Surface water frequency maps and surface water areas during 1989–2016
Surface water frequencies (FW) of individual pixels in 2016 varied substantially across China (Fig. 1a). There were 1444 million pixels with annual surface water frequency of FW > 0 in 2016, amounting to ~1.3 × 106 km2 maximum SWA in 2016. Based on the surface water frequency in a year, a water pixel was defined as year-long surface water (FW ≥ 0.75), seasonal surface water (0.05 ≤ FW < 0.75) or ephemeral surface water (FW < 0.05)21,23,27,32. The year-long SWA in 2016 was ∼0.155 × 106 km2, most of which was in large rivers, lakes, and reserves (Fig. 1a) between 80°–90°E and 110°–120°E longitude and 35°–45°N latitude in China (Supplementary Fig. 2a). The seasonal and ephemeral surface water areas in 2016 were 0.571 × 106 and 0.542 × 106 km2, respectively, most of which were located at the edge of large surface water bodies, small ponds and streams, and flooded rice fields (Fig. 1a). The 28-year surface water frequency of individual pixels during 1989–2016 also had large spatial variation in China (Fig. 1b). There were 181 million pixels with 28-year surface water frequency of FW ≥ 0.75, amounting to 0.163 × 106 km2 SWA, which was 5% higher than that in 2016. The spatial distribution of annual surface water frequency map in 2016 agreed statistically well with that of 28-year surface water frequency map (Supplementary Fig. 3), but they did have notable differences, which might be related to climate, dam and reservoir construction, water management, and water use.
a Surface water body frequency map in 2016 for China, and (1, 2, 3) are its three zoom-in views for Qinghai Lake, Poyang Lake, and Taihu Lake, respectively. b The 28-year surface water body frequency map during 1989–2016. c Water body areas within China during 1989–2016 with different annual water frequencies. The permanent water body areas from the Joint Research Centre (JRC) data set2 during 1989–2015 are also shown.
At the national scale, we calculated SWA in a year with various surface water frequencies, ranging from FW > 0 to FW ≥ 0.75 (Fig. 1c). The year-long SWA (FW ≥ 0.75) in China varied from 0.135 × 106 km2 in 1996 to 0.172 × 106 km2 in 1990 over the period of 1989–2016 (Fig. 1c), and it had the smallest standard deviation and extremum (Supplementary Fig. 4). The year-long SWA in China increased significantly during 1991–2016 (slope = 0.36 ± 0.33 × 103 km2 year−1, p < 0.05). The seasonal SWA (0.05 ≤ FW < 0.75) had large increases during 1989–1998 and then remained relatively stable during 1999–2016 (Fig. 1c). The ephemeral SWA (0 < FW < 0.05) had large increases during 1989–2003 and then remained stable during 2004–2016 (Fig. 1c), and the large drop in ephemeral SWA in 2012 was related to drought33 and the smaller number of good-quality Landsat observations (Supplementary Fig. 12b).
We compared the year-long SWA in China from our data set with the permanent SWA from the JRC data set2 (Fig. 1c). The JRC data set, which reported the permanent and seasonal surface water areas at 30-m resolution in the world from 1984 to 20152, represents significant progress in remote sensing of surface water. In 2015, the year-long SWA in China from our data set (0.157 × 106 km2) was moderately (12.9%) higher than the permanent SWA from the JRC data set (0.139 × 106 km2). Over the period of 1989–2015, the year-long SWA in China in our data set agreed well with permanent SWA from the JRC data set (slope = 0.98 ± 1.1 × 103 km2 year−1, R2 = 0.99, standard error = 0.56 × 103 km2, N = 832), except 1997 and 1998 (Fig. 1c; Supplementary Fig. 5). The permanent and seasonal surface water areas in South China in 1997 and 1998 from the JRC data set were substantially lower than those from our data set (Supplementary Figs. 1 and 5), which raises concern on the use of the JRC data set for the study of extreme flood events in South China in 19987. The differences in SWA estimates between our data set and the JRC data set can be attributed to the definition of surface water types (year-long vs permanent surface water), number of Landsat images used, Landsat image data types (top-of-atmosphere reflectance vs surface reflectance), training and validation data, and mapping algorithms. The JRC data set used the Landsat top-of-atmosphere reflectance images as data resources, many ancillary maps from other sources as masks, and 40,124 points for accuracy assessment of the global surface water maps. In this study, we used Landsat surface reflectance images, and 18,397 points for accuracy assessment of the surface water maps of China. The accuracy assessment showed that the user’s accuracies of our surface water body maps (year-long surface water: 99.7% (±0.12), seasonal surface water: 98.6% (±0.47)) are similar to those from the JRC data set, but the producer’s accuracy of seasonal surface water of our data set (86.4% (±3.57)) was higher than that of the JRC data set (68.4%) (Supplementary Table 4). Therefore, our surface water data set provides improved and reliable information about the surface water bodies in China during 1989–2016.
