Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Characteristics of meteorological disasters and their impacts on the agricultural ecosystems in the northwest of China: a case study in Xinjiang

Characteristics of meteorological disasters and their impacts on the agricultural ecosystems in... Background: 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. Results: (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. Conclusions: 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. Keywords: Climate extremes; Palmer Drought Severity Index (PDSI); Agricultural disaster area; Xinjiang Background higher frequencies of extreme weather compared to other According to the World Meteorological Organization countries (Zhang et al. 1991). The crop disaster area caused (WMO) estimate, losses caused by meteorological disasters by various meteorological disasters has been as high as have accounted for 85% of the total losses cause by natural 5× 10 hectares every year, and the population affected by disasters (Qiang et al. 2001; Qing 2008). In the last 20 years, major meteorological disasters, such as typhoons, rain- the direct economic losses caused by meteorological storms, droughts, heat waves, and sand storms, has reached extreme events have increased exponentially, and human 4× 10 (Qing 2008). During the period 1990–2006, the mortality has also significantly increased (Loukas et al. direct economic losses caused by meteorological disasters 2010). China is one of the countries that has been most in China was 1859 × 10 RMB per year, accounting for an severely affected by the meteorological disasters around the average of for 2.8% of the annual GDP (Qing 2008). world due to its complex terrain conditions, leading to 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 * Correspondence: chenyn@ms.xjb.ac.cn State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of derivative disasters have accounted for 83% of the total loss Ecology and Geography, Chinese Academy of Sciences (CAS), CAS, No. 818 due to all natural disasters, and the number of deaths has South Beijing Road, Urumqi, Xinjiang 830011, China accounted for 85% of the total (Liu 1995). These disasters Full list of author information is available at the end of the article © 2015 Wu et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 2 of 10 have also exacerbated the deterioration of the ecological South Xinjiang are located between these three moun- environment. Some new characteristics of these meteoro- tains. Xinjiang is divided into southern and northern areas logical disasters and their derivative disasters have been found by its natural landscape, resulting in two mountain-basin since the 1980s, including the increase in the types of events, systems with different hydrological and thermal condi- increased frequency, and increased strength. According to in- tions (Xu et al. 2004). The unique mountain-basin land- 7 7 complete statistics, the average annual loss was 1 × 10 -5 × 10 scape is responsible for the frequent occurrence of natural 8 8 8 RMB, 1.4 × 10 RMB, and 20 × 10 -50 × 10 RMB during disasters. Alpine areas are distributed widely in Xinjiang the period 1950–1970, during the 1980s, and since the and possess numerous glaciers and permanent snow, 1990s, respectively, accounting for 2 ~ 3.5% of the GDP in which increase the frequency of outburst floods. The mid- Xinjiang. Since the 1980s, the drought disaster area, inun- dle area experiences the highest precipitation in Xinjiang dated area, and various economic losses have increased (Gao et al. 2002), and water erosion is very strong; there- significantly (Xu et al. 2008; Bai et al. 2012; Chen and Gao fore, floods and snowfalls result in most of the extreme 2010; Chen et al. 2008). events (Hong and Adler 2008). In the low mountain areas, Xinjiang is located in the arid region of northwestern active fault zones increase the occurrence of floods, China; its ecological environment is very fragile. The recov- landslides, and debris flows during rainstorms and ery period of the agricultural ecological system to disasters seasonal snowmelt (Sun et al. 2014). is relatively long. Analyzing the effects of meteorological disasters on the agricultural ecosystem in Xinjiang is very Methods important for formulating corresponding disaster preven- Daily data, include the maximum temperature, mini- tion countermeasures that can protect the ecological and mum temperature, precipitation, relative humidity, environmental security. In addition, such an analysis is also sunshine hours, air pressure, and wind speed (1 January favorable for formulating agricultural adaptation and 1960 to 28 February 2011) covering the Xinjiang region mitigation measures in response to climate change and for were provided by the National Climate Center (NCC) of maintaining social stability, which are very important for the China Meteorological Administration (CMA). For promoting the sustainable development of the national this region, 53 stations passed the internal homogeneity economy. check of the China National Meteorological Center (CNMC), including a moving t test (Peterson et al. 1998), standard normal homogeneity test (Alexandersson Description of study area 1986), and departure accumulating method (Buishand Xinjiang Province (Figure 1) is located in northwestern 1982). Data were analyzed using the RclimDex package 8 2 China. With an area of 16.61 × 10 km , Xianjiang is the (software and documentation available for download largest province in China. Xinjiang is a typical inland arid from http://etccdi.pacificclimate.org/). Table 1 lists the region. The Altai Mountains, Tian Mountains, and Kunlun indices used in this study. The regional average is the Mountains extend from north to south in Xinjiang. The arithmetic mean of all stations in Xinjiang. The Xinjiang Junggar Basin in North Xinjiang and the Tarim Basin in disaster area data were derived from the China Statistical Yearbook and Chinese agricultural statistics. The effect- ive irrigation area and rural power data were also col- lected from the China Statistical Yearbook and Chinese agricultural statistics. 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 theavailablewater content(AWC).The AWCwas determined from the State Soil Geographic Database (STATSGO) for the top 100 cm of the soil profile. However, acommoncritiqueofthe PDSI is that thebehaviorofthe index at various locations is inconsistent, making spatial comparisons of the PDSI difficult. The SC-PDSI automatic- ally calibrates the behavior at any location by replacing Figure 1 Study area and weather stations in Xinjiang Province. empirical constants with dynamically calculated values. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 3 of 10 Table 1 Definitions of 6 temperature indices and 5 precipitation indices used in this study; all of the indices are calculated using RClimDex ID Indicator name Definitions UNITS Cold extremes TN10p Cool nights Percentage of days when TN < 10th percentile Days TNn Coldest nights Monthly minimum value of daily minimum temp °C CSDI Cold spell duration indicator Annual count of days with at least 6 consecutive days Days when TN < 10th percentile Warm extremes TX90p Warm days Percentage of days when TX > 90th percentile Days TXx Warmest days Monthly maximum value of daily maximum temp °C WSDI Warm spell duration indicator Annual count of days with at least 6 consecutive days Days when TX > 90th percentile Variability extremes DTR Diurnal temperature range Monthly mean difference between TX and TN °C More details regarding the PDSI and SC-PDSI can be −0.28°C/decade. Winter exhibit the most significant de- found in the works of Liu et al. (2012) and Wells et al. creasing trend, with a value of −4°C/decade (Figure 3d). (2004). The decrease in the DTR was because the minimum We used a nonparametric Kendall’s tau-based slope temperature increased faster than the maximum estimator in this study (Sen 1968); statistical significance temperature. for the trends in extreme climate indices was determined The spatial distribution of the trends in precipitation using the Mann-Kendall test (Kendall 1975; Mann 1945). extremes is show in Figure 4. For R0.1 (Figure 4c), which A trend was considered to be statistically significant if it represents the precipitation frequency, 40% of stations was significant at the 5% level. The results of the M-K test exhibited statistically significant changes. As for SDII are substantially affected by serial correlation; therefore, (Figure 4b), positive trends were found at 83% of the we adopted the Yue and Pilon method, which uses the stations; all stations were less coherent with positive/ Rpackage “ZYP” to remove lag-1 autocorrelations (Yue negative changes. Heavy precipitation, which was repre- et al. 2002). sented by R10 (Figure 4a), was dominated by increasing trends. The R0.1 and CWD trends were 1.26 days/decade Results and discussion and 0.047 days/decade, respectively. The SDII trend was Characteristics of climate extremes weaker. Therefore, the precipitation extremes exhibited Figure 2 depicts the spatial trends in the temperature significant changes in Xinjiang, specifically regarding the indices for cold extreme (Figure 2a-b). For cold nights change in rainy days (R0.1), heavy precipitation events (TN10), 51 stations (96%) had significant decreasing (R10) and the mean simple daily intensity index (SDII), trends, with a regional trend of −1.89 days/decade. The indicating that the precipitation changes were reflected temperature on the coldest nights (TNn) also exhibited a in both the frequency and intensity. statistically significant increase at approximately 70% of the stations, and the regional trend was 0.7°C/decade. The cold spell duration indicator (CSDI) (not shown) Characteristics of climate extremes decreased at a rate of −0.7 days/decade, while only 25% For the annual PDSI in Xinjiang (Figure 5a), 42 stations of the studied stations exhibited a significant change. had significant increasing trends. Stations along the For the warm extremes (Figure 2c-d), 83% of the Tianshan Mountains and North Xinjiang exhibited more stations exhibited statistically significant trends for warm pronounced trends. The seasonal pattern of the PDSI nights (TX90); the regional trend for TN90 was 1.25 days/ was same as the annual PDSI (Figure 5b-c), with correl- decade. The warmest days (TXx) also exhibited an ation coefficients exceeding 0.94. A detailed analysis increasing trend, while only 25% of the stations exhibited resulted in two contrasting periods (Figure 6). Wet significant trends. The warm spell duration indicator conditions occurred during the period 1987–2010, while (WSDI) (not shown) also increased, and the regional trend persistent drought conditions dominated during the was 1.98 days/decade. period 1960–1987. The annual PDSI was negative before The regional seasonal trends in the DTR are shown in 1986, and the mean was −1.5, which indicated frequent Figure 3. The regional trend in the annual DTR was drought events during these years. Thereafter, the PDSI Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 4 of 10 Figure 2 Spatial patterns of the changes in temperatures extremes per decade during the period 1960–2010 in Xinjiang. a: annual TN10p; b: annual TNn; c: annual TX90p; d: annual TXx. Positive/negative trends are shown as up/down triangles; the filled triangles are related to statistically significant trends (significant at the 0.05 level). The triangle sizes are proportional to the magnitude of the trends. changed to −0.5; therefore, the PDSI also exhibited a Influence of meteorological disasters on Agriculture step-wise change around 1986. These results were in Agriculture is the most sensitive sector to climate change. accordance with climate (temperature and precipitation) Affected by climate change, the agricultural growth process change (Shi et al. 2007). Moreover, the PDSI exhibited is constantly changing. Climate change has a direct effect decreasing trends after 2005, indicating the apparent on agricultural ecosystems, often lead to increased instabil- increase in drought severity. ity and volatility in agricultural production. The crop Figure 3 Regional trends in the DTR (the column is the annual anomaly series; the solid line is the 5-year-smoothed average; and the dashed line is the linear regression). a: spring DTR; b: summer DTR; c: autumn DTR; d: winter DTR. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 5 of 10 Figure 4 Spatial distribution of the trends and trend magnitudes for precipitation extremes. a: annual R10; b: annual SDII; c: annual R0.1; d: annual CDD. Positive/negative trends are shown as up/down triangles; the filled triangles are related to statistically significant trends (significant at the 0.05 level). The triangle sizes are proportional to the magnitude of the trends. acreage in Xinjiang increased at a rate of 6.21 × 10 mu/ area decreased substantially after 1970 before reversing in year; the changes can be divided into several distinct 2008. The area of planted wheat and corn changed in a phases. The area increased significantly during the periods similar manner to that of the crop acreage, experiencing a 1950–1970 and 1995–2012, while the area remained stable progression of increases, decreases, and ultimately increase during the period 1970–1995 (Figure 7a). The grain crop again. Because of the increased yield per mu, the wheat and Figure 5 Spatial pattern of decadal trends and spatial characteristic for the seasonal PDIS and the annual PDSI in Xinjiang. a: Spatial trends of annual PDSI and b, c: correlations between the seasonal and annual series. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 6 of 10 Figure 6 Regional time series of the PDIS in Xinjiang: a: regional time series of the annual PDSI; b: regional five-year moving average of the annual and seasonal PDSIs; and c, d: correlations between the seasonal and annual series. corn yields increased substantially (Figure 7c-d). After 2000, the storm disaster area was 210000 acres in the 1960’sand the yield/mu maintained a relatively constant level; an increased to 2430000 acres in the 2000’s. The proportion increasing trend was not well defined. Therefore, when affected by low temperatures increased after 1995, espe- agriculture developed to a certain level, it was challenging cially in 2009, which is when the proportion reached 20% to achieve increasing yields through improved irrigation (Figure 8e). This finding indicates that the climate warmed, measures and increased input in the agriculture sector. The which simultaneously exacerbated the climate instability economic crop area exhibited a continuous increasing and resulted in extreme low temperature events during trend. Moreover, in 1998, the planted area exceeded the some periods. grain crop area. In the case of cotton, the area, yield and The correlation coefficients between the trend in regional yield per mu increased after 1980 (Figure 7b). Based on the climateextremesand thedisasterareaare showninTable 2. above analysis, the crop acreage increased in Xinjiang The statistical data representing the affected areas may be primarily due to increased economic crops. not comprehensive before 1986; therefore, we also discuss The statistical results show that the devastated farmland data since1986via a separateanalysis(Table3). Thedisas- resulting from meteorological disasters increased (either ter area exhibited a significant correlation with extreme based on the disaster area or the disaster ratio), especially temperature drought indices, especially the TN10p and after 1970 (Figure 8a). Flood disaster areas (Figure 8b) were TX90p, indicating that the disaster area was primarily con- primarily concentrated after 1987, which confirms that the trolled by temperature extremes. When the summertime climate in Xinjiang shifted from warm and dry to warm temperature was warm, the corresponding affected farm- and wet around 1987. Droughts and storms occurred land area increased. The reasons for floods/droughts are primarily after 1970 (Figure 8c-d). The increasing trend in complex and are closely related to the weather, geograph- the storm disaster area was very well defined. For example, ical conditions, water conservancy facilities, soil structure, Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 7 of 10 Figure 7 Crop time series in Xinjiang during the period 1949–2012. a: the time series of crop area, and b, c, d: the characteristics of cotton, wheat and corn, respectively. and primarily precipitation extremes. Regardless, changes variability. For example, within a single year, extremely in the frequency (R0.1), intensity (SDII), amplitude (R10) warm events can occur in combination extremely cold and duration (CWD) of precipitation events will increase events. The cold events lead to rapid increases in the disas- the flood disaster area (Table 2). Drought events exhibited a ter area. Xinjiang has a typical irrigation agriculture; the negative correlation with extreme precipitation events, i.e., shortage of water resources is very serious due to the sharp when the precipitation intensity and frequency increased, increase in irrigated area. Based on the exponential growth the number of drought events decreased. The enhanced in rural consumption (Figure 9), we can speculate that the warm temperature extremes (WSDI) resulted in intensified annual extracted groundwater volume increased. Moreover, drought. For example, high temperatures in winter increase the effective irrigation area decreased substantially in recent soil moisture evaporation, affect the safe overwintering of years, which has also resulted in an increase in the disaster crops, decrease soil entropy, and increase the possibility of area, especially due to the expansion of drought-affected soil drought. The PDSI exhibited a negative correlation areas. with drought events, especially after 1986 (Table 3), which is when the correlation was significant. This result indicates Conclusions that the PDSI partially reflects the drought extent, which The characteristics of Xinjiang meteorological disasters can be used to assess drought events in Xinjiang. The storm were analyzed using extreme indices and the PDSI. disaster area was related to the temperature index, indicat- Combined with data for the farmland disaster area, we ing that extreme temperatures play a vital role in the forma- also analyzed the influence of meteorological disasters tion of strong convective weather. Moreover, the low on the agricultural and ecological systems. The primary temperature disaster area was also associated with the conclusions of this study are as follows: extreme temperature index; however, the correlation was found to be positive when compared with warm 1) In Xinjiang, the climate became warmer and wetter, temperature extremes, which is inconsistent with the actual with cold extremes decreasing and warm extremes relationship. This result may be related to climate change increasing. Moreover, climate extremes derived from Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 8 of 10 Figure 8 The characteristics of the agricultural disaster area in Xinjiang. a, b, c, d, e is the disaster area and proportion for total disaster area, flood, drought, storm and low temperature, respectively. Table 2 Correlations between climate extremes and the agricultural disaster area in Xinjiang Area (Million mu) Proportion (%) Total Flood Drought Storm Low temperature Total Flood Drought Storm Low temperature ** ** CWD .057 .581 -.119 .199 .123 -.018 .551 -.175 .192 .083 ** ** SDII -.067 .572 -.223 .081 -.011 -.112 .546 -.231 .049 -.033 ** ** R0.1 .043 .547 -.105 .249 .068 -.024 .549 -.179 .261 .008 ** * ** * R10 .051 .738 -.207 .280 .148 -.038 .703 -.267 .277 .113 ** ** RX1day .057 .656 -.110 .176 .040 .014 .621 -.147 .182 .026 * ** CSDI .005 -.138 .158 -.349 -.063 .046 -.114 .195 -.390 -.091 ** ** * ** ** ** ** ** ** TN10p -.651 -.532 -.356 -.662 -.589 -.518 -.460 -.212 -.627 -.571 * ** TNn .101 .207 -.074 .358 .090 .082 .196 -.066 .408 .164 ** ** ** * * ** ** * DTR -.380 -.628 -.159 -.477 -.311 -.299 -.607 -.073 -.484 -.291 ** ** ** ** ** * ** ** TX90p .533 .397 .276 .492 .534 .441 .335 .168 .482 .542 * ** * ** TXx .329 -.070 .277 .209 .377 .296 -.106 .222 .205 .376 ** ** ** ** ** * ** ** WSDI .470 .375 .236 .412 .551 .368 .317 .116 .419 .553 ** ** PDSI .046 .565 -.088 .147 .060 .013 .561 -.103 .176 .068 *Indicates significance at <0.05 and **indicates significance at <0.01. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 9 of 10 Table 3 Correlations between climate extremes and the agricultural disaster area in Xinjiang after 1986 Area (Million mu) Proportion (%) Total Flood Drought Storm Low temperature Total Flood Drought Storm Low temperature * * CWD -.289 .490 -.363 -.242 -.146 -.363 .449 -.339 -.270 -.202 ** ** * ** ** SDII -.319 .635 -.527 -.162 -.186 -.408 .604 -.566 -.163 -.197 * * * * R0.1 -.299 .438 -.423 -.043 -.248 -.403 .438 -.448 -.069 -.313 ** ** ** ** R10 -.211 .710 -.542 .003 -.062 -.327 .654 -.612 -.017 -.091 ** ** * ** ** RX1day -.382 .610 -.531 -.213 -.279 -.464 .568 -.545 -.224 -.287 CSDI .316 .208 .213 -.050 .404 .283 .268 .115 -.079 .346 ** * ** ** * TN10p -.672 -.177 -.401 -.441 -.528 -.536 -.029 -.184 -.321 -.474 TNn -.310 -.206 -.287 .019 -.281 -.303 -.211 -.230 .010 -.210 DTR -.030 -.410 .102 -.090 .018 .104 -.348 .198 -.014 .098 ** * * ** TX90p .544 .186 .282 .295 .510 .502 .091 .132 .253 .517 * * TXx .343 -.174 .193 .163 .436 .288 -.265 .085 .157 .427 ** * ** ** ** WSDI .567 .120 .406 .177 .517 .564 .026 .293 .158 .520 ** * * * PDSI -.521 .352 -.461 -.420 -.366 -.513 .364 -.351 -.392 -.374 *Indicates significance at <0.05 and **indicates significance at <0.01. daily minimum temperatures were more numerous ecosystem and the oasis crop yield and quality in that the extremes derived from daily maximum various ways, leading to increased instability in temperatures, which was found to be consistent agricultural production. This study showed that the with the significantly decreased DTR. Precipitation strengthening of climate warming and evaporation extremes also increased due to the increase in both capacity not only increased the soil moisture but also precipitation frequency and intensity. Frequent and decrease the soil entropy, increased spring drought, long droughts occurred from 1960 to 1986, although accelerated the production of organic matter due to wet climates prevailed from 1987 to 2010. However, soil microbial decomposition, resulting in the decline after 2003, droughts became more prolific, which in soil fertility and decreased yields. To maintain soil was reflected in a drastic decrease in the PDSI. fertility, the fertilization amount must also increase. 2) Extreme temperatures had a significant effect on the Although the climate warming can improve grain farmland disaster area in Xinjiang. For example, yields in some regions, climate warming can cause the summertime warm events resulted in an increase in agriculture water consumption per unit to increase, the affected farmland area. Floods were primarily which also increases production costs. Especially in related to extreme precipitation, while the storm winter, an increase in extreme minimum temperatures disaster area and the low temperature disaster area will reduce overwinter human mortality, expand were the result of changes in temperature extremes. agricultural pest regions, increase the pesticide Climate change can also affect the agricultural control difficulty and application amount. Figure 9 Changes in rural electricity consumption and the effective irrigation area in Xinjiang. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 10 of 10 Competing interests Sun G, Chen Y, Li W, Pan C, Li J, Yang Y (2014) Intra-annual distribution and The authors declare that they have no competing interests. decadal change in extreme hydrological events in Xinjiang, Northwestern China. Nat Hazards 70(1):119–133 Wells N, Goddard S, Hayes MJ (2004) A self-calibrating Palmer Drought Severity Authors’ contributions Index. J Clim 17(12):2335–2351 MW carried out data analysis and write the manuscript. YC provided Xu ZX, Chen YN, Li JY (2004) Impact of climate change on water resources in the suggestions and advices to the study and checked the manuscript. HW and Tarim River basin. Water Resour Manag 18:439–458 GS provided unpublished data and added some useful comments to improve Xu GH, Mao WY, Lu GY (2008) Xinjiang meteorological weather changes and the paper. All authors read and approved the final manuscript. general statement on the work of disaster prevention and reduction. Desert Oasis Meteorol 2(01):50–54 (in Chinese with English abstract) Acknowledgements Ye MQ, Chen BH (1996) Research on natural disaster zoning in Xinjiang. J Nat The research is supported the Natural Sciences Foundation of China (Grant Disasters 5(01):14–21 (in Chinese with English abstract) No.41361093). The authors thank the National Climate Central, China Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on Meteorological Administration, for providing the meteorological data for this the ability to detect trend in hydrological series. Hydrol Process study. 16(9):1807–1829 Zhang YC, He WX, Li SK (1991) Introduction to Chinese agricultural Author details 1 meteorological disasters [M]. China Meteorological Press, Beijing Key Laboratory of Oasis Ecology, School of Resource and Environmental Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences (CAS), CAS, No. 818 South Beijing Road, Urumqi, Xinjiang 830011, China. Forestry and Horticulture Department, Xinjiang Agriculture University, Urumqi, Xinjiang 830052, China. Received: 4 December 2014 Accepted: 17 January 2015 References Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6(6):661–675 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) Buishand TA (1982) Some methods for testing the homogeneity of rainfall records. J Hydrol 58(1–2):11–27 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) 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) Gao XJ, Zhao ZC, Giorgi F (2002) Changes of extreme events in regional climate simulations over East Asia. Adv Atmos Sci 19:927–942 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 23:249–257 Kendall MG (1975) Rank-correlation measures. Charles Griffin, London, p 202 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) 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):2761–2779 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):1895–1897 Mann HB (1945) Non-parametric tests against trend. Econometrica 13:245–259 Palmer WC (1965) Meteorological drought. Office of Climatology Research Paper Submit your manuscript to a 45, Weather Bureau, Washington, D.C, p 58 journal and benefi t from: Peterson TC, Easterling DR, Karl TR, Groisman P, Nicholls N, Plummer N, Parker D (1998) Homogeneity adjustments of in situ atmospheric climate data: a 7 Convenient online submission review. Int J Climatol 18(13):1493–1517 7 Rigorous peer review 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 7 Immediate publication on acceptance 6(01):28–38 (in Chinese with English abstract) 7 Open access: articles freely available online Qing DH (2008) China's major meteorological disasters and development trend. 7 High visibility within the fi eld China Emerg Rescue 6:4–6 (in Chinese with English abstract) 7 Retaining the copyright to your article Sen PK (1968) Estimates of the regression coefficient based on Kendall's Tau. J. Am. Stat. Assoc 63:1379-1389 Shi YF, Shen YP, Kang E, Li DL, Ding YJ, Zhang GW, Hu RJ (2007) Recent and Submit your next manuscript at 7 springeropen.com future climate change in northwest china. Clim Change 80:379–39 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geoenvironmental Disasters Springer Journals

Characteristics of meteorological disasters and their impacts on the agricultural ecosystems in the northwest of China: a case study in Xinjiang

Download PDF
10 pages

Loading next page...
 
/lp/springer-journals/characteristics-of-meteorological-disasters-and-their-impacts-on-the-nOCLb3Qxfe

References (28)

Publisher
Springer Journals
Copyright
Copyright © 2015 by Wu et al.; licensee Springer.
