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- Copyright © 2020 Ming-jin Zhan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
Hindawi Advances in Meteorology Volume 2020, Article ID 8839111, 11 pages https://doi.org/10.1155/2020/8839111 Research Article Evaluation and Analysis of Soil Temperature Data over Poyang Lake Basin, China 1,2,3 1 2 4 Ming-jin Zhan , Lingjun Xia , Longfei Zhan , and Yuanhao Wang Jiangxi Eco-Meteorological Centre, Nanchang 330046, China Jiangxi Climate Change Centre, Nanchang 330046, China Institute for Disaster Risk Management (IDRM), School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing 210044, China Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China Correspondence should be addressed to Yuanhao Wang; wyh1983@mail.iap.ac.cn Received 10 March 2020; Revised 22 October 2020; Accepted 2 November 2020; Published 8 December 2020 Academic Editor: Antonio Donateo Copyright © 2020 Ming-jin Zhan et al. -is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Soil temperature reflects the impact of local factors, such as the vegetation, soil, and atmosphere of a region. -erefore, it is important to understand the regional variation of soil temperature. However, given the lack of observations with adequate spatial and/or temporal coverage, it is often difficult to use observational data to study the regional variation. Based on the observational data from Nanchang and Ganzhou stations and ERA-Interim/Land reanalysis data, this study analyzed the spatiotemporal distribution characteristics of soil temperature over Poyang Lake Basin. Four soil depths were examined, 0–7, 7–28, 28–100, and 100–289 cm, recorded as ST1, ST2, ST3, and ST4, respectively. -e results showed close correlations between observation data and reanalysis data at different depths. Reanalysis data could reproduce the main spatiotemporal distributions of soil temperature over the Poyang Lake Basin but generally underestimated their magnitudes. Temporally, there was a clear warming trend in the basin. Seasonally, the temperature increase was the most rapid in spring and the slowest in summer, except for ST4, which increased the fastest in spring and the slowest in winter. -e temperature increase was faster for ST1 than the other depths. -e warming trend was almost the same for ST2, ST3, and ST4. An abrupt change of annual soil temperature at all depths occurred in 1997, and annual soil temperatures at all depths were abnormally low in 1984. Spatially, annual soil temperature decreased with latitude, except for the summer ST1. Because of the high temperature and precipitation in summer, the ST1 values were higher around the lake and the river. -e climatic trend of soil temperature generally increased from south to north, which was opposite to the distribution of soil temperature. -ese findings provide a basis for understanding and assessing the variation of soil temperature in the Poyang Lake Basin. result in variation of terrain and hydrological conditions, 1. Introduction alteration of the distribution and growth rate of vegetation, Soil temperature, an important parameter to characterize the enhancement of soil organic carbon decomposition, and thermal properties of soil, plays a key role in the land surface increased CO2 emissions from the soil to the atmosphere processes [1]. It can also affect climate change by affecting [6–11]. -ese effects could have major consequences both the energy distribution, exchange, and water budget on the locally and globally. surface. Moreover, it gradually acts on the upper atmosphere Under the influence of global warming, the mean surface through its influence on the surface boundary layer [2–5]. temperature around the world increased by an average of ° ° -erefore, soil temperature plays an important role in the 0.85 C (0.65–1.06 C) from 1880 to 2012 [12]. Between 1961 interaction between land and air [1, 2]. Soil temperature also and 2011, the mean temperature of China increased by 1.1 C, plays an important role in climate change [3–5]. Changes in which is greater than the global and the Northern Hemi- soil temperature associated with climate warming could sphere values [13]. Driven by the air temperature, the soil 2 Advances in Meteorology temperature in China has also shown a warming trend. Since climate change. However, owing to the lack of sufficient the 1990s, based on the monthly soil temperature data of 532 observation stations, it was not realistic to use observation data to study regional soil temperature variation. -erefore, stations in China, the annual mean soil temperature has remarkably increased. Regionally, the soil temperatures in we first evaluated the