File:Southwest usa ppt gdd fahrenheit days 100 1700 ad 1.svg

1. SW USA 2000 Year Growing Degree Days and Precipitation Reconstructions
2. -----------------------------------------------------------------------
3. World Data Service for Paleoclimatology, Boulder
4. and
5. NOAA Paleoclimatology Program
6. National Centers for Environmental Information (NCEI)
7. -----------------------------------------------------------------------
9. NOTE: Please cite Publication, and Online_Resource and date accessed when using these data.
10. If there is no publication information, please cite Investigators, Title, and Online_Resource and date accessed.
12. Online_Resource: https://www.ncdc.noaa.gov/paleo/study/19783
13. Online_Resource: http://www1.ncdc.noaa.gov/pub/data/paleo/treering/reconstructions/northamerica/usa/bocinsky2016/readme-bocinsky2016.txt
15. Original_Source_URL:
17. Description/Documentation lines begin with #
18. Data lines have no #
20. Archive: Climate Reconstructions
22. Parameter_Keywords: precipitation
23. --------------------
24. Contribution_Date
25. Date: 2016-04-01
26. --------------------
27. Title
28. Study_Name: SW USA 2000 Year Growing Degree Days and Precipitation Reconstructions
29. --------------------
30. Investigators
31. Investigators: Bocinsky, R.K.; Rush, J.; Kintigh, K.W.; Kohler, T.A.
32. --------------------
33. Description_and_Notes
34. Description: High spatial resolution (30 arc-second) Southwestern United States tree-ring reconstructions of
35. May-September Growing-degree Days (GDD), reported in Fahrenheit units, and Net Water-year Precipitation
36. (previous October - current November), reported in millimeters of precipitation. The reconstructions
37. were performed using the "PaleoCAR" method detailed in Bocinsky and Kohler (2014) Nature Communications.
38. Reconstructions are delivered in 1x1 degree netCDF files.
39. East-west spatial resolution: 30 arc-seconds (1/120 of a degree)
40. North-south spatial resolution: 30 arc-seconds (1/120 of a degree)
41. Z resolution: integer units
42. --------------------
43. Publication
44. Authors: R. Kyle Bocinsky, Johnathan Rush, Keith W. Kintigh and Timothy A. Kohler
45. Published_Date_or_Year: 2016-04-01
46. Published_Title: Exploration and exploitation in the macrohistory of the pre-Hispanic Pueblo Southwest
47. Journal_Name: Science Advances
48. Volume: 2
49. Edition: e1501532
50. Issue: 4
51. Pages:
52. Report_Number:
53. DOI: 10.1126/sciadv.1501532
54. Online_Resource: http://advances.sciencemag.org/content/2/4/e1501532
55. Full_Citation:
56. Abstract: Cycles of demographic and organizational change are well documented in Neolithic societies, but the social and ecological processes underlying them are debated. Such periodicities are implicit in the "Pecos classification," a chronology for the pre-Hispanic U.S. Southwest introduced in Science in 1927 which is still widely used. To understand these periodicities, we analyzed 29,311 archaeological tree-ring dates from A.D. 500 to 1400 in the context of a novel high spatial resolution, annual reconstruction of the maize dry-farming niche for this same period. We argue that each of the Pecos periods initially incorporates an "exploration" phase, followed by a phase of "exploitation" of niches that are simultaneously ecological, cultural, and organizational. Exploitation phases characterized by demographic expansion and aggregation ended with climatically driven downturns in agricultural favorability, undermining important bases for social consensus. Exploration phases were times of socio-ecological niche discovery and development.
57. --------------------
58. Publication
59. Authors: R. Kyle Bocinsky and Timothy A. Kohler
60. Published_Date_or_Year: 2014-12-04
61. Published_Title: A 2,000-year reconstruction of the rain-fed maize agricultural niche in the US Southwest
62. Journal_Name: Nature Communications
63. Volume: 5
64. Edition:
65. Issue: 5618
66. Pages:
67. Report_Number:
68. DOI: 10.1038/ncomms6618
69. Online_Resource: http://www.nature.com/ncomms/2014/141204/ncomms6618/full/ncomms6618.html
70. Full_Citation:
71. Abstract: Humans experience, adapt to and influence climate at local scales. Paleoclimate research, however, tends to focus on continental, hemispheric or global scales, making it difficult for archaeologists and paleoecologists to study local effects. Here we introduce a method for high-frequency, local climate-field reconstruction from tree-rings. We reconstruct the rain-fed maize agricultural niche in two regions of the southwestern United States with dense populations of prehispanic farmers. Niche size and stability are highly variable within and between the regions. Prehispanic rain-fed maize farmers tended to live in agricultural refugia - areas most reliably in the niche. The timing and trajectory of the famous thirteenth century Pueblo migration can be understood in terms of relative niche size and stability. Local reconstructions like these illuminate the spectrum of strategies past humans used to adapt to climate change by recasting climate into the distributions of resources on which they depended.
72. ------------------
73. Funding_Agency
74. Funding_Agency_Name:
75. Grant:
76. ------------------
77. Site_Information
78. Site_Name: Southwestern USA
79. Location: North America>United States Of America
80. Country: United States Of America
81. Northernmost_Latitude: 43.0
82. Southernmost_Latitude: 31.0
83. Easternmost_Longitude: -102.0
84. Westernmost_Longitude: -115.0
85. Elevation: m
86. ------------------
87. Data_Collection
88. Collection_Name: Bocinsky2016
89. Earliest_Year: 0
90. Most_Recent_Year: 2000
91. Time_Unit: AD
92. Core_Length: m
93. Notes:
94. ------------------

