Data Paper

Dataset of Inland Waters Nitrogen Deposition in China (1990s-2010s)

  • LI Zhaoxi , 1 ,
  • ZHOU Feng , 2, * ,
  • MIAO Chiyuan 3 ,
  • SHI Kun , 4, * ,
  • GAO Yang , 1, *
  • 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
  • 3. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
  • 4. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
*ZHOU Feng, E-mail: ;
SHI Kun, E-mail: ;
GAO Yang, E-mail:

LI Zhaoxi, E-mail:

Received date: 2021-09-28

  Accepted date: 2022-03-10

  Online published: 2023-04-21

Supported by

The National Natural Science Foundation of China(41922003)

The National Natural Science Foundation of China(41871080)


Dramatic increases in global atmospheric N deposition have had adverse effects on inland water ecosystems. China is one of the three hot spots of N deposition in the world. In order to fully understand the impact of N deposition on inland waters in China and formulate a reasonable water pollution treatment plan, we need to clearly quantify the N deposition flux in inland water. Therefore, using the LMDZ-OR-INCA model combined with inland water area data, we obtained the dataset of inland water N deposition in China from the 1990s to the 2010s, which lays a foundation for further understanding the spatial-temporal pattern of inland water N deposition and its impact mechanisms. At the same time, we publicly share this dataset and provide online access and download services at

Cite this article

LI Zhaoxi , ZHOU Feng , MIAO Chiyuan , SHI Kun , GAO Yang . Dataset of Inland Waters Nitrogen Deposition in China (1990s-2010s)[J]. Journal of Resources and Ecology, 2023 , 14(3) : 675 -680 . DOI: 10.5814/j.issn.1674-764x.2023.03.021

1 Introduction

Global atmospheric nitrogen (N) deposition has increased dramatically due to human activities (Galloway et al., 2008). N compounds (NOy and NHx) in the atmosphere enter water bodies in the form of precipitation (wet deposition) or dust (dry deposition), which is called water N deposition. Excessive N input to water can cause damage to aquatic ecosystems and produce a range of environmental problems, such as hypoxia in aquatic organisms, eutrophication, and algae blooms (Bergström and Jansson, 2006; Peng et al., 2019). Since China is in one of the three hot spots of N deposition in the world (north America, western Europe and east Asia), the quantitative monitoring of N deposition in China began in the late 1970s (Lu and Shi, 1979). Atmospheric N deposition is one of the most important sources of watershed non-point-source pollution (Yu et al., 2019). Because the sources are complex and greatly affected by the basin meteorological factors (precipitation, wind direction, etc.), the N deposition pattern has significant spatio-temporal variation characteristics (Wang et al., 2018).
China is experiencing severe air pollution as a result of reactive nitrogen (Nr) emissions from human activities, which is accelerating the deposition of N from the atmosphere into terrestrial and aquatic ecosystems, with adverse effects on human health and ecosystem safety (Clark and Tilman, 2008; Liu et al., 2011). In the past three decades, the Nr deposition in China increased from 13.2 kg ha-1 yr-1 in the 1980s to 21.1 kg ha-1 yr-1 in the 2000s, for an increase of 59.8% (Law, 2013). In addition, China’s inland water area increased by 6462 km2, resulting in increased direct exposure to atmospheric N deposition (Gao et al., 2020). The accelerated N deposition rate and the increased inland water area have jointly promoted the N deposition in inland water in China, and the N deposition situation faced by inland water in China is not optimistic. Fully understanding the spatial and temporal distribution patterns of N deposition in inland water of China is a prerequisite for exploring the current impacts of N deposition on aquatic ecosystems. The N deposition records in China are mainly based on site monitoring, so the time span is often short and the scope usually focuses on the study of terrestrial ecosystems (Jia et al., 2019). For example, Jia et al. (2021) produced the spatial pattern data set for atmospheric inorganic N dry deposition in China from 2006 to 2015 based on the available atmospheric N dry deposition station data and the NO2 and NH3 remote sensing column concentration data. For inland waters, the shared data of N deposition with a long time frame and high spatial resolution have not yet been produced. In this study, based on the global aerosol chemical climate model LMDZ-OR-INCA and the Annual Report on Sensing Monitoring of Global Ecosystem and Environment (large terrestrial water area) released by National Remote Sensing Center, the N deposition fluxes of ten watersheds in China from 1990 to 2013 were calculated. This dataset is conducive to deepening and improving our understanding of the temporal changes and spatial patterns of N deposition in China in the past three decades.
Table 1 Dataset profile
English title Dataset of nitrogen deposition in inland waters of China (1990s-2010s)
First author Li Zhaoxi (
Corresponding author Zhou Feng (
Shi Kun (
Gao Yang (
Data author(s) Zhou Feng, Miao Chiyuan, Gao Yang
Foundation The National Natural Science Foundation of China (41922003 and 41871080)
Time range 1990s, 2000s and 2010s
Geographical scope China (15.67°N-54.37°N, 71.25°E-136.55°E)
Spatial resolution 0.01°×0.01°
Data volume 600 M, 1.83 M after compression
Data format *.tif
Data service system
Dataset composition The dataset consists of six data files, including three for China N deposition data and three for inland water N deposition data. Unit: mg m-2 yr-1. Open with ArcGIS or MATLAB.

