Generate variables to capture heat stress

[Shailaja Tiwari]

Dear all
I have daily minimum and maximum temperature data from 697 weather stations across 311 districts.
For each district i in month m of year t, I need to aggregate the daily temperature data into two threshold-based heat stress variables which are (1) growing degree-days GDD and (2) stress degree-days or SDD.

The GDD and SDD variables are defined as follows

Where T^low and T^high are the maximum and minimum temperature in ^oC for district i on day d of month m and year t.
T^low is the lower threshold and takes the value of 10, T^high is upper threshold and takes the value 30 and T^k takes the value 35

GDD_imt is heat accumulated within temperatures T^low and T^high and SDD_imt is heat accumulated above T^k.

Any suggestions how to implement these specifications?

[Mike Lacy]

Detailed advice is not possible without seeing an excerpt of your data using -dataex-, as described in the FAQ. Having formulae and your description of their meaning is helpful, and you've done that *much* better than most postershere, but having a -dataex- excerpt would nevertheless be essential to getting detailed advice.

So, without those, my suggestion is that you look at the min() and max() function described in -help egen-, and the use of "by varlist" with this command. The documentation is not entirely easy, but that's where you need to start. I presume your data is stored in the long format, with each each observation representing a particular station for a particular day/month/year. If not, you'll need to create that.

I'd start here by computing the various max and min values using -egen- and then eyeballing them to see if they make sense. Then, I'd look at using the -total- function in -egen-, with a "by list," to add up these values.

Also, another thing I find unclear is what you want for a final data file to analyze. It seems like you might want a data set in which each observation is a district for a particular year/month. It's possible in that case that you might be able to do with -collapse- what I describe doing with -egen-. It would be helpful if you said something like "In the end, I want a data set in which each observation is ...., containing variables that describe .... The ultimate purpose is to examine .... with ..... as the unit of analysis."

[Shailaja Tiwari]

Dear Mike thank you for your answer. Very remiss of me, here is an excerpt of my data. I have the mean, minimum and maximum temperature (in degree Celsius) defined for day, month and year for different weather stations. You are correct my data is in the long format.

