Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern Rice Production Area of China

Journal of Integrative Agriculture 2013, 12(12): 2260-2279

December 2013

RESEARCH ARTICLE

Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern Rice Production Area of China YE Qing1, 2, YANG Xiao-guang1, DAI Shu-wei1, 3, LI Yong4 and GUO Jian-ping5 1

College of Resources and Environmental Science, China Agricultural University, Beijing 100193, P.R.China Forestry Institute, Jiangxi Agricultural University, Nanchang 330045, P.R.China 3 School of Natural Resources, University of Nebraska-Lincoln, NE 68583, USA 4 Guizhou Key Laboratory of Mountainous Climate and Resources, Guiyang 550002, P.R.China 5 Chinese Academy of Meteorological Sciences, Bejing 100081, P.R.China 2

Abstract The spatiotemporal characteristics of hydrothermal resources in southern rice production area of China have changed under the background of climate change, and this change would affect the effectiveness of hydrothermal resources during local rice growing period. According to the cropping system subdivision in southern rice production area of China during 1980s, this study used climate data from 254 meteorological stations and phonological data from 168 agricultural observation stations in the south of China, and adopted 6 international evaluation indices about the effectiveness of hydrothermal resources to analyze the temporal and spatial characteristics of hydrothermal resources during the growing period of single cropping rice system and double cropping rice system for 16 planting zones in the whole study area. The results showed that: in southern rice production area of China, the effectiveness of thermal resources of single cropping rice area (SCRA) was less than that of double cropping rice area (DCRA), whereas the effectiveness of thermal resources of both SARA and DCRA showed a decreasing trend. The index value of effective precipitation satisfaction of SCRA was higher than that of DCRA, nevertheless the index value of effective precipitation satisfaction of both SCRA and DCRA showed a decreasing trend. There was a significant linear relationship between effective thermal resource and water demand, likely water demand increased by 18 mm with every 100°C d increase of effective heat. Effective precipitation satisfaction index (EPSI) showed a negative correlation with effective heat, yet showed a positive correlation with effective precipitation. EPSI reduced by 1% when effective heat resource increased by 125°C d. This study could provide insights for policy makers, land managers or farmers to improve water and heat resource uses and rationally arrange rice production activities under global climate change condition. Key words: climate change, rice production area, effectiveness of hydrothermal resources, spatiotemporal characteristics

INTRODUCTION The change of agro-climatic resources directly affects the options of local cropping system, crops structure,

and planting patterns (Han and Qu 1991), meanwhile, it affects the growth and development as well as the yield and quality of crop production (Rosenzweig and Parry 1994; Rosenzweig and Hillel 1995; Ju et al. 2013). The change of agro-climatic resources mainly

Received 3 November, 2012 Accepted 21 January, 2013 YE Qing, Tel: +86-10-62733939, E-mail: [email protected]; Correspondence YANG Xiao-guang, Tel: +86-10-62733939, E-mail: [email protected]

© 2013, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(13)60403-7

Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern

reflects on the quantity variation and effectiveness variation. Predecessors analyzed the effectiveness of climatic resources from different angles, and the research on heat resource effectiveness mainly involved the following three aspects: (1) Using accumulated effective temperature (daily temperature above lower temperature limit) method to evaluate the variation of effective heat during the growing periods of different crops under the background of climate change (Wang 1982; Liu 1993; Li et al. 2008; Miao et al. 2009; Jiang 2011). Most previous researches failed to take into account of the effect of accumulated heat above upper temperature limit on crop growth and development. However, temperature either above the upper temperature limit or below the lower temperature limit suppressed the growth and development of rice (Han and Qu 1991). Some scholars used three fundamental point temperatures of crop to study the effective temperature for crop growth, and defined the temperature which was above the lower limit and below the upper limit as effective temperature (Yu et al. 1991; Challinor et al. 2004; Tao et al. 2009), and the accumulation of effective temperature as effective accumulated temperature, then used the ratio of effective accumulated temperature and active accumulated temperature as heat resource effectiveness rate to illustrate heat effectiveness (Yu et al. 1991). (2) Using thermal time concept to evaluate the effectiveness of heat resources during crop growing period, like as applying growing degree-days (GDD), which was an extension of thermal time concept, into crops models to project the process of crop development. Many scholars had found that there was a close correlation between GDD and crop development rate (Hartz and Moore 1978; Huang et al. 1998; Caton et al. 1998; Liu et al. 1998). In addition, GDD was used in many cropping models to predict crop phonology (Leong and Ong 1983; Default 1997; McMaster and Wilhelm 1997; Black and Ong 2000; Butler et al. 2002; Calıskan et al. 2008a, b) and crop yield (Idso 1978; Lobell 2011). (3) From the perspective of heat usage efficiency to study the obtained crop yield per unit effective heat (Ravindra et al. 2008; Xiao et al. 2011), Dong (1988) used the total obtained heat during crop growing period divided by 0°C accumulated temperature to calculate the heat utilization. Dastane (1978) defined the precipitation effective-

2261

ness as “the availability of precipitation resource in a certain region”, and pointed out that from the angle of agricultural meteorology, precipitation effectiveness indicated the satisfaction degree to ETc. In addition, the summarized three methods to evaluate precipitation effectiveness were: (1) Using the matching condition with other climatic resources to evaluate the effectiveness of precipitation. This method primarily took into account of water index, like as using 10 times of precipitation divided by 0°C accumulated temperature to get the hydrothermal coefficients or named aridity index (de Martonne 1926; Han 1999), or using monthly precipitation and evaporation to calculate Thornthwaite index (Thornthwaite 1948). Prentice (1993) used yearly actual evapotranspiration divided by yearly potential evapotranspiration to calculate humidity index, and index indicating drought stress etc. to illustrate precipitation effectiveness, however, this type of method could only reflect water condition in the study area, but could not implicitly explain precipitation effectiveness during the growing period of a certain crop. (2) From the angle of water demand satisfaction for crop growth and development, precipitation effectiveness was defined as the availability of precipitation in a certain area (Dastane 1978), which was the percentage of effective precipitation to ETc. Effective precipitation was precipitation minus surface runoff, evaporation and deep percolation losing water, it could be stored by the soil layers, was the most closely correlated with crop growth and development, and could reflect the amount of available precipitation resource in a certain area. This type of method implicitly illustrated the satisfaction degree of effective precipitation resource to crop growth and development (Cahoon et al. 1992). (3) From the angle of water production efficiency, the obtainable crop yield and economic income per unit water (precipitation usage efficiency) were used to evaluate the effectiveness of precipitation resources. This type of method encountered a problem when applied in the comparison of regional precipitation resource effectiveness due to the fact that different crops, varieties, and climatic regions had different precipitation interception conditions, resulting in different effectiveness of precipitation resource (Xiao et al. 2011; Jiang et al. 2013). Under the background of global climate change, the

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2262

effectiveness of climatic resources changed. These changes would affect the length of regional crop growing season, crop planting area, etc., and would ultimately affect the grain yield. This study chose southern rice production area in China as study area because it is the domestic main rice production area, with main planting pattern as rice and other crops to form double cropping or triple cropping. We and analyzed the effectiveness change of agro-climatic resources during the growing season of rice, explicated the distribution characteristics and variation trend of climatic resources during the growing season of both single cropping rice and double cropping rice, aiming to provide basis and reference for technical adaptation measurements in rice production under the background of climate change.