2 Methods

2.1 Production of N deposition data for inland waters of China

We used the N deposition data modeled by the LMDZ- OR-INCA model, combined with the inland water area data, and obtained the inland water N deposition dataset for nearly 30 years in China (spanning the 1990s, 2000s and 2010s). The specific process flow is shown in Fig. 1.
Fig. 1 Flow chart for N deposition data processing of inland waters in China

2.2 Data sources for the inland water area of China

The changes in the inland water area of China from the 1990s to the 2010s were obtained from the Annual Report of Sensing Monitoring of Global Ecosystem and Environment (Large Terrestrial Water area) issued by the National Remote Sensing Center (

2.3 Data of N deposition fluxes in China

The aerosol chemical climate model LMDZ-OR-INCA is a global model coupled with LMDs (Laboratoire de Météorologie Dynamique, version 4) and the INCA (IN teraction with Chemistry and Aerosols, version 4) (Hauglustaine et al., 2014). Through this model, the dry and wet deposition of all forms of N can be quantified. Emissions data include the marine emission of N (NH3), the vegetation emission of N (NO), agricultural activities as sources of N (including fertilizer use and livestock) and fuel combustion of N (NOy and NHx), which are input into the model. With regard to N-containing aerosols and gases, LMDZ-INCA operates with a fully interactive atmospheric N cycle (Hauglustaine et al., 2014). Its horizontal resolution is 1.27° latitude by 2.5° longitude and there are 39 vertical layers in the atmosphere to simulate the dry and wet deposition of NOy and NHx for 1980, 1990 and 1997-2013. The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis meteorological fields have been used in the current configuration to facilitate the annual pattern transfer and removal process in 1990, and for each year during the recent 1997-2013 period. Before model assessment using these observations, we collected additional data from the National Acid Deposition Monitoring Network (NADMN) established by the China Meteorological Administration to expand the coverage of the wet N deposition data.

3 Data records

The data files include China’s N deposition dataset and inland water N deposition dataset with the resolution of 0.01°×0.01° in the 1990s, 2000s and 2010s, wherein the inland water body N deposition data set has been extracted on the basis of the former. The unit of the N deposition dataset is mg N m-2 yr-1, and the file format is ArcGIS TIFF. It can be opened with either ArcGIS or ENVI.

4 Technical validation

4.1 Uncertainty analysis of the inland water area

There are two main sources of uncertainty. First, some of the uncertainty comes from the data sources on land-use changes in China. Although there may be some errors in the interpretation of remote sensing images, such as in the classification and boundary determination of different land use types, these sources have a qualitative accuracy of more than 95% and are widely used in China (Liu et al., 2003). Second, the interannual variability and seasonal dynamics of water bodies may also contain uncertainties. For example, the expansion rate of lakes in the Qinghai-Tibet Plateau from 2009 to 2014 ranged from 6.2% to 18.7%, and the shrunken rate ranged from -1.6% to 16.3%. Due to changes in rainfall and temperature, the coefficient of change of the lake area in different seasons was about 0.17%-0.43% (Yang, 2017). However, our data cover multi-year time scales, so fluctuations in nitrogen deposition due to seasonal water area changes are almost negligible. In terms of interannual variability, the total area of China’s inland waters increased by 6462 km2 from the 1990s to the 2010s, only accounting for about 1.5% of China’s land area (Gao et al., 2020). Therefore, this uncertainty has little impact on the objectives of this work due to the relatively small change in water area.

