Increased nitrous oxide emissions from global lakes and reservoirs since the pre-industrial era

Domain: earth_science
Published: 2024-01-31
Editorial outcome: major_revision

Abstract
Lentic systems (lakes and reservoirs) are emission hotspots of nitrous oxide (N2O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N2O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N2O-N yr−1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N2O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N2O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr−1. This suggests a global emission factor of 0.051% for inland water N2O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories.

Introduction
Nitrous oxide (N2O) is a potent greenhouse gas, with ~273 times the warming potential of carbon dioxide on a 100-year time horizon, and also contributes to stratospheric ozone destruction1–3. Nitrogen (N) processes in inland waters, as a critical component of the global N cycle, are gaining recognition for their important contribution to N2O emissions through nitrification and denitrification4,5. These emissions, expressed in carbon dioxide (CO2) equivalents, will offset ~4% of the land carbon sink6. Several preceding studies have been dedicated to assessing the magnitude of N2O emissions from inland waters on regional and global scales5,7,8. However, the global estimates are still weakly constrained, particularly for lentic systems such as lakes and reservoirs.

Sizeable human activities have contributed to a notable increase in anthropogenic N loads that are transported from land to lentic systems, thereby playing a significant role in N2O emissions originating from these systems8–10. However, based only on sparse and unevenly distributed local measurements, most previous estimates on N2O emissions from lentic systems are varied by approximately four-fold (160.00-583.00 Gg N yr−1)5,11,12. Furthermore, human-induced N2O emission from lentic systems are implicitly incorporated, as the indirect agricultural N2O emissions, into the recent national N2O emission inventory from the United Nations Framework Convention on Climate Change (UNFCCC), which is calculated based on anthropogenic N additions and global mean emission factors13,14. Nevertheless, the use of constant and linear emission factors in emission inventory fails to capture the spatial variability of N2O emissions from lentic systems8 and cannot dynamically attribute them to environmental changes, such as climate warming and agricultural N application. This limitation hinders the accurate estimation of N2O emissions from lentic systems at the regional level, consequently impacting the precision of national N2O emission inventories13. Considering the anticipated rise in terrestrial N loads to inland waters15, there is a pressing need for a more mechanistic research framework to enhance our understanding of N cycling within aquatic environments and to refine the estimation of N2O emissions from lentic systems.

Considering the strong correlation between terrestrial N loads and N2O emissions in aquatic systems, previous modeling studies have been dedicated to predicting N2O emissions from lentic systems based on spatially explicit terrestrial N input8,16. However, a short-coming of existing models is that N2O production is represented as a function of dissolved inorganic nitrogen (DIN) availability, and when nitrate content reaches zero, denitrification will cease to produce N2O. Nevertheless, in nature, denitrifying bacteria continue to denitrify N2O in absence of nitrate, and this leads to a decrease of N2O levels in the water, and ultimately the system can function as N2O sinks. This occurs in low-DIN systems, particularly in tropical lakes17 and tropical rivers with a relatively low human impact18,19. Additionally, these studies represent single-point snapshots in time and have not fully integrated the temporally-evolving dynamically coupled N cycles of terrestrial-aquatic continuum from a mechanistic perspective, limiting their ability in representing the response of N2O budgets of lentic systems when the watershed environment experiences significant changes (such as climate change and land management). Some studies have revealed that global changes, including climate change, land use change, and atmospheric N deposition, have a substantial influence on N cycling in lentic systems20–22. Hence, it is imperative to develop a dynamic mechanistic model that incorporates intricate environmental changes and integrates multiple N processes for regional and global assessments.

Benefiting from our past modeling efforts in simulating the dynamic riverine N2O emissions23, we incorporated the N2O sub-model of lentic systems with significant improvement in water transporting and the associated biogeochemical processes. This integration forms a comprehensive stream-river-lake-reservoir corridor within the framework of the Dynamic Land Ecosystem Model (DLEM) to represent the dynamic interaction of three N species (DIN, dissolved organic N and particulate organic N) across the terrestrial-aquatic continuum. We compared the simulated inland water N2O fluxes and aquatic nitrate concentration with the observations around the globe to showcase the good performance of our model, with R2 values exceeding 0.6 and Nash-Sutcliffe efficiency coefficient (NSE) exceeding 0.5. Then, we assess global N2O emissions from lakes and reservoirs (N2O-LR) from the pre-industrial period to the recent decade (1850-2019) and examine their sensitivity to environmental changes. Derived from two global lentic system datasets (HydroLAKES and GRanD database)24,25, we categorize lakes and reservoirs into “small” or “large”, depending on their upstream catchment area and the connectivity to subnetwork flows or main river channels in this study. Those with an upstream catchment area greater than the area of a 0.5° grid cell are defined as large lentic systems, and linked to the main channel corridor; correspondingly, the remaining lentic systems with a smaller upstream area are defined as small lentic systems, and linked to the subnetwork corridor. Furthermore, for management purposes, we quantified emission factors for global countries, which is applicable to national greenhouse gas emission inventories for estimating agricultural contributions to N2O emissions from inland waters. Here, we define the agricultural N2O emission factor for inland waters (EF_Ag) as the proportion of agricultural N2O emissions from inland waters (attributed to synthetic fertilizer and manure application) relative to the total agricultural N additions.

Results
The spatiotemporal patterns of global lake and reservoir N2O emissions during the 1850s-2010s

Our simulation utilized high-resolution data for global lentic systems that is derived from the HydroLAKES and GRanD datasets24,25. The total surface area covered by these lentic systems is 2,900,538 km2, where lakes account for 85% (50% for large lakes and 35% for small lakes) and reservoirs accounting for 15% (14% for large reservoirs and 1% for small reservoirs), respectively (Supplementary Table 1). Driven by these lentic system data, our study shows that the estimated N2O-LR in the 2010s (2010-2019) totaled 64.6 ± 12.1 Gg N yr−1 (mean ± standard deviation of the annual average), with 88% from lakes (56.9 ± 10.6 Gg N yr−1) and 12% from reservoirs (7.8 ± 1.5 Gg N yr−1) (Fig. 1a). The decadal mean N2O-LR increased significantly (p < 0.01) from the pre-industrial period (the 1850s, 28.6 ± 6.8 Gg N yr−1) to the 2010s, with an average increase rate of 0.2 Gg N yr−1 (Fig. 1a). The most notable increase in total N2O-LR was found from the 1940s to the 1980s, with an annual increase rate of 0.4 Gg N yr−1. Since the 1980s, the increase rate in N2O-LR had slowed down to a rate of 0.3 Gg N yr−1 during the 1980s-2010s (Fig. 1a).