2. 1. python3 usa sw drought data visualization
4. already tree ring dataset netcdf interpolated
6. 1. 11.01.2024 0000.0003

import numpy as np import math

import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import cm

from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)

from scipy import interpolate import scipy.signal

import xarray as xr

def find_nearest(array, value):

   array = np.asarray(array)
   idx = (np.abs(array - value)).argmin()
   return (idx, array[idx])

1. 1. chaco canyon
2. pname1="Chaco Canyon"

plon1= -107.96 plat1= 36.06

1. 1. sw test site
2. pname1="SW test site"
3. plon1= -107.5
4. plat1= 36.5

beginx=1 endx=1700

dataname1 = './108W36N_GDD.nc4' dataname2 = './108W36N_PPT.nc4'

convert_bp_to_ad=0

beginy=1900 endy=3100

1. beginy=0
2. endy=800

captioni="Chaco Canyon - GDD (curve) and PPT (color bar)"

xxname1="Year AD/CE" yyname1="GDD in deg F days"

1. yyname1="annual precipitation "

size0=17 size1=17 size2=18 size3=22

ds1 = xr.open_dataset(dataname1) ds2 = xr.open_dataset(dataname2)

1. 1. precip !

x1=ds1["Year"].values x2=ds2["Year"].values

1. y1=ds1["GDD"].values[:,1,1]
2. y1=ds1["GDD"].values[:,1,1]
3. y2=ds2["PPT"].values[:,1,1]

1. y1=ds1["GDD"].values[:,60,60]
2. y2=ds2["PPT"].values[:,60,60]

lons1=ds1["longitude"].values[:] lats1=ds1["latitude"].values[:]

1. print (np.shape(ds1["GDD"].values))

print(lons1) print(lats1)

londex1, slon1=find_nearest(lons1, plon1) latdex1, slat1 =find_nearest(lats1, plat1)

print(plon1, plat1) print(slon1, slat1) print(londex1, latdex1)

y1=ds1["GDD"].values[:,latdex1,londex1] y2=ds2["PPT"].values[:,latdex1,londex1]

1. quit(-1)

yearspan=int(math.fabs(endx-beginx))

x_smooth = np.linspace(beginx,endx, yearspan)

1. quit(-1)

smooth_funktion = interpolate.interp1d(x2, y2, kind="cubic")

1. smooth_funktion = interpolate.interp1d(x1, y1, kind="cubic")

1. quit(-1)

y_smooth = smooth_funktion(x_smooth)

1. print(y_smooth)

1. quit(-1)

1. print (np.shape(y2))

y1_smooth20 = np.convolve(y1, np.ones((20,))/20, mode='same') y1_smooth200 = np.convolve(y1, np.ones((200,))/200, mode='same')

y2_smooth20 = np.convolve(y2, np.ones((20,))/20, mode='same') y2_smooth200 = np.convolve(y2, np.ones((200,))/200, mode='same')

1. quit(-1)

deltay1_200=y1-y1_smooth200 deltay1_20=y1_smooth20-y1_smooth200

deltay2_200=y2-y2_smooth200 deltay2_20=y2_smooth20-y2_smooth200

1. plt.plot(x1,y2)
2. plt.plot(x1,y2_smooth20)

1. plt.plot(x1,y2)
2. plt.xlim((500,1100))
3. plt.ylim((-500,300))
4. plt.ylim((-200,200))

1. plt.plot(x1,deltay1_200)
2. plt.plot(x1,deltay1_20)

1. plt.plot(x2,deltay2_200)
2. plt.plot(x2,deltay2_20)

1. plt.show()

n_lines = yearspan

c = np.arange(1, n_lines + 1)

1. c = np.arange(beginx, endx + 1)

1. ytab=np.linspace(0, (n_lines-1), n_lines)

maxy=np.max(y_smooth) miny=np.min(y_smooth) meany=np.mean(y_smooth) amplitudey=maxy-miny

ytab=((y_smooth-miny)/amplitudey)*n_lines

ytab2=np.int_(ytab)

plt.axes().set_aspect(1/2)

plt.xlim([beginx, endx]) plt.ylim([beginy, endy])

plt.plot(x1,y1)

norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())

1. cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.RdBu_r)
2. cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.terrain_r)
3. cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.gist_earth_r)

cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.BrBG)

1. 1. gouldian rainbow2 rainbow4

cmap.set_array([])

1. fig, ax = plt.subplots(dpi=100)

ii=0

for i in range(1,n_lines): fux=i+beginx idex=int(ytab2[i]) coloro=cmap.to_rgba(idex + 1) plt.axvline(fux, color=coloro, alpha=0.5) ii=ii+1

plt.plot(x1,y1, c='b', lw=1, alpha=1.0)

1. plt.scatter(x1,y1, c=cm.gist_rainbow_r(np.abs(y1)), s=199, marker="o", edgecolor='none')

1. plt.scatter(x1,y1, c='b', s=20, marker="o", edgecolor='none', alpha=1.0)

plt.locator_params(axis='x', nbins=20)

plt.title(captioni, fontsize=size3, color="#0000af") plt.xlabel(xxname1, color="darkgreen", fontsize=size1) plt.ylabel(yyname1, color="darkgreen", fontsize=size1) plt.xticks(fontsize=size2) plt.yticks(fontsize=size2)

1. plt.gca().invert_yaxis()

1. plt.gca().grid()

plt.gca().xaxis.set_major_locator(plt.MultipleLocator(100)) plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(10))

plt.gca().yaxis.set_major_locator(plt.MultipleLocator(100)) plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(10))

print("..") plt.show()

print(".")