reanalysis data based on the obser- northeastern China have increased most significantly, vation data. Subsequently, we used the reanalysis data to whereas, in the eastern part of southwestern China, soil study the temporal and spatial variation characteristics of temperature has tended to decrease [14]. In Lhasa (Tibet), soil temperature in the Poyang Lake Basin. -e findings of from 1961 to 2005, the annual mean soil temperatures at this study will provide a scientific basis for improved un- shallow layers (0–40 cm) showed increasing trends, with derstanding and assessment of the impact of climate change increase rates of (0.45–0.66 C)/10a, which was greater than on terrestrial ecosystems. the increase trend of air temperature in the same period [15]. In Alxa Left Banner (Inner Mongolia Autonomous region, 2. Data northwestern China), from 1961 to 2005, the increase rates ° ° 2.1. Study Area. -e Poyang Lake Basin (28 22′–29 45′N, of annual soil temperatures at 0–80 cm soil depths were 5 2 ° ° 115 47′–116 45′E, area of 1.62 ×10 km ) is located in the (0.28–0.46 C)/10a, which was lower than that of the air middle–lower reaches of the Yangtze River, within the temperature (0.46 C/10a). Seasonally, the responses of sphere of the East Asian monsoon, and has a typical summer, autumn, and winter air temperature to climate monsoon climate (Figure 1). From 1961 to 2018, the annual change are more substantial than soil temperatures, while temperature is 18.1 C and annual precipitation is approxi- the responses of soil temperatures to climate change are mately 1650 mm. -e region has four distinct seasons: spring more significant than air temperature in spring [16]. Based (March–May), summer (June–August), autumn (Septem- on the analysis of the long-term changes (1961–2018) in soil ber–November), and winter (December–February). It plays temperature at Nanchang (the middle–lower reaches of the an important ecological and hydrological role in the middle Yangtze River, southern China), Zhan (2019) found that the and lower Yangtze River Region [20]. annual variation of air temperature correlated very well with ° ° -e Nanchang (28 36′N, 115 55′E; elevation: 46.9 m) soil temperatures at 0–320 cm soil depth [11]. -e increase ° ° and Ganzhou (28 52′N, 115 ; elevation: 58.6 m) weather rates of soil temperature were reported as 0.074–0.186 C/ station were selected for this study because they had 10a, lower than those of annual air temperature, 0.255 C/ remained at the same location since 1960. Nanchang station 10a. is located in the northern part of the basin and Ganzhou is To date, many studies have investigated the variations located in the southern, thus Nanchang and Ganzhou could of soil temperatures at specific research stations, but reflect the overall climate characteristics of the basin. examinations of the spatiotemporal variations of soil Moreover, the time series data of soil temperatures (depths temperature remain largely comparative. However, given of 0, 20, 80, and 320 cm) recorded at the two stations are long the lack of observations with adequate spatial and/or term, and the data integrity is considered satisfactory (i.e., temporal coverage, it is difficult to use the observation the amount of missing data annually is <5%). data of stations to study the regional variation. Because of the continuity and adequate spatial and temporal cov- erage, reanalysis data plays an important role in soil 2.2. Observed Data. -e soil temperature date (0, 20, 80, and temperature regional study. Yang and Zhang (2017) 320 cm soil depth) from Nanchang and Ganzhou National evaluated four reanalysis datasets of soil temperature, the Weather stations span from 1961 to the present (black circles land surface reanalysis of the European Centre for Me- in Figure 1). Prior to further analysis, these data were tested dium-Range Weather Forecasts (ERA-Interim/Land), the for homogeneity. -e missing data, less than 1%, have little second modern-era retrospective analysis for research and or no effect on the research results. applications (MERRA-2), the National Centre for Envi- ronmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), and version 2 of the Global Land Data 2.3. ERA-Interim/Land Reanalysis Data. ERA-Interim/Land Assimilation System (GLDAS-2.0) [17]. -ey found that is a global land surface reanalysis dataset covering the period reanalysis data could reproduce the main spatial distri- 1979 to the present. It describes the evolution of soil bution of soil temperature in summer and winter, espe- moisture, soil temperature, and snowpack. ERA-Interim/ cially over the east of China but generally underestimated Land is the result of a single 32-year