N2O emissions intensities in small lentic systems were higher than those from large lentic systems. Specifically, in the 1850s, the N2O emission per unit area from small lakes was 13.2 ± 0.1 mg N m−2 yr−1, whereas it was 10.4 ± 2.9 mg N m−2 yr−1 for large lakes. Due to the larger area of global large lakes, N2O emissions from these lakes contributed larger to the overall N2O emissions than small lakes in the 1850s, with N2O emissions shares of large and small lakes being 53% and 47%, respectively (Fig. 1b). In the 2010s, N2O emission per unit area from small reservoirs (128.2 ± 22.3 mg N m−2 yr−1) is three times higher than that from small lakes (30.6 ± 5.0 mg N m−2 yr−1), which is followed by large lakes (17.6 ± 3.8 mg N m−2 yr−1) and large reservoirs (10.0 ± 2.2 mg N m−2 yr−1). Therefore, despite the small lentic systems comprising only 36% of the total surface area, they contributed to 55% of the total N2O emissions in the 2010s. There had been a 133% increase in N2O emissions from small lakes during the 1850s-2010s, which is approximately twice that of large lakes (Fig. 1b). From the 1850s to the 2010s, the total increase in N2O-LR was primarily attributed to small lakes (50%), followed by the large lakes (29%), the large reservoirs (11%), and the small reservoirs (10%) (Fig. 1c).

In the pre-industrial period (the 1850s), North America (8.3 Gg N yr−1), East Asia (5.5 Gg N yr−1), and South America (4.5 Gg N yr−1) were hotspots for N2O-LR (Fig. 2a), collectively accounting for 64% of the global N2O-LR (Supplementary Fig. 1). From the 1850s to the 2010s, around 75% of the increasing global N2O-LR was from northern mid- to high-latitudes (30°N-60°N) (Fig. 2c). During the 2010s, N2O-LR showed the peaks in northern mid- to high-latitudes (30°N-60°N) and the tropics (5°N-5°S) (Fig. 2c). The regions with intensive agricultural activities including East Asia (16.3 Gg N yr−1), North America (14.0 Gg N yr−1), Europe (8.6 Gg N yr−1) and Africa (7.1 Gg N yr−1), contributed 71% of total N2O-LR (Supplementary Fig. 1), becoming hotspots of N2O-LR in the 2010s (Fig. 2a). At the regional level, the amount of N2O-LR from Europe, East Asia, South Asia, Southeast Asia, and West/Central Asia have increased more than twofold (Supplementary Fig. 1), with up to eightfold increases in some grids since the 1850s (Fig. 2b). N2O-LR in most regions increased significantly especially after the 1960s (Supplementary Fig. 2). The increasing rate of N2O emissions from small lentic systems in most regions, except Africa, South Asia, and West/Central Asia, is 2–8 times higher than the rate in large lentic systems (Supplementary Fig. 2). The increase in rates of N2O emission from small lentic systems in Europe (0.03 Gg N yr−1), North America (0.02 Gg N yr−1), and East Asia (0.04 Gg N yr−1) were much higher than those in other regions.

Relative roles of natural and anthropogenic environmental factors on lentic system N2O emissions over time and regions

Globally, N2O-LR were enhanced by climate change, land use change, and agricultural N additions, but reduced by elevated atmospheric CO2 concentration (Fig. 3). It should be noted that the initiation of reservoir simulations on the grid cells is determined by the year of reservoir construction. As a result, when conducting factorial experiments, the effect of reservoir construction will undoubtedly be included in the influence of environmental changes on N2O-LR. Since N2O emissions from reservoirs constituted 12% of N2O-LR in the 2010s, we assume that the impact of reservoir construction represents only a minor portion of the overall impact. From the 1850s to the 1940s, climate change and land use change collectively contributed to 85% of the increase in N2O-LR, which was primarily attributed to higher terrestrial N input driven by global warming and increased agricultural activities. During the 1940s-1980s, the global N2O-LR experienced a threefold increase, when agricultural N addition, including synthetic fertilizer and manure application, contributed 68% of this increase, while the contribution of climate change and land use change was only 22%. In the 1980s to the 2010s, agricultural N addition remained the dominant driver for increased N2O-LR, with its contribution on global increased N2O-LR being twice higher than that of climate change and six times higher than that of land use change. Nevertheless, the increased magnitude of N2O-LR originating from agricultural N addition, climate change, and land use change were comparatively lower than that in the previous period. In the 2010s, agricultural N addition is the primary factor responsible for enhancing N2O-LR in most regions, except in Africa and Russia where climate change remains dominant. Agricultural N addition contributes up to 60% of increased N2O-LR in Southeastern Asia, Southern Asia, Europe, Eastern Asia, and West/Central Asia (Supplementary Fig. 3). Notably, from the pre-industrial period to the recent decade, the elevated atmospheric CO2 concentration accelerated plant growth and N uptake, and thus inhibited terrestrial N loss to lentic systems and N2O-LR. The inhibitory effect of elevated atmospheric CO2 concentration on N2O-LR has been on the rise from 0.9 Gg N yr−1 in the 1850s-1940s to 3.3 Gg N yr−1 in the 1980s-2010s, which resulting in a 52% reduction in N2O-LR and nearly offset the promoting effect induced by climate change and land use change. We also found that small lentic systems showed greater response to global changes (Supplementary Fig. 4). Induced by climate change and human disturbance, N2O emissions from small lentic systems increased by 211% (3.5 Gg N yr−1) during the 1850s-1940s and by 247% (12.6 Gg N yr−1) during the 1940s-1980s. In comparison, the rates of increase of N2O emissions from large lentic systems were slower, with 66% (1.9 Gg N yr−1) increase during the 1850s-1940s and 185% (8.8 Gg N yr−1) increase during the 1940s-1980s. Although the area of small lentic systems only accounts for 36% of the total lentic system area, they show a greater response to environmental change. Specifically, during the 1850s-1940s, the strong responses of N2O emissions from small lentic systems were attributable to climate change (63%), agricultural N addition (90%), atmospheric N deposition (72%), and increased atmospheric CO2 concentration (-54%), and outweighed the responses of large lentic systems, constituting more than half of the total responses. During the 1940s-1980s, while the influence of climate change on N2O emissions from small lentic systems diminished (decreasing from 63% to 36%), the responses of small lentic systems to agricultural N additions (68%), atmospheric N deposition (54%), and increased atmospheric CO2 concentration (-64%) still amounted to over half of the total responses. In the period of the 1980s-2010s, N2O emissions from small lentic systems have greater responses to natural disturbances such as climate change (56%) and increased atmospheric CO2 concentration (-63%) (Supplementary Fig. 4).

