Episodic Master Recession techniques to evaluate hydrographs for aquifer recharge and streamflow

Introduction

Rate-of-change-based analysis tools employing a master recession curve (MRC) can be beneficially applied to different types of hydrographs. An MRC is the relation between the value of a measured response R and its rate of change with time, dR/dt, within a period when there is no external input to the system. We have developed MRC and episodic master recession (EMR) methods to evaluate hydrographs for groundwater levels and streamflow. Applications extend to diverse hydrologic quantities, including aquifer recharge, preferential flow, and stormflow characterization. The determination of a parameterized MRC through this type of structured procedure provides a basis for quantification of hydrologic variables and characteristics that can be validly compared among different events, sites, and periods of time. The EMR method provides means to evaluate long-term hydrographs to discern and illuminate trends with storm characteristics, seasons, soil conditions, and other factors. These expert-guided iterative methods can serve as an alternative or supplement to methods of more fully manual or fully automated evaluation. No approach can totally eliminate the subjectivity of certain hydrologic judgments, whether they enter into the evaluation through event-by-event decisions, through the choices made in the coding of computerized algorithms, or through a structured iterative process as we present here. The structured iterative approach affords much flexibility in formulating expert judgments, and serves to confine the judgments to statements and procedures that can be quantified and documented.

Critical parameter values that characterize water behavior at a given site can be established once for that site so that subjective influences do not affect episode-to-episode comparisons. By centralizing the elements requiring judgment, this method keeps the most subjective elements openly apparent, making it easy to maintain consistency. If applied to a data set with diverse recharge episodes, with broadly differing characteristics, the method serves in evaluating how storm characteristics and antecedent conditions affect recharge and stormflow, with application to climate change and other important issues.

The MRC and EMR programs are written in the R programming language and can be easily run using R studio, an open-source platform available at www.rstudio.com. The downloadable versions of these codes have been set up with filenames and parameter values for a sample data set, which must be changed for the user’s data.

The flow chart below illustrates the way that the MRC and EMR programs are used in conjunction.

Downloadable Resources

Primary publication describing the method:

Nimmo, J.R., and K.S. Perkins. 2018. Episodic master recession evaluation of groundwater and streamflow hydrographs for water-resource estimation. Vadose Zone J. 17:180050. doi:10.2136/vzj2018.03.0050. (PDF)

Publication and document describing the original form of the method:

Nimmo, J.R., Horowitz, C., and Mitchell, L., 2015, Discrete-storm water-table fluctuation method to estimate episodic recharge: Groundwater, v.53, no. 2, doi: 10.1111/gwat.12177. ( PDF)

EMR Program information ( PDF)

References for the water table fluctuation method:

Heppner, C.S., and Nimmo, J.R., 2005, A computer program for predicting recharge with a master recession curve: U.S. Geological Survey Scientific Investigations Report 2005-5172. ( PDF)

Healy, R.W., and Cook, P.G., 2002, Using groundwater levels to estimate recharge: Hydrogeology Journal, v. 10, no. 1, p. 91-109.

Published studies utilizing the method:

Allocca, V., De Vita, P., Manna, F., and Nimmo, J.R., 2015, Groundwater recharge assessment at local and episodic scale in a soil mantled perched karst aquifer in southern Italy, Journal of Hydrology 529:843–853. ( PDF)

Tashie, A.M., Mirus, B.B., and Pavelsky, T.M., 2015, Identifying longterm empirical relationships between storm characteristics and episodic groundwater recharge, Water Resour. Res., 51, doi:10.1002/2015WR017876.

Zhang, M., Singh, H.V., Migliaccio, K.W., and Kisekka, I., 2017, Evaluating water table response to rainfall events in shallow aquifer and canal system, Hydrological Processes, 31:3907-3919, doi:10.1002/hyp.11306.