Influence of future climate change on crop water demand

The main crops planted in Shijiazhuang Plain are winter wheat and summer corn, accounting for more than 70% of the total crop planting area, which are planted in two seasons a year. Therefore, this study takes winter wheat and summer corn as representative crops to calculate. The calculation period is 20 1 1 ~ 2060.

In order to compare future climate scenarios with current climate conditions, meteorological data of current climate conditions (RCP) from 20 1 1 to 2060 were generated by NCC/GU-WG(2.0) weather generator software developed by China National Climate Center. The simulation software was developed by China National Climate Center according to the daily meteorological data of 67 1 weather station in China, with high accuracy, as shown in Table 7- 1. This software is easy to operate. Directly select the corresponding simulation station and click the output button, mainly 20 16544.

Table 7- 1 Comparison between simulated meteorological data and measured data

Note: The maximum temperature in the table is the multi-year average daily maximum temperature, the minimum temperature is the multi-year average daily minimum temperature, the precipitation is the multi-year average precipitation, and the sunshine hours are the multi-year average sunshine hours. The measured data comes from China meteorological data sharing service network.

First, the calculation method

The Man Penman-Monteith formula is used to calculate the crop water demand, and the calculation formula is as follows:

Study on evolution characteristics and scale effect of groundwater flow field in Shijiazhuang plain area

Where: ETo is the reference crop water requirement, mm; Rn is the surface net radiation, mj/m2; G is soil heat flux, mj/m2; T is the daily average temperature at a height of 2.0m,℃; U2 is the wind speed at a height of 2.0m, m/s; Es is saturated water pressure, kPa；; Ea is the actual water pressure, kPa；; δ is the slope of saturated water pressure curve, kPa/℃; R is the hygrometer constant, kPa/℃. The basic calculation data required by the above formula include daily maximum temperature, daily minimum temperature, average wind speed, average relative humidity and sunshine hours. The remaining calculation parameters can be calculated by corresponding empirical formulas. The calculation process of this paper is realized on the software EToCalculatorV32 developed by FAO. The air humidity (%) selection button Tdew=Tmin+2℃, the wind speed (m/s) selection button light tomoderate wind and the interior lacation button.

Crop irrigation water demand is calculated according to the following formula:

IR =KcETo-Pe (7-2)

Where: IR is irrigation water demand, mm; Kc is the coefficient of crop water demand, using the measured data of Liu Yu et al. (2009); Pe is the effective precipitation in crop growth period, mm.

Effective precipitation (Pe) during crop growth period is calculated according to the following formula, and the calculation time unit is ten days.

Study on evolution characteristics and scale effect of groundwater flow field in Shijiazhuang plain area

Where: p is the precipitation in the growing period of crops, mm.

Second, the data source.

Because the resolution of MPI-ESM-MR atmospheric circulation model output data is low (1.865× 1.875), it needs to be scaled down. In this paper, the statistical downscaling software SDSM(4.2) is used to downscale the daily maximum temperature and daily minimum temperature of RCP4.5 climate scenario model. The surface temperature field and sea level pressure field with a prediction factor of 2.0m. The verification period of statistical model is 196 1 ~ 1975, and the verification period is 1976 ~ 65438.

Figures 7- 1 and 7-2 give the measured data and simulated data of monthly maximum temperature and monthly minimum temperature in the study area 1976 ~ 20 10. Normalized root mean square difference (RMSE) is used to measure the difference between the measured sequence and the simulated sequence, and the calculation formula is Formula (7-4), and the consistency is measured by the correlation between them.

Fig. 7- 1 month comparison between measured data and simulated data of maximum temperature

Fig. 7-Comparison between measured data and simulated data of minimum temperature in February.

Generally speaking, RMSE < 10% is excellent, 10% < RMSE < 20% is good, 20% < RMSE < 30% is medium, and RMSE > 30% is poor. The closer the correlation coefficient is to 1, the better the correlation is (Figure 7-3).

Figure 7-3 Correlation between Measured Monthly Temperature Data and Downscaled Data

A- maximum temperature; B- minimum temperature

Study on evolution characteristics and scale effect of groundwater flow field in Shijiazhuang plain area

Where: Si is the analog value,℃; Ri is the measured value,℃; R is the average value of measurement,℃. After calculation, the normalized root mean square difference (RMSE) of the average maximum temperature in 1976 ~ 1990 is 8.9%, which is an excellent level, and the average minimum temperature is 22.6%, which is a medium level. Judging from the correlation coefficient, the highest temperature is 0.98 and the lowest temperature is 0.99, both of which are very high, indicating that the measured values are in good agreement with the simulated values.