Spatial–temporal dynamics of year-long SWA during 1989–2016
Year-long SWA at the provincial level was unevenly distributed across China with various interannual variations (Fig. 2a). The mean of year-long SWA (ha) per unit land area (km2) in a province during 1989–2016 varied between 0.16 ha km−2 in Gansu and Guizhou and 9.5 ha km−2 in Jiangsu. The standard deviation of year-long SWA per unit land area in a province ranged from 0.01 ha km−2 in Shanghai to 7.0 ha km−2 in Tibet. All the provinces in the Loess Plateau, the Mongolia Plateau, the Yunan-Guizhou Plateau, and mountainous areas (Fujian, Guangxi) had <1 ha km−2 SWA (Fig. 2a). Hebei and Henan Provinces also had <1 ha km−2 SWA, where annual precipitation was moderate and unevenly distributed34,35 and surface and groundwater withdrawal for public water supply and irrigation substantially increased34,36. Xinjiang had <1 ha km−2 SWA because of its arid climate and large land area. Three provinces in Northeast China had 1–2 ha km−2 SWA. Provinces in the Qinghai-Tibetan Plateau had 2–3 ha km−2 SWA because of its large number of lakes and increased precipitation and glacial meltwater20,37,38,39. Provinces in East China and Southeast China are associated with the lower streams of Yellow River (Shandong), Yangtze River (Hubei, Jiangxi, Anhui, Jiangsu), Pearl River (Guangdong), and large lakes (Jiangxi), and thus had 2–3 ha km−2 or higher SWA. Overall, Southwest and Southeast China had much more SWA than other regions, especially North China, which is similar to the spatial patterns of annual precipitation in China35.
a Average year-long surface water area (SWA) (ha) per unit land area (km2) and standard deviation at the provincial scale. b Interannual trends of year-long SWA and standard errors at the provincial scale. c Interannual trends of year-long SWA at the watershed scale (slope value). d Interannual trends of year-long SWA at the watershed scale. Note that in 1996 SWA values in Tianjin, Hebei, and Shandong were extremely low (b), which was partially attributed to severe drought in the year. We analyzed the trend during 1989–2016 with 1996 data and without 1996 data, the slope values of the trend varied slightly in these three places. Here we keep the entire time series data in the graph, but only using the data without 1996 for regression model.
The year-long SWA in a province had divergent interannual trends during 1989–2016 in China (Fig. 2b). Fourteen provinces had significantly increasing trends of the year-long SWA during 1989–2016, ranging from 4.9 ± 1.5 km2 year−1 in Hebei to 301.1 ± 140.7 km2 year−1 in Tibet. Qinghai Province ranked the second in its increase of SWA (110.5 ± 52.4 km2 year−1). Among the provinces under arid and semi-arid climate, Xinjiang was the only province with significant increase of SWA (100.0 ± 68.4 km2 year−1). Increased annual precipitation and water from melting glaciers resulted in an increase of year-long SWA in Xinjiang Province37. In contrast, seven provinces had significantly decreasing trends of the year-long SWA during 1989–2016, ranging from −3.9 ± 1.7 km2 year−1 in Beijing to −84.9 ± 32.0 km2 year−1 in Inner Mongolia. The year-long SWA in Inner Mongolia shrank from 4660.6 km2 in 1991 to 3071.4 km2 in 2009, a loss of 1589.2 km2 or 34.1%32. The coal mining industry is one of major reasons for the large loss of lakes in Inner Mongolia, as the number of mining enterprises in Inner Mongolia increased markedly from 156 in 2000 to 865 in 2010 and annual coal production increased from 72 to 789 million tons18.
