# Impact of Climate Change on the Growth of Typical Crops in Karst Areas: A Case Study of Guizhou Province

Authors: Jun Ma; Baisha Weng; Wuxia Bi; Dan Xu; Ting Xu; Dengming Yan
Journal: Advances in Meteorology
 (2019)

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## Abstract

Climate change has emerged as a significant man-made global environmental challenge marked by rising temperature. The global rising temperature is supposed to alter climatic patterns like floods and droughts, thereby affecting human life supporting system and global food production. In order to clarify the impact of weather events on agricultural production in karst landforms, this study selected the indices of the growth period of crops (start time and duration), growing season precipitation, intense precipitation, number of consecutive rainless days, and number of drought-flood abrupt alternation events to evaluate the variation trend of future weather events and their impact on crop growth in Guizhou Province, China. The results show that (1) the climate is generally getting warmer. From 2019 to 2050, the sowing period of winter wheat and rice tends to be postponed. The duration of maize and rice’s growth period will be shortened, and the life cycle of wheat also emerges as having a decreasing tendency except for those from the southern region. Comparing with the mean value during 1961 to 2018, the average crop cycle length of winter wheat, summer maize, and rice was shortened. The rate of shortening of crop cycle length is faster than the value during 1961 to 2018. (2) In the next 30 years, extreme precipitation concentrates in June and mainly falls in the central and southeast parts of Guizhou Province. In addition, summer is the outbreak period of drought events and drought-flood abrupt alternation events, which has a great impact on crop’s growth. This study can provide references for the planting system, structure, layout, and management of crops in the karst region.


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## Body

## 1. Introduction

Climate change has already affected global ecosystems, biodiversity, and social economy [1], and the impacts are likely to be more pronounced in the future [2]. IPCC Fifth Assessment Report has pointed out that global climate change is undoubtable [3]. Climate change has emerged as a significant man-made global environmental challenge marked by rising temperature. Global mean temperature has increased by 0.8°C over the past century and is anticipated to rise from 1.5°C to 4.8°C over the next hundred years [4]. Global warming trends may benefit crop production in cooler regions. Some areas such as northern Europe might benefit from climate change to some extent, in the short and medium terms, e.g., by increasing crop yield, better forest growth, and augmented tourism demand [5]. However, the negative impacts of climate change will be so severe in arid or semiarid areas such as Iran [6]. The agricultural water has been reducing with a steep downward trend in south of the Iran as a result of climate change [7]. Increased temperatures are likely to shorten the crop cycle, thus reducing crop production [8]. Recent increases in climate variability may have affected crop yields in countries across Europe since around the mid-1980s [9]. Global rising temperature is supposed to alter climatic patterns like floods, droughts, and incidents of the El Nino and La Nina, which could also reduce the yield in other regions where optimal temperatures have already existed, thereby affecting human life supporting system and global food production, further leading to food insecurity in terms of food availability, accessibility, utilization, and food system stability [4, 10–13].The existence of climate change in China is unequivocal. From 1961 to 2017, the average annual surface temperature in China increased by 0.24°C every 10 years, and the heating rate was higher than the global average. From 1961 to 2017, there was no significant increase or decrease in the average annual precipitation in China, but the extreme precipitation events showed an increasing trend [14]. The impact of climate change on Chinese agriculture is evident [15]. Statistics reveal that during a 28-year period from 1980 to 2008 in China, climatic changes led to a crop yield reduction of 1.27%, 1.73%, and 0.41% for wheat, corn, and soybean, respectively, while there was an increment of 0.56% in rice yield [16]. Lv et al. [17] reported a decrease in wheat yield in northern China and an increase in southern China in the future due to the presence of the rain-fed conditions.Therefore, the study on the climate change impacts on crop growth was mainly in two aspects: one is the impact of normal climatic factors on agriculture, such as, temperature change, precipitation change, and others; the other is the impact of extreme climate events on the agriculture, such as rainstorm and drought. Southwest China is one of the main food producing areas. Guizhou Province has poor surface water storage capacity with karst topography. It has been found that a suitable climate plays a significant role in promoting food production [18]. Therefore, the growth period of crops (start time and duration), growing season precipitation, intense precipitation, number of consecutive rainless days, and number of drought-flood abrupt alternation events were selected as indices. Based on the observation data of 84 meteorological stations and climate model prediction simulation results, the evolution characteristics of extreme weather events and their effects on crop growth were evaluated. It is of great significance to the future agriculture development in Guizhou Province.


## 2. Study Site

Guizhou Province is located in the subtropical monsoon climate zone. The landform type is karst topography, with poor surface water storage capacity. Guizhou is the only province in China without plains. Thus, terrace fields are the main type of farmland. In 2017, the cultivated land area of Guizhou Province accounted for 2.72% of China’s total cultivated land area, while the corn production and rice yield accounted for 2.03% and 1.45% of the national total output, respectively [19]. Guizhou has abundant agricultural biological resources, of 207 kinds [20], such as rice, corn, soybean, potato, sorghum, wheat, and so on. However, rice and corn account for about 70% of the total grain output, and winter wheat is responsible for about 50% of the summer harvest [21, 22]. Therefore, rice, corn, winter wheat, and other typical crops were selected to analyze the impact of climate change on crop growth and yield. Figure 1 shows the location of Guizhou Province.Figure 1
Location and meteorological monitoring points in Guizhou Province. Note: the figure includes the digital elevation model (DEM) data and the Thiessen polygons based on the location of the 84 weather stations.


## 3. Data and Methods

### 3.1. Research Idea

This paper conducts research according to the idea of “data sorting-model screening-extreme indices selection-future trend analysis-impact analysis” (details are shown in Figure2).Figure 2
Technical steps for the study. Note: NSE represents the Nash–Sutcliffe efficiency coefficient.


### 3.2. Climatic Data

#### 3.2.1. Measured Meteorological Data

Daily temperature and precipitation of 84 meteorological stations used in this study (from January 1961 to December 2016) were provided by the Meteorological Bureau of Guizhou Province. And daily temperature and precipitation of meteorological data (from January 2017 to December 2018) were obtained fromhttp://data.cma.cn/site/index.html and https://www.wcrp-climate.org/data-etccdi, respectively. In this paper, the data in the province level were calculated through the Thiessen polygon based on the data of weather stations. The annual average temperature of 58 years in Guizhou was 15.6°C. The annual precipitation was 1183 mm.


#### 3.2.2. Data Simulated by Model

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) brought together 28 global impact models from five different sectors (water, agriculture, biomes, coastal infrastructure, and malaria). However, models involved in the agricultural component of ISIMIP were GFDL-ESM2M, IPSL-CM5A-LR, HadGEM2-ES, NorESM1-M, and MIROC-ESM-CHEM, with resolution of 0.5° × 0.5°, and were bias-corrected towards an observation-based dataset by using a trend-preserving method which is a common method in climate change studies [23–25]. Therefore, this study predicts the impact of climate change on agriculture based on these five models. Table 1 shows the details of five global climate models [26, 27].Table 1
Details of five global climate models provided by ISIMIP.
R&D unit (country)
Name
Biogeochemical characteristics of the Earth system
External forcing factors considered in the simulation
Geophysical Fluid Dynamics Laboratory (GFDL) (USA)
GFDL-ESM2M
A (simple), LC, OBGC
GHG, SD, Oz, LU, SI, VI, SS, BC, MD, OC
Hadley Centre for Climate Prediction and Research, Met Office (UK)
HADGEM2-ES
A (complex), AC, LC, OBGC
GHG, SA, Oz, LU, SI, VI, BC, OC
L’Institut Pierre-Simon Laplace (IPSL) (France)
IPSL-CM5A-LR
A (simple), LC, OBGC
Nat, Ant, GHG, SA, Oz, LU, SS, Ds, BC, MD, OC, AA
Technology, Atmosphere and Ocean Research Institute, and National Institute for Environmental Studies (Japan)
MIROC-ESM-CHEM
A (complex), LC, OBGC
GHG, SA, Oz, LU, Sl, Vl, MD, BC, OC
Norwegian Climate Centre (Norway)
NORESM1-M
A (complex), AC
GHG, SA, Oz, S1, V1, BC, OC
Note. A: aerosol; AC: atmospheric chemistry; LC: terrestrial carbon cycle; OBGC: marine biogeochemistry; Nat: natural forcing; Ant: artificial forcing; GHG: completely mixed with greenhouse gases; SD: artificial sulphide aerosol (only direct effect); SI: a sulphide aerosol (only indirect effect); SA: a direct and indirect effect of sulphide aerosol; Oz: tropospheric and stratospheric ozone; LU: land use change; Sl: solar radiation; Vl: volcanic aerosol; SS: sea salt; Ds: dust; BC: black carbon; MD: mineral dust; OC: organic carbon; AA: artificial aerosol.