Estimate of global inland water N2O emissions: integrating updated riverine estimation

The transport and transformation of N in inland waters have significant cascading effects. For instance, the nutrient N is transported to lakes and reservoirs through upstream rivers; as a potent reactor of N species, lakes or reservoirs have a significant impact on nutrient N exported further to the downstream rivers. Therefore, in this study, we present an updated estimation of N2O emissions from global streams and rivers, encompassing the processes of lentic systems that were excluded from our previous estimates (Supplementary Fig. 5). In the pre-industrial period (the 1850s), global streams and rivers emitted 83.8 ± 22.8 Gg N yr−1 of N2O into atmosphere. Since the pre-industrial era, there has been a significant growth in global riverine N2O emissions, particularly from the 1960s to the 2010s, exhibiting a linear growth rate of 26.2 Gg N per decade (Supplementary Fig. 5). The remarkable increase of global riverine N2O emissions can be attributed primarily to the application of agricultural N fertilizer and N manure (Supplementary Fig. 6). In the recent decade, global riverine N2O emissions reached 254.9 ± 46.2 Gg N yr−1, marking a twofold increase from the estimated levels in the 1850s (Supplementary Fig. 5). Combining the updated amount of riverine N2O emissions, the estimated N2O emission from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr−1 (Fig. 4). In the 2010s, riverine N2O emissions constitute 80% of the total emissions from inland waters worldwide, with N2O-LR being accountable for the remaining 20%. N2O emissions from global inland waters have substantially increased by 207.2 Gg N yr−1 since the 1850s, of which the increased N2O-LR induced by anthropogenic perturbation contributes to 17% of the total increase of N2O emissions from inland waters.

Dynamic anthropogenic emission factors of N2O emissions from inland waters

Regarding the finding that agricultural activities are responsible for the accelerated growth in N2O emissions from inland waters, here we propose the revised EF_Ag to improve the overall clarity of anthropogenic indirect N2O emissions within national greenhouse gas emission inventories. Our factorial experiments reveal the dynamic contribution of agricultural N additions on N2O emissions from inland waters (Supplementary Table 2 and Table 10). From the 1850s to the 1910s, manure application resulted in negligible N2O emissions from inland waters (Fig. 5a). In the early years of synthetic N fertilizers being applied in agricultural practices (around the 1920s), agricultural N additions led to a slight increase in N2O emissions from inland waters, with a EF_Ag of only 0.002%. By the 1990s, the fraction of synthetic N fertilizer in agricultural N addition increased from around zero to 45% (Fig. 5b). More importantly, global mean EF_Ag constantly raised and reached 0.053% in the 1990s (Fig. 5a; Supplementary Table 2). However, despite the ongoing growth in agricultural N addition, EF_Ag in recent decades are lower than that in the 1990s. In the 2010s, the global mean EF_Ag stand at 0.051%, with national-level EF_Ag ranging from 0.000 to 0.341%. EF_Ag of thirty-nine countries are exceeding global mean EF_Ag in the 2010s (Supplementary Table 3).

Methods
Dynamic Land Ecosystem Model-Terrestrial-Aquatic Continuum (DLEM-TAC)

To quantify N2O emissions from global inland waters (rivers, lakes, and reservoirs), we use a process-based coupled terrestrial-aquatic model, which is built on the framework of the Dynamic Land Ecosystem Model (DLEM). DLEM-TAC is a fully distributed, process-based land surface model which couples the major land processes (terrestrial hydrology, plant phenology and physiology, soil biogeochemistry) and aquatic dynamics (lateral transport and in-stream biogeochemistry)23,63–65. The land component of DLEM-TAC explicitly simulates the carbon, N, and water fluxes between plants, soil, and atmosphere, and the surface and drainage runoff and N load from land module are used as the input of the riverine module. The simulated N load includes DIN, dissolved organic nitrogen (DON), particulate organic nitrogen (PON), and runoff, which serve as the major inputs to the aquatic module.

The DLEM-TAC aquatic module calculates lateral water transport and the associated aquatic biogeochemical processes by adopting a scale-adaptive scheme (Supplementary Fig. 7). The water transport scheme couples hillslope flow, subnetwork flow, and main channel flow with a grid cell as subgrid processes, which allows the representation of small scale physical and biogeochemical processes at larger spatial scales. The subnetwork flow, which is conventionally known as the 1st-5th order rivers in the 0.5° grid cell66,67, receives water from hillslope flow and drains into the main channel. Based on our previous study23, we coupled the lentic systems into the subnetwork and river routing to form a river-lake-reservoir corridor in this so improved model. Furthermore, lentic systems where the upstream area is smaller than the area of the 0.5° grid cell are classified as small lentic systems, and assigned to the linked subnetwork corridor; correspondingly, the remainders with the upstream catchment area larger than the grid area are classified as large lentic systems, and assigned to the linked main channel corridor. The surface area of large/small lakes or large/small reservoirs are shown in Supplementary Table 1. The incoming water and nutrient flows of sub grid lakes and reservoirs linked to subnetworks depend on their upstream area obtained from the high-resolution dataset24, which determines the fraction of flows from hillslope and subsurface that are intercepted. The water of a linked river-lake-reservoir corridor of a subnetwork drains to lakes and reservoirs first, and the outflow rate of lakes and reservoirs is determined based on the predefined residence time obtained from the global lake dataset24,68,69. The aquatic N module was developed based on the scale adaptive water transport scheme23,70, including lateral transport, decomposition of organic matter, particulate organic matter deposition, nitrification, and denitrification.

Following our previous work referring to the representation of water transport and biogeochemical cycling, we developed an inland water N2O module within the aquatic biogeochemical component of the DLEM-TAC framework71 (Supplementary Fig. 8). The net fluxes of dissolved N2O (including physical and biogeochemical processes) in inland waters are estimated as:

(ΔM_N2O)/Δt = F_a + Y_water + D - R - E (1)

where M_N2O is the total mass of dissolved N2O in inland waters (g N), Δt is the timestep, F_a is advective N2O fluxes (g N d−1) (Supplementary Text 1), Y_water is the N2O production within inland waters (g N d−1) (Supplementary Text 2), D is the dissolved N2O from rainfall to inland waters (i.e. deposition) (g N d−1) with an initial concentration equal to the atmospheric equilibrium N2O concentration, R is the flux from N2O reduction (g N d−1) to dinitrogen gas (Supplementary Text 3), and E is N2O efflux (g N d−1) through the air-water interface (Supplementary Text 4).

Input data

The driving data of the DLEM-TAC include the climate variables, atmospheric CO2 concentration, land use change, N deposition, N fertilizer, and manure application with a spatial resolution of 0.5° × 0.5°. The daily climate variables (precipitation, mean temperature, maximum temperature, minimum temperature, and shortwave radiation) were obtained from the CRUNCEP dataset (https://vesg.ipsl.upmc.fr) for 1901-2019. Climate data during 1850-1900 cycled early 20th century (1901-1920) climate72. Annual atmospheric CO2 concentration from 1900-2019 was obtained from the NOAA GLOBALVIEW-CO2 dataset (https://www.esrl.noaa.gov). The annual land use change data were derived from a potential natural vegetation map (synergetic land cover product) and a prescribed cropland area dataset from the history database of the global environment v.3.2 (HYDE 3.2, ftp://ftp.pbl.nl/hyde). The data of N fertilizer, manure N application, and N deposition were obtained from Tian, et al.73.