The year-long SWA in a watershed also had divergent interannual trends during 1989–2016 (Fig. 2c, d). Sixty-one watersheds, mostly in the western and northern Tibetan Plateau, had significantly increasing trends of year-long SWA during 1989–2016, ranging from 0.004 ± 0.001 km2 year−1 in the Qindanhe Watershed in Shanxi to 6.5 ± 1.1 km2 year−1 in the Qiangtang Plateau watershed in western Tibet. Water from melting glaciers and increased annual precipitation over the recent decades were considered as the major driving factors for the expansion of large amounts of lakes in the Tibetan Plateau20,37,38,39. Annual precipitation increased by 20 mm and annual mean air temperature increased by 1.6 °C from 2000 to 201440. In addition, successful water conservation through the Chinese Ecological Protection and Construction Projects also contributed to the increasing trends of SWA in eastern and northern Tibetan Plateau41. Forty-four watersheds, mostly in North China and southeastern Tibet, had significantly decreasing trends of year-long SWA, ranging from −0.0023 ± 0.0018 km2 year−1 in the Suifenhe watershed in Heilongjiang Province to −2.4 ± 1.3 km2 year−1 in the Kashgar River watershed in Xinjiang. The decreasing trends of SWA in North China were caused by the disappearance of a number of lakes, which was driven by both natural and anthropogenic factors18,32. The remaining 104 watersheds had no significant trends in year-long SWA during 1989–2016. Therefore, in general, the water-rich regions of the southeastern China were becoming richer (gainers), whereas the water-poor regions of the northern China were becoming poorer (losers).
Changes of TWS and SWA during 2002–2016
We assessed the spatial–temporal dynamics of the year-long SWA and the terrestrial water storage (TWS) from the GRACE satellite during 2002–2016 in China. At the provincial scale, ten provinces had significantly decreasing trends of TWS, which ranged from −0.1 ± 0.08 cm year−1 in Gansu to −1.7 ± 0.7 cm year−1 in Shandong (Fig. 3a). For the provinces in North China Plain, agriculture intensification and increased groundwater use were the major driving forces for the decreased TWS26. For example, in Shandong Province the amount of groundwater use exceeded the amount of groundwater recharge by the natural processes over the past several decades, and excessive withdrawal of groundwater resulted in the largest decreasing trend of TWS (−1.7 ± 0.7 cm year−1) in Shandong34,42. The mass losses of glaciers in Tibet and Xinjiang contributed considerably to the losses of TWS37,41. Xinjiang is one of the world’s largest producers of coal, thus groundwater use by coal mining in the area might have also contributed to the decrease of TWS43. Qinghai had a significantly increasing trend of TWS (Fig. 3a), which was related to the increased SWA with high R square and small standard error (Fig. 3b, c; Supplementary Fig. 6a, b) and other factors44. Guangxi and Guizhou Provinces had significantly increasing trends of TWS and SWA, where substantial vegetation recoveries driven by various ecological engineering projects were also observed45. In comparison, Inner Mongolia, Gansu, and Shaanxi, where major ecological engineering projects were also implemented46, did not have significant changes of TWS and SWA during 2002–2016, which raises the concern on the effectiveness of these projects on conservation of water resources in these provinces.
a Trend of terrestrial water storage (TWS) at the provincial scale. b Trend of surface water area (SWA) at the provincial scale. c Trend of linear regressions between SWA and TWS at the provincial scale. d Trend of TWS at the watershed scale. e Trend of SWA at the watershed scale. f Trend of linear regressions between SWA and TWS at the watershed scale. g Trend of TWS at the 0.5° gridcell scales. h Trend of SWA at the 0.5° gridcell scales. i Trend of linear regressions between SWA and TWS at the 0.5° gridcell scales.
