Journal of Geographical Sciences >
Evidence for a recent warming and wetting in the source area of the Yellow River (SAYR) and its hydrological impacts
*Corresponding author: Lan Yongchao (1957-), Professor, E-mail:lyc@lzb.ac.cn
Author: Tian Hui (1970-), PhD, specialized in land surface processes observation and remote sensing, climate change and water resources. E-mail:htian@lzb.ac.cn
Received date: 2014-08-20
Accepted date: 2015-01-10
Online published: 2015-06-15
Supported by
The Key Deployment Project of the Chinese Academy of Sciences, No.Y322G73001
National Natural Science Foundation of China, No.91225302, No.91437217, No.41375022, No.41175027
Copyright
Climate change investigation at a watershed-scale plays a significant role in revealing the historical evolution and future trend of the runoff variation in watershed. This study examines the multisource hydrological and meteorological variables over the source area of the Yellow River (SAYR) from 1961 to 2012 and the future climate scenarios in the region during 2006-2100 based on the CMIP5 projection data. It recognizes the significant characteristics of the recent climate change in the SAYR and predicts the change trend of future flow in the region. It is found that (1) The climate in the SAYR has experienced a significant warm-wet change since the early 2000s, which is very different from the antecedent warm-dry trend since the late 1980s; (2) The warm-wet trend in the northwestern SAYR (the headwater area of the Yellow River (HAYR), is more obvious than that in the whole SAYR; (3) With precipitation increase, the runoff in the region also experienced an increasing process since 2006. The runoff variations in the region are sensitive to the changes of precipitation, PET and maximum air temperature, but not very sensitive to changes in mean and minimum air temperatures; (4) Based on the CMIP5 projection data, the warm-wet climate trend in SAYR are likely to continue until 2049 if considering three different (i.e. RCP2.6, RCP4.5 and RCP8.5) greenhouse gas emission scenarios, and the precipitation in SAYR will not be less than the current level before 2100; however, it is estimated that the recent flow increase in the SAYR is likely to be the decadal change and it will at most continue until the 2020s; (5) The inter-annual variations of the East Asian winter monsoon are found to be closely related to the variations of annual precipitation in the region. Meanwhile, the increased precipitation as well as the increase of potential evapotranspiration (PET) being far less than that of precipitation in the recent period are the main climate causes for the flow increase in the region.
TIAN Hui , LAN Yongchao , WEN Jun , JIN Huijun , WANG Chenghai , WANG Xin , KANG Yue . Evidence for a recent warming and wetting in the source area of the Yellow River (SAYR) and its hydrological impacts[J]. Journal of Geographical Sciences, 2015 , 25(6) : 643 -668 . DOI: 10.1007/s11442-015-1194-7
Figure 1 26 weather stations operated by China Meteorological Administration (CMA) and 5 hydrological stations operated by Yellow River Conservancy Commission (YRCC) in the source area of the Yellow River (SAYR) and the headwaters area of the Yellow River (HAYR) |
Table 1 China Meteorological Administration (CMA) weather stations located in SAYR and its bordering areas |
Name and location of station | Mean annual values | |||||
---|---|---|---|---|---|---|
Elevation (m a.s.l.) | Name | Abbreviation | Lat. (°N) | Long. (°E) | Precip. (mm) | Air temp. (°C) |
