There have been few studies of greenhouse gas emissions from reservoirs,
despite the remarkable growth in the number of reservoirs in developing
countries. We report a case study that focuses on the littoral zone of a
major Chinese reservoir, where we established measurements of N
Reservoirs are increasing rapidly in number and area, growing with the continuing demand for water and hydropower. In rapidly developing countries like China, India and Brazil this growth is likely to continue for many years (Yang and Lu, 2014; Kumar et al., 2011). There are several environmental impacts of reservoirs, particularly sediment accumulation and vegetation change. Moreover, when fertile agricultural lands are inundated by rising water there may be a strong enhancement of greenhouse gas emissions (Tranvik et al., 2009; L. Yang et al., 2014).
The pelagic zones of reservoirs have more often been studied (Beaulieu et
al., 2014; Guérin et al., 2008; Huttunen et al., 2002; X. L. Liu et al.,
2011) but few researchers have investigated the littoral zone, which could
be a hotspot of N
Because of the strong gradients in water level and water level fluctuations,
compared to the more or less stable pelagic zone and strictly terrestrial
areas nearby (e.g. grassland and farmland), the environment of the littoral
zone is more diverse and dynamic in terms of soil moisture, plant species and
soil nutrients across scales of both space and time (Peng et al., 2011; Ahn
et al., 2014; Trost et al., 2013). These variables may be expected to
influence N
To be more specific, the objectives of this present study included (i)
capturing the spatial and temporal variation of the N
The research was carried out at Miyun Reservoir (40
Experimental design. WL: water level. The difference
between high WL and low WL was caused by summer flooding. m
We divided the littoral zone into five areas based on water level (Fig. 1).
Sites were selected ranging from locations in open water to the dry area on
higher ground, to provide five contrasting environments: (i) deep water area
(DW); (ii) shallow water area (SW); (iii) seasonal (August and September)
flooded area (SF); (iv) “seasonally flooded control area” (SFC), which was
500 m away from SF, had the same plant species as SF, but escaped the flood
in August and September because of its slightly (about 1 m) higher
elevation; and (v) an area which is seldom flooded (the last flooding was
several years ago) which hereafter we call the non-flooded area (NF). Three
typical plant communities in each water level were selected. At SW, SF, SFC
and NF, land cropped with maize (
Dominant plant species at each plot in different months.
DW: deep water site, SW: shallow water site, SF: seasonally flooded site,
SFC: “control site” for seasonally flooded site, NF: non-flooded site. A, B
and C indicate sample plot with different vegetation. Species with aerenchyma
are denoted
Environmental characteristics (mean
Nitrous oxide flux was measured in November 2011, then May, July, August, September and October 2012. Measurements at site SFC were carried out just after the flooding and during the time when the water level dropped from August to October 2012. In order to reduce uncertainty in the average daily flux, a sampling protocol designed to capture any diurnal variation was performed at three-hourly intervals (local time: 6, 9, 12, 15, 18, 21 and 24 h). Each plot had four replicate chambers located within 3 m from each other. To eliminate trampling disturbance to the soil–sediment during sampling, wooden access platforms were built.
The static opaque chamber technique was used to determine the N
Physicochemical properties (mean
Chambers were reset into new positions near the old positions each sampling
month. All positions at each site were within an area of 20 m
Weekly precipitation was accessed through the China Meteorological Data
Sharing Service System
(
Water level was measured after gas sampling at DW, SW and SF (when SF had standing water in August and September 2012). At site SF (when there was no standing water in November 2011, May, July and October 2012) and SFC, a 1m PVC tube was inserted vertically into the soil under the chamber after all monthly gas sampling was complete, allowing 2 h for the water level to equilibrate before measuring the level. The water table of site NF was calculated according to the elevation measured by a Global Navigation Satellite System receiver (BLH-L90, Daheng International, China).
Soil water content (SWC) was measured every month after all gas sampling with
a soil water sensor (UNI1000, Shunlong, China). Soil–sediment samples
(0–30 cm) at site DW, SW, SF and NF were collected at each replicate
location in November 2011, except site SFC in October 2012. Fresh
soil–sediment samples were used for NH
N
ANOVA table to test the effects of water level, sampling
month and time of day on N
Monthly N
Relationship between flux, air temperature and
soil NO
Relationship between flux and water DO (mean
Flux differences were tested using a three-way ANOVA, and then using LSD for
multiple comparisons (Table 2 and Fig. 4). A one-sample
Precipitation occurred from March to November. The highest rainfall was in
July, which accounted for one-fourth of the total (Fig. 2a). Water levels
rose rapidly after the summer monsoon, and then declined after August
(Fig. 2d). Temperature peaked during summer (Fig. 2c). The diurnal range in
temperature was about 10
Spearman's rank correlation (
The mean flux from the littoral zone of the Miyun Reservoir was
6.8
Rank correlation analysis was carried out between N
We present the relationship between nitrate and N
Comparison of N
Variations of N
The mean flux from the littoral zone of the Miyun Reservoir was 6.8, from
How do these fluxes compare to those reported from elsewhere? Our fluxes are
comparable to those from the littoral zone of temperate-zone lakes, for
example, a shallow lake in eastern Austria (Soja et al., 2014). However, in
most of the cases, our fluxes were lower, as shown by the following
comparisons. One similar-latitude lake, Lake Baiyangdian, had nearly 10
times higher N
N
Elsewhere, we presented data on methane emissions from this reservoir
(M. Yang et al., 2014). The global-warming potential (GWP) of N
Unlike the specific influence of flooding on CH
Besides, floods may influence N
Positive correlations between N
N
Negative relationships between N
Soil NO
Based on the above discussion and discussion in a previous paper (M. Yang et
al., 2014), the influence of environmental factors on N
Reclamation of the shore by local farmers, to supplement their income, is not
rare. In this research we compared the N
Reservoirs are being developed, in part, for “clean energy”, and reports of
high greenhouse gas emissions from reservoirs have already led some authors
to question the “clean” concept, especially in relation to the mitigation
of climate change (Gunkel, 2009). To evaluate the role that reservoirs play
in climate change, their greenhouse gas emissions ought to be compared with
those of the prior ecosystem (Tremblay et al., 2005). Farmland is one of the
several ecosystems which are lost by flooding during reservoir construction
in China. Total emission of N
Finally, we return to our original hypothesis: the littoral zone
is a hotspot of N
This study was financially supported by State Forestry Administration of China under grant 200804005 and China Scholarship Council. We thank Yi Zhu, Lei Guan, Yamian Zhang, Nana Li, Jialin Lei, Rui Li, Duoduo Feng, Hairui Duo, Lei Jing, Qing Zeng, Chu Lang, Xu Luo, Jiayuan Li, Yonghong Gao, Defeng Bai, Siyu Zhu, and Jianing Xu for their great support during the course of this study. We also thank Beijing North Miyun Reservoir Eco-agriculture Co. Ltd for granting us permission to conduct the study on its land. We sincerely appreciate suggestions and help from editors and reviewers on this paper. Edited by: T. J. Battin