Study of dynamic behaviour of recession curves

Abstract

Since Brutsaert and Neiber (1977), recession curves are widely used to analyse subsurface systems of river basins by expressing − dQ/dt as a function of Q, which typically take a power law form: − dQ/dt = kQα, where Q is the discharge at a basin outlet at time t. Traditionally recession flows are modelled by single reservoir models that assume a unique relationship between − dQ/dt and Q for a basin. However, recent observations indicate that − dQ/dt ‐ Q relationship of a basin varies greatly across recession events, indicating the limitation of such models. In this study, the dynamic relationship between − dQ/dt and Q of a basin is investigated through the geomorphological recession flow model which models recession flows by considering the temporal evolution of its active drainage network (the part of the stream network of the basin draining water at time t). Two primary factors responsible for the dynamic relationship are identified: (i) degree of aquifer recharge (ii) spatial variation of rainfall. Degree of aquifer recharge, which is likely to be controlled by (effective) rainfall patterns, influences the power law coefficient, k. It is found that k has correlation with past average streamflow, which confirms the notion that dynamic − dQ/dt ‐ Q relationship is caused by the degree of aquifer recharge. Spatial variation of rainfall is found to have control on both the exponent, α, and the power law coefficient, k. It is noticed that that even with same α and k, recession curves can be different, possibly due to their different (recession) peak values. This may also happen due to spatial variation of rainfall. Copyright © 2012 John Wiley & Sons, Ltd.

Streamflow sensitivity to water storage changes across Europe

“…the exponent β can vary with the chosen methodology [Rupp and Selker, 2006;Stoelzle et al, 2013;Dralle et al, 2015;Thomas et al, 2015], spatial variation of rainfall and groundwater discharge [Biswal and Nagesh Kumar, 2014], and the quality of data [Rupp and Selker, 2006;Stoelzle et al, 2013]. Questions still remain about whether the α parameter is a meaningful way to summarize recession behavior.…”

Objective hydrograph baseflow recession analysis

“…In general, groundwater elevations tend to be highest in the spring and lowest in the fall as evidenced in monthly groundwater elevation data obtained from the USGS across New Jersey. Such changes in groundwater elevations may alter active stream networks as described by Biswal and Marani (2010) and Biswal and Nagesh Kumar (2014). To characterize seasonal impacts on hydrograph recessions, we separated recession data from each site into 5 categories: winter (December-February), spring (March-May), summer (June-August), fall (September-November), and annual datasets which include all streamflow data for the period 1990-2009.…”

“…Szilagyi et al (2007) and Shaw et al (2013) evaluated the influence of watershed evapotranspiration on the behavior of baseflow recessions. Studies have evaluated the influence of watershed geomorphology (Biswal and Marani, 2010;Biswal and Nagesh Kumar, 2013;Biswal and Nagesh Kumar, 2014) and watershed storage on stream network dynamics. Kirchner (2009) characterized catchment behavior by deriving a sensitivity function related to nonlinear storage-discharge relationships.…”

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

“…That is, as indicated by Equation (11), those data points with the same recession time (t) between different recession events would be located on a straight line with a slope of 1 and a y-intercept of −log(2t). It should be noted that this behavior is reflected only by the very early recession data, since Equations (4) and (6) are only valid for short recession times.…”