Subject
Environment; Environment, general; Earth Sciences, general; Geography (general); Geoecology/Natural Processes; Natural Hazards; Environmental Science and Engineering
eISSN
2197-8670
DOI
10.1186/s40677-015-0015-8
Publisher site
See Article on Publisher Site

Abstract

Background: 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. Results: (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. Conclusions: 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. Keywords: Climate extremes; Palmer Drought Severity Index (PDSI); Agricultural disaster area; Xinjiang Background higher frequencies of extreme weather compared to other According to the World Meteorological Organization countries (Zhang et al. 1991). The crop disaster area caused (WMO) estimate, losses caused by meteorological disasters by various meteorological disasters has been as high as have accounted for 85% of the total losses cause by natural 5× 10 hectares every year, and the population affected by disasters (Qiang et al. 2001; Qing 2008). In the last 20 years, major meteorological disasters, such as typhoons, rain- the direct economic losses caused by meteorological storms, droughts, heat waves, and sand storms, has reached extreme events have increased exponentially, and human 4× 10 (Qing 2008). During the period 1990–2006, the mortality has also significantly increased (Loukas et al. direct economic losses caused by meteorological disasters 2010). China is one of the countries that has been most in China was 1859 × 10 RMB per year, accounting for an severely affected by the meteorological disasters around the average of for 2.8% of the annual GDP (Qing 2008). world due to its complex terrain conditions, leading to 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 * Correspondence: chenyn@ms.xjb.ac.cn State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of derivative disasters have accounted for 83% of the total loss Ecology and Geography, Chinese Academy of Sciences (CAS), CAS, No. 818 due to all natural disasters, and the number of deaths has South Beijing Road, Urumqi, Xinjiang 830011, China accounted for 85% of the total (Liu 1995). These disasters Full list of author information is available at the end of the article © 2015 Wu et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 2 of 10 have also exacerbated the deterioration of the ecological South Xinjiang are located between these three moun- environment. Some new characteristics of these meteoro- tains. Xinjiang is divided into southern and northern areas logical disasters and their derivative disasters have been found by its natural landscape, resulting in two mountain-basin since the 1980s, including the increase in the types of events, systems with different hydrological and thermal condi- increased frequency, and increased strength. According to in- tions (Xu et al. 2004). The unique mountain-basin land- 7 7 complete statistics, the average annual loss was 1 × 10 -5 × 10 scape is responsible for the frequent occurrence of natural 8 8 8 RMB, 1.4 × 10 RMB, and 20 × 10 -50 × 10 RMB during disasters. Alpine areas are distributed widely in Xinjiang the period 1950–1970, during the 1980s, and since the and possess numerous glaciers and permanent snow, 1990s, respectively, accounting for 2 ~ 3.5% of the GDP in which increase the frequency of outburst floods. The mid- Xinjiang. Since the 1980s, the drought disaster area, inun- dle area experiences the highest precipitation in Xinjiang dated area, and various economic losses have increased (Gao et al. 2002), and water erosion is very strong; there- significantly (Xu et al. 2008; Bai et al. 2012; Chen and Gao fore, floods and snowfalls result in most of the extreme 2010; Chen et al. 2008). events (Hong and Adler 2008). In the low mountain areas, Xinjiang is located in the arid region of northwestern active fault zones increase the occurrence of floods, China; its ecological environment is very fragile. The recov- landslides, and debris flows during rainstorms and ery period of the agricultural ecological system to disasters seasonal snowmelt (Sun et al. 2014). is relatively long. Analyzing the effects of meteorological disasters on the agricultural ecosystem in Xinjiang is very Methods important for formulating corresponding disaster preven- Daily data, include the maximum temperature, mini- tion countermeasures that can protect the ecological and mum temperature, precipitation, relative humidity, environmental security. In addition, such an analysis is also sunshine hours, air pressure, and wind speed (1 January favorable for formulating agricultural adaptation and 1960 to 28 February 2011) covering the Xinjiang region mitigation measures in response to climate change and for were provided by the National Climate Center (NCC) of maintaining social stability, which are very important for the China Meteorological Administration (CMA). For promoting the sustainable development of the national this region, 53 stations passed the internal homogeneity economy. check of the China National Meteorological Center (CNMC), including a moving t test (Peterson et al. 1998), standard normal homogeneity test (Alexandersson Description of study area 1986), and departure accumulating method (Buishand Xinjiang Province (Figure 1) is located in northwestern 1982). Data were analyzed using the RclimDex package 8 2 China. With an area of 16.61 × 10 km , Xianjiang is the (software and documentation available for download largest province in China. Xinjiang is a typical inland arid from http://etccdi.pacificclimate.org/). Table 1 lists the region. The Altai Mountains, Tian Mountains, and Kunlun indices used in this study. The regional average is the Mountains extend from north to south in Xinjiang. The arithmetic mean of all stations in Xinjiang. The Xinjiang Junggar Basin in North Xinjiang and the Tarim Basin in disaster area data were derived from the China Statistical Yearbook and Chinese agricultural statistics. The effect- ive irrigation area and rural power data were also col- lected from the China Statistical Yearbook and Chinese agricultural statistics. 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 theavailablewater content(AWC).The AWCwas determined from the State Soil Geographic Database (STATSGO) for the top 100 cm of the soil profile. However, acommoncritiqueofthe PDSI is that thebehaviorofthe index at various locations is inconsistent, making spatial comparisons of the PDSI difficult. The SC-PDSI automatic- ally calibrates the behavior at any location by replacing Figure 1 Study area and weather stations in Xinjiang Province. empirical constants with dynamically calculated values. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 3 of 10 Table 1 Definitions of 6 temperature indices and 5 precipitation indices used in this study; all of the indices are calculated using RClimDex ID Indicator name Definitions UNITS Cold extremes TN10p Cool nights Percentage of days when TN < 10th percentile Days TNn Coldest nights Monthly minimum value of daily minimum temp °C CSDI Cold spell duration indicator Annual count of days with at least 6 consecutive days Days when TN < 10th percentile Warm extremes TX90p Warm days Percentage of days when TX > 90th percentile Days TXx Warmest days Monthly maximum value of daily maximum temp °C WSDI Warm spell duration indicator Annual count of days with at least 6 consecutive days Days when TX > 90th percentile Variability extremes DTR Diurnal temperature range Monthly mean difference between TX and TN °C More details regarding the PDSI and SC-PDSI can be −0.28°C/decade. Winter exhibit the most significant de- found in the works of Liu et al. (2012) and Wells et al. creasing trend, with a value of −4°C/decade (Figure 3d). (2004). The decrease in the DTR was because the minimum We used a nonparametric Kendall’s tau-based slope temperature increased faster than the maximum estimator in this study (Sen 1968); statistical significance temperature. for the trends in extreme climate indices was determined The spatial distribution of the trends in precipitation using the Mann-Kendall test (Kendall 1975; Mann 1945). extremes is show in Figure 4. For R0.1 (Figure 4c), which A trend was considered to be statistically significant if it represents the precipitation frequency, 40% of stations was significant at the 5% level. The results of the M-K test exhibited statistically significant changes. As for SDII are substantially affected by serial correlation; therefore, (Figure 4b), positive trends were found at 83% of the we adopted the Yue and Pilon method, which uses the stations; all stations were less coherent with positive/ Rpackage “ZYP” to remove lag-1 autocorrelations (Yue negative changes. Heavy precipitation, which was repre- et al. 2002). sented by R10 (Figure 4a), was dominated by increasing trends. The R0.1 and CWD trends were 1.26 days/decade Results and discussion and 0.047 days/decade, respectively. The SDII trend was Characteristics of climate extremes weaker. Therefore, the precipitation extremes exhibited Figure 2 depicts the spatial trends in the temperature significant changes in Xinjiang, specifically regarding the indices for cold extreme (Figure 2a-b). For cold nights change in rainy days (R0.1), heavy precipitation events (TN10), 51 stations (96%) had significant decreasing (R10) and the mean simple daily intensity index (SDII), trends, with a regional trend of −1.89 days/decade. The indicating that the precipitation changes were reflected temperature on the coldest nights (TNn) also exhibited a in both the frequency and intensity. statistically significant increase at approximately 70% of the stations, and the regional trend was 0.7°C/decade. The cold spell duration indicator (CSDI) (not shown) Characteristics of climate extremes decreased at a rate of −0.7 days/decade, while only 25% For the annual PDSI in Xinjiang (Figure 5a), 42 stations of the studied stations exhibited a significant change. had significant increasing trends. Stations along the For the warm extremes (Figure 2c-d), 83% of the Tianshan Mountains and North Xinjiang exhibited more stations exhibited statistically significant trends for warm pronounced trends. The seasonal pattern of the PDSI nights (TX90); the regional trend for TN90 was 1.25 days/ was same as the annual PDSI (Figure 5b-c), with correl- decade. The warmest days (TXx) also exhibited an ation coefficients exceeding 0.94. A detailed analysis increasing trend, while only 25% of the stations exhibited resulted in two contrasting periods (Figure 6). Wet significant trends. The warm spell duration indicator conditions occurred during the period 1987–2010, while (WSDI) (not shown) also increased, and the regional trend persistent drought conditions dominated during the was 1.98 days/decade. period 1960–1987. The annual PDSI was negative before The regional seasonal trends in the DTR are shown in 1986, and the mean was −1.5, which indicated frequent Figure 3. The regional trend in the annual DTR was drought events during these years. Thereafter, the PDSI Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 4 of 10 Figure 2 Spatial patterns of the changes in temperatures extremes per decade during the period 1960–2010 in Xinjiang. a: annual TN10p; b: annual TNn; c: annual TX90p; d: annual TXx. Positive/negative trends are shown as up/down triangles; the filled triangles are related to statistically significant trends (significant at the 0.05 level). The triangle sizes are proportional to the magnitude of the trends. changed to −0.5; therefore, the PDSI also exhibited a Influence of meteorological disasters on Agriculture step-wise change around 1986. These results were in Agriculture is the most sensitive sector to climate change. accordance with climate (temperature and precipitation) Affected by climate change, the agricultural growth process change (Shi et al. 2007). Moreover, the PDSI exhibited is constantly changing. Climate change has a direct effect decreasing trends after 2005, indicating the apparent on agricultural ecosystems, often lead to increased instabil- increase in drought severity. ity and volatility in agricultural production. The crop Figure 3 Regional trends in the DTR (the column is the annual anomaly series; the solid line is the 5-year-smoothed average; and the dashed line is the linear regression). a: spring DTR; b: summer DTR; c: autumn DTR; d: winter DTR. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 5 of 10 Figure 4 Spatial distribution of the trends and trend magnitudes for precipitation extremes. a: annual R10; b: annual SDII; c: annual R0.1; d: annual CDD. Positive/negative trends are shown as up/down triangles; the filled triangles are related to statistically significant trends (significant at the 0.05 level). The triangle sizes are proportional to the magnitude of the trends. acreage in Xinjiang increased at a rate of 6.21 × 10 mu/ area decreased substantially after 1970 before reversing in year; the changes can be divided into several distinct 2008. The area of planted wheat and corn changed in a phases. The area increased significantly during the periods similar manner to that of the crop acreage, experiencing a 1950–1970 and 1995–2012, while the area remained stable progression of increases, decreases, and ultimately increase during the period 1970–1995 (Figure 7a). The grain crop again. Because of the increased yield per mu, the wheat and Figure 5 Spatial pattern of decadal trends and spatial characteristic for the seasonal PDIS and the annual PDSI in Xinjiang. a: Spatial trends of annual PDSI and b, c: correlations between the seasonal and annual series. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 6 of 10 Figure 6 Regional time series of the PDIS in Xinjiang: a: regional time series of the annual PDSI; b: regional five-year moving average of the annual and seasonal PDSIs; and c, d: correlations between the seasonal and annual series. corn yields increased substantially (Figure 7c-d). After 2000, the storm disaster area was 210000 acres in the 1960’sand the yield/mu maintained a relatively constant level; an increased to 2430000 acres in the 2000’s. The proportion increasing trend was not well defined. Therefore, when affected by low temperatures increased after 1995, espe- agriculture developed to a certain level, it was challenging cially in 2009, which is when the proportion reached 20% to achieve increasing yields through improved irrigation (Figure 8e). This finding indicates that the climate warmed, measures and increased input in the agriculture sector. The which simultaneously exacerbated the climate instability economic crop area exhibited a continuous increasing and resulted in extreme low temperature events during trend. Moreover, in 1998, the planted area exceeded the some periods. grain crop area. In the case of cotton, the area, yield and The correlation coefficients between the trend in regional yield per mu increased after 1980 (Figure 7b). Based on the climateextremesand thedisasterareaare showninTable 2. above analysis, the crop acreage increased in Xinjiang The statistical data representing the affected areas may be primarily due to increased economic crops. not comprehensive before 1986; therefore, we also discuss The statistical results show that the devastated farmland data since1986via a separateanalysis(Table3). Thedisas- resulting from meteorological disasters increased (either ter area exhibited a significant correlation with extreme based on the disaster area or the disaster ratio), especially temperature drought indices, especially the TN10p and after 1970 (Figure 8a). Flood disaster areas (Figure 8b) were TX90p, indicating that the disaster area was primarily con- primarily concentrated after 1987, which confirms that the trolled by temperature extremes. When the summertime climate in Xinjiang shifted from warm and dry to warm temperature was warm, the corresponding affected farm- and wet around 1987. Droughts and storms occurred land area increased. The reasons for floods/droughts are primarily after 1970 (Figure 8c-d). The increasing trend in complex and are closely related to the weather, geograph- the storm disaster area was very well defined. For example, ical conditions, water conservancy facilities, soil structure, Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 7 of 10 Figure 7 Crop time series in Xinjiang during the period 1949–2012. a: the time series of crop area, and b, c, d: the characteristics of cotton, wheat and corn, respectively. and primarily precipitation extremes. Regardless, changes variability. For example, within a single year, extremely in the frequency (R0.1), intensity (SDII), amplitude (R10) warm events can occur in combination extremely cold and duration (CWD) of precipitation events will increase events. The cold events lead to rapid increases in the disas- the flood disaster area (Table 2). Drought events exhibited a ter area. Xinjiang has a typical irrigation agriculture; the negative correlation with extreme precipitation events, i.e., shortage of water resources is very serious due to the sharp when the precipitation intensity and frequency increased, increase in irrigated area. Based on the exponential growth the number of drought events decreased. The enhanced in rural consumption (Figure 9), we can speculate that the warm temperature extremes (WSDI) resulted in intensified annual extracted groundwater volume increased. Moreover, drought. For example, high temperatures in winter increase the effective irrigation area decreased substantially in recent soil moisture evaporation, affect the safe overwintering of years, which has also resulted in an increase in the disaster crops, decrease soil entropy, and increase the possibility of area, especially due to the expansion of drought-affected soil drought. The PDSI exhibited a negative correlation areas. with drought events, especially after 1986 (Table 3), which is when the correlation was significant. This result indicates Conclusions that the PDSI partially reflects the drought extent, which The characteristics of Xinjiang meteorological disasters can be used to assess drought events in Xinjiang. The storm were analyzed using extreme indices and the PDSI. disaster area was related to the temperature index, indicat- Combined with data for the farmland disaster area, we ing that extreme temperatures play a vital role in the forma- also analyzed the influence of meteorological disasters tion of strong convective weather. Moreover, the low on the agricultural and ecological systems. The primary temperature disaster area was also associated with the conclusions of this study are as follows: extreme temperature index; however, the correlation was found to be positive when compared with warm 1) In Xinjiang, the climate became warmer and wetter, temperature extremes, which is inconsistent with the actual with cold extremes decreasing and warm extremes relationship. This result may be related to climate change increasing. Moreover, climate extremes derived from Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 8 of 10 Figure 8 The characteristics of the agricultural disaster area in Xinjiang. a, b, c, d, e is the disaster area and proportion for total disaster area, flood, drought, storm and low temperature, respectively. Table 2 Correlations between climate extremes and the agricultural disaster area in Xinjiang Area (Million mu) Proportion (%) Total Flood Drought Storm Low temperature Total Flood Drought Storm Low temperature ** ** CWD .057 .581 -.119 .199 .123 -.018 .551 -.175 .192 .083 ** ** SDII -.067 .572 -.223 .081 -.011 -.112 .546 -.231 .049 -.033 ** ** R0.1 .043 .547 -.105 .249 .068 -.024 .549 -.179 .261 .008 ** * ** * R10 .051 .738 -.207 .280 .148 -.038 .703 -.267 .277 .113 ** ** RX1day .057 .656 -.110 .176 .040 .014 .621 -.147 .182 .026 * ** CSDI .005 -.138 .158 -.349 -.063 .046 -.114 .195 -.390 -.091 ** ** * ** ** ** ** ** ** TN10p -.651 -.532 -.356 -.662 -.589 -.518 -.460 -.212 -.627 -.571 * ** TNn .101 .207 -.074 .358 .090 .082 .196 -.066 .408 .164 ** ** ** * * ** ** * DTR -.380 -.628 -.159 -.477 -.311 -.299 -.607 -.073 -.484 -.291 ** ** ** ** ** * ** ** TX90p .533 .397 .276 .492 .534 .441 .335 .168 .482 .542 * ** * ** TXx .329 -.070 .277 .209 .377 .296 -.106 .222 .205 .376 ** ** ** ** ** * ** ** WSDI .470 .375 .236 .412 .551 .368 .317 .116 .419 .553 ** ** PDSI .046 .565 -.088 .147 .060 .013 .561 -.103 .176 .068 *Indicates significance at <0.05 and **indicates significance at <0.01. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 9 of 10 Table 3 Correlations between climate extremes and the agricultural disaster area in Xinjiang after 1986 Area (Million mu) Proportion (%) Total Flood Drought Storm Low temperature Total Flood Drought Storm Low temperature * * CWD -.289 .490 -.363 -.242 -.146 -.363 .449 -.339 -.270 -.202 ** ** * ** ** SDII -.319 .635 -.527 -.162 -.186 -.408 .604 -.566 -.163 -.197 * * * * R0.1 -.299 .438 -.423 -.043 -.248 -.403 .438 -.448 -.069 -.313 ** ** ** ** R10 -.211 .710 -.542 .003 -.062 -.327 .654 -.612 -.017 -.091 ** ** * ** ** RX1day -.382 .610 -.531 -.213 -.279 -.464 .568 -.545 -.224 -.287 CSDI .316 .208 .213 -.050 .404 .283 .268 .115 -.079 .346 ** * ** ** * TN10p -.672 -.177 -.401 -.441 -.528 -.536 -.029 -.184 -.321 -.474 TNn -.310 -.206 -.287 .019 -.281 -.303 -.211 -.230 .010 -.210 DTR -.030 -.410 .102 -.090 .018 .104 -.348 .198 -.014 .098 ** * * ** TX90p .544 .186 .282 .295 .510 .502 .091 .132 .253 .517 * * TXx .343 -.174 .193 .163 .436 .288 -.265 .085 .157 .427 ** * ** ** ** WSDI .567 .120 .406 .177 .517 .564 .026 .293 .158 .520 ** * * * PDSI -.521 .352 -.461 -.420 -.366 -.513 .364 -.351 -.392 -.374 *Indicates significance at <0.05 and **indicates significance at <0.01. daily minimum temperatures were more numerous ecosystem and the oasis crop yield and quality in that the extremes derived from daily maximum various ways, leading to increased instability in temperatures, which was found to be consistent agricultural production. This study showed that the with the significantly decreased DTR. Precipitation strengthening of climate warming and evaporation extremes also increased due to the increase in both capacity not only increased the soil moisture but also precipitation frequency and intensity. Frequent and decrease the soil entropy, increased spring drought, long droughts occurred from 1960 to 1986, although accelerated the production of organic matter due to wet climates prevailed from 1987 to 2010. However, soil microbial decomposition, resulting in the decline after 2003, droughts became more prolific, which in soil fertility and decreased yields. To maintain soil was reflected in a drastic decrease in the PDSI. fertility, the fertilization amount must also increase. 2) Extreme temperatures had a significant effect on the Although the climate warming can improve grain farmland disaster area in Xinjiang. For example, yields in some regions, climate warming can cause the summertime warm events resulted in an increase in agriculture water consumption per unit to increase, the affected farmland area. Floods were primarily which also increases production costs. Especially in related to extreme precipitation, while the storm winter, an increase in extreme minimum temperatures disaster area and the low temperature disaster area will reduce overwinter human mortality, expand were the result of changes in temperature extremes. agricultural pest regions, increase the pesticide Climate change can also affect the agricultural control difficulty and application amount. Figure 9 Changes in rural electricity consumption and the effective irrigation area in Xinjiang. Wu et al. Geoenvironmental Disasters (2015) 2:3 Page 10 of 10 Competing interests Sun G, Chen Y, Li W, Pan C, Li J, Yang Y (2014) Intra-annual distribution and The authors declare that they have no competing interests. decadal change in extreme hydrological events in Xinjiang, Northwestern China. Nat Hazards 70(1):119–133 Wells N, Goddard S, Hayes MJ (2004) A self-calibrating Palmer Drought Severity Authors’ contributions Index. J Clim 17(12):2335–2351 MW carried out data analysis and write the manuscript. YC provided Xu ZX, Chen YN, Li JY (2004) Impact of climate change on water resources in the suggestions and advices to the study and checked the manuscript. HW and Tarim River basin. Water Resour Manag 18:439–458 GS provided unpublished data and added some useful comments to improve Xu GH, Mao WY, Lu GY (2008) Xinjiang meteorological weather changes and the paper. All authors read and approved the final manuscript. general statement on the work of disaster prevention and reduction. Desert Oasis Meteorol 2(01):50–54 (in Chinese with English abstract) Acknowledgements Ye MQ, Chen BH (1996) Research on natural disaster zoning in Xinjiang. J Nat The research is supported the Natural Sciences Foundation of China (Grant Disasters 5(01):14–21 (in Chinese with English abstract) No.41361093). The authors thank the National Climate Central, China Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on Meteorological Administration, for providing the meteorological data for this the ability to detect trend in hydrological series. Hydrol Process study. 16(9):1807–1829 Zhang YC, He WX, Li SK (1991) Introduction to Chinese agricultural Author details 1 meteorological disasters [M]. China Meteorological Press, Beijing Key Laboratory of Oasis Ecology, School of Resource and Environmental Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences (CAS), CAS, No. 818 South Beijing Road, Urumqi, Xinjiang 830011, China. Forestry and Horticulture Department, Xinjiang Agriculture University, Urumqi, Xinjiang 830052, China. Received: 4 December 2014 Accepted: 17 January 2015 References Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6(6):661–675 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) Buishand TA (1982) Some methods for testing the homogeneity of rainfall records. J Hydrol 58(1–2):11–27 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) 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) Gao XJ, Zhao ZC, Giorgi F (2002) Changes of extreme events in regional climate simulations over East Asia. Adv Atmos Sci 19:927–942 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 23:249–257 Kendall MG (1975) Rank-correlation measures. Charles Griffin, London, p 202 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) 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):2761–2779 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):1895–1897 Mann HB (1945) Non-parametric tests against trend. Econometrica 13:245–259 Palmer WC (1965) Meteorological drought. Office of Climatology Research Paper Submit your manuscript to a 45, Weather Bureau, Washington, D.C, p 58 journal and benefi t from: Peterson TC, Easterling DR, Karl TR, Groisman P, Nicholls N, Plummer N, Parker D (1998) Homogeneity adjustments of in situ atmospheric climate data: a 7 Convenient online submission review. Int J Climatol 18(13):1493–1517 7 Rigorous peer review 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 7 Immediate publication on acceptance 6(01):28–38 (in Chinese with English abstract) 7 Open access: articles freely available online Qing DH (2008) China's major meteorological disasters and development trend. 7 High visibility within the fi eld China Emerg Rescue 6:4–6 (in Chinese with English abstract) 7 Retaining the copyright to your article Sen PK (1968) Estimates of the regression coefficient based on Kendall's Tau. J. Am. Stat. Assoc 63:1379-1389 Shi YF, Shen YP, Kang E, Li DL, Ding YJ, Zhang GW, Hu RJ (2007) Recent and Submit your next manuscript at 7 springeropen.com future climate change in northwest china. Clim Change 80:379–39

Journal

Geoenvironmental Disasters – Springer Journals

Published: Feb 12, 2015

There are no references for this article.