In the aquatic module, the required channel dataset included channel slope, channel width, and channel length generated from the HydroSHEDs dataset70,74 and DDM30 dataset75. The flow direction and distance data were obtained from the Dominant River Tracing (DRT) dataset76. For modeling water dynamics in lakes and reservoirs, we generated 0.5° grid level surface water area, upstream area, volume, depth, and average residence time for lakes based on the HydroLAKES dataset24, while the GRanD v1.01 database provided the same information for reservoirs69.

Simulation protocol

The DLEM-TAC simulation includes three steps: equilibrium run, spin-up run and two transit runs, one with dam operation closed, and another one with dam operation open. First, the equilibrium run is required to obtain the initial and steady state condition of carbon, N, and water pools at the pre-industrial level in each grid cell77. In this step, we held all the driving forces such as climate data, atmospheric CO2 concentration, land use data, and N additions consistent with the first year’s data we used in the simulation. Second, we conducted a 30-year spin-up run by randomly selecting climate data within the 1850s78. This step can alleviate the disturbance of driving data changes in the transit run. Then we conducted the natural flow simulation with the dam model temporarily deactivated (no dams), and all the driving forces change over time. After the natural flow simulation, we set up a management flow simulation with the dam module open, because the dam module needs natural flow in the previous run as model input68. Based on natural flow, the management flow simulation for newly constructed reservoirs over time were conducted starting from the constructed years provided by GRanDv1.01 database. To evaluate the model performance, we compared the simulated N2O emission to 106 observed N2O fluxes from global inland waters including lakes, reservoirs and rivers. In addition, we also validated the simulated aquatic nitrate concentration. The simulated results agreed well with the observation with the value of R2 above 0.6 and NSE above 0.6 in most cases (Supplementary Fig. 9).

To quantify the effects of environmental factors such as climate change, atmospheric CO2 concentration, land use change, N deposition, and agricultural N application (fertilizer and manure) on N2O-LR, we conducted other five factorial experiments though holding each environmental factor consistent with the first year of corresponding environmental data (Supplementary Table 10).

Calculation of agricultural N2O emission factors for inland waters

In the UNFCCC national GHG emission inventories, EFs applied in estimates of agriculture-induced inland water N2O emissions are derived from the methodology provided by IPCC’s report14. In that report, EFs produced from leaching and runoff of N addition are defined as the fraction of N leaching and runoff that is lost through N2O emissions, and further assumes that 24% of the agricultural N addition in managed land of wet climates is lost through leaching and runoff. To facilitate the calculation of agriculture-induced inland water N2O emissions in individual countries, we calculate EF_Ag as the percentage of agriculture-induced inland water N2O emissions relative to the agricultural N additions to avoid applying a constant as the proportion of agricultural N addition loss:

EF_Ag = (N2O_inlandwater_Ag / Agricultural N additions) × 100% (2)

where N2O_inlandwater_Ag is agriculture-induced N2O emissions from inland waters, which is calculated as the difference of inland water N2O emissions between Simulation 1 and Simulation 6 (Simulation 1 is the all-combined simulation with all the driving forces changing over time; Simulation 6 is factorial simulation experiment by holding agricultural N application at the first year, see Supplementary Table 10). The amount of N additions is obtained from Tian, et al.73.

Raw EF_Ag we calculated are negative between the 1850s-1910s, which can be explained by the unsaturated N2O concentrations in inland waters under the small amount of manure application. Therefore, we forced EF_Ag to zero for the period of the 1850s-1910s. Negative EF_Ag at specific countries were treated accordingly, which usually located in regions less affected by agriculture.