RESULTS Analyses of heat resource effectiveness during rice growing season in southern rice production area of China This study used 10°C accumulated temperature to analyze the spatiotemporal characteristics of available heat resource (∑T 10°C) during rice growing season, and growing degree days between 10 and 35°C to analyze the spatiotemporal characteristics of effective heat resource (GDD10-35°C) during rice growing season, and used accumulated temperature effectiveness rate, which was the ratio of effective heat and 10°C accumulated temperature, to evaluate the effectiveness of heat resource (ET) during rice growing season. We plotted 80% guarantee rate values and climatic trend rate of these indices with ArcGIS10.0 (Figs. 1-3).

Spatial characteristics of ∑T 10°C during rice growing season in southern rice production area of China In Fig. 1, spatial distribution of 80% guarantee rate values and climatic trend rate of ∑T 10°C during period 1951-1980 and period 1981-2010 showed the total ∑T 10°C in each subzone of southern rice production area of China.

YE Qing et al.

The ∑ T 10°C in southern rice production area of China showed a decreasing trend from southeast to northwest (Fig. 1-A and C). In Fig. 1-E and F, single cropping rice area (SCRA) showed the most ∑T 10°C in Sichuan Pandong area (S7) rather than Yunnan plateau area (S4) which was in lower latitude, double cropping rice area (DCRA) showed a very significant latitudinal distribution, and with the most ∑T 10°C in coastal Xishuangbanna tropical triple-cropping area (D7). During period 1951-1980, the average ∑T 10°C in SCRA and DCRA was 4 701, and 6 265°C d, respectively; during 1981-2010, the average ∑T 10°C in SCRA and DCRA was 4 694 and 6 366°C d, respectively. Compared with the former period, average ∑ T 10°C in DCRA during the latter period increased slightly, yet the average ∑T 10°C in SCRA during the latter period decreased slightly. Spatial distribution of ∑T 10°C in each subzone was relatively more uniform, and the spatial difference was less. As to the variation of ∑T 10°C during rice growing period in the study area (Table 1, Fig. 1-B and D), ∑T 10°C in SCRA showed a decreasing trend in 63% stations during 1951-1980, with a decreasing trend of 25.5°C d/decade, ∑T 10°C in DCRA showed an increasing trend in 64% stations of all, yet the increasing trend was not significant (8.4°C d/decade). During 1981-2010, ∑T 10°C in both SCRA and DCRA generally showed an increasing trend, about 98% stations showed an increasing trend, with an increasing amplitude of 1 44.5 and 179°C d/decade, respectively.

Spatial characteristics of GDD10-35°C during rice growing season Fig. 2 showed the spatial distribution of 80% guarantee rate values and climatic trend rate of GDD10-35°C during rice growing season for period 1951-1980 and period 1981-2010. In the study area, GDD10-35°C showed a decreasing trend from southeast to northwest (Fig. 2-A and C). In Fig. 2-E and F, GDD10-35°C during rice growing season in SCRA was below 2 200°C d, significantly less than that in DCRA (2 800-4 000°C d). In SCRA, average GDD10-35°C was 2 030-2 190°C d in the lower plateau area at the border of Sichuan-Hubei-HunanGuizhou (S2), Chengdu Plain (S6), S7, Jianghuai Plain (S8), and hilly plain terrain at the border of Hubei-

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Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern

Henan-Anhui (S9); the rest area had less GDD10-35°C on average (<2 000°C d), including Qinling and Bashan mountain areas (S1), Guizhou plateau area (S3), S4, and plateau mountain area at the border of YunnanGuizhou (S5). In DCRA, average GDD10-35°C was 3 1603 900°C d with an exception of southern Yunnan (D6) below 3 000°C d. In Fig. 2-B and D and Table 1, it showed the change trend of GDD10-35°C in each subzone in the study area. In southern rice production area of China, climatic trend rate of GDD10-35°C in each subzone showed an opposite result during two periods. Generally, GDD 10-35°C during period 1951-1980 showed a decreasing trend (Fig. 2-A and B), about 67% stations in DCRA showed a decreasing trend, except D6 and D7, other subzones showed a significant decreasing trend; about 80% stations in SCRA showed a decreasing trend, and S4 and hilly plain terrain at the border of S9 showed a decreasing trend in all stations. In all, during 1951-1980, GDD 10-35°C both in SCRA and DCRA showed a decreasing trend, yet SCRA showed a more significant decreasing trend than DCRA. During 1981-2010, GDD10-35°C generally showed an increasing trend, about 98% stations in DCRA showed an increasing trend (Fig. 2-C and D), and 96% stations in SCRA showed an increasing trend (Table 1), DCRA showed a more significant increasing trend than SCRA. It turned out that GDD10-35°C both in SCRA and DCRA increased during the most recent three decades,

yet DCRA showed a more significant increasing trend.

Change characteristics of E T during rice growing season Fig. 3 showed the calculated 80% guarantee rate of of ET during period 1951-1980 and period 1981-2010 in southern rice production area of China. In the study area, during 1951-1980, average ET in SCRA was 40%, and average ET in DCRA was 53%, which was 13% higher than SCRA. During 19812010, average E T in SCRA was 39%, and average ET in DCRA was 53%, which was 14% higher than SCRA. During two study periods, general distribution characteristics in different cropping rice areas showed no significant change, high ET area (>50%) (Fig. 3-A, C, E and F) mainly located along the middle and lower reaches of the Yangtze River (D1), the Plain of Hubei and Hunan (D2), Zhejiang-Fujian area (D3), and Nan Mountains (D4), etc., reflecting relatively better use of heat resource for rice production in these four zones; medium ET area (40-50%) mainly located in lower plain late triple-cropping area in South China (D5), D6, and D7; low ET area (<40%) mainly located in SCRA, like as lower plateau area at the border of S2, S3, S4, S5, S6 and S7. Within the whole study area, the highest ET occurred in the middle and lower reaches of D1, and the lowest ET rate occurred in S4. ET during rice growing season in southern rice pro-

Table 1 Percentage of stations with increasing/decreasing trend of available heat resource (∑T heat resource effectiveness rate (ET) (%) ∑T Subzone S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7

1951-1980 60 64 91 86 64 75 62 27 38 40 43 53 30 57 11 20

+ 40 36 9 14 36 25 38 73 63 60 57 47 70 43 89 80

2263

10°C

), effective heat resource (GDD10-35°C) and ET

GDD10-35°C

10°C

1981-2010 --9 -9 ----------7

+ 100 100 91 100 91 100 100 100 100 100 100 100 100 100 100 93

1951-1980 80 58 63 100 81 75 75 91 100 93 90 95 75 73 22 20

+ 20 42 36 -18 25 25 9 -7 9 6 25 27 77 81

1981-2010 -8 18 -9 -------5 3 -7

+ 100 91 81 100 91 100 99 100 100 100 100 100 95 97 100 93

1951-1980 80 58 45 57 91 25 50 73 100 97 95 82 89 44 73 33

+ 20 42 55 43 9 75 50 27 -3 5 18 11 56 27 67

1981-2010 60 75 82 100 64 50 67 100 100 63 90 71 100 56 60 7

+ 40 25 18 -36 50 33 --37 10 29 -44 40 93

+ stood for increasing trend; - stood for decreasing trend; -- meant no station showed a corresponding trend. The same as below.