At the watershed scale, the interannual trends of TWS during 2002–2016 had a distinct spatial pattern (Fig. 3d). Most of the watersheds in southern Tibet and northern China had significantly decreasing trends of TWS (Fig. 3d), but few of them had significantly increasing trends of SWA (Fig. 3e). The significantly negative correlations between TWS and SWA in these watersheds (Fig. 3f) suggest that excessive use of groundwater might have contributed to the losses of TWS in these watersheds27. A recent study that examined groundwater level data from 801 wells in China had reported large decreasing trends of groundwater levels among the wells in those regions29. Significantly positive correlations between TWS and SWA over the watersheds in Northern Tibet and Qinghai with high R squares suggest that SWA contributed significantly to the TWS dynamics (Supplementary Fig. 6c). A recent study reported strong relationship between TWS and surface water storage of large lakes (>10 km2) in the Tibetan Plateau20. Many watersheds along the Yangtze River had significantly increasing trends of TWS (Fig. 3d). The temporal dynamics of TWS and SWA in many watersheds in Guangxi and Guizhou Provinces were highly correlated (Fig. 3d–f). In total, 18 watersheds (27% of China’s total land area) had significantly positive correlations between TWS and SWA, and 59 watersheds (7% of China’s total land area) had significantly negative correlations between TWS and SWA (Fig. 3f).
We further investigated the temporal relationships between TWS and year-long SWA within individual 0.5° gridcells (longitude/latitude). TWS increased significantly in 1268 gridcells (34.7% of 3654 gridcells in China), most of which were distributed in northern Tibet, South China, and Northeast China (Fig. 3g). TWS decreased significantly in 1408 gridcells (38.5%), mostly in Xinjiang Province, southern and southeastern Tibet, and the Yellow River Basin. The southeastern Tibet and Tianshan Mountains in Xinjiang had relatively higher decrease rates than other regions because of high retreat rates of glaciers39. Year-long SWA increased significantly in 996 gridcells (27.3%), mostly in northwestern Tibet, and the Sichuan, Haihe, and Songhuajiang Basins (Fig. 3h). SWA decreased significantly in 409 gridcells (11.2%), mostly in southeastern Tibet, the Tian Shan and Kunlun Mountains. The substantial losses of both surface and terrestrial water resources in southeastern Tibet clearly pose threats to water security and economy in Southern China, as southeastern Tibet is the headwater of many large rivers in the region. The temporal relationships between TWS and SWA was significantly positive in 963 gridcells and negative in 268 gridcells (Fig. 3i). Overall, our results revealed the spatial–temporal divergence and consistency between TWS and SWA during 2002–2016 in China, and extensive and continued losses of TWS, together with small to moderate changes of SWA in North China, indicated long-term water stress in the region.
Anthropogenic and climatic drivers for TWS and SWA dynamics
Large-scale hydrological projects (e.g., dam construction and long-distance water transfer) have been carried out in China in an effort to meet the growing needs of water resources for an increasing population4,47,48. One example is the Three Gorges Dam (TGD) in western Hubei Province, the largest hydroelectric dam in the world (Supplementary Fig. 7). It had large impacts on its neighboring provinces and watersheds along the Yangtze River since its first impoundment in 2003. The water level of the TGD reservoir increased to 156 m in September 2006, 172.5 m in September 2008, and 175 m (the maximum height by dam design) in October 201021,49,50 (Fig. 4a–c). Chongqing, which is an upstream municipality of the TGD, had stepwise increases of TWS and SWA during 2002–2010 (Fig. 4a), corresponding well to the water level changes of the TGD reservoir. These data suggested that the TGD clearly increased upstream SWA and TWS. In Hubei Province, where TGD is located, TWS increased in 2003 but then remained relatively stable (Fig. 4b). However, SWA dropped substantially after 2003, and did not recover until the mid-2010s. Jiangxi is a downstream province of the TGD, and both TWS and SWA have dropped substantially since 2003 (Fig. 4c). These results clearly indicated that the TGD affected water resources in the upstream and downstream areas, especially SWA and TWS in the downstream of TGD31,51.
a Chongqing. b Hubei. c Jiangxi. d Shandong. e Tibet. f Qinghai Province. The SPEI is the standardized precipitation-evapotranspiration index. The dashed lines and the numbers in subfigure (a–c) showed the water level (H) of the Three Gorge Reservoir and their corresponding years. The EUWR project in Shandong Province is the Ecological Urgent Water Replenishing (EUWR) project to divert water from the Yangtze River to Nansihu Lake during 2002–2003.