### 3.3. Indicators for Evaluating Climate Events

In order to analyze the impact of climate change on crop growth, five indicators of growth period were selected based on accumulated temperature, growing season precipitation, intense precipitation, number of consecutive rainless days, and number of drought-flood abrupt alternation events.


#### 3.3.1. Growth Period of Crops (Start Time and Duration)

In order to analyze the impact of climate change on crops, the growth period is calculated based on the accumulated temperature threshold. The average daily temperature between 15°C and 18°C is most suitable for winter wheat sowing. When the daily average temperature is within the abovementioned temperature range (15°C to 18°C) during five consecutive days since late September, the first day of the period is defined as the sowing day of winter wheat [28]. Based on the research performed by Lu and Wang [29], the accumulated temperature ≥10°C (AT10) was chosen as an indicator to identify the stage of winter wheat growth. The AT10 threshold of each stage is given in Table 2 [30]. For summer maize, suitable sowing temperature ranges from 20°C to 25°C [31, 32]. When the average temperature for 5 consecutive days is within the abovementioned temperature range (20°C to 25°C) for the first time after 5 days before the ripening of winter wheat, the first day of the period is defined as the sowing day of summer maize. Table 3 shows the AT10 threshold of each stage [33]. The minimum temperature for safe seeding of japonica rice is 10°C and that of indica rice is 12°C [34, 35]. In this study, the next day when the average temperature for 5 consecutive days is finally lower than 12°C is selected as the date of rice planting [36]. AT10 thresholds of rice in different growth stages are given in Table 4 [29, 37].Table 2
AT10 thresholds of winter wheat at different growth stages.
Growth stage
Sowing-emergence
Emergence-tillering
Tillering-jointing
Jointing-heading
Heading-dough
AT10 (°C)
134
148
179
311
686Table 3
AT10 thresholds of summer maize at different growth stages.
Growth stage
Sowing-emergence
Emergence-silking
Silking-dough
AT10 (°C)
158
1086
906Table 4
AT10 thresholds of rice at different growth stages.
Growth stage
Sowing-transplanting
Transplanting-tillering
Tillering-jointing
Jointing-heading
Heading-dough
Medium japonica rice AT10 (°C)
800
384
588
557
636
Medium indica rice AT10 (°C)
810
584
494
484
590


#### 3.3.2. Growing Season Precipitation

To study the effects of climate change on agriculture development in Guizhou, the precipitation changes during the crop growing season need to be clarified.


#### 3.3.3. Intense Precipitation

Based on the daily precipitation, this study used the criteria in Table5 to calculate the precipitation times in different grades and their change rate [38].Table 5
Precipitation grade standards.
Climatic factors
Light rain
Moderate rain
Heavy rain
Rainstorm
Heavy rainstorm
Extraordinary rainstorm
24 h precipitation (mm/day)
S < 10
10 ≤S < 25
25 ≤S < 50
50 ≤S < 100
100 ≤S < 200
200 ≤S
Note. S represents 24 h precipitation.


#### 3.3.4. Number of Consecutive Rainless Days

In order to evaluate the degree of drought, this paper utilized the indicators of consecutive rainless days for analysis and calculation. The number of consecutive rainless days refers to the number of consecutive days without effective precipitation during the crop growth period. According to the Standard of Classification for Drought Severity (SL424-2008) [39], it is considered to be a rainless day when the daily precipitation is less than 3 mm in spring (from March to May), autumn (from September to November), and winter (from December to February) and when the daily precipitation is less than 5 mm in summer (from June to August) (Table 6).Table 6
Consecutive rainless days: drought grade standards.
Season
Region
Number of consecutive rainless days in different drought grades (days)
Light drought
Moderate drought
Severe drought
Extraordinary drought
Spring (Mar–May)
North China
15∼30
31∼50
51∼75
>75
South China
10∼20
21∼45
46∼60
>60
Summer (Jun–Aug)
North China
15∼30
31∼50
51∼75
>75
South China
10∼20
21∼45
46∼60
>60
Autumn (Sep–Nov)
North China
10∼20
21∼30
31∼50
>50
South China
5∼10
11∼15
16∼30
>30
Winter (Dec–Feb)
North China
20∼30
31∼60
61∼80
>80
South China
15∼25
26∼45
46∼70
>70


#### 3.3.5. Number of Drought-Flood Abrupt Alternation Events

According the analysis of drought-flood abrupt alternation, a drought-flood abrupt alternation event concludes longest consecutive rainless days and following intense precipitaion. The longest consecutive rainless days reach the level of moderate drought. That is, the longest consecutive rainless days are no less than 31 days in spring and autumn and no less than 21 days in summer. The first precipitation after the drought is intense precipitation (with 24 h accumulated precipitation of more than 25 mm). Figure3 presents an intense precipitation after the summer drought [33].Figure 3
Drought-flood abrupt alternation event.


### 3.4. Adaptive Analysis of Climate Models to Simulate Extreme Events

In order to reduce the impact of model uncertainty on the simulation of Guizhou, four indices were chosen to select the optimal plan: first is the Nash–Sutcliffe efficiency coefficient (NSE) of daily simulated value and measured value; second is the correlation coefficient of month simulated value and measured value; the third one is the sum of the absolute difference between the simulated value and the measured value of the annual average temperature or precipitation of each mode; and the fourth one is the proportion of precipitation amount of different grades in total precipitation amount.(1)E=1−∑t=1TQ0t−Qmt2∑t=1TQ0t−Q0−2,where E is the value of NSE, Q0 is the measured value, and Qm is the value of simulation.(2)ρxy=Covx,yDxDy,where ρxy is the value of correlation coefficient, Covx,y is the covariance of x and y, and D(x) and D(y) are the variances of x and y, respectively.


## 3.1. Research Idea

This paper conducts research according to the idea of “data sorting-model screening-extreme indices selection-future trend analysis-impact analysis” (details are shown in Figure2).Figure 2
Technical steps for the study. Note: NSE represents the Nash–Sutcliffe efficiency coefficient.


## 3.2. Climatic Data

### 3.2.1. Measured Meteorological Data

Daily temperature and precipitation of 84 meteorological stations used in this study (from January 1961 to December 2016) were provided by the Meteorological Bureau of Guizhou Province. And daily temperature and precipitation of meteorological data (from January 2017 to December 2018) were obtained fromhttp://data.cma.cn/site/index.html and https://www.wcrp-climate.org/data-etccdi, respectively. In this paper, the data in the province level were calculated through the Thiessen polygon based on the data of weather stations. The annual average temperature of 58 years in Guizhou was 15.6°C. The annual precipitation was 1183 mm.


### 3.2.2. Data Simulated by Model

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) brought together 28 global impact models from five different sectors (water, agriculture, biomes, coastal infrastructure, and malaria). However, models involved in the agricultural component of ISIMIP were GFDL-ESM2M, IPSL-CM5A-LR, HadGEM2-ES, NorESM1-M, and MIROC-ESM-CHEM, with resolution of 0.5° × 0.5°, and were bias-corrected towards an observation-based dataset by using a trend-preserving method which is a common method in climate change studies [23–25]. Therefore, this study predicts the impact of climate change on agriculture based on these five models. Table 1 shows the details of five global climate models [26, 27].Table 1
Details of five global climate models provided by ISIMIP.
R&D unit (country)
Name
Biogeochemical characteristics of the Earth system
External forcing factors considered in the simulation
Geophysical Fluid Dynamics Laboratory (GFDL) (USA)
GFDL-ESM2M
A (simple), LC, OBGC
GHG, SD, Oz, LU, SI, VI, SS, BC, MD, OC
Hadley Centre for Climate Prediction and Research, Met Office (UK)
HADGEM2-ES
A (complex), AC, LC, OBGC
GHG, SA, Oz, LU, SI, VI, BC, OC
L’Institut Pierre-Simon Laplace (IPSL) (France)
IPSL-CM5A-LR
A (simple), LC, OBGC
Nat, Ant, GHG, SA, Oz, LU, SS, Ds, BC, MD, OC, AA
Technology, Atmosphere and Ocean Research Institute, and National Institute for Environmental Studies (Japan)
MIROC-ESM-CHEM
A (complex), LC, OBGC
GHG, SA, Oz, LU, Sl, Vl, MD, BC, OC
Norwegian Climate Centre (Norway)
NORESM1-M
A (complex), AC
GHG, SA, Oz, S1, V1, BC, OC
Note. A: aerosol; AC: atmospheric chemistry; LC: terrestrial carbon cycle; OBGC: marine biogeochemistry; Nat: natural forcing; Ant: artificial forcing; GHG: completely mixed with greenhouse gases; SD: artificial sulphide aerosol (only direct effect); SI: a sulphide aerosol (only indirect effect); SA: a direct and indirect effect of sulphide aerosol; Oz: tropospheric and stratospheric ozone; LU: land use change; Sl: solar radiation; Vl: volcanic aerosol; SS: sea salt; Ds: dust; BC: black carbon; MD: mineral dust; OC: organic carbon; AA: artificial aerosol.