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YE Qing et al.

2264 Tendency of ∑T ≥ 10°C (°C d/decade)

∑T ≥ 10°C (°C d) <4 000 4 000 to 5 000

5 000 to 6 000

6 000 to 7 500

>7 500

<0

0 to 100

100 to 300

>300

B

1951-1980

A

South China Sea Islands

South China Sea Islands

D

1981-2010

C

South China Sea Islands

South China Sea Islands

10 000 ∑ T ≥10 °C (°C d)

9 000

E

F

8 000 7 000 6 000 5 000 4 000 3 000 2 000

S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7 S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7

Fig. 1 The temporal and spatial characteristics of available heat resource (∑T 10°C) during 1951-1980 and 1981-2010 in 16 subzones in southern rice production area of China. A and C, the distribution of 80% guarantee rate values of ∑T 10℃ during 1951-1980 and 1981-2010, respectively. B and D, the climatic trend rate of ∑T 10°C during 1951-1980 and 1981-2010, respectively. E and F, spatial variation of ∑T 10℃ in 16 subzones during 1951-1980 and 1981-2010, respectively. Box-boundaries are the 25th and 75th percentiles, and whiskers are the 5th and 95th percentile. The same as below.

duction area of China showed a decreasing trend in the two study periods (Fig. 3-E and F, Table 1). During 1951-1980, about 65% stations in SCRA showed a decreasing trend, and 78% stations in DCRA showed a decreasing trend (Fig. 3-B). During 1981-2010, about 77% stations in SCRA showed a decreasing trend, and 66% stations in DCRA showed a decreasing trend (Fig. 3-D). Table 1 showed the change trend of ET during the two study periods in each subzone. During 19511980, S3, S6, D5 and D7 showed an increasing trend, S7 failed to show a clear trend because half and half

stations were showing increasing trend and decreasing trend, the other 11 subzones all showed a decreasing trend in different degrees. During 1981-2010, D7 showed an increasing trend, S6 showed no clear trend due to half and half stations showing increasing trend and decreasing trend, and the other 14 subzones showed a decreasing trend in different degrees. As to the percentage of stations with increasing trend or decreasing trend, plateau mountain area at the border of S5, D1 and D7 showed a significant increasing trend of ET during 1981-2010, S1, S8, D2, S3, S4, S9, S6, S7, D4 and D5 showed a significant decreasing trend,

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Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern GDD 10-35°C (°C d)

<2 000

2 000 to 3 000

3 000 to 4 000

2265

Tendency of GDD 10-35°C (°C d/decade)

>4 000

<-90

-90 to -30

-30 to 0

0 to 30

30 to 90

>90

B

1951-1980

A

South China Sea Islands

South China Sea Islands

D

1981-2010

C

GDD 10-35°C (°C d)

South China Sea Islands

4 500 4 000 3 500 3 000 2 500 2 000 1 500 1 000 500 0

E

South China Sea Islands

F

S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7 S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7

Fig. 2 The temporal and spatial characteristics of effective heat resource (GDD10-35°C) during 1951-1980 and 1981-2010 in 16 subzones in southern rice production area of China. A and C, the distribution of 80% guarantee rate values of GDD10-35°C during 1951-1980 and 19812010, respectively. B and D, the climatic trend rate of GDD10-35°C during 1951-1980 and 1981-2010, respectively. E and F, the spatial variation of GDD10-35°C in 16 subzones during 1951-1980 and 1981-2010, respectively.

yet D3 and D6 failed to show a noticeable trend. According to the above analyses on mean ET during the two study periods in SCRA as well as DCRA, there was only slight impact of climate change on ET in southern rice area of China. Afterwards, we did a deeper analysis on the climatic trend rates of ET and ∑T 10°C as well as the ratio of GDD10-35°C and ∑T 10°C in the 16 subzones, and found that both of them showed a decreasing trend during 1951-1980, and the mean ET in SCRA and DCRA was 0.37 and 0.42, respectively. During 1981-2010, both GDD 10-35°C and ∑ T 10°C showed an increasing trend, and E T in SCRA and

DCRA was 0.33 and 0.49, respectively. ET changed slightly during the two study periods mainly because of the simultaneous decreasing trend or increasing trend for both GDD10-35°C and ∑T 10°C (Table 2). In addition, we analyzed the accumulated heat resource above 35°C (upper temperature limit for rice growth and development) and found that increasing of extreme high temperature days led to the increasing of accumulated heat resource above 35°C but with small amplitude, and the mean value during 1951-2010 in SCRA and DCRA was 14.3 and 8.3°C d/decade, respectively, yet the effect on ET was negligible.

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YE Qing et al.

2266

In all, it was seen that: (1) During the two study periods, SCRA showed less ∑T 10°C (Fig. 1-A and C), GDD10-35°C (Fig. 2-A and C), and ET (Fig. 3-A and C) than DCRA; (2) during 1951-1980, both of SCRA and DCRA in southern rice production area of China showed a decreasing trend of ∑T 10°C and GDD10-35°C; during 1981-2010, both of SCRA and DCRA in southern rice production area of China showed an increasing trend of ∑T 10°C and GDD10-35°C; (3) ET in southern rice production area of China generally showed a decreasing trend in the two study periods, and SCRA showed a more significant decreasing trend than DCRA

during 1951-1980, vice versa during 1981-2010; (4) in SCRA, the least ET was in S4, yet the local ∑T 10°C (Fig. 1-A, C, E and F) was relatively rich (4 688°C d). In DCRA, ET was relatively less in D5, D6 and D7, yet these three subzones were relatively rich in ∑T 10°C (Fig. 1-A, C, E and F) of 8 290.7, 6 000, and 7 540°C d, respectively. Hence, it was suggested to take appropriate measurements to improve GDD10-35°C in order to improve zonal ET, like as changing planting date to extend crop growing season, replacing middle or early maturity varieties with late maturity variety, and adopting multiple-cropping system etc.; (5) during

Thermal utilization (%) <40

40 to 50

Tendency of thermal utilization (%/decade) >50

<-0.5

A

-0.5 to 0

>0

1951-1980

B

South China Sea Islands

South China Sea Islands

D

1981-2010

C

South China Sea Islands

Thermal utilization (%)

64 60

E

South China Sea Islands

F

56 52 48 44 40 36 32

S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7 S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7

Fig. 3 The temporal and spatial characteristics of heat resource effectiveness rate (ET) during 1951-1980 and 1981-2010 in 16 subzones in southern rice production area of China. A and C, the distribution of 80% guarantee rate values of ET during 1951-1980 and 1981-2010 respectively. B and D, the climatic trend rate of ET during 1951-1980 and 1981-2010, respectively. E and F, the spatial variation of ET in 16 subzones during 1951-1980 and 1981-2010, respectively.

