Lauren Hay: Hi.
This is Lauren Hay.
This training video will explain the climate distribution modules in PRMS.
There are nine climate distribution module options in PRMS: three for maximum and minimum
temperature; three for precipitation; and three that are used to distribute precipitation
and temperature.
With the exception of the climate_hru module, all of these modules distribute climate station
data.
If you are not using climate_hru, then you will need a station data file that contains
daily precipitation and maximum and minimum temperature.
The data should be examined in terms of quality and quantity.
Before you begin, make sure you have a copy of the PRMS manual.
I will be using the Merced River basin example in the manual.
This is a table I made for climate station data in and around the Merced River basin
in California, listing the station ID, station type, longitude, latitude, and elevation.
This is the example basin that is in the PRMS manual.
There are 26 climate stations in and around the Merced River basin.
This simple map shows the basin area in pink with the climate station locations labeled.
As you can see, only one climate station actually resides within the basin.
Do not make the mistake of only pulling climate stations that are inside your basin boundaries.
The next thing you need to do is examine the station data.
Here is a table showing the number of missing days per water year for temperature at the
26 stations in and around the Merced River basin.
I highlighted the stations that are missing the entire period of record.
The other stations have a lot of missing data as well so you really need to be aware of
how good your data is before you start working with it.
Here is a similar table for the precipitation data.
The missing record is not quite as bad as the temperature data.
You need to go through your station data and make sure that you think it is OK to use in
your study.
I would advise plotting the data as well to look for obvious outliers.
I will first go over the temperature distribution modules.
These modules are used to compute evaporation, transpiration, sublimation, and snow melt.
In your control file you need to set the control parameter temp_module to a valid temperature
distribution module name.
The table shows the choices.
This is straight out of Table 2 in the PRMS manual.
The first temperature distribution module is temp_1sta.
This is the simplest way to distribute maximum and minimum temperature in the basin.
Temp_1sta distributes temperature to each HRU based on a measured value.
You must designate one station for each HRU with the parameter hru_tsta.
I have clipped equation 1-13 out of the PRMS manual.
The bold names in the equation indicate parameters that must be defined.
You need to supply a lapse rate by month and an adjustment factor by HRU for maximum and
minimum temperature.
The temp laps module uses two measurement stations and computes the lapse rates between
those two stations for each HRU.
Here are the equations from the PRMS manual.
For each HRU the base station is specified by the hru_tsta parameter and the lapse station
is specified by the hru_tlaps parameter.
If you want to use this method, make sure you have good quality temperature data because
lapse rates are computed on a daily basis from the station data.
The temp_dist2 module is a little more complicated.
This module calculates a lapse rate, an elevation factor, and an inverse distance squared weighting
factor.
The lapse rates are limited by the monthly lapse-rate parameter values that must be defined.
The dist_max parameter sets the maximum distance for stations to be included.
If you are going to use this method you should read the manual for further information.
Now we will discuss the Precipitation distribution modules.
All the precipitation distribution modules determine precipitation form (rain or snow).
This can either be input in the data file using form_data, or it will be estimated using
temperature data.
When precipitation form is estimated, the parameters tmax_allrain, tmax_allsnow, and
adjmix_rain are used.
Here is a flow chart that explains how PRMS uses these parameters to determine whether
the precipitation is rain, or snow, or a mix of rain and snow.
In your control file you need to set the control parameter precip_module to a valid precipitation
distribution module name.
The table shows the choices.
This is straight out of Table 2 in the PRMS manual.
The first precipitation distribution module choice is precip_1sta.
precip_1sta distributes precipitation to each HRU based on a measured value.
You must designate one station for each HRU with the parameter hru_psta.
This uses the measurements from a single station and the associated rain_adj and snow_adj parameters
to adjust that stations data.
Here are the equations from the manual.
The rain_adj and snow_adj parameters are by HRU and by month.
The precip_laps module assigns two measurement stations to each HRU and computes a daily
lapse rate between those stations at each HRU.
It has an adjustment factor for rain or snow.
Equation 1-31 from the PRMS manual shows the precipitation calculations if it is all snow.
The precip_dist2 module distributes precipitation by computing weights based on lapse rates
and the inverse of the square of the distance between the centroid of the HRU and the location
of multiple precipitation stations.
The parameter dist_max sets the distance between a measurement station and the HRU centroid
for inclusion.
If you are going to use this method you should read the manual for further information.
Now we will discuss the modules for precipitation and temperature distribution.
In your control file you need to set the control parameter precip_module and temp_module to
the same precipitation and temperature distribution module name.
The table shows the choices.
This is straight out of Table 2 in the PRMS manual.
Climate_hru is probably the easiest of all of these distribution modules to use.
It reads in the precipitation and the temperature by HRU.
There are many gridded climate data sets that can be summarized by HRU.
The adjustment parameters can be used to further adjust the climate values defined by HRU within
PRMS.
You can use the Geo Data portal to get the climate data by HRU.
You will need to submit an HRU shape file and select a gridded climate data set.
The Geo Data Portal will over lay your shape file on the gridded climate and give you back
summaries by HRU of your climate variables.
The Geo Data portal returns a comma separated variable, or csv file.