## 3.2.1. Measured Meteorological Data

Daily temperature and precipitation of 84 meteorological stations used in this study (from January 1961 to December 2016) were provided by the Meteorological Bureau of Guizhou Province. And daily temperature and precipitation of meteorological data (from January 2017 to December 2018) were obtained fromhttp://data.cma.cn/site/index.html and https://www.wcrp-climate.org/data-etccdi, respectively. In this paper, the data in the province level were calculated through the Thiessen polygon based on the data of weather stations. The annual average temperature of 58 years in Guizhou was 15.6°C. The annual precipitation was 1183 mm.


## 3.2.2. Data Simulated by Model

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) brought together 28 global impact models from five different sectors (water, agriculture, biomes, coastal infrastructure, and malaria). However, models involved in the agricultural component of ISIMIP were GFDL-ESM2M, IPSL-CM5A-LR, HadGEM2-ES, NorESM1-M, and MIROC-ESM-CHEM, with resolution of 0.5° × 0.5°, and were bias-corrected towards an observation-based dataset by using a trend-preserving method which is a common method in climate change studies [23–25]. Therefore, this study predicts the impact of climate change on agriculture based on these five models. Table 1 shows the details of five global climate models [26, 27].Table 1
Details of five global climate models provided by ISIMIP.
R&D unit (country)
Name
Biogeochemical characteristics of the Earth system
External forcing factors considered in the simulation
Geophysical Fluid Dynamics Laboratory (GFDL) (USA)
GFDL-ESM2M
A (simple), LC, OBGC
GHG, SD, Oz, LU, SI, VI, SS, BC, MD, OC
Hadley Centre for Climate Prediction and Research, Met Office (UK)
HADGEM2-ES
A (complex), AC, LC, OBGC
GHG, SA, Oz, LU, SI, VI, BC, OC
L’Institut Pierre-Simon Laplace (IPSL) (France)
IPSL-CM5A-LR
A (simple), LC, OBGC
Nat, Ant, GHG, SA, Oz, LU, SS, Ds, BC, MD, OC, AA
Technology, Atmosphere and Ocean Research Institute, and National Institute for Environmental Studies (Japan)
MIROC-ESM-CHEM
A (complex), LC, OBGC
GHG, SA, Oz, LU, Sl, Vl, MD, BC, OC
Norwegian Climate Centre (Norway)
NORESM1-M
A (complex), AC
GHG, SA, Oz, S1, V1, BC, OC
Note. A: aerosol; AC: atmospheric chemistry; LC: terrestrial carbon cycle; OBGC: marine biogeochemistry; Nat: natural forcing; Ant: artificial forcing; GHG: completely mixed with greenhouse gases; SD: artificial sulphide aerosol (only direct effect); SI: a sulphide aerosol (only indirect effect); SA: a direct and indirect effect of sulphide aerosol; Oz: tropospheric and stratospheric ozone; LU: land use change; Sl: solar radiation; Vl: volcanic aerosol; SS: sea salt; Ds: dust; BC: black carbon; MD: mineral dust; OC: organic carbon; AA: artificial aerosol.


## 3.3. Indicators for Evaluating Climate Events

In order to analyze the impact of climate change on crop growth, five indicators of growth period were selected based on accumulated temperature, growing season precipitation, intense precipitation, number of consecutive rainless days, and number of drought-flood abrupt alternation events.


### 3.3.1. Growth Period of Crops (Start Time and Duration)

In order to analyze the impact of climate change on crops, the growth period is calculated based on the accumulated temperature threshold. The average daily temperature between 15°C and 18°C is most suitable for winter wheat sowing. When the daily average temperature is within the abovementioned temperature range (15°C to 18°C) during five consecutive days since late September, the first day of the period is defined as the sowing day of winter wheat [28]. Based on the research performed by Lu and Wang [29], the accumulated temperature ≥10°C (AT10) was chosen as an indicator to identify the stage of winter wheat growth. The AT10 threshold of each stage is given in Table 2 [30]. For summer maize, suitable sowing temperature ranges from 20°C to 25°C [31, 32]. When the average temperature for 5 consecutive days is within the abovementioned temperature range (20°C to 25°C) for the first time after 5 days before the ripening of winter wheat, the first day of the period is defined as the sowing day of summer maize. Table 3 shows the AT10 threshold of each stage [33]. The minimum temperature for safe seeding of japonica rice is 10°C and that of indica rice is 12°C [34, 35]. In this study, the next day when the average temperature for 5 consecutive days is finally lower than 12°C is selected as the date of rice planting [36]. AT10 thresholds of rice in different growth stages are given in Table 4 [29, 37].Table 2
AT10 thresholds of winter wheat at different growth stages.
Growth stage
Sowing-emergence
Emergence-tillering
Tillering-jointing
Jointing-heading
Heading-dough
AT10 (°C)
134
148
179
311
686Table 3
AT10 thresholds of summer maize at different growth stages.
Growth stage
Sowing-emergence
Emergence-silking
Silking-dough
AT10 (°C)
158
1086
906Table 4
AT10 thresholds of rice at different growth stages.
Growth stage
Sowing-transplanting
Transplanting-tillering
Tillering-jointing
Jointing-heading
Heading-dough
Medium japonica rice AT10 (°C)
800
384
588
557
636
Medium indica rice AT10 (°C)
810
584
494
484
590


### 3.3.2. Growing Season Precipitation

To study the effects of climate change on agriculture development in Guizhou, the precipitation changes during the crop growing season need to be clarified.


### 3.3.3. Intense Precipitation

Based on the daily precipitation, this study used the criteria in Table5 to calculate the precipitation times in different grades and their change rate [38].Table 5
Precipitation grade standards.
Climatic factors
Light rain
Moderate rain
Heavy rain
Rainstorm
Heavy rainstorm
Extraordinary rainstorm
24 h precipitation (mm/day)
S < 10
10 ≤S < 25
25 ≤S < 50
50 ≤S < 100
100 ≤S < 200
200 ≤S
Note. S represents 24 h precipitation.


### 3.3.4. Number of Consecutive Rainless Days

In order to evaluate the degree of drought, this paper utilized the indicators of consecutive rainless days for analysis and calculation. The number of consecutive rainless days refers to the number of consecutive days without effective precipitation during the crop growth period. According to the Standard of Classification for Drought Severity (SL424-2008) [39], it is considered to be a rainless day when the daily precipitation is less than 3 mm in spring (from March to May), autumn (from September to November), and winter (from December to February) and when the daily precipitation is less than 5 mm in summer (from June to August) (Table 6).Table 6
Consecutive rainless days: drought grade standards.
Season
Region
Number of consecutive rainless days in different drought grades (days)
Light drought
Moderate drought
Severe drought
Extraordinary drought
Spring (Mar–May)
North China
15∼30
31∼50
51∼75
>75
South China
10∼20
21∼45
46∼60
>60
Summer (Jun–Aug)
North China
15∼30
31∼50
51∼75
>75
South China
10∼20
21∼45
46∼60
>60
Autumn (Sep–Nov)
North China
10∼20
21∼30
31∼50
>50
South China
5∼10
11∼15
16∼30
>30
Winter (Dec–Feb)
North China
20∼30
31∼60
61∼80
>80
South China
15∼25
26∼45
46∼70
>70


### 3.3.5. Number of Drought-Flood Abrupt Alternation Events

According the analysis of drought-flood abrupt alternation, a drought-flood abrupt alternation event concludes longest consecutive rainless days and following intense precipitaion. The longest consecutive rainless days reach the level of moderate drought. That is, the longest consecutive rainless days are no less than 31 days in spring and autumn and no less than 21 days in summer. The first precipitation after the drought is intense precipitation (with 24 h accumulated precipitation of more than 25 mm). Figure3 presents an intense precipitation after the summer drought [33].Figure 3
Drought-flood abrupt alternation event.