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Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern

2267

Table 2 Climatic trend rate of effective heat resource and available heat resource during 1951-2010 in southern rice production area of China Subzones S1 S2 S3 S4 S5 S6 S7 S8 S9 Average D1 D2 D3 D4 D5 D6 D7 Average

TRGDD -22.90 -7.06 -10.22 -29.88 -21.10 -8.63 -26.72 -6.73 -29.10

1951-1980 TR∑T -31.88 12.12 -3.46 -7.90 -22.44 -78.61 -32.33 1.77 17.70

-31.44 -30.13 -22.00 -11.69 -14.95 18.44 33.93

-3.95 5.43 -7.35 23.41 73.84 -6.67 34.09

TRGDD /TR∑T 0.72 -0.58 2.95 3.78 0.94 0.11 0.83 -3.79 -1.64 0.37 7.95 -5.55 2.99 -0.50 -0.20 -2.77 1.00 0.42

TRGDD 71.13 33.06 20.42 46.80 45.47 71.78 61.31 63.97 48.73

1981-2010 TR∑T 172.59 185.27 175.33 142.56 82.64 231.54 124.91 196.59 169.92

117.71 86.39 99.65 61.27 61.36 94.51 65.11

200.92 186.24 218.98 166.43 187.81 168.69 100.82

TRGDD /TR∑T 0.41 0.18 0.12 0.33 0.55 0.31 0.49 0.33 0.29 0.33 0.59 0.46 0.46 0.37 0.33 0.56 0.65 0.49

TRGDD is the climatic trend rate of effective heat resource (oC d/decade); TR∑T is the climatic trend rate of available heat resource (oC d/decade).

1951-1980, increasing of ET was correlated with the increasing of GDD10-35°C in some subzones, such as S3, S6, D5 and D7. During 1981-2010, GDD10-35°C in each subzone showed a significant increasing trend, with an exception of D7, the other 15 zones showed a decreasing trend in ET because increasing amplitude of ∑T 10°C was greater than that of GDD10-35°C during rice growing season; (6) ∑T 10°C of 3 700 and 4 900°C d was required for single cropping rice system and double cropping rice system from sowing to maturity (Han and Qu 1991), GDD10-35°C of 1 300 and 2 900°C d was required for single cropping rice system and double cropping rice system from sowing to maturity period. In SCRA, S2, S6, S6, S7 were rich in GDD10-35°C, if the single cropping rice system were replaced by double cropping rice system, ET during 1951-1980 would increase from 38.7 to 54.5%, 44.3 to 59%, and 37.2 to 54%, respectively; and ET during 1981-2010 would increase from 38.7 to 56.3%, 44 to 59%, and 37 to 55%, respectively.

Analyses of water resource effectiveness In order to evaluate precipitation resource effectiveness in the study area, we calculated effective precipitation (Pe), crop water demand (ETc) and effective precipitation satisfaction index (EPSI) during rice growing period, and plotted 80% guarantee rate values

and climatic trend rate of the above indices with ArcGIS 10.0 (Figs. 4-6).

Spatiotemporal characteristics of effective precipitation during rice growing season Fig. 4 showed the distribution of 80% guarantee rate values (Fig. 4-A and C), climatic trend rate (Fig. 4-B and D) and spatial variation of Pe (Fig. 4-E and F) during rice growing season in each subzone in southern rice production area of China in the two study periods. In southern rice production area of China, Pe during rice growing season showed a decreasing trend from southeast to northwest (Fig. 4-A and C), and Pe in SCRA (221.3 mm) was significantly less than that in DCRA (314.5 mm on average). During the two study periods, average Pe in each subzone had no significant difference (Fig. 4-E and F). In the whole study area, area of effective precipitation >300 mm during rice growing season reduced towards the south, and mainly located in D3, D6 and D7. Effective precipitation during rice growing season Pe was generally decreasing in southern rice production area of China. During 1951-1980, Pe during rice growing season in southern rice production area of China generally showed an increasing trend (Fig. 4-B), and DCRA showed a greater increasing amplitude (4.9 mm/decade) than

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YE Qing et al.

2268 Effective precipitation (mm) <200

200 to 300

300 to 400

Tendency of effective precipitation (mm/decade) >400

A

<-10

-10 to 0

0 to 10

>10

1951-1980

B

South China Sea Islands

South China Sea Islands

D

1981-2010

C

Effective precipitation (mm)

South China Sea Islands

500 450 400 350 300 250 200 150 100 50 0

E

South China Sea Islands

F

S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7 S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7

Fig. 4 The temporal and spatial characteristics of effective precipitation (Pe) during 1951-1980 and 1981-2010 in 16 subzones in southern rice production area of China. A and C, the distribution of 80% guarantee rate values of Pe during 1951-1980 and 1981-2010, respectively. B and D, the climatic trend rate of Pe during 1951-1980 and 1981-2010, respectively. E and F, the spatial variation of Pe in 16 subzones during 1951-1980 and 1981-2010, respectively.

SCRA (0.8 mm/decade). In SCRA, more than 71% stations showed an increasing trend in S2, S3, S4 and S9, yet more than 54% stations showed a decreasing trend in the other 5 subzones (Table 4). Subzones with increasing Pe in SCRA was potentially possible to cultivate double cropping rice. During 1981-2010, Pe during rice growing season in southern rice production area of China generally showed a decreasing trend, and DCRA showed a greater decreasing amplitude (6.8 mm/decade) than SCRA (5.1 mm/decade). In SCRA, about 68% stations showed a decreasing trend, 7 subzones showed a decreasing trend, and more than 90% stations showed a

decreasing trend in S6, S7, S1 and S5. However, more than 58% stations showed an increasing trend in S2 and S4 (Table 4). In DCRA, about 70% stations showed a decreasing trend of effective precipitation during rice growing season.

Distribution characteristics of ETc during rice growing season in southern rice production area of China In Fig. 5, it was shown the distribution of 80% guarantee rate values (Fig. 5-A and C) and climatic trend rate

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Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern

(Fig. 5-B and D) of ETc during rice growing season in period 1951-1980 and 1981-2010 in southern rice production area of China, as well as spatial variation of Pe in each subzone (Fig. 5-E and F). ETc during rice growing season in southern rice production area of China showed a decreasing trend from southeast to northwest (Fig. 5-A and C). During 1951-1980, ETc during rice growing season in DCRA was 919 mm on average, which was 313 mm more than that in SCRA (606 mm). During 1981-2010, ETc during rice growing season in DCRA was 922 mm on average, which was 333 mm more than that in SCRA

(589 mm). In the study area, the largest ETc during rice growing season occurred in D7, with 1 015 mm on average, and the least ETc during rice growing season occurred in S6, with 524 mm on average (Fig. 5-E and F). Spatial variation difference of ETc in each subzone was less significant in SCRA than in DCRA. In the whole study area, spatial variation difference of ETc during rice growing season was more significant during period 1951-1980 than period 1981-2010. From the perspective of temporal change trend, during 1951-1980, ET c during rice growing season in SCRA generally showed an increasing trend

Rice ET c (mm) <500

500 to 700

700 to 1 000

2269

Tendency of rice ET c (mm/decade) >1 000

A

<-20

-20 to 0

0 to 20

>20

1951-1980

B

South China Sea Islands

South China Sea Islands

D

1981-2010

C

Rice ET c (mm)

South China Sea Islands

1 200 1 100 1 000 900 800 700 600 500 400 300 0

E

South China Sea Islands

F

S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7 S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7

Fig. 5 The temporal and spatial characteristics of ETc during 1951-1980 and 1981-2010 in 16 subzones in southern rice production area of China. A and C, the distribution of 80% guarantee rate values of ETc during 1951-1980 and 1981-2010, respectively. B and D, the climatic trend rate of ETc during 1951-1980 and 1981-2010, respectively. E and F, the spatial variation of ETc in 16 subzones during 1951-1980 and 1981-2010, respectively.

