├── in ├── organic.txt ├── sites.txt ├── initial.txt ├── mineral.txt ├── grid.txt ├── rsnow.txt ├── snow.txt └── bound.txt ├── results.png ├── gipl.yml ├── Makefile ├── gipl_config.cfg ├── LICENSE.md ├── compare.m ├── gipl_mods.f90 ├── README.md ├── gipl.f90 ├── plot_input_data.ipynb └── mesres.txt /in/organic.txt: -------------------------------------------------------------------------------- 1 | 1 2 | 1 0 3 | -------------------------------------------------------------------------------- /in/sites.txt: -------------------------------------------------------------------------------- 1 | 1 2 | 246 1 1 1 1 0.00 3 | -------------------------------------------------------------------------------- /results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Elchin/GIPL/HEAD/results.png -------------------------------------------------------------------------------- /gipl.yml: -------------------------------------------------------------------------------- 1 | # Build GIPL from source. 2 | name: gipl 3 | channels: 4 | - conda-forge 5 | dependencies: 6 | - python =3 7 | - fortran-compiler 8 | - make 9 | -------------------------------------------------------------------------------- /in/initial.txt: -------------------------------------------------------------------------------- 1 | 1 13 2 | DEPTH TEMP 3 | -1.5 14.9 4 | 0.0 13.8 5 | 0.087 10.6 6 | 0.137 9.0 7 | 0.213 6.5 8 | 0.289 4.63 9 | 0.363 2.74 10 | 0.44 1.12 11 | 0.517 -0.367 12 | 0.594 -1.09 13 | 0.745 -2.28 14 | 0.89 -3.33 15 | 1.11 -4.71 16 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | CC=gfortran 2 | 3 | CFLAGS=-c 4 | 5 | all: gipl 6 | 7 | gipl: gipl_mods.o gipl.o 8 | $(CC) gipl_mods.o gipl.o -o gipl 9 | 10 | gipl_mods.o: gipl_mods.f90 11 | $(CC) $(CFLAGS) gipl_mods.f90 12 | 13 | gipl.o: gipl.f90 14 | $(CC) $(CFLAGS) gipl.f90 15 | 16 | clean: 17 | rm *o gipl 18 | -------------------------------------------------------------------------------- /in/mineral.txt: -------------------------------------------------------------------------------- 1 | 1 2 | 1 6 3 | 0.39 0.07 -0.19 2000000.0 1600000.0 1.05 2.05 0.21 4 | 0.41 0.001 -0.9 2600000.0 2400000.0 0.812 2.03 0.15 5 | 0.38 0.06 -0.6 2600000.0 2400000.0 1.21 2.13 0.60 6 | 0.35 0.06 -0.324 2900000.0 2000000.0 1.42 2.52 7.04 7 | 0.28 0.018 -0.109 3100000.0 2000000.0 1.78 2.04 17.0 8 | 0.05 0.067 -0.215 3000000.0 2500000.0 2.45 2.62 8.0 9 | -------------------------------------------------------------------------------- /gipl_config.cfg: -------------------------------------------------------------------------------- 1 | input files 2 | in/sites.txt 3 | in/bound.txt 4 | in/snow.txt 5 | in/rsnow.txt 6 | in/initial.txt 7 | in/grid.txt 8 | in/organic.txt 9 | in/mineral.txt 10 | 11 | output files 12 | out/mean.txt 13 | out/result.txt 14 | out/start.txt 15 | 16 | 0/1: start from previous time step / start from the beginning 17 | 1 18 | step | taum | tmin : 19 | 1.0 0.1 0.0010 20 | begin end : start and end, in the example it runs over one year from 0 to 1 21 | 0 2 22 | smoothing factor | unfrozen water parameter | max number of iterations 23 | 0.0100 0.0100 5 24 | number of second in a day [sec] | number of time steps (in the example number of days in a year ) 25 | 86400.0 365 26 | sea level | max number of freezing fronts [integer number] 27 | 0.000 4 28 | freezing front min and max depth [meters] 29 | 0.05 10.00 30 | saturation coefficient (fraction of 1) 31 | 0.95 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 GIPL 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /compare.m: -------------------------------------------------------------------------------- 1 | function compare(k); 2 | % s: sight 3 | % k: 0 turn off, 1 turn on 4 | %clear all 5 | if k==1, status = dos('./gipl'); end 6 | m1=load('dump/result.txt'); 7 | m2=load('mesres.txt'); 8 | 9 | 10 | calc=m1(:,5:end); 11 | mes=m2(:,2:end); 12 | 13 | Depths=[0.001 0.072 0.125 0.2 0.277 0.354 0.424 0.506 0.583 0.741 0.885 1.1]; 14 | num=[3,5,8,11]; % indexes 15 | 16 | n=length(calc(1:end,1)); 17 | % 1 2 3 4 5 6 7 8 9 10 11 12 18 | % J F M A M J J A S O N D 19 | mon=[31 28 31 30 31 30 31 31 30 31 30 31]; 20 | beg=6; k=1; 21 | for i=1+beg:12+beg 22 | if i==12, curr=i; else curr=rem(i,12); end 23 | if k==1, month(k)=mon(curr); 24 | else month(k)=month(k-1)+mon(curr); 25 | end 26 | k=k+1; 27 | end 28 | 29 | yy=fix(n/365); 30 | for i=1:yy 31 | for j=1:12 32 | k=j+(i-1)*12; 33 | xtick(k)=month(j)+365*(i-1); 34 | end 35 | xxtick(i)=(365-182)+365*(i-1); 36 | end 37 | 38 | nx=length(xtick); 39 | xmonth={ 'A' 'S' 'O' 'N' 'D' 'J' 'F' 'M' 'A' 'M' 'J' 'J'}; 40 | xyear={ '2008' '2009' '2010' '2011' '2012'}; 41 | figure(1); 42 | for i=1:4 43 | subplot(4,1,i); 44 | depth=num(i); 45 | plot(1:n,calc(:,depth),'b-','LineWidth',2); hold on % calculated 46 | plot(1:n,mes(1:n,depth),'r-','LineWidth',2); % mesaured 47 | err(i)=mean(abs(calc(:,depth)-mes(1:n,depth))); 48 | set(gca,'XTick',xtick); set(gca,'XTickLabel',xmonth) 49 | title(['Depth=',strcat(num2str(Depths(depth)),' m'), ', MAE=',num2str(err(i))]); 50 | ylabel 'Temperature (^oC)'; hold off; grid on; 51 | axis([1 n min(mes(1:n,depth))-3 max(mes(1:n,depth))+3]) 52 | end 53 | xlabel 'Time (day)' 54 | err(5)=sum(err(:))/4; disp([err]); 55 | legend('Simulated','Observed',1); 56 | -------------------------------------------------------------------------------- /in/grid.txt: -------------------------------------------------------------------------------- 1 | 176 2 | -1.5 3 | -1.45 4 | -1.4 5 | -1.35 6 | -1.3 7 | -1.25 8 | -1.2 9 | -1.15 10 | -1.1 11 | -1.05 12 | -1 13 | -0.95 14 | -0.9 15 | -0.85 16 | -0.8 17 | -0.75 18 | -0.7 19 | -0.65 20 | -0.6 21 | -0.55 22 | -0.5 23 | -0.45 24 | -0.4 25 | -0.35 26 | -0.3 27 | -0.25 28 | -0.2 29 | -0.15 30 | -0.1 31 | -0.08 32 | -0.06 33 | -0.04 34 | -0.03 35 | -0.02 36 | -0.01 37 | -0.005 38 | -0.002 39 | -0.001 40 | 0 41 | 0.001 42 | 0.002 43 | 0.005 44 | 0.01 45 | 0.02 46 | 0.03 47 | 0.04 48 | 0.05 49 | 0.08 50 | 0.1 51 | 0.12 52 | 0.14 53 | 0.16 54 | 0.18 55 | 0.2 56 | 0.22 57 | 0.24 58 | 0.26 59 | 0.28 60 | 0.3 61 | 0.32 62 | 0.34 63 | 0.36 64 | 0.38 65 | 0.4 66 | 0.42 67 | 0.44 68 | 0.46 69 | 0.48 70 | 0.5 71 | 0.52 72 | 0.54 73 | 0.56 74 | 0.58 75 | 0.6 76 | 0.62 77 | 0.64 78 | 0.66 79 | 0.68 80 | 0.7 81 | 0.72 82 | 0.74 83 | 0.76 84 | 0.78 85 | 0.8 86 | 0.82 87 | 0.84 88 | 0.86 89 | 0.88 90 | 0.9 91 | 0.92 92 | 0.94 93 | 0.96 94 | 0.98 95 | 1 96 | 1.05 97 | 1.1 98 | 1.15 99 | 1.2 100 | 1.25 101 | 1.3 102 | 1.35 103 | 1.4 104 | 1.45 105 | 1.5 106 | 1.55 107 | 1.6 108 | 1.65 109 | 1.7 110 | 1.75 111 | 1.8 112 | 1.85 113 | 1.9 114 | 1.95 115 | 2 116 | 2.1 117 | 2.2 118 | 2.3 119 | 2.4 120 | 2.5 121 | 2.6 122 | 2.7 123 | 2.8 124 | 2.9 125 | 3 126 | 3.1 127 | 3.2 128 | 3.3 129 | 3.4 130 | 3.5 131 | 3.6 132 | 3.7 133 | 3.8 134 | 3.9 135 | 4 136 | 4.1 137 | 4.2 138 | 4.3 139 | 4.4 140 | 4.5 141 | 4.6 142 | 4.7 143 | 4.8 144 | 5 145 | 5.3 146 | 5.7 147 | 6 148 | 6.5 149 | 7 150 | 7.5 151 | 8 152 | 8.5 153 | 9 154 | 9.5 155 | 10 156 | 10.5 157 | 11 158 | 11.5 159 | 12 160 | 12.5 161 | 13 162 | 14 163 | 15 164 | 16 165 | 17 166 | 18 167 | 19 168 | 20 169 | 21 170 | 23 171 | 28 172 | 33 173 | 50 174 | 60 175 | 70 176 | 80 177 | 90 178 | 12 179 | 39 180 | 48 181 | 51 182 | 55 183 | 58 184 | 62 185 | 66 186 | 70 187 | 74 188 | 81 189 | 89 190 | 97 191 | -------------------------------------------------------------------------------- /gipl_mods.f90: -------------------------------------------------------------------------------- 1 | module const 2 | 3 | real*8, parameter :: hcap_snow=840000.0 ! heat capacity of snow (constant) 4 | real*8, parameter :: Lf=333.2*1.D+6 ! Latent of water fusion 5 | integer, parameter :: lbound=2 ! 1 const temp, 2 heat flux condition at the bottom boundary 6 | integer, parameter :: n_lay=10 ! total allowed number of soil layer 7 | 8 | end module const 9 | 10 | module bnd 11 | integer :: n_temp ! number of upper boundary points for temperature (input) 12 | real*8,allocatable:: utemp_time(:), utemp(:,:) ! upper boundary time and temperature (input) 13 | real*8,allocatable:: utemp_time_i(:), utemp_i(:,:) ! time and upper boundary temprature (interpolated) 14 | integer :: n_snow ! number of upper boundary points for snow (input) 15 | real*8 ,allocatable:: snd_time(:),snd(:,:) ! upper boundary snow time and snow depth (input) 16 | integer :: n_stcon 17 | real*8 ,allocatable:: stcon_time(:),stcon(:,:) ! snow thermal conductivity time and itself (input) 18 | real*8 ,allocatable:: snd_i (:,:), stcon_i (:,:) ! snow depth and thermal conductivity (interpolated) 19 | real*8 :: TINIR 20 | real*8 :: time_restart ! restart time in restart file 21 | 22 | 23 | ! Parameter read from cmd file 24 | integer :: restart ! 