## 3.3.1. Growth Period of Crops (Start Time and Duration)

In order to analyze the impact of climate change on crops, the growth period is calculated based on the accumulated temperature threshold. The average daily temperature between 15°C and 18°C is most suitable for winter wheat sowing. When the daily average temperature is within the abovementioned temperature range (15°C to 18°C) during five consecutive days since late September, the first day of the period is defined as the sowing day of winter wheat [28]. Based on the research performed by Lu and Wang [29], the accumulated temperature ≥10°C (AT10) was chosen as an indicator to identify the stage of winter wheat growth. The AT10 threshold of each stage is given in Table 2 [30]. For summer maize, suitable sowing temperature ranges from 20°C to 25°C [31, 32]. When the average temperature for 5 consecutive days is within the abovementioned temperature range (20°C to 25°C) for the first time after 5 days before the ripening of winter wheat, the first day of the period is defined as the sowing day of summer maize. Table 3 shows the AT10 threshold of each stage [33]. The minimum temperature for safe seeding of japonica rice is 10°C and that of indica rice is 12°C [34, 35]. In this study, the next day when the average temperature for 5 consecutive days is finally lower than 12°C is selected as the date of rice planting [36]. AT10 thresholds of rice in different growth stages are given in Table 4 [29, 37].Table 2
AT10 thresholds of winter wheat at different growth stages.
Growth stage
Sowing-emergence
Emergence-tillering
Tillering-jointing
Jointing-heading
Heading-dough
AT10 (°C)
134
148
179
311
686Table 3
AT10 thresholds of summer maize at different growth stages.
Growth stage
Sowing-emergence
Emergence-silking
Silking-dough
AT10 (°C)
158
1086
906Table 4
AT10 thresholds of rice at different growth stages.
Growth stage
Sowing-transplanting
Transplanting-tillering
Tillering-jointing
Jointing-heading
Heading-dough
Medium japonica rice AT10 (°C)
800
384
588
557
636
Medium indica rice AT10 (°C)
810
584
494
484
590


## 3.3.2. Growing Season Precipitation

To study the effects of climate change on agriculture development in Guizhou, the precipitation changes during the crop growing season need to be clarified.


## 3.3.3. Intense Precipitation

Based on the daily precipitation, this study used the criteria in Table5 to calculate the precipitation times in different grades and their change rate [38].Table 5
Precipitation grade standards.
Climatic factors
Light rain
Moderate rain
Heavy rain
Rainstorm
Heavy rainstorm
Extraordinary rainstorm
24 h precipitation (mm/day)
S < 10
10 ≤S < 25
25 ≤S < 50
50 ≤S < 100
100 ≤S < 200
200 ≤S
Note. S represents 24 h precipitation.


## 3.3.4. Number of Consecutive Rainless Days

In order to evaluate the degree of drought, this paper utilized the indicators of consecutive rainless days for analysis and calculation. The number of consecutive rainless days refers to the number of consecutive days without effective precipitation during the crop growth period. According to the Standard of Classification for Drought Severity (SL424-2008) [39], it is considered to be a rainless day when the daily precipitation is less than 3 mm in spring (from March to May), autumn (from September to November), and winter (from December to February) and when the daily precipitation is less than 5 mm in summer (from June to August) (Table 6).Table 6
Consecutive rainless days: drought grade standards.
Season
Region
Number of consecutive rainless days in different drought grades (days)
Light drought
Moderate drought
Severe drought
Extraordinary drought
Spring (Mar–May)
North China
15∼30
31∼50
51∼75
>75
South China
10∼20
21∼45
46∼60
>60
Summer (Jun–Aug)
North China
15∼30
31∼50
51∼75
>75
South China
10∼20
21∼45
46∼60
>60
Autumn (Sep–Nov)
North China
10∼20
21∼30
31∼50
>50
South China
5∼10
11∼15
16∼30
>30
Winter (Dec–Feb)
North China
20∼30
31∼60
61∼80
>80
South China
15∼25
26∼45
46∼70
>70


## 3.3.5. Number of Drought-Flood Abrupt Alternation Events

According the analysis of drought-flood abrupt alternation, a drought-flood abrupt alternation event concludes longest consecutive rainless days and following intense precipitaion. The longest consecutive rainless days reach the level of moderate drought. That is, the longest consecutive rainless days are no less than 31 days in spring and autumn and no less than 21 days in summer. The first precipitation after the drought is intense precipitation (with 24 h accumulated precipitation of more than 25 mm). Figure3 presents an intense precipitation after the summer drought [33].Figure 3
Drought-flood abrupt alternation event.


## 3.4. Adaptive Analysis of Climate Models to Simulate Extreme Events

In order to reduce the impact of model uncertainty on the simulation of Guizhou, four indices were chosen to select the optimal plan: first is the Nash–Sutcliffe efficiency coefficient (NSE) of daily simulated value and measured value; second is the correlation coefficient of month simulated value and measured value; the third one is the sum of the absolute difference between the simulated value and the measured value of the annual average temperature or precipitation of each mode; and the fourth one is the proportion of precipitation amount of different grades in total precipitation amount.(1)E=1−∑t=1TQ0t−Qmt2∑t=1TQ0t−Q0−2,where E is the value of NSE, Q0 is the measured value, and Qm is the value of simulation.(2)ρxy=Covx,yDxDy,where ρxy is the value of correlation coefficient, Covx,y is the covariance of x and y, and D(x) and D(y) are the variances of x and y, respectively.


## 4. Result

### 4.1. Selection of Optimal Climate Model

Calculation results of four indices are presented in Tables7–9 and Figures 4 and 5. A comparison and analysis of the four indicators has revealed that the mean of five models is the most accurate in temperature and precipitation simulation. For extreme precipitation, the simulation effect of the HadGEM2-ES model is better. Therefore, we chose the mean value of the five models to predict the future temperature and precipitation data and used the HadGEM2-ES model to predict extreme precipitation events.Table 7
Comparison of simulated and measured values of temperature in Guizhou from 1961 to 2000.
Indices
GFDL-ESM2M
HadGEM2-ES
IPSL-CM5A-LR
MIROC-ESM-CHEM
NorESM1-M
Mean of 5 models
NSE
0.63
0.70
0.67
0.67
0.69
0.81
Correlation coefficient
0.82
0.85
0.84
0.84
0.84
0.90
T (°C)
23.04
20.19
23.62
26.12
22.33
17.42
Note. T represents the sum of the absolute difference between the measured value and the simulated value of the annual average temperature of each mode.Table 8
Comparison of simulated and measured values of precipitation in Guizhou from 1961 to 2000.
Indices
GFDL-ESM2M
HadGEM2-ES
IPSL-CM5A-LR
MIROC-ESM-CHEM
NorESM1-M
Mean of 5 models
NSE
−0.67
−0.35
−0.83
−0.89
−0.44
−0.01
correlation coefficient
0.14
0.20
0.14
0.13
0.20
0.27
P (mm)
6398.98
5858.67
6854.68
7082.90
6432.12
4768.24
Note. P represents the sum of the absolute difference between the measured value and the simulated value of the annual average precipitation of each mode.Table 9
The difference between simulated value of the precipitation of in different grades simulated by each mode and the measured precipitation from 1961 to 2000.
Prediction schemes
Light rain
Moderate rain
Heavy rain
Rainstorm
Heavy rainstorm
Extraordinary rainstorm
GFDL-ESM2M
−3.86
10.03
−0.54
−5.90
−0.20
0.30
HadGEM2-ES
3.86
2.91
−1.93
−4.71
−0.11
−0.18
IPSL-CM5A-LR
−7.44
9.14
0.43
−3.35
1.24
−0.18
MIROC-ESM-CHEM
−8.67
3.21
6.82
−1.26
−0.91
0.65
NorESM1-M
0.73
9.39
0.09
−7.70
−2.61
−0.18
Mean of 5 models
13.6
21.58
−17.60
−14.24
−3.32
−0.18Figure 4
Spatial distribution of temperature. (a)–(g) represent measure, GFDL-ESM2M, IPSL-CM5A-LR, HadGEM2-ES, NorESM1-M, MIROC-ESM-CHEM, and the mean of the five models, respectively.
(a)
(b)
(c)
(d)
(e)
(f)
(g)Figure 5
Spatial distribution of precipitation. (a)–(g) represent measure, GFDL-ESM2M, IPSL-CM5A-LR, HadGEM2-ES, NorESM1-M, MIROC-ESM-CHEM, and the mean of the five models, respectively.
(a)
(b)
(c)
(d)
(e)
(f)
(g)


### 4.2. Evolution of Temperature and Precipitation

#### 4.2.1. The Variation Trend from 1961 to 2018

Figure6 shows the evolution trend of the annual average temperature from 1961 to 2018. Figure 7 shows the evolution trend of annual precipitation from 1961 to 2018. Notably, we used linear trends and not some Mann-Kendall-like trend analysis because the linear trends could reflect the evolution trend and rate more directly. It can be seen that the temperature shows an increasing trend, with a rate of 0.14°C per 10 years. However, the precipitation shows a decreasing trend, with a rate of 15.02 mm per 10 years.Figure 6
Evolution trends of the average annual temperature from 1961 to 2018.Figure 7
Evolution trends of annual precipitation from 1961 to 2018.


