Under the background of global warming, deep understanding for drought-related index is important. The spatial distributions and trends in annual mean (AM) climatic data, including
It has been widely recognized that the increment of greenhouse gases in the atmosphere is the primary cause of the observed global warming [
General circulation models (GCMs), which include numerical coupled models and represent various earth systems, are important tools to assessing climate change effects on droughts [
The SDS technique involves developing a quantitative relationship between large-scale climate variables (predictors) and local surface variables (predictands). SDS has many advantages. SDS can provide point-scale climatic variables from GCM-scale output; it can be used to derive variables not available from regional climate models (RCMs); it is easily transferred to other regions based on standard and accepted statistical procedures; and it is able to directly incorporate observations into method [
Xinjiang Autonomous Region has an area of 1.66 million km2 and takes up roughly one sixth of the total area of China. It is located in the northwest inland of China and is distant from any coast. The climate in Xinjiang is dry with many hours of sunshine and large temperature differences between day and night. The surface water and groundwater resources in Xinjiang rank 14 and 4, respectively, out of 31 provinces in China with highly uneven spatial distributions. The average annual precipitation in Xinjiang is only 147 mm while the average annual evaporation is 1512 mm [
The objectives of this study are
Data used in this study include
HadCM3 (Hadley Centre Coupled Model, version 3, [
Potential evapotranspiration is based on the calculated rate of evapotranspiration from a hypothetical reference crop with a height of 0.12 m, an albedo of 0.23, and a fixed surface resistance of 70 s m−1. FAO-56 Penman-Monteith equation is used for estimating
SDSM version 4.2 is used to project future daily
The established empirical relationship, taking
Daily precipitation variations in the whole region (
The dryness index (
Inverse distance weight (IDW) interpolation method in Arcmap 10.2 is used to interpret the spatial distributions of
The existence of annual trends in the data series was analyzed based on the Mann-Kendall (MK) statistical test [
The standardized test statistic
The magnitude of the slope of the trend is estimated according to Sen [
A total of 41 weather stations in Xinjiang, China, with observed datasets over 1961–2000 were selected. The longitude and latitude ranges of the 41 sites were between E36.9 and 48.1° and N75.2 and 94.7°, respectively. The elevation
Digital elevation model (DEM) showing the geological location of the weather stations used in this study.