0/1 start from previous time step / start from the begining 25 | real*8 :: time_step ! step is the timestep in the example it is 1 yr 26 | real*8 :: TAUM ! taum is the convergence parameter used by the stefan subroutine 27 | real*8 :: TMIN ! tmin minimal timestep used in the Stefan subroutine 28 | real*8 :: time_beg,time_end ! inbegin time, end time 29 | integer :: itmax ! maximum number of iterations in Stefan subroutine 30 | integer :: n_time ! number of time steps that temp will be averaged over 31 | integer :: n_frz_max ! maximum number of freezing fronts 32 | real*8 :: smooth_coef ! smoothing factor 33 | real*8 :: unf_water_coef ! unfrozen water coefficient 34 | real*8 :: n_sec_day ! number of second in a day 35 | real*8 :: frz_frn_max,frz_frn_min ! freezing front min and max depth [meters] 36 | real*8 :: sat_coef ! saturation coefficient [dimensionless, fraction of 1] 37 | ! output file names 38 | character(64) :: restart_file,result_file,aver_res_file 39 | 40 | type site_gipl 41 | real*8 :: time 42 | end type site_gipl 43 | 44 | end module bnd 45 | 46 | module thermo 47 | real*8 L_fus ! Latent heat of fusion [W/mK] 48 | real*8 sea_level ! how many meter above the sea level the borehole is 49 | 50 | ! thermo physical parameters of soil for each soil layer 51 | real*8,allocatable:: vwc(:,:) ! volumetric water content 52 | real*8,allocatable:: a_coef(:,:),b_coef(:,:) ! a and b unfrozen water curve coefficients 53 | real*8,allocatable:: temp_frz(:,:) ! temperature freezing depression 54 | real*8,allocatable:: EE(:,:) 55 | real*8,allocatable:: hcap_frz(:,:),hcap_thw(:,:) ! soil layer heat capacity thawed/frozen 56 | real*8,allocatable:: tcon_frz(:,:),tcon_thw(:,:) ! soil layer thermal conductivity thawed/frozen 57 | 58 | 59 | real*8 :: hcap_s ! heat capacity of snow (constant) nondimentional 60 | 61 | real*8, allocatable :: temp(:,:) ! soil temperature 62 | real, allocatable:: n_bnd_lay(:,:) ! number of boundaries between layer in soil 63 | integer k0 64 | 65 | 66 | integer, allocatable :: snow_code(:),veg_code(:) ! (not necccessary) required for runing in parallel 67 | integer, allocatable :: geo_code(:),gt_zone_code(:) ! (not necccessary) required for runing in parallel 68 | real*8, allocatable :: temp_grd(:) ! temprature gradient at the lower boundary 69 | 70 | real*8 ,allocatable:: RES(:,:) ! unified variable for the writing results into the file 71 | 72 | end module thermo 73 | 74 | module grd 75 | 76 | integer,allocatable:: n_lay_cur(:) ! current number of soil layers <= n_lay 77 | ! calclulated as a sum of organic and mineral soil layers 78 | integer :: n_site ! number of sites 79 | integer :: n_grd ! total number of grid points with depth (grid.txt) 80 | real*8,allocatable:: zdepth(:),dz(:) ! vertical grid and distance between grid point 'zdepth(n_grd)' 81 | integer,allocatable:: lay_id(:,:) ! layer index 82 | integer :: m_grd ! number of grid points to store in res file 83 | integer,allocatable:: zdepth_id(:) ! index vector of stored grid points 'zdepth_id(m_grid)' 84 | integer :: n_ini ! number of vertical grid cells in init file 85 | real*8, allocatable :: zdepth_ini(:),ztemp_ini(:,:) ! depth and correspoding initial temperature (time=0) 'zdepth_ini(n_ini)' 86 | character(210) :: FMT1,FMT2 ! results formating type 87 | 88 | end module grd 89 | 90 | module alt 91 | integer,allocatable::n_frz_frn(:,:) ! number of freezing front (e.g. when freezup is about to happened) 92 | integer,allocatable::i_time(:) ! internal time step with the the main loop 93 | real*8 ,allocatable::z_frz_frn(:,:,:) ! depth of the freezing front 94 | end module alt 95 | 96 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GIPL (Geophysical Institute Permafrost Laboratory) Model 2 | 3 | GIPL is a permafrost numerical model that employs phase changes and the effect of unfrozen volumetric water content in non-homogeneous soil texture. 4 | The original version of the model was originally developed by Romanovsky and Tipenko (2004) at the University of Alaska Fairbanks, and described in Marchenko et al., (2008). This version has been significantly modified from its predecessor and was adapted to the IRF (Initialize, Run, Finalize) coding standard structure (see [Basic Model Interface](http://csdms.colorado.edu/wiki/BMI_Description)). 5 | This version is maintained by [Elchin Jafarov](https://www.woodwellclimate.org/staff/elchin-jafarov/). Please cite Jafarov et al., (2012) when using the model. This model is a part of [permamodel project](https://github.com/permamodel/permamodel). 6 | 7 | # References: 8 | Jafarov, E. E., Marchenko, S. S., and Romanovsky, V. E.: Numerical modeling of permafrost dynamics in Alaska using a high spatial resolution dataset, The Cryosphere, 6, 613–624, https://doi.org/10.5194/tc-6-613-2012, 2012. 9 | 10 | Jafarov, E. E., Romanovsky, V. E., Genet, H., McGuire A., D., Marchenko, S. S.: The effects of fire on the thermal stability of permafrost in lowland and upland black spruce forests of interior Alaska in a changing climate, Environmental Research Letters, 8, 035030, 2013. https://doi.org/10.1088/1748-9326/8/3/035030 11 | 12 | Jafarov, E.E., Nicolsky, D.J., Romanovsky, V.E., Walsh, J.E., Panda, S.K., Serreze, M.C. 2014. The effect of snow: How to better model ground surface temperatures. Cold Regions Science and Technology, Volume 102, Pages 63-77, ISSN 0165-232X, [doi: 10.1016/j.coldregions.2014.02.007](http://www.sciencedirect.com/science/article/pii/S0165232X1400038X). 13 | 14 | Zlotnik, V.A., Harp, D.R., Jafarov, E.E., Abolt, C.J. A Model of Ice Wedge Polygon Drainage in Changing Arctic Terrain. Water 2020, 12, 3376. [https://doi.org/10.3390/w12123376](https://www.mdpi.com/2073-4441/12/12/3376) 15 | 16 | # Installation: 17 | 18 | **Windows**: Compile the `gipl.f90` using [gfortran](https://gcc.gnu.org/wiki/GFortran) or similar compiler and name the executable file `gipl.exe`. 19 | 20 | **Linux**: Use Makefile to create an executable. Navigate to the GIPL folde and type `make` in the terminal. 21 | 22 | **Mac**: 23 | ```bash 24 | conda env create --file gipl.yml 25 | source activate gipl 26 | make 27 | ``` 28 | # Run: 29 | Make sure to create a folder called `out` before running the executable file (see [`gipl_config.cfg`](https://github.com/Elchin/GIPL/blob/master/gipl_config.cfg)).
30 | 31 | # Visualize: 32 | The file with measured temperatures is [`mesres.txt`](https://github.com/Elchin/GIPL/blob/master/mesres.txt). The header for the `mesres.txt` can be found in the `compare.m` script. Type the command `>>compare(0)` to execute the script in the [Matlab](https://www.mathworks.com/products/matlab.html). The script plots the daily measured against calculated ground temperatures at four specified depth as shown in the figure below. Also checkout the jupyter notebook [example](https://github.com/Elchin/GIPL/blob/master/plot_results.ipynb). 33 | ![results](https://github.com/Elchin/GIPL/blob/master/results.png) 34 | 35 | # Input/Output Setup: 36 | [`gipl_config.cfg`](https://github.com/Elchin/GIPL/blob/master/gipl_config.cfg) configuration file that includes paths to input and output files. All input files stored in `in` folder. The path for the output files is prescribed in the config file. The model outputs three files: `results.txt` file with daily output, `mean.txt` with the yearly averaged data such as active layer thickness and freeze-up day, and the file `start.txt` includes the temperature profile with depth for the last day of simulation. The `result.txt` has the structure: `time`, `upper_bnd_temperature`, `snow_depth, ground_temperatures`, where 'ground_temperatures' assigned in [`grid.txt`](https://github.com/Elchin/GIPL/blob/master/in/grid.txt) (see below). The `mean.txt` file has the same configuration and includes 3 more columns. Everything in `mean.txt` is averaged yearly (see `number_of_time_steps` in `gipl_config.cfg`). 37 | 38 | ## Input data: 39 | All input files are located in the [`in`](https://github.com/Elchin/GIPL/tree/master/in) folder. 40 | 41 | ### **gipl_config.cfg** 42 | Includes paths for input and output files and the correspoding setup paramteres.
43 | ``` 44 | 0/1: start from previous time step / start from the begining 45 | step | taum | tmin : 46 | step is the timestep in the example it is 1 47 | taum is the convergence parameter used by the stefan subroutine 48 | tmin minimal timestep used in the Stefan subroutine 49 | begin | end : start and end, in the example it runs over one year from 0 to 1 50 | smoothing_factor | unfrozen_water_parameter | max number of iterations 51 | number_of_second_per_day [sec] | number_of_time_steps (in the example number of days in a year ) 52 | sea_level | max_number_of_freezing_fronts [integer number] 53 | freezing_front_min | freezing_front_max depth [meters] 54 | saturation_coefficient (fraction of 1) 55 | ``` 56 | 57 | ### **input.txt** 58 | Includes the total number of sites and the corresponding ids for the organic and mineral soils. In the current version, the number of sites is equal to 1. 59 | 60 | ### **bound.txt** 61 | Include upper boundary condition (in the example it is an air temperature)
62 | ``` 63 | First row is a number of observations (in the example number of day) 64 | Column 1: Time parameter (day number) 65 | Column 2: Temperature (daily averaged temperature [Celsius]) 66 | ``` 67 | 68 | ### **snow.txt** 69 | Include snow depth (in the example it is daily snow depth)
70 | ``` 71 | First row is a number of observations (in the example number of day) 72 | Column 1: Time parameter (day number) 73 | Column 2: Snow depth (daily averaged [m]) 74 | ``` 75 | 76 | ### **rsnow.txt** 77 | Include snow thermal conductivity (in the example it is daily snow conductivity)
