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Delaigue Olivier authored30317b54
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DataAltiExtrapolation_Valery <- function(DatesR, Precip, PrecipScale = TRUE, TempMean, TempMin = NULL, TempMax = NULL, ZInputs, HypsoData, NLayers, verbose = TRUE) { ##Altitudinal_gradient_functions_______________________________________________________________ ##unique_gradient_for_precipitation GradP_Valery2010 <- function() { return(0.00041) ### value from Valery PhD thesis page 126 } ##daily_gradients_for_mean_min_and_max_air_temperature GradT_Valery2010 <- function() { RESULT <- matrix(c( 01, 01, 0.434, 0.366, 0.498, 02, 01, 0.434, 0.366, 0.500, 03, 01, 0.435, 0.367, 0.501, 04, 01, 0.436, 0.367, 0.503, 05, 01, 0.437, 0.367, 0.504, 06, 01, 0.439, 0.367, 0.506, 07, 01, 0.440, 0.367, 0.508, 08, 01, 0.441, 0.368, 0.510, 09, 01, 0.442, 0.368, 0.512, 10, 01, 0.444, 0.368, 0.514, 11, 01, 0.445, 0.368, 0.517, 12, 01, 0.446, 0.368, 0.519, 13, 01, 0.448, 0.369, 0.522, 14, 01, 0.450, 0.369, 0.525, 15, 01, 0.451, 0.369, 0.527, 16, 01, 0.453, 0.370, 0.530, 17, 01, 0.455, 0.370, 0.533, 18, 01, 0.456, 0.370, 0.537, 19, 01, 0.458, 0.371, 0.540, 20, 01, 0.460, 0.371, 0.543, 21, 01, 0.462, 0.371, 0.547, 22, 01, 0.464, 0.372, 0.550, 23, 01, 0.466, 0.372, 0.554, 24, 01, 0.468, 0.373, 0.558, 25, 01, 0.470, 0.373, 0.561, 26, 01, 0.472, 0.374, 0.565, 27, 01, 0.474, 0.374, 0.569, 28, 01, 0.476, 0.375, 0.573, 29, 01, 0.478, 0.375, 0.577, 30, 01, 0.480, 0.376, 0.582, 31, 01, 0.483, 0.376, 0.586, 01, 02, 0.485, 0.377, 0.590, 02, 02, 0.487, 0.377, 0.594, 03, 02, 0.489, 0.378, 0.599, 04, 02, 0.492, 0.379, 0.603, 05, 02, 0.494, 0.379, 0.607, 06, 02, 0.496, 0.380, 0.612, 07, 02, 0.498, 0.381, 0.616, 08, 02, 0.501, 0.381, 0.621, 09, 02, 0.503, 0.382, 0.625, 10, 02, 0.505, 0.383, 0.630, 11, 02, 0.508, 0.384, 0.634, 12, 02, 0.510, 0.384, 0.639, 13, 02, 0.512, 0.385, 0.643, 14, 02, 0.515, 0.386, 0.648, 15, 02, 0.517, 0.387, 0.652, 16, 02, 0.519, 0.387, 0.657, 17, 02, 0.522, 0.388, 0.661, 18, 02, 0.524, 0.389, 0.666, 19, 02, 0.526, 0.390, 0.670, 20, 02, 0.528, 0.391, 0.674, 21, 02, 0.530, 0.392, 0.679, 22, 02, 0.533, 0.393, 0.683, 23, 02, 0.535, 0.393, 0.687, 24, 02, 0.537, 0.394, 0.691, 25, 02, 0.539, 0.395, 0.695,7172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140
26, 02, 0.541, 0.396, 0.699,
27, 02, 0.543, 0.397, 0.703,
28, 02, 0.545, 0.398, 0.707,
29, 02, 0.546, 0.399, 0.709,
01, 03, 0.547, 0.399, 0.711,
02, 03, 0.549, 0.400, 0.715,
03, 03, 0.551, 0.401, 0.718,
04, 03, 0.553, 0.402, 0.722,
05, 03, 0.555, 0.403, 0.726,
06, 03, 0.557, 0.404, 0.729,
07, 03, 0.559, 0.405, 0.732,
08, 03, 0.560, 0.406, 0.736,
09, 03, 0.562, 0.406, 0.739,
10, 03, 0.564, 0.407, 0.742,
