (KGE), etc. The results of this study suggest that event-based modelling of EPA-SWMM outperforms the continuous simulation approach in producing both total runoff hydrograph (TRH) and direct runoff hydrograph (DRH).

show abstract

Section: Modelling Errors Limitations and Future Directionsmentioning

confidence: 99%

A Comparison of Continuous and Event-Based Rainfall–Runoff (RR) Modelling Using EPA-SWMM

Hossain

Hewa

Wella-Hewage

2019

Water

View full text Add to dashboard Cite

show abstract

“…Onondaga Creek has the second largest drainage area (298 km 2 ) in the Onondaga Lake watershed and contributes the greatest surface water inflow. The creek is approximately 26.7 mi (43 km) in length and descends more than 0.19 mi (0.30 km) from its headwaters [17]. Land use is predominantly forest (50%) and agriculture (31%), with approximately 19% considered developed [18].…”

Section: Study Sitementioning

confidence: 99%

Microbial Source-Tracking Reveals Origins of Fecal Contamination in a Recovering Watershed

Green

Weller

Johnson

et al. 2019

Water

View full text Add to dashboard Cite

Fecal contamination of waterbodies due to poorly managed human and animal waste is a pervasive problem that can be particularly costly to address, especially if mitigation strategies are ineffective at sufficiently reducing the level of contamination. Identifying the most worrisome sources of contamination is particularly difficult in periurban streams with multiple land uses and requires the distinction of municipal, agricultural, domestic pet, and natural (i.e., wildlife) wastes. Microbial source-tracking (MST) methods that target host-specific members of the bacterial order Bacteroidales and others have been used worldwide to identify the origins of fecal contamination. We conducted a dry-weather study of Onondaga Creek, NY, where reducing fecal contamination has been approached mainly by mitigating combined sewer overflow events (CSOs). Over three sampling dates, we measured in-stream concentrations of fecal indicator bacteria; MST markers targeting human, ruminant, and canine sources; and various physical-chemical parameters to identify contaminants not attributable to CSOs or stormwater runoff. We observed that despite significant ruminant inputs upstream, these contaminants eventually decayed and/or were diluted out and that high levels of urban bacterial contamination are most likely due to failing infrastructure and/or illicit discharges independent of rain events. Similar dynamics may control other streams that transition from agricultural to urban areas with failing infrastructure.

show abstract

“…Uncertainties in hydrological models include (i) errors and limitations in input datasets, (ii) parameterization or assumptions in the model, (iii) calibration errors, (iv) land classification/cover errors, and (v) catchment scale/transfer errors (Coon and Reddy 2008). It has also been noted that when dealing with precipitation and hydrology, a deterministic forecast gives the user an “illusion of certainty” whereas a probabilistic system inherently accounts for uncertainty.…”

Section: Uncertaintymentioning

confidence: 99%

Floods in a Changing Climate

Andersen¹,

Shepherd²

2013

Geography Compass

View full text Add to dashboard Cite

Atmospheric warming and associated hydrological changes have implications for regional flood intensity and frequency. Climate models and hydrological models have the ability to integrate various contributing factors and assess potential changes to hydrology at global to local scales through the century. This survey of floods in a changing climate reviews flood projections based on sources of precipitation, ice and snow melt, and coastal inundation. Topographic and anthropogenic influences that exacerbate or reduce flood risks by altering surface runoff, infiltration, storage, and precipitation development are also considered. Flood mitigation and adaptation strategies for infrastructure, agriculture, public health, and local communities are explored along with uncertainties and challenges in flood research. Recent and upcoming datasets to help with future flood monitoring and prediction include satellite missions, advanced radar, and in-situ networks.

show abstract

Hydrologic and water-quality characterization and modeling of the Onondaga Lake Basin, Onondaga County, New York

Cited by 7 publications

References 43 publications

A Comparison of Continuous and Event-Based Rainfall–Runoff (RR) Modelling Using EPA-SWMM

A Comparison of Continuous and Event-Based Rainfall–Runoff (RR) Modelling Using EPA-SWMM

Microbial Source-Tracking Reveals Origins of Fecal Contamination in a Recovering Watershed

Floods in a Changing Climate

Contact Info

Product

Resources

About