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YE Qing et al.

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(Fig. 5-B), about 54% stations showed an increasing trend (Table 4), however, ETc during rice growing season in DCRA showed a decreasing trend, around 63% stations showed a decreasing trend. During 1981-2010, ETc during rice growing season showed an increasing trend both in SCRA and DCRA (Fig. 5-D),

and about 58 and 69% stations showed an increasing trend, respectively. Other than that, we analyzed the climatic trend rate of each climate element for typical stations with the climatic trend rate of ETc above 20 mm/decade during the entire rice growing season in 1981-2010 in the

Table 3 Changing trend and significance level of climate elements for typical stations during 1981-2010 Subzones S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7

N 4 0 0 1 0 1 0 3 0 12 5 12 1 7 2 4

Tmax (°C/decade) 0.60 (4) --0.38 (1) -0.66 (1) -0.49 (3) -0.66 (12) 0.43 (5) 0.48 (10) 0.14 (0) 0.34 (6) 0.34 (2) 0.15 (1)

Tmin (°C/decade) 0.22 (2) --0.53 (1) -0.58 (1) -0.53 (3) -0.54 (11) 0.48 (5) 0.41 (12) 0.16 (0) 0.36 (7) 0.60 (2) 0.22 (3)

Tmean (°C/decade) 0.33 (3) --0.41 (1) -0.68 (1) -0.50 (3) -0.57 (12) 0.44 (5) 0.43 (12) 0.17 (0) 0.32 (6) 0.44 (2) 0.18 (1)

U (m s-1/decade) 0.26 (4) ---0.02 (0) -0.18 (0) --0.08 (1) --0.10 (8) -0.04 (2) -0.05 (6) 0.28 (1) -0.19 (5) -0.001 (0) 0.14 (2)

P (mm/decade) -55.07 (1) --32.61 (0) --62.22 (0) -2.19 (0) --45.35 (1) 26.15 (0) 25.87 (0) -46.76 (0) -19.38 (1) -27.69 (0) 37.15 (0)

R (h/decade) 65.86 (3) --72.25 (1) --32.55 (0) -6.22 (0) -13.39 (2) 36.40 (1) 20.65 (2) 39.00 (0) 56.89 (3) 105.55 (2) 55.36 (1)

RH (%/decade) -1.22 (1) ---1.39 (1) --3.31 (1) --2.57 (3) --3.02 (12) -3.02 (5) -2.15 (12) -1.46 (1) -2.33 (7) -2.17 (2) -1.01 (3)

N, the number of typical stations in each subzone, Tmax, maximum temperature during the entire rice growing season, Tmin, minimum temperature during the entire rice growing season, Tmean, mean temperature during the entire rice growing season, U, mean wind speed during the entire rice growing season, P, the total precipitation during the entire rice growing season, R, the total hours of sunshine duration during the entire rice growing season, RH, mean relative humidity during the entire rice growing season, and the number in parentheses stood for the number of stations passing α=0.05 significance test.

research area (Table 3). In Table 3, for those stations with significant increasing ET c , 87% stations showed significant increasing trend in maximum temperature, 90% stations showed significant increasing trend in minimum temperature, 88% stations showed significant increasing trend in mean temperature, and 92% stations showed significant decreasing trend in mean relative humidity. Therefore, we concluded that temperature increasing and relative humidity decreasing were the main factors causing ET c increasing.

Distribution characteristics of precipitation satisfaction degree during rice growing period in southern rice production area of China Fig. 6 showed the distribution of 80% guarantee rate values (Fig. 6-A and C) and climatic trend rate (Fig. 6-B and D) of EPSI during rice growing season

during period 1951-1980 and 1981-2010 in southern rice production area of China, as well as spatial variation of EPSI during rice growing season in each subzone (Fig. 6-E and F). It was found that EPSI during rice growing season in DCRA was less than in SCRA (Fig. 6-A and C). During 1951-1980, EPSI during rice growing season in DCRA was 32.3% on average, which was 3.5% lower than that in SCRA (35.8%). During 1981-2010, EPSI during rice growing season in DCRA was 32.1% on average, which was 3.5% lower than that in SCRA (35.6%). It was shown that EPSI during rice growing season in DCRA was generally lower than that in SCRA. In the study area, the highest EPSI during rice growing season occurred in S1, with 51.7% on average during the whole study period, and the lowest EPSI during rice growing season occurred in S9 (Fig. 6-E and F). The lower the EPSI, the less precipitation effectiveness, yet the more dependence of rice production on irrigation source. Therefore, SCRA had less ir-

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Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern EPSI (%) <20%

20 to 30%

30 to 40%

2271

Tendency of rice EPSI (%/decade) 40 to 55%

<-3

>55%

A

-3 to 0

0 to 3

>3

1951-1980

B

South China Sea Islands

South China Sea Islands

D

1981-2010

C

EPSI (%)

South China Sea Islands

100 90 80 70 60 50 40 30 20 10 0

E

South China Sea Islands

F

S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7 S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7

Fig. 6 The temporal and spatial characteristics of EPSI during 1951-1980 and 1981-2010 in 16 subzones in southern rice production area of China. A and C, the distribution of 80% guarantee rate values of EPSI during 1951-1980 and 1981-2010, respectively. B and D, the climatic trend rate of EPSI during 1951-1980 and 1981-2010, respectively. E and F, the spatial variation of EPSI in 16 subzones during 1951-1980 and 1981-2010, respectively.

rigation demand than DCRA. In the study area, spatial variation of EPSI during rice growing season showed little change during the two periods. Nevertheless, subzonal variation of EPSI during rice growing season in DCRA was less than that in SCRA. In southern rice production area of China, during 1951-1980, EPSI during rice growing season in SCRA generally showed a decreasing trend of 2.4%/decade, about 57% stations showed a decreasing trend, while EPSI during rice growing season in DCRA generally showed an increasing trend of 2.1%/decade, about 58% stations showed an increasing trend. During

1981-2010, EPSI during rice growing season showed a decreasing trend both in SCRA and DCRA, with the same amplitude of 1.8%/decade, about 69 and 76% stations showed a decreasing trend, respectively (Table 4). In all, analyses on precipitation effectiveness indices during rice growing season showed that: (1) In the study area, effective precipitation during rice growing season in DCRA was 93 mm more than that in SCRA on average, but ET c during rice growing season in DCRA was 322 mm more than that in SCRA on average, which meant DCRA had 229 mm more irrigation demand than SCRA; (2) EPSI in DCRA was

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YE Qing et al.

