sustainability Article
Assessment of Drought Impact on Main Cereal Crops Using a Standardized Precipitation Evapotranspiration Index in Liaoning Province, China Taotao Chen 1 , Guimin Xia 1 , Tiegang Liu 2 , Wei Chen 3 and Daocai Chi 1, * 1 2
3
*
College of Water Resources, Shenyang Agricultural University, Shenyang 110866, China;
[email protected] (T.C.);
[email protected] (G.X.) State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource & Hydropower, Sichuan University, Chengdu 610065, China;
[email protected] Water Conservancy and Hydropower Science Research Institute of Liaoning province, Shenyang 110003, China;
[email protected] Correspondence:
[email protected] or
[email protected]; Tel.: +86-24-8848-7138
Academic Editor: Iain Gordon Received: 11 August 2016; Accepted: 17 October 2016; Published: 22 October 2016
Abstract: Global warming has resulted in increasingly frequent and severe drought and/or precipitation events. Severe drought limits crop water availability and impacts agricultural productivity and socioeconomic development. To quantify drought-induced yield loss during the main crop stages in Liaoning province, China, aspects of drought episodes (magnitude, duration, and frequency) were investigated during the period 1960–2015 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), respectively. Then the relationship between the SPI/SPEI and the standardized yield residuals series (SYRS), and the drought-induced yield loss were analyzed for maize, rice, sorghum, soybean, and millet. Liaoning underwent a province-wide increase in temperature, reduced precipitation, and reduced reference crop evapotranspiration. As expected, Liaoning experienced province-wide meteorological drying trends during the main crop growth stages, while the drought frequency, duration, and magnitude were not as serious as revealed by using the SPI. As compared to the SPI, the SPEI considering potential evapotranspiration explained 39%–78% yield variability of SYRS and evaluated the drought-induced yield loss more accurately. The increased drought frequency mainly affected the rain-fed crops (maize, sorghum, soybean, and millet), while it did not reduce irrigated rice production. No major impact was exerted on the rain-fed crops caused by mild drought. However, severe drought (SPEI < −1.0) markedly reduced yield performance, in particular at the anthesis-silking stage for maize, the jointing-booting stage for sorghum, the flowering-podding stage for soybean, and the sowing-milking stage for millet. It is concluded that the SPEI is a more useful measure for the identification of drought episodes and the assessment of drought impact on agricultural production in Liaoning province. Keywords: yield loss; spatiotemporal variability; drought frequency; drought duration; drought magnitude
1. Introduction The global climate has undergone significant and unprecedented changes during the past 100 years. There is increasing evidence that global warming has resulted in increasingly frequent and severe drought and/or precipitation events [1]. Drought is one of the most complex and damaging natural disasters that impacts agricultural productivity and socioeconomic development [2,3]. Many Sustainability 2016, 8, 1069; doi:10.3390/su8101069
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areas of the world still suffer from drought-induced crop failure and water shortage problems, particularly in rainfed agriculture regions [4,5]. Liaoning province is located in East Asia’s monsoon region and has one of the largest climate change rates in the world [6]. Drought is a major problem in this area, which has been responsible for great losses of regional agricultural production. The socioeconomic activities of Liaoning province are predominantly rain-dependent. Liaoning has experienced an unprecedented severity in the intensity, extent, and duration of extreme events (e.g., drought) [7,8]. Therefore, it is imperative to quantify the drought risk and its drought impact on main crop yields, which benefit farmers by mitigating or adapting to drought-induced impact on agricultural systems. Drought indices, such as the Palmer Drought Severity Index (PDSI) [9], the Standardized Precipitation Index (SPI) [10], etc., have great potential for characterizing agricultural impacts associated with droughts [11,12]. Among the various drought indices, the PDSI is one of the most widely used measures. The PDSI’s main advantage lies in its sensitivity to changes in climatic water balance, which makes comprehensive assessments for both precipitation and evapotranspiration anomalies, and soil water-holding capacity [13]. Nevertheless, the PDSI is weaker at demonstrating the effects of the short-term drought, and lacks the multi-scalar character essential for assessing drought [3,14]. Additionally, drought assessment in snowmelt regions or during the winter months (e.g., parts of Liaoning in April) is not available, for all snows are assumed as rain [15]. The SPI is a relatively easy index for assessing the variability of dryness/wetness conditions, and is widely used by meteorologists and climatologists around the world [16], due to its limited input data requirements, multi-scalar characters, and simplicity of calculations. However, in the context of current global warming, the changes of the atmospheric evaporative demand cannot be neglected in drought research, even though precipitation is the main driver of drought severity [15,17]. The main disadvantage is therefore that the SPI uses only precipitation, without taking into account temperature and evapotranspiration [18]. Recently, the Standardized Precipitation- Evapotranspiration Index (SPEI) [13] has been proposed for detecting and monitoring drought. The index combines the sensitivity of the PDSI to changes in evaporation demand under a warming climate and the multi-temporal nature of the SPI with the simplicity of calculation [2,13]. Rising temperature plays an important role in explaining the recent trends in the development of natural vegetation and crops, and agricultural productions [13,19]. Liaoning province is characterized by increased temperature and reduced precipitation, which usually resulted in more frequent and severe drought episodes [15,20,21]. However, Liaoning province experiences a decline in the reference crop evapotranspiration (ET0 ) with a gradual warming trend [22]. Therefore, it is essential to combine the reduced precipitation with reduced ET0 to better understand the drought characteristics and the spatial–temporal variabilities of Liaoning province under a warming climate using the SPEI. Previous studies have revealed drought characteristics in Liaoning province using different drought indices [15,20,21,23]. However, most of these studies focus on the spatial–temporal variabilities of drought episodes, and only a limited number of studies have addressed the impact of drought on main cereal crops, in particular using the SPEI index. Therefore, the objectives of this study are (1) to detect and characterize the temporal–spatial distribution of drought variability (drought frequency, magnitude and duration, and significant level) using the SPEI index for Liaoning province during the main crop growth stages; and (2) evaluate the drought-induced decline and yield sensitivity of maize, rice, sorghum, soybean, and millet in Liaoning province, China. 2. Materials and Methods 2.1. Study Region Liaoning province, with a total area of 148,000 km2 , lies between 38◦ 430 and 43◦ 260 N latitude and 118◦ 530 and 125◦ 460 E longitude in China (Figure 1). Liaoning is also known as “the Golden Triangle” from its shape and strategic location, and consists essentially of a central lowland, flanked
