Estimates of Flow Duration, Mean Flow, and Peak-Discharge Frequency Values for Kansas Stream Locations

Perry, Charles A.; Wolock, David M.; Artman, Joshua C.

doi:10.3133/sir20045033

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Some of the streamgage criteria described above may be considered by some to be too relaxed for load calculation purposes, but the criteria were intended to provide a large number of suitable gages to choose from. For example, the drainage area ratio criteria (item #2 above) is slightly more relaxed than the guidelines (0.5-1.5) noted by Perry et al (2004) for estimating streamflow statistics at ungaged sites. However, >97% of the sites used in the SPARROW models are within the 0.5-1.5 ratio range.…”

Section: Description Of Water-quality and Streamflow Data And Data Smentioning

confidence: 99%

A Multi-Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models1

Saad

Schwarz

Robertson

et al. 2011

JAWRA Journal of the American Water Resources Association

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Stream-loading information was compiled from federal, state, and local agencies, and selected universities as part of an effort to develop regional SPAtially Referenced Regressions On Watershed attributes (SPARROW) models to help describe the distribution, sources, and transport of nutrients in streams throughout much of the United States. After screening, 2,739 sites, sampled by 73 agencies, were identified as having suitable data for calculating long-term mean annual nutrient loads required for SPARROW model calibration. These sites had a wide range in nutrient concentrations, loads, and yields, and environmental characteristics in their basins. An analysis of the accuracy in load estimates relative to site attributes indicated that accuracy in loads improve with increases in the number of observations, the proportion of uncensored data, and the variability in flow on observation days, whereas accuracy declines with increases in the root mean square error of the water-quality model, the flow-bias ratio, the number of days between samples, the variability in daily streamflow for the prediction period, and if the load estimate has been detrended. Based on compiled data, all areas of the country had recent declines in the number of sites with sufficient water-quality data to compute accurate annual loads and support regional modeling analyses. These declines were caused by decreases in the number of sites being sampled and data not being entered in readily accessible databases.

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Section: Description Of Water-quality and Streamflow Data And Data Smentioning

confidence: 99%

A Multi-Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models1

Saad

Schwarz

Robertson

et al. 2011

JAWRA Journal of the American Water Resources Association

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“…Annual flows were summed from the Americus and Plymouth sites, which were 83.9 km 3 in 2007, 159.1 km 3 in 2008 and 133.2 km 3 in 2009; the median combined annual flow from these sites was 106.1 km 3 from 1964 to 2006. Rivers exceeded the 2‐year (50% annual) USGS flood‐frequency estimates 14 times at the Plymouth and Neosho Rapids sites (Perry et al ., ), indicating that relatively extreme storms (and corresponding high sediment loads) were frequently observed during the study period. Increased rainfall and flow during the study period indicate that sediment flux to (and likely from) the reservoir is likely larger than during a typical, 4‐year study period.…”

Section: Resultsmentioning

confidence: 99%

Assessing the potential of reservoir outflow management to reduce sedimentation using continuous turbidity monitoring and reservoir modelling

Lee

Foster

2012

Hydrological Processes

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In‐stream sensors are increasingly deployed as part of ambient water quality‐monitoring networks. Temporally dense data from these networks can be used to better understand the transport of constituents through streams, lakes or reservoirs. Data from existing, continuously recording in‐stream flow and water quality monitoring stations were coupled with the two‐dimensional hydrodynamic CE‐QUAL‐W2 model to assess the potential of altered reservoir outflow management to reduce sediment trapping in John Redmond Reservoir, located in east‐central Kansas. Monitoring stations upstream and downstream from the reservoir were used to estimate 5.6 million metric tons of sediment transported to John Redmond Reservoir from 2007 through 2010, 88% of which was trapped within the reservoir. The two‐dimensional model was used to estimate the residence time of 55 equal‐volume releases from the reservoir; sediment trapping for these releases varied from 48% to 97%. Smaller trapping efficiencies were observed when the reservoir was maintained near the normal operating capacity (relative to higher flood pool levels) and when average residence times were relatively short. An idealized, alternative outflow management scenario was constructed, which minimized reservoir elevations and the length of time water was in the reservoir, while continuing to meet downstream flood control end points identified in the reservoir water control manual. The alternative scenario is projected to reduce sediment trapping in the reservoir by approximately 3%, preventing approximately 45 000 metric tons of sediment from being deposited within the reservoir annually. This article presents an approach to quantify the potential of reservoir management using existing in‐stream data; actual management decisions need to consider the effects on other reservoir benefits, such as downstream flood control and aquatic life. Copyright © 2012 John Wiley & Sons, Ltd.

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“…The available data in Kansas City has been downloaded from the source: http://waterdata.usgs.gov. Elevation, mean slope permeability and precipitation are taken from Perry et al [28].…”

Section: Data Colmentioning

confidence: 99%

Prediction of Flow Duration Curves for Ungauged Basins with Quasi-Newton Method

ar¹,

Baykan²

2013

JWARP

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Prediction of flow-duration-curves (FDC) is an important task for water resources planning, management and hydraulic energy production. Classification of the basins as carstic and non-carstic may be used to estimate parameters of the FDC with predictive tools for catchments with/without observed stream flow. There is a need for obtaining FDC for ungauged stations for efficient water resource planning. Thus, study proposes a quite new approach, called the EREFDC model, for estimating the parameters of the FDC for which the parameters of the FDC are obtained with quasi-Newton method. Estimation are made for using the bv gauged stations at first than the FDC parameters are estimated for ungauged stations based on drainage area, annual mean precipitation, mean permeability, mean slope, latitude, longitude, and elevation from the mean sea level are used. The EREFDC model consists of various type of linear- and nonlinear mathematical equations, is able to predict a wide range of the FDC parameters for gauged and ungauged basins. The method is applied to 72 unimpaired catchments studied are about for 50 years average daily measured stream flow. Results showed that the EREFDC model may be used for estimating. FDC parameters for ungauged hydrological basins in order to find FDC for ungauged stations. Results also showed that the EREFDC model performs better in carstic regions than non-carstic regions. In addition, parameters of FDC for tributaries at the basins with insufficient flow data or without flow data may be determined by using basin characteristics.

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Estimates of Flow Duration, Mean Flow, and Peak-Discharge Frequency Values for Kansas Stream Locations

Cited by 7 publications

References 10 publications

A Multi-Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models1

A Multi-Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models1

Assessing the potential of reservoir outflow management to reduce sedimentation using continuous turbidity monitoring and reservoir modelling

Prediction of Flow Duration Curves for Ungauged Basins with Quasi-Newton Method

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