11, 03, 0.566, 0.408, 0.745,
12, 03, 0.567, 0.409, 0.748,
13, 03, 0.569, 0.410, 0.750,
14, 03, 0.570, 0.411, 0.753,
15, 03, 0.572, 0.412, 0.756,
16, 03, 0.573, 0.413, 0.758,
17, 03, 0.575, 0.414, 0.761,
18, 03, 0.576, 0.415, 0.763,
19, 03, 0.577, 0.416, 0.765,
20, 03, 0.579, 0.417, 0.767,
21, 03, 0.580, 0.417, 0.769,
22, 03, 0.581, 0.418, 0.771,
23, 03, 0.582, 0.419, 0.773,
24, 03, 0.583, 0.420, 0.774,
25, 03, 0.584, 0.421, 0.776,
26, 03, 0.585, 0.422, 0.777,
27, 03, 0.586, 0.422, 0.779,
28, 03, 0.587, 0.423, 0.780,
29, 03, 0.588, 0.424, 0.781,
30, 03, 0.589, 0.425, 0.782,
31, 03, 0.590, 0.425, 0.783,
01, 04, 0.591, 0.426, 0.784,
02, 04, 0.591, 0.427, 0.785,
03, 04, 0.592, 0.427, 0.785,
04, 04, 0.593, 0.428, 0.786,
05, 04, 0.593, 0.429, 0.787,
06, 04, 0.594, 0.429, 0.787,
07, 04, 0.595, 0.430, 0.787,
08, 04, 0.595, 0.431, 0.788,
09, 04, 0.596, 0.431, 0.788,
10, 04, 0.596, 0.432, 0.788,
11, 04, 0.597, 0.432, 0.788,
12, 04, 0.597, 0.433, 0.788,
13, 04, 0.597, 0.433, 0.788,
14, 04, 0.598, 0.434, 0.788,
15, 04, 0.598, 0.434, 0.788,
16, 04, 0.598, 0.435, 0.787,
17, 04, 0.599, 0.435, 0.787,
18, 04, 0.599, 0.436, 0.787,
19, 04, 0.599, 0.436, 0.786,
20, 04, 0.599, 0.436, 0.786,
21, 04, 0.600, 0.437, 0.785,
22, 04, 0.600, 0.437, 0.785,
23, 04, 0.600, 0.437, 0.784,
24, 04, 0.600, 0.438, 0.784,
25, 04, 0.600, 0.438, 0.783,
26, 04, 0.601, 0.438, 0.783,
27, 04, 0.601, 0.438, 0.782,
28, 04, 0.601, 0.439, 0.781,
29, 04, 0.601, 0.439, 0.781,
30, 04, 0.601, 0.439, 0.780,
01, 05, 0.601, 0.439, 0.779,
02, 05, 0.601, 0.439, 0.778,
03, 05, 0.601, 0.439, 0.778,
04, 05, 0.601, 0.440, 0.777,
05, 05, 0.601, 0.440, 0.776,
141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210
06, 05, 0.601, 0.440, 0.775,
07, 05, 0.601, 0.440, 0.775,
08, 05, 0.601, 0.440, 0.774,
09, 05, 0.601, 0.440, 0.773,
10, 05, 0.602, 0.440, 0.772,
11, 05, 0.602, 0.440, 0.772,
12, 05, 0.602, 0.440, 0.771,
13, 05, 0.602, 0.440, 0.770,
14, 05, 0.602, 0.440, 0.770,
15, 05, 0.602, 0.440, 0.769,
16, 05, 0.602, 0.440, 0.768,
17, 05, 0.602, 0.440, 0.768,
18, 05, 0.602, 0.440, 0.767,
19, 05, 0.602, 0.440, 0.767,
20, 05, 0.602, 0.440, 0.766,
21, 05, 0.602, 0.440, 0.766,
22, 05, 0.602, 0.440, 0.765,
23, 05, 0.602, 0.440, 0.765,
24, 05, 0.602, 0.440, 0.764,
25, 05, 0.602, 0.440, 0.764,
26, 05, 0.602, 0.440, 0.764,
27, 05, 0.602, 0.439, 0.763,
28, 05, 0.602, 0.439, 0.763,
29, 05, 0.602, 0.439, 0.763,
30, 05, 0.602, 0.439, 0.762,
31, 05, 0.602, 0.439, 0.762,
01, 06, 0.602, 0.439, 0.762,
02, 06, 0.602, 0.439, 0.762,
03, 06, 0.602, 0.439, 0.762,
04, 06, 0.602, 0.439, 0.762,
05, 06, 0.602, 0.439, 0.762,
06, 06, 0.602, 0.438, 0.761,
07, 06, 0.602, 0.438, 0.761,
08, 06, 0.602, 0.438, 0.761,
09, 06, 0.602, 0.438, 0.761,
10, 06, 0.602, 0.438, 0.761,
