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INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: 1181–1195 (2014) Published online 18 June 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3755 Regional temperature change over the Huang-Huai-Hai Plain of China: the roles of irrigation versus urbanization Wenjiao Shi, Fulu Tao* and Jiyuan Liu Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China ABSTRACT: Irrigation and urbanization, two widely occurring land-use/land-cover changes, have important influences on regional climate, especially on temperature. The effect of irrigation and urbanization on temperature is separately documented in several studies. However, there are few studies analysing the combined effects of irrigation and urbanization on temperature. In this study, changes in surface temperature were analysed in relation to irrigation and urbanization on the Huang-Huai-Hai Plain of China from 1955 to 2007. To better characterize the combined effects of these two processes on temperatures, long-term weather observations are used along with irrigation and urbanization data sets. The results indicated that irrigation had a significant cooling effect of 0.17–0.20 ◦ C decade−1 on average daily maximum temperature of the hottest 1, 5, and 30 d of each year on the Huang-Huai-Hai Plain. Compared with the reference conditions, irrigation also indicated a cooling effect of 0.12 ◦ C decade−1 on summertime daily maximum temperature. In contrast to its effect on maximum temperature, irrigation appeared to induce a warming effect of 0.43 ◦ C/decade on average daily minimum temperature of the coldest 1 d of each year. Where irrigation interfaced with urbanization, the urbanization warming of extreme daily maximum temperature seemed to only partly counteract the irrigation cooling effect. The findings of this study deepen our insight into the effects of irrigation and urbanization on temperature dynamics, and the combined implications for regional climate change. Further efforts to understand irrigation and urbanization effects on climate should not only use observations, but should also be coupled with dynamic land-use and regional climate models to understand the complex processes and controlling mechanisms. KEY WORDS irrigation; urbanization; climate change; extreme temperature; land-use change Received 27 February 2012; Revised 23 January 2013; Accepted 19 May 2013 1. Introduction Two important processes of human activities, the emission of greenhouse gases and land-use/land-cover changes, have great influence on climate change (Pielke et al., 2002; Paeth et al., 2009). Greenhouse gas emissions are embedded in climate models for IPCC climate change assessments, and land-cover changes in forcing scenarios for future climate change studies are also documented (Feddema et al., 2005). In recent years, there has been growing interest in land-use/land-cover change and its effect on climate change (Kalnay and Cai, 2003; Roy et al., 2003; Parker, 2004; Pitman et al., 2004; Feddema et al., 2005; Lobell et al., 2006a, 2008; Kueppers et al., 2007; Deo et al., 2009; Hu et al., 2010). Particularly, urbanization (Kalnay and Cai, 2003; Zhou et al., 2004) and irrigation (Mahmood et al., 2006; Bonfils and Lobell, 2007; Kueppers et al., 2007; Lobell et al., 2008; Lobell et al., 2009; Puma and Cook, 2010; Wu et al., 2010) are the two main causes of rapid land-use change with significant influence on climate change of regional scale. * Correspondence to: F. Tao, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. E-mail: [email protected] 2013 Royal Meteorological Society Irrigated lands account for approximately 40% of global food production. Thus food security depends on the magnitude of climatic changes, especially in the irrigated regions of the world (Lobell et al., 2006b). The impact of irrigation on temperature is detailed in several studies driven by model simulations (Lobell et al., 2006a; Kueppers et al., 2007, 2008; Diffenbaugh, 2009; Lobell et al., 2009; Sacks et al., 2009; Jin and Miller, 2010) and observational data analyses (Mahmood et al., 2006; Bonfils and Lobell, 2007). Several previous studies also demonstrate that irrigation reduces soil albedo, increases transpiration and evaporation, and alters local soil moisture, energy balance and climate. On hourly to seasonal timescales, the effects of irrigation on climate change sometimes exceed those of greenhouse gases (Bonfils and Lobell, 2007; Kueppers et al., 2007; Lobell et al., 2009). Model simulations indicate that the impact of land use change due to irrigation on temperature is most pronounced in summer (Kueppers et al., 2008). Furthermore, substantial regional differences exist in the irrigation cooling effect (Lobell et al., 2009). None of the climate models in the fourth assessment report (AR4) of IPCC include irrigation-driven changes in soil moisture (Lobell et al., 2008). In addition, the effects of irrigation on extreme temperatures are consistently ignored (Lobell