78 | ``` 79 | First row is a number of observations (in the example number of day) 80 | Column 1: Time parameter (day number) 81 | Column 2: Snow conductivity (daily averaged [W/(mK)]) 82 | ``` 83 | 84 | ### **grid.txt** 85 | Includes number of grid point (`n`),
86 | In the example, the vertical grid starts from the 1.5 meters above the ground up to 90 meters deep. 87 | The minus sign corresponds to the values above the ground surface and plus corresponds to the values below the surface. 88 | For more clarity, copy and paste the grid into the excel file. The `n+1` element of the grid corresponds to the number of output points (in the example it is 12). The rest of the grid file correspond to indexes of the grid points (e.g. the number 40 below the 12 is the index of the gr(40)=0.001). 89 | 90 | ### **initial.txt** 91 | Includes the initial temperatures with depth profile. 92 | The model reads `initial.txt` file when in `cmd.txt` the first element is equal to 1 (e.i. start from the initial time step). 93 | The first parameter in `initial.txt` can be ignored, the second parameter is the number of points (in the example it is equal 13). 94 | The first column corresponds to the depth [m] and the second column to the temperature [Celsius] measured at that depth at time=0. 95 | 96 | ### **mineral.txt** 97 | Includes all thermo-physical properties of the multilayered soil column. 98 | 99 | The first row can be ignored. 100 | In the second row, the first element can be also ignored. The second element in this row corresponds to the number of layers, 101 | starting from row 3 to row 8 are thermo-physical properties of each layer. 102 | The first column is the volumetric water content (WVC) is a fraction of 1. 103 | The second and the third columns are "a" and "b" coefficients of the unfrozen water curve (obtained from unfrozen water curve fitting) [dimensionless]. 104 | The forth and the fifth columns are the thawed and frozen volumetric heat capacities [J/(m^3K)]. 105 | The six and the seven columns are thawed and frozen heat conductivities [W/(mK)]. 106 | The eighths column is the thickness of the corresponding layer. 107 | 108 | 109 | ### **organic.txt** 110 | Includes a similar structure to `mineral.txt` and carries the parameters for the organic soil layer/s. 111 | 112 | ### **sites.txt** 113 | This file includes `site_id`, `snow_code`, `veg_code`, `geo_code`, `gt_zone_code`, and `temp_grd`. This file is useful when we run multiple sites at the same time and have multiple organic and mineral soils, snow parameters, and so on. The current setup uses a temperature gradient equal 0.0 at the lower boundary of the grid. 114 | -------------------------------------------------------------------------------- /in/rsnow.txt: -------------------------------------------------------------------------------- 1 | 757 2 | 1 0.3 3 | 2 0.3 4 | 3 0.3 5 | 4 0.3 6 | 5 0.3 7 | 6 0.3 8 | 7 0.3 9 | 8 0.3 10 | 9 0.3 11 | 10 0.3 12 | 11 0.3 13 | 12 0.3 14 | 13 0.3 15 | 14 0.3 16 | 15 0.3 17 | 16 0.3 18 | 17 0.3 19 | 18 0.3 20 | 19 0.3 21 | 20 0.3 22 | 21 0.3 23 | 22 0.3 24 | 23 0.3 25 | 24 0.3 26 | 25 0.3 27 | 26 0.3 28 | 27 0.3 29 | 28 0.3 30 | 29 0.3 31 | 30 0.3 32 | 31 0.3 33 | 32 0.3 34 | 33 0.3 35 | 34 0.3 36 | 35 0.3 37 | 36 0.3 38 | 37 0.3 39 | 38 0.3 40 | 39 0.3 41 | 40 0.3 42 | 41 0.3 43 | 42 0.3 44 | 43 0.3 45 | 44 0.3 46 | 45 0.3 47 | 46 0.3 48 | 47 0.3 49 | 48 0.3 50 | 49 0.3 51 | 50 0.3 52 | 51 0.3 53 | 52 0.3 54 | 53 0.3 55 | 54 0.3 56 | 55 0.3 57 | 56 0.3 58 | 57 0.3 59 | 58 0.3 60 | 59 0.3 61 | 60 0.3 62 | 61 0.3 63 | 62 0.3 64 | 63 0.3 65 | 64 0.3 66 | 65 0.3 67 | 66 0.3 68 | 67 0.3 69 | 68 0.3 70 | 69 0.3 71 | 70 0.3 72 | 71 0.3 73 | 72 0.3 74 | 73 0.3 75 | 74 0.3 76 | 75 0.3 77 | 76 0.3 78 | 77 0.3 79 | 78 0.3 80 | 79 0.3 81 | 80 0.3 82 | 81 0.3 83 | 82 0.3 84 | 83 0.3 85 | 84 0.3 86 | 85 0.3 87 | 86 0.3 88 | 87 0.3 89 | 88 0.3 90 | 89 0.3 91 | 90 0.3 92 | 91 0.3 93 | 92 0.3 94 | 93 0.3 95 | 94 0.3 96 | 95 0.3 97 | 96 0.3 98 | 97 0.3 99 | 98 0.3 100 | 99 0.3 101 | 100 0.3 102 | 101 0.3 103 | 102 0.3 104 | 103 0.3 105 | 104 0.3 106 | 105 0.3 107 | 106 0.3 108 | 107 0.3 109 | 108 0.3 110 | 109 0.3 111 | 110 0.3 112 | 111 0.3 113 | 112 0.3 114 | 113 0.3 115 | 114 0.3 116 | 115 0.3 117 | 116 0.3 118 | 117 0.3 119 | 118 0.3 120 | 119 0.3 121 | 120 0.3 122 | 121 0.3 123 | 122 0.3 124 | 123 0.3 125 | 124 0.3 126 | 125 0.3 127 | 126 0.3 128 | 127 0.3 129 | 128 0.3 130 | 129 0.3 131 | 130 0.3 132 | 131 0.3 133 | 132 0.3 134 | 133 0.3 135 | 134 0.3 136 | 135 0.3 137 | 136 0.3 138 | 137 0.3 139 | 138 0.3 140 | 139 0.3 141 | 140 0.3 142 | 141 0.3 143 | 142 0.3 144 | 143 0.3 145 | 144 0.3 146 | 145 0.3 147 | 146 0.3 148 | 147 0.3 149 | 148 0.3 150 | 149 0.3 151 | 150 0.3 152 | 151 0.3 153 | 152 0.3 154 | 153 0.3 155 | 154 0.3 156 | 155 0.3 157 | 156 0.3 158 | 157 0.3 159 | 158 0.3 160 | 159 0.3 161 | 160 0.3 162 | 161 0.3 163 | 162 0.3 164 | 163 0.3 165 | 164 0.3 166 | 165 0.3 167 | 166 0.3 168 | 167 0.3 169 | 168 0.3 170 | 169 0.3 171 | 170 0.3 172 | 171 0.3 173 | 172 0.3 174 | 173 0.3 175 | 174 0.3 176 | 175 0.3 177 | 176 0.3 178 | 177 0.3 179 | 178 0.3 180 | 179 0.3 181 | 180 0.3 182 | 181 0.3 183 | 182 0.3 184 | 183 0.3 185 | 184 0.3 186 | 185 0.3 187 | 186 0.3 188 | 187 0.3 189 | 188 0.3 190 | 189 0.3 191 | 190 0.3 192 | 191 0.3 193 | 192 0.3 194 | 193 0.3 195 | 194 0.3 196 | 195 0.3 197 | 196 0.3 198 | 197 0.3 199 | 198 0.3 200 | 199 0.3 201 | 200 0.3 202 | 201 0.3 203 | 202 0.3 204 | 203 0.3 205 | 204 0.3 206 | 205 0.3 207 | 206 0.3 208 | 207 0.3 209 | 208 0.3 210 | 209 0.3 211 | 210 0.3 212 | 211 0.3 213 | 212 0.3 214 | 213 0.3 215 | 214 0.3 216 | 215 0.3 217 | 216 0.3 218 | 217 0.3 219 | 218 0.3 220 | 219 0.3 221 | 220 0.3 222 | 221 0.3 223 | 222 0.3 224 | 223 0.3 225 | 224 0.3 226 | 225 0.3 227 | 226 0.3 228 | 227 0.3 229 | 228 0.3 230 | 229 0.3 231 | 230 0.3 232 | 231 0.3 233 | 232 0.3 234 | 233 0.3 235 | 234 0.3 236 | 235 0.3 237 | 236 0.3 238 | 237 0.3 239 | 238 0.3 240 | 239 0.3 241 | 240 0.3 242 | 241 0.3 243 | 242 0.3 244 | 243 0.3 245 | 244 0.3 246 | 245 0.3 247 | 246 0.3 248 | 247 0.3 249 | 248 0.3 250 | 249 0.3 251 | 250 0.3 252 | 251 0.3 253 | 252 0.3 254 | 253 0.3 255 | 254 0.3 256 | 255 0.3 257 | 256 0.3 258 | 257 0.3 259 | 258 0.3 260 | 259 0.3 261 | 260 0.3 262 | 261 0.3 263 | 262 0.3 264 | 263 0.3 265 | 264 0.3 266 | 265 0.3 267 | 266 0.3 268 | 267 0.3 269 | 268 0.3 270 | 269 0.3 271 | 270 0.3 272 | 271 0.3 273 | 272 0.3 274 | 273 0.3 275 | 274 0.3 276 | 275 0.3 277 | 276 0.3 278 | 277 0.3 279 | 278 0.3 280 | 279 0.3 281 | 280 0.3 282 | 281 0.3 283 | 282 0.3 284 | 283 0.3 285 | 284 0.3 286 | 285 0.3 287 | 286 0.3 288 | 287 0.3 289 | 288 0.3 290 | 289 0.3 291 | 290 0.3 292 | 291 0.3 293 | 292 0.3 294 | 293 0.3 295 | 294 0.3 296 | 295 0.3 297 | 296 0.3 298 | 297 0.3 299 | 298 0.3 300 | 299 0.3 301 | 300 0.3 302 | 301 0.3 303 | 302 0.3 304 | 303 0.3 305 | 304 0.3 306 | 305 0.3 307 | 306 0.3 308 | 307 0.3 309 | 308 0.3 310 | 309 0.3 311 | 310 0.3 312 | 311 0.3 313 | 312 0.3 314 | 313 0.3 315 | 314 0.3 316 | 315 0.3 317 | 316 0.3 318 | 317 0.3 319 | 318 0.3 320 | 319 0.3 321 | 320 0.3 322 | 321 0.3 323 | 322 0.3 324 | 323 0.3 325 | 324 0.3 326 | 325 0.3 327 | 326 0.3 328 | 327 0.3 329 | 328 0.3 330 | 329 0.3 331 | 330 0.3 332 | 331 0.3 333 | 332 0.3 334 | 333 0.3 335 | 334 0.3 336 | 335 0.3 337 | 336 0.3 338 | 337 0.3 339 | 338 0.3 340 | 339 0.3 341 | 340 0.3 342 | 341 0.3 343 | 342 0.3 344 | 343 0.3 345 | 344 0.3 346 | 345 0.3 347 | 346 0.3 348 | 347 0.3 349 | 348 0.3 350 | 349 0.3 351 | 350 0.3 352 | 351 0.3 353 | 352 0.3 354 | 353 0.3 355 | 354 0.3 356 | 355 0.3 357 | 356 0.3 358 | 357 0.3 359 | 358 0.3 360 | 359 0.3 361 | 360 0.3 362 | 361 0.3 363 | 362 0.3 364 | 363 0.3 365 | 364 0.3 366 | 365 0.3 367 | 366 0.3 368 | 367 0.3 369 | 368 0.3 370 | 369 0.3 371 | 370 0.3 372 | 371 0.3 373 | 372 0.3 374 | 373 0.3 375 | 374 0.3 376 | 375 0.3 377 | 376 0.3 378 | 377 0.3 379 | 378 0.3 380 | 379 0.3 381 | 380 0.3 382 | 381 0.3 383 | 382 0.3 384 | 383 0.3 385 | 384 0.3 386 | 385 0.3 387 | 386 0.3 388 | 387 0.3 389 | 388 0.3 390 | 389 0.3 391 | 390 0.3 392 | 391 0.3 393 | 392 0.3 394 | 393 0.3 395 | 394 0.3 396 | 395 0.3 397 | 396 0.3 398 | 397 0.3 399 | 398 0.3 400 | 399 0.3 401 | 400 0.3 402 | 401 0.3 403 | 402 0.3 404 | 403 0.3 405 | 404 0.3 406 | 405 0.3 407 | 406 0.3 408 | 407 0.3 409 | 408 0.3 410 | 409 0.3 411 | 410 0.3 412 | 411 0.3 413 | 412 0.3 414 | 413 0.3 415 | 414 0.3 416 | 415 0.3 417 | 416 0.3 418 | 417 0.3 419 | 418 0.3 420 | 419 0.3 421 | 420 0.3 422 | 421 0.3 423 | 422 0.3 424 | 423 0.3 425 | 424 0.3 426 | 425 0.3 427 | 426 0.3 428 | 427 0.3 429 | 428 0.3 430 | 429 0.3 431 | 430 0.3 432 | 431 0.3 433 | 432 0.3 434 | 433 0.3 435 | 434 0.3 436 | 435 0.3 437 | 436 0.3 438 | 437 0.3 439 | 438 0.3 440 | 439 0.3 441 | 440 0.3 442 | 441 0.3 443 | 442 0.3 444 | 443 0.3 445 | 444 0.3 446 | 445 0.3 447 | 446 0.3 448 | 447 0.3 449 | 448 0.3 450 | 449 0.3 451 | 450 0.3 452 | 451 0.3 453 | 452 0.3 454 | 453 0.3 455 | 454 0.3 456 | 455 0.3 457 | 456 0.3 458 | 457 0.3 459 | 458 0.3 460 | 459 0.3 461 | 460 0.3 462 | 461 0.3 463 | 462 0.3 464 | 463 0.3 465 | 464 0.3 466 | 465 0.3 467 | 466 0.3 468 | 467 0.3 469 | 468 0.3 470 | 469 0.3 471 | 470 0.3 472 | 471 0.3 473 | 472 0.3 474 | 473 0.3 475 | 474 0.3 476 | 475 0.3 477 | 476 