Derivation of Base Flow Recession Curves and Parameters

Singh, Krishan P.; Stall, John B.

doi:10.1029/wr007i002p00292

Cited by 63 publications

(52 citation statements)

References 6 publications

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“…Shaw and Riha (2012) end the extracted recession 2 days before dQ/dt changes from negative to positive following a recession start. Some studies use the inflection point of the recession curve -the first day following a rainfall event for which the hydrograph is concave down -to identify the start of the extracted recession (Singh and Stall, 1971;Wittenberg and Sivapalan, 1999). A similar concavity criterion, paired with the requirement of decreasing flow, could also be used to define the end of a recession event.…”

Section: Identifying the End Of A Recession Event (C)mentioning

confidence: 99%

Event-scale power law recession analysis: quantifying methodological uncertainty

Dralle

Karst

Charalampous

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The study of single streamflow recession events is receiving increasing attention following the presentation of novel theoretical explanations for the emergence of power law forms of the recession relationship, and drivers of its variability. Individually characterizing streamflow recessions often involves describing the similarities and differences between model parameters fitted to each recession time series. Significant methodological sensitivity has been identified in the fitting and parameterization of models that describe populations of many recessions, but the dependence of estimated model parameters on methodological choices has not been evaluated for event-by-event forms of analysis. Here, we use daily streamflow data from 16 catchments in northern California and southern Oregon to investigate how combinations of commonly used streamflow recession definitions and fitting techniques impact parameter estimates of a widely used power law recession model. Results are relevant to watersheds that are relatively steep, forested, and rain-dominated. The highly seasonal mediterranean climate of northern California and southern Oregon ensures study catchments explore a wide range of recession behaviors and wetness states, ideal for a sensitivity analysis. In such catchments, we show the following: (i) methodological decisions, including ones that have received little attention in the literature, can impact parameter value estimates and model goodness of fit; (ii) the central tendencies of event-scale recession parameter probability distributions are largely robust to methodological choices, in the sense that differing methods rank catchments similarly according to the medians of these distributions; (iii) recession parameter distributions are method-dependent, but roughly catchment-independent, such that changing the choices made about a particular method affects a given parameter in similar ways across most catchments; and (iv) the observed correlative relationship between the power-law recession scale parameter and catchment antecedent wetness varies depending on recession definition and fitting choices. Considering study results, we recommend a combination of four key methodological decisions to maximize the quality of fitted recession curves, and to minimize bias in the related populations of fitted recession parameters.

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Section: Identifying the End Of A Recession Event (C)mentioning

confidence: 99%

Event-scale power law recession analysis: quantifying methodological uncertainty

Dralle

Karst

Charalampous

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Szilagyi and Parlange, 1998;Peña-Arancibia et al, 2010), but studies can also be found that eliminated an interval between 1 and 10 days (Zecharias and Brutsaert, 1988a;Vogel and Kroll, 1992;Parlange et al, 2001;Malvicini et al, 2005;van Dijk, 2010;Wang and Cai, 2010). Singh and Stall (1971) divided the declining hydrograph at the inflection point and analyzed only the latter part to reduce influence of surface flows at the beginning of recession. Other studies have extended this restriction and used only recessions that started two days after the inflection point (Wittenberg and Sivapalan, 1999;Wang and Cai, 2010).…”

Section: Introductionmentioning

confidence: 99%

Are streamflow recession characteristics really characteristic?

Stoelzle

Stahl

Weiler

2013

Hydrol. Earth Syst. Sci.

109

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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“…Also, other methods that use some form of hydrological reasoning have been developed without a physically based mathematical framework. Currently, the separation of baseflow from the streamflow hydrograph can be carried out utilising methods that can be grouped into the following four categories: (1) graphical separation (Sloto and Crouse, 1996), (2) recession analysis (Tallaksen, 1995), (3) conceptual models (Barnes, 1939;Singh and Stall, 1971;Furey and Gupta, 2001;Eckhardt, 2005;Huyck et al, 2005) and (4) recursive digital filters (Nathan and McMahon, 1990;Arnold and Allen, 1999).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of outputs from automated baseflow separation methods against simulated baseflow from a physically based, surface water-groundwater flow model

Partington

Brunner

Simmons

et al. 2012

Journal of Hydrology

143

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s u m m a r yBaseflow is often considered to be the groundwater discharge component of streamflow. It is commonly estimated using conceptual models, recursive filters or a combination of the two. However, it is difficult to validate these methods due to the current challenges of measuring baseflow in the field. In this study, simulation of a synthetic catchment's response to rainfall is carried out using a fully integrated surface water-groundwater flow model. A series of rainfall events with differing recovery periods and varied antecedent moisture conditions is considered to span a range of different streamflow generation dynamics. Baseflow is estimated for the outlet hydrograph of the synthetic catchment using a selection of commonly used automated baseflow separation methods. These estimates are compared to the baseflow signal obtained from the numerical model, which serves as the control experiment. Results from these comparisons show that depending on the method used, automated baseflow separation underestimates the simulated baseflow by as much as 28%, or overestimates it by up to 74%, during rainfall events. No separation method is found to be clearly superior to the others, as the performance of the various methods varies with different soil types, antecedent moisture conditions and rainfall events. The differences between the various approaches clearly demonstrate that the baseflow separation methods investigated are not universally applicable.

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Derivation of Base Flow Recession Curves and Parameters

Cited by 63 publications

References 6 publications

Event-scale power law recession analysis: quantifying methodological uncertainty

Event-scale power law recession analysis: quantifying methodological uncertainty

Are streamflow recession characteristics really characteristic?

Evaluation of outputs from automated baseflow separation methods against simulated baseflow from a physically based, surface water-groundwater flow model

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