Other human water uses, for agriculture, industry, and human settlements, also affect the temporal dynamics of SWA and TWS. In the North China Plain, more than 60% of water resources used for human activities came from groundwater52. For example, in Shandong Province42, SWA increased slightly from early 1990s and early 2000s, and remained stable in late 2000s (Fig. 4d). Shandong suffered from a severe drought in 2002, as shown by very low SPEI value in 2002. The Ecological Urgent Water Replenishing (EUWR) project was implemented to divert water from the Yangtze River to Nansihu Lake during 2002–2003 to sustain the lake33, which contributed to the elevated increase of SWA and TWS during 2002–2004. After the EUWR project, SWA remained stable during 2004–2016 (Supplementary Fig. 8). However, TWS continued to decrease substantially after 2004, clearly suggesting the overexploitation of groundwater34,42. The production of grain, meat, fruit, and vegetables in Shandong was ~8%, 9%, 12%, and 13% of the total production in China, respectively53, and water use for agriculture (mostly irrigation) contributed to prolonged depletion of groundwater in the province. Therefore, although water transfer project increased SWA after 2004, excessive withdrawal of groundwater has resulted in the observed decrease of TWS in Shandong Province52, which poses serious challenges for decision makers and stakeholders in the region who tackle and cope with increased water stress and rising concerns on water and food security.
Climate change has affected glacial dynamics, annual precipitation, and surface water resources in the Tibetan Plateau20,41, which is known as “the Third Pole” of the world and “Water Tower of Asia” because of its abundant rivers, lakes, and glaciers37. SWA in Tibet dropped substantially during 1994–1995, following severe droughts during 1993–1995, as shown in large negative standardized precipitation evapotranspiration index (SPEI) values (Fig. 4e), which takes into account of both precipitation and potential evapotranspiration54. SWA in Tibet recovered in the late 1990s as SPEI became positive in those years. TWS and SWA remained relatively stable during 2002–2012 but started to drop substantially after 2012 at a rate of −1.93 cm year−1 (TWS) and −8.2 × 102 km2 year−1 (SWA), respectively. SWA in Qinghai Province also dropped substantially in 1994–1995 and then gradually recovered in late 1990s and increased in 2000s (Fig. 4f). Both SWA and TWS peaked in 2012 and then decreased slowly in 2013–2016. Therefore, as there were no large dam or reservoir construction projects in the Tibetan Plateau2, precipitation was the main reason for variations in SWA and TWS in the Tibetan Plateau20, and glacier meltwater driven by rising temperature also contributed to the changes in SWA and TWS20,37.
Interannual climate variability and new reservoirs are considered as major factors contributing to the interannual variations of SWA at the provincial scale27. For example, in Jiangxi Province, extensive flood events in 2010 resulted in a large gain of SWA and TWS, but severe drought in 2011 resulted in a large loss of SWA and TWS (Fig. 4c). In Guangxi Province, additional new reservoirs/dams caused an elevated (stepwise) increase of SWA over years (Supplementary Note 1). Thus, here we used multi-variate stepwise regression models to identify the effects of four variables (precipitation, temperature, year-long SWA of the previous year, and areas of new reservoirs) on the changes of SWA in each province of China. These variables had little collinearity with small variance inflation factor (VIF) values (Supplementary Fig. 9). The statistical analysis indicated that annual precipitation had a significantly strong positive effect on SWA in 10 provinces (Supplementary Fig. 10a), and the increased precipitation in Heilongjiang of Northeast China had the largest contribution to the increased SWA than do other provinces55. Annual mean temperature had a strong negative effect on SWA in Liaoning Province (Supplementary Fig. 10b). The areas of new reservoirs in 10 provinces in China had significantly positive effects on SWA, meaning that the areas of new reservoirs significantly contributed to the increase of SWA in these 10 provinces (Supplementary Fig. 10c). In addition, year-long SWA in the previous year also had significantly positive effects in 13 provinces, especially in northern and western China (Supplementary Fig. 10d), indicating that there were strong legacy effects on the SWA dynamics27.
