Relating low‐flow characteristics to the base flow recession time constant at partial record stream gauges

Eng, Ken; Milly, P. C. D.

doi:10.1029/2006wr005293

Cited by 54 publications

(69 citation statements)

References 13 publications

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“…Some studies have linked slopes b to the spatial scale and found that a linear relationship for storage-outflow (b = 1) was suitable for hillslopes but turned more and more into non-linear behavior (b > 1) with increasing catchment scale and catchment heterogeneity (Clark et al, 2009;Harman et al, 2009). We found no systematic pattern between slopes b and catchment area (not shown), although area often has been identified as a catchment characteristic for regional low-flow regression models (Eng and Milly, 2007). This may be due to the non-characteristic results of a RAM caused by aquifer heterogeneity (Rupp and Selker, 2005;Harman et al, 2009) or the unknown number of aquifers that contribute recession streamflow to each catchment (Ajami et al, 2011).…”

Section: The Effect Of Different Rams To Distinguish Catchments' Recementioning

confidence: 81%

Are streamflow recession characteristics really characteristic?

Stoelzle

Stahl

Weiler

2013

Hydrol. Earth Syst. Sci.

108

134

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Abstract. Streamflow recession has been investigated by a variety of methods, often involving the fit of a model to empirical recession plots to parameterize a non-linear storageoutflow relationship based on the dQ/dt-Q method. Such recession analysis methods (RAMs) are used to estimate hydraulic conductivity, storage capacity, or aquifer thickness and to model streamflow recession curves for regionalization and prediction at the catchment scale. Numerous RAMs have been published, but little is known about how comparably the resulting recession models distinguish characteristic catchment behavior. In this study we combined three established recession extraction methods with three different parameter-fitting methods to the power-law storage-outflow model to compare the range of recession characteristics that result from the application of these different RAMs. Resulting recession characteristics including recession time and corresponding storage depletion were evaluated for 20 mesoscale catchments in Germany. We found plausible ranges for model parameterization; however, calculated recession characteristics varied over two orders of magnitude. While recession characteristics of the 20 catchments derived with the different methods correlate strongly, particularly for the RAMs that use the same extraction method, not all rank the catchments consistently, and the differences among some of the methods are larger than among the catchments. To elucidate this variability we discuss the ambiguous roles of recession extraction procedures and the parameterization of the storage-outflow model and the limitations of the presented recession plots. The results suggest strong limitations to the comparability of recession characteristics derived with different methods, not only in the model parameters but also in the relative characterization of different catchments. A multiple-methods approach to investigating streamflow recession characteristics should be considered for applications whenever possible.

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Section: The Effect Of Different Rams To Distinguish Catchments' Recementioning

confidence: 81%

Are streamflow recession characteristics really characteristic?

Stoelzle

Stahl

Weiler

2013

Hydrol. Earth Syst. Sci.

108

134

View full text Add to dashboard Cite

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“…Juston et al (2009) demonstrate that, if selected in an intelligent way, a small fraction of data points in a longer time series might contain almost all information of the entire data series. Eng and Milly (2007) found that "a single pair of strategically timed streamflow measurements" considerably improved the estimation of base flow recession coefficients compared to estimates based on catchment area. Rode et al (2007) applied a water quality model to the Elbe River and found that a subset of the entire calibration data provided good results.…”

Section: Prediction Of Ungauged Basins (Pub)mentioning

confidence: 99%

Gauging the ungauged basin: how many discharge measurements are needed?

Seibert

Beven

2009

Hydrol. Earth Syst. Sci.

220

168

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Abstract. Runoff estimation in ungauged catchments is probably one of the most basic and oldest tasks of hydrologists. This long-standing issue has received increased attention recently due to the PUB (Prediction in Ungauged Basins) initiative. Given the challenges of predicting runoff for ungauged catchments one might argue that the best course of action is to take a few runoff measurements. In this study we explored how implementing such a procedure might support predictions in an ungauged basin. We used a number of monitored Swedish catchments as hypothetical ungauged basins where we pretended to start with no runoff data and then added different sub-sets of the available data to constrain a simple catchment model. These sub-sets consisted of a limited number of single runoff measurements; in other words these data represent what could be measured with limited efforts in an ungauged basin. We used a Monte Carlo approach and predicted runoff as a weighted ensemble mean of simulations using acceptable parameter sets. We found that the ensemble prediction clearly outperformed the predictions using single parameter sets and that surprisingly little runoff data was necessary to identify model parameterizations that provided good results for the "ungauged" test periods. These results indicated that a few runoff measurements can contain much of the information content of continuous runoff time series. However, the study also indicated that results may differ significantly between catchments and also depend on the days chosen for taking the measurements.

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“…There is considerable controversy over the assumption that watersheds exhibit a fixed time constant, known as the baseflow recession constant, in the relation between aquifer storage and baseflow discharge (Zecharias and Brutsaert, 1988;Troch et al, 1993;Vogel and Kroll, 1996;Wittenberg, 1999;Eng and Milly, 2007;Harman and Sivapalan, 2009). Thus, another goal of our study is to improve our ability to construct hypothesis tests which can effectively evaluate whether or not watershed recessions are characterized by a time constant known as the baseflow recession constant, K b .…”

Section: Introductionmentioning

confidence: 99%

Objective hydrograph baseflow recession analysis

Thomas

Vogel

Famiglietti

2015

Journal of Hydrology

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Keywords:Hydromorphology Groundwater/surface water interaction Quantile regression Numerical derivative Linear reservoir Water withdrawal s u m m a r y A streamflow hydrograph recession curve expresses the theoretical relationship between aquifer structure and groundwater outflow to a stream channel. That theoretical relationship is often portrayed empirically using a recession plot defined as a plot of ln(ÀdQ/dt) versus ln(Q), where Q is streamflow discharge. Such hydrograph recession plots are commonly used to estimate recession parameters, aquifer properties and for evaluating alternative hydrologic hypotheses. We introduce a comprehensive and objective approach to analyze baseflow recessions with innovations including the use of quantile regression, efficient and objective numerical estimation of dQ/dt, inclusion of groundwater withdrawals, and incorporation of seasonal effects. We document that these innovations when all combined, lead to significant improvements, over previous studies, in our ability to discern the theoretical behavior of stream aquifer systems. A case study reveals that our methodology enables us to reject the simple linear reservoir hypothesis of stream aquifer interactions for watersheds in New Jersey and results in improved correlations between low flow statistics and aquifer properties for those same watersheds.

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Relating low‐flow characteristics to the base flow recession time constant at partial record stream gauges

Cited by 54 publications

References 13 publications

Are streamflow recession characteristics really characteristic?

Are streamflow recession characteristics really characteristic?

Gauging the ungauged basin: how many discharge measurements are needed?

Objective hydrograph baseflow recession analysis

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