2791 3192 2836 2238 3324 3202 3145 4176 3682 4273 4418 4201 3720 3969 3501 3630 3473 3441 2911 3394 3530 3896 2666 3493 2369 2852 | Nuomuhong Dulan Qiabuqia Guide Xinghai Guinan Tongren Qumalai (Qumaléb) Yushu (Ghegu) Maduo (Madoi) Qingshuihe Shiqu Guoluo Dari Henan Jiuzhi Maqu Ruoergai (Zoîgé) Hezuo Ganzi Banma Seda Maerkang Hongyuan Xiaojin Songpan | NMH DLN QBQ GDE XHI* GNN* TGR QML GHG MDO* QSH SQU GLO* DRI* HNN* JUZ* MQU* ZRG* HZO GZI BMA SDA MRK HYN* XJN SPN | 36.37 36.33 36.27 36.37 35.80 35.58 35.52 34.53 33.10 34.95 33.80 32.98 34.48 33.75 34.73 33.43 34.00 33.58 35.00 31.62 32.93 32.28 31.90 32.80 31.00 32.65 | 96.45 98.03 100.30 101.37 99.67 100.73 102.02 95.47 96.75 98.13 97.13 98.10 100.23 99.65 101.60 101.48 102.08 102.97 102.90 100.00 100.75 100.33 102.23 102.55 102.35 103.57 | 46.7 203.8 319.1 255.7 366.9 415.8 414.5 418.0 486.7 321.7 516.5 569.6 515.8 553.8 587.4 746.4 603.7 652.6 542.2 643.3 661.0 662.3 778.6 750.2 616.0 717.1 | 5.0 3.4 4.2 7.5 1.4 2.4 5.9 -2.0 3.4 -3.6 -4.5 -0.6 -0.2 -0.7 -0.2 0.8 1.7 1.2 4.1 6.3 4.2 0.7 9.0 1.6 12.1 6.4 |
Note: The data period in the table is 1961-2012. * The weather stations which are located within SAYR. |
Figure 2 MK abrupt change detection of the 1961-2012 mean annual air temperature (MAAT) in the SAYR (a). C1 and C2 are confidence lines at confidence level of p = 0.05. UF and UB are forward and backward time series of the dimensionless variable u of MK abrupt change detection, respectively. Time series of MAAT, maximum annual air temperature (MaxAAT) and minimum annual air temperature (MinAAT) in the SAYR in the period 1961-1994 (b). As Figure 2b shows, but in the period 1995-2012 (c) |
Figure 3 MK trends of the 1961-2012 total annual precipitation (TAP) series of 26 CAM weather stations (a); The 1980-2010 average of TAPs in the SAYR based on the ITPCAS data (b); Spatial distribution of estimated Sen slope (c-d) and spatial distribution of confidence level corresponding with the standardized test statistic Z of MK-Trend detection for the ITPCAS TAP in the SAYR (e-f) and its surrounding areas in the periods of1980-2000 (c, e) and 2001-2010 (d, f) |
Figure 4 The 10-station mean and 26-station mean annual precipitation time series (a), the cumulative anomaly (CA) of the 10-station mean annual precipitation series (b), the CA of the 26-station mean annual precipitation series (c), MTT change detection for the 10-station precipitation series (d), and MTT change detection for the 26-station precipitation series (e). C1 and C2 in (d) and (e) are confidence lines at the significant level of 0.01. |
Figure 5 TRMM 3B43 based areal averaged annual precipitation series in SAYR and HAYR during 1998-2012 |
Figure 6 Abrupt change detection for the 1980-2010 ITPCAS-based TAP series in the SAYR (left) and the HAYR (right) by MK approach (a-b), CA approach (c-d) and MTT approach (e-f) |
Table 2 Climate trends in the seasonal precipitation variability based on the 10-station monthly precipitation series for 1961-2012 |
Month | Abrupt point | PBA | PAA | Average for PBA (mm) | Average for PAA (mm) | Average of 1961- 2012 (mm) | Line slope for PBA (mm/yr) | Line slope for PAA (mm/yr) | CC for PBA | CC for PAA |
---|---|---|---|---|---|---|---|---|---|---|
Jan | 2000 | 1993-2000 | 2001-2012 | 5.4 | 4.7 | 4.0 | -1.319 | 0.209 | 0.918 | 0.457 |
Feb | 2002 | 1989-2002 | 2003-2012 | 7.1 | 6.4 | 5.7 | -0.455 | 0.103 | 0.755 | 0.108 |
Mar | 2002 | 1993-2002 | 2003-2012 | 15.0 | 13.3 | 12.8 | -0.960 | 0.330 | 0.592 | 0.195 |
Apr | 2002 | 1994-2002 | 2003-2012 | 27.7 | 24.9 | 25.8 | -1.880 | 0.343 | 0.563 | 0.168 |
May | 2000 | 1985-2000 | 2001-2012 | 66.4 | 70.6 | 66.3 | -0.857 | -1.188 | 0.319 | 0.262 |
Jun | 2001 | 1987-2001 | 2002-2012 | 96.8 | 101.5 | 95.8 | -2.516 | 1.358 | 0.419 | 0.256 |
Jul | 2000 | 1989-2000 | 2001-2012 | 109.6 | 116.5 | 112.0 | -1.210 | 5.228 | 0.220 | 0.752 |
Aug | 2002 | 1981-2002 | 2003-2012 | 87.4 | 104.8 | 96.8 | -0.444 | 0.093 | 0.135 | 0.165 |