Takelamagan desert was located in the middle south part of the region; there were sparse weather stations located within the desert zone. The annual mean weather conditions of the 41 sites are given in Table
Basic information of meteorological stations.
Site | Longitude (°) | Latitude (°) | Elevation |
|
|
|
|
|
---|---|---|---|---|---|---|---|---|
Tulufan | 89.2 | 42.9 | 30 | 14.5 | 1.3 | 40.6 | 8.1 | 0.04 |
Jinghe | 82.9 | 44.6 | 304 | 7.8 | 1.7 | 61.3 | 7.1 | 0.24 |
Kelamayi | 85.0 | 45.6 | 329 | 8.6 | 3.2 | 48.3 | 7.4 | 0.28 |
Shihezi | 86.2 | 44.3 | 470 | 7.2 | 1.5 | 64.5 | 7.5 | 0.49 |
Fuhai | 87.5 | 47.1 | 498 | 4.1 | 2.6 | 62.4 | 7.9 | 0.30 |
Habahe | 86.4 | 48.1 | 539 | 4.8 | 4.0 | 60.6 | 8.3 | 0.45 |
Tacheng | 83.0 | 46.8 | 550 | 6.9 | 2.3 | 60.3 | 8.1 | 0.67 |
Wusu | 84.7 | 44.5 | 684 | 8.1 | 2.2 | 58.5 | 7.4 | 0.41 |
Hami | 93.5 | 42.8 | 763 | 10.0 | 2.2 | 43.0 | 9.1 | 0.10 |
Yining | 81.5 | 44.0 | 790 | 8.9 | 1.9 | 65.5 | 7.8 | 0.66 |
Urumqi | 87.6 | 43.8 | 836 | 6.9 | 2.4 | 58.2 | 7.3 | 0.64 |
Ruoqiang | 88.2 | 39.0 | 895 | 11.9 | 2.6 | 39.4 | 8.4 | 0.08 |
Aletai | 88.1 | 47.8 | 939 | 4.5 | 2.4 | 57.6 | 8.2 | 0.46 |
Tuoli | 83.6 | 46.0 | 946 | 5.3 | 2.9 | 56.3 | 7.7 | 0.58 |
Kuerle | 86.2 | 41.8 | 948 | 11.8 | 2.5 | 45.3 | 7.9 | 0.15 |
Jimunai | 85.9 | 47.4 | 951 | 4.2 | 3.8 | 57.2 | 8.0 | 0.50 |
Luntai | 84.3 | 41.8 | 982 | 11.1 | 1.3 | 49.3 | 7.4 | 0.19 |
Bohu | 86.6 | 42.0 | 1057 | 8.5 | 1.9 | 56.8 | 8.3 | 0.20 |
Kuche | 83.0 | 41.7 | 1071 | 11.4 | 2.3 | 45.4 | 7.7 | 0.18 |
Akesu | 80.3 | 41.2 | 1106 | 10.4 | 1.6 | 57.3 | 7.8 | 0.19 |
Bachu | 78.6 | 39.8 | 1119 | 12.1 | 1.6 | 48.0 | 7.8 | 0.15 |
Keping | 79.1 | 40.5 | 1161 | 11.7 | 1.7 | 44.8 | 7.5 | 0.24 |
Shache | 77.3 | 38.4 | 1233 | 11.7 | 1.5 | 53.7 | 7.9 | 0.14 |
Baicheng | 81.9 | 41.8 | 1236 | 7.9 | 0.8 | 63.9 | 7.9 | 0.30 |
Qieme | 85.5 | 38.2 | 1246 | 10.6 | 1.9 | 41.7 | 7.7 | 0.08 |
Qitai | 90.4 | 45.2 | 1278 | 5.2 | 3.1 | 60.8 | 8.2 | 0.45 |
Hebukesaier | 85.7 | 46.8 | 1293 | 3.7 | 2.6 | 53.6 | 8.0 | 0.36 |
Fuyun | 89.5 | 47.0 | 1304 | 3.1 | 1.8 | 59.2 | 7.9 | 0.42 |
Shufu | 75.9 | 39.4 | 1331 | 11.9 | 1.9 | 50.8 | 7.6 | 0.16 |
Pishan | 78.3 | 37.6 | 1373 | 12.2 | 1.5 | 44.1 | 7.1 | 0.13 |
Hetian | 79.8 | 37.1 | 1388 | 12.6 | 1.9 | 42.1 | 7.2 | 0.10 |
Minfeng | 82.7 | 37.1 | 1416 | 11.6 | 1.6 | 41.1 | 7.9 | 0.10 |
Yutian | 81.7 | 36.9 | 1432 | 11.7 | 1.5 | 45.0 | 7.7 | 0.13 |
Balikun | 93.0 | 43.6 | 1632 | 1.9 | 2.4 | 56.2 | 8.5 | 0.53 |
Wenquan | 81.1 | 44.9 | 1697 | 3.9 | 2.2 | 64.6 | 7.5 | 0.75 |
Qinghe | 90.4 | 46.7 | 1730 | 0.7 | 1.3 | 60.6 | 8.5 | 0.40 |
Zhaosu | 81.1 | 43.2 | 1890 | 3.3 | 2.3 | 67.4 | 7.3 | 0.13 |
Aheqi | 78.5 | 40.9 | 1990 | 6.6 | 2.7 | 50.0 | 7.8 | 0.55 |
Yiwu | 94.7 | 43.3 | 1995 | 3.9 | 3.5 | 42.0 | 8.9 | 0.28 |
Wuqia | 75.3 | 39.7 | 2181 | 7.3 | 2.4 | 45.6 | 7.8 | 0.43 |
Tashikuergan | 75.2 | 37.8 | 3095 | 3.6 | 2.0 | 39.9 | 7.9 | 0.17 |
Figure
Spatial distributions of the multiyear mean climatic elements in the study region.