11, 06, 0.602, 0.438, 0.762,
12, 06, 0.602, 0.438, 0.762,
13, 06, 0.602, 0.438, 0.762,
14, 06, 0.602, 0.438, 0.762,
15, 06, 0.602, 0.437, 0.762,
16, 06, 0.602, 0.437, 0.762,
17, 06, 0.602, 0.437, 0.762,
18, 06, 0.602, 0.437, 0.762,
19, 06, 0.602, 0.437, 0.763,
20, 06, 0.602, 0.437, 0.763,
21, 06, 0.602, 0.437, 0.763,
22, 06, 0.602, 0.436, 0.763,
23, 06, 0.602, 0.436, 0.763,
24, 06, 0.602, 0.436, 0.764,
25, 06, 0.602, 0.436, 0.764,
26, 06, 0.601, 0.436, 0.764,
27, 06, 0.601, 0.436, 0.764,
28, 06, 0.601, 0.436, 0.764,
29, 06, 0.601, 0.435, 0.765,
30, 06, 0.601, 0.435, 0.765,
01, 07, 0.601, 0.435, 0.765,
02, 07, 0.600, 0.435, 0.765,
03, 07, 0.600, 0.435, 0.765,
04, 07, 0.600, 0.434, 0.766,
05, 07, 0.600, 0.434, 0.766,
06, 07, 0.599, 0.434, 0.766,
07, 07, 0.599, 0.434, 0.766,
08, 07, 0.599, 0.434, 0.766,
09, 07, 0.598, 0.433, 0.766,
10, 07, 0.598, 0.433, 0.766,
11, 07, 0.598, 0.433, 0.766,
12, 07, 0.597, 0.433, 0.766,
13, 07, 0.597, 0.432, 0.767,
14, 07, 0.597, 0.432, 0.767,
211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280
15, 07, 0.596, 0.432, 0.767,
16, 07, 0.596, 0.432, 0.766,
17, 07, 0.595, 0.431, 0.766,
18, 07, 0.595, 0.431, 0.766,
19, 07, 0.594, 0.431, 0.766,
20, 07, 0.594, 0.430, 0.766,
21, 07, 0.593, 0.430, 0.766,
22, 07, 0.593, 0.430, 0.766,
23, 07, 0.592, 0.429, 0.765,
24, 07, 0.592, 0.429, 0.765,
25, 07, 0.591, 0.428, 0.765,
26, 07, 0.590, 0.428, 0.765,
27, 07, 0.590, 0.428, 0.764,
28, 07, 0.589, 0.427, 0.764,
29, 07, 0.588, 0.427, 0.764,
30, 07, 0.588, 0.426, 0.763,
31, 07, 0.587, 0.426, 0.763,
01, 08, 0.586, 0.425, 0.762,
02, 08, 0.586, 0.425, 0.762,
03, 08, 0.585, 0.424, 0.761,
04, 08, 0.584, 0.424, 0.761,
05, 08, 0.583, 0.423, 0.760,
06, 08, 0.583, 0.423, 0.760,
07, 08, 0.582, 0.422, 0.759,
08, 08, 0.581, 0.421, 0.758,
09, 08, 0.580, 0.421, 0.758,
10, 08, 0.579, 0.420, 0.757,
11, 08, 0.578, 0.420, 0.756,
12, 08, 0.578, 0.419, 0.755,
13, 08, 0.577, 0.418, 0.754,
14, 08, 0.576, 0.418, 0.754,
15, 08, 0.575, 0.417, 0.753,
16, 08, 0.574, 0.416, 0.752,
17, 08, 0.573, 0.415, 0.751,
18, 08, 0.572, 0.415, 0.750,
19, 08, 0.571, 0.414, 0.749,
20, 08, 0.570, 0.413, 0.748,
21, 08, 0.569, 0.413, 0.747,
22, 08, 0.569, 0.412, 0.746,
23, 08, 0.568, 0.411, 0.745,
24, 08, 0.567, 0.410, 0.744,
25, 08, 0.566, 0.409, 0.743,
26, 08, 0.565, 0.409, 0.742,
27, 08, 0.564, 0.408, 0.741,
28, 08, 0.563, 0.407, 0.740,
29, 08, 0.562, 0.406, 0.738,
30, 08, 0.561, 0.405, 0.737,
31, 08, 0.560, 0.405, 0.736,
01, 09, 0.558, 0.404, 0.735,
02, 09, 0.557, 0.403, 0.734,
03, 09, 0.556, 0.402, 0.732,
04, 09, 0.555, 0.401, 0.731,
05, 09, 0.554, 0.401, 0.730,
06, 09, 0.553, 0.400, 0.728,
07, 09, 0.552, 0.399, 0.727,
08, 09, 0.551, 0.398, 0.725,
09, 09, 0.550, 0.397, 0.724,
10, 09, 0.549, 0.396, 0.723,
