Variability of Empirical Flow Quantiles

Loáiciga, Hugo A.

doi:10.1061/(asce)0733-9429(1989)115:1(82)

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…The daily E. coli concentrations were then used to construct an E. coli duration curve, similar to a flow duration curve (Loaiciga 1989). Daily E. coli concentration was ranked and the probability of exceedance (p) was calculated as:…”

Section: E Colimentioning

confidence: 99%

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Quantifying the variability in Escherichia coli (E. coli) throughout storm events at a karst spring in northwestern Arkansas, United States

Knierim

Hays

Bowman³

2015

Environ Earth Sci

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In northwestern Arkansas, karst aquifers have experienced degraded water quality due to impacts of landuse changes, such as increased urbanization or intensification of agriculture. Water quality at a karst spring was characterized by quantifying the variability of fecal-indicator bacteria, specifically Escherichia coli (E. coli), throughout base-flow periods and storm events. Waterquality change over time was assessed using an intermittent record of nitrate and chloride data since the 1990s. A Geographic Information System was used to quantify changes in land use over time and identify housing subdivisions using on-site septic systems. E. coli was significantly greater during storm events (649 CFU/100 mL or MP/ 100 mL) than base-flow periods (41 CFU/100 mL or MPN/ 100 mL). The increase in E. coli at the spring following storm events was interpreted to result from flushing of bacteria from the land surface, through the epikarst and karst conduits, and into groundwater. Nitrate and chloride have increased significantly since the 1990s, following the general pattern of increased urbanization in the recharge area. Septic-tank effluent may be degrading the water quality of the karst spring based on the dominance of on-site septic tank usage in the recharge area, unsuitable topography and soil type for septic tank absorption fields, increased nitrate and chloride concentrations concomitant with increased urbanization, and increase of the fecal-indicator bacteria E. coli following storm events.

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Section: E Colimentioning

confidence: 99%

“…where k is the rank of daily E. coli concentration and n is the number of days (Loaiciga 1989). The E. coli duration curve estimates the amount of time that BS was above an E. coli concentration threshold, such as the primary or secondary contact limits for the state of Arkansas.…”

Section: E Colimentioning

confidence: 99%

Quantifying the variability in Escherichia coli (E. coli) throughout storm events at a karst spring in northwestern Arkansas, United States

Knierim

Hays

Bowman³

2015

Environ Earth Sci

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“…Droughts were defined by at least 3 years of below-median annual streamflow in all or any subset of the basins. This particular definition of drought fits well with the water supply/demand situation in the study areas and with the hydrologic perspective of the study[Loaiciga, 1989]. Various other definitions of drought have been used with peculiarities depending on the meteorologic, climatic, ecologic, or economic emphasis given to drought phenomena.Figure 10displays the conceptual representation of drought as a stochastic process introduced by Loaiciga et al[1992].…”

mentioning

confidence: 99%

dendrohydrology and long‐term hydrologic phenomena

Loáiciga¹,

Haston²,

Michaelsen³

1993

Reviews of Geophysics

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Dendrohydrology provides accurate methods for studying long‐term hydrologic variability at regional scales. A substantial literature and body of knowledge exists, attesting to the value of tree ring based hydrologic reconstructions to discern patterns of long‐term hydrologic variability. Application studies encompass drought analysis, analysis of extremes, periodicity of rare hydrologic phenomena, regional interdependence of surface moisture conditions, and, in general, the probabilistic analysis of key hydroclimatic variables such as runoff, precipitation, and temperature. The probabilistic analysis includes distributional properties, frequency duration analysis, severity of events, and spatial variability of hydrologic indicators. This paper reviews some fundamental aspects of dendrohydrology, with a perspective on its value to hydrologists in pursuit of an understanding of long‐term hydrologic spatial‐temporal behavior and provides also a selective citation of previous work conducted within the dendrohydrologic discipline.

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“…The 50, 75, 80, 90, and 100% exceedance‐probability streamflows (streamflow values that are equalled or exceeded, say 90% of the time) are calculated using the entire daily‐streamflow record for each streamgage (Searcy, 1959). The daily‐streamflow values are ranked from largest to smallest and a probability is calculated for each value based on the California‐plotting position (Loaiciga, 1989). The probabilities are insensitive to the choice of plotting position due to the large number of daily‐streamflow values in each record, so we do not consider other plotting positions.…”

Section: Data and Synthetic Partial‐record Measurement Generationmentioning

confidence: 99%

“…Circles represent the 1120 continuous streamgages used in this study. Black is for the streamgages in the east region, blue for the central region, and red for the west region of the United States from largest to smallest and a probability is calculated for each value based on the California-plotting position (Loaiciga, 1989). The probabilities are insensitive to the choice of plotting position due to the large number of daily-streamflow values in each record, so we do not consider other plotting positions.…”

Section: Data and Synthetic Partial-record Measurement Generationmentioning

confidence: 99%

Causes of systematic over‐ or underestimation of low streamflows by use of index‐streamgage approaches in the United States

Eng¹,

Kiang²,

Chen³

et al. 2011

Hydrological Processes

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Abstract:Low-flow characteristics can be estimated by multiple linear regressions or the index-streamgage approach. The latter transfers streamflow information from a hydrologically similar, continuously gaged basin ('index streamgage') to one with a very limited streamflow record, but often results in biased estimates. The application of the index-streamgage approach can be generalized into three steps: (1) selection of streamflow information of interest, (2) definition of hydrologic similarity and selection of index streamgage, and (3) application of an information-transfer approach. Here, we explore the effects of (1) the range of streamflow values, (2) the areal density of streamgages, and (3) index-streamgage selection criteria on the bias of three information-transfer approaches on estimates of the 7-day, 10-year minimum streamflow (Q 7,10 ). The three information-transfer approaches considered are maintenance of variance extension, base-flow correlation, and ratio of measured to concurrent gaged streamflow (Q-ratio invariance). Our results for 1120 streamgages throughout the United States suggest that only a small portion of the total bias in estimated streamflow values is explained by the areal density of the streamgages and the hydrologic similarity between the two basins. However, restricting the range of streamflow values used in the index-streamgage approach reduces the bias of estimated Q 7,10 values substantially. Importantly, estimated Q 7,10 values are heavily biased when the observed Q 7,10 values are near zero. Results of the analysis also showed that Q 7,10 estimates from two of the three index-streamgage approaches have lower root-mean-square error values than estimates derived from multiple regressions for the large regions considered in this study. Published in

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Variability of Empirical Flow Quantiles

Cited by 15 publications

References 14 publications

Quantifying the variability in Escherichia coli (E. coli) throughout storm events at a karst spring in northwestern Arkansas, United States

Quantifying the variability in Escherichia coli (E. coli) throughout storm events at a karst spring in northwestern Arkansas, United States

dendrohydrology and long‐term hydrologic phenomena

Causes of systematic over‐ or underestimation of low streamflows by use of index‐streamgage approaches in the United States

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