Sunshine hour
Air temperature (°C)
Relative humidity (%)
Wind speed (m/s)
The spatial distributions of annual mean precipitation
Spatial distributions and trends of
Overall decreasing distributions of
Area weights (
Site |
|
---|---|
Aheqi | 0.0133 |
Akesu | 0.0306 |
Aletai | 0.0121 |
Bachu | 0.0216 |
Balikun | 0.0338 |
Baicheng | 0.0220 |
Bohu | 0.0237 |
Fuhai | 0.0201 |
Fuyun | 0.0173 |
Habahe | 0.0094 |
Hami | 0.0542 |
Hebukesaier | 0.0121 |
Hetian | 0.0537 |
Jimunai | 0.0047 |
Jinghe | 0.0197 |
Kalamayi | 0.0132 |
Keping | 0.0132 |
Kuche | 0.0305 |
Kuerle | 0.0257 |
Luntai | 0.0328 |
Minfeng | 0.0539 |
Pishan | 0.0382 |
Qiemo | 0.0780 |
Qinghe | 0.0087 |
Qitai | 0.0301 |
Ruoqiang | 0.1210 |
Shache | 0.0221 |
Shihezi | 0.0220 |
Shufu | 0.0166 |
Tacheng | 0.0057 |
Tashikuergan | 0.0231 |
Tulufan | 0.0615 |
Tuoli | 0.0127 |
Urumqi | 0.0275 |
Wenquan | 0.0088 |
Wuqia | 0.0183 |
Wusu | 0.0197 |
Yining | 0.0109 |
Yiwu | 0.0285 |
Yutian | 0.0352 |
The calculated MK statistics
Trend detection results over 1961–2010.
Site |
|
|
DIAM | |||
---|---|---|---|---|---|---|
|
|
|
|
|
|
|
Aheqi | 1.27 | 3.32* | 2.58* (1) | −3.35* | −1.44 (10) | |
Akesu | 2.76* | 1.70 (2) | 1.27 | 0.84 (1) | −1.29 | −0.95 (1) |
Aletai | 4.13* | 2.73* (1) | 4.64* | 1.55 (9) | −4.60* | −2.74* (3) |
Bachu | −2.88* | 2.19* | −2.44* | |||
Baicheng | 3.98* | 1.44 (8) | 2.386* | 1.37 (1) | −3.65* | −1.89 (4) |
Balikun | 3.75* | 2.67* (2) | 3.98* | 1.58 (8) | −1.95 | −1.41 (1) |
Bohu | −0.65 | −0.50 (1) | 1.11 | −1.15 | ||
Fuhai | −0.40 | 2.359* | 1.78 (1) | −2.38* | ||
Fuyun | 6.54* | 2.92* (5) | 4.27* | 1.46 (8) | −3.81* | −2.05* (3) |
Habahe | −2.49* | −1.78 (1) | 4.32* | 1.81 (7) | −4.25* | |
Hami | −4.25* | −1.67 (8) | 3.681* | −3.76* | ||
Hebukesaier | 0.42 | 1.36 | −1.99* | |||
Hetian | 1.74 | 1.10 (2) | 2.02* | −1.99* | ||
Jimunai | −0.82 | −0.58 (1) | 4.35* | 2.03* (4) | −4.08* | −2.04* (4) |
Jinghe | 1.42 | 0.99 (1) | 1.87 | −1.87 | ||
Kelamayi | 2.76* | 1.70 (2) | 2.61* | 1.62 (3) | −1.51 | |
Keping | 1.51 | 1.17 (1) | 1.46 | −2.74* | ||
Kuche | 1.36 | 0.79 (2) | 2.46* | 1.68 (2) | −2.39* | |
Kuerle | −2.29* | −1.43 (1) | 1.10 | −1.29 | ||
Luntai | 2.07* | 1.67 (1) | 4.90* | 2.28* (5) | −5.02* | −2.31* (6) |
Minfeng | 3.51* | 2.49* (1) | 1.91 | −1.84 | ||
Pishan | 4.17* | 1.54 (6) | 2.05* | −1.82 | ||
Qieme | −0.87 | 2.93* | 1.94 (2) | −3.71* | −1.47 (9) | |
Qinghe | 3.38* | 3.88* | 1.43 (8) | −2.84* | ||
Qitai | 2.64* | 1.51 (3) | 3.87* | 1.52 (9) | −3.80* | −1.43 (10) |
Ruoqiang | 2.89* | 2.09* (2) | 3.44* | −3.48* | ||
Shache | −0.72 | 1.49 | −4.32* | |||
