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Sandra Villamizar

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ABSTRACT:

This compressed folder contains all the files and scripts necessary to reproduce the results for the manuscript "Producing long-term series of whole-stream metabolism using readily available data to assess river ecosystem response to flow disturbances". The user should download the file and extract it to a preferred location within a computer.

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ABSTRACT:

We describe the procedure for the consolidation of the daily metabolic rate estimates along with summary weather, reach and water derived data that may be used for the interpretation of the processes happening along the reach of interest.
Sections 1 and 2 of the R script define the working directory and read the file containing the time series of daily metabolic rate estimates for the study period (related variable: ´table1´). Section 3 converts to NAs the daily metabolic rates that are of bad quality. The considerations to do this are first, daily metabolic rate estimates for days in which the available number of DO or temperature data points were less than 65% of the maximum daily total (this criterion may be modified by the user) and second, daily outputs that reflect failure on model performance due to either reaeration rates much higher than metabolic processes or DO concentrations increasing during night time (these resulted in positive values of the estimated CR24 and/or negative values of GPP).
Section 4 reads the daily solar radiation and reference PAR estimates and together with the filtered metabolic rates conform the final results table (related variable: ´main1´). Next, in section 5, the script reads the daily averages of ancillary water data (turbidity and chlorophyll for our case) and appends them to the final results table (related variable: ´main 4´). In section 6 the script creates figures 2, 3, and 4 of the publication. Figure 2 presents stacked panels of time series of mean daily discharge, water temperature, and DO (daily maximum, minimum and range); figure 3 presents stacked panels of the daily metabolic estimates (GPP, CR24 and NDM) in the context of the flow dynamics of the reach; figure 4 presents the ancillary data that is expected to support the analysis of the daily metabolic rates within the reach (turbidity, chlorophyll and reference PAR). Finally, section 7 prints the final results table that contains all the daily variables for the period of analysis.
DOI: 10.6084/m9.figshare.3427892

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ABSTRACT:

Generating daily estimates of WSM. We show the procedure to generate daily estimates of one station whole-stream metabolism using water, reach and weather data for the period of interest. Read the "Supplemental_Text_S4.pdf" for detailed description of the Daily.Metabolism.R script.

DOI: 10.6084/m9.figshare.3422272

Note: the "AAAA-MM_inputs.csv" files are the monthly dates files used for the estimation of WSM for the period of August 2010 through December 2014. To run the Daily.Metabolism.R script only one of these files is required.

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ABSTRACT:

We present the procedure to obtain daily averages of ancillary water data (e.g., turbidity and chlorophyll) that may be used to support the interpretation of the daily metabolic rate estimates. The Ancillary_DarilyAvg.R script needs to be executed as many times as the number of available ancillary water parameters (twice for our case). For the case of the SJR restoration reach, the data can be accessed by doing a query on the CDEC website indicating the sensor number (28 for chlorophyll, 27 for turbidity), the interval of the data (event or hourly), and the starting and ending dates of the period of interest. A comma separated value file is produced after the request, and each of these files will be the input to this R script.
Section 1 of the script sets up the working directory; the ancillary water data input file is read in section 2 and particular configuration parameters are specified in section 3. According to these parameters, a new table (‘table1’) with only the columns of interest is created in section 4; section 5 converts into NA’s all the cell values that report missing data (“m”) or errors (“-9998” or “-9997”). The actual daily averages are calculated in section 6 and stored in the variable ‘table4’. Section 7 deals with the transformation of the date column to an actual date format in order to identify whether or not there are missing days within the time series. The output of this section (dates and intervals between dates) as well as the daily averages contained in ‘table4’, are merged into a new variable ‘table5’. Finally, section 8 prints the output of this script as a tab separated text file.
DOI: 10.6084/m9.figshare.3413023

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ABSTRACT:

Generating estimates of daily reference photosynthetically Active Radiation (PAR). We show the procedure to generate estimates of daily reference PAR using solar radiation data. The input for the R script (CalculateDailyPAR.R) is a raw time series of hourly solar radiation (stored in variable ‘ws’) that for our case was obtained from the CIMIS website (station id: 105) [California Department of Water Resources, 2015]. The script processes the data set to format the date and time columns, and to identify missing data points reporting their position within the time series (variable ‘na.id’). The user fills the gaps using adequate strategies and creates a new input file (stored in variable ‘fill.points’) containing the values to fill in within the time series. A reference PAR estimate is obtained as a constant fraction of solar radiation using the conversion factor proposed by [Meek et al., 1984]. The script then calculates an average daily value of solar radiation and integrates the reference PAR over the daytime period to obtain a daily value. The script ends by generating a final table (‘ws.results’) reporting daily values of solar radiation (maximum and mean in W m-2), and maximum, mean, and minimum reference PAR values in units of (μmol m-2 d-1) and (mol m-2 d-1).
DOI:10.6084/m9.figshare.3412765

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Generic Generic

ABSTRACT:

Generating estimates of daily reference photosynthetically Active Radiation (PAR). We show the procedure to generate estimates of daily reference PAR using solar radiation data. The input for the R script (CalculateDailyPAR.R) is a raw time series of hourly solar radiation (stored in variable ‘ws’) that for our case was obtained from the CIMIS website (station id: 105) [California Department of Water Resources, 2015]. The script processes the data set to format the date and time columns, and to identify missing data points reporting their position within the time series (variable ‘na.id’). The user fills the gaps using adequate strategies and creates a new input file (stored in variable ‘fill.points’) containing the values to fill in within the time series. A reference PAR estimate is obtained as a constant fraction of solar radiation using the conversion factor proposed by [Meek et al., 1984]. The script then calculates an average daily value of solar radiation and integrates the reference PAR over the daytime period to obtain a daily value. The script ends by generating a final table (‘ws.results’) reporting daily values of solar radiation (maximum and mean in W m-2), and maximum, mean, and minimum reference PAR values in units of (μmol m-2 d-1) and (mol m-2 d-1).
DOI:10.6084/m9.figshare.3412765

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Generic Generic

ABSTRACT:

We present the procedure to obtain daily averages of ancillary water data (e.g., turbidity and chlorophyll) that may be used to support the interpretation of the daily metabolic rate estimates. The Ancillary_DarilyAvg.R script needs to be executed as many times as the number of available ancillary water parameters (twice for our case). For the case of the SJR restoration reach, the data can be accessed by doing a query on the CDEC website indicating the sensor number (28 for chlorophyll, 27 for turbidity), the interval of the data (event or hourly), and the starting and ending dates of the period of interest. A comma separated value file is produced after the request, and each of these files will be the input to this R script.
Section 1 of the script sets up the working directory; the ancillary water data input file is read in section 2 and particular configuration parameters are specified in section 3. According to these parameters, a new table (‘table1’) with only the columns of interest is created in section 4; section 5 converts into NA’s all the cell values that report missing data (“m”) or errors (“-9998” or “-9997”). The actual daily averages are calculated in section 6 and stored in the variable ‘table4’. Section 7 deals with the transformation of the date column to an actual date format in order to identify whether or not there are missing days within the time series. The output of this section (dates and intervals between dates) as well as the daily averages contained in ‘table4’, are merged into a new variable ‘table5’. Finally, section 8 prints the output of this script as a tab separated text file.
DOI: 10.6084/m9.figshare.3413023

Show More
Generic Generic

ABSTRACT:

Generating daily estimates of WSM. We show the procedure to generate daily estimates of one station whole-stream metabolism using water, reach and weather data for the period of interest. Read the "Supplemental_Text_S4.pdf" for detailed description of the Daily.Metabolism.R script.

DOI: 10.6084/m9.figshare.3422272

Note: the "AAAA-MM_inputs.csv" files are the monthly dates files used for the estimation of WSM for the period of August 2010 through December 2014. To run the Daily.Metabolism.R script only one of these files is required.

Show More
Generic Generic

ABSTRACT:

We describe the procedure for the consolidation of the daily metabolic rate estimates along with summary weather, reach and water derived data that may be used for the interpretation of the processes happening along the reach of interest.
Sections 1 and 2 of the R script define the working directory and read the file containing the time series of daily metabolic rate estimates for the study period (related variable: ´table1´). Section 3 converts to NAs the daily metabolic rates that are of bad quality. The considerations to do this are first, daily metabolic rate estimates for days in which the available number of DO or temperature data points were less than 65% of the maximum daily total (this criterion may be modified by the user) and second, daily outputs that reflect failure on model performance due to either reaeration rates much higher than metabolic processes or DO concentrations increasing during night time (these resulted in positive values of the estimated CR24 and/or negative values of GPP).
Section 4 reads the daily solar radiation and reference PAR estimates and together with the filtered metabolic rates conform the final results table (related variable: ´main1´). Next, in section 5, the script reads the daily averages of ancillary water data (turbidity and chlorophyll for our case) and appends them to the final results table (related variable: ´main 4´). In section 6 the script creates figures 2, 3, and 4 of the publication. Figure 2 presents stacked panels of time series of mean daily discharge, water temperature, and DO (daily maximum, minimum and range); figure 3 presents stacked panels of the daily metabolic estimates (GPP, CR24 and NDM) in the context of the flow dynamics of the reach; figure 4 presents the ancillary data that is expected to support the analysis of the daily metabolic rates within the reach (turbidity, chlorophyll and reference PAR). Finally, section 7 prints the final results table that contains all the daily variables for the period of analysis.
DOI: 10.6084/m9.figshare.3427892

Show More
Generic Generic

ABSTRACT:

This compressed folder contains all the files and scripts necessary to reproduce the results for the manuscript "Producing long-term series of whole-stream metabolism using readily available data to assess river ecosystem response to flow disturbances". The user should download the file and extract it to a preferred location within a computer.

Show More