0.3 478 | 477 0.3 479 | 478 0.3 480 | 479 0.3 481 | 480 0.3 482 | 481 0.3 483 | 482 0.3 484 | 483 0.3 485 | 484 0.3 486 | 485 0.3 487 | 486 0.3 488 | 487 0.3 489 | 488 0.3 490 | 489 0.3 491 | 490 0.3 492 | 491 0.3 493 | 492 0.3 494 | 493 0.3 495 | 494 0.3 496 | 495 0.3 497 | 496 0.3 498 | 497 0.3 499 | 498 0.3 500 | 499 0.3 501 | 500 0.3 502 | 501 0.3 503 | 502 0.3 504 | 503 0.3 505 | 504 0.3 506 | 505 0.3 507 | 506 0.3 508 | 507 0.3 509 | 508 0.3 510 | 509 0.3 511 | 510 0.3 512 | 511 0.3 513 | 512 0.3 514 | 513 0.3 515 | 514 0.3 516 | 515 0.3 517 | 516 0.3 518 | 517 0.3 519 | 518 0.3 520 | 519 0.3 521 | 520 0.3 522 | 521 0.3 523 | 522 0.3 524 | 523 0.3 525 | 524 0.3 526 | 525 0.3 527 | 526 0.3 528 | 527 0.3 529 | 528 0.3 530 | 529 0.3 531 | 530 0.3 532 | 531 0.3 533 | 532 0.3 534 | 533 0.3 535 | 534 0.3 536 | 535 0.3 537 | 536 0.3 538 | 537 0.3 539 | 538 0.3 540 | 539 0.3 541 | 540 0.3 542 | 541 0.3 543 | 542 0.3 544 | 543 0.3 545 | 544 0.3 546 | 545 0.3 547 | 546 0.3 548 | 547 0.3 549 | 548 0.3 550 | 549 0.3 551 | 550 0.3 552 | 551 0.3 553 | 552 0.3 554 | 553 0.3 555 | 554 0.3 556 | 555 0.3 557 | 556 0.3 558 | 557 0.3 559 | 558 0.3 560 | 559 0.3 561 | 560 0.3 562 | 561 0.3 563 | 562 0.3 564 | 563 0.3 565 | 564 0.3 566 | 565 0.3 567 | 566 0.3 568 | 567 0.3 569 | 568 0.3 570 | 569 0.3 571 | 570 0.3 572 | 571 0.3 573 | 572 0.3 574 | 573 0.3 575 | 574 0.3 576 | 575 0.3 577 | 576 0.3 578 | 577 0.3 579 | 578 0.3 580 | 579 0.3 581 | 580 0.3 582 | 581 0.3 583 | 582 0.3 584 | 583 0.3 585 | 584 0.3 586 | 585 0.3 587 | 586 0.3 588 | 587 0.3 589 | 588 0.3 590 | 589 0.3 591 | 590 0.3 592 | 591 0.3 593 | 592 0.3 594 | 593 0.3 595 | 594 0.3 596 | 595 0.3 597 | 596 0.3 598 | 597 0.3 599 | 598 0.3 600 | 599 0.3 601 | 600 0.3 602 | 601 0.3 603 | 602 0.3 604 | 603 0.3 605 | 604 0.3 606 | 605 0.3 607 | 606 0.3 608 | 607 0.3 609 | 608 0.3 610 | 609 0.3 611 | 610 0.3 612 | 611 0.3 613 | 612 0.3 614 | 613 0.3 615 | 614 0.3 616 | 615 0.3 617 | 616 0.3 618 | 617 0.3 619 | 618 0.3 620 | 619 0.3 621 | 620 0.3 622 | 621 0.3 623 | 622 0.3 624 | 623 0.3 625 | 624 0.3 626 | 625 0.3 627 | 626 0.3 628 | 627 0.3 629 | 628 0.3 630 | 629 0.3 631 | 630 0.3 632 | 631 0.3 633 | 632 0.3 634 | 633 0.3 635 | 634 0.3 636 | 635 0.3 637 | 636 0.3 638 | 637 0.3 639 | 638 0.3 640 | 639 0.3 641 | 640 0.3 642 | 641 0.3 643 | 642 0.3 644 | 643 0.3 645 | 644 0.3 646 | 645 0.3 647 | 646 0.3 648 | 647 0.3 649 | 648 0.3 650 | 649 0.3 651 | 650 0.3 652 | 651 0.3 653 | 652 0.3 654 | 653 0.3 655 | 654 0.3 656 | 655 0.3 657 | 656 0.3 658 | 657 0.3 659 | 658 0.3 660 | 659 0.3 661 | 660 0.3 662 | 661 0.3 663 | 662 0.3 664 | 663 0.3 665 | 664 0.3 666 | 665 0.3 667 | 666 0.3 668 | 667 0.3 669 | 668 0.3 670 | 669 0.3 671 | 670 0.3 672 | 671 0.3 673 | 672 0.3 674 | 673 0.3 675 | 674 0.3 676 | 675 0.3 677 | 676 0.3 678 | 677 0.3 679 | 678 0.3 680 | 679 0.3 681 | 680 0.3 682 | 681 0.3 683 | 682 0.3 684 | 683 0.3 685 | 684 0.3 686 | 685 0.3 687 | 686 0.3 688 | 687 0.3 689 | 688 0.3 690 | 689 0.3 691 | 690 0.3 692 | 691 0.3 693 | 692 0.3 694 | 693 0.3 695 | 694 0.3 696 | 695 0.3 697 | 696 0.3 698 | 697 0.3 699 | 698 0.3 700 | 699 0.3 701 | 700 0.3 702 | 701 0.3 703 | 702 0.3 704 | 703 0.3 705 | 704 0.3 706 | 705 0.3 707 | 706 0.3 708 | 707 0.3 709 | 708 0.3 710 | 709 0.3 711 | 710 0.3 712 | 711 0.3 713 | 712 0.3 714 | 713 0.3 715 | 714 0.3 716 | 715 0.3 717 | 716 0.3 718 | 717 0.3 719 | 718 0.3 720 | 719 0.3 721 | 720 0.3 722 | 721 0.3 723 | 722 0.3 724 | 723 0.3 725 | 724 0.3 726 | 725 0.3 727 | 726 0.3 728 | 727 0.3 729 | 728 0.3 730 | 729 0.3 731 | 730 0.3 732 | 731 0.3 733 | 732 0.3 734 | 733 0.3 735 | 734 0.3 736 | 735 0.3 737 | 736 0.3 738 | 737 0.3 739 | 738 0.3 740 | 739 0.3 741 | 740 0.3 742 | 741 0.3 743 | 742 0.3 744 | 743 0.3 745 | 744 0.3 746 | 745 0.3 747 | 746 0.3 748 | 747 0.3 749 | 748 0.3 750 | 749 0.3 751 | 750 0.3 752 | 751 0.3 753 | 752 0.3 754 | 753 0.3 755 | 754 0.3 756 | 755 0.3 757 | 756 0.3 758 | 757 0.3 -------------------------------------------------------------------------------- /in/snow.txt: -------------------------------------------------------------------------------- 1 | 757 2 | 1 0 3 | 2 0 4 | 3 0 5 | 4 0 6 | 5 0 7 | 6 0 8 | 7 0 9 | 8 0 10 | 9 0 11 | 10 0 12 | 11 0 13 | 12 0 14 | 13 0 15 | 14 0 16 | 15 0 17 | 16 0 18 | 17 0 19 | 18 0 20 | 19 0 21 | 20 0 22 | 21 0 23 | 22 0 24 | 23 0 25 | 24 0 26 | 25 0 27 | 26 0 28 | 27 0 29 | 28 0 30 | 29 0 31 | 30 0 32 | 31 0 33 | 32 0 34 | 33 0 35 | 34 0 36 | 35 0 37 | 36 0 38 | 37 0 39 | 38 0 40 | 39 0 41 | 40 0 42 | 41 0 43 | 42 0 44 | 43 0 45 | 44 0 46 | 45 0 47 | 46 0 48 | 47 0 49 | 48 0 50 | 49 0 51 | 50 0 52 | 51 0 53 | 52 0 54 | 53 0 55 | 54 0 56 | 55 0 57 | 56 0 58 | 57 0 59 | 58 0 60 | 59 0 61 | 60 0 62 | 61 0 63 | 62 0 64 | 63 0 65 | 64 0 66 | 65 0 67 | 66 0.017 68 | 67 0 69 | 68 0.013 70 | 69 0.02 71 | 70 0.004 72 | 71 0.007 73 | 72 0.006 74 | 73 0.01 75 | 74 0.013 76 | 75 0.012 77 | 76 0.012 78 | 77 0.008 79 | 78 0.004 80 | 79 0.044 81 | 80 0.032 82 | 81 0.032 83 | 82 0.036 84 | 83 0.034 85 | 84 0.032 86 | 85 0.031 87 | 86 0.027 88 | 87 0.025 89 | 88 0.027 90 | 89 0.028 91 | 90 0.027 92 | 91 0.025 93 | 92 0.026 94 | 93 0.026 95 | 94 0.018 96 | 95 0.023 97 | 96 0.023 98 | 97 0.023 99 | 98 0.022 100 | 99 0.022 101 | 100 0.021 102 | 101 0.023 103 | 102 0.022 104 | 103 0.03 105 | 104 0.056 106 | 105 0.051 107 | 106 0.043 108 | 107 0.045 109 | 108 0.049 110 | 109 0.049 111 | 110 0.046 112 | 111 0.048 113 | 112 0.053 114 | 113 0.052 115 | 114 0.066 116 | 115 0.062 117 | 116 0.061 118 | 117 0.079 119 | 118 0.111 120 | 119 0.11 121 | 120 0.116 122 | 121 0.113 123 | 122 0.109 124 | 123 0.123 125 | 124 0.132 126 | 125 0.131 127 | 126 0.134 128 | 127 0.133 129 | 128 0.131 130 | 129 0.131 131 | 130 0.13 132 | 131 0.131 133 | 132 0.131 134 | 133 0.131 135 | 134 0.131 136 | 135 0.132 137 | 136 0.141 138 | 137 0.138 139 | 138 0.136 140 | 139 0.132 141 | 140 0.131 142 | 141 0.131 143 | 142 0.131 144 | 143 0.131 145 | 144 0.129 146 | 145 0.129 147 | 146 0.129 148 | 147 0.13 149 | 148 0.129 150 | 149 0.129 151 | 150 0.136 152 | 151 0.143 153 | 152 0.129 154 | 153 0.129 155 | 154 0.13 156 | 155 0.129 157 | 156 0.129 158 | 157 0.129 159 | 158 0.128 160 | 159 0.128 161 | 160 0.125 162 | 161 0.125 163 | 162 0.125 164 | 163 0.127 165 | 164 0.128 166 | 165 0.126 167 | 166 0.124 168 | 167 0.122 169 | 168 0.122 170 | 169 0.123 171 | 170 0.126 172 | 171 0.127 173 | 172 0.126 174 | 173 0.126 175 | 174 0.126 176 | 175 0.125 177 | 176 0.124 178 | 177 0.124 179 | 178 0.127 180 | 179 0.127 181 | 180 0.129 182 | 181 0.132 183 | 182 0.134 184 | 183 0.153 185 | 184 0.154 186 | 185 0.156 187 | 186 0.156 188 | 187 0.155 189 | 188 0.154 190 | 189 0.154 191 | 190 0.154 192 | 191 0.154 193 | 192 0.15 194 | 193 0.144 195 | 194 0.143 196 | 195 0.139 197 | 196 0.132 198 | 197 0.134 199 | 198 0.134 200 | 199 0.143 201 | 200 0.155 202 | 201 0.153 203 | 202 0.156 204 | 203 0.155 205 | 204 0.155 206 | 205 0.153 207 | 206 0.153 208 | 207 0.154 209 | 208 0.156 210 | 209 0.151 211 | 210 0.149 212 | 211 0.147 213 | 212 0.149 214 | 213 0.149 215 | 214 0.145 216 | 215 0.15 217 | 216 0.152 218 | 217 0.168 219 | 218 0.183 220 | 219 0.156 221 | 220 0.153 222 | 221 0.155 223 | 222 0.158 224 | 223 0.15 225 | 224 0.147 226 | 225 0.147 227 | 226 0.147 228 | 227 0.148 229 | 228 0.149 230 | 229 0.148 231 | 230 0.147 232 | 231 0.148 233 | 232 0.149 234 | 233 0.148 235 | 234 0.147 236 | 235 0.147 237 | 236 0.15 238 | 237 0.147 239 | 238 0.148 240 | 239 0.148 241 | 240 0.146 242 | 241 0.147 243 | 242 0.148 244 | 243 0.146 245 | 244 0.152 246 | 245 0.145 247 | 246 0.146 248 | 247 0.153 249 | 248 0.172 250 | 249 0.147 251 | 250 0.145 252 | 251 0.146 253 | 252 0.143 254 | 253 0.142 255 | 254 0.141 256 | 255 0.143 257 | 256 0.145 258 | 257 0.144 259 | 258 0.145 260 | 259 0.148 261 | 260 0.144 262 | 261 0.144 263 | 262 0.145 264 | 263 0.144 265 | 264 0.144 266 | 265 0.142 267 | 266 0.143 268 | 267 0.143 269 | 268 0.142 270 | 269 0.143 271 | 270 0.142 272 | 271 0.14 273 | 272 0.139 274 | 273 0.139 275 | 274 0.141 276 | 275 0.143 277 | 276 0.141 278 | 277 0.14 279 | 278 0.138 280 | 279 0.139 281 | 280 0.141 282 | 281 0.14 283 | 282 0.141 284 | 283 0.142 285 | 284 0.143 286 | 285 0.144 287 | 286 0.143 288 | 287 0.136 289 | 288 0.138 290 | 289 0.138 291 | 290 0.141 292 | 291 0.141 293 | 292 0.139 294 | 293 0.141 295 | 294 0.141 296 | 295 0.136 297 | 296 0.141 298 | 297 0.141 299 | 298 0.134 300 | 299 0.128 301 | 300 0.125 302 | 301 0.127 303 | 302 0.139 304 | 303 0.122 305 | 304 0.127 306 | 305 0.129 307 | 306 0.128 308 | 307 0.128 309 | 308 0.175 310 | 309 0.176 311 | 310 0.175 312 | 311 0.174 313 | 312 0.174 314 | 313 0.171 315 | 314 0.172 316 | 315 0.175 317 | 316 0.174 318 | 317 0.177 319 | 318 0.178 320 | 319 0.173 321 | 320 0.172 322 | 321 0.17 323 | 322 0.169 324 | 323 0.176 325 | 324 0.177 326 | 325 0.184 327 | 326 0.177 328 | 327 0.173 329 | 328 0.175 330 | 329 0.171 331 | 330 0.172 332 | 331 0.171 333 | 332 0.172 334 | 333 0.166 335 | 334 0.151 336 | 335 0.141 337 | 336 0.124 338 | 337 0.108 339 | 338 0.096 340 | 339 0.087 341 | 340 0.069 342 | 341 0.03 343 | 342 0.006 344 | 343 0 345 | 344 0 346 | 345 0 347 | 346 0 348 | 347 0 349 | 348 0 350 | 349 0 351 | 350 0 352 | 351 0.003 353 | 352 0.005 354 | 353 0.006 355 | 