simulation with the their magnitudes. Moreover, four reanalysis products (the latest ECMWF (European Centre for Medium-Range ERA-Interim reanalysis, ERA-Interim/Land, MERRA- Weather Forecasts) land surface model driven by meteo- Land, and NOAA-CIRES 20CR) were used to analyze the rological forcing from the ERA-Interim atmospheric re- soil temperature variation over middle and high latitudes analysis and precipitation adjustments based on the monthly of East Asia [18]. In addition, the ERA-Interim land GPCP v2.1 (Global Precipitation Climatology Project) surface temperature dataset has also been used in mapping [21, 22]. -ere are four soil depths 0–7, 7–28, 28–100, and the permafrost distribution over the Tibetan Plateau [19]. 100–289 cm, written as ST1, ST2, ST3, and ST4, respectively. ° ° In this study, soil temperatures at different depths were -e horizontal resolution is about 1 × 1 and the time fre- examined in the Poyang Lake Basin, located in the sub- quency is monthly (grid in Figure 1), from January 1979 to tropical monsoon region of China, a region sensitive to December 2018. Advances in Meteorology 3 114° E 115° E 116° E 117° E 118° E 119° E 2.4.2. Sen’s Slope Estimator. Sen (1968) developed the nonparametric procedure for estimating the long-term trend [23]. Compared with the least squares linear regression, Yangtze 30° N Sen’s slope estimator is not sensitive to outliers and thus the river estimated linear trend is significantly more accurate and robust for skewed data. -e slope of N pairs of data points 29° N can be estimated by the following relation: Nanchang x − x j l β � median , (j> l> 1), (2) 28° N j − l where x and x are data values at time l and time j, re- l j 27° N spectively. -is method is widely applied to hydrological and climatic time series because of its robustness for estimating the magnitude of a trend [24–28]. 26° N Ganzhou 2.4.3. Test of Abrupt Change. -e Mann–Kendall test was 25° N developed by Mann and Kendall [29, 30] and was originally used to detect trend changes in the sequence. Goossens and 0 100 200 km Berger (1986) improved and further developed the test, 24° N allowing it to determine the year of the abrupt change in the trend [31]. Weather station For time series x with n sample sizes, a rank series S is Grid constructed: River 1 x > x ⎧ ⎨ i j Figure 1: Location of the Nanchang and Guangzhou National S � r , r � , j � 1, 2, . . . , i, (3) i i Weather stations, China. 0 else i�1 S − E S k k ������� 2.4. Method UF � , k � 1, 2, . . . , n. (4) Var S 2.4.1. Applicability Evaluation of ERA-Interim/Land Re- analysis Data. -e evaluation of the ERA-Interim/Land In equation (4), UF � 0 and E(S ) and Var(S ) are the k k reanalysis data using the observed data focused on mean and variance of the S : monthly variations. -e correlation coefficients, mean n(n + 1) error (ME), mean absolute error (MAE), and root mean E S � , square error (RMSE) between ERA soil temperature and (5) observational soil temperature were calculated to in- n(n − 1)(2n + 5) Var S � . vestigate their agreement at monthly time scales. -e two k observed time series are from Nanchang and Ganzhou stations. -eir counterparts, the ERA-Interim/Land re- Arrange x in reverse chronological order, x , x , . . . , x . Repeat the process again to obtain UB: analysis data, are the average values of the two grid cells n n−1 1 closest to the weather stations (red grid cells in UB � −UF , k � n, n − 1, . . . , 1. (6) k k Figure 1). STERA represented the soil temperature series of the ERA-Interim/Land reanalysis data, while STobs UF is a standard normal distribution, which is in the represented the soil temperature series of the observa- time series x order x , x , . . . , x . Look up the normal 1 2 n tional data: distribution table at the given significance level α. If |UF |> U , it indicates that there is an obvious trend change i α in the sequence. If α � 0.05, U � ± 1.96. 0.05 ME � ST − ST /n , ERA obs. If the UF and UB intersect and |UF |> 1.96, x would i�1 change abruptly at the intersection. -e Mann–Kendall test has been frequently used to quantify the abrupt changes in MAE � ST − ST /n, ERA obs. hydrometeorological time series [32, 33]. . (1) i�1 ���� 2.4.4. Anomaly and Standard Deviation. Climate anomalies RMSE � ST − ST . ERA obs. are considered conditions in which anomalies of climatic i�1 elements reach a certain magnitude. -e World Meteoro- logical Organization has stated that when an anomaly of a 4 Advances in Meteorology climatic element is more than double the standard deviation, the climatic element should be considered abnormal [11]. 