Shihezi | 3.73* | 1.57 (7) | 3.68* | 1.62 (6) | −3.53* | −1.95 (3) |
Shufu | −3.48* | 1.874 | −1.81 | |||
Tacheng | 2.09* | −1.51 (1) | 3.61* | 1.63 (6) | −3.93* | −2.39* (3) |
Tashikuergan | 4.20* | 1.97* (7) | 2.91* | −2.64* | ||
Tulufan | −1.00 | −0.75 (1) | 1.55 | −1.57 | ||
Tuoli | 2.74* | 1.78 (3) | 2.22* | −2.09* | ||
Urumqi | 2.06* | 1.50 (3) | 4.63* | 1.54 (9) | −3.40* | |
Wenquan | −1.26 | −0.99 (1) | 3.46* | −4.62* | −1.72 (9) | |
Wuqia | 4.18* | 1.80 (7) | 1.79 | −1.62* | ||
Wusu | −0.08 | 3.20* | 1.87 (3) | −3.21* | −1.76 (3) | |
Yining | −5.44* | −2.85* (3) | 3.88* | 1.68 (6) | −4.28* | −1.64 (8) |
Yiwu | 2.99* | 4.38* | 2.55* (3) | −4.32* | ||
Yutian | 3.15* | 2.18* (1) | 0.084 | 1.17 | 0.89 (1) | |
Zhaosu | 1.15 | 2.85* | −2.34* |
In Table
The spatial distributions of the final trends (using the combined MK and MMK test results) for
Predictors were selected for the region (Table
The selected predictors used for developing a relationship between the predictand and predictor.
Number | PR | NPP | NPE |
---|---|---|---|
1 | Mean sea level pressure | 7 | 13 |
2 | Surface airflow strength | 2 | 3 |
3 | Surface zonal velocity | 5 | 9 |
4 | Surface meridional velocity | 12 | 6 |
5 | Surface velocity | 24 | 12 |
6 | Surface wind direction | 2 | 1 |
7 | Surface divergence | 10 | 1 |
8 | 500 hPa airflow strength | 10 | 1 |
9 | 500 hPa zonal velocity | 13 | 3 |
10 | 500 hPa meridional velocity | 19 | 7 |
11 | 500 hPa velocity | 24 | 1 |
12 | 500 hPa geopotential height | 29 | 20 |
13 | 500 hPa wind direction | 0 | 0 |
14 | 500 hPa divergence | 0 | 0 |
15 | 850 hPa airflow strength | 1 | 2 |
16 | 850 hPa zonal velocity | 9 | 10 |
17 | 850 hPa meridional velocity | 8 | 8 |
18 | 850 hPa velocity | 6 | 7 |
19 | 850 hPa geopotential height | 0 | 0 |
20 | 850 hPa wind direction | 0 | 0 |
21 | 850 hPa divergence | 0 | 0 |
22 | 500 hPa relative humidity | 17 | 5 |
23 | 850 hPa relative humidity | 17 | 14 |
24 | Near surface relative humidity | 8 | 8 |
25 | Surface specific humidity | 1 | 25 |
26 | Mean temperature at 2 m | 6 | 41 |
PR: Predictor, NPP and NPE: numbers of stations used for establishing predictor-predictand relationship of rainfall and
The observed historical daily
Determination of coefficients (
Site |
|
|
---|---|---|
Aheqi | 0.992 | 0.923 |
Akesu | 0.945 | 0.984 |
Aletai | 0.998 | 0.984 |
Bachu | 0.979 | 0.924 |
Balikun | 0.989 | 0.970 |
Baicheng | 0.987 | 0.786 |
Bohu | 0.969 | 0.865 |
Fuhai | 0.997 | 0.988 |
Fuyun | 0.962 | 0.910 |
Habahe | 0.993 | 0.971 |
Hami | 0.911 | 0.566 |
Hebukesaier | 0.989 | 0.978 |
Hetian | 0.962 | 0.914 |