354 0.006 356 | 355 0.005 357 | 356 0.003 358 | 357 0 359 | 358 0 360 | 359 0.001 361 | 360 0.002 362 | 361 0.004 363 | 362 0.001 364 | 363 0 365 | 364 0 366 | 365 0.001 367 | 366 0 368 | 367 0 369 | 368 0 370 | 369 0 371 | 370 0 372 | 371 0 373 | 372 0 374 | 373 0 375 | 374 0.002 376 | 375 0.002 377 | 376 0.003 378 | 377 0.002 379 | 378 0 380 | 379 0 381 | 380 0 382 | 381 0 383 | 382 0 384 | 383 0 385 | 384 0 386 | 385 0 387 | 386 0 388 | 387 0 389 | 388 0 390 | 389 0 391 | 390 0 392 | 391 0 393 | 392 0 394 | 393 0 395 | 394 0 396 | 395 0 397 | 396 0 398 | 397 0 399 | 398 0 400 | 399 0 401 | 400 0 402 | 401 0 403 | 402 0 404 | 403 0 405 | 404 0 406 | 405 0 407 | 406 0 408 | 407 0 409 | 408 0 410 | 409 0 411 | 410 0 412 | 411 0 413 | 412 0 414 | 413 0 415 | 414 0 416 | 415 0 417 | 416 0 418 | 417 0 419 | 418 0 420 | 419 0 421 | 420 0 422 | 421 0 423 | 422 0 424 | 423 0 425 | 424 0 426 | 425 0 427 | 426 0 428 | 427 0 429 | 428 0 430 | 429 0 431 | 430 0 432 | 431 0 433 | 432 0 434 | 433 0 435 | 434 0 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5.596 753 | 752 7.297 754 | 753 7.071 755 | 754 8.131 756 | 755 8.083 757 | 756 2.516 758 | 757 1.597 -------------------------------------------------------------------------------- /gipl.f90: -------------------------------------------------------------------------------- 1 | ! Geophysical Instatitue Permafrost Laboratory model version 2 GIPLv2 2 | ! version 2 is a numerical transient model that employs phase changes and the effect of the unfrozen volumetric water content in the non-homogeniuos soil texture 3 | ! Original version of the model developed by Romanovsky and Tipenko 2004 and described in Marchenko et al., (2008) 4 | ! Current version been significanlty modefied from its predicessor and using the IRF coding design 5 | ! This version is maintained by E. Jafarov at INSTAAR, CU Boulder 6 | ! Please cite Jafarov et al., (2012) work when using it. 7 | 8 | program gipl2 9 | use bnd 10 | use thermo 11 | use grd 12 | use alt 13 | 14 | call initialize 15 | call run_model 16 | call finalize 17 | 18 | end ! end of main program 19 | 20 | 21 | subroutine run_model 22 | use const 23 | use bnd 24 | use thermo 25 | use grd 26 | use alt 27 | 28 | implicit none 29 | 30 | ! variables 31 | real*8 :: res_save(m_grd+3,n_site) ! save results into 2D array 32 | real*8 :: dfrz_frn(n_time) ! depth of the freezing front 33 | real :: frz_up_time_cur ! freezeup time current (within a year) 34 | real :: frz_up_time_tot ! freezeup time global 35 | ! counters (time,steps) 36 | real*8 :: time_s,time_e ! internal start and end times 37 | real*8 :: time_loop ! main looping time 38 | real*8 :: time_cur ! current time (e.g. current day) 39 | integer :: i_site,j_time,i_grd,i_lay 40 | 41 | time_s=time_step*DBLE(n_time*time_beg) 42 | time_e=time_step*DBLE(n_time*time_end) 43 | i_time=1 44 | time_loop=0.0D0 45 | TINIR=0.0D0 46 | do while (time_loop.LT.time_e) 47 | do i_site=1,n_site 48 | time_cur=time_loop+time_restart 49 | call save_results(i_site,time_cur,time_loop) 50 | 6666 continue 51 | 52 | !do while (i_time(i_site).LT.n_time) 53 | !write(*,*)time_loop, i_site, i_time, n_time 54 | call stefan1D(temp(i_site,:),n_grd,dz,time_loop,i_site,lay_id(i_site,:), & 55 | temp_grd(i_site)) 56 | time_loop=time_loop+time_step 57 | time_cur=time_loop+time_restart 58 | if(i_time(i_site).LT.n_time) then 59 | i_time(i_site)=i_time(i_site)+1 60 | call save_results(i_site,time_cur,time_loop) 61 | call active_layer(i_site) 62 | ! write(*,*) 'goto', i_time,time_loop 63 | GOTO 6666 64 | endif 65 | !enddo 66 | if(time_s.LT.time_e.AND.time_loop.GT.time_s)then 67 | do j_time=1,n_time ! WRITTING RESULTS 68 | write(1,FMT1) i_site, (RES(j_time,i_grd),i_grd=1,m_grd+3) 69 | enddo 70 | endif 71 | do i_grd=1,m_grd+3 72 | res_save(i_grd,i_site)=sum((RES(:,i_grd))) 73 | enddo 74 | enddo 75 | 76 | i_time=1 77 | do i_site=1,n_site 78 | frz_up_time_cur=-7777.D0 79 | frz_up_time_tot=frz_up_time_cur 80 | do j_time=2,n_time 81 | if((n_frz_frn(j_time,i_site)-n_frz_frn(j_time-1,i_site)).EQ.-2)then 82 | if(z_frz_frn(j_time-1,n_frz_frn(j_time-1,i_site),i_site).GE.frz_frn_min) frz_up_time_cur=SNGL(RES(j_time,1)) 83 | endif 84 | enddo 85 | 86 | if(frz_up_time_cur.GT.0.0)then 87 | frz_up_time_tot=AMOD(frz_up_time_cur,REAL(n_time)) 88 | if(frz_up_time_tot.EQ.0.0)frz_up_time_tot=REAL(n_time) 89 | endif 90 | dfrz_frn=z_frz_frn(:,1,i_site) 91 | 92 | call save_results(i_site,time_cur,time_loop) 93 | call active_layer(i_site) 94 | 95 | !____WRITTING MEAN 96 | write(2,FMT2) i_site,(res_save(i_grd,i_site)/DBLE(n_time),i_grd=1,m_grd+3), & 97 | dfrz_frn(n_time),frz_up_time_cur,frz_up_time_tot 98 | do j_time=1,n_time+2 99 | utemp_time_i(j_time)=time_cur+DBLE(j_time-1)*time_step 100 | enddo 101 | call interpolate(utemp_time,utemp(:,i_site),n_temp,utemp_time_i,utemp_i(:,i_site),n_time+2) 102 | call interpolate(snd_time,snd(:,i_site),n_snow,utemp_time_i,snd_i(:,i_site),n_time+2) 103 | call snowfix(utemp_i(:,i_site),snd_i(:,i_site),n_time+2) 104 | call interpolate(stcon_time,stcon(:,i_site),n_stcon,utemp_time_i,stcon_i(:,i_site),n_time+2) 105 | enddo 106 | call save_restart 107 | 108 | TINIR=time_loop 109 | enddo 110 | 111 | end subroutine run_model 112 | 113 | 114 | subroutine save_restart 115 | use bnd 116 | use thermo 117 | use grd 118 | implicit none 119 | integer :: i_site,i_grd 120 | 121 | rewind(3) 122 | write(3, * ) time_restart 123 | do i_grd=1,n_grd 124 | write (3,* ) ( temp(i_site,i_grd),i_site=1,n_site) 125 | enddo 126 | 127 | end subroutine save_restart 128 | 129 | 130 | subroutine finalize 131 | 132 | close(1);close(2);close(3) 133 | 134 | end subroutine finalize 135 | 136 | 137 | subroutine initialize 138 | use const 139 | use bnd 140 | use thermo 141 | use grd 142 | use alt 143 | 144 | implicit none 145 | 146 | integer IREAD,ierr 147 | integer :: i,j,k,z_num,i_grd,j_time,i_site,i_lay 148 | 149 | 150 | real*8 ,allocatable ::gtzone(:,:) 151 | character*64 stdummy 152 | character*64 fconfig 153 | 154 | character*64 file_sites,file_bound,file_snow,file_rsnow,file_init 155 | character*64 file_grid,file_organic,file_mineral 156 | 157 | real*8,allocatable:: A1(:,:),A2(:,:),A3(:,:),A4(:,:),A5(:,:) 158 | real*8,allocatable:: A6(:,:),A7(:,:),A8(:,:),A9(:,:),A10(:,:) 159 | integer, allocatable :: veg_class(:), num_vl(:) 160 | integer :: vln 161 | 162 | real*8,allocatable:: B1(:,:),B2(:,:),B3(:,:),B4(:,:),B5(:,:) 163 | real*8,allocatable:: B6(:,:),B7(:,:),B8(:,:) 164 | integer, allocatable :: geo_class(:), num_gl(:) 165 | real*8 :: layer_thick 166 | integer :: gln 167 | real*8, allocatable :: z(:) ! vertical grid 168 | real*8 :: hcscale 169 | 170 | fconfig='gipl_config.cfg' 171 | call filexist(fconfig) 172 | open(60,file=fconfig) 173 | !read input files 174 | read(60,'(A)')stdummy 175 | read(60,'(A)')file_sites 176 | read(60,'(A)')file_bound 177 | read(60,'(A)')file_snow 178 | read(60,'(A)')file_rsnow 179 | read(60,'(A)')file_init 180 | !read(60,'(A)')cmdf 181 | read(60,'(A)')file_grid 182 | read(60,'(A)')file_organic 183 | read(60,'(A)')file_mineral 184 | 185 | ! read output files 186 | read(60,'(A)')stdummy 187 | read(60,'(A)')stdummy 188 | read(60,'(A)')aver_res_file 189 | read(60,'(A)')result_file 190 | read(60,'(A)')restart_file 191 | 192 | ! read input parameters 193 | read(60,'(A)')stdummy 194 | read(60,'(A)')stdummy 195 | read(60,*)restart 196 | read(60,'(A)')stdummy 197 | read(60,*)time_step,TAUM,TMIN 198 | read(60,'(A)')stdummy 199 | read(60,*) time_beg,time_end 200 | read(60,'(A)')stdummy 201 | read(60,*) smooth_coef,unf_water_coef,itmax 202 | !smoothing factor | unfrozen water parameter | max number of iterations 203 | read(60,'(A)')stdummy 204 | read(60,*) n_sec_day,n_time 205 | ! number of second in a day [sec] | number of time steps (in the example number of days in a year ) 206 | read(60,'(A)')stdummy 207 | read(60,*) sea_level,n_frz_max 208 | read(60,'(A)')stdummy 209 | read(60,*) frz_frn_min,frz_frn_max 210 | read(60,'(A)')stdummy 211 | read(60,*) sat_coef 212 | 213 | close(60) 214 | 215 | call filexist(file_sites) 216 | call filexist(file_bound) 217 | call filexist(file_snow) 218 | call filexist(file_rsnow) 219 | call filexist(file_grid) 220 | call filexist(file_init) 221 | call filexist(file_mineral) 222 | call filexist(file_organic) 223 | 224 | open(60,FILE=file_sites) 225 | read(60,*)n_site 226 | allocate(snow_code(n_site),STAT=IERR) 227 | allocate(veg_code(n_site),STAT=IERR) 228 | allocate(geo_code(n_site),STAT=IERR) 229 | allocate(gt_zone_code(n_site),STAT=IERR) 230 | allocate(temp_grd(n_site),STAT=IERR) 231 | do i_site=1,n_site 232 | read(60,*) IREAD,snow_code(i_site),veg_code(i_site),geo_code(i_site),& 233 | gt_zone_code(i_site),temp_grd(i_site) 234 | enddo 235 | close(60) 236 | ! print*, trim(file_sites),' has been read' 237 | 238 | open(60,file=file_bound) 239 | read(60,*)n_temp 240 | allocate(utemp_time(n_temp),STAT=IERR) 241 | allocate(utemp(n_temp,n_site),STAT=IERR) 242 | do i=1,n_temp 243 | read(60,*) utemp_time(I),(utemp(I,i_site),i_site=1,n_site) 244 | enddo 245 | close(60) 246 | ! print*,trim(file_bound),' has been read' 247 | 248 | open(60,file=file_rsnow) 249 | read(60,*)n_stcon 250 | allocate(stcon_time(n_stcon),STAT=IERR) 251 | allocate(stcon(n_stcon,n_site),STAT=IERR) 252 | do i=1,n_stcon 253 | read(60,*) stcon_time(i),(stcon(i,i_site),i_site=1,n_site) 254 | enddo 255 | close(60) 256 | ! print*,trim(file_rsnow),' has been read' 257 | 258 | open(60,file=file_snow) 259 | read(60,*)n_snow 260 | allocate(snd_time(n_snow),STAT=IERR) 261 | allocate(snd(n_snow,n_site),STAT=IERR) 262 | do I=1,n_snow 263 | read(60,*) snd_time(i),(snd(i,i_site),i_site=1,n_site) 264 | enddo 265 | close(60) 266 | ! print*,trim(file_snow),' has been read' 267 | 268 | open(60,file=file_init,action='read') 269 | read(60,*)z_num,n_ini!,time_restart 270 | allocate(zdepth_ini(n_ini),STAT=IERR) 271 | allocate(ztemp_ini(n_ini,n_site),STAT=IERR) 272 | allocate(gtzone(n_ini,z_num+1),STAT=IERR) 273 | read(60,*)stdummy 274 | do i=1,n_ini 275 | read(60,*) (gtzone(i,j),j=1,z_num+1) 276 | enddo 277 | close(60) 278 | ! print*,trim(file_init),'has been read' 279 | 280 | time_restart=utemp_time(1) 281 | zdepth_ini(:)=gtzone(:,1) 282 | do i=1,n_site 283 | k=gt_zone_code(i) 284 | ztemp_ini(:,I)=gtzone(:,k+1) 285 | enddo 286 | 287 | open(60,file=file_grid) 288 | read(60,*)n_grd 289 | allocate(zdepth(n_grd),STAT=IERR) 290 | do i=1,n_grd 291 | read(60,*) zdepth(i) 292 | enddo 293 | read(60,*)m_grd 294 | allocate(zdepth_id(m_grd),STAT=IERR) 295 | do j=1,m_grd 296 | read(60,*)zdepth_id(j) 297 | enddo 298 | close(60) 299 | ! print*,trim(file_grid),' has been read' 300 | 301 | ! note: that all max n_lay_cur layers has to be read or it will a give segmantation error 302 | ! n_lay=10!MAXVAL(n_lay_cur) 303 | !---------------------------------------------------- 304 | open (60, file=file_organic) 305 | read(60,*) vln ! reads numbers of classes 306 | allocate(A1(n_lay,vln),STAT=IERR) ! vwc 307 | allocate(A2(n_lay,vln),STAT=IERR) ! a_coef 308 | allocate(A3(n_lay,vln),STAT=IERR) ! b_coef 309 | allocate(A4(n_lay,vln),STAT=IERR) ! hcap_frz 310 | allocate(A5(n_lay,vln),STAT=IERR) !hcap_thw 311 | allocate(A6(n_lay,vln),STAT=IERR) !tcon_frz 312 | allocate(A7(n_lay,vln),STAT=IERR) !tcon_thw 313 | allocate(A8(vln,n_lay),STAT=IERR) !bot_cond 314 | allocate(veg_class(vln),STAT=IERR) !veg_class 315 | allocate(num_vl(vln),STAT=IERR) !num_vl number of vegetation layers 316 | do I = 1,vln 317 | read(60,*)veg_class(i),num_vl(i) 318 | do j=1,num_vl(i) 319 | read(60,*)A1(J,I),A2(J,I),A3(J,I), & 320 | A4(J,I),A5(J,I),A6(J,I),A7(J,I),A8(I,J) 321 | enddo 322 | enddo 323 | close(60) 324 | ! print*,trim(file_organic),' has been read' 325 | 326 | open (60, file=file_mineral) 327 | read(60,*) gln ! reads numbers of classes 328 | allocate(B1(n_lay,gln),STAT=IERR) ! vwc 329 | allocate(B2(n_lay,gln),STAT=IERR) ! a_coef 330 | allocate(B3(n_lay,gln),STAT=IERR) ! b_coef 331 | allocate(B4(n_lay,gln),STAT=IERR) ! hcap_frz 332 | allocate(B5(n_lay,gln),STAT=IERR) !hcap_thw 333 | allocate(B6(n_lay,gln),STAT=IERR) !tcon_frz 334 | allocate(B7(n_lay,gln),STAT=IERR) !tcon_thw 335 | allocate(B8(gln,n_lay),STAT=IERR) !bot_cond 336 | allocate(geo_class(gln),STAT=IERR) !geo_class 337 | allocate(num_gl(gln),STAT=IERR) !num_vl number of lithologic layers 338 | do I = 1,gln 339 | read(60,*)geo_class(i),num_gl(i) 340 | do j=1,num_gl(i) 341 | read(60,*)B1(J,I),B2(J,I),B3(J,I), & 342 | B4(J,I),B5(J,I),B6(J,I),B7(J,I),B8(I,J) 343 | enddo 344 | enddo 345 | close(60) 346 | ! print*,trim(file_mineral),' has been read' 347 | 348 | 349 | allocate(vwc(n_lay,n_site),STAT=IERR) 350 | allocate(a_coef(n_lay,n_site),STAT=IERR) 351 | allocate(b_coef(n_lay,n_site),STAT=IERR) 352 | allocate(EE(n_lay,n_site),STAT=IERR) 353 | allocate(hcap_frz(n_lay,n_site),STAT=IERR) 354 | allocate(hcap_thw(n_lay,n_site),STAT=IERR) 355 | allocate(tcon_frz(n_lay,n_site),STAT=IERR) 356 | allocate(tcon_thw(n_lay,n_site),STAT=IERR) 357 | allocate(n_lay_cur(n_site),STAT=IERR) 358 | allocate(n_bnd_lay(n_site,n_lay+1),STAT=IERR) 359 | 360 | do i = 1,n_site 361 | layer_thick=0 362 | n_bnd_lay(i,1)=layer_thick 363 | layer_thick=0 364 | n_bnd_lay(i,1)=layer_thick 365 | do j=1,num_vl(veg_code(i)) 366 | vwc(J,I)=A1(j,veg_code(i)); 367 | a_coef(J,I)=A2(j,veg_code(i)); 368 | b_coef(J,I)=A3(j,veg_code(i)); 369 | hcap_thw(J,I)=A4(j,veg_code(i)); 370 | hcap_frz(J,I)=A5(j,veg_code(i)); 371 | tcon_thw(J,I)=A6(j,veg_code(i)); 372 | tcon_frz(J,I)=A7(j,veg_code(i)); 373 | if (j.eq.1) then 374 | layer_thick=A8(veg_code(i),j) 375 | else 376 | layer_thick=layer_thick+A8(veg_code(i),j); 377 | endif 378 | n_bnd_lay(i,j+1)=layer_thick 379 | EE(J,I)=0 380 | ! write(*,'(3(f8.3),2(f12.1),3(f8.3))') vwc(J,I),a_coef(J,I),b_coef(J,I), & 381 | ! hcap_thw(J,I),hcap_frz(J,I),tcon_thw(J,I),tcon_frz(J,I),n_bnd_lay(i,j+1) 382 | enddo 383 | k=1 384 | n_lay_cur(I)=num_vl(veg_code(i))+num_gl(geo_code(i)) ! maximum number of soil layer = organic layers + mineral layers 385 | do j=num_vl(veg_code(i))+1,n_lay_cur(I) 386 | vwc(J,I)=B1(k,geo_code(i)); 387 | a_coef(J,I)=B2(k,geo_code(i)); 388 | b_coef(J,I)=B3(k,geo_code(i)); 389 | hcap_thw(J,I)=B4(k,geo_code(i)); 390 | hcap_frz(J,I)=B5(k,geo_code(i)); 391 | tcon_thw(J,I)=B6(k,geo_code(i)); 392 | tcon_frz(J,I)=B7(k,geo_code(i)); 393 | EE(J,I)=0 394 | layer_thick=layer_thick+B8(geo_code(i),k); 395 | n_bnd_lay(i,j+1)=layer_thick!B8(geo_code(i),j) 396 | k=k+1 397 | enddo 398 | n_bnd_lay(i,n_lay_cur(I)+1)=zdepth(n_grd) 399 | enddo 400 | 401 | allocate(z(n_grd),STAT=IERR) 402 | allocate(dz(n_grd),STAT=IERR) 403 | allocate(temp(n_site,n_grd),STAT=IERR) 404 | allocate(lay_id(n_site,n_grd),STAT=IERR) 405 | allocate(i_time(n_site),STAT=IERR) 406 | allocate(z_frz_frn(n_time,n_frz_max,n_site),STAT=IERR) 407 | allocate(n_frz_frn(n_time,n_site),STAT=IERR) 408 | allocate(temp_frz(n_lay,n_site),STAT=IERR) 409 | allocate(RES(n_time,m_grd+3),STAT=IERR) 410 | i_time=1 ! active_layer uses it below, needs to be initialized here 411 | 412 | z=zdepth/zdepth(n_grd) 413 | do i_grd=2,n_grd 414 | dz(i_grd)=z(i_grd)-z(i_grd-1) 415 | enddo 416 | 417 | hcscale=zdepth(n_grd)*zdepth(n_grd)/n_sec_day 418 | hcap_frz=hcap_frz*hcscale 419 | hcap_thw=hcap_thw*hcscale 420 | hcap_s=hcap_snow*hcscale 421 | L_fus=hcscale*Lf 422 | call assign_layer_id(n_lay,n_lay_cur,n_site,n_grd,zdepth,n_bnd_lay,lay_id) 423 | call init_cond(restart,n_site) 424 | 425 | allocate(utemp_time_i(n_time+2),STAT=IERR) ! allocating interval varialbe after interation 426 | allocate(utemp_i(n_time+2,n_site),STAT=IERR) 427 | allocate(snd_i(n_time+2,n_site),STAT=IERR) 428 | allocate(stcon_i(n_time+2,n_site),STAT=IERR) 429 | 430 | do j_time=1,n_time+2 431 | utemp_time_i(j_time)=time_restart+DBLE(j_time-1)*time_step 432 | enddo 433 | do i_site=1,n_site 434 | if (lbound.EQ.2)temp_grd(i_site)=temp_grd(i_site)*zdepth(n_grd) 435 | do i_lay=1,n_lay_cur(i_site) 436 | temp_frz(i_lay,i_site)=-(vwc(i_lay,i_site)/a_coef(i_lay,i_site))**(1.d0/b_coef(i_lay,i_site)) 437 | enddo 438 | call interpolate(utemp_time,utemp(:,i_site),n_temp,utemp_time_i,utemp_i(:,i_site),n_time+2) 439 | call interpolate(snd_time,snd(:,i_site),n_snow,utemp_time_i,snd_i(:,i_site),n_time+2) 440 | call snowfix(utemp_i(:,i_site),snd_i(:,i_site),n_time+2) 441 | call interpolate(stcon_time,stcon(:,i_site),n_stcon,utemp_time_i,stcon_i(:,i_site),n_time+2) 442 | call active_layer(i_site) 443 | enddo 444 | 445 | open(1,file=result_file,STATUS='unknown') 446 | open(2,file=aver_res_file,STATUS='unknown') 447 | open(3,file=restart_file,STATUS='unknown') 448 | write(FMT1,'(A30,I0,A12)')'(1x,I10,1x,F12.3,2(1x,F16.12),',m_grd,'(1x,F16.12))' 449 | write(FMT2,'(A28,I0,A40)')'(1x,I10,1x,F12.3,2(1x,F8.3),',m_grd,'(1x,F8.3),(1x,F8.3,1x,F12.3),(1x,F12.3))' 450 | 451 | end subroutine initialize 452 | 453 | subroutine init_cond(q,last) 454 | 455 | use bnd 456 | use thermo 457 | use grd 458 | 459 | implicit none 460 | integer q,last 461 | integer i,j 462 | character*64 file_init 463 | 464 | if(q.EQ.1)then !restart=1 means reading initial data from 465 | do I=1,last 466 | call interpolate(zdepth_ini,ztemp_ini(:,I),n_ini,zdepth,temp(I,:),n_grd) 467 | enddo 468 | elseif(restart.EQ.0)then !restart=0 enbales spinup 469 | write(file_init,'(A14)') 'dump/start.txt' 470 | open(60,file=file_init,action='READ') 471 | read(60,*)time_restart ! day number in restart file 472 | do J=1,n_grd 473 | read (60,* ) ( temp(i,j),i=1,last) 474 | enddo 475 | close(60) 476 | endif 477 | 478 | end subroutine init_cond 479 | 480 | subroutine active_layer(k) 481 | 482 | use bnd 483 | use thermo 484 | use grd 485 | use alt 486 | 487 | implicit none 488 | 489 | integer :: k,j,jj 490 | real*8 GA,GB,YFRON,GX,GY 491 | real*8 fsat_unf_water 492 | 493 | z_frz_frn(i_time(k),:,k)=sea_level 494 | n_frz_frn(i_time(k),k)=0 495 | do 1329 JJ=1,n_grd-1 496 | J=n_grd-JJ 497 | if (zdepth(J).GE.sea_level.AND.zdepth(J+1).LE.frz_frn_max)then 498 | GA=fsat_unf_water(temp(k,J),lay_id(k,J),k) 499 | GB=fsat_unf_water(temp(k,J+1),lay_id(k,J+1),k) 500 | if((GA-sat_coef)*(GB-sat_coef).LE.0.D0) then 501 | GY=(GA-GB)/(zdepth(J)-zdepth(J+1)) 502 | GX=(GA+GB-GY*(zdepth(J)+zdepth(J+1)))/2.D0 503 | if(GY.EQ.0.D0) then 504 | YFRON=(zdepth(J)+zdepth(J+1))/2.D0 505 | else 506 | YFRON=(sat_coef-GX)/GY 507 | endif 508 | else 509 | GOTO 1329 510 | endif 511 | if(n_frz_frn(i_time(k),k).LT.n_frz_max)then 512 | n_frz_frn(i_time(k),k)=n_frz_frn(i_time(k),k)+1 513 | z_frz_frn(i_time(k),n_frz_frn(i_time(k),k),k)=YFRON 514 | endif 515 | endif 516 | 1329 CONTINUE 517 | 518 | end subroutine active_layer 519 | 520 | subroutine save_results(k,time1,time2) 521 | use thermo 522 | use grd 523 | use alt 524 | 525 | implicit none 526 | integer :: k,j 527 | real*8 :: time1,time2 528 | real*8 :: futemp,fsnow_level 529 | 530 | RES(i_time(k),1)=time1 531 | RES(i_time(k),2)=futemp(time2,k) 532 | RES(i_time(k),3)=fsnow_level(k,time2) 533 | do J=1,m_grd 534 | RES(i_time(k),J+3)=temp(k,zdepth_id(J)) 535 | enddo 536 | 537 | end subroutine save_results 538 | 539 | !