et al., 2008; Hu et al., 2010). Because 1182 W. SHI et al. Figure 1. The location of the study area in China (left) and the location of the 95 meteorological stations used in this study (right). there is a lack of studies directly comparing surface temperature using observed data (Mahmood et al., 2006), there is the need to examine the effects of irrigation on extreme temperatures via observational data analysis (Hu et al., 2010; Jin and Miller, 2010). The cooling effects of irrigation continue to attract the interest of research communities across the globe, including the United States (Mahmood et al., 2006; Kueppers et al., 2008), India (De Rosnay et al., 2003; Douglas et al., 2006) and Thailand (Haddeland et al., 2006). However, there is little work in this direction for the Huang-Huai-Hai Plain of China, which is one of the most intensively irrigated regions in the world (Siebert et al., 2005). Urbanization induces heat islands, which is partially responsible for observed warming in urban land surfaces in the last few decades (Zhou et al., 2004). Changes in surface temperature depend on the nature of ‘urban’ (Hua et al., 2008; Jones et al., 2008), spatial scales (Jones et al., 2008; Ren et al., 2008) and seasonal differences among urban regions (Kueppers et al., 2008). For example, cities with dense populations, buildings and limited greenbelts exhibit urbanization effects are different from cities with the reverse characteristics (Hua et al., 2008). There is a wider effect of urbanization on annual mean surface air temperature in large cities than 2013 Royal Meteorological Society in small cities (Ren et al., 2008). Two thirds of the constituent data come from the marine realm, where any urban influence cannot be present (Jones et al., 2008). Consequently, the urbanization effect is hardly noticeable at global scale. However, urban heat islands (UHI) could greatly influence natural variations in local climate (Hua et al., 2008). Tayan et al. (1997) showed that the effect of urbanization on UHI is most obvious in spring, whereas its highest effect on the lowering of minimum temperature occurs in winter. Hua et al. (2008) showed that during spring and summer, the effects of UHI on temperature are relatively weaker in coastal than in inland cities. Changes in surface temperature could result from changes in soil albedo and soil moisture (Sailor, 1995; Jacobson, 1999). Rapid urbanization alters moisture flux associated with deep-rooted trees and concrete. This in turn alters the local surface energy balance by increasing sensible heat flux and decreasing latent heat flux (Kueppers et al., 2008). Other studies contend that compared with largescale changes in land use, the impact of urbanization on temperature is relatively small (Kalnay and Cai, 2003). In the rapidly urbanizing southeast China, studies suggest significant effect of urbanization on climate change (Zhou et al., 2004). These conflicting reports indicate the need for further research into the effects of urbanization on land surface air temperature. Int. J. Climatol. 34: 1181–1195 (2014) 1183 IRRIGATION COOLING EFFECT Figure 2. Locations of the agrometeorological stations in Hebei Province. At the end of 2007, the total population in the HuangHuai-Hai Plain was 422.59 million, which was increased by 206.68 million people as compared to that in the end of 1995. On the basis of remote sensing and land-use change interpretations, the Huang-Huai-Hai Plain (the socalled ‘Granary of China’) has had significant expansion in irrigated lands and urbanization in recent decades (Liu et al., 2003, 2010). This greatly influences land surface air temperature and regional climate change (Mahmood et al., 2006; Hu et al., 2010). The effects of irrigation and urbanization on temperature and climate change are separately analysed in several studies. However, there are few studies that integrate the effects of these land physiographic factors on temperature change. In fact, the effects of irrigation and urbanization on temperature 2013 Royal Meteorological Society climate change in the Huang-Huai-Hai Plain largely remain uncertain. The rapid expansions in urbanization and irrigated land in the Plain in recent decades provides a unique opportunity for the combined analysis of the effects of these two processes on temperature and climate change. The objectives of this study were to (1) investigate the impacts of irrigation on long-term (1955–2007) temperature trends in the Huang-Huai-Hai Plain of China, (2) determine the influences of irrigation and urbanization effects on daily extreme temperature over the Plain, and (3) compare the relative effects of irrigation and urbanization on extreme daily maximum temperature. Int. J. Climatol. 