The downscaling of precipitation series is relatively complicated, and the data obtained by downscaling with SDSM(4.2) software has a larger error than the measured data in the same period. Referring to the research method of Cong et al. (20 10), this paper adopts the following steps to scale:

(1) The historical output data of atmospheric circulation model MPI-ESM-MR (196 1 ~ 2000) and the output data of RCP4.5 climate scenario (1~ 65438+February) are counted respectively.

(2) Contrastively analyze and calculate the increase degree of average precipitation from 1 to 12 relative to the historical output data 1 to 12, respectively.

3) Calculate and superimpose Shijiazhuang Station 20 1 1 to 65438 generated by NCC/GU-WG(2.0) weather generator, and calculate the increase range of monthly average precipitation relative to the historical output data in RCP4.5 scenario mode as 1 to 65438.

The main calculation process is shown in Figure 7-4:

Figure 7-4 Downscaling Calculation Process of Daily Precipitation

Third, the result analysis

With air temperature as the abscissa and crop water demand as the ordinate, a correlation diagram is established (Figure 7-5). As can be seen from the figure, with the increase of temperature, the crop water demand in both climate scenarios increases linearly, but the increase range is different. Under the current climate conditions, the crop water demand will increase by 40.7 mm for every temperature increase of 65438 0.0℃, and by 27.8 mm in RCP4.5 scenario. Judging from the average water demand of crops in the next 50 years (20 1 1 ~ 2060), the current climate condition is 1 107mm, and the RCP4.5 scenario is increased to 1 139mm.

Figure 7-5 Influence of annual maximum temperature on crop water demand under different climate scenarios

a-RCP； b—RCP4.5

Equations (7-2) and (7-3) can be used to calculate the crop irrigation water demand in Shijiazhuang Plain from 201/kloc-0 to 2060. With precipitation as the abscissa and irrigation water demand as the ordinate, a correlation diagram is established (Figure 7-6). It can be seen that with the increase of precipitation, the irrigation water demand under the two climate scenarios decreases linearly, but the reduction extent is different. Under the current climate conditions, the irrigation water demand will be reduced by 40 mm for every increase of precipitation 100 mm, and by 45 mm in RCP4.5 scenario.

Figure 7-6 Influence of annual maximum temperature on crop irrigation water demand under different climate scenarios

a-RCP； b—RCP4.5

According to the average level of many years (20 1 1 ~ 2060), the irrigation water demand under current climate conditions is 7 15mm, 201~ 2035 is 709mm, and 2036 ~ 2060 is 729 mm. The water demand of RCP4.5 is 7 12mm, 707mm from 2065,438+065,438+0 to 2035, and 7 17mm from 2036 to 2060. In order to quantitatively evaluate the impact of climate change on annual water demand, water demand greater than 750mm is high-intensity irrigation water demand, and 700 ~ 750 mm is less than 700mm is low-intensity irrigation water demand. Under the current climatic conditions (RCP), the annual low-intensity irrigation water demand accounts for 42% (20 1 1 ~ 2060), and the medium-intensity irrigation water demand accounts for 34%. Under the RCP4.5 climate scenario, compared with the current climate conditions, the annual proportion of low-intensity water demand increased by 8%, medium-intensity water demand decreased by 6%, and high-intensity water demand decreased by 2%.

From an interannual point of view, under the current climate conditions, the irrigation water demand showed an obvious downward trend at a significant level of 5% from 201/kloc-0 to 2035, and the decline rate was 13.5mm/ 10a, and there was no obvious upward or downward trend from 2036 to 2060. Compared with the current climate conditions, the decline rate of irrigation water demand increased to 15.7mm/ 10a in 2065433, and there was no obvious upward or downward trend of irrigation water demand from 2036 to 2060 (Figure 7-8).

Figure 7-7 Evolution characteristics of irrigation water demand in Shijiazhuang Plain from 2011to 2060 under current climate conditions.

Fig. evolution characteristics of irrigation water demand in Shijiazhuang plain from 2011to 2060 under RCP 4.5 climate scenario.