#### 4.2.2. The Variation Trend from 2019 to 2050

Three RCPs (Representative Concentration Pathways) adopted in the IPCC’s Fifth Assessment Report AR5 (RCPs 2.6, 4.5, and 8.5) were applied. For RCP4.5 scenario, the average temperature from 2019 to 2050 will go up, with a rate of 0.29°C per 10 years (Figure8). The precipitation also shows an increasing tendency, with the rate being 27.9 mm/10 years (Figure 9). Compared with historical data, the increase rate of temperature is about 2 times that in history. The average annual precipitation in the next 30 years is lower than that in the past 60 years. The temperature increases the most in winter (Figure 10). The precipitation decreases in spring, autumn, and winter (Figure 11). In the next 30 years, annual average temperature increases from 1.36°C to 1.5°C compared with baseline period (from 1961 to 2018) (Figure 12).Figure 8
Evolution trends of the average annual temperature from 2019 to 2050.Figure 9
Evolution trends of annual precipitation from 2019 to 2050.Figure 10
Relative changes of monthly average temperature between 2019–2050 and 1961–2018.Figure 11
Relative change of monthly average precipitation between 2019–2050 and 1961–2018.Figure 12
Change of annual average temperature and annual precipitation from 2019 to 2050 compared with the baseline period in Guizhou. (a) RCP2.6. (b) RCP4.5. (c) RCP8.5.
(a)
(b)
(c)
(d)
(e)
(f)


#### 4.2.3. Growth Period of Crops (Start Time and Duration)

Climate change has changed the cycle length of crops [37, 40]. Studying the changes in crop growth period can provide a basis for adjusting crop types. A land use map, developed in 2014, was obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences. The spatial distribution of main crops in Guizhou was from the land use map in Geographic Information System (GIS) using the ArcMap platform (Figure 13(a)). This study chose the same precision as the climate model data and divided Guizhou Province into 65 grid cells each with the size of 0.5° × 0.5° (Figure 13(b)). The main crops in Guizhou are winter wheat, summer maize, and rice. The varieties of rice mainly include single-cropping medium japonica rice and single-cropping medium indica rice [41, 42]. The spatial distribution of rice species is shown in Figure 13(c). According to the accumulated temperature, the growth period of 1961 to 2018 and 2019 to 2050 crops was analyzed. In the study, there is only one cultivar for each crop, from the past to the future.Figure 13
Cultivated field and grid map of superimposed area in Guizhou.
(a)
(b)
(c)From 1961 to 2018, the average winter wheat cycle length was 200 days. The growth period of winter wheat showed a decreasing trend with a rate of −2 days/10 years. The average summer maize cycle length was 98 days. The growth period of summer maize showed a decreasing trend with a rate of −0.9 days/10 years. The average summer maize cycle length was 141 days. The growth period of summer maize showed a decreasing trend with a rate of −1 days/10 years.From 2019 to 2050, taking the RCP4.5 scenario as an example, the wheat sowing period will be postponed. The average crop cycle length of winter wheat, summer maize, and rice is 177 days, 94 days, and 132 days, respectively. From the spatial analysis (Figure14(a)), the crop cycle length of winter wheat is shortened except for the southern region, with the rate of −2.1 days per 10 years. The linear tendency rate of corn planting date is small. From the spatial analysis (Figure 14(b)), the crop cycle length of summer maize is shortened in most areas of Guizhou, with the rate of −1.2 days per 10 years. The rice sowing period will be postponed in the next 30 years. From the spatial analysis (Figure 14(c)), the total number of days in rice growth period is shortened, with the rate of −1.6 days per 10 years.Figure 14
Distribution of the change rate of total days in crop growth period: (a) winter wheat, (b) summer maize, and (c) rice.
(a)
(b)
(c)


#### 4.2.4. Growing Season Precipitation

According to the calculation of the growth period of crops, the whole growth period of maize and rice is from April to September, and the growth period of wheat is from October to next April. The yield of wheat is positively correlated with the precipitation from December to March [43, 44].For RCP4.5 scenario, the linear tendency rate of the precipitation tendency is small in the wheat growing season but large in the interannual variation (Figure15). The precipitation in the growing season of rice and corn showed an increasing trend, with a rate of 20.2 mm/10 years (Figure 16).Figure 15
Precipitation trends during the wheat growing season (December to March) from 2019 to 2050.Figure 16
Precipitation trends during the growing season of rice and corn (April to September) from 2019 to 2050.


### 4.3. Trends in Extreme Events from 2019 to 2050

#### 4.3.1. Intense Precipitation

In the next 30 years, intense precipitation (≥50 mm/day) events occur in April to October under three scenarios, with a difference in spatial distribution. For example, in the RCP4.5 scenario, extreme precipitation is mainly concentrated in the central and southeastern parts of Guizhou (Figure17). And extreme precipitation accounts for 2.9% of total precipitation, with 1.1%, 0.56%, and 0.43% in June, July, and September, respectively.Figure 17
Spatial distribution of the proportion of extreme precipitation to total precipitation. (a) RCP2.6. (b) RCP4.5. (c) RCP8.5.
(a)
(b)
(c)


#### 4.3.2. Consecutive Rainless Days

Figure18 plots the probabilities of different levels of drought in Guizhou from 2019 to 2050, and Figure 19 shows the probabilities of drought (except light drought) in different seasons from 2019 to 2050.Figure 18
The probabilities of different levels of drought in Guizhou from 2019 to 2050. (a) Light drought. (b) Moderate drought. (c) Severe drought. (d) Extraordinary drought.
(a)
(b)
(c)
(d)Figure 19
The probabilities of drought (except mild drought) in different seasons from 2019 to 2050. (a) Spring. (b) Summer. (c) Autumn.
(a)
(b)
(c)Drought is considered to be the most serious meteorological disaster affecting agriculture, and the drought in Guizhou Province has obvious seasonality and regionality [45]. Therefore, this paper analyzed the different levels of drought and seasonal drought in Guizhou in the next 30 years. Spring drought and summer drought are the main disaster-causing factors for rice and corn. Autumn and spring droughts are the main hazard factors for wheat and rapeseed [46]. This study mainly analyzed the probability of drought in spring, summer, and autumn.Under the RCP4.5 scenario, in the next 30 years, the occurrence probability of drought in western Guizhou is higher than that in eastern Guizhou. In eastern Guizhou, drought is frequent in summer, while much less in spring and autumn. A number of droughts (except light drought) might occur most in the summer followed by spring. Therefore, the greatest impact on agriculture in the next 30 years is summer drought, followed by spring drought. Frequent drought events will increase the irrigation water demand and augment the pressure on freshwater resources.


#### 4.3.3. Drought-Flood Abrupt Alternation Events

Soil fertility, rice growth period, physiological characteristics, and others will be affected by drought-flood abrupt alternation events, and the latter results in large reduction of production [47]. Figure 20 shows spatial distribution of drought-flood abrupt alternation events in Guizhou.Figure 20
Spatial distribution of the frequency of drought-flood abrupt alternation events in Guizhou from 2019 to 2050. (a) RCP2.6. (b) RCP4.5. (c) RCP8.5.
(a)
(b)
(c)Low incidence of drought-flood abrupt alternation events is present in the RCP4.5 scenario, but each event has the longest drought duration with severe disasters. In the RCP4.5 scenario, drought-flood alternation change events might occur in spring, summer, and autumn.


## 4.1. Selection of Optimal Climate Model

Calculation results of four indices are presented in Tables7–9 and Figures 4 and 5. A comparison and analysis of the four indicators has revealed that the mean of five models is the most accurate in temperature and precipitation simulation. For extreme precipitation, the simulation effect of the HadGEM2-ES model is better. Therefore, we chose the mean value of the five models to predict the future temperature and precipitation data and used the HadGEM2-ES model to predict extreme precipitation events.Table 7
Comparison of simulated and measured values of temperature in Guizhou from 1961 to 2000.
Indices
GFDL-ESM2M
HadGEM2-ES
IPSL-CM5A-LR
MIROC-ESM-CHEM
NorESM1-M
Mean of 5 models
NSE
0.63
0.70
0.67
0.67
0.69
0.81
Correlation coefficient
0.82
0.85
0.84
0.84
0.84
0.90
T (°C)
23.04
20.19
23.62
26.12
22.33
17.42
Note. T represents the sum of the absolute difference between the measured value and the simulated value of the annual average temperature of each mode.Table 8
Comparison of simulated and measured values of precipitation in Guizhou from 1961 to 2000.
Indices
GFDL-ESM2M
HadGEM2-ES
IPSL-CM5A-LR
MIROC-ESM-CHEM
NorESM1-M
Mean of 5 models
NSE
−0.67
−0.35
−0.83
−0.89
−0.44
−0.01
correlation coefficient
0.14
0.20
0.14
0.13
0.20
0.27
P (mm)
6398.98
5858.67
6854.68
7082.90
6432.12
4768.24
Note. P represents the sum of the absolute difference between the measured value and the simulated value of the annual average precipitation of each mode.Table 9
The difference between simulated value of the precipitation of in different grades simulated by each mode and the measured precipitation from 1961 to 2000.
Prediction schemes
Light rain
Moderate rain
Heavy rain
Rainstorm
Heavy rainstorm
Extraordinary rainstorm
GFDL-ESM2M
−3.86
10.03
−0.54
−5.90
−0.20
0.30
HadGEM2-ES
3.86
2.91
−1.93
−4.71
−0.11
−0.18
IPSL-CM5A-LR
−7.44
9.14
0.43
−3.35
1.24
−0.18
MIROC-ESM-CHEM
−8.67
3.21
6.82
−1.26
−0.91
0.65
NorESM1-M
0.73
9.39
0.09
−7.70
−2.61
−0.18
Mean of 5 models
13.6
21.58
−17.60
−14.24
−3.32
−0.18Figure 4
Spatial distribution of temperature. (a)–(g) represent measure, GFDL-ESM2M, IPSL-CM5A-LR, HadGEM2-ES, NorESM1-M, MIROC-ESM-CHEM, and the mean of the five models, respectively.
(a)
(b)
(c)
(d)
(e)
(f)
(g)Figure 5
Spatial distribution of precipitation. (a)–(g) represent measure, GFDL-ESM2M, IPSL-CM5A-LR, HadGEM2-ES, NorESM1-M, MIROC-ESM-CHEM, and the mean of the five models, respectively.
(a)
(b)
(c)
(d)
(e)
(f)
(g)