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Table 4 Percentage of stations with increasing/decreasing trend of effective precipitation (Pe), crop water demand (ETc) and effective precipitation effectiveness index (EPSI) (%) ETc

Pe Subzone S1 S2 S3 S4 S5 S6 S7 S8 S9 D1 D2 D3 D4 D5 D6 D7

1951-1980 80 27 -29 58 100 50 73 14 55 38 50 10 17 44 58

+ 20 72 100 71 41 -50 27 86 45 62 50 90 83 55 42

1981-2010 90 36 55 29 91 100 100 54 57 96 81 70 45 73 89 35

+ 10 64 45 71 8 --45 43 4 19 30 55 26 11 65

EPSI

1951-1980 30 45 54 43 50 17 14 73 86 85 67 45 80 40 55 65

1981-2010

+ 70 54 45 57 50 83 85 27 14 15 33 55 20 60 45 35

-91 75 29 41 -36 18 85 22 34 15 45 46 22 36

lower than that in SCRA; (3) Pe during period 19511980 showed an increasing trend both in SCRA and DCRA. However, Pe during period 1981-2010 showed a decreasing trend both in SCRA and DCRA, yet DCRA had a more decreasing amplitude than SCRA; (4) in the whole study area, EPSI showed a decreasing trend in the two study periods.

+ 100 9 25 71 58 100 64 82 14 77 67 85 55 53 78 65

80 36 27 71 83 100 57 55 14 44 38 56 15 37 67 57

1981-2010 100 36 43 57 92 100 100 73 43 89 81 89 40 84 89 57

+ 20 63 73 29 17 -43 45 86 55 62 44 85 64 33 43

+ -64 57 43 8 --27 57 11 19 11 60 17 11 43

1 400

A

1 200 1 000 800 600

D

400 ET c (mm)

Correlation between effective heat resource and precipitation effectiveness during rice growing season

1951-1980

S

200

ETc=0.1911GDD10-35°C+244.75 R2=0.7953

P<0.001

1 400

B

1 200 1 000 800

Some researches showed a positive correlation between growing degree days (GDD) and rice yield (Sarma et al. 2008). The fact that climate warming had caused rice yield increasing due to the increase of GDD during crop growing season was based on the condition of greater consumption than evapotranspiration (Narongrit and Chankao 2009). In southern rice production area of China, GDD increased significantly during 1981-2010 (Fig. 2-D), more consumption water was needed to keep rice yield increasing. We found a good linear relationship between GDD 10-35°C and ETc during rice growing season (Fig. 7), and this also reflected the correlation between GDD10-35°C and reference crop evapotranspiration (Solantie 2004). The more the GDD10-35°C, the more ETc, and ETc during rice growing season increased by 18 mm with an 100°C d increase of GDD10-35°C on average. Therefore, if single

600 D

400 S

200

ETc=0.1877GDD10-35°C+266.51 R2=0.73

0

P<0.001

1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 5 000 GDD10-35°C (°C d)

Fig. 7 Linear relation between effective heat resource (GDD10-35°C) and ETc during rice growing season in single cropping rice area (S) and double cropping rice area (D) during 1951-1980 (A) and 19812010 (B).

cropping rice was replaced by double cropping rice, during 1951-1980, GDD10-35°C during rice growing season in S2, S6 and S7 would increase from 2 058, 2 260, and 2 047 to 2 900°C d, respectively, and ETc during rice growing season would increase by 152 mm, 115

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Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern

2273

usage of rice. Meanwhile, according to the previous planting system subdivision (Liu and Han 1987), this study divided the whole study area into two first-class areas, SCRA and DCRA, and then 16 subzones, taking fully into account the effects of different cropping system and topography on hydrothermal resource distribution. In the analyses of heat resource effectiveness, according to the concept of thermal time, single sine method (Zalom et al. 1983) was adopted to calculate daily GDD10-35°C, accumulating heat resource which was above lower crop temperature limit and below upper crop temperature limit, and the results showed a better reflection of crop effective heat demand than previous study (Wang 1982; Yu et al. 1991; Liu 1993; Miao et al. 2009; Jiang 2011). Some scholars had pointed out that, the index assimilation intensity measuring dry matter yield was proportional to accumulated effective temperature during daytime, therefore diurnal effective temperature accumulation should be used to study GDD10-35°C during crop growing period (Solantie 2004), like as temperature increase during nighttime could cause rice yield reducing (Peng et al. 2004). However, some scholars hold the opposite opinion, like as temperature increase during nighttime could increase crop yield of maize and rice (Lobell 2007), and temperature increase during nighttime resulted in more effective temperature accumulation and then accelerated leaf emergence of main stem during rice seedling stage (Wei and Pan 2009). The relation between daily temperature and crop growth development was the principle issue influencing heat resource effectiveness during crop growing period, we used accumulated GDD10-35°C which was above lower crop temperature limit and below upper crop temperature limit to study heat resource effectiveness during rice growing season, taking into account of the effect of thermal time on crop growth and development, yet failing to consid-

mm, and 154 mm, respectively. During 1981-2010, if single cropping rice system was replaced by double cropping rice system, ETc during rice growing season in S2, S6, and S7 would increase by 152, 116, and 159 mm, respectively. Lack of precipitation was the main reason limiting rice cropping system change. Under the background of climate warming, GDD10-35°C and ETc during rice growing season increased. How would this change influence regional precipitation effectiveness? In order to answer this scientific question, we did regression analyses between GDD10-35°C and Pe (Table 5), the resulted regression model was as equations 1 and 2. I (1951-1980): EPSI=23.328+0.11Pe-0.008GDD10-35°C (1) (adjusted R2=0.808, P<0.0001) II (1981-2010): EPSI=24.908+0.106Pe-0.007GDD10-35°C (2) (adjusted R2=0.776, P<0.0001) GDD 10-35°C together with effective precipitation could well simulate precipitation effectiveness; GDD10-35°C had a negative effect on precipitation effectiveness, while Pe had a positive effect on precipitation effectiveness. During years with relatively more constant precipitation, precipitation effectiveness decreased by 1% with a 125°C d increase of GDD10-35°C. Therefore, under the background of climate warming, improving water usage efficiency would be the principal approach to make rice production benefit from the increase of GDD10-35°C.

DISCUSSION This study was based on the averaged rice growing period to calculate heat resource effectiveness and precipitation effectiveness during the two study periods, and eliminated the contribution of agricultural technology development on improving climatic resource

Table 5 Correlation coefficients and collinearity diagnostics of regression models Regression model I

II

Constant GDD10-35°C Pe Constant GDD10-35°C Pe

Unstandardized coefficient B Std. error 23.328 1.071 -0.008 0.000 0.110 0.004 24.908 1.052 -0.007 0.000 0.106 0.004

Standardized coefficient Beta --0.784 0.955 --0.839 0.970

t

Significance

21.779 -23.291 28.354 23.683 -22.268 25.734

0.000 0.000 0.000 0.000 0.000 0.000

Zero-order --0.327 0.579 --0.323 0.523

Correlation Partial --0.845 0.887 --0.833 0.867

Part --0.689 0.838 --0.710 0.821

Collinearity diagnostics Tolerance VIF --0.771 1.298 0.771 1.298 --0.717 1.395 0.717 1.395

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2274

er crop effective temperature demand during different rice growing stages. In addition, shallow-water rice dominated in southern rice production area of China, nocturnal field water temperature also played a significant role in crop growth (Thakur et al. 2010). Therefore, further study with consideration of filed water temperature effect will get a better illustration of heat resource effectiveness during rice growing season. Varieties with higher water usage efficiency usually had lower water crop demand and turned out to be higher precipitation effectiveness. Besides, analyses of precipitation effectiveness in this study were only from the perspective of precipitation resource availability. Lastly, crop hydrothermal resource demand was dependent on different crops and different cropping systems (Han and Qu 1991), and the corresponding effectiveness would vary, however, only two firstclass divisions (SCRA and DCRA) in southern rice production area of China failed to clearly illustrate the actual complex hydrothermal resource situation. Hence, further study on crop heat resource effectiveness should focus on combination with diurnal and nocturnal temperature, as well as varying effect of GDD10-35°C on crop growth and development during different growing stages, and further study on crop precipitation effectiveness should involve precipitation usage efficiency.