34: 1181–1195 (2014) 1184 W. SHI et al. Figure 3. Decadal trends in temperature for different levels of irrigation on the Huang-Huai-Hai Plain of China in the period 1955–2007. The label of on the abscissa shows the irrigation region followed by the number of stations in parenthesis. The decadal trends of T max shown are the average of the 1 (top 1), 5 (top 5), and 30 (top 30) highest daily T max per year and the average annual daily maximum temperatures (T max_mean ). The decadal trends of T min shown are the average of the 1 (bottom 1), 5 (bottom 5), and 30 (bottom 30) coldest daily T min per year and the average annual daily maximum temperature (T min_mean ). The decadal trend of T mean shown is the average annual daily mean temperatures (T mean_mean ). Error bars indicate 95% confidence interval based on bootstrap resampling. Dark blue bars represent the standard deviation of bottom 1, and red bars represent the standard deviation of top 1. 2. 2.1. Data and methods Study area The study area is comprised of two metropolises (Beijing and Tianjin) and five provinces (Anhui, Hebei, Henan, Jiangsu and Shandong). It is located between 110◦ 1 E–124◦ 21 E and 28◦ 59 N–43◦ 9 N (Figure 1). The Huang-Huai-Hai Plain, which is the largest and most important agricultural base in China, also lies in the study area. Annual average temperature (1955–2007) in the plain gradually varies from 3.1 ◦ C in the north to 16.8 ◦ C in the south, with an average annual precipitation of 386–2333 mm. Double cropping of winter wheat and summer maize is the main production system in the region. Winter wheat is sown from early October and harvested by the following June, and summer maize grows immediately after winter wheat is harvested till late September (Sun et al., 2010). In cultivated lands, annual water consumption far exceeds annual precipitation, which is mainly sustained by irrigation. Intensive irrigation therefore offsets not only the water deficit, but also maintains high crop yield in the Plain. The HuangHuai-Hai Plain has experienced significant expansions in built-up and residential areas in last several decades (Liu et al., 2005; Sun et al., 2010). 2013 Royal Meteorological Society 2.2. Data 2.2.1. Climate data The temperature and precipitation data used in this article are from records collected and processed by the National Meteorological Center of the China Meteorological Administration. This data set includes measurements from 108 meteorological stations distributed throughout the study area from 1951 to the present. Because some stations were removed (Zhou et al., 2004), we use the temperature [daily maximum (T max ), minimum (T min ) and mean (T mean ) land surface air temperatures] and annual precipitation data from 95 stations for the period from 1955 to 2007 in this study (Figure 1). 2.2.2. Irrigation data We used a gridded irrigation data set based on the geographical distribution of areas equipped for irrigation around the year 2000 (Siebert et al., 2005). The data set is of high resolution and is available at 5 arc minute × 5 arc minute grid cells. Two country-specific indicators were computed that take into account the geo-spatial information density, including indicator for the density of sub-national irrigation statistics and indicator for the density of geo-spatial records. Then the map was compared Int. J. Climatol. 34: 1181–1195 (2014) IRRIGATION COOLING EFFECT 1185 Figure 4. Decadal trends for the average of the 1, 5, and 30 highest daily T max and coldest daily T min values, along with the average June–August (JJA) T max and December–February (DJF) T min , and the mean annual T max (T max_mean ) and T min (T min_mean ) in irrigated and reference regions on the Huang-Huai-Hai Plain of China in the period 1955–2007. Irr–Ref is the difference of temperature trends between irrigated and reference regions. Error bars indicate 95% confidence interval based on bootstrap resampling. to the irrigated areas of two global land-cover inventories from remote sensing data (Siebert et al., 2005). The quality of the irrigation map is graded as ‘very good’ by the above indicators and comparison for the region over China (Siebert et al., 2005). To assess the quality of this irrigation data set in regional scale, we compared records about irrigation equipment and real practices at the agrometeorological stations in Hebei Province collected and processed by the National Meteorological Center of the China Meteorological Administration with the irrigation data set in Hebei Province (Figure 2). The comparison showed that most of irrigation records of the 24 stations were very consistent with the irrigation data set. There were only two stations located at Zhangbei and Huanghua Counties that had records inconsistent with the irrigation data set (Figure 2). For example, Huanghua station is with irrigation in the agrometeorological station records while the grid which is the station located is without any irrigation area at the irrigation map. In general, this irrigation data set for the study area is considered to be reliable. 