Recession analysis across scales: The impact of both random and nonrandom spatial variability on aggregated hydrologic response

Chen, Bo; Krajewski, Witold F.

doi:10.1016/j.jhydrol.2015.01.049

Cited by 20 publications

(17 citation statements)

References 59 publications

(128 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, nearly all event-scale recession studies set a minimum duration for chosen recession periods. Reasons for this choice vary; authors cite the removal of noise from short events (Ye et al, 2014), the necessity of capturing late time flow processes (Chen and Krajewski, 2015), and data quality concerns related to sample size (Shaw, 2016). Event-scale recession analyses have typically chosen a minimum of 4 to 5 days of recession for daily data (e.g., Shaw and Riha, 2012;Biswal and Marani, 2010), although values upwards of 10 days (e.g., Howe, 1966) and as low as 12 h (e.g., McMillan et al, 2014, for high-frequency data) have been used.…”

Section: Defining the Minimum Allowable Length Of Recession Event (M)mentioning

confidence: 99%

Event-scale power law recession analysis: quantifying methodological uncertainty

Dralle

Karst

Charalampous

et al. 2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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.

show abstract

Section: Defining the Minimum Allowable Length Of Recession Event (M)mentioning

confidence: 99%

Event-scale power law recession analysis: quantifying methodological uncertainty

Dralle

Karst

Charalampous

et al. 2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Numerous studies have unveiled various natural causes of recession variability, to name a few but not limited to: the vertical heterogeneity of hydraulic conductivity (Troch et al 1993, Rupp andSelker 2006b), the horizontal heterogeneity recession processes across hillslopes (Harman et al 2009), landscape organization (e.g. Clark et al 2009, Chen andKrajewski 2015), the shrinkage of the feeding river network or area to streamflow (Biswal and Marani 2010, Mutzner et al 2013, Biswal and Nagesh Kumar 2014, and watershed moisture storage (Shaw et al 2013). These studies have greatly advanced our knowledge of the hydrological functioning of catchments.…”

Section: Artificial Variability Can Blur the Natural Variability Of Rmentioning

confidence: 99%

“…This approach gained attention in the past and is found increasingly in the recent literature (e.g. Vogel and Kroll 1996, Sujono et al 2004, van de Giesen et al 2005, Hammond and Han 2006, Blume et al 2007, Rupp et al 2009, Shaw et al 2013, Biswal and Nagesh Kumar 2014, Chen and Krajewski 2015.…”

Section: Introductionmentioning

confidence: 99%

Analysing individual recession events: sensitivity of parameter determination to the calculation procedure

Chen

Krajewski

2016

Hydrological Sciences Journal

Self Cite

View full text Add to dashboard Cite

This study assesses the sensitivity to model fitting methods and segment selection of the estimated parameters A and B of the model dQ/dt = −AQ B for individual events. We investigated about 750 recession events observed at 25 US Geological Survey gauges in the Iowa and Cedar river basins in the United States, with drainage areas ranging from 7 to 17 000 km 2. The parameters of these recession events were estimated using three commonly adopted methods and recession segments with different extraction criteria. The results showed that the variations of the parameter estimates for the same recession event were comparable to the variations of parameters between different events due to using different model fitting methods and recession segments. This raises cautions for comparative analysis of individual recessions. The result also implies that the nonlinear direct fitting method is the most robust among the three model fitting methods compared.

show abstract

“…Otherwise, Equation (8) will lead to an unrealistically large value of L when ASNL shrinks significantly. Based on the empirically observed dynamic of the stream network [24], the hillslopes including order-one streams stop contributing to streamflow first, indicating the shrinkage of the contribution area. As the catchment drainage continues, only the areas connected to high order streams contribute.…”

Section: Estimation Of Aquifer Breadthmentioning

confidence: 99%

Estimation of Active Stream Network Length in a Hilly Headwater Catchment Using Recession Flow Analysis

Zhang

Long

et al. 2017

Water

View full text Add to dashboard Cite

Abstract:Varying active stream network lengths (ASNL) is a common phenomenon, especially in hilly headwater catchment. However, direct observations of ASNL are difficult to perform in mountainous catchments. Regarding the correlation between active stream networks and stream recession flow characteristics, we developed a new method to estimate the ASNL, under different wetness conditions, of a catchment by using streamflow recession analysis as defined by Brutsaert and Nieber in 1977. In our study basin, the Sagehen Creek catchment, we found that aquifer depth is related to a dimensionless parameter defined by Brutsaert in 1994 to represent the characteristic slope magnitude for a catchment. The results show that the estimated ASNL ranges between 9.8 and 43.9 km which is consistent with direct observations of dynamic stream length, ranging from 12.4 to 32.5 km in this catchment. We also found that the variation of catchment parameters between different recession events determines the upper boundary characteristic of recession flow plot on a log-log scale.

show abstract

Recession analysis across scales: The impact of both random and nonrandom spatial variability on aggregated hydrologic response

Cited by 20 publications

References 59 publications

Event-scale power law recession analysis: quantifying methodological uncertainty

Event-scale power law recession analysis: quantifying methodological uncertainty

Analysing individual recession events: sensitivity of parameter determination to the calculation procedure

Estimation of Active Stream Network Length in a Hilly Headwater Catchment Using Recession Flow Analysis

Contact Info

Product

Resources

About