11, 09, 0.548, 0.396, 0.721,
12, 09, 0.546, 0.395, 0.720,
13, 09, 0.545, 0.394, 0.718,
14, 09, 0.544, 0.393, 0.717,
15, 09, 0.543, 0.392, 0.715,
16, 09, 0.542, 0.391, 0.713,
17, 09, 0.541, 0.391, 0.712,
18, 09, 0.540, 0.390, 0.710,
19, 09, 0.538, 0.389, 0.709,
20, 09, 0.537, 0.388, 0.707,
21, 09, 0.536, 0.388, 0.705,
22, 09, 0.535, 0.387, 0.703,
281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350
23, 09, 0.533, 0.386, 0.702,
24, 09, 0.532, 0.385, 0.700,
25, 09, 0.531, 0.385, 0.698,
26, 09, 0.530, 0.384, 0.696,
27, 09, 0.528, 0.383, 0.694,
28, 09, 0.527, 0.383, 0.692,
29, 09, 0.526, 0.382, 0.690,
30, 09, 0.525, 0.381, 0.688,
01, 10, 0.523, 0.381, 0.686,
02, 10, 0.522, 0.380, 0.684,
03, 10, 0.521, 0.379, 0.682,
04, 10, 0.519, 0.379, 0.680,
05, 10, 0.518, 0.378, 0.678,
06, 10, 0.517, 0.377, 0.676,
07, 10, 0.515, 0.377, 0.674,
08, 10, 0.514, 0.376, 0.671,
09, 10, 0.512, 0.376, 0.669,
10, 10, 0.511, 0.375, 0.667,
11, 10, 0.510, 0.375, 0.664,
12, 10, 0.508, 0.374, 0.662,
13, 10, 0.507, 0.374, 0.659,
14, 10, 0.505, 0.373, 0.657,
15, 10, 0.504, 0.373, 0.654,
16, 10, 0.502, 0.372, 0.652,
17, 10, 0.501, 0.372, 0.649,
18, 10, 0.499, 0.372, 0.647,
19, 10, 0.498, 0.371, 0.644,
20, 10, 0.496, 0.371, 0.641,
21, 10, 0.495, 0.371, 0.639,
22, 10, 0.493, 0.370, 0.636,
23, 10, 0.492, 0.370, 0.633,
24, 10, 0.490, 0.370, 0.630,
25, 10, 0.489, 0.369, 0.628,
26, 10, 0.487, 0.369, 0.625,
27, 10, 0.485, 0.369, 0.622,
28, 10, 0.484, 0.368, 0.619,
29, 10, 0.482, 0.368, 0.616,
30, 10, 0.481, 0.368, 0.613,
31, 10, 0.479, 0.368, 0.610,
01, 11, 0.478, 0.368, 0.607,
02, 11, 0.476, 0.367, 0.604,
03, 11, 0.475, 0.367, 0.601,
04, 11, 0.473, 0.367, 0.598,
05, 11, 0.471, 0.367, 0.595,
06, 11, 0.470, 0.367, 0.592,
07, 11, 0.468, 0.367, 0.589,
08, 11, 0.467, 0.366, 0.586,
09, 11, 0.465, 0.366, 0.583,
10, 11, 0.464, 0.366, 0.580,
11, 11, 0.462, 0.366, 0.577,
12, 11, 0.461, 0.366, 0.574,
13, 11, 0.459, 0.366, 0.571,
14, 11, 0.458, 0.366, 0.568,
15, 11, 0.456, 0.366, 0.565,
16, 11, 0.455, 0.366, 0.562,
17, 11, 0.454, 0.366, 0.559,
18, 11, 0.452, 0.365, 0.556,
19, 11, 0.451, 0.365, 0.553,
20, 11, 0.450, 0.365, 0.550,
21, 11, 0.448, 0.365, 0.547,
22, 11, 0.447, 0.365, 0.544,
23, 11, 0.446, 0.365, 0.542,
24, 11, 0.445, 0.365, 0.539,
25, 11, 0.443, 0.365, 0.536,
26, 11, 0.442, 0.365, 0.533,
27, 11, 0.441, 0.365, 0.531,
28, 11, 0.440, 0.365, 0.528,
29, 11, 0.439, 0.365, 0.526,
30, 11, 0.438, 0.365, 0.523,
01, 12, 0.437, 0.365, 0.521,
351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420
02, 12, 0.436, 0.365, 0.519,
03, 12, 0.435, 0.365, 0.517,
04, 12, 0.434, 0.365, 0.515,
05, 12, 0.434, 0.365, 0.513,
06, 12, 0.433, 0.365, 0.511,
07, 12, 0.432, 0.365, 0.509,
08, 12, 0.431, 0.365, 0.507,
09, 12, 0.431, 0.365, 0.505,