________________________________________________ 540 | !__________________FUNCTIONS_____________________ 541 | !________________________________________________ 542 | real*8 function funf_water(T,NNN,I) 543 | use thermo 544 | implicit none 545 | real*8, intent(in) :: T ! temprature 546 | integer, intent(in) :: NNN, I 547 | real*8 :: temp_dep 548 | real*8 :: a,b,e 549 | real*8 :: theta 550 | 551 | temp_dep=temp_frz(NNN,I) ! change I to k0 everywhere except temp_dep 552 | e=EE(NNN,I) 553 | theta=vwc(NNN,I) 554 | a=a_coef(NNN,I) 555 | b=b_coef(NNN,I) 556 | 557 | IF(T.LE.temp_dep-e)THEN 558 | funf_water=a*((DABS(T))**b) 559 | ELSEIF(T.GT.temp_dep)THEN 560 | funf_water=theta 561 | ELSE 562 | funf_water=a*((DABS(temp_dep-e))**b) 563 | funf_water=funf_water+(theta-funf_water)*(T+e-temp_dep)/e 564 | endif 565 | return 566 | end function funf_water 567 | !----------------------------------------------- 568 | real*8 function fsat_unf_water(T,NNN,I)!Saturated unforzen water 569 | use thermo 570 | 571 | !IMPLICIT REAL*8(A-H,O-Z) 572 | implicit none 573 | real*8, intent(in) :: T 574 | integer, intent(in) :: NNN, I 575 | real*8 :: temp_dep 576 | real*8 :: a,b,e 577 | real*8 :: theta 578 | 579 | temp_dep=temp_frz(NNN,I) ! freezing temprature depression 580 | e=EE(NNN,I) 581 | theta=vwc(NNN,I) 582 | a=a_coef(NNN,I) 583 | b=b_coef(NNN,I) 584 | IF(T.LE.temp_dep-e)THEN 585 | fsat_unf_water=a*((DABS(T))**b) 586 | ELSEIF(T.GT.temp_dep)THEN 587 | fsat_unf_water=theta 588 | ELSE 589 | fsat_unf_water=a*((DABS(temp_dep-e))**b) 590 | fsat_unf_water=fsat_unf_water+(theta-fsat_unf_water)*(T+e-temp_dep)/e 591 | ENDIF 592 | fsat_unf_water=fsat_unf_water/theta 593 | return 594 | 595 | end function fsat_unf_water 596 | !----------------------------------------------- 597 | real*8 function fdunf_water(T,NNN,I) 598 | use thermo 599 | implicit none 600 | real*8, intent(in) :: T ! temprature 601 | integer, intent(in) :: NNN, I 602 | real*8 :: temp_dep 603 | real*8 :: a,b,e 604 | real*8 :: theta 605 | 606 | temp_dep=temp_frz(NNN,I) 607 | e=EE(NNN,I) 608 | theta=vwc(NNN,I) 609 | a=a_coef(NNN,I) 610 | b=b_coef(NNN,I) 611 | 612 | if(T.LE.temp_dep-e)THEN 613 | fdunf_water=-b*a*((DABS(T))**(b-1.0D0)) 614 | elseif(T.GT.temp_dep)THEN 615 | fdunf_water=0.0D0 616 | else 617 | fdunf_water=a*((DABS(temp_dep-e))**b) 618 | fdunf_water=(b-fdunf_water)/e 619 | endif 620 | return 621 | 622 | end function fdunf_water 623 | !---------------------------------------- 624 | real*8 function futemp(T,I) 625 | use bnd 626 | implicit none 627 | real*8 T 628 | integer I,II 629 | 630 | II=1+IDINT((T-TINIR)/time_step) 631 | futemp=utemp_i(II,I)+(T+time_restart-utemp_time_i(II)) & 632 | *(utemp_i(II+1,I)-utemp_i(II,I))/(utemp_time_i(II+1)-utemp_time_i(II)) 633 | return 634 | end function futemp 635 | !---------------------------------------- 636 | subroutine snowfix(air_temp,stcon,n) 637 | 638 | real*8, intent (in) :: air_temp(n) 639 | real*8, intent (out) :: stcon(n) 640 | integer :: n 641 | 642 | if(air_temp(1).gt.0.and.stcon(1).gt.0)stcon(1)=0 643 | do i=2,n 644 | if(air_temp(i).gt.0.and.stcon(i).gt.0)then 645 | if (stcon(i-1).eq.0)stcon(i)=0 ! puts zeros only at the begining of the year 646 | endif 647 | enddo 648 | 649 | return 650 | end subroutine snowfix 651 | 652 | !---------------------------------------- 653 | subroutine interpolate(XIN,YIN,NIN,XOUT,YOUT,n_itime) 654 | ! Linear interpolation 655 | real*8, intent(in) :: XIN(NIN),YIN(NIN) 656 | real*8, intent(out) :: XOUT(n_itime),YOUT(n_itime) 657 | integer :: NIN,n_itime 658 | do I=1,n_itime 659 | if(XOUT(I).LE.XIN(1))THEN 660 | YOUT(I)=YIN(1) 661 | GOTO 1 662 | elseif(XOUT(I).GT.XIN(NIN))THEN 663 | YOUT(I)=YIN(NIN) 664 | GOTO 1 665 | else 666 | do J=1,NIN-1 667 | if (XIN(J).LT.XOUT(I).AND.XOUT(I).LE.XIN(J+1))THEN 668 | YOUT(I)=YIN(J)+(XOUT(I)-XIN(J))*(YIN(J+1)-YIN(J))/(XIN(J+1)-XIN(J)) 669 | GOTO 1 670 | endif 671 | enddo 672 | endif 673 | 1 continue 674 | enddo 675 | return 676 | end 677 | 678 | !---------------------------------------- 679 | subroutine assign_layer_id(n_lay,n_lay_cur,n_site,n_grd,zdepth,n_bnd_lay,lay_id) 680 | !assigns correspond layer id to the grid point 681 | !starting from surface to the bottom 682 | implicit none 683 | 684 | integer :: n_site,n_grd,n_lay 685 | integer :: lay_id(n_site,n_grd),n_lay_cur(n_site) 686 | real*8 :: zdepth(n_grd) 687 | real :: n_bnd_lay(n_site,n_lay+1) 688 | integer :: isite,igrd,ilay 689 | 690 | do isite=1,n_site 691 | do 6 igrd=1,n_grd 692 | lay_id(isite,igrd)=n_lay_cur(isite) 693 | do ilay=1,n_lay_cur(isite)-1 694 | if ( n_bnd_lay(isite,ilay).LE.zdepth(igrd).AND.zdepth(igrd).LT.n_bnd_lay(isite,ilay+1))then 695 | lay_id(isite,igrd)=ilay 696 | GOTO 6 697 | endif 698 | enddo 699 | 6 continue 700 | enddo 701 | return 702 | end 703 | !---------------------------------------- 704 | real*8 function fsnow_level(site_id,time) 705 | use bnd 706 | real*8 :: time 707 | integer :: site_id,II 708 | 709 | II=1+IDINT((time-TINIR)/time_step) 710 | fsnow_level=snd_i(II,site_id)+(time+time_restart-utemp_time_i(II))* & 711 | (snd_i(II+1,site_id)-snd_i(II,site_id))/(utemp_time_i(II+1)-utemp_time_i(II)) 712 | return 713 | end function fsnow_level 714 | !----------------------------------------------- 715 | real*8 function ftcon(T,id,j,time_cur) 716 | use bnd 717 | use grd 718 | use thermo 719 | 720 | implicit real*8(A-H,O-Z) 721 | integer :: II 722 | 723 | gr_sur=sea_level 724 | dsnow=sea_level-fsnow_level(id,time_cur) 725 | NS=lay_id(id,j) 726 | if(zdepth(j).le.dsnow)then !atmosphere 727 | ftcon=1.d4 728 | elseif (zdepth(j).Lt.gr_sur)then !snow 729 | II=1+IDINT((time_cur-tinir)/time_step) 730 | ftcon=stcon_i(II,id)+(time_cur+time_restart-utemp_time_i(II))* & 731 | (stcon_i(II+1,id)-stcon_i(II,id))/(utemp_time_i(II+1)-utemp_time_i(II)) 732 | else !ground 733 | WC=funf_water(T,NS,id)/vwc(NS,id) 734 | ftcon=(tcon_thw(NS,id)**WC)*(tcon_frz(NS,id)**(1.0-WC)) 735 | endif 736 | return 737 | end function ftcon 738 | !---------------------------------------- 739 | real*8 function fhcap(T,NNUS,I) 740 | use thermo 741 | 742 | IMPLICIT REAL*8(A-H,O-Z) 743 | DIMENSION NNUS(2),T(2) 744 | 745 | H=1/(T(1)-T(2)) 746 | if(DABS(T(1)-T(2)).LT.1.D-6) THEN 747 | fhcap=0.5d0*(fdunf_water(T(1),NNUS(1),I)+fdunf_water(T(2),NNUS(2),I)) 748 | else 749 | if (nnus(1).ne.nnus(2))THEN 750 | fhcap=0.5D0*( H*(funf_water(T(1),NNUS(1),I)-funf_water(T(2),NNUS(1),I))+ & 751 | H*(funf_water(T(1),NNUS(2),I)-funf_water(T(2),NNUS(2),I)) ) 752 | else 753 | fhcap=H*(funf_water(T(1),NNUS(1),I)-funf_water(T(2),NNUS(2),I)) 754 | endif 755 | endif 756 | fhcap=L_fus*DABS(fhcap) 757 | return 758 | end function fhcap 759 | !---------------------------------------- 760 | !---------------------------------------- 761 | real*8 function fapp_hcap(T,I,J) ! Apparent heat capacity 762 | use thermo 763 | use grd 764 | 765 | implicit real*8(A-H,O-Z) 766 | DIMENSION T(n_grd),WW(2),NN(2) 767 | 768 | li=lay_id(I,J) ! layer index 769 | gr_sur=sea_level ! ground surface 770 | if(zdepth(J).lE.gr_sur)then 771 | fapp_hcap=hcap_s ! heat capacity for snow 772 | else 773 | WC=funf_water(T(J),li,I)/vwc(li,I) 774 | fapp_hcap=hcap_thw(li,I)*WC+hcap_frz(li,I)*(1.0-WC) 775 | if(J.GT.(1).AND.J.LT.n_grd)then 776 | WW(1)=(T(J-1)+T(J))/2.D0 777 | NN(1)=lay_id(I,J-1) 778 | WW(2)=T(J) 779 | NN(2)=lay_id(I,J) 780 | fapp_hcap=fapp_hcap+fhcap(WW,NN,I)*dz(J)/(dz(J+1)+dz(J)) 781 | WW(1)=T(J) 782 | NN(1)=lay_id(I,J) 783 | WW(2)=(T(J+1)+T(J))/2.D0 784 | NN(2)=lay_id(I,J+1) 785 | fapp_hcap=fapp_hcap+fhcap(WW,NN,I)*dz(J+1)/(dz(J+1)+dz(J)) 786 | elseif(J.EQ.1)then 787 | WW(1)=T(J) 788 | NN(1)=lay_id(I,J) 789 | WW(2)=(T(J+1)+T(J))/2.D0 790 | NN(2)=lay_id(I,J+1) 791 | fapp_hcap=fapp_hcap+fhcap(WW,NN,I) 792 | elseif(J.EQ.n_grd)then 793 | WW(1)=(T(J-1)+T(J))/2.D0 794 | NN(1)=lay_id(I,J-1) 795 | WW(2)=T(J) 796 | NN(2)=lay_id(I,J) 797 | fapp_hcap=fapp_hcap+fhcap(WW,NN,I) 798 | endif 799 | endif 800 | 801 | return 802 | end 803 | !