4.2 Validation of modeled N deposition fluxes

The recent global data set of measured wet N deposition rates from 2002 to 2006 was used to evaluate the modeled N deposition rates (Vet et al., 2014), along with dry N deposition data from previous study estimates (Lamarque et al., 2005; Dentener et al., 2006). Before model assessment using these observations, we further collected wet N deposition data from South America and Africa to increase the coverage. A more complete description of model performance can be found in Wang et al. (2017). Based on the evaluation of modeled spatial distribution of N wet deposition by global in situ monitoring, the correlation coefficient (R2) between the log-transformed deposition rates and wet N deposition is 0.55, which shows that the correlation between the model output and the data is significant (P < 0.001) (Fig. 2).
Fig. 2 The coefficient of correlation (R2) of log-transformed deposition rates
For N wet deposition, the normalized mean bias (NMB) is -8%. Statistical analysis shows that 50% of the modeled wet N deposition data have a bias of -25% to 50%. The observed wet N deposition data allowed us to further evaluate the modeled wet N deposition in the oxidized form (NO3) and the reduced form (NH4) by region (Fig. 3). Modeled wet NO3 deposition NMB was -12% in North America, -31% in Europe, and -28% in East Asia. Previous studies have shown that the NO3 deposition NMB values are -28%, 13%, and -54% in these three regions, respectively (Hauglustaine et al., 2014). Additionally, our modeled NMB values for wet NH4 deposition were -20% in North America, -30% in Europe, and -28% in East Asia, compared to -32%, -4.5%, and -60% in previous studies (Hauglustaine et al., 2014). The negative bias for N deposition in East Asia may be due to the use of the ACCMIP inventory in that study, which would have underestimated the active N emissions in the region. We further updated the emissions associated with reactive N using the latest inventory from the ECLIPSE GAINS. 4a inventory (Höglund-Isaksson, 2013) and confirmed this assumption.
Fig. 3 Comparison of modeled and observed wet deposition of different forms of N in North America (a), Europe (b), Asia (c), Africa (d), South America (e) and other regions (f)

Note: Data for measurements the total deposition of reduced and oxidized forms of N are shown as grey crosses. The percentages in brackets show the normalized mean bias of log-transformed deposition rates with the numbers of data points shown in brackets after each regional name.

5 Discussion

As shown in Fig. 4, N deposition showed an increasing trend in China over the past three decades, especially in the Huaihe River Basin and the middle and lower reaches of the Yangtze River, which were the hot areas of N deposition in China. Spatially, N deposition decreased from east to west, and the peak region of N deposition was always located in the Huaihe River Basin. Similarly, N deposition in the inland waters of China has been on the rise in the past three decades, especially for some lakes in the middle and lower reaches of the Yangtze River, such as Poyang Lake and Taihu Lake. N deposition in the lakes of the Qinghai-Tibet Plateau has always remained at a low level in the past. In general, the growth rate of the N deposition flux in inland water (40.98%) is much higher than the average growth rate of N deposition in China (30.41%) (Fig. 5). This difference further reflects the important ecological position of inland water as a sentinel of climate change for the increase of N deposition.
Fig. 4 N deposition in China (a, c, e) and inland waters (b, d, f) in the past three decades
Fig. 5 Average N deposition and inland water N deposition in the past three decades in China

6 Conclusions

We used the LMDZ-OR-INCA model to model the N deposition data for China in the past three decades, and combined the results with data on the water area change to obtain the inland water N deposition dataset. Over the past three decades, N deposition in China and China’s inland waters had upward trends in time, and trends of high in the east and low in the west in space. The regions with the highest N deposition fluxes were located in the Huaihe River Basin and the middle and lower reaches of the Yangtze River, which was also consistent with the economic and social development trends in China. This dataset is of great significance for guiding the regulation of water pollution treatment and ecological risk assessments.

7 Usage notes

The dataset can be viewed at After logging in, the reader can download the data by clicking on the “paper data” icon on the home page, or by selecting “paper data” in the data resources section. The format of the data is ArcGIS TIFF, and the size of the compressed data is 1.83 Mb, with a spatial resolution of 0.01°×0.01°, and the unit is mg m-2 yr-1.
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