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Sustainability 2016, and 8, 1069strategic location, and consists essentially of a central lowland, flanked3 of from its shape by16 mountains to the east and west. Based on the topographical and hydrographic characteristics, Liaoning is comprised of and Northeastern Mountain and Liaodong in the east (also by mountains to the east west. Based on the topographical andPeninsula hydrographic characteristics, collectively referred to as Eastern Mountain), Western Highland, and a Central Plain zone [24]. The Liaoning is comprised of Northeastern Mountain and Liaodong Peninsula in the east (also collectively Northeastern sub-regionWestern is dominated by the ranges, referred to asMountain Eastern Mountain), Highland, andChangbai a Central Mountain Plain zoneand [24].Qianshan The Northeastern which extends into the sea to form the Liaodong Peninsula. Cultivated land area (4.09 Mountain sub-region is dominated by the Changbai Mountain and Qianshan ranges, whichmillion extends hectare) of Peninsula. the total land area, ofland which 80% are distributed in the into the accounted sea to formfor the27.65% Liaodong Cultivated area about (4.09 million hectare) accounted for Central Plain zone and Western Highland. The annual mean air temperature ranges from 7 to 11 °C 27.65% of the total land area, of which about 80% are distributed in the Central Plain zone and Western and the frost-free period lastsair from 130 to 200ranges days. from The prevailing cereal include maize, Highland. The annual mean temperature 7 to 11 ◦ C and thecrops frost-free period lastsrice, from sorghum, soybean, and millet in this agro-climatic region. Precipitation is the highest in the Eastern 130 to 200 days. The prevailing cereal crops include maize, rice, sorghum, soybean, and millet in this Mountain, and region. lowest in the WesternisHighland, Central Plain receives a moderate agro-climatic Precipitation the highestwhile in thethe Eastern Mountain, and lowest in theamount Western ofHighland, rainfall. The average annual rainfall is 500–1000 mm, and more than 85% is received during the while the Central Plain receives a moderate amount of rainfall. The average annual rainfall main crop growth stages (April October). is 500–1000 mm, and more thanto 85% is received during the main crop growth stages (April to October).
Figure 1. Spatial distribution of the 56 meteorological stations in Liaoning province, China. Figure 1. Spatial distribution of the 56 meteorological stations in Liaoning province, China.
2.2. Meteorological Data and Drought Identification 2.2. Meteorological Data and Drought Identification Meteorological data were provided by the Northeast Regional Meteorological Center, China Meteorological data were provided by datasets the Northeast Regional Meteorological Center, stations China Meteorological Administration (CMA). The covering the selected 56 meteorological Meteorological Administration (CMA). The datasets covering the selected 56 meteorological (Figure 1) in Liaoning province included the daily minimum, mean, and maximum air temperatures, stations 1) wind in Liaoning included the daily minimum, mean, andand maximum air relative (Figure humidity, velocity province (at 10 m height), sunshine duration, pan evaporation, precipitation temperatures, relative humidity, wind velocity (at 10 m height), sunshinedata duration, pan during the period 1960–2015. The homogeneity and reliability of the meteorological were checked evaporation, and precipitation during the period 1960–2015. The homogeneity and reliability of the and controlled as described by Liu et al. [25]. In order to identify and characterize the evolution meteorological data were checked and controlled as described by Liu et al. [25]. In order to identify of drought episodes, the SPI series was computed based on monthly precipitation series following and theofevolution drought episodes, theaccumulative SPI series wasprecipitation computed based on monthly the characterize methodology McKee etofal. [10]. The monthly was estimated by precipitation series following the methodology of McKee et al. [10]. The monthly accumulative summing the daily precipitation data. The SPI series was fitted to a gamma probability distribution precipitation was estimated by summing the daily precipitation The SPI series was fitted a and normalized to a standard normal probability distribution,data. as described by McKee et al.to[10]. gamma probability distribution and normalized to a standard normal probability distribution, as The SPEI [13] has been proposed for identifying drought episodes. The calculation procedures for the described bysimilar McKeetoetthe al.SPI, [10].but Thethe SPEI [13](precipitation has been proposed for crop identifying drought episodes. SPEI were P-ET reference evapotranspiration) series 0 The the SPEI towere similar the The SPI,SPEI but is the 0 (precipitation wascalculation fitted to theprocedures log-logisticfor distribution calculate thetoSPEI. anP-ET extension of the SPI,
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which attempts to rectify a shortcoming of the SPI—its calculation is only based on precipitation data [17]. For the SPEI, the FAO56 Penman–Monteith method was employed for monthly reference crop evapotranspiration (ET0 ) estimation, which is recommended by many international organizations such as the FAO [26]. Both the SPI and SPEI for different month lags were implemented using the SPEI package [27] in R software. The threshold of 0 in both the SPI and SPEI series (representing 50% of the probability distribution of the standardized variable) was used to identify and explore individual drought episodes, which considered that all the negative values were related to dry conditions [28]. The drought duration was the longest period of consecutive months with the values