CONCLUSION Based on the climate data from 254 meteorological stations and phonological data from 168 agricultural experiment stations in southern rice production area of China, we analyzed spatiotemporal characteristics of indices including ∑T 10°C, GDD10-35°C, ET, Pe, ETc and EPSI during rice growing period, and studied hydrothermal resource effectiveness during rice growing season according to the relative definitions from predecessors. Then we analyzed distribution of available hydrothermal resource during rice growing season and percentage of effective resource amount to available resource amount to evaluate the resource effectiveness during rice growing season both in SCRA and DCRA. The results showed that in the study area, ∑ T 10°C, GDD10-35°C and ET in DCRA was higher than in SCRA. During 1951-1980, both of SCRA and DCRA showed

YE Qing et al.

a decreasing trend of ∑T 10°C and GDD10-35°C, while an increasing trend during 1981-2010. However, in the whole study area, ET showed a decreasing trend in the two study periods, yet SCRA showed a more significant decreasing trend than DCRA during 19511980, and vice versa during 1981-2010. In southern rice production area of China, subzones with rich ∑T 10°C turned out to have a low ET. Therefore, agricultural technology and agronomic practices should be adopted to improve local E T, such as replacing early maturity varieties with middle or late maturity varieties to extend rice growing season, greenhouse seedling or reforming single cropping rice to double cropping rice to increase multiple crop index and so on. On average, DCRA had 93 mm more effective precipitation during rice growing season than SCRA, but 322 mm more ETc than SCRA resulting 229 mm more irrigation water demand during rice growing season, therefore EPSI in DCRA was lower than in SCRA. During 1951-1980, both of SCRA and DCRA showed an increasing trend in effective precipitation while a decreasing trend during 19812010, yet DCRA showed greater decreasing amplitude than SCRA. EPSI in southern rice production area of China showed a decreasing trend during the two study periods, and we found that temperature increasing and relative humidity decreasing were the main reasons causing ETc increasing. Climate warming increased non-physiological water consumption (e.g., water surface evaporation) and extended crop growing season, leading to more ETc. There was a significant linear relation between GDD 10-35°C and ETc during rice growing season, and ETc would increase by 18 mm with each additional 100°C d of GDD10-35°C. Precipitation effectiveness had a negative correlation with GDD10-35°C yet a positive correlation with effective precipitation, within those years with constant precipitation during rice growing season, precipitation effectiveness reduced by 1% with each additional 125°C d of GDD10-35°C. From the angle of heat resource effectiveness, improving multiple-cropping index could increase accumulation of GDD10-35°C, and then improved heat resource effectiveness. However, from the angle of water effectiveness, improving multiple-cropping index would necessarily increase ET c , without changing the current agronomic practices or crop growth environ-

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Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern

change (Qin et al. 2007; Yang et al. 2011), and 1951-1980 was the fledging period of China agricultural technique development (Deng 2001), the impact of climate change

99°E 102°E 105°E 108°E 111°E 114°E 117°E 120°E 123°E

A

15°N 18°N 21°N 24°N 27°N 30°N 33°N

ment, precipitation effectiveness during rice growing season would decrease. In other words, short supply of effective precipitation for ETc would aggravate. In all, under the background of climate warming and GDD10-35°C increasing, improving output rate of per unit water and increasing precipitation interception in the field was principal for improving precipitation effectiveness.

MATERIALS AND METHODS Study region description This study chose multiple-cropping area in the south of China, covering 99-123°E, 18-34°N. The study area included 16 subzones, which was 9 single cropping rice zones (S1, S2, S3, S4, S5, S6, S7, S8 and S9) and 7 double cropping rice zones (D1, D2, D3, D4, D5, D6 and D7). Table 6 showed the geographic location and average altitude of each zone, and Fig. 8 showed the distribution of meteorological stations and agricultural sites in the research area.

*

Demarcation line Weather station Single rice station

Yel loBeijing w

River

N

g Ya n

Double rice station

B

Study region

Data

S7

-140 m

S8

S9

D1

S2 D2

S3

S4 D5

South China Sea Islands

0 500 1,000 2,000 km

S1 S6

D3

D4 D6

D7

D7

N 0

z iR iv er

Elevation

7 148 m

S5

Climate data was from 254 climate stations of the National Meteorological Networks of China Meteorological Administration, and the distribution of climate stations was shown in Fig. 8. The rice phonological data (sowing, booting, flowering and maturity dates) was from local Agrometeorological Experimental Stations, including observation data during 1981-2010. The digital elevation model (DEM) data set was provided by International Scientific & Technical Data Mirror Site of Computer Network Information Center in Chinese Academy of Sciences, Beijng, China. The period of the 1980s was the turning point of climate

2275

250 500

Si: The single-rice zone South China 1 000 Di: The double-rice zone Sea Islands km Rice-cultivation boundary

Fig. 8 Distribution of meteorological stations and agricultural sites in the study area. A, geographic location of the study area and distribution of meteorological stations and agricultural sites. B, subdivision zones in single cropping rice area (SCRA) and double cropping rice area (DCRA).

Table 6 Overview of each subzone in the study area1) Subzones S1 S2 S3 S4 S5 S6 S7 S8 S9 1)

Single cropping rice area (SCRA) Latitude range Longitude range 30°45´-34°32´ 103°27´-111°34´ 25°19´-31°28´ 107°41´-111°39´ 24°56´-29°10´ 104°50´-109°4´ 23°21´-26°41´ 98°35´-105°10´ 25°21´-32°15´ 98°35´-105°10´ 29°12´-32°20´ 102°49´-105°16´ 27°40´-32°42´ 103°33´-109°45´ 31°7´-34°6´ 116°47´-121°51´ 30°26´-32°42´ 109°19´-122°27´

Average elevation (m) 1 292.0 782.8 1 063.0 1 956.8 2 325.1 805.1 566.0 183.2 16.7

Subzones D1 D2 D3 D4 D5 D6 D7

Double cropping rice area (DCRA) Latitude range Longitude range Average elevation (m) 26°16´-31°35´ 109°19´-122°27´ 220.3 24°59´-30°5´ 110°52´-117°46´ 181.6 24°27´-29°45´ 115°45´-121°55´ 482.2 23°11´-26°47´ 104°31´-116°35´ 477.2 21°33´-24°44´ 105°33´-119°5´ 1 437.9 22°28´-25°51´ 98°7´-107°21´ 217.5 18°11´-25°19´ 97°33´-111°24´ 711.2

Referencing to the subdivision method from Liu and Han (1987), according to the relative consistency of natural condition and societal economic condition, the relative consistency of crop varieties, crop structure, and cropping system, basically keeping the integrity of county-level administrative division. Firstly, according to heat index to subdivide cropping system patterns, secondly, according to water condition, geomorphic feature, crop pattern to subdivide planting system patterns.