------------------------------------------------------- 804 | subroutine stefan1D(temps,n_grd,dz,time_loop,isite,lay_idx,flux) 805 | 806 | use thermo 807 | use bnd 808 | use const 809 | 810 | implicit none 811 | 812 | integer, intent(inout) :: n_grd 813 | real*8, intent(inout) :: dz(n_grd),temps(n_grd) 814 | integer, intent(inout) :: lay_idx(n_grd) 815 | real*8, intent(inout) :: time_loop 816 | real*8 :: futemp,flux,fapp_hcap,ftcon,fsat_unf_water 817 | 818 | integer :: isite,i_grd,IT 819 | 820 | ! tridiagonal variables 821 | real*8 :: RAB1,RAB2,AKAPA2,AMU2,Q2 822 | real*8 :: A,B,C,D 823 | real*8 :: ALF(n_grd),BET(n_grd) 824 | real*8 :: EEY,EEY1,abs1,abs2 825 | 826 | real*8 :: temp_o(n_grd) ! old temperature before tridiagonal method 827 | real*8 :: temp_n(n_grd) ! new temperature after tridiagonal method 828 | 829 | ! time counter internal to this subroutine 830 | real*8 :: time_l ! loop time in a subroutine 831 | real*8 :: time_p ! present time in a subroutine 832 | real*8 :: timei ! main subroutine timer 833 | real :: time_swith ! for timei 834 | 835 | time_l=time_loop 836 | time_swith=-1.0 837 | timei=TAUM 838 | temps=temp(isite,:) 839 | 64 continue 840 | time_p=time_l+timei 841 | temp_o=temps 842 | IT=1 843 | ALF(2)=0.D0 844 | BET(2)=futemp(time_p,isite) 845 | 22 continue 846 | if(IT.GT.ITMAX) then 847 | timei=timei/2.D0 848 | time_swith=-1.0 849 | GOTO 64 850 | endif 851 | 852 | do i_grd=2,n_grd-1 853 | D=fapp_hcap(temp_o,isite,i_grd)/timei 854 | A=2.D0*ftcon(temp_o(i_grd),isite,i_grd,time_p)/(dz(i_grd)*(dz(i_grd)+dz(i_grd+1))) 855 | B=2.D0*ftcon(temp_o(i_grd+1),isite,i_grd+1,time_p)/(dz(i_grd+1)*(dz(i_grd)+dz(i_grd+1))) 856 | C=A+B+D 857 | ALF(i_grd+1)=B/(C-A*ALF(i_grd)) 858 | BET(i_grd+1)=(A*BET(i_grd)+D*temps(i_grd))/(C-A*ALF(i_grd)) 859 | enddo 860 | 861 | RAB1=ftcon(temp_o(n_grd),isite,n_grd,time_p) 862 | RAB2=fapp_hcap(temp_o,isite,n_grd) 863 | AKAPA2=2.D0*RAB1/(((RAB2*dz(n_grd)*dz(n_grd))/timei+2.D0*RAB1)) 864 | Q2=RAB1*flux 865 | AMU2=(temps(n_grd)*RAB2/timei+2.D0*Q2/dz(n_grd))/(RAB2/timei+2.D0*RAB1 & 866 | /dz(n_grd)**2.D0) 867 | if(DABS(AKAPA2)>1.D0) then 868 | print*,'Tridiagonal method is failed - chang you time step tau' 869 | print*,rab1,rab2,akapa2 870 | STOP 871 | endif 872 | 873 | ! assigns boundary condition check 874 | if (lbound.EQ.2)then 875 | temp_n(n_grd)=(AMU2+AKAPA2*BET(n_grd))/(1.D0-ALF(n_grd)*AKAPA2) 876 | else 877 | temp_n(n_grd)=flux 878 | endif 879 | 880 | ! calculates new tempratures 881 | do i_grd=1,n_grd-1 882 | temp_n(n_grd-i_grd)=ALF(n_grd-i_grd+1)*temp_n(n_grd-i_grd+1)+BET(n_grd-i_grd+1) 883 | enddo 884 | 885 | if(timei>tmin) then 886 | do i_grd=1,n_grd 887 | EEY=fsat_unf_water(temp_n(i_grd),lay_idx(i_grd),isite) 888 | EEY1=fsat_unf_water(temp_o(i_grd),lay_idx(i_grd),isite) 889 | abs1=DABS(EEY-EEY1) 890 | abs2=DABS(temp_o(i_grd)-temp_n(i_grd)) 891 | if((abs1.GT.unf_water_coef).or.(abs2.GT.smooth_coef)) then 892 | temp_o=temp_n 893 | IT=IT+1 894 | GOTO 22 895 | endif 896 | enddo 897 | endif 898 | 899 | if(time_p.LT.time_loop+time_step-1.D-12)then 900 | time_l=time_p 901 | temps=temp_n 902 | if(time_swith>0) then 903 | if(timei.LT.TAUM) then 904 | timei=timei*2.D0 905 | time_swith=-1.0 906 | endif 907 | else 908 | time_swith=1.0 909 | endif 910 | GOTO 64 911 | elseif(time_p.GT.time_loop+time_step+1.D-12)then 912 | timei=(time_loop+time_step-time_l) 913 | goto 64 914 | else 915 | temps=temp_n 916 | endif 917 | 918 | end subroutine stefan1D 919 | 920 | subroutine filexist(filename) 921 | character*64 filename 922 | logical chf 923 | inquire(file=filename,exist=chf) 924 | if (.not.chf) then 925 | write(*,'(/'' FILE '',a, '' DOESNT EXIST'')')trim(filename) 926 | stop 927 | endif 928 | end subroutine filexist!----------------------------------------------- 929 | 930 | -------------------------------------------------------------------------------- /plot_input_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "This script visualizes input data from the example folder. Note, that measurements correspond to the beginning of the hydrologic year (July 1). " 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "%matplotlib inline\n", 19 | "import numpy as np\n", 20 | "from matplotlib import pyplot as plt" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "metadata": { 27 | "collapsed": true 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "def plot_data(input_file,ylabel):\n", 32 | " input_data = np.loadtxt(input_file, skiprows=3, unpack=False)\n", 33 | " data=input_data[:,1]\n", 34 | " plt.plot(data,'b',linewidth=2.0)\n", 35 | " plt.xlim([0, len(data)]);\n", 36 | " plt.ylabel(ylabel, fontsize=16); \n", 37 | " plt.tick_params(labelsize=16)" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 3, 43 | "metadata": { 44 | "collapsed": false 45 | }, 46 | "outputs": [ 47 | { 48 | "data": { 49 | "image/png": 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G3Lnc17lzeN8fQRAEQRDCT2oq40INixZxrDNpUmgTtkuX8v//sssoDOLiuG/QIK2/+sr3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\nF9WeFOOCx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50 | "text/plain": [ 51 | "
" 52 | ] 53 | }, 54 | "metadata": {}, 55 | "output_type": "display_data" 56 | } 57 | ], 58 | "source": [ 59 | "fig = plt.figure()\n", 60 | "fig.set_figheight(8); fig.set_figwidth(11)\n", 61 | "\n", 62 | "fig.add_subplot(3,1,1)\n", 63 | "bound_file='in/bound.txt'\n", 64 | "plot_data(bound_file,'Air temp [C]')\n", 65 | "plt.plot(np.zeros(365),'k--',linewidth=2.0)\n", 66 | "\n", 67 | "fig.add_subplot(3,1,2)\n", 68 | "snow_file='in/snow.txt'\n", 69 | "plot_data(snow_file,'snow depth [m]')\n", 70 | "\n", 71 | "fig.add_subplot(3,1,3)\n", 72 | "rsnow_file='in/rsnow.txt'\n", 73 | "plot_data(rsnow_file,'snow density [kg m-3]')\n", 74 | "plt.xlabel('time [days]', fontsize=16);\n", 75 | "fig.tight_layout()" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": null, 81 | "metadata": { 82 | "collapsed": true 83 | }, 84 | "outputs": [], 85 | "source": [] 86 | } 87 | ], 88 | "metadata": { 89 | "anaconda-cloud": {}, 90 | "kernelspec": { 91 | "display_name": "Python [Root]", 92 | "language": "python", 93 | "name": "Python [Root]" 94 | }, 95 | "language_info": { 96 | "codemirror_mode": { 97 | "name": "ipython", 98 | "version": 2 99 | }, 100 | "file_extension": ".py", 101 | "mimetype": "text/x-python", 102 | "name": "python", 103 | "nbconvert_exporter": "python", 104 | "pygments_lexer": "ipython2", 105 | "version": "2.7.12" 106 | } 107 | }, 108 | "nbformat": 4, 109 | "nbformat_minor": 0 110 | } 111 | -------------------------------------------------------------------------------- /mesres.txt: -------------------------------------------------------------------------------- 1 | 13.806 10.623 9 6.525 4.626 2.742 1.117 -0.367 -1.085 -2.276 -3.328 -4.712 2 | 9.73 8.418 7.675 6.128 4.644 3.038 1.62 -0.066 -0.711 -1.868 -2.925 -4.339 3 | 6.159 5.512 5.178 4.334 3.408 2.33 1.357 0.107 -0.52 -1.603 -2.619 -4.014 4 | 6.017 4.577 4.061 3.167 2.39 1.605 0.916 0.093 -0.463 -1.467 -2.432 -3.773 5 | 7.742 5.666 4.866 3.587 2.617 1.696 0.938 0.082 -0.465 -1.408 -2.322 -3.603 6 | 11.9 8.592 7.175 5.114 3.641 2.32 1.281 0.16 -0.437 -1.352 -2.234 -3.469 7 | 11.828 9.419 8.196 6.226 4.651 3.1 1.817 0.35 -0.383 -1.281 -2.149 -3.354 8 | 9.528 7.598 6.896 5.549 4.319 3.011 1.881 0.53 -0.299 -1.185 -2.036 -3.228 9 | 11.803 8.85 7.594 5.709 4.287 2.95 1.858 0.623 -0.241 -1.103 -1.94 -3.111 10 | 7.727 7.243 6.807 5.702 4.555 3.289 2.171 0.855 -0.173 -1.024 -1.845 -2.994 11 | 9.375 6.763 5.878 4.532 3.489 2.506 1.669 0.706 -0.123 -0.954 -1.764 -2.891 12 | 4.784 4.726 4.7 4.217 3.499 2.623 1.801 0.808 -0.086 -0.905 -1.69 -2.8 13 | 3.107 2.684 2.6 2.339 1.968 1.519 1.059 0.478 -0.086 -0.86 -1.631 -2.715 14 | 3.877 2.939 2.546 1.972 1.516 1.099 0.714 0.299 -0.125 -0.854 -1.589 -2.642 15 | 3.837 3.007 2.734 2.227 1.723 1.226 0.791 0.307 -0.159 -0.858 -1.587 -2.598 16 | 9.591 6.404 5.154 3.487 2.438 1.603 0.973 0.368 -0.155 -0.857 -1.555 -2.554 17 | 10.427 7.979 6.971 5.314 4.025 2.809 1.817 0.764 -0.108 -0.829 -1.529 -2.508 18 | 9.75 8.093 7.222 5.72 4.455 3.205 2.157 0.981 -0.047 -0.796 -1.492 -2.46 19 | 8.485 7.444 6.748 5.482 4.355 3.214 2.212 1.087 0.037 -0.75 -1.434 -2.399 20 | 6.625 5.538 5.091 4.268 3.472 2.645 1.876 0.978 0.115 -0.699 -1.378 -2.345 21 | 6.098 5.141 4.748 3.961 3.194 2.402 1.691 0.899 0.134 -0.657 -1.326 -2.272 22 | 2.489 2.717 2.841 2.745 2.392 1.904 1.387 0.762 0.131 -0.623 -1.292 -2.228 23 | 2.881 2.089 1.81 1.5 1.233 0.977 0.69 0.371 0.027 -0.622 -1.265 -2.181 24 | 3.002 2.644 2.477 2.059 1.607 1.176 0.788 0.393 0.004 -0.619 -1.262 -2.149 25 | 2.707 1.999 1.788 1.457 1.158 0.873 0.589 0.293 -0.02 -0.622 -1.232 -2.13 26 | 4.079 2.911 2.551 1.954 1.462 1.03 0.662 0.312 -0.036 -0.619 -1.223 -2.096 27 | 6.359 4.028 3.439 2.577 1.935 1.356 0.872 0.412 -0.028 -0.619 -1.225 -2.082 28 | 6.583 4.92 4.336 3.421 2.647 1.909 1.257 0.622 0.016 -0.624 -1.225 -2.053 29 | 7.757 5.496 4.74 3.669 2.845 2.072 1.393 0.713 0.056 -0.609 -1.187 -2.027 30 | 5.987 5.279 4.901 4.082 3.267 2.425 1.664 0.876 0.122 -0.581 -1.173 -2.003 31 | 1.519 1.92 2.168 2.289 2.083 1.701 1.246 0.695 0.108 -0.566 -1.146 -1.964 32 | 1.776 1.381 1.293 1.151 0.961 0.763 0.541 0.289 -0.001 -0.54 -1.123 -1.928 33 | 2.784 1.98 1.711 1.308 0.982 0.694 0.453 0.216 -0.035 -0.572 -1.104 -1.921 34 | 1.258 1.319 1.337 1.242 1.025 0.781 0.513 0.246 -0.036 -0.582 -1.104 -1.899 35 | 0.534 0.393 0.469 0.532 0.466 0.381 0.249 0.108 -0.071 -0.584 -1.106 -1.88 36 | 0.301 0.296 0.335 0.373 0.294 0.223 0.124 0.038 -0.088 -0.592 -1.114 -1.877 37 | -0.029 -0.015 0.044 0.126 0.109 0.09 0.04 -0.01 -0.115 -0.623 -1.151 -1.88 38 | 0.648 0.215 0.184 0.135 0.058 0.024 -0.018 -0.038 -0.141 -0.629 -1.146 -1.885 39 | 2.777 1.242 0.983 0.635 0.392 0.228 0.091 0.003 -0.162 -0.662 -1.147 -1.88 40 | 4.032 2.525 2.075 1.48 1.04 0.669 0.369 0.093 -0.194 -0.66 -1.157 -1.881 41 | 4.749 3.181 2.675 1.989 1.466 0.981 0.574 0.159 -0.195 -0.68 -1.185 -1.881 42 | 6.7 4.798 4.072 2.984 2.177 1.451 0.856 0.271 -0.187 -0.667 -1.163 -1.847 43 | 7.133 5.486 4.806 3.663 2.76 1.891 1.16 0.4 -0.152 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