## 4.2. Evolution of Temperature and Precipitation

### 4.2.1. The Variation Trend from 1961 to 2018

Figure6 shows the evolution trend of the annual average temperature from 1961 to 2018. Figure 7 shows the evolution trend of annual precipitation from 1961 to 2018. Notably, we used linear trends and not some Mann-Kendall-like trend analysis because the linear trends could reflect the evolution trend and rate more directly. It can be seen that the temperature shows an increasing trend, with a rate of 0.14°C per 10 years. However, the precipitation shows a decreasing trend, with a rate of 15.02 mm per 10 years.Figure 6
Evolution trends of the average annual temperature from 1961 to 2018.Figure 7
Evolution trends of annual precipitation from 1961 to 2018.


### 4.2.2. The Variation Trend from 2019 to 2050

Three RCPs (Representative Concentration Pathways) adopted in the IPCC’s Fifth Assessment Report AR5 (RCPs 2.6, 4.5, and 8.5) were applied. For RCP4.5 scenario, the average temperature from 2019 to 2050 will go up, with a rate of 0.29°C per 10 years (Figure8). The precipitation also shows an increasing tendency, with the rate being 27.9 mm/10 years (Figure 9). Compared with historical data, the increase rate of temperature is about 2 times that in history. The average annual precipitation in the next 30 years is lower than that in the past 60 years. The temperature increases the most in winter (Figure 10). The precipitation decreases in spring, autumn, and winter (Figure 11). In the next 30 years, annual average temperature increases from 1.36°C to 1.5°C compared with baseline period (from 1961 to 2018) (Figure 12).Figure 8
Evolution trends of the average annual temperature from 2019 to 2050.Figure 9
Evolution trends of annual precipitation from 2019 to 2050.Figure 10
Relative changes of monthly average temperature between 2019–2050 and 1961–2018.Figure 11
Relative change of monthly average precipitation between 2019–2050 and 1961–2018.Figure 12
Change of annual average temperature and annual precipitation from 2019 to 2050 compared with the baseline period in Guizhou. (a) RCP2.6. (b) RCP4.5. (c) RCP8.5.
(a)
(b)
(c)
(d)
(e)
(f)


### 4.2.3. Growth Period of Crops (Start Time and Duration)

Climate change has changed the cycle length of crops [37, 40]. Studying the changes in crop growth period can provide a basis for adjusting crop types. A land use map, developed in 2014, was obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences. The spatial distribution of main crops in Guizhou was from the land use map in Geographic Information System (GIS) using the ArcMap platform (Figure 13(a)). This study chose the same precision as the climate model data and divided Guizhou Province into 65 grid cells each with the size of 0.5° × 0.5° (Figure 13(b)). The main crops in Guizhou are winter wheat, summer maize, and rice. The varieties of rice mainly include single-cropping medium japonica rice and single-cropping medium indica rice [41, 42]. The spatial distribution of rice species is shown in Figure 13(c). According to the accumulated temperature, the growth period of 1961 to 2018 and 2019 to 2050 crops was analyzed. In the study, there is only one cultivar for each crop, from the past to the future.Figure 13
Cultivated field and grid map of superimposed area in Guizhou.
(a)
(b)
(c)From 1961 to 2018, the average winter wheat cycle length was 200 days. The growth period of winter wheat showed a decreasing trend with a rate of −2 days/10 years. The average summer maize cycle length was 98 days. The growth period of summer maize showed a decreasing trend with a rate of −0.9 days/10 years. The average summer maize cycle length was 141 days. The growth period of summer maize showed a decreasing trend with a rate of −1 days/10 years.From 2019 to 2050, taking the RCP4.5 scenario as an example, the wheat sowing period will be postponed. The average crop cycle length of winter wheat, summer maize, and rice is 177 days, 94 days, and 132 days, respectively. From the spatial analysis (Figure14(a)), the crop cycle length of winter wheat is shortened except for the southern region, with the rate of −2.1 days per 10 years. The linear tendency rate of corn planting date is small. From the spatial analysis (Figure 14(b)), the crop cycle length of summer maize is shortened in most areas of Guizhou, with the rate of −1.2 days per 10 years. The rice sowing period will be postponed in the next 30 years. From the spatial analysis (Figure 14(c)), the total number of days in rice growth period is shortened, with the rate of −1.6 days per 10 years.Figure 14
Distribution of the change rate of total days in crop growth period: (a) winter wheat, (b) summer maize, and (c) rice.
(a)
(b)
(c)


### 4.2.4. Growing Season Precipitation

According to the calculation of the growth period of crops, the whole growth period of maize and rice is from April to September, and the growth period of wheat is from October to next April. The yield of wheat is positively correlated with the precipitation from December to March [43, 44].For RCP4.5 scenario, the linear tendency rate of the precipitation tendency is small in the wheat growing season but large in the interannual variation (Figure15). The precipitation in the growing season of rice and corn showed an increasing trend, with a rate of 20.2 mm/10 years (Figure 16).Figure 15
Precipitation trends during the wheat growing season (December to March) from 2019 to 2050.Figure 16
Precipitation trends during the growing season of rice and corn (April to September) from 2019 to 2050.


## 4.2.1. The Variation Trend from 1961 to 2018

Figure6 shows the evolution trend of the annual average temperature from 1961 to 2018. Figure 7 shows the evolution trend of annual precipitation from 1961 to 2018. Notably, we used linear trends and not some Mann-Kendall-like trend analysis because the linear trends could reflect the evolution trend and rate more directly. It can be seen that the temperature shows an increasing trend, with a rate of 0.14°C per 10 years. However, the precipitation shows a decreasing trend, with a rate of 15.02 mm per 10 years.Figure 6
Evolution trends of the average annual temperature from 1961 to 2018.Figure 7
Evolution trends of annual precipitation from 1961 to 2018.


## 4.2.2. The Variation Trend from 2019 to 2050

Three RCPs (Representative Concentration Pathways) adopted in the IPCC’s Fifth Assessment Report AR5 (RCPs 2.6, 4.5, and 8.5) were applied. For RCP4.5 scenario, the average temperature from 2019 to 2050 will go up, with a rate of 0.29°C per 10 years (Figure8). The precipitation also shows an increasing tendency, with the rate being 27.9 mm/10 years (Figure 9). Compared with historical data, the increase rate of temperature is about 2 times that in history. The average annual precipitation in the next 30 years is lower than that in the past 60 years. The temperature increases the most in winter (Figure 10). The precipitation decreases in spring, autumn, and winter (Figure 11). In the next 30 years, annual average temperature increases from 1.36°C to 1.5°C compared with baseline period (from 1961 to 2018) (Figure 12).Figure 8
Evolution trends of the average annual temperature from 2019 to 2050.Figure 9
Evolution trends of annual precipitation from 2019 to 2050.Figure 10
Relative changes of monthly average temperature between 2019–2050 and 1961–2018.Figure 11
Relative change of monthly average precipitation between 2019–2050 and 1961–2018.Figure 12
Change of annual average temperature and annual precipitation from 2019 to 2050 compared with the baseline period in Guizhou. (a) RCP2.6. (b) RCP4.5. (c) RCP8.5.
(a)
(b)
(c)
(d)
(e)
(f)