3. Results 30 3.1. Validation of ERA-Interim/Land Reanalysis Data for 25 Applicability. In this study, the observational soil temper- atures of the Nanchang and Ganzhou stations were used to evaluate the ERA-Interim/Land reanalysis data at its nearby grid points (Figure 1). -ere were four soil depths in the observational (obs.) data (0, 20, 80, and 320 cm, given as obs. ST1, obs. ST2, obs. ST3, and obs. ST 4, resp.). Comparing the month-by-month average data from January 1979 to December 2018, we found a very strong correlation between ERA and obs. soil temperature data (Figure 2 and Table 1). Taking the obs. ST1 of Nanchang and ST1 of ERA as an example, the correlation coefficient is 0.99, Date (MM/YY) and the averages of obs. and the ERA are 20.2 (3.2, 38.0) and 19.1 (4.9, 32.1), respectively. -e ME, MAE, and RMSE are ERA ° ° ° Obs −1.1 C, 1.7 C, and 2.2 C. -is shows that there is a high degree of consistency between ERA and obs. at the ST1. At Figure 2: Monthly variations in ERA ST1 and obs. ST1 at Nan- the same depth, the ERA-Interim/Land reanalysis data are in chang station. good agreement with the observed data (Figure 2 and Ta- ble 1). -e ERA-Interim/Land reanalysis data are shown to 3.3. Abrupt Change of Soil Temperatures. Based on equations be reliable for regional soil temperature research in the (3)–(6), the years of abrupt change in soil temperatures were Poyang Lake Basin. then calculated. Most of the annual and seasonal soil tem- peratures show abrupt changes; in general, the soil temper- atures changed from a relatively cold period to a comparatively 3.2. Monthly, Seasonal, and Yearly Changes of Soil warm period (Figure 5 and Table 2). An abrupt change of Temperatures. -e mean temperatures from 1979 to 2018 in ° annual soil temperature occurred at all depths in 1997 (Fig- Poyang Lake Basin, were 19.8, 19.0, 19.1, and 19.1 C for ST1–4, ure 5), while an abrupt change of spring soil temperature respectively. In ST1 (0–7 cm), the temperature reaches a peak ° ° occurred in 1996. Abrupt changes in summer soil tempera- in July (28.8 C) and a minimum in January (9.6 C). -e tures of ST1, ST2, and ST3 occurred in 2002, but the summer temperature rises from January, peaks in July, and then soil temperature of ST4 had its abrupt change in 1999. In gradually decreases. In ST2 (7–28 cm) and ST3 (28–100 cm), autumn, the abrupt changes of ST2, ST3, and ST4 occurred in the temperature changes are very similar to those for ST1, 2000, while the abrupt change of ST1 occurred in 1997. In except for the temperature peaks in August rather than July. winter, ST2 and ST3 showed no abrupt changes, whereas ST1 Unlike other depths, in ST4 (100–289 cm), the temperature ° and ST4 showed an abrupt change in 1992. starts increasing in March (12.6 C), peaks in September (25.4 C), and then declines to February of the following year (Figure 3). From March to September, the temperature of ST1 3.4. Anomalous Characteristics of Soil Temperatures. In is higher than the temperature of ST4. -erefore, the heat spring, the soil temperatures at depths of ST1, ST2, and ST3 travels from the surface to depth and the soil is in a state of were abnormally low in 1996 and abnormally high in 2018. In energy absorption. From October to February of the following 1984, the soil temperature of ST4 was abnormally low. In year, the temperature of ST4 is higher than that of ST1. -e summer, the soil temperature of ST1 was abnormally low in energy path thus reverses, from the deep soil to the surface, 1982. -e soil temperatures of ST1, ST2, and ST3 were ab- and the soil becomes a source of heat energy. normally high in 2013, whereas that of ST4 was abnormally Monthly values are averaged to obtain the seasonal high in both 2007 and 2018. In autumn, the soil temperature of temperature. Seasons are defined as follows: winter- ST1 was abnormally low in 1979 and that of ST2 was also � December, January, and February; spring � March, April, abnormally low in 1981. -e soil temperature of ST4 was and May; summer � June, July, and August; and abnormally high in 2009. In winter, soil temperatures at all autumn � September, October, and November. In general, depths were abnormally high in 1999 and 2017 and low in the temperature at each depth and each season increases year 1984. In terms of the annual mean, soil temperatures at all by year. In terms of seasons, the temperature increases the depths were abnormally low in 1984 (Table 3). fastest in spring and the slowest in summer, except for the ST4, which increases the fastest in spring and the slowest in winter. In terms of depth, the temperature of ST1 rises the 3.5. Spatial Variations of Soil Temperatures. In the Poyang fastest, but the warming trend is similar among the other Lake Basin, annual