Jimunai | 0.990 | 0.968 |
Jinghe | 0.994 | 0.936 |
Kalamayi | 0.998 | 0.968 |
Keping | 0.970 | 0.793 |
Kuche | 0.951 | 0.799 |
Kuerle | 0.973 | 0.738 |
Luntai | 0.988 | 0.914 |
Minfeng | 0.877 | 0.520 |
Pishan | 0.888 | 0.752 |
Qiemo | 0.950 | 0.873 |
Qinghe | 0.993 | 0.971 |
Qitai | 0.882 | 0.761 |
Ruoqiang | 0.955 | 0.627 |
Shache | 0.928 | 0.808 |
Shihezi | 0.994 | 0.978 |
Shufu | 0.896 | 0.765 |
Tacheng | 0.951 | 0.867 |
Tashikuergan | 0.995 | 0.942 |
Tulufan | 0.991 | 0.765 |
Tuoli | 0.986 | 0.950 |
Urumqi | 0.993 | 0.975 |
Wenquan | 0.996 | 0.989 |
Wuqia | 0.988 | 0.935 |
Wusu | 0.985 | 0.941 |
Yining | 0.992 | 0.969 |
Yiwu | 0.987 | 0.959 |
Yutian | 0.969 | 0.719 |
Zhaosu | 0.972 | 0.798 |
Daily
Spatial distributions and the trend test results for
Scenario A2 for
Scenario B2 for
Scenario A2 for
Scenario B2 for
Scenario A2 for
Scenario B2 for
From the trend test results of
Series of
Variations of
Variations of
Variations of
Variations of
The trend test results for
Trend detection results of
Item |
|
|
|
|
|
|
DIWR | DIWR | DIWR |
---|---|---|---|---|---|---|---|---|---|
Period | 1 | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 2 |
Scenario | A2 | B2 | A2 | B2 | A2 | B2 | |||
|
−0.003 | −0.003 | −0.002 | 0.003 | 0.002 | 0 | −0.096 | −0.02 | −0.013 |
|
−5.72* | −1.84 | −1.73 | 4.97* | 2.31* | 0.875 | −4.43* | −6.20* | −4.31* |
|
−1.93 (10) | — | — | 1.56 (12) | — | — | −2.30* (6) | −1.87 (15) | −3.01* (2) |
In Table
Uncertainty exists when assessing climate change impacts on hydrologic or meteorological elements. Several studies affirmed that GCMs were the largest uncertainty contributors [
Only one statistical downscaling method was used in this study. Uncertainty was not assessed but could refer to the other research mentioned above. SDSM 4.2 software is convenient for projecting future daily
The trends for
Overall,
The statistical downscaling model (SDSM), combined with HadCM3 data and NCEP reanalysis data, was used in this study to project future daily
Ranges of
The robust trend test combining MK with MKK methods indicated that for the whole region in Xinjiang, trends in
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors acknowledge the Xinjiang Joint Project of China National Science Foundation (U1203182), International Cooperation Key Project in Shannxi, China (2012KW-24-01), and Basic Science-Technology Foundation for the Talent Young Scientist in the Universities of China (YQ2013006). Mark Sigouin helped edit the paper. The authors thank the anonymous reviewers, who gave them very constructive and helpful comments.