Quantifying the uncertainty induced by terrestrial nitrogen inputs

The previous studies have identified N loads as a major source of uncertainty in inland water N2O emission estimates16,23. Here we evaluate the uncertainty in estimating N2O-LR induced by variations in N loads. We collected 62 field data points of N leaching, which covers five N species (NO3-, NH4+, DON, TDN, TN) and four types of land use (cropland, forests, grassland, peatland), and validated against the simulated N leaching by DLEM-TAC (Supplementary Fig. 10). Then, we calculated the 95% uncertainty ranges of N loading using the Origin software. Finally, we determine the uncertainty range of ±22%, ±50%, ±37% and ±26% for NO3-, NH4+, DON and PON loads, respectively. We then conducted two model simulations from 1850 to 2019 with the parameters of terrestrial loads of NO3-, NH4+, DON and PON varying ±22%, ±50%, ±37% and ±26%, respectively.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "L 55-57 : This statement is incorrect. N2O emissions from lakes have been reported recently in using an observational approach at regional scale (Borges et al. 2022) and modelling approach at global scale (Lauerwald et al. 2019).\nL77-88 : An additional short-coming of existing models is that N2O production parameterized directly a function of DIN availability, and in the case of NO3- = 0, denitrification will no longer produce N2O. However, in nature, denitrifying bacteria continue to denitrify N2O in absence of NO3-, and this leads to a decrease of N2O levels in the water, and ultimately the system can function as sinks of N2O in particular in systems with low DIN levels. This occurs in several tropical lakes (Borges et al. 2022). Actually this seems to occur quite systematically in low DIN environments, and is a generalized feature in tropical rivers with a relatively low human impact (Borges et al. 2019; Chiriboga and Borges 2023).\n89-93 : It is essential to explain here what is the difference between the present work and the one of Lauerwald et al. (2019) since it seems (at least to me) to be from the same group, and a very similar model setup.\nL 111 : Please explain here how you define « large » and « small » lakes/reservoirs.\nL157-174: It is unclear if the effect of changes of the surface area of lakes in response of climate change are accounted in the simulations (Zhao et al. 2022; Zhou et al. 2021).\nL 265 : The two cited references are for a single system. It is unreasonable to make a generalization on the response of N2O emissions from lentic systems to warming based on two sites.\nL275-296: Borges et al. (2022) suggested that N2O in lakes could change as function of depth that modulates the coupling between surface waters and sediments. Since there is a general relation between surface area and depth (larger systems are in general deeper) (Wetzel 2001) this could also play a role in explaining differences between small and large systems.\nL316-317: There have been several past papers reporting that the IPCC EF are too high. It would be useful to include these in the discussion here.\nL 340 : Unfortunatly the authors did not provide a Table with fluxes from observational studies. I have the impression the data from African lakes from the recent paper of Borges et al. (2022) are missing from this comparison.\nL342: the r2 alone is not sufficient for validation. You can have a very good correlation (high r2) and a poor validation due to a very large off-set (Y-intercept > or < 0) or over-/under-estimate (slope > or < 1) or both. So please make much more rigorous statistical evaluation of the validation.\nL346: There’s some room for debate. Borges et al. (2022) shows on the contrary that smaller and shallower African lakes have lower N2O concentration than larger and deeper ones.\nL 348 : do you mean seasonal or long-term changes of surface area ?\nPlease note that the HydroLAKES data-base in some cases over-estimates the lake surface area because some of data are quite old (HydroLAKES builds on the GLWD) and some lakes have shrunken with global warming. This is the case for arid and semi-arid climates, for instance Lake Chad, as discussed by Borges et al. (2022).", "reviewer_2": "This study presents global predictions of N2O emissions from lakes and reservoirs using a process based, coupled terrestrial-aquatic model. They find that while large lakes dominate lentic N2O emissions, smaller lakes and small reservoirs have bigger per unit area emission. The study predicts smaller lentic N2O emissions than most previous studies, but more than double the preindustrial emission rate.\nA major strength of this study is the coupling of terrestrial and aquatic models that allow the exploration of how the major global change factors contribute to the increased N2O emissions. The portioning of effects is very interesting. Not surprisingly, the major contributor is excess agricultural nitrogen. An interesting finding is that in recent decades, CO2 fertilization seems to have led to reduced N2O emissions, contributing to the slower increases in recent decades. This is based on the terrestrial model, which has its dynamics presumably described in earlier papers. It would be worth pointing out in this manuscript as part of discussion that, while aquatic N2O emissions are predicted to be leveling off, no leveling off N2O increases in the global atmosphere have been noted, instead they appear to be accelerating.\nThis manuscript emphasizes lentic N2O emission, bringing in lotic N2O emissions only as discussion points and as part of the emission factor calculation. This is surprising and I think a major weakness for several reasons. First, the results highlight the inland water emission factor, which includes streams and rivers, but their response to the global change factors studied in scenarios is not considered at all. The paper seems to be disconnected in this sense. Second, rivers actually dominate inland water N2O emissions. Lentic waters are only about 10% of total inland water emissions. This is a point that I think is important to highlight. Third, river and lake processes interact as the authors discuss. However, the authors emphasize how denitrification by lakes limits N2O emissions downstream, but not the fact that denitrification by streams and rivers limits N2O emissions by lakes and reservoirs. This would be a compelling paper if these results were to be compared together.\nGlobal inland water N2O emissions are lower than most other studies, including those that incorporate the role of streams, rivers, lakes, and reservoirs, and that account for sub grid river networks (based on Beaulieu et al. 2011 which used the global aquatic N model in Wollheim et al. 2008). Little is discussed as to why the current model results in lower N2O emissions. Is it because N loading from the landscape is less (Beaulieu model is based on annual N export from land to water in Green et al. 2004), the denitrification uptake velocity is less, the proportion of incomplete denitrification or nitrification is less, or some other reason. No parameter values are given (or calculated from the more complex model used here), but for the paper to be useful, they should be. Otherwise the results are just assertions.\nI did not understand the numbers that went into the global emission factor calculation. Please explain clearly how the 137.02 Gg N yr-1 was derived.\nAnother weaknesses is that the representation of the processes that lead to N2O emissions and key parameter values are not provided. It is mentioned (L91) that significant improvements have been incorporated, but these are not identified. In the current manuscript, only the equation for mass balance of N2O is provided, and variable, Ywater, is the only mention of N2O production. There is nothing about what controls N2O production, which is essential for a paper like this. I had to go back to the Yao et al. 2020 paper, where more detail is given in the supplemental, but here there were errors in description of the equations (supplemental eqns 5 -9) that I was not sure what was going on. No values for uptake velocity were given here, so the reader could not evaluate why the N2O emissions were lower than other global studies. Maybe I could go back even further to earlier papers, but I should not have to as this is essential for understanding the results of this study.\nIt was also not clear how the lakes at the 0.5 degree grid scale were integrated into the river network from the description. Are all small lakes within a grid cell combined into one and “placed” at the mouth of the grid cell, intercepting N that has been transported through the sub grid cell river network where denitrification and N2O emissions can also occur? It is likely that N enters streams at the sub grid scale first, before reaching any lake at the minimum lake sizes considered. See Wollheim et al. 2008 for a very similar approach (Wollheim, W.M., C.J. Vorosmarty, A.F. Bouwman, P.A. Green, J. Harrison, E. Linder, B.J. Peterson, S. Seitzinger, and J.P.M. Syvitski. 