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(DATE MONTH YEAR) double(LAT LON) float(MEANT MINT MAXT id)
1 1 1969 7.5 94.5  25.2 20.32 30.08   1
1 1 1970 7.5 94.5  27.7 25.42 29.97   2
1 1 1971 7.5 94.5 26.72 24.26 29.18   3
1 1 1972 7.5 94.5 25.39 21.73 29.05   4
1 1 1973 7.5 94.5 27.63 24.88 30.38   5
1 1 1974 7.5 94.5 26.39 24.41 28.38   6
1 1 1975 7.5 94.5 26.74 23.62 29.86   7
1 1 1978 7.5 94.5 27.17 23.86 30.48   8
1 1 1980 7.5 94.5 26.98 23.75  30.2   9
1 1 1981 7.5 94.5  26.1 22.04 30.15  10
1 1 1982 7.5 94.5 26.98 24.41 29.55  11
1 1 1983 7.5 94.5 25.73 21.64 29.81  12
1 1 1988 7.5 94.5 25.88 22.01 29.76  13
1 1 1989 7.5 94.5 27.24 23.92 30.55  14
1 2 1969 7.5 94.5 25.27 20.83 29.71  15
1 2 1970 7.5 94.5 27.71 25.08 30.35  16
1 2 1971 7.5 94.5 26.45 23.21 29.68  17
1 2 1972 7.5 94.5 27.03 23.47 30.59  18
1 2 1973 7.5 94.5 27.99 24.42 31.57  19
1 2 1974 7.5 94.5 27.09  24.8 29.39  20
1 2 1975 7.5 94.5 26.82 23.28 30.36  21
1 2 1978 7.5 94.5 27.59 24.79 30.38  22
1 2 1981 7.5 94.5 27.81 24.44 31.17  23
1 2 1983 7.5 94.5 24.82 20.63    29  24
1 2 1988 7.5 94.5 26.59 21.78 31.39  25
1 2 1989 7.5 94.5 26.85 22.54 31.15  26
1 3 1969 7.5 94.5  26.8 22.44 31.17  27
1 3 1970 7.5 94.5 26.33 21.67 30.98  28
1 3 1971 7.5 94.5 25.58 22.51 28.65  29
1 3 1972 7.5 94.5 24.35 18.69    30  30
1 3 1973 7.5 94.5 28.19 24.51 31.86  31
1 3 1974 7.5 94.5 26.94 24.24 29.64  32
1 3 1975 7.5 94.5 27.57 24.92 30.22  33
1 3 1977 7.5 94.5 26.89  23.4 30.37  34
1 3 1978 7.5 94.5 28.09 24.97 31.22  35
1 3 1980 7.5 94.5 25.85 20.79 30.92  36
1 3 1981 7.5 94.5 26.94 22.66 31.23  37
1 3 1982 7.5 94.5 27.32 23.39 31.25  38
1 3 1983 7.5 94.5 27.43 23.34 31.53  39
1 3 1989 7.5 94.5 27.54 23.88  31.2  40
1 4 1969 7.5 94.5 27.52 23.36 31.68  41
1 4 1970 7.5 94.5 27.12 22.96 31.27  42
1 4 1971 7.5 94.5 27.65 24.48 30.81  43
1 4 1972 7.5 94.5 27.06 22.27 31.84  44
1 4 1973 7.5 94.5 27.42 24.01 30.84  45
1 4 1974 7.5 94.5 27.93 24.71 31.15  46
1 4 1975 7.5 94.5 27.54 23.48 31.61  47
1 4 1977 7.5 94.5 28.46 25.27 31.66  48
1 4 1978 7.5 94.5 27.48 23.04 31.92  49
1 4 1980 7.5 94.5 27.37 22.24  32.5  50
1 4 1981 7.5 94.5 27.99 23.42 32.55  51
1 4 1982 7.5 94.5 26.87 21.08 32.67  52
1 4 1983 7.5 94.5 27.57 22.87 32.28  53
1 4 1988 7.5 94.5 26.82 21.91 31.72  54
1 4 1989 7.5 94.5 28.52 24.38 32.66  55
1 5 1969 7.5 94.5 28.94 24.53 33.35  56
1 5 1970 7.5 94.5 27.13 23.75  30.5  57
1 5 1971 7.5 94.5 26.81 23.57 30.05  58
1 5 1972 7.5 94.5 27.72 23.82 31.61  59
1 5 1973 7.5 94.5 28.35 25.11 31.59  60
1 5 1974 7.5 94.5 27.29 25.12 29.46  61
1 5 1975 7.5 94.5 27.35 23.19 31.51  62
1 5 1977 7.5 94.5 28.82 24.66 32.97  63
1 5 1978 7.5 94.5 27.85 23.84 31.85  64
1 5 1981 7.5 94.5 28.36 24.54 32.18  65
1 5 1982 7.5 94.5 28.54 24.14 32.93  66
1 5 1988 7.5 94.5 28.21 24.99 31.44  67
1 5 1989 7.5 94.5 28.38  24.9 31.86  68
1 6 1969 7.5 94.5 27.39 25.02 29.76  69
1 6 1970 7.5 94.5 27.04 23.83 30.24  70
1 6 1971 7.5 94.5 27.22 24.46 29.98  71
1 6 1972 7.5 94.5 27.93 24.77  31.1  72
1 6 1973 7.5 94.5  26.9 24.38 29.43  73
1 6 1974 7.5 94.5 28.36 25.88 30.84  74
1 6 1975 7.5 94.5  26.8 23.25 30.36  75
1 6 1977 7.5 94.5 28.64 25.72 31.55  76
1 6 1978 7.5 94.5  27.8 24.89 30.71  77
1 6 1980 7.5 94.5 28.34 24.45 32.22  78
1 6 1981 7.5 94.5 27.62 24.05 31.19  79
1 6 1982 7.5 94.5 26.48 23.02 29.93  80
1 6 1983 7.5 94.5 27.47  23.8 31.15  81
1 6 1988 7.5 94.5 29.14 27.06 31.22  82
1 6 1989 7.5 94.5 26.41 22.69 30.13  83
1 7 1969 7.5 94.5 26.05 23.46 28.65  84
1 7 1970 7.5 94.5  26.8 23.64 29.97  85
1 7 1971 7.5 94.5 25.89 23.35 28.43  86
1 7 1972 7.5 94.5 25.67 22.83 28.51  87
1 7 1973 7.5 94.5 25.94 23.55 28.33  88
1 7 1974 7.5 94.5 27.54 24.54 30.54  89
1 7 1975 7.5 94.5 25.97 23.29 28.65  90
1 7 1977 7.5 94.5 28.61  26.3 30.92  91
1 7 1978 7.5 94.5 25.86 22.81 28.91  92
1 7 1980 7.5 94.5 26.71 23.31  30.1  93
1 7 1981 7.5 94.5 25.72 21.79 29.65  94
1 7 1982 7.5 94.5 27.83 24.36  31.3  95
1 7 1983 7.5 94.5 27.54 23.81 31.27  96
1 7 1988 7.5 94.5 27.05 23.37 30.73  97
1 7 1989 7.5 94.5 28.36 25.37 31.35  98
1 8 1969 7.5 94.5 28.39 26.18 30.61  99
1 8 1970 7.5 94.5 26.08 23.21 28.96 100
end

My purpose to see the impact of the GDD and SDD variables on yield. I have the yield data for 311 districts (my panel variable) from 1970 to 2018 in a separate file.
However, I need to calculate the GDD and SDD variables using the formula above and the data in the excerpt.
This is where I am getting stuck. I am not sure how to code the above formula.
Also I assume that I will have to implement the formula above and calculate the GDD and SDD variables and then use the collapse option??

[Abdur Rub]

Shailaja Tiwari Hi! I was wondering if you managed to find a way to code the GDD and SDD formulae? I am stuck in a similar situation.