2.2.3. Urbanization data The gridded urbanization data of the study area was from the 1 × 1 km land-use set of China built through visual interpretation of Landsat TM images for 1990, 2000 and 2005 with a spatial resolution of 30 × 30 m2 (Liu et al., 2002, 2010, 2005, 2003). We use 2005 data as an example to show the derivation of the urbanization data and their reliability. Firstly, a total of 411 Landsat TM images with charge-coupled device (CCD) images 2013 Royal Meteorological Society from CBERS-2 as a supplement were collected. Then they were geo-referenced using 1 : 50 000 topographic maps, and the root mean squared error (RMSE) of geometric rectification was less than 1.5 pixels (or 45 m). Secondly, according to the field validation, the accuracy of identification for built-up land was 98% (Liu et al., 2010). Thirdly, in order to overlay the grid data of irrigation and urbanization, these vector land-use data were converted into grid data with 1 × 1 km resolution through a method which was introduced in the study of Liu et al. (2010). This method can make the grid data reflect area proportion for the built-up area and eliminate the scale effect of different data sources during data fusion (Liu et al., 2003). 2.3. Analyses Following the methods of Hegerl et al. (2004) and Lobell et al. (2008), decadal trends of temperature extremes using daily T max and T min were computed, including the average T max on the hottest 1, 5, and 30 d (top 1, top 5 and top 30) and average T min on the coldest 1, 5, and 30 d (bottom 1, bottom 5 and bottom 30) for each year. Average monthly and quarterly T max and T min , and average annual T max , T min and T mean (T max_mean , T min_mean and T mean_mean ) were also computed. Linear trends for 1955–2007 for all temperature and precipitation values were computed using ordinary least squares regression. On the basis of bootstrap resampling (n = 1000) of the original time series, the 95% confidence interval was computed nonparametrically. Int. J. Climatol. 34: 1181–1195 (2014) 1186 W. SHI et al. Figure 5. Decadal trends from 1955 to 2007 of average monthly T max in irrigation and reference regions on the Huang-Huai-Hai Plain of China. Irr–Ref is the difference of temperature trends between irrigated and reference regions. Error bars indicate 95% confidence interval based on bootstrap resampling. High-irrigation regions (with irrigated land fraction of 50–100%) are designated as irrigation regions and then low-irrigation regions (with irrigated land fraction of 0.1–10%) are designated as reference regions (Bonfils and Lobell, 2007; Lobell et al., 2008). High-urbanization regions (with built-up land fraction of 70–100%) are designated as urbanized regions and then low-urbanization regions (with built-up land fraction <30%) are designated as reference regions. 3. Results and discussion 3.1. Decadal trends of temperature under different irrigation levels Decadal trends of the average T max on the hottest 1, 5 and 30 d (top 1, top 5 and top 30) and average T min on the coldest 1, 5 and 30 d (bottom 1, bottom 5 and bottom 30), along with the average annual T max , T min and T mean (T max_mean , T min_mean and T mean_mean ) for the period 1955–2007 are shown in Figure 3. For the extremes of daily maximum temperature (T max ), the cooling effect of increased irrigation on the top 1, top 5 and top 30 was similar and the trends for these were distinguishable from that for T max_mean . The greatest decrease of temperature was in the region irrigated up to 60–70% for top 1 (−0.29 ◦ C decade−1 ), top 5 (−0.25 ◦ C decade−1 ) and top 30 (−0.23 ◦ C decade−1 ). The effect of irrigation can be attributed to the enhanced evaporative cooling and increase of soil moisture, which causes a decrease in sensible heat flux during daytime through shifts in 2013 Royal Meteorological Society the Bowen ratio from sensible heating (Bonfils and Lobell, 2007; Kueppers et al., 2007; Sacks et al., 2009). However, the stations located in 50–60% irrigation have not much cooling effect (nearly 0 ◦ C decade−1 ), and those located in 70–80% irrigation had a smaller cooling effect than 60–70% irrigation. There are two possible reasons for this phenomenon. First, it may be, in some part, a result of urbanization occurs in the region with 50–60% irrigation (Shi et al. 2013), which could increase the temperature trends. Shi et al. (2013) showed that more urbanization occurred in the region with 50–60% irrigation, compared to the region with any other percentages of irrigation in Huang-Huai-Hai Plain. Second, the locations with 70–80% irrigation area (only two locations) were few compared to those with 50–60 and 60–70% irrigation (11 and 13 locations, respectively; Figure 3). In contrast to the cooling effect on T max , a warming effect of increased irrigation for the lowest 1 (bottom 1), 5 (bottom 5) and 30 (bottom 30) temperatures was shown. The greatest increase of temperature was with 60–70% irrigation for bottom 1 (1.30 ◦ C decade−1 ), bottom 5 (1.18 ◦ C decade−1 ) and bottom 30 (0.92 ◦ C decade−1 ). It appears that irrigation had a greater warming effect on extreme T min than the cooling effect on extreme T max values. A similar irrigation impact, which was much larger during the day than at night, was also shown by Bonfils and Lobell (2007) for research in California and Nebraska, USA. The mechanism for the warming effect on T min can be explained as follows. During daytime, irrigation increases evaporation (Boucher et al., Int. J. Climatol. 