Changes of water resources and population
We investigated the relationships between the change in water resources (TWS, SWA) during 1989–2016 and the change in population during 2000–2015 at the provincial scale (Fig. 5a, b). In the 9 provinces with significantly decreasing trends in TWS (Fig. 5a), there was a total increase of 53.4 million people. In the 14 provinces with significantly increasing trends in TWS (Fig. 5a), there was a total increase of 146.3 million people. In the 7 provinces with significantly decreasing trends in SWA (Fig. 5b), population increased by 92.2 million. In the 14 provinces with significantly increasing trends of SWA (Fig. 5b), population increased by 57.1 million. In Beijing, population increased by 10 million and both TWS and SWA decreased substantially during 1989–2016. In total, over 135 million population lived in 15 provinces that experienced significant losses of TWS or SWA during 1989–2016.
a Relationship between population density changed from 2000 to 2015 and terrestrial water storage (TWS) trends at the provincial scale. b Relationship between population density changed from 2000 to 2015 and surface water area (SWA) trends at the provincial scale. c Relationship between population, gross domestic product (GDP) in 2015, and TWS trends at the provincial scale. d Relationship between population, GDP in 2015, and SWA trends at the provincial scale. e Relationship between TWS trends and Gridded Population of the World (GPW) in 2015 at the 0.5° gridcell scale. f Relationship between SWA trends and GPW in 2015 at the 0.5° gridcell scale.
To further investigate the likely challenge China’s population and economy may face in terms of water resources, we analyzed population and gross domestic product (GDP) in 2015 and the changes of TWS and SWA during 1989–2016. Human population and gross domestic product (GDP) data in 2015 had a strong linear relationship at the provincial scale (Fig. 5c, d). However, the temporal changes of water resources as measured by the trends of TWS and SWA varied among the provinces (Fig. 5c, d), which indicates that each province experienced different water resource challenges for its population and economy. Guangdong Province had the largest population, the highest GDP, and a significantly increasing TWS trend and a non-significant change in SWA (Fig. 5c), which suggests that water resources are unlikely to be a major constraint for Guangdong. On the contrary, Shandong Province ranked second in population and third in GDP, but it had a significantly decreasing trend in TWS (Fig. 5c) because of overexploitation of groundwater34,42, which suggests that Shandong Province is likely to face major challenge for its water security and economic development. Henan Province ranked third in population and 5th in GDP, and it also had a significantly decreasing trend in TWS (Fig. 5c), which suggests that Henan Province is also likely to face major challenges for its water security and economic development. Both Shandong and Henan Provinces had significantly increasing trends in SWA, but the significantly decreasing trends in TWS in these two provinces suggest that they need to have large structural changes in agriculture, which uses a lot of groundwater for irrigation and industry. Geographically, at the 0.5° gridcell scale, approximately 460 million (34.9%) people live in 1408 (38.5%) gridcells with significantly decreasing trends in TWS (Fig. 5e), and 109 million (8.3%) people live in 409 (11.2%) gridcells with significantly decreasing trends in SWA in 2015 (Fig. 5f; Supplementary Table 1).
Surface water resources and water security have been a major concern in China over the past decades18,29,35,36,38. To date, our surface water data set at 30-m spatial resolution during 1989–2016 for China is an accurate, updated, reliable, and spatially detailed data set. Our estimate of ~0.155 × 106 km2 year-long SWA in China in 2016 reveals that surface water resources in China are very limited, where over 1.4 billion people now live. In comparison, a recent study that used Landsat images and the same mapping algorithms reported ~0.257 × 106 km2 year-long surface water area in the contiguous United States27, where ~330 million people live. Our results also reveal the large and geographically divergent trends in terrestrial water storage in China during 2002–2016. Large and continued losses of SWA and TWS and decoupling (inconsistency) of temporal dynamics between TWS and SWA indicate decade-to-century-scale deficit of groundwater resources in the northern parts of China. As of 2015, ~569 million people lived in the areas that experienced significant losses of TWS or SWA during 1989–2016. A number of climate and hydrological models have predicted large interannual variations in climate in northern China, including frequent droughts and heatwaves35,45. Further population growth and climate changes pose enormous challenges for water resources management in China. The surface water data set and the findings from this study can be used to assist water resources managers, stakeholders, decision makers, and the public to develop evidence-based planning and management of limited water resources in China, in particular under increasing water demand and use, more frequent droughts and heatwaves, and climate change.
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