10, 12, 0.430, 0.365, 0.504,
11, 12, 0.430, 0.365, 0.502,
12, 12, 0.429, 0.365, 0.501,
13, 12, 0.429, 0.365, 0.500,
14, 12, 0.429, 0.365, 0.498,
15, 12, 0.428, 0.365, 0.497,
16, 12, 0.428, 0.365, 0.496,
17, 12, 0.428, 0.365, 0.496,
18, 12, 0.428, 0.365, 0.495,
19, 12, 0.428, 0.365, 0.494,
20, 12, 0.428, 0.365, 0.494,
21, 12, 0.428, 0.365, 0.494,
22, 12, 0.428, 0.365, 0.493,
23, 12, 0.429, 0.365, 0.493,
24, 12, 0.429, 0.366, 0.493,
25, 12, 0.429, 0.366, 0.493,
26, 12, 0.430, 0.366, 0.494,
27, 12, 0.430, 0.366, 0.494,
28, 12, 0.431, 0.366, 0.495,
29, 12, 0.431, 0.366, 0.495,
30, 12, 0.432, 0.366, 0.496,
31, 12, 0.433, 0.366, 0.497), ncol = 5, byrow = TRUE)
dimnames(RESULT) <- list(1:366, c("day", "month", "grad_Tmean", "grad_Tmin", "grad_Tmax"))
return(RESULT)
}
##Format_______________________________________________________________________________________
HypsoData <- as.double(HypsoData)
ZInputs <- as.double(ZInputs)
##ElevationLayers_Creation_____________________________________________________________________
ZLayers <- as.double(rep(NA, NLayers))
if (!identical(HypsoData, as.double(rep(NA, 101)))) {
nmoy <- 100 %/% NLayers
nreste <- 100 %% NLayers
ncont <- 0
for (iLayer in 1:NLayers) {
if (nreste > 0) {
nn <- nmoy + 1
nreste <- nreste - 1
} else {
nn <- nmoy
}
if (nn == 1) {
ZLayers[iLayer] <- HypsoData[ncont + 1]
}
if (nn == 2) {
ZLayers[iLayer] <- 0.5 * (HypsoData[ncont + 1] + HypsoData[ncont + 2])
}
if (nn > 2) {
ZLayers[iLayer] <- HypsoData[ncont + nn / 2]
}
ncont <- ncont + nn
421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490
}
}
##Precipitation_extrapolation__________________________________________________________________
##Initialisation
if (identical(ZInputs, HypsoData[51]) & NLayers == 1) {
LayerPrecip <- list(as.double(Precip))
} else {
##Elevation_gradients_for_daily_mean_precipitation
GradP <- GradP_Valery2010() ### single value
TabGradP <- rep(GradP, length(Precip))
##Extrapolation
##Thresold_of_inputs_median_elevation
Zthreshold <- 4000
LayerPrecip_mat <- sapply(1:NLayers, function(iLayer) {
##If_layer_elevation_smaller_than_Zthreshold
if (ZLayers[iLayer] <= Zthreshold) {
prcp <- as.double(Precip * exp(TabGradP * (ZLayers[iLayer] - ZInputs)))
##If_layer_elevation_greater_than_Zthreshold
} else {
##If_inputs_median_elevation_smaller_than_Zthreshold
if (ZInputs <= Zthreshold) {
prcp <- as.double(Precip * exp(TabGradP * (Zthreshold - ZInputs)))
##If_inputs_median_elevation_greater_then_Zthreshold
} else {
prcp <- as.double(Precip)
}
}
return(prcp)
})
if (PrecipScale) {
LayerPrecip_mat <- LayerPrecip_mat / rowMeans(LayerPrecip_mat) * Precip
LayerPrecip_mat[is.nan(LayerPrecip_mat)] <- 0
}
LayerPrecip <- as.list(as.data.frame(LayerPrecip_mat))
}
