Related Experiment Videos
Fully automated objective-based method for master recession curve separation
Kristijan Posavec1, Jelena Parlov, Zoran Nakić
1Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia. kristijan.posavec@rgn.hr
Abstract:
The fully automated objective-based method for master recession curve (MRC) separation was developed by using Microsoft Excel spreadsheet and Visual Basic for Applications (VBA) code. The core of the program code is used to construct an MRC by using the adapted matching strip method (Posavec et al. 2006). Criteria for separating the MRC into two or three segments are determined from the flow-duration curve and are represented as the probable range of percent of flow rate duration. Successive separations are performed automatically on two and three MRCs using sets of percent of flow rate duration from selected ranges and an optimal separation model scenario, having the highest average coefficient of determination R(2), is selected as the most appropriate one. The resulting separated master recession curves are presented graphically, whereas the statistics are presented numerically, all in separate sheets. Examples of field data obtained from two springs in Istria, Croatia, are used to illustrate its application. The freely available Excel spreadsheet and VBA program ensures the ease of use and applicability for larger data sets.
Related Concept Videos
Response Surface Methodology
The process of RSM involves several key steps:
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
First Derivative Test: Problem Solving
Area Between Curves: Problem Solving
Calibration Curves: Linear Least Squares
For data that follow a straight line, the standard method for fitting is the linear...