mean ST1 decreases with latitude. -ere layers (Figure 4). are two low-value areas in the northeast and northwest of the Soil temperature (°C) Jan-79 Jun-83 Nov-87 Apr-92 Sep-96 Mar-01 Aug-05 Jan-10 Jun-14 Nov-18 Advances in Meteorology 5 Table 1: -e monthly variations of ERA and obs. soil temperature data. ° ° ° Correlation coefficients ME ( C) MAE ( C) RMSE ( C) ST1 0.99 −1.1 1.7 2.2 ST2 0.99 −1.3 1.3 1.5 ERA versus Nanchang ST3 0.99 −1.1 1.2 3.7 ST4 0.94 −1.2 2.7 3.2 ST1 0.99 −2.5 2.6 3.2 ST2 0.99 −2.6 2.6 2.7 ERA versus Ganzhou ST3 0.99 −2.5 2.5 2.6 ST4 0.87 −2.6 3.1 4 35.0 30.0 ST1 ST2 30.0 25.0 25.0 20.0 20.0 15.0 15.0 10.0 10.0 5.0 5.0 0.0 0.0 123456789 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Month Month 30.0 30.0 ST3 ST4 25.0 25.0 20.0 20.0 15.0 15.0 10.0 10.0 5.0 5.0 0.0 0.0 123456789 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Month Month Figure 3: Monthly change of the soil temperature at the four soil depths. Poyang Lake Basin (Figure 6(a)). -e spatial distributions of of soil temperature. -ere are high-value areas in the almost all soil temperatures are very similar to the annual northeast region of the Poyang Lake Basin (Figure 8). We ST1 soil temperature; the spatial correlation coefficients are plotted a distribution map of soil temperature for the four over 0.9, except for the summer soil temperatures of ST1 and depths at different scales (data not presented), the corre- ST2 (Table 4). -e spatial correlation coefficient between lation coefficients of which with annual ST1 are around 0.9, summer soil temperatures ST1 and ST2 is 0.860. Unlike the except for the trends of ST1 and ST2 in summer (Table 5). annual soil temperature ST1, the summer soil temperature -e only difference is that the low-value area of the trend of ST1 values are higher around the lake and the river summer ST1 is more northward. Annual ST1 shows a clear upward trend; in most areas, it can reach 0.3 (Figure 6(b)). C/10a Summer air temperature and summer precipitation may (Figure 8(a)). Summer ST1 also shows a clear upward trend be the main factors influencing the spatial distribution of the in the whole area, but the increase rate is lower than that of summer ST1. In terms of high temperature regions, the annual ST1. spatial distribution of summer air temperature and ST1 are basically the same, except for the northeast part of the basin. 4. Discussion Heavy precipitation in the northeast part may cause the soil temperature to fail to increase (Figure 7). Over the past century, the effects of global warming have In general, the climatic trend of soil temperature pres- affected not only the air temperature but also precipitation ents a generally increasing trend from the south area to the patterns and soil temperatures [34]. Soil temperature is the north in the Poyang Lake Basin, opposite to the distribution main factor affecting the length of the growing season, rates Temperature (°C) Temperature (°C) Temperature (°C) Temperature (°C) 6 Advances in Meteorology 22.0 30.0 Spring Summer 21.0 29.0 20.0 28.0 19.0 27.0 18.0 26.0 17.0 25.0 16.0 24.0 15.0 23.0 14.0 22.0 13.0 21.0 12.0 20.0 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Year Year ST1 ST3 ST1 ST3 ST2 ST4 ST2 ST4 26.0 20.0 Autumn Winter 25.0 18.0 24.0 16.0 23.0 14.0 22.0 12.0 21.0 10.0 20.0 8.0 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Year Year ST1 ST3 ST1 ST3 ST2 ST4 ST2 ST4 21.0 Year 20.5 Climatic trend Spring Summer Autumn Winter Year rate (°C/decade) 20.0 ST1 0.43 0.19 0.33 0.29 0.31 19.5 ST2 0.41 0.19 0.26 0.20 0.29 19.0 ST3 0.42 0.22 0.27 0.19 0.29 ST4 0.37 0.29 0.25 0.20 0.30 18.5 18.0 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Year ST1 ST3 ST2 ST4 Figure 4: Changes in seasonal and annual soil temperatures over the Poyang Lake Basin. of mineralization and nutrient assimilation, and plant we need further to detect if the reanalysis data perform well productivity [35–37]. It is therefore very important to de- in complex terrain. Poyang Lake Basin locates in the middle and lower Yangtze river and South China. We also need termine the variation of soil temperature. Based on the observation and reanalysis data from 1979 to 2018, this study further to investigate the differences between soil temper- analyzed the variation of seasonal and annual soil temper- ature in Poyang Lake Basin and that in the big area. In ature, abrupt changes and abnormal years, and the spatial context, we have found an abrupt change of soil tempera- distribution of the soil temperature and its trends. In fact, the ture: we also need to know why it happened and what is the Poyang Lake Basin includes plains, basins, and mountains; subsequent impact. Another