34: 1181–1195 (2014) IRRIGATION COOLING EFFECT 1187 Figure 6. Decadal trends from 1955 to 2007 of average seasonal T max in irrigated and reference regions on the Huang-Huai-Hai Plain of China. Irr–Ref is the difference of temperature trends between irrigated and reference regions. Error bars indicate 95% confidence interval based on bootstrap resampling. 2004) and the heat capacity of the soil and vegetation (Kalnay and Cai, 2003; Bonfils and Lobell, 2007), and moist ground and vegetation also absorb solar energy on cloudless days (Christy et al., 2006). The energy absorbed during the day can be released at night and release the energy in the evening (Christy et al., 2006). Increased level of irrigation had no clear effect on the decadal change of annual T min (T min_mean ) and T mean (T mean_mean ) (Figure 3). 3.2. Comparisons of extreme temperature between irrigated and reference regions Figure 4 shows decadal trends of average T max on the hottest 1, 5 and 30 d and average T min on the coldest 1, 5 and 30 d, along with the averages for June–August T max (JJA) and December–February T min (DJF) and the average annual T max (T max_mean ) and T min (T min_mean ). Decadal temperature changes for top 1, top 5 and top 30 and the June–August (summer) period of T max were negative for irrigated areas. On average, differences between the irrigated and reference regions were −0.20, −0.18, −0.17, −0.12 and −0.07 ◦ C decade−1 for top 1, top 5, top 30, summer (JJA) and annual T max (T max_mean ), respectively. Independent samples t-test showed that a significant irrigation effect was observed for the top 1 (P = 0.037), top 5 (P = 0.040) and top 30 (P = 0.040), but not for the average for summer and average annual T max (T max_mean ). This was in accordance with our expectation that irrigation would have larger effects on hot days when sensible heat fluxes were higher (Lobell et al., 2008). The changes of lands to irrigated agriculture areas have the potential to alter surface temperature by 2013 Royal Meteorological Society modifying the energy budget at the land–atmosphere interface (Kueppers et al., 2008). A regional climate model showed that a regional irrigation cooling effect exists (Kueppers et al., 2007). Jin and Miller (2010) explain that irrigation lowers surface temperature, which in turn reduces the sensible heat flux during the day, is caused by stronger surface evaporation as a result of manually adding water during irrigation. Trends for T min in irrigated regions were all positive (0.40–1.16 ◦ C decade−1 ). The difference between irrigated and reference areas was largest for bottom 1 (0.43 ◦ C decade−1 , P = 0.03). Although the similar differences were shown for bottom 5 and bottom 30, they were not significant. The finding of warming trend for T min is consistent with previous modelling studies of irrigation effects (Christy et al., 2006; Lobell et al., 2006a). For example, Lobell et al. (2006a) showed that latent heat fluxes peak during the daytime, so the cooling effect was much stronger for daily maximum than minimum temperatures. This is because the primary cause of local cooling in irrigation was an increase in latent heat flux and the associated decrease in sensible heat (Lobell et al., 2006a). Christy et al. (2006) also indicated that the warming trend of minimum temperatures at a highly significant rate in all seasons in the California Central Valley is likely related to irrigation in their modelling results. However, there also some other modelling study shows that irrigation also produces stronger surface evaporation, which cools the surface, weakens the sensible heat flux, and further dominates the nighttime surface cooling process (Jin and Miller, 2010). Int. J. Climatol. 34: 1181–1195 (2014) 1188 W. SHI et al. Figure 7. Decadal trends from 1955 to 2007 of average monthly T min in irrigation and reference regions on the Huang-Huai-Hai Plain of China. Irr–Ref is the difference of temperature trends between irrigated and reference regions. Error bars indicate 95% confidence interval based on bootstrap resampling. Figure 8. Decadal trends from 1955 to 2007 of average seasonal T min for irrigated and reference regions on the Huang-Huai-Hai Plain of China. Irr–Ref is the difference of temperature trends between irrigated and reference regions. Error bars indicate 95% confidence interval based on bootstrap resampling. 3.3. Seasonal variations of the irrigation effect on extreme temperature Figure 5 shows the decadal trends of average monthly T max from 1955 to 2007 for irrigated and reference regions. The cooling irrigation effect occurred only in 2013 Royal Meteorological Society June, July and August, with trends of −0.06, −0.11 and −0.05 ◦ C decade−1 , respectively. From the differences of decadal trends of average monthly T max between irrigated and reference regions (Irr–Ref), the decadal trends of irrigated regions were lower than reference regions for Int. J. Climatol. 