2008b. Global N removal by freshwater aquatic systems: a spatially distributed, within-basin approach. Global Biogeochemical Cycles. GB2026", "reviewer_doi": "10.1029/2007GB002963.\nI appreciated the effort at validation of N2O emissions, based on 60 lake locations, as well as the incorporation of uncertainty in the loading, which I agree is an essential issue that must be considered. But I would like to know where these observational data come from. There were no units in the figures showing these comparisons with observations. Given that this is a global model, and that nitrate concentrations likely drive N2O emissions, in order to understand the usefulness of the model I would also like to see how well the model is tested against nitrate concentration data in streams, rivers, or lakes. Since the complete N cycle is represented, it would also be useful to compare the % of incomplete denitrification predicted to measurements, which based on LINX2 studies (Beaulieu et al 2011) demonstrated a fairly low average of 0.9% of denitrification is as N20. Are predictions here similar?\nI also wondered about about the mechanisms behind a few of the results. Why does Africa have such large increase in N2O emissions, while ag N increase is minimal. Change is attributed to climate change in Africa, but I think it is getting drier, so why would that lead to more N2O emissions? Table S1 reports the residence times of lakes and reservoirs. Why would small lakes have a longer residence time than large lakes (I don’t think this is expected), and do reservoirs really have 10’s of years of residence time?\nSpecific\nL97. Define small and large lakes.\nL106. I would suggest one fewer decimal place when reporting fluxes. I’m sure estimates are not that precise, and would make easier to read.\nL162. It was not clear what this statement means.\nL192. Why do smaller systems have a greater response in the model?\nL195. Confusing section here.\nL203. What about the role of streams and rivers upstream of lakes? Should be mentioned.\nL210. This is only half of the contemporary estimate from the global model of lakes, rivers, reservoirs reported in Beualeigh et al. 2011. Can you include some discussion of why?\nL247. Lake emissions should also be lower when incorporated into river network because of stream and river reductions. i.e. it works in both directions.\nL277. Are small lakes hotspots not just because more effective denitrification (uptake velocity / hydraulic load), but also because they are upstream from large lakes, and therefore have greater inputs? It is not clear why residence times should be greater in small lakes, as it is likely that they will also have less watershed area. Please support this is case from literature.\nL283. Longer residence times lead to more denitrification, but not more complete conversion of nitrate to N2, at least based on this reference. This statement requires more support.\nL292. Is this really an important dynamic? If so, provide some numbers.\nL297. Please be clear that this includes streams and rivers, in addition to lentic waters. Since this is highlighted, stream and river results should be added to results section.\nL315-317. This discounting is unclear.\nL331-346. Consider putting model validation in a brief study design paragraph before the results, to increase trust in the model results.\nL371. Did the sub grid cell river network account for serial transformations from 1st through 4th order? The integration of lakes is unclear – do small lakes intercept all sub grid river flow?. The global inland water N model of Wollheim et al. 2008 used a similar approach, including a statistical N removal model to account for small river dynamics.\nL384. If lakes and reservoir residence times are predefined, does that mean it does not change with changes in hydrology (i.e. climate change)?\nL396. More in how Ywater is controlled is really needed.\nL422. Unclear what “dam operation closed” means. Is it that there are no reservoirs at all, or just input = output, or there is active management for hydroelectricity based on some rules?\nL449. Does this mean that 76% of added N is harvested, stored, or denitrifed on land? Isn’t this part of the terrestrial model used here?\nL479. The data availability is all raw input data from other sources, none of the data produced by this study. I would expect key model predictions would be shared, as well as the model validation data that was compiled.\nFig. 4. Color reference are confusing, as red represents both lake and changing emissions.\nFig 5b. Change secondary Y axis to “proportion manure”\nTable S2. Sort based on Country name, or EFag.\nTable S4. I’m not sure this table is worth including, given the very different assumption, with mist not relevant to the IPCC report.\nFigure S2. Make legend larger to read\nTable S4. CO3 should have negative values, right?\nFigure S5 and S8. Units needed, more informative caption (where are data from, how collected, ecosystem, biome, time). References to where they are from, and include in data that this study makes available.", "reviewer_3": "I have published quite a lot concerning GHG emissions from lakes so the literature is well-known to me. N2O is a minor issue in lakes as it probably constitutes less than 3% of lakes’ atmospheric impact so that does diminish the interest of this manuscript somewhat. I have a major concern about this manuscript that would need to be cleared up before publication anywhere, in my view. This paper is basically a lengthy modeling effort base on the premise that N limits N2O emissions from lakes. The authors cite a couple of manuscripts as support of this idea (Maavara et al. 2018 [not 2019 as listed] and Lauerwald et al 2019) but both of these are modeling exercises and neither of them demonstrates the critical role of N in driving N2O emissions from lakes. Instead, it is much more likely (see Delsontro et al 2018) that N2O emissions are driven by P (or primary production) and lake size. Most limnologists agree that N is almost always in excess in inland waters so the amount of N as substrate for N2O emission should rarely be an issue. A credible study would be based on empirical analyses of the factors driving N2O emissions in lakes. In its present state, this paper appears to be a thought study based on the query, “what would be happening if N limited N2O emissions”.\nSome smaller suggestions are listed below\nLines 55-56. There are lots of studies of N2O emissions in lakes and reservoirs. This statement is false or misleading.\nLines 229-230. Ranges of estimated impacts are listed as N here, not as CO2 equivalents as in most similar studies.\nLines 232-234: “Observation-based” studies are suggested to be inaccurate compared to this model. Science normally depends on observations so the authors should make the case why their model might actually be more accurate. This seems to be somewhat non-scientific, to me.\nLines 371-372. Quite a lot is known about the global distribution of rivers and streams. For example, Downing et al. 2012. Why not use the best available estimates? In any case, I think the authors should explain why stream order (which is often criticized as having little biogeochemical relevance) would be relevant.\nLine 394. The authors should better explain where variables like Fa or Ywater could be determined and where these came from.\nLine 401. It should be clarified how one gets from modeled or simulated N transport to N2O emissions. Especially given the paucity of global empirical data suggested in the Introduction."}, "author_response": "Revisions of Manuscript: NCOMMS-23-28044\nTitle: Increased nitrous oxide emissions from global lakes and reservoirs since the pre-industrial era\nAuthor(s): Ya Li, Hanqin Tian, Yuanzhi Yao, Hao Shi, Zihao Bian, Yu Shi, Siyuan Wang, Taylor Maavara, Ronny Lauerwald, Shufen Pan\nWe thank the reviewers and editors for taking the time to read and review this manuscript as well as for their constructive comments and feedback. We have carefully revised the manuscript in line with the feedback from the reviewers, and have added text and figures to the revised version of the manuscript.\nWe have included more details in the Method section, especially regarding model processes, underlying mechanisms, and model validations to better ground our findings related to dynamic N O 2 emissions from inland waters. In addition, the more important results and information for riverine (lotic) N O emissions, and observed data used to validation were added in the revised manuscript and supplementary information.\nThe revisions are outlined below with a point-by-point response to each reviewer’s comments.Please note that reviewers’ comments are in italics while our responses are not. All text from the manuscript is colored blue, and citation numbers correspond to the reference section of this manuscript.\nReviewer #1 (Remarks to the Author):\n1. Comments L 55-57: This statement is incorrect. N2O emissions from lakes have been reported recently in using an observational approach at regional scale (Borges et al. 2022) and modelling approach at global scale (Lauerwald et al. 2019).