This README.txt file was generated on 12-Oct-2024 by Yasas Upeakshika Bandara.


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GENERAL INFORMATION
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1. Title of Dataset: Parameters of multi-linear regressions for reconstructing peak flow in Contiguous United States (CONUS)

# Authors: Yasas Upeakshika Bandara and Giuseppe Mascaro

2. Author Information

First Author Contact Information
    Name: Yasas Upeakshika Bandara
    Institution: Arizona State University
    Address: 777 E University Dr Tempe, AZ 85281
    Email: ybandara@asu.edu
	

Corresponding Author Contact Information
    Name: Giuseppe Mascaro
    Institution: Arizona State University
    Address: 777 E University Dr Tempe, AZ 85281
    Email: gmascaro@asu.edu 
	

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DATA & FILE OVERVIEW
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Directory of Files:
   A. Filename: Gauge_info.csv         
      Information about the gauges for which the multilinear regressions were developed.
      		             
   B. Filename: MR_parameters.csv       
      Parameters (intercept and coefficients) of the multilinear regressions at each gauge.
	 

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DATA DESCRIPTION FOR: Gauge_info.csv 
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1. Number of variables: 4


2. Number of gauges/rows: 672


3. Variable List

    A. Name: Gauge_ID
       Description: ID of the gauge (USGS - Gages-II HCDN ID)

    B. Name: Gauge_name 
       Description: Name of the gauge

    C. Name: LAT_GAGE
       Description: Latitude of the gauge (Geographical Coordinate System - NAD 83)

    D. Name: LNG_GAGE
       Description: Longitude of the gauge (Geographical Coordinate System - NAD 83)

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DATA DESCRIPTION FOR: MR_parameters.csv 
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1. Number of variables: 8


2. Number of gauges/rows: 672


3. Variable List

    A. Name: Gauge_ID
       Description: ID of the gauge (USGS - Gages-II HCDN ID)

    B. Name: b_0, b_1, b_2, b_3, b_4, b_5 
       Description: Intercept and coefficients of the multi-linear regressions at each gauge

    C. Name: CF
       Description: Correction factor 

   

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METHODOLOGICAL INFORMATION
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This dataset was developed to expand the available peak flow (Q_p) data across 672 gauges in the Contiguous United States (CONUS).
The information on the gauges is available in Gauge_info.csv

We developed multi-linear regressions with 5 predictors:

	a) daily mean on the day of the peak (Q_m,s)
	b) daily mean on the day before (Q_m,b)
	c) daily mean of the day after (Q_m,a)
	d) antecedent precipitation depth (P_dep)
	e) antecedent precipitation duration (P_dur)

To apply the MRs at a gauge:

1. Select a set of peaks from the time series of daily mean (Q_m,s)

2. Identify the values of the five predictors for each peak (see Bandara and Mascaro (2024, under review) for details)

3. Get the natural log of each predictor

4. The regression is applied in the form:
	log(Q_p) = b_0 + b_1*log(Q_m,s) + b_2*log(Q_m,b) + b_3*log(Q_m,a) + b_4*log(P_dep) + b_5*log(P_dur)

5. Obtain the coefficient values for your gauge from MR_parameters.csv. Note that the significant predictors were selected at each gauge, implying zero coefficients for the non-significant predictors.

6. Compute log(Q_p) and, then, Q_p = CF*exp[log(Q_p)] using the correction factor CF available in MR_parameters.csv
	

Reference:

Bandara, Y. U., G. Mascaro (2024). Parameters of multi-linear regressions for reconstructing peak flow in Contiguous United States (CONUS), HydroShare, http://www.hydroshare.org/resource/4b455c9ac1864e1ba7a3bc90e91e8e0e

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