Sep | 2000 | 1984-2000 | 2001-2012 | 73.3 | 85.0 | 85.6 | -1.526 | -0.346 | 0.440 | 0.126 |
Oct | 2002 | 1988-2002 | 2003-2012 | 38.9 | 41.4 | 38.0 | -1.094 | -1.344 | 0.328 | 0.454 |
Nov | 2003 | 1989-2003 | 2004-2012 | 4.9 | 6.5 | 5.6 | 0.004 | 0.391 | 0.000 | 0.281 |
CC is correlation coefficient. PBA is the period before abrupt change point. PAA is the period after abrupt change point. |
Figure 7 (Upper panel) The 1961-2012 annual runoff series at TNH. MA10 denotes the 10-year moving average, and so on. (Lower panel) abrupt climate change detection for the 1961-2012 annual runoff series at TNH by CA approach (a) and by MK approach (b), and MK abrupt change detection for the monthly runoff series in May (c), June (d), July (e), August (f), September (g) and October (h) at α = 0.05 significant level |
Table 3 The year for each monthly runoff series at TNH since then the monthly runoff in most years was above the long-term (1961-2012) average |
Season | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Year | 2006 | 2009 | 2006 | 2008 | 2009 | 2007 | 2005 | 2009 | - | 2008 | 2008 | 2005 |
Figure 8 Abrupt climate change detection for CT series by MK approach at TNH (a), JNG (b), MQU (c), JMI (d), HHY (e) and by CA approach at TNH (f), JNG (g) and MQU (h) at α= 0.05 significant level |
Table 4 Climatic abrupt changes of CT series in the recent decade at 5 YRCC hydrological stations |
YRCC station | Recent abrupt point | PBA | PAA | Average for PBA (month) | Average for PAA (month) |
---|---|---|---|---|---|
HHY | 2006 | 1981-2006 | 2007-2012 | 7.35 | 7.73 |
JMI | 2002 | 1981-2002 | 2003-2012 | 7.27 | 7.46 |
MQU | 2002 | 1988-2002 | 2003-2012 | 7.30 | 7.57 |
JNG | 2002 | 1983-2002 | 2003-2012 | 7.31 | 7.52 |
TNH | 2002 | 1985-2002 | 2003-2012 | 7.29 | 7.53 |
Figure 9 The projection of mean, maximum, minimum annual air temperature (AAT) (a), annual precipitation (b) over the SAYR in the period 2006-2100 derived from the NCCC-provided CMIP5 data over China. The correction for the original data is based on the Delta method (Hay et al., 2000) by using the reference period (1961-1990) CMA weather-station data over the SAYR. |
Figure 10 Observed, simulated, and forecasted and average annual runoff rates in the SAYR during 1920-2060 |
Figure 11 The competing relationship between the runoff and Hargreaves potential evapotranspiration (PET) (a), Penman PET (b) in the distribution of precipitation at the annual scale over the SAYR during the period 1961-2012. For lacking the regional actual evapotranspiration observation over the SAYR, Penman PET and Hargreaves PET series are used to replace the actual evapotranspiration. (c) Linear correlation between the Penman-PET and Hargreaves-PET series during the period 1961-2012 in the SAYR. |
Figure 12 Projection of the 2006-2100 Hargreaves-PET followed by (Hargreaves and Samani, 1985) derived from the NCCC-provided CMIP5 projection of average, maximum and minimum annual air temperature over the SAYR |
Figure 13 The projection of the runoff depth over the SAYR based on the formula R=k·Pa·PETb (a), and (b) using the NCCC-provided CMIP5 projection of the annual precipitation and Hargreaves-PET in the period 2006-2100 |
Figure 14 Anomaly series of EAWMI (a), LSWM (b) and SH (c) indexes in the period 1961-2012 |
Figure 15 The 10-station TAP and PET_PM series in 2001-2012 (a), and the ratio of annual runoff to TAP and that of PET_PM to TAP in 2001-2012 (b) |
The authors have declared that no competing interests exist.
1 |
|
2 |
|
3 |
|
4 |
|
5 |
|
6 |
|
7 |
|
8 |
|
9 |
|
10 |
|
11 |
|
12 |
|
13 |
|
14 |
|
15 |
|
16 |
|
17 |
|
18 |
|
19 |
|
20 |
|
21 |
|
22 |
|
23 |
|
24 |
|
25 |
|
26 |
|
27 |
|
28 |
|
29 |
|
30 |
|
31 |
|
32 |
|
33 |
|
34 |
|
35 |
|
36 |
|
37 |
|
38 |
|
39 |
|
/
〈 | 〉 |