\nResponse: We have corrected the statement in the revised main text:\nL52-53:“However, the global estimates are still weakly constrained, particularly for lentic systems such as lakes and reservoirs.”\n2. Comments L 77-88: An additional short-coming of existing models is that N2O production parameterized directly a function of DIN availability, and in the case of NO3- = 0, denitrification will no longer produce N2O. However, in nature, denitrifying bacteria continue to denitrify N2O in absence of NO3-, and this leads to a decrease of N2O levels in the water, and ultimately the system can function as sinks of N2O in particular in systems with low DIN levels. This occurs in several tropical lakes (Borges et al. 2022). Actually this seems to occur quite systematically in low DIN environments, and is a generalized feature in tropical rivers with a relatively low human impact (Borges et al. 2019; Chiriboga and Borges 2023).\nResponse: Thank you for the great suggestion. We have added this shortcoming of the existing models in the revised main text. The specific modifications are found in L76-82.\nL76-82:“However, a short-coming of existing models is that N O production is represented as a 2 function of dissolved inorganic nitrogen (DIN) availability, and when nitrate content reaches zero, denitrification will ceases the production of N O. Nevertheless, in nature, denitrifying bacteria 2 continue to denitrify N O in the absence of nitrate, and this leads to a decrease of N O levels in the 2 2 water, and ultimately the system can function as N O sinks. This occurs in low-DIN systems, 2 particularly in tropical lakes (17) and tropical rivers with a relatively low human impact (18, 19).”\nReference:\n17. Borges, A. V., et al. Greenhouse gas emissions from African lakes are no longer a blind spot. Sci. Adv. 8, eabi8716 (2022).\n18. Chiriboga, Gonzalo & Borges, Alberto V. Andean headwater and piedmont streams are hot spots of carbon dioxide and methane emissions in the Amazon basin. Communications Earth & Environment 4, 76 (2023).\n19. Borges, Alberto V, et al. Variations in dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial-wetland connectivity. Biogeosciences 16, 3801-3834 (2019).\n3. Comments L 89-93: It is essential to explain here what is the difference between the present work and the one of Lauerwald et al. (2019) since it seems (at least to me) to be from the same group, and a very similar model setup.\nIn this paragraph, we discuss the limitation of existing models, namely their failure to effectively integrate terrestrial and aquatic systems. Building upon comment #2, we also highlight another drawback of the existing models, which lies in its incomplete depiction of the nitrogen cycle in water systems. Furthermore, in the next paragraph, we revised the description of the strengths of our model. The specific modifications are found in L76-87, L94-98.\nL76-87:“However, a short-coming of existing models is that N O production is represented as a 2 function of dissolved inorganic nitrogen (DIN) availability, and when nitrate content reaches zero, denitrification will ceases produce N O. Nevertheless, in nature, denitrifying bacteria continue to 2 denitrify N O in absence of nitrate, and this leads to a decrease of N O levels in the water, and 2 2 ultimately the system can function as N O sinks. This occurs in low-DIN systems, particularly in 2 tropical lakes (17) and tropical rivers with a relatively low human impact (18, 19).Additionally, these studiesrepresent single-point snapshots in time and have not fully integrated the temporally-evolving dynamically coupled N cycles of terrestrial-aquatic continuum from a mechanistic perspective, limiting their ability in representing the response of N O budgets of lentic systems when the 2 watershed environment experiences significant changes (such as climate change and land management).”\nL94-98:“This integration forms a comprehensive stream-river-lake-reservoir corridor within the framework of the Dynamic Land Ecosystem Model (DLEM) to represent the dynamic interaction of three N species (DIN, dissolved organic N and particulate organic N) across the terrestrial-aquatic continuum.”\nReference:\n17. Borges, A. V., et al. Greenhouse gas emissions from African lakes are no longer a blind spot. Sci. Adv. 8, eabi8716 (2022).\n18. Chiriboga, Gonzalo & Borges, Alberto V. Andean headwater and piedmont streams are hot spots of carbon dioxide and methane emissions in the Amazon basin. Communications Earth & Environment 4, 76 (2023).\n19. Borges, Alberto V, et al. Variations in dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial-wetland connectivity. Biogeosciences 16, 3801-3834 (2019).\n4. Comments L 111: Please explain here how you define « large » and « small » lakes/reservoirs.\nResponse Thank you for the suggestion. In our previous study (Yao et al., 2020), we divided the simulations of river routing and the biogeochemical processes into two levels: main channel corridor at the grid cell level and subnetwork corridor within the grid cells. In our current model version, the lentic systems (lakes and reservoirs) are further connected to the river systems. Therefore, we 3 classified lentic systems with an upstream catchment area larger than the grid area as “large lentic systems”, and linked them to the main channel corridor; correspondingly, the remaining lentic systems with a smaller upstream area are classified as “small lentic systems”, and linked to the subnetwork corridor.\nWe explained a definition of large or small lentic systems in the revised main text when the words firstly appeared. The specific modifications are found in L104-110.\nL104-110:“Derived from two global lentic system datasets (HydroLAKES and GRanD database) (24, 25), we categorize lakes and reservoirs into “small” or “large”, depending on their upstream catchment area and the connectivity to subnetwork flows or main river channels in this study. Those with an upstream catchment area greater than the area of a 0.5° grid cell are defined as large lentic systems, and linked to the main channel corridor; correspondingly, the remaining lentic systems with a smaller upstream area are defined as small lentic systems, and linked to the subnetwork corridor.”\nReference:\n24. Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications 7, 13603 (2016).\n+.(6ADIAM'1('AO=G(4ECDVMANJGPOEJIH=KKEICJBODARJMG@$NMANAMQJEMN=I@@=HNBJMNPNO=EI=>GAMEQAMVBGJR management. Frontiers in Ecology and the Environment 9, 494-502 (2011).\n5. Comments L 157-174: It is unclear if the effect of changes of the surface area of lakes in response of climate change are accounted in the simulations (Zhao et al. 2022; Zhou et al. 2021).\nResponse: In our study, we used the lake surface area data derived from the HydroLAKES dataset to drive the model. Due to the limitations of remote sensing products, this dataset can be considered as the most robust one to represent hydrological characteristics of the global lake systems despite it is static. Nevertheless, the changes of lake surface area due to climate change may result in non- negligible effects on lacustrine N O emissions in the real world. We further addressed this research 2 gap in the Discussion section. The specific statement is found in L410-415.\nL410-415:“Although we included additional N O emissions from newly constructed reservoirs over 2 time, we did not consider the impact of lake area changes on N O emissions due to the limited 2 availability of dynamic lake surface data. Considering that a recent study demonstrated the increasing trend of global lake area in recent decades (60), it is likely that our study gives a conservative estimate for N O-LR.”\n60. Pi, X., et al. Mapping global lake dynamics reveals the emerging roles of small lakes. Nature Communications 13, 5777 (2022).\n6. Comments L 265: The two cited references are for a single system. It is unreasonable to make a generalization on the response of N2O emissions from lentic systems to warming based on two sites.\nResponse: We have revised the statement in the main text as:\nL305-309:“Additionally, several local observational studies indicate that temperature increases are likely to stimulate the nitrifying and denitrifying microbial activity in water systems and increased 4 N O emissions (39, 40). It is worth noting that the increase in N O-LR caused by changes in terrestrial 2 2 N loads may outweigh the effect of temperature on the control of microbial metabolism (39, 41)”\nReference:\n39. Beaulieu, J. J., Shuster, W. D. & Rebholz, J. A. Nitrous oxide emissions from a large, impounded river: The Ohio River. Environ. Sci. Technol. 44, 7527-7533 (2010).\n40. Xiao, Q., et al. Coregulation of nitrous oxide emissions by nitrogen and temperature in China's third largest freshwater lake (Lake Taihu). Limnology and Oceanography 64, 1070-1086 (2018).\n41. Royer, T. V., David, M. B. & Gentry, L. E. Timing of riverine export of nitrate and phosphorus from agricultural watersheds in Illinois: Implications for reducing nutrient loading to the Mississippi River. Environ. Sci. Technol. 40, 4126-4131 (2006).