## 4.2.3. Growth Period of Crops (Start Time and Duration)

Climate change has changed the cycle length of crops [37, 40]. Studying the changes in crop growth period can provide a basis for adjusting crop types. A land use map, developed in 2014, was obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences. The spatial distribution of main crops in Guizhou was from the land use map in Geographic Information System (GIS) using the ArcMap platform (Figure 13(a)). This study chose the same precision as the climate model data and divided Guizhou Province into 65 grid cells each with the size of 0.5° × 0.5° (Figure 13(b)). The main crops in Guizhou are winter wheat, summer maize, and rice. The varieties of rice mainly include single-cropping medium japonica rice and single-cropping medium indica rice [41, 42]. The spatial distribution of rice species is shown in Figure 13(c). According to the accumulated temperature, the growth period of 1961 to 2018 and 2019 to 2050 crops was analyzed. In the study, there is only one cultivar for each crop, from the past to the future.Figure 13
Cultivated field and grid map of superimposed area in Guizhou.
(a)
(b)
(c)From 1961 to 2018, the average winter wheat cycle length was 200 days. The growth period of winter wheat showed a decreasing trend with a rate of −2 days/10 years. The average summer maize cycle length was 98 days. The growth period of summer maize showed a decreasing trend with a rate of −0.9 days/10 years. The average summer maize cycle length was 141 days. The growth period of summer maize showed a decreasing trend with a rate of −1 days/10 years.From 2019 to 2050, taking the RCP4.5 scenario as an example, the wheat sowing period will be postponed. The average crop cycle length of winter wheat, summer maize, and rice is 177 days, 94 days, and 132 days, respectively. From the spatial analysis (Figure14(a)), the crop cycle length of winter wheat is shortened except for the southern region, with the rate of −2.1 days per 10 years. The linear tendency rate of corn planting date is small. From the spatial analysis (Figure 14(b)), the crop cycle length of summer maize is shortened in most areas of Guizhou, with the rate of −1.2 days per 10 years. The rice sowing period will be postponed in the next 30 years. From the spatial analysis (Figure 14(c)), the total number of days in rice growth period is shortened, with the rate of −1.6 days per 10 years.Figure 14
Distribution of the change rate of total days in crop growth period: (a) winter wheat, (b) summer maize, and (c) rice.
(a)
(b)
(c)


## 4.2.4. Growing Season Precipitation

According to the calculation of the growth period of crops, the whole growth period of maize and rice is from April to September, and the growth period of wheat is from October to next April. The yield of wheat is positively correlated with the precipitation from December to March [43, 44].For RCP4.5 scenario, the linear tendency rate of the precipitation tendency is small in the wheat growing season but large in the interannual variation (Figure15). The precipitation in the growing season of rice and corn showed an increasing trend, with a rate of 20.2 mm/10 years (Figure 16).Figure 15
Precipitation trends during the wheat growing season (December to March) from 2019 to 2050.Figure 16
Precipitation trends during the growing season of rice and corn (April to September) from 2019 to 2050.


## 4.3. Trends in Extreme Events from 2019 to 2050

### 4.3.1. Intense Precipitation

In the next 30 years, intense precipitation (≥50 mm/day) events occur in April to October under three scenarios, with a difference in spatial distribution. For example, in the RCP4.5 scenario, extreme precipitation is mainly concentrated in the central and southeastern parts of Guizhou (Figure17). And extreme precipitation accounts for 2.9% of total precipitation, with 1.1%, 0.56%, and 0.43% in June, July, and September, respectively.Figure 17
Spatial distribution of the proportion of extreme precipitation to total precipitation. (a) RCP2.6. (b) RCP4.5. (c) RCP8.5.
(a)
(b)
(c)


### 4.3.2. Consecutive Rainless Days

Figure18 plots the probabilities of different levels of drought in Guizhou from 2019 to 2050, and Figure 19 shows the probabilities of drought (except light drought) in different seasons from 2019 to 2050.Figure 18
The probabilities of different levels of drought in Guizhou from 2019 to 2050. (a) Light drought. (b) Moderate drought. (c) Severe drought. (d) Extraordinary drought.
(a)
(b)
(c)
(d)Figure 19
The probabilities of drought (except mild drought) in different seasons from 2019 to 2050. (a) Spring. (b) Summer. (c) Autumn.
(a)
(b)
(c)Drought is considered to be the most serious meteorological disaster affecting agriculture, and the drought in Guizhou Province has obvious seasonality and regionality [45]. Therefore, this paper analyzed the different levels of drought and seasonal drought in Guizhou in the next 30 years. Spring drought and summer drought are the main disaster-causing factors for rice and corn. Autumn and spring droughts are the main hazard factors for wheat and rapeseed [46]. This study mainly analyzed the probability of drought in spring, summer, and autumn.Under the RCP4.5 scenario, in the next 30 years, the occurrence probability of drought in western Guizhou is higher than that in eastern Guizhou. In eastern Guizhou, drought is frequent in summer, while much less in spring and autumn. A number of droughts (except light drought) might occur most in the summer followed by spring. Therefore, the greatest impact on agriculture in the next 30 years is summer drought, followed by spring drought. Frequent drought events will increase the irrigation water demand and augment the pressure on freshwater resources.


### 4.3.3. Drought-Flood Abrupt Alternation Events

Soil fertility, rice growth period, physiological characteristics, and others will be affected by drought-flood abrupt alternation events, and the latter results in large reduction of production [47]. Figure 20 shows spatial distribution of drought-flood abrupt alternation events in Guizhou.Figure 20
Spatial distribution of the frequency of drought-flood abrupt alternation events in Guizhou from 2019 to 2050. (a) RCP2.6. (b) RCP4.5. (c) RCP8.5.
(a)
(b)
(c)Low incidence of drought-flood abrupt alternation events is present in the RCP4.5 scenario, but each event has the longest drought duration with severe disasters. In the RCP4.5 scenario, drought-flood alternation change events might occur in spring, summer, and autumn.


## 4.3.1. Intense Precipitation

In the next 30 years, intense precipitation (≥50 mm/day) events occur in April to October under three scenarios, with a difference in spatial distribution. For example, in the RCP4.5 scenario, extreme precipitation is mainly concentrated in the central and southeastern parts of Guizhou (Figure17). And extreme precipitation accounts for 2.9% of total precipitation, with 1.1%, 0.56%, and 0.43% in June, July, and September, respectively.Figure 17
Spatial distribution of the proportion of extreme precipitation to total precipitation. (a) RCP2.6. (b) RCP4.5. (c) RCP8.5.
(a)
(b)
(c)


## 4.3.2. Consecutive Rainless Days

Figure18 plots the probabilities of different levels of drought in Guizhou from 2019 to 2050, and Figure 19 shows the probabilities of drought (except light drought) in different seasons from 2019 to 2050.Figure 18
The probabilities of different levels of drought in Guizhou from 2019 to 2050. (a) Light drought. (b) Moderate drought. (c) Severe drought. (d) Extraordinary drought.
(a)
(b)
(c)
(d)Figure 19
The probabilities of drought (except mild drought) in different seasons from 2019 to 2050. (a) Spring. (b) Summer. (c) Autumn.
(a)
(b)
(c)Drought is considered to be the most serious meteorological disaster affecting agriculture, and the drought in Guizhou Province has obvious seasonality and regionality [45]. Therefore, this paper analyzed the different levels of drought and seasonal drought in Guizhou in the next 30 years. Spring drought and summer drought are the main disaster-causing factors for rice and corn. Autumn and spring droughts are the main hazard factors for wheat and rapeseed [46]. This study mainly analyzed the probability of drought in spring, summer, and autumn.Under the RCP4.5 scenario, in the next 30 years, the occurrence probability of drought in western Guizhou is higher than that in eastern Guizhou. In eastern Guizhou, drought is frequent in summer, while much less in spring and autumn. A number of droughts (except light drought) might occur most in the summer followed by spring. Therefore, the greatest impact on agriculture in the next 30 years is summer drought, followed by spring drought. Frequent drought events will increase the irrigation water demand and augment the pressure on freshwater resources.


## 4.3.3. Drought-Flood Abrupt Alternation Events

Soil fertility, rice growth period, physiological characteristics, and others will be affected by drought-flood abrupt alternation events, and the latter results in large reduction of production [47]. Figure 20 shows spatial distribution of drought-flood abrupt alternation events in Guizhou.Figure 20
Spatial distribution of the frequency of drought-flood abrupt alternation events in Guizhou from 2019 to 2050. (a) RCP2.6. (b) RCP4.5. (c) RCP8.5.
(a)
(b)
(c)Low incidence of drought-flood abrupt alternation events is present in the RCP4.5 scenario, but each event has the longest drought duration with severe disasters. In the RCP4.5 scenario, drought-flood alternation change events might occur in spring, summer, and autumn.


