Hydrologic Connectivity Regulates Riverine N2O Sources and Dynamics.

Indirect nitrous oxide (N2O) emissions from streams and rivers are a poorly constrained term in the global N2O budget. Current models of riverine N2O emissions place a strong focus on denitrification in groundwater and riverine environments as a dominant source of riverine N2O, but do not explicitly consider direct N2O input from terrestrial ecosystems. Here, we combine N2O isotope measurements and spatial stream network modeling to show that terrestrial-aquatic interactions, driven by changing hydrologic connectivity, control the sources and dynamics of riverine N2O in a mesoscale river network within the U.S. Corn Belt. We find that N2O produced from nitrification constituted a substantial fraction (i.e., >30%) of riverine N2O across the entire river network. The delivery of soil-produced N2O to streams was identified as a key mechanism for the high nitrification contribution and potentially accounted for more than 40% of the total riverine emission. This revealed large terrestrial N2O input implies an important climate-N2O feedback mechanism that may enhance riverine N2O emissions under a wetter and warmer climate. Inadequate representation of hydrologic connectivity in observations and modeling of riverine N2O emissions may result in significant underestimations.

[Scale Effects of Landscape Pattern on Impacts of River Water Quality： A Meta-analysis].

The landscape pattern determines water pollution source and sink processes and plays an important role in regulating river water quality. Due to scale effects, studies on the relationship between landscape pattern and river water quality showed variance at different scales. However, there is still a lack of integrated study on the scale effect of landscape pattern and river water quality dynamics. This study collected 4 041 data from results of previous publications to address the characteristics of landscape pattern and river water quality dynamics at different scales and to identify the key temporal and spatial scales as well as landscape pattern indices for regulating river water quality. The results indicated that, compared to precipitation events, base flow periods, and interannual scales, the high-flow period was the key temporal scale for linking landscape pattern on river water quality. Compared to the watershed scale, the landscape pattern of buffer zones had a greater impact on river water quality. The high-flow period-buffer zone scale was the key spatiotemporal coupling scale for linking landscape pattern and river water quality. Compared to croplands, water bodies, grasslands, and the overall landscape of the watershed, the landscape pattern of forests and urban areas had a greater impact on river water quality. Fragmentation degree was the most important landscape pattern factor regulating river water quality. In river water quality management, it is important to focus on the landscape configuration of buffer zones, increase forest area, reduce patch density of forests and water bodies, and decrease the aggregation degree of urban areas.

[Nitrate Pollution Characteristics and Its Quantitative Source Identification of Major River Systems in China].

Accurate source identification/apportionment is essential for optimizing water NO3--N pollution control strategies. This study conducted a meta-analysis based on data from 167 rivers across China from 2000 to 2022 to analyze the spatial and temporal variation patterns of nitrate pollution in seven major river systems and to quantitatively identify the source composition of riverine nitrate. The average ρ（NO3--N） in the seven major river systems was （4.54±3.99） mg·L-1， with 9.6% of river ρ（NO3--N） exceeding 10 mg·L-1. The riverine ρ（NO3--N） in eastern China were higher than that in western China， and the highest concentration was observed in the Haihe River system. Additionally， tributaries experienced more serious NO3--N pollution than that in the main stream. The ρ（NO3--N） in most river systems in the dry season was higher than that in the wet season， except in the Yellow River system. There was significant nitrification in the Pearl River system， the middle and lower reaches of the Yellow River system， the middle reaches of the Liaohe River system， the Songhua River system， and the Haihe River system， whereas there was significant denitrification in the Yangtze River system， the Huaihe River system， and the lower reaches of the Pearl River system. Based on the dual stable isotopes-based MixSIAR model， the major NO3--N source was sewage/manure （ > 50%） in the Yangtze River system， Haihe River system， Liaohe River system， and Southeast River system. Soil nitrogen was the main NO3--N source in the Songhua River system （56.4%）， and the contribution of fertilizer nitrogen， soil nitrogen， and sewage/manure to NO3--N pollution in the Pearl River system， Huai River system， and Yellow River system was 20%-40%. The contribution rate of sewage/manure to NO3--N in the tributaries was higher than that in the main stream， whereas the contribution rate of soil nitrogen to NO3--N in the main stream was higher than that in the tributaries. The contribution rate of soil nitrogen， fertilizer nitrogen， and atmospheric deposition nitrogen to nitrate nitrogen in the wet season was higher than that in the dry season， whereas the contribution rate of sewage/manure to NO3--N pollution in the dry season was higher than that in the wet season. Therefore， point source pollution such as domestic and production sewage discharge should be controlled