important issue is how regional Temperature (°C) Temperature (°C) Temperature (°C) Temperature (°C) Temperature (°C) Advances in Meteorology 7 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 –2 –4 Figure 5: Years of abrupt change in annual ST1. Table 2: Abrupt change years of annual and seasonal soil temperatures over the Poyang Lake Basin. Index Spring Summer Autumn Winter Annual ST1 1996 2002 1997 1992 1997 ST2 1996 2002 2000 — 1997 ST3 1996 2002 2000 — 1997 ST4 1996 1999 2000 1992 1997 Note. “—“indicates no abrupt change. Table 3: Years of anomalous annual and seasonal mean soil temperatures over the Poyang Lake Basin. Index Spring Summer Autumn Winter Annual ST1 1996 (－), 2018 (+) 1982 (－), 2013 (+) 1979 (－) 1984 (－), 1999, 2017 (+) 1984 (－), ST2 1996 (－), 2018 (+) 2013 (+) 1981 (－) 1984 (－), 1999, 2017 (+) 1984 (－), ST3 1996 (－), 2018 (+) 2007, 2013 (+) — 1984 (－), 1999, 2017 (+) 1984 (－), ST4 1984 (－) 2007, 2018 (+) 2009 (+) 1984 (－), 1999, 2017 (+) 1984 (－), Note. (+) abnormally high, (−) abnormally low. 30°N 30°N 29°N 29°N 28°N 28°N 27°N 27°N Annual ST1 (°C) Summer ST1 (°C) 26°N 26°N 21.5 28.7 17.5 26.8 25°N 25°N 0 50 100 0 50 100 Km Km 114°E 115°E 116°E 117°E 118°E 119°E 114°E 115°E 116°E 117°E 118°E 119°E (a) (b) Figure 6: Spatial distribution of annual soil temperature (a) and summer soil temperature (b) of ST1 over the Poyang Lake Basin. Statistical value 8 Advances in Meteorology Table 4: Spatial correlation coefficient between annual ST1 and soil temperatures for other depths and time periods. Correlation coefficient Annual ST1 Annual — Spring 0.992 ST1 Summer 0.497 Autumn 0.997 Winter 0.986 Annual 0.995 Spring 0.996 ST2 Summer 0.642 Autumn 0.996 Winter 0.984 Annual 0.995 Spring 0.993 ST3 Summer 0.872 Autumn 0.991 Winter 0.991 Annual 0.995 Spring 0.992 ST4 Summer 0.975 Autumn 0.926 Winter 0.978 30°N 30°N 29°N 29°N 28°N 28°N 27°N 27°N Summer air Summer precipitation 26°N temperature (°C) 26°N (mm) 28.7 734.7 21.7 439.7 25°N 25°N 0 50 100 0 50 100 Km Km 114°E 115°E 116°E 117°E 118°E 119°E 114°E 115°E 116°E 117°E 118°E 119°E (a) (b) Figure 7: Spatial distribution of summer air temperature (a) and summer precipitation (b) over the Poyang Lake Basin. Advances in Meteorology 9 30°N 30°N 29°N 29°N 28°N 28°N 27°N 27°N Annual ST1 Summer ST1 26°N (°C/decade) 26°N (°C/decade) 0.45 0.35 0.25 0.08 25°N 25°N 0 50 100 0 50 100 Km Km 114°E 115°E 116°E 117°E 118°E 119°E 114°E 115°E 116°E 117°E 118°E 119°E (a) (b) Figure 8: Spatial distribution of the climatic trend of annual soil temperature (a) and summer soil temperature (b) of ST1 over the Poyang Lake Basin. Table 5: Spatial correlation coefficient between the climatic trend the studied depths over the past 40 years (correlation of annual ST1 and soil temperatures for other depths and time coefficients ≥0.87) and a significant upward trend. periods. Compared with the observation data, the reanalysis data generally underestimates their magnitudes. Correlation coefficient Annual ST1 Compared with Nanchang (Ganzhou), the ME is Annual — −1.1 to −1.2 (−2.5 to −2.6). -e ERA-Interim/Land Spring 0.951 reanalysis data are reliable for regional soil tem- ST1 Summer 0.768 perature research in the Poyang Lake basin. Autumn 0.933 Winter 0.886 (2) Monthly, from March to September, the temperature Annual 0.989 of ST1 is higher than the temperature of ST4. -is Spring 0.933 indicates that the heat travels from the surface to ST2 Summer 0.790 depth. From October to February of the following Autumn 0.952 year, the temperature of ST4 is higher than the Winter 0.900 temperature of ST1, indicating that the energy path Annual 0.989 had reversed, now traveling from the deep soil to the Spring 0.946 surface. Seasonally and annually, the soil tempera- ST3 Summer 0.862 tures mostly increased during the study period. In Autumn 0.943 terms of seasons, the temperature increase was the Winter 0.921 fastest in spring (0.37–0.43 C/10a) and the slowest in Annual 0.987 summer (0.19–0.29 C/10a), except for ST4, in- Spring 0.946 creasing the fastest in spring (0.37 C/10a) and the ST4 Summer 0.938 slowest in winter (0.20 C/10a). Annually, the tem- Autumn 0.908 Winter 0.946 perature increased the fastest for ST1 (0.31 C/10a). For the other layers, the warming trend is almost the same. climate change and soil temperature interact. What role does (3) In general, the soil temperatures changed from a soil temperature play in the monsoon climate is also a relatively cold period to a comparatively warm pe- question worth studying. riod. Abrupt changes of the annual soil temperature at all depths occurred in 1997, while the abrupt 5. Conclusions change of spring soil temperature occurred in 1996. Abrupt changes in summer soil temperatures of ST1, -e main conclusions of this study are as follows: ST2, and ST3 occurred in 2002, except for ST4, (1) -e relationships between the observation data and which showed an abrupt change in 1999. In autumn, reanalysis data all showed good correlation at each of the abrupt changes of ST2, ST3, and ST4 occurred in 10 Advances in Meteorology Asian monsoon in a warming climate,” Earth System Dy- 2000, whereas the ST1 had its abrupt change in 1997. namics, vol. 6, no. 2, pp. 569–582, 2015. In winter, the ST2 and ST3 showed no abrupt [5] A. Ruiz-Barradas and S. Nigam, “Atmosphere-land surface change, whereas ST1 and ST4 showed abrupt interactions over the southern great plains: characterization changes in 1992. from pentad analysis of DOE ARM field observations and (4) Annually, the years of anomalous soil temperatures NARR,” Journal of Climate, vol. 26, no. 3, pp. 875–886, 2013. at all depths were consistent and were anomalously [6] M. Bilgli, B. Sahin, and L. Sangun, “Estimating soil temper- low in 1984. Seasonally, the years of anomalous soil ature using neighboring station data via multi-nonlinear temperatures of ST1, ST2, and ST3 cm were con- regression and artificial neural network models,” Environ- sistent, but they were inconsistent with the soil mental Monitoring and Assessment, vol. 185, no. 1, pp. 347– temperature of ST4. 358, 2013. [7] T. R. H. Holmes, M. Owe, R. A. M. Dejeu et al., “Estimating (5) Spatially, in the Poyang Lake Basin, the soil tem- the soil temperature profile from a single depth observation: a perature decreases with latitude, except for ST1 in simple empirical heatflow solution,” Water Resources Re- summer. -e high air temperature and heavy pre- search, vol. 44, no. 2, pp. 103–112, 2008. cipitation in summer may be the main causes of the [8] B. Qian, E. G. Gregorich, S. Gameda, D. W. Hopkins, and spatial distribution of the summer ST1. Unlike the X. L. Wang, “Observed soil temperature trends associated other soil temperatures, the summer soil tempera- with climate change in Canada,” Journal of Geophysical Re- ture of ST1 is higher around the lake and the river. search, vol. 116, no. D2, Article ID D02106, 2011. -e climatic trend of soil temperature presents a [9] Y. Xue, R. Vasic, Z. Janjic, Y. M. Liu, and P. C. Chu, “-e general increasing trend from the south to the north impact of spring subsurface soil temperature anomaly in the in Poyang Lake Basin, opposite to the distribution of western U. S. on North American summer precipitation: a soil temperature. case study using regional climate model downscaling,” Journal of Geophysical Research: Atmospheres, vol. 117, no. D11, 2012. Data Availability [10] Y. Wang, W. Chen, J. Zhang, and D. Nath, “Relationship between soil temperature in May over Northwest China and -e observed data at depths of 0, 20, 80, and 320 cm the East Asian summer monsoon precipitation,” Acta (1979–2018) used to support the findings of this study were Meteorologica Sinica, vol. 27, no. 5, pp. 716–724, 2013. accessed from http://data.cma.cn/under account and [11] S. E. Trumbore, O. A. Chadwick, and R. Amundson, “Rapid therefore they cannot be made freely available. -e ERA- exchange between soil carbon and atmospheric carbon di- Interim/Land reanalysis data can be downloaded from oxide driven by temperature change,” Science, vol. 272, http://apps.ecmwf.int/datasets/. pp. 393–395, 1996. [12] M. L. Goulden, S. C. Wofsy, J. W. Harden et al., “Sensitivity of Conflicts of Interest boreal forest carbon balance to soil thaw,” Science, vol. 279, no. 5348, pp. 214–217, 1998. -e authors declare that there are no conflicts of interest [13] F. E. Nelson, “Geocryology: enhanced: (Un) frozen in time,” regarding the publication of this paper. Science, vol. 299, no. 5613, pp. 1673–1675, 2003. [14] F. E. Nelson, O. A. Anisimov, and N. I. Shiklomanov, Acknowledgments “Subsidence risk from thawing permafrost,” Nature, vol. 410, no. 6831, pp. 889-890, 2001. -is study was supported by the Jiangxi Meteorological [15] Y. Zhang and W. Y. Chen, “Soil temperature in Canada Bureau and Jiangxi Eco-Meteorological Center and a bi- during the twentieth century: complex responses to atmo- spheric climate change,” Journal of Geophysical Research, lateral cooperation project between the Natural Science vol. 110, no. D3, Article ID D03112, 2005. Foundation of China and the Pakistan Science Foundation [16] M. J. Zhan, L. J. Xia, L. F. Zhan et al., “Recognition of changes (41661144027). in air and soil temperatures at a station typical of China’s subtropical monsoon region (1961–2018),” Advances in Me- References teorology, vol. 2019, Article ID 6927045, 9 pages, 