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YE Qing et al.

2276

the better heat resource effectiveness and the better usage of heat resource.

on agriculture played an important role. During the most recent three decades, agricultural technology in China has been in a rapid developing stage (Lu and Sun 2004). According to the world meteorological organization (WMO) period requirement for calculating climate (WMO 1989), we divided the period 1951-2010 into two periods (19511980 as period I, 1981-2010 as period II), and studied the effectiveness change of climatic resources during these two three-decade periods.

ET (%) =

GDD10 − 35 ° C ∑T≥10°C

×100 %

(3)

Where ET is heat resource effectiveness rate (%); GDD10-35°C is growing degree days between 10°C and 35°C during rice growing period (°C d); ∑T 10°C is available heat resource, calculated by 10°C accumulated temperature (°C d). The computation of GDD10-35°C was referred to the degreeday calculation method (Zalom et al. 1983) and single sine method (Ye et al. 2011).

Determination of rice growing period and crop coefficients

Precipitation effectiveness index

This study used averaged rice growing period during 1981-2010 from all agricultural experiment sites in each subzone (Fig. 8) as zonal rice growing period (Table 7), we subdivided rice growing period into three development stages according to the rules of rice growth and development (Allen et al. 1998; Li et al. 2011): early stage (from sowing to booting), middle stage (from booting to flowering), and late stage (from flowering to maturity). Crop coefficients in each growing stage were determined referring to FAO recommended Kc values (Allen et al. 1998) (Table 8).

According to Dastane’s (1978) definition of precipitation effectiveness, we adopted the ratio of effective precipitation and ETc during rice growing season as EPSI to evaluate precipitation effectiveness for crop growth and development during rice growing, seen details in eq. (4). n

EPSI (%) =

∑ Pei i=1 n

∑ ETci

×100

(i=1, 2, 3, …, n)

(4)

i=1

Heat resource effectiveness index

Where EPSI is effective precipitation satisfaction index (%); Pei is effective precipitation (mm d-1) in the ith d; ETci is crop water demand (mm d-1) in the ith d; n is the number of growing stage days. Precipitation satisfaction degree illustrates degree of coupling between Pe and ETc, the greater EPSI during crop growing period, the greater precipitation effectiveness. As to the computation of P e we directly adopted the method recommended by United States Department of Agriculture Soil Conservation Service (USDASCS). When daily total precipitation is more than 8.3 mm, the Pe equals to the amount of saturated soil moisture content plus the field capacity, when daily total precipitation is less than 8.3 mm, the effective precipitation equals to the amount of total precipitation subtract the percolation amount (eq. 5).

On the basis of predecessors’ research, this study defined accumulated temperature which was below upper temperature limit of 35°C and above lower temperature limit of 10°C during rice growing period (Yoshida 1978, 1981; Gao and Li 1992; Shimono 2007) as the GDD10-35°C (Lobell 2011). This study defined heat effectiveness in a certain area as the availability of heat resource for rice growth. 10°C accumulated temperature was an index commonly used to evaluate heat resource of thermophilic crops (Han and Qu 1991), and represented the ∑T 10°C for crop growth. Therefore, heat effectiveness during rice growing season was expressed as the ratio of GDD 10-35°C and 10°C accumulated temperature (eq. 3). The greater the value,

Table 7 Rice growing period in each sub zone in southern rice production area of China (mon-d) Subzones S1 S2 S3 S4 S5 S6 S7 S8 S9

S 4-11 4-19 4-11 3-19 4-1 3-30 3-20 4-22 5-10

Single rice B F 8-1 8-11 7-26 8-5 8-2 8-12 7-18 7-30 7-29 8-8 7-22 8-1 7-13 7-24 7-30 8-12 8-13 8-22

M 9-16 9-7 9-21 9-12 9-25 9-4 8-24 9-14 10-3

Subzones D1 D2 D3 D4 D5 D6 D7

S 4-2 3-29 3-21 3-25 2-5 3-9 1-23

Early rice B F 6-16 6-25 6-8 6-20 6-11 6-21 6-15 6-23 5-23 6-3 6-5 6-15 5-2 5-13

M 7-18 7-14 7-20 7-21 7-1 7-12 6-9

S 6-20 6-21 6-24 7-4 6-26 7-11 6-27

Late rice B 9-4 9-3 9-8 9-13 9-11 9-24 9-16

F 9-13 9-12 9-19 9-22 9-25 10-4 9-27

M 10-21 10-17 10-30 10-26 10-22 11-6 10-27

S, sowing date; B, booting date; F, flowering date; M, maturity date.

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Variation Characteristics of Hydrothermal Resources Effectiveness Under the Background of Climate Change in Southern

Table 8 Crop coefficients during rice growing stages Growing stage Sowing to booting Booting to flowering Flowering to maturity

Crop coefficient 1.05 1.20 1.00

 P (4.17- 0.2 P ) / 4.17 Pe =  4.17 + 0.1P

P < 8.3 P≥8.3

(5)

Where Pe is daily effective precipitation (mm d-1); P is daily total precipitation (mm d-1); and 4.17, 0.1 and 0.2 are empirical parameters. Dastane (1978) had evaluated many computing methods for effective precipitation, and thought this is the handiest method. Secondly, this method was recognized for general use (Cuenca 1989; Jensen et al. 1990; Patwardhan et al. 1990; Smith 1992; Petra 2002; Li et al. 2011). ETc (mm d-1) was calculated with crop coefficients (Kc) and reference crop evapotranspiration (ET0) (Allen 1998) (eqs. 6 and 7): ETc=ET0 ×Kc

ET0 =

(6)

0.408∆( Rn − G ) + γ

900

U 2 ( ea − ed )

T+273

∆ +γ (1 + 0.34U 2 )

(7)

Where ET c is crop water demand (mm d -1 ); ET 0 is reference crop evapotranspiration (mm d -1 ); R n is net radiation at the crop surface (MJ m-2 d-1); G is soil heat flux density (MJ m-2 d-1); T is the daily mean air temperature at 2 m (°C); U2 is wind speed at 2 m (m s-1); ed is the saturation vapor pressure at air temperature (kPa); ea is the actual vapor pressure (kPa); Δ is the slope of saturation water vapor pressure-temperature curve (kPa °C-1); γ is psychometric constan (kPa °C-1). Rn, G, Δ and U2 could be calculated through the observed data from meteorological stations.

Acknowledgements Th is w o r k w a s support e d by t he Spe c i a l F und for Meteorology-Scientific Research in the Public Interest, China (GYHY201106020) and the National 973 Program of China (2010CB951502).

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