This needs to be converted to a PRMS climate_hru input file.
The input files need to be designated in the control file as listed here.
Prcp.cbh, tmax.cbh, and tmin.cbh are the csv-converted files.
Cbh stands for climate by HRU.
The csv files are easy to convert.
CSV files from the Geo Data portal, or GDP, look like this.
The first line is a descriptor.
The second line lists the number of HRUs.
This is followed by a units header line.
The data is then listed.
The csv file needs to be converted to a CBH file for PRMS as shown here.
For precipitation the prcp.cbh file has a header line, in this case -- Created by Me.
This is followed by “precip” – indicating this is precipitation data and the designated
number of HRUs.
A line of #s is inserted before the actual data.
The data is listed as follows: "Year," "Month," "Day," and "Hour," "Minute," "Second," (which
are always 0 0 0), followed by the data for each HRU on that day.
Make sure when you convert that you are using the right units.
The next module is the xyz_dist module.
You can use this when there are one or more climate stations.
The xyz_dist module uses a multiple linear regression based on longitude, latitude, and
elevation to distribute the climate variable (CV) to the HRU.
These are the equations from the PRMS manual.
Equation 1-35 is the multiple linear regression equation.
You have to determine the lapse rates for x, y, and z.
The module solves for the intercept (b0) in equation 1-36.
The b0 value is then put into equation 1-37 with the HRU's longitude, latitude, and elevation
or xyz to get the HRU CV.
Here is a 1 predictor example using elevation, z, to distribute daily precipitation from
a set of stations.
For each day you are solving for b0 which is the y intercept.
The y intercept is equal to the mean of the precipitation stations that you have chosen,
minus the slope times the mean of the elevations of those precipitation stations.
Graphically, each day the mean station elevation is plotted versus the mean station precipitation.
The slope through that point is used to find the y-intercept value.
The y-intercept is the green X on the plot.
The y- intercept value is plugged back into the equation.
Precipitation at the HRU is equal to the slope times the HRU elevation plus the y-intercept.
There are many parameters that you need to calculate for the xyz_dist module.
You will need to calculate multiple linear regression slope values for maximum temperature,
minimum temperature, and precipitation.
You will need to calculate the station locations and elevations, or X, Y, Zs.
You will need to calculate for each station the mean maximum and minimum temperature and
precipitation in case all the chosen stations are all missing on a day.
You will need to calculate transformation parameters.
The multiple linear regression equation needs to be developed with transformed CVs because
the CVs have drastically different magnitudes.
The transformation parameters are used by PRMS.
These are further explained in the PRMS manual.
The station use indicators tell PRMS which stations in your data file are to be used
in the multiple linear regression.
As shown earlier, the Merced River basin example had 26 climate stations.
You do not want to use all of the stations in the xyz calculations.
The tsta_nuse and psta_nuse are 0, 1 indicators for station use.
The parameter psta_freq_nuse indicates the set of stations to use to determine if there
is precipitation that day.
The rain_code parameter is used with the psta_freq_nuse parameter.
The rain_code is a monthly parameter that indicates the rule for precipitation station
use.
A rain_code of 1 means that PRMS calculates precipitation if at least one of the stations
that you've chosen with the psta_nuse parameter has precipitation on that day.
A rain_code of 2 means that PRMS examines all the stations in your data file, and calculates
precipitation if any one of those stations has precipitation.
A rain_code of 3 means PRMS calculates precipitation even if all stations have zero precipitation.
In general, precipitation increases with elevation.
Climate stations tend to be at lower elevations in the basin.
So even if the mean station precipitation is zero, PRMS will probably produce precipitation
at the higher elevations using xyz.
A rain_code of 4 means precipitation is calculated if the rain_day variable in your input file
is set to 1.
A rain_code of 5 uses the psta_freq_nuse parameter.
This is a binary parameter that can indicate a separate set of stations from psta_nuse
for precipitation frequency.
It is good to examine the rainfall frequency at the stations in and around your basin to
determine how to set these parameters.
The last distribution module is the ide_dist module.
You need adequate station coverage to use this module or it won't work very well.
If you don't have a lot of good quality climate data and/or you don't have good coverage in
your basin, then you shouldn't use this module.
This module uses a combination of inverse distance and elevation weighting.
The inverse distance finds the neighborhood of climate stations around each HRU based
on the ndist_psta or ndist_tsta parameter, and then computes a weighted average of the
climate station values within the neighborhood.
The inverse distance weight can be raised to a power with the dist_exp parameter if
you want to reduce the effect of climate stations that are located farther away.
The elevation weighting determines the climate stations with the closest elevations above
and below a given HRU's elevation.
Then it interpolates to the HRU based on the elevation values.
PRMS calculates the climate variable from the inverse distance weighting and the elevation
weighting.
These values are weighted and summed based on the prcp_wght_dist and temp_wght_dist parameters.
These are monthly parameters.
So say in the winter time, you think the precipitation should be more heavily weighted towards elevation
because of orographic influences.
Then you would give more weight to the winter months using the elevation weight.
In the summertime you think that precipitation is more convective and things are more localized.
Then you'd want to use more of the inverse distance weight in the summer time.
This concludes the presentation on Climate Distribution Modules in PRMS.
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