Analysis of the HSPF Water Quality Parameter Uncertainty in Predicting Peak in-Stream Fecal Coliform Concentrations

Paul, Siddhartho Shekhar; Haan, P. K.; Matlock, Marty D.; Mukhtar, Saqib; Pillai, Suresh D.

doi:10.13031/2013.15872

Cited by 47 publications

(32 citation statements)

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“…The ET prediction was also assessed using statistical evaluation parameters that included mean absolute error (MAE) and mean absolute percent difference (MAPD), as described by Paul et al (2004) and Wang et al (2009), respectively, the coefficient of determination (r 2 ), and The Nash-Sutcliffe coefficient of efficiency, E (Nash and Sutcliffe 1970) to evaluate goodness of fit. Table 3.…”

Section: Supporting Field Measurementsmentioning

confidence: 99%

Camelina water use and seed yield response to irrigation scheduling in an arid environment

2012

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Camelina sativa (L.) Crantz is a promising, biodiesel-producing oilseed that could potentially be implemented as a low-input alternative crop for production in the arid southwestern USA. However, little is known about camelina's water use, irrigation management, and agronomic characteristics in this arid environment. Camelina experiments were conducted for 2 years (January to May in 2008 and 2010) in Maricopa, Arizona, to evaluate the effectiveness of previously developed heat unit and remote sensing basal crop coefficient (K cb ) methods for predicting camelina crop evapotranspiration (ET) and irrigation scheduling. Besides K cb methods, additional treatment factors included two different irrigation scheduling soil water depletion (SWD) levels (45 and 65 %) and two levels of seasonal N applications within a randomized complete block design with 4 blocks. Soil water content measurements taken in all treatment plots and applied in soil water balance calculations were used to evaluate the predicted ET. The heat-unit K cb method was updated and validated during the second experiment to predict ET to within 12-13 % of the ET calculated by the soil water balance. The remote sensing K cb method predicted ET within 7-10 % of the soil water balance. Seasonal ET from the soil water balance was significantly greater for the remote sensing than heat-unit K cb method and significantly greater for the 45 than 65 % SWD level. However, final seed yield means, which varied from 1,500 to 1,640 kg ha -1 for treatments, were not significantly different between treatments or years. Seed oil contents averaged 45 % in both years. Seed yield was found to be linearly related to seasonal ET with maximum yield occurring at about 470-490 mm of seasonal ET. Differences in camelina seed yields due to seasonal N applications (69-144 kg N ha -1 over the 2 years) were not significant. Further investigations are needed to characterize camelina yield response over a wider range of irrigation and N inputs.

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Section: Supporting Field Measurementsmentioning

confidence: 99%

Camelina water use and seed yield response to irrigation scheduling in an arid environment

2012

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“…Currently, nonpoint source (NPS) pollution models are most frequently used to determine the maximum allowable loading rates of fecal indicators from diffuse sources that are necessary to meet water quality standards, and most NPS models simulate microorganism transport in the unattached or dissolved state (Paul et al 2004). Previous studies have reported that fecal bacteria attach to particles in streams and estuaries (Characklis et al 2005;Fries et al 2006, Goulder 1977.…”

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confidence: 99%

Escherichia coli and Enterococci Attachment to Particles in Runoff from Highly and Sparsely Vegetated Grassland

Soupir

Mostaghimi

2010

Water Air Soil Pollut

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Limited data on microbial partitioning between the freely suspended and particulate attached phases during transport along overland flow pathways have resulted in high uncertainty in bacterial fate and transport models and the application of these models to watershed management plans. The objectives of this study were to examine differences in attachment between E. coli and enterococci in runoff from plots with highly and sparsely vegetated grassland; investigate relations between flow regime, total suspended solids, and E. coli and enterococci attachment; and identify the particle size categories to which the attached cells were associated. Two rainfall simulations were conducted on large field plots 3 m wide by 18.3 m long with highly and both highly and sparsely vegetated covers and treated with standard cowpats. Results from the first experiment representing pasture with highly vegetated cover indicate that the majority of E. coli and enterococci are transported from the fresh manure source in the unattached state with only 4.8% of E. coli and 13% of enterococci associated with particles. The second experiment which compared partitioning in runoff from both highly and sparsely vegetated covers found lower bacterial attachment rates: the average E. coli percent attached was 0.06% from plots with highly vegetated cover and 2.8% from plots with sparsely vegetated cover while the corresponding values for enterococci were 0.98% and 1.23%, respectively. The findings from this study provide the first set of data on bacterial partitioning in overland flow from large field plots, and results may be helpful for parameterizing water quality models and designing conservation practices.