## 5. Discussion

### 5.1. Effect of Normal Climatic Factors on Crop Growth

In Guizhou, the whole climate is getting warmer. Compared with historical data, the increase rate of temperature based on model projections is about 2 times that in history.The temperature increase can lead to the northward shifting of suitable cropping areas of rice, maize, and wheat [48]. In the past 30 years, the north boundary of the double cropping rice growing area in southern China has been pushed northward for nearly 300 km [49]. Growth period is advanced and shortened [50]. The decomposition of soil organic matter is accelerated, and the soil fertility is reduced [51, 52]. It may also expand the activity scope of some pests subjected to temperature restrictions, further shorten the growth period of most pests, and increase the number of reproductive generations [53]. In the next 30 years, annual average temperature may increase from 1.36°C to 1.5°C compared with baseline period (from 1961 to 2018). It may change the suitable cropping areas in Guizhou and increase pests and diseases. Ao [54] also obtained this conclusion when she studied the Liupanshui area in Guizhou. It may also reduce crop yield while increasing utilization of fertilizers and pesticides, which will pollute the environment and water resources.From 2019 to 2050, under RCP4.5 scenario, the sowing period of winter wheat and rice tends to be postponed. The duration of maize and rice’s growth period will be shortened, and the life cycle of wheat also emerges as having a decreasing tendency except for those from the southern region. The average crop cycle length of winter wheat, summer maize, and rice was shortened by 23 days, 4 days, and 9 days, respectively, compared with the mean value of 1961 to 2018. Meanwhile, the rate of shortening of crop cycle length is faster than the value of 1961 to 2018. Changes in food quality are to be expected, e.g., decreased protein and mineral nutrient concentrations, as well as altered lipid composition [55]. The linear tendency rate of the precipitation tendency is small in the wheat growing season but large in the interannual variation, which increases the uncertainty of the irrigation amount of water resources. While most parts of Guizhou are rain-fed agricultural areas, the yield of wheat is positively correlated with the precipitation. Thus, the adverse effects will affect the wheat growth.


### 5.2. Effect of Extreme Events on Crop Growth

Extreme precipitation can affect soil fertility, and storm floods can cause sudden increase in diseases [56, 57]. The main stage of rice tillering in Guizhou is in June, which is the key period for rice growing [43]. Extreme precipitation in the next 30 years is mainly concentrated in June and July, which will increase the incidence of diseases. This phenomenon is more evident in central and southeastern Guizhou. In the future, the rice planting layout can be adjusted according to the distribution of extreme precipitation.Rapeseed is also one of the crops in Guizhou. It blooms in March and matures in the end of April and early May. From the spatial distribution of extreme precipitation, it can be explored that the occurrence probability of extreme precipitation is relatively high in the two major rapeseed producing areas, Buyei and Miao Autonomous Prefecture of QianNan and Guiyang. In April, extreme precipitation accounted for 0.16% of the annual precipitation.Drought is considered to be the most serious meteorological disaster affecting agriculture. Under the RCP4.5 scenario, in the next 30 years, a number of droughts (except light drought) might occur most in the summer followed by spring. Therefore, the greatest impact on agriculture in the next 30 years is summer drought, followed by spring drought. Frequent drought events will increase the irrigation water demand and augment the pressure on freshwater resources. In the RCP4.5 scenario, drought-flood abrupt alternation events might occur in spring, summer, and autumn, which will seriously affect soil quality and increase fertilizers.


## 5.1. Effect of Normal Climatic Factors on Crop Growth

In Guizhou, the whole climate is getting warmer. Compared with historical data, the increase rate of temperature based on model projections is about 2 times that in history.The temperature increase can lead to the northward shifting of suitable cropping areas of rice, maize, and wheat [48]. In the past 30 years, the north boundary of the double cropping rice growing area in southern China has been pushed northward for nearly 300 km [49]. Growth period is advanced and shortened [50]. The decomposition of soil organic matter is accelerated, and the soil fertility is reduced [51, 52]. It may also expand the activity scope of some pests subjected to temperature restrictions, further shorten the growth period of most pests, and increase the number of reproductive generations [53]. In the next 30 years, annual average temperature may increase from 1.36°C to 1.5°C compared with baseline period (from 1961 to 2018). It may change the suitable cropping areas in Guizhou and increase pests and diseases. Ao [54] also obtained this conclusion when she studied the Liupanshui area in Guizhou. It may also reduce crop yield while increasing utilization of fertilizers and pesticides, which will pollute the environment and water resources.From 2019 to 2050, under RCP4.5 scenario, the sowing period of winter wheat and rice tends to be postponed. The duration of maize and rice’s growth period will be shortened, and the life cycle of wheat also emerges as having a decreasing tendency except for those from the southern region. The average crop cycle length of winter wheat, summer maize, and rice was shortened by 23 days, 4 days, and 9 days, respectively, compared with the mean value of 1961 to 2018. Meanwhile, the rate of shortening of crop cycle length is faster than the value of 1961 to 2018. Changes in food quality are to be expected, e.g., decreased protein and mineral nutrient concentrations, as well as altered lipid composition [55]. The linear tendency rate of the precipitation tendency is small in the wheat growing season but large in the interannual variation, which increases the uncertainty of the irrigation amount of water resources. While most parts of Guizhou are rain-fed agricultural areas, the yield of wheat is positively correlated with the precipitation. Thus, the adverse effects will affect the wheat growth.


## 5.2. Effect of Extreme Events on Crop Growth

Extreme precipitation can affect soil fertility, and storm floods can cause sudden increase in diseases [56, 57]. The main stage of rice tillering in Guizhou is in June, which is the key period for rice growing [43]. Extreme precipitation in the next 30 years is mainly concentrated in June and July, which will increase the incidence of diseases. This phenomenon is more evident in central and southeastern Guizhou. In the future, the rice planting layout can be adjusted according to the distribution of extreme precipitation.Rapeseed is also one of the crops in Guizhou. It blooms in March and matures in the end of April and early May. From the spatial distribution of extreme precipitation, it can be explored that the occurrence probability of extreme precipitation is relatively high in the two major rapeseed producing areas, Buyei and Miao Autonomous Prefecture of QianNan and Guiyang. In April, extreme precipitation accounted for 0.16% of the annual precipitation.Drought is considered to be the most serious meteorological disaster affecting agriculture. Under the RCP4.5 scenario, in the next 30 years, a number of droughts (except light drought) might occur most in the summer followed by spring. Therefore, the greatest impact on agriculture in the next 30 years is summer drought, followed by spring drought. Frequent drought events will increase the irrigation water demand and augment the pressure on freshwater resources. In the RCP4.5 scenario, drought-flood abrupt alternation events might occur in spring, summer, and autumn, which will seriously affect soil quality and increase fertilizers.


## 6. Conclusions

(1)
From 1961 to 2018, the temperature showed an increasing trend, with a rate of 0.14°C per 10 years. The precipitation showed a decreasing trend, with a rate of −15.02 mm per 10 years. Compared with historical data, under RCP4.5 scenario, the increase rate of temperature is about 2 times that in history.
(2)
The growth period of crops has been influenced by climate change. The average crop cycle length of winter wheat, summer maize, and rice was shortened by 23 days, 4 days, and 9 days, respectively, compared with the mean value of 1961–2018. The sowing period of wheat and rice will be postponed in the next 30 years. The winter wheat cycle length is shortened except for the southern region, with the rate from −4.83 to 2.18 (days/10 years). The life cycle of corn also emerges as having a decreasing tendency in most areas of Guizhou, with the rate from −2.12 to 0.15 (days/10 years). The rice sowing period will be postponed in the next 30 years. The rice cycle length is shortened, with the rate from −4.65 to −0.07 (days/10 years). In the next 30 years. The precipitation tendency is low in the wheat growing season but large in the interannual variation, which increases the uncertainty of the irrigation amount of water resources. The precipitation in the growing season of rice and corn shows an increasing trend, with a rate of 16.6 mm/10 years.
(3)
In the next 30 years, intense precipitation (≥50 mm/day) events might occur during April to October under three scenarios. Under the RCP4.5 scenario, extreme precipitation is mainly concentrated in the central and southeastern parts of Guizhou. And extreme precipitation accounts for 2.9% of total precipitation, 1.1% in June. The occurrence probability of extreme precipitation is relatively high in the two major rapeseed producing areas, Buyei and Miao Autonomous Prefecture of QianNan and Guiyang.
(4)
Under the RCP4.5 scenario, in the next 30 years, the occurrence probability of drought in western Guizhou is higher than that in eastern Guizhou. In eastern Guizhou, drought is frequent in summer, while much less in spring and autumn. A number of droughts (except mild drought) might occur most in the summer followed by spring. Therefore, the greatest impact on agriculture in the next 30 years is summer drought, followed by spring drought. In the RCP4.5 scenario, drought-flood abrupt alternation events might occur in spring, summer, and autumn, which will seriously affect soil quality and increase fertilizers.



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*Source: 1401402-2019-11-03.xml*