##Temperature_extrapolation____________________________________________________________________
##Initialisation
LayerTempMean <- list()
LayerTempMin <- list()
LayerTempMax <- list()
if (identical(ZInputs, HypsoData[51]) & NLayers == 1) {
LayerTempMean[[1]] <- as.double(TempMean)
if (!is.null(TempMin) & !is.null(TempMax)) {
LayerTempMin[[1]] <- as.double(TempMin)
LayerTempMax[[1]] <- as.double(TempMax)
}
} else {
##Elevation_gradients_for_daily_mean_min_and_max_temperature
GradT <- as.data.frame(GradT_Valery2010())
iday <- match(format(DatesR, format = "%d%m"),
sprintf("%02i%02i", GradT[, "day"], GradT[, "month"]))
TabGradT <-
GradT[iday, c("grad_Tmean", "grad_Tmin", "grad_Tmax")]
##Extrapolation
##On_each_elevation_layer...
for (iLayer in 1:NLayers) {
LayerTempMean[[iLayer]] <- as.double(TempMean + (ZInputs - ZLayers[iLayer]) * abs(TabGradT[, "grad_Tmean"]) / 100)
if (!is.null(TempMin) & !is.null(TempMax)) {
LayerTempMin[[iLayer]] <- as.double(TempMin + (ZInputs - ZLayers[iLayer]) * abs(TabGradT[, "grad_Tmin"]) / 100)
LayerTempMax[[iLayer]] <- as.double(TempMax + (ZInputs - ZLayers[iLayer]) * abs(TabGradT[, "grad_Tmax"]) / 100)
}
}
}
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##Solid_Fraction_for_each_elevation_layer______________________________________________________
LayerFracSolidPrecip <- list()
##Thresold_of_inputs_median_elevation
Zthreshold <- 1500
##Option
Option <- "USACE"
if (!is.na(ZInputs)) {
if (ZInputs < Zthreshold & !is.null(TempMin) & !is.null(TempMax)) {
Option <- "Hydrotel"
}
}
##On_each_elevation_layer...
for (iLayer in 1:NLayers) {
##Turcotte_formula_from_Hydrotel
if (Option == "Hydrotel") {
TempMin <- LayerTempMin[[iLayer]]
TempMax <- LayerTempMax[[iLayer]]
SolidFraction <- 1 - TempMax / (TempMax - TempMin)
SolidFraction[TempMin >= 0] <- 0
SolidFraction[TempMax <= 0] <- 1
}
##USACE_formula
if (Option == "USACE") {
USACE_Tmin <- -1.0
USACE_Tmax <- 3.0
TempMean <- LayerTempMean[[iLayer]]
SolidFraction <- 1 - (TempMean - USACE_Tmin) / (USACE_Tmax - USACE_Tmin)
SolidFraction[TempMean > USACE_Tmax] <- 0
SolidFraction[TempMean < USACE_Tmin] <- 1
}
LayerFracSolidPrecip[[iLayer]] <- as.double(SolidFraction)
}
namesLayer <- sprintf("L%i", seq_along(LayerPrecip))
names(LayerPrecip) <- namesLayer
names(LayerTempMean) <- namesLayer
if (!is.null(TempMin) & !is.null(TempMax)) {
names(LayerTempMin) <- namesLayer
names(LayerTempMax) <- namesLayer
}
names(LayerFracSolidPrecip) <- namesLayer
##END__________________________________________________________________________________________
return(list(LayerPrecip = LayerPrecip,
LayerTempMean = LayerTempMean,
LayerTempMin = LayerTempMin,
LayerTempMax = LayerTempMax,
LayerFracSolidPrecip = LayerFracSolidPrecip,
ZLayers = ZLayers))
}