34: 1181–1195 (2014) 1189 IRRIGATION COOLING EFFECT all months except for February. The most significant differences were in June and July, with cooling of −0.14 and −0.16 ◦ C decade−1 , respectively. The irrigation cooling effect on T max only occurred in summer but not in the other seasons with stations in the irrigated regions colder on average than reference regions by 0.12 ◦ C decade−1 in summer (Figure 6). This can be explained by the possibility that higher sensible heat fluxes at summer days can induce larger cooling effects on hot days (Lobell et al., 2008). The result of irrigation cooling effect on T max in this study differed a little from results of research in a similar location in China undertaken by Bonfils and Lobell (2007), which showed that T max only responded modestly with summer time irrigation trend. Bonfils and Lobell (2007) found for Eastern China that a cooling effect on T max was associated with increasing irrigation fraction in summer from 0.1–10 to 30–40%, but there was no further cooling with the increase of irrigation area to 40–50%. A possible explanation is that Bonfils and Lobell (2007) used the CRU2.1 data set, in which the underlying station network had considerably lower resolution than the data set we used. The warming trends for the average monthly T min occurred in every month for both irrigated and reference regions from 1955 to 2007. Except for February and October, the warming trends were larger in reference regions than those in irrigated regions for all months (Figure 7). Jin and Miller (2010) also shows that T min has a positive trend for all seasons and is consistent with the observed global warming trend. Warming trends of T min were lower in irrigated regions than those in reference regions for all seasons and the greatest difference between irrigated and reference regions occurred in summer with 0.08 ◦ C decade−1 (Figure 8). The difference with seasons between irrigated and reference regions were not significant according to the independent samples t-tests. 3.4. Spatial analysis of the irrigation effect On the basis of the above analysis of the irrigation effect on T max and T min , top 1, 5, and 30 and bottom 1 provided the best indicators of irrigation related cooling or warming in the study area. As the top 1, top 5 and top 30 have similar spatial distributions, only the decadal trend of the top 5 with irrigation spatial pattern is shown (Figure 9). The decadal trend of the bottom 1 with irrigation spatial pattern is shown in Figure 10. Most of the stations located in irrigated grid cells (fraction of 50–100%) had larger cooling trends for extreme T max and larger warming trends for extreme T min , while in the reference grid cells (fractions of 0.1–10%), the trend was opposite. However, a few stations located in the irrigated areas showed warming trends for extreme daily maximum temperature. This could be attributed to urbanization which will be discussed in the Section 3.5. Although the difference of bottom 1 at irrigated and reference stations are significant (P < 0.05), the spatial distribution of bottom 1 with irrigation shows 2013 Royal Meteorological Society not much strong relationship between bottom 1 and irrigation as extreme T max . This is because most of the decadal trends for bottom 1 are warmer than zero, and different factors affect the extent of warming, including greenhouse gases, clouds, local land-use patterns and so on. The climate regime is important to the overall impacts of irrigation on temperature. Decadal trends of precipitation indicate that the drier north of the Huang-Huai-Hai Plain has become drier and the wetter south has become wetter from 1955 to 2007 (Figure 11). In order to determine the impacts of the average annual precipitation and the decadal trends of annual precipitation on irrigation cooling, we selected 26 irrigated stations (fraction of 50–100%), and divided them into three groups according to the average annual precipitation and the decadal trends of annual precipitation for 1955–2007. Stations at drier locations (300–600 mm and 600–900 mm) cooled more than those at wetter locations (900–1200 mm; Figure 12(a)). Stations with decreasing decadal trends of annual precipitation showed larger irrigation cooling effects than those with increasing trends (Figure 12(b)). The regional climate model showed that the expansion of irrigation in regions with low precipitation can increase the irrigation cooling effect, and the irrigation cooling effect is most pronounced during warm, dry times of the year, and during relatively warm, dry years (Kueppers et al., 2007, 2008). 