\n7. Comments L 275-296: Borges et al. (2022) suggested that N2O in lakes could change as function of depth that modulates the coupling between surface waters and sediments. Since there is a general relation between surface area and depth (larger systems are in general deeper) (Wetzel 2001) this could also play a role in explaining differences between small and large systems.\nResponse: Thank you for the valuable suggestion. The study by Borges et al. (2022) described the high denitrification rates in sediments of shallow African lakes, which supports the higher N O 2 emission rates from the small lakes simulated in our study. We have added the standpoint in revised main text. The specific modifications are found in L337-341.\nL337-341:“The shallow African lakes, with considerable organic matter deposition on the sediment, sustain high benthic denitrification rates, as suggested by Borges, et al. (17). Since there is a general relation between lake surface area and depth (52), that standpoint also supports the higher N O 2 emission rates from the small lakes in our study.”\nReference:\n17. Borges, A. V., et al. Greenhouse gas emissions from African lakes are no longer a blind spot. Sci. Adv. 8, eabi8716 (2022).\n52. Wetzel, R. G. Limnology: lake and river ecosystems (gulf professional publishing) (2001).\n8. Comments L 316-317: There have been several past papers reporting that the IPCC EF are too high. It would be useful to include these in the discussion here.\nResponse: We have revised the statement in the main text as:\nL379-382:“…we find that our results yield lower estimates than those reported in most of previous studies (7, 11, 14, 28) and align with the lower boundary of the range estimated by Maavara, et al. (8) (SI Appendix, Table S5).”\nReference:\n7. Beaulieu, J. J., et al. Nitrous oxide emission from denitrification in stream and river networks. Proc. Natl. Acad. Sci. U. S. A. 108, 214-219 (2011).\n8. Maavara, T., et al. Nitrous oxide emissions from inland waters: Are IPCC estimates too high? Glob. Change Biol. 25, 473-488 (2019).\n11. Zheng, Y., et al. Global methane and nitrous oxide emissions from inland waters and estuaries. Glob. Change Biol. 28, 4713–4725 (2022).\n5\n14. IPCC 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories Volume 4 Agriculture, Forestry and Other Land Use. (2019).\n28. Tian, L., Cai, Y. & Akiyama, H. A review of indirect N2O emission factors from agricultural nitrogen leaching and runoff to update of the default IPCC values. Environ. Pollut. 245, 300-306 (2019).\n9. Comments L 340: Unfortunatly the authors did not provide a Table with fluxes from observational studies. I have the impression the data from African lakes from the recent paper of Borges et al. (2022) are missing from this comparison.\nResponse: We apologize for the oversight in not including detailed information regarding the observations used for model validation. We have added the three tables (SI Appendix, Table S7, Table S8, and Table S9) regarding the detailed information including location, sampled year, collected method, land use types, N O fluxes, aquatic nitrate concentration, N loads, and data sources of 2 observation in Supplementary information. We also appreciate your valuable suggestion. We have incorporated the observed data from Borges et al., 2022 for validation (SI Appendix, Fig. S9), and the relevant detailed information can be found in Table S7 and Table S8.\n10. Comments L 342: the r2 alone is not sufficient for validation. You can have a very good correlation (high r2) and a poor validation due to a very large off-set (Y-intercept > or < 0) or over-/under-estimate (slope > or < 1) or both. So please make much more rigorous statistical evaluation of the validation.\nResponse Thank you for the valuable suggestion. We incorporated Nash-Sutcliffe efficiency coefficient (referred to as NSE hereafter) (Nash and Sutcliffe, 1970) as an additional metric, to enhance the reliability assessment of the simulations. NSE is considered to be a robust and objective quality measure and is widely used for assessing the goodness of fit of a variety of models, especially for hydrologic models (Clarke, 2008; McCuen et al., 2006; Lin et al., 2017). In the updated validation (SI Appendix, Fig. S9), the simulated N O emissions and aquatic nitrate concentrations for lakes, 2 reservoirs, and rivers were comparable to observed values with R2 values higher than 0.6 and NSEs higher than 0.5 in most cases. Additionally, we also plotted a 1:1 line on the validated figures to help identifying over-or underestimations.\nReference:\nClarke R T. A critique of present procedures used to compare performance of rainfall-runoff models. Journal of hydrology, 2008, 352(3-4): 379-387.\nLin F, Chen X, Yao H. Evaluating the use of Nash-Sutcliffe efficiency coefficient in goodness-of-fit measures for daily runoff simulation with SWAT. Journal of Hydrologic Engineering, 2017, 22(11): 05017023.\nMcCuen R H, Knight Z, Cutter A G. Evaluation of the Nash–Sutcliffe efficiency index[J]. Journal of hydrologic engineering, 2006, 11(6): 597-602.\nNash J E, Sutcliffe J V. River flow forecasting through conceptual models part I—A discussion of principles. Journal of hydrology, 1970, 10(3): 282-290.\n11. Comments L 346: There’s some room for debate. Borges et al. (2022) shows on the contrary that smaller and shallower African lakes have lower N2O concentration than larger and deeper ones.\nResponse: Thank you for the suggestion. The study by Borges et al. (2022) provides us with very useful information. We added the discussion for the different findings between our study and that by Borges et al. (2022). The specific modifications are found in L337-345.\nL337-345:“The shallow African lakes, with considerable organic matter deposition on the sediment, sustain high benthic denitrification rates, as suggested by Borges, et al. (17). Since there is a general relation between lake surface area and depth (52), that standpoint also supports the higher N O 2 emission rates from the small lakes in our study. However, if the inorganic nitrogen concentration 7 cannot support high denitrification rates on sediment, the N O produced in the water column will be 2 absorbed by sediment to fuel benthic denitrification (17). This explains the contrasting findings in Borges, et al. (17) which showed the N O undersaturation in the shallow African lakes.” 2\nReference:\n17. Borges, A. V., et al. Greenhouse gas emissions from African lakes are no longer a blind spot. Sci. Adv. 8, eabi8716 (2022).\n52. Wetzel, R. G. Limnology: lake and river ecosystems (gulf professional publishing) (2001).\n12. Comments L 348: do you mean seasonal or long-term changes of surface area?\nPlease note that the HydroLAKES data-base in some cases over-estimates the lake surface area because some of data are quite old (HydroLAKES builds on the GLWD) and some lakes have shrunken with global warming. This is the case for arid and semi-arid climates, for instance Lake Chad, as discussed by Borges et al. (2022).\nResponse: The input data of lake surface area in our study is static. We concur with your point that global changes have indeed exerted a discernible influence on the hydrological characteristics of lakes. Consequently, the employment of data confined to a single period may introduce a degree of error into our estimations—a factor we have comprehensively addressed as a source of research uncertainty within the Discussion section.\nA recent study conducted the estimate of the changes in global natural lake areas over the past few decades (Pi et al., 2022). The findings revealed that the area of natural lakes increased both within and outside the permafrost regions during the 1980s-2000s; however, during the 2000s-2010s, the area of natural lakes within the permafrost regions increased, while the area of natural lakes outside the permafrost regions decreased. Overall, the study demonstrated a net increase in the area of natural lakes across the six continents over the entire period. Incorporating this expansion of lake surface area could potentially result in a more substantial magnitude of actual N O emissions from lakes, 2 indicating that our estimation of the increase in lake N O emissions may be underestimated. 2\nReference:\nPi X, Luo Q, Feng L, et al. Mapping global lake dynamics reveals the emerging roles of small lakes[J]. nature communications, 2022, 13(1): 5777.\nWe added the following discussion to the revised main text:\nL413-415:“Considering that a recent study demonstrated the increasing trend of global lake area in recent decades (60), it is likely that our study gives a conservative estimate for N O-LR.” 2\nReference:\n60. Pi, X., et al. Mapping global lake dynamics reveals the emerging roles of small lakes. Nature Communications 13, 5777 (2022).\n13. Comments L 349: REFERENCES\nBorges AV, L Deirmendjian, S Bouillon, W Okello, T Lambert, FAE Roland, VF Razanamahandry, NRG Voarintsoa, F Darchambeau, IA Kimirei, J-P Descy, GH Allen, C Morana (2022) Greenhouse gas emissions from African lakes are no longer a blind spot, Science Advances, 8, eabi8716, 1-17"}]

Decision letter