- Research article
- Open Access
Characteristics of meteorological disasters and their impacts on the agricultural ecosystems in the northwest of China: a case study in Xinjiang
© Wu et al.; licensee Springer. 2015
- Received: 4 December 2014
- Accepted: 17 January 2015
- Published: 12 February 2015
In recent years, the meteorological extreme events have caused the direct economic losses and human mortality increased significantly. While there has been a paucity of information regarding trends in meteorological disasters in Xinjiang. Based on two extreme climate measurements, i.e., the Palmer Drought Severity Index (PDSI) and the agricultural disaster area, the influence of meteorological disasters on agriculture were analyzed during the period 1960-2010.
(1) Temperature extremes exhibited patterns consistent with warming, with a large proportion of stations having statistically significant trends. The warming trends in the indices derived using daily minimum temperatures were greater than those obtained using maximum temperatures. Most of the precipitation indices exhibited increasing trends across the region, and the increased precipitation was due to the increase in both precipitation frequency and intensity. (2) The drought indices increased significantly in most regions of Xinjiang, and the seasonal PDSI exhibited significant correlations with the annual PDSI. For the entire geographical study area, two contrasting periods were evident in the PDSI between 1961 and 2010. Wet conditions dominated from 1987 to 2010, whereas persistent drought conditions occurred from 1960 to 1986. (3) Increased climate extremes resulted in increased agricultural disaster area. During warm summers, the droughts intensified; the corresponding snowmelt flood also became stronger. In addition, the sharply reduced effective irrigation area exacerbated the increased agricultural disaster area.
Climate change has affected the local agricultural oasis ecosystem and the yield and quality of crops in Xinjiang, leading to increased instability in agricultural production.
- Climate extremes
- Palmer Drought Severity Index (PDSI)
- Agricultural disaster area
According to the World Meteorological Organization (WMO) estimate, losses caused by meteorological disasters have accounted for 85% of the total losses cause by natural disasters (Qiang et al. 2001; Qing 2008). In the last 20 years, the direct economic losses caused by meteorological extreme events have increased exponentially, and human mortality has also significantly increased (Loukas et al. 2010). China is one of the countries that has been most severely affected by the meteorological disasters around the world due to its complex terrain conditions, leading to higher frequencies of extreme weather compared to other countries (Zhang et al. 1991). The crop disaster area caused by various meteorological disasters has been as high as 5 × 107 hectares every year, and the population affected by major meteorological disasters, such as typhoons, rainstorms, droughts, heat waves, and sand storms, has reached 4 × 108 (Qing 2008). During the period 1990–2006, the direct economic losses caused by meteorological disasters in China was 1859 × 108 RMB per year, accounting for an average of for 2.8% of the annual GDP (Qing 2008).
Xinjiang has complex natural conditions that lead to a high frequency of various types of strong natural disasters (Ye and Chen 1996). Meteorological disasters and their derivative disasters have accounted for 83% of the total loss due to all natural disasters, and the number of deaths has accounted for 85% of the total (Liu 1995). These disasters have also exacerbated the deterioration of the ecological environment. Some new characteristics of these meteorological disasters and their derivative disasters have been found since the 1980s, including the increase in the types of events, increased frequency, and increased strength. According to incomplete statistics, the average annual loss was 1 × 107-5 × 107 RMB, 1.4 × 108 RMB, and 20 × 108 -50 × 108 RMB during the period 1950–1970, during the 1980s, and since the 1990s, respectively, accounting for 2 ~ 3.5% of the GDP in Xinjiang. Since the 1980s, the drought disaster area, inundated area, and various economic losses have increased significantly (Xu et al. 2008; Bai et al. 2012; Chen and Gao 2010; Chen et al. 2008).
Xinjiang is located in the arid region of northwestern China; its ecological environment is very fragile. The recovery period of the agricultural ecological system to disasters is relatively long. Analyzing the effects of meteorological disasters on the agricultural ecosystem in Xinjiang is very important for formulating corresponding disaster prevention countermeasures that can protect the ecological and environmental security. In addition, such an analysis is also favorable for formulating agricultural adaptation and mitigation measures in response to climate change and for maintaining social stability, which are very important for promoting the sustainable development of the national economy.