3.5. Spatial analysis of the urbanization effect There were some observations located in the irrigated cell areas that had warming trends for extreme temperature (Figure 9). On the basis of the trend analysis for the irrigation effect on temperatures, urbanization impacts on extreme high-temperature events were analysed. In order to detect if urbanization had counteracted the irrigation cooling effect, the spatial distribution of builtup land and cropland in 1990, 2000 and 2005 are plotted together with top 5 T max . Because they are very similar in 1990, 2000 and 2005, only the spatial distributions of built-up land and cropland in 2005 are shown in Figure 13(a) and (b). It is noted that a few of stations (for example, Beijing station) with higher urbanization levels (Figure 13(a)) located in high-cropland regions (Figure 13(b)) and high-irrigation regions (Figure 9) have had larger warming trends compared to stations where lower urbanization with high irrigation have occurred. Stations in high-irrigation area fractions that showed warming trends were always located in provincial or developed cities (Figure 13). For example, the trend of the station in Beijing was for an increase of 0.07–0.09 ◦ C decade−1 for top 1, top 5 and top 30. Due to the rapid urbanization associated with industrialization, the creation of an UHI was a likely cause for the observed warming at the meteorological station in Beijing (Zhou et al., 2004). Int. J. Climatol. 34: 1181–1195 (2014) 1190 W. SHI et al. Figure 9. Spatial distribution of percent irrigation areas, along with decadal trends (◦ C decade−1 ) of the top 5 extreme maximum temperatures (T max ) for the 95 meteorological stations on the Huang-Huai-Hai Plain of China. Pink triangles represent decreasing trends and red circles represent increasing decadal trends of temperature. 3.6. Comparison of irrigation cooling and urbanization warming effects We selected 26 irrigated stations (fraction of 50–100%), and divided them into three groups of 7 urbanized stations (more than 70% built-up areas in 1 × 1 km grid), 3 midurbanized stations (30–70% built-up areas in 1 × 1 km grid) and 16 reference stations (less than 30% built-up areas in 1 × 1 km grid). Decadal trends for the top 1, top 5 and top 30 of the first and the third group are compared in Figure 14, which indicates that urbanization weakened the irrigation cooling effect. For example, for top 1, top 5 and top 30 the decadal trends of irrigated stations with urbanization decreased 0.04–0.09 ◦ C decade−1 , but decreased 0.19–0.21 ◦ C decade−1 for reference regions. The differences between the urbanized and reference region of decadal trends for top 1, top 5 and top 30 varied from 0.12 to 0.16 ◦ C. These urbanization warming 2013 Royal Meteorological Society effects were not as strong as we expected relative to the irrigation cooling effect, so that the average warming values of urbanized stations for top 1, top 5 and top 30 were modified by irrigation cooling effects during 1955–2007 (Figure 14). Kalnay and Cai (2003) have shown that the urban impacts on temperature are much less than those of other large-scale land-use changes in the United States and may be more easily compensated by irrigation cooling effects. To explain the roles of irrigation versus urbanization better, we compared two groups of stations with different fractions of irrigation and urbanization: (1) irrigation (more than 50%) with urbanization (more than 70%; Irr and Urb), and (2) irrigation (more than 50%) with less urbanization (less than 30%; Irr and Ref; Figure 15). Greater cooling effect (top 1 for −0.14 ◦ C, top 5 for −0.19 ◦ C and top 30 for −0.18 ◦ C) occurred for the Irr and Ref stations. The cooling effect was also Int. J. Climatol. 34: 1181–1195 (2014) IRRIGATION COOLING EFFECT 1191 Figure 10. Spatial distribution of percent irrigation areas, along with decadal trends (◦ C decade−1 ) of the bottom 1 extreme minimum temperature (T min ) for the 95 meteorological stations on Huang-Huai-Hai Plain of China. Blue triangles represent decreasing trends and red circles represent increasing decadal trends of temperature. shown for the Irr and Urb stations (top 1 for −0.09 ◦ C, top 5 for −0.07 ◦ C and top 30 for −0.05 ◦ C) but was smaller than the Irr and Ref stations. This suggests that the T max is affected by interaction of urbanization and irrigation, but the effect of urbanization may be less than that of irrigation. In addition, if irrigated area declines due to lack of sufficient water supply or conversion of irrigated agriculture to urban land, the temperature will be increasing because greenhouse gas-driven warming may be reinforced by regional land-use change (Kueppers et al., 2007). 