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“…However, several watershed-scale models have become more common in their application and appearance in published literature. These include AnnAGNPS (Yuan et al, 2001;Suttles et al, 2003), ANSWERS-2000 (Bouraoui andDillaha, 2000;Niu et al, 2001), HSPF (Paul et al, 2004;Saleh and Du, 2004), SWAT (Saleh et al, 2000;White and Chaubey, 2005), and WAM (Ouyang, 2003;Bottcher et al, 2005). Additional information on these and other watershedscale models may also be found in Bora and Bera (2003,2004).…”

Section: Commonly Used Watershed-scale Modelsmentioning

confidence: 99%

“…Another watershed-scale model is HSPF, which is a hydrologic and water quality model often used to simulate stream bacteria (Paul et al, 2004;Benham et al, 2005). Although HSPF is one of the most comprehensive watershedscale models, its application may be more difficult for the user than that of other watershed-scale models.…”

Section: Commonly Used Watershed-scale Modelsmentioning

confidence: 99%

Landscape Models for Simulating Water Quality at Point, Field, and Watershed Scales

Srivastava¹,

Migliaccio²,

Šimůnek³

2007

Transactions of the ASABE

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ABSTRACT. In the last four decades, a plethora of models has been developed to simulate nonpoint-source (NPS) odeling nonpoint-source (NPS) pollutant fate and transport processes across multiple scales is fundamental to addressing a number of environmental and natural resource issues, including degradation of soil and contamination of surface and ground waters. Landscape NPS models are currently used for a variety of purposes, including registration of pesticides and other agrochemicals, designing soil conservation practices, water table management, prevention of chemical pollution of surface water bodies and groundwater, protection of aquatic biota and development of Total Maximum Daily Loads (TMDLs). Virtually all federal agencies currently have their own models or support models developed elsewhere. According to Singh and Woolhiser (2002), "In the years ahead, the models will become even more common and will play an increasing role in our day-to-day lives." Since NPS pollutant transport is mainly driven by meteorological events, the early to mid-20th century saw the development of mathematical descriptions of individual hydrologic components (e.g., infiltration, runoff, evapotranspiration, and interception). The digital revolution of 1960s witnessed the integration of individual hydrologic components (Singh and Woolhiser, 2002) into functional models that can be applied at various spatial and temporal scales. The development of NPS models in the U.S. began in response to the Clean Water Act (Arnold and Fohrer, 2005). Examples of these models include AGNPS (Young et al., 1987), AnnAGNPS (Bingner and Theurer, 2003), ANSWERS Dillaha, 1996, 2000), APEX (Williams and Izaurralde, 2005), CREAMS (Knisel 1980), DRAINMOD (Skaggs, 2007, EPIC (Williams et al., 1984), GLEAMS (Leonard et al., 1987;Knisel and Davis, 1999), GWLF (Haith and Shoemaker, 1987), HSPF (Bicknell et al., 2001), HY-DRUS (Šimůnek et al., 2005, 2006, KINEROS (Woolhiser et al., 1990), LEACHM (Hutson and Wagenet, 1992), MAC-RO (Jarvis, 1994), NLEAP (Shaffer et al., 1991), RUSLE (Renard et al., 1991), RZWQM (Ahuja and Hebson, 1992) , SWAT (Neitsch et al., 2002), and WEPP (Laflen et al., 1991). These models tend to be scale dependent and are usually suited for profile/horizon/pedon (point scale), field/farm (field scale), or watershed scale; however, there are a few exceptions.The land phase of the hydrologic cycle is complicated and includes a great deal of uncertainty (Singh and Woolhiser, 2002) because of the spatial and temporal heterogeneity of soil properties, land use, precipitation, etc. Currently, a model that simultaneously operates at multiple spatial scales does not exist (the hierarchies of scales at which NPS models are M

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Analysis of the HSPF Water Quality Parameter Uncertainty in Predicting Peak in-Stream Fecal Coliform Concentrations

Cited by 47 publications

References 0 publications

Camelina water use and seed yield response to irrigation scheduling in an arid environment

Camelina water use and seed yield response to irrigation scheduling in an arid environment

Escherichia coli and Enterococci Attachment to Particles in Runoff from Highly and Sparsely Vegetated Grassland

Landscape Models for Simulating Water Quality at Point, Field, and Watershed Scales

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