Description of study area
Definitions of 6 temperature indices and 5 precipitation indices used in this study; all of the indices are calculated using RClimDex
Percentage of days when TN < 10th percentile
Monthly minimum value of daily minimum temp
Cold spell duration indicator
Annual count of days with at least 6 consecutive days when TN < 10th percentile
Percentage of days when TX > 90th percentile
Monthly maximum value of daily maximum temp
Warm spell duration indicator
Annual count of days with at least 6 consecutive days when TX > 90th percentile
Diurnal temperature range
Monthly mean difference between TX and TN
The PDSI is widely used in drought evaluation studies, which was developed by Palmer (1965) to measure the cumulative departure in atmospheric moisture supply and demand. The PDSI not only accounts for precipitation but also accounts for temperature, which has a large effect on evapotranspiration and soil moisture (Liu et al. 2012). The PDSI soil parameter that is used for bucket water balance is the available water content (AWC). The AWC was determined from the State Soil Geographic Database (STATSGO) for the top 100 cm of the soil profile. However, a common critique of the PDSI is that the behavior of the index at various locations is inconsistent, making spatial comparisons of the PDSI difficult. The SC-PDSI automatically calibrates the behavior at any location by replacing empirical constants with dynamically calculated values. More details regarding the PDSI and SC-PDSI can be found in the works of Liu et al. (2012) and Wells et al. (2004).
We used a nonparametric Kendall’s tau-based slope estimator in this study (Sen 1968); statistical significance for the trends in extreme climate indices was determined using the Mann-Kendall test (Kendall 1975; Mann 1945). A trend was considered to be statistically significant if it was significant at the 5% level. The results of the M-K test are substantially affected by serial correlation; therefore, we adopted the Yue and Pilon method, which uses the R package “ZYP” to remove lag-1 autocorrelations (Yue et al. 2002).
Characteristics of climate extremes
For the warm extremes (Figure 2c-d), 83% of the stations exhibited statistically significant trends for warm nights (TX90); the regional trend for TN90 was 1.25 days/decade. The warmest days (TXx) also exhibited an increasing trend, while only 25% of the stations exhibited significant trends. The warm spell duration indicator (WSDI) (not shown) also increased, and the regional trend was 1.98 days/decade.
Characteristics of climate extremes
Influence of meteorological disasters on Agriculture
Correlations between climate extremes and the agricultural disaster area in Xinjiang
Area (Million mu)
Correlations between climate extremes and the agricultural disaster area in Xinjiang after 1986
Area (Million mu)
In Xinjiang, the climate became warmer and wetter, with cold extremes decreasing and warm extremes increasing. Moreover, climate extremes derived from daily minimum temperatures were more numerous that the extremes derived from daily maximum temperatures, which was found to be consistent with the significantly decreased DTR. Precipitation extremes also increased due to the increase in both precipitation frequency and intensity. Frequent and long droughts occurred from 1960 to 1986, although wet climates prevailed from 1987 to 2010. However, after 2003, droughts became more prolific, which was reflected in a drastic decrease in the PDSI.
Extreme temperatures had a significant effect on the farmland disaster area in Xinjiang. For example, summertime warm events resulted in an increase in the affected farmland area. Floods were primarily related to extreme precipitation, while the storm disaster area and the low temperature disaster area were the result of changes in temperature extremes. Climate change can also affect the agricultural ecosystem and the oasis crop yield and quality in various ways, leading to increased instability in agricultural production. This study showed that the strengthening of climate warming and evaporation capacity not only increased the soil moisture but also decrease the soil entropy, increased spring drought, accelerated the production of organic matter due to soil microbial decomposition, resulting in the decline in soil fertility and decreased yields. To maintain soil fertility, the fertilization amount must also increase. Although the climate warming can improve grain yields in some regions, climate warming can cause the agriculture water consumption per unit to increase, which also increases production costs. Especially in winter, an increase in extreme minimum temperatures will reduce overwinter human mortality, expand agricultural pest regions, increase the pesticide control difficulty and application amount.
The research is supported the Natural Sciences Foundation of China (Grant No.41361093). The authors thank the National Climate Central, China Meteorological Administration, for providing the meteorological data for this study.
- Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6(6):661–675View ArticleGoogle Scholar
- Bai YG, Musha RZ, Lei XY, Zhang JH (2012) Analysis on characteristics and affecting factor of drought disaster of Xinjiang. Yellow River 34(07):61–63 (in Chinese with English abstract)Google Scholar
- Buishand TA (1982) Some methods for testing the homogeneity of rainfall records. J Hydrol 58(1–2):11–27View ArticleGoogle Scholar
- Chen YF, Gao G (2010) An analysis to losses caused by meteorological disasters in China during 1989–2008. Meteorological Mon 36(02):76–80 (in Chinese with English abstract)Google Scholar
- Chen XJ, Liu XF, Yang XH (2008) Current position of natural disasters in Xinjiang and their control countermeasures. Territory Nat Resour Stud 3(030):64–65 (in Chinese with English abstract)Google Scholar
- Gao XJ, Zhao ZC, Giorgi F (2002) Changes of extreme events in regional climate simulations over East Asia. Adv Atmos Sci 19:927–942View ArticleGoogle Scholar
- Hong Y, Adler RF (2008) Predicting global landslide spatiotemporal distribution: integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates. Int J Sediment Res 23:249–257View ArticleGoogle Scholar
- Kendall MG (1975) Rank-correlation measures. Charles Griffin, London, p 202Google Scholar
- Liu GC (1995) Discussion on the effects of meteorological disasters on the national economy of Xinjiang. Arid Zone Res 12(03):7–14 (in Chinese with English abstract)Google Scholar
- Liu L, Hong Y, Bednarczyk CN, Yong B, Shafer MA, Riley R, Hocker JE (2012) Hydro-climatological drought analyses and projections using meteorological and hydrological drought indices: a case study in Blue River Basin, Oklahoma. Water Resour Manag 26(10):2761–2779View ArticleGoogle Scholar
- Loukas A, Llasat MC, Ulbrich U (2010) “Extreme events induced by weather and climate change: evaluation, forecasting and proactive planning” Preface. Nat Hazards Earth Syst Sci 10(9):1895–1897View ArticleGoogle Scholar
- Mann HB (1945) Non-parametric tests against trend. Econometrica 13:245–259View ArticleGoogle Scholar
- Palmer WC (1965) Meteorological drought. Office of Climatology Research Paper 45, Weather Bureau, Washington, D.C, p 58Google Scholar
- Peterson TC, Easterling DR, Karl TR, Groisman P, Nicholls N, Plummer N, Parker D (1998) Homogeneity adjustments of in situ atmospheric climate data: a review. Int J Climatol 18(13):1493–1517View ArticleGoogle Scholar
- Qiang ZA, Wu TW, Song MH, Ma XB, Cai Y, Liang XY (2001) Arid disaster and advances in arid climate researched over northwest China. Adv Earth Sci 6(01):28–38 (in Chinese with English abstract)Google Scholar
- Qing DH (2008) China's major meteorological disasters and development trend. China Emerg Rescue 6:4–6 (in Chinese with English abstract)Google Scholar
- Sen PK (1968) Estimates of the regression coefficient based on Kendall's Tau. J. Am. Stat. Assoc 63:1379-1389Google Scholar
- Shi YF, Shen YP, Kang E, Li DL, Ding YJ, Zhang GW, Hu RJ (2007) Recent and future climate change in northwest china. Clim Change 80:379–39View ArticleGoogle Scholar
- Sun G, Chen Y, Li W, Pan C, Li J, Yang Y (2014) Intra-annual distribution and decadal change in extreme hydrological events in Xinjiang, Northwestern China. Nat Hazards 70(1):119–133View ArticleGoogle Scholar
- Wells N, Goddard S, Hayes MJ (2004) A self-calibrating Palmer Drought Severity Index. J Clim 17(12):2335–2351View ArticleGoogle Scholar
- Xu ZX, Chen YN, Li JY (2004) Impact of climate change on water resources in the Tarim River basin. Water Resour Manag 18:439–458View ArticleGoogle Scholar
- Xu GH, Mao WY, Lu GY (2008) Xinjiang meteorological weather changes and general statement on the work of disaster prevention and reduction. Desert Oasis Meteorol 2(01):50–54 (in Chinese with English abstract)Google Scholar
- Ye MQ, Chen BH (1996) Research on natural disaster zoning in Xinjiang. J Nat Disasters 5(01):14–21 (in Chinese with English abstract)Google Scholar
- Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process 16(9):1807–1829View ArticleGoogle Scholar
- Zhang YC, He WX, Li SK (1991) Introduction to Chinese agricultural meteorological disasters [M]. China Meteorological Press, BeijingGoogle Scholar
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