2019. [17] R. K. Pachauri and L. A. Meyer, “IPCC Climate Change 2014: [1] X. Meng, H. Wang, Y. Wu et al., “Investigating spatiotemporal Synthesis Report Summary for Policymakers,” in Contribution changes of the land-surface processes in Xinjiang using high- of Working Groups I, II, and III to the Fifth Assessment Report resolution CLM3.5 and CLDAS: soil temperature,” Scientific of the Intergovernmental Panel on Climate Change, Cambridge Reports, vol. 7, Article ID 13286, 2017. University Press, Cambridge, UK, 2014. [2] A. Boone, P. Samuelsson, S. Gollvik et al., “-e interactions [18] L. W. Zhang, R. C. Yu, Z. L. Ding et al., Cird National between soil-biosphere-atmosphere land surface model with a Assessment Report on Climate Change, Science Press, Beijing, multi-energy balance (ISBA-MEB) option in SURFEXv8-Part China, 2015. 1: model description,” Geoscientific Model Development, [19] X. B. Lu, H. M. Xu, C. H. Sun et al., “Characteristics of soil vol. 10, no. 2, pp. 843–872, 2017. temperature variations in China in recent 50 years,” Journal of [3] A. D. L. Morten, H. C. Jens, D. Martin et al., “Local control on Nanjing Institute of Meteorology, vol. 29, no. 5, pp. 706–712, precipitation in a fully coupled climate-hydrology model,” Scientific Reports, vol. 6, Article ID 22927, 2016. 2006. [20] J. Du, C. Li, J. Liao et al., “Responses of climatic change on [4] M. V. S. Ramarao, R. Krishnan, J. Sanjay, and T. P. Sabin, “Understanding land surface response to changing South soil temperature at shallow layers in Lhasa from 1961 to Advances in Meteorology 11 2005 (in Chinese),” Meteorological Monthly, vol. 33, no. 10, pp. 61–67, 2007. [21] C. Chen and G. S. Zhou, “Characteristics of air temperature and ground temperature in alxa Left banner from 1961 to 2010,” Journal of Natural Resources, vol. 29, no. 1, pp. 91–103, [22] K. Yang and J. Zhang, “Evaluation of reanalysis datasets against observational soil temperature data over China,” Climate Dynamics, vol. 50, no. 1-2, p. 317, 2017. [23] Z. D. Yu, G. Q. Zhou, and Y. J. Zhou, “Evaluation and analysis of soil temperature data over middle and high latitudes of East Asia,” Chinese Journal of Atmospheric Sciences, vol. 41, no. 1, pp. 147–166, 2017. [24] Y. H. Qin, T. H. Wu, R. Li et al., “-e applicability of ERA- Interim land surface temperature dataset to map the per- mafrost distribution over the Tibetan Plateau,” Journal of Glaciology and Geocryology, vol. 37, no. 6, pp. 1534–1543, [25] Q. Hu, S. Feng, H. Guo et al., “Interactions of the Yangtze river flow and hydrologic processes of the Poyang Lake, China,” Journal of Hydrology, vol. 347, no. 1-2, pp. 90–100, [26] P. Berrisford, D. P. Dee, P. Poli et al., “-e ERA-Interim archive version 2.0,” ERA Report Series, vol. 123 pages, 2011. [27] G. Balsamo, C. Albergel, A. Beljaars et al., “ERA-Interim/ Land: a global land surface reanalysis data set,” Hydrology and Earth System Sciences, vol. 19, no. 1, pp. 389–407, 2015. [28] P. K. Sen, “Estimates of the regression coefficient based on Kendall’s tau,” Journal of the American Statistical Association, vol. 63, no. 324, pp. 1379–1389, 1968. [29] J. M. Cunderlik and D. H. Burn, “Non-stationary pooled flood frequency analysis,” Journal of Hydrology, vol. 276, no. 1–4, pp. 210–223, 2003. [30] H. Tabari and P. H. Talaee, “Temporal variability of precip- itation over Iran: 1966–2005,” Journal of Hydrology, vol. 396, no. 3-4, pp. 313–320, 2011. [31] D. Jhajharia, Y. Dinpashoh, E. Kahya, V. P. Singh, and A. Fakheri-Fard, “Trends in reference evapotranspiration in the humid region of northeast India,” Hydrological Processes, vol. 26, no. 3, pp. 421–435, 2012. [32] G. Wang, A. J. Dolman, R. Blender et al., “Fluctuation regimes of soil moisture in ERA-40 re-analysis data,” Ceoretical and Applied Climatology, vol. 99, no. 1-2, pp. 1–8, 2010. [33] M. Zhan, Y. Wang, G. Wang et al., “Long-term changes in soil moisture conditions and their relation to atmospheric cir- culation in the Poyang Lake basin, China,” Quaternary In- ternational, vol. 440, pp. 23–29, 2017. [34] H. B. Mann, “Nonparametric tests against trend,” Econo- metrica, vol. 13, no. 3, pp. 245–259, 1945. [35] M. G. Kendall, Rank Correlation Methods, Griffin, London, UK, 1975. [36] C. H. Goossens and A. Berger, “Annual and seasonal climatic variations over the Northern Hemisphere and Europe during the last century,” Annals of Geophysics, vol. 4, pp. 385–400, [37] C. B. Fu and Q. Wang, “-e definition and detection of the abrupt climate change,” Scientia Atmospheric Sinica, vol. 16, no. 4, pp. 482–492, 1992.
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Advances in Meteorology – Hindawi Publishing Corporation
Published: Dec 8, 2020