4. Conclusions There has been a very large increase of both irrigation and urbanization on the Huang-Huai-Hai Plain of China since 1955. This study comprehensively analysis the effects of these two processes on surface temperatures on the Plain. Our results, as significantly tested by independent 2013 Royal Meteorological Society samples t-tests, suggest that irrigation has cooled extreme daily maximum temperatures and warmed extreme daily minimum temperatures. For example, the average values of daily maximum temperature for the hottest 1, 5, and 30 d at irrigated stations were colder by 0.17–0.20 ◦ C decade−1 in irrigated regions than in reference regions of the study area. However, only the daily minimum temperature on the coldest 1 d at irrigated stations was obviously warmer than that at reference regions by 0.43 ◦ C decade−1 . Cooling of extreme daily maximum temperatures is particularly noted in the summer months of June, July and August. On the basis of the findings, it is concluded that irrigation generally modulates extreme maximum temperatures of the hottest days of the year. As well, irrigation generally increases the extreme minimum temperatures of the coldest days on the Huang-Huai-Hai Plain of China. Moreover, high precipitation regions of the Plain exhibit a lower irrigation cooling effect than low precipitation regions. In regions where irrigation Int. J. Climatol. 34: 1181–1195 (2014) 1192 W. SHI et al. Figure 11. Spatial distribution of average annual precipitation for 1955–2007, along with decadal trends (mm per decade) in annual precipitation for the 95 meteorological stations on Huang-Huai-Hai Plain of China. Blue triangles represent decreasing trends and red circles represent increasing decadal trends of precipitation. interfaces with urbanization, urbanization warming of extreme daily maximum temperatures appears to have only partly counteracted the cooling effect of irrigation on temperature. Stations in urban and irrigation regions also depict an overall cooling trend over decades. However, as this work is limited in spatial scale and data sample size, the combined effect of irrigation and urbanization in their regions of overlap requires further study. It is needed to use modelling tools to isolate the role of irrigation and urbanization from that of climate change to qualify the detailed physical processes of the impact of the former two events. It is important to note that the differences in temperature between irrigated and reference regions are induced by other factors as well. For example, variations in climate regimes, clouds, aerosol and land characteristics 2013 Royal Meteorological Society (elevation, slope, aspect, latitude and proximity to the sea) also obfuscate the atmospheric signal, especially when its spatial pattern correlates with that of irrigation. Further studies should therefore be conducted to quantify the net impact of irrigation on regional climate dynamics. This study sheds further light on the important issue of uncertainties introduced by the effect of land-use change on regional climate dynamics. Relationships among irrigation, urbanization, precipitation and other climatic variables could be useful in studying the land-state processes in the Huang-Huai-Hai Plain. On the other hand, the effects of land-use change, such as deforestation, grazing and abandonment of barren lands on regional climate dynamics may provide additional insight into land-state process in other regions of the world. Detailed Int. J. Climatol. 34: 1181–1195 (2014) IRRIGATION COOLING EFFECT 1193 Figure 14. The comparisons of decadal trends of top 1, top 5 and top 30 for irrigated stations with urbanization (Urb, 70–100% urbanization) and reference (Ref, less than 30% urbanization) on the Huang-Huai-Hai Plain of China. Urb–Ref is the difference between Urb and Ref. Error bars indicate 95% confidence interval based on bootstrap resampling. effects of irrigation and urbanization on climate change should not only use observational data, but should also be coupled with dynamic land-use models and regional climate models to understand the complex processes and controlling mechanisms. Figure 12. Impacts of (a) average annual precipitation and (b) decadal trends in annual precipitation for 1955–2007 on decadal trends of irrigation cooling effects for the top 1, top 5 and top 30 extreme maximum temperatures (T max ) on the Huang-Huai-Hai Plain of China. Error bars indicate 95% confidence interval based on bootstrap resampling. model simulations of land use and regional climate are also needed to foster even deeper understandings of the dynamics of temperature, precipitation and other agroclimatic and human factors. Further efforts to explain the Acknowledgements This research was supported by the National Basic Research Program of China (2010CB950902), the National Natural Science Foundation of China (41001057 and 41071030) and the ‘Strategic Priority Research Program’ of the Chinese Academy of Sciences, Climate Change: Carbon Budget and Relevant Issues grant no. XDA05090310. We gratefully acknowledge Prof. Figure 13. Spatial distribution of (a) built-up area (urban) and (b) cropland fraction on a 1 × 1 km grid for the Huang-Huai-Hai Plain in 2005 together with the top 5 of T max . The value indicates the percentage of built-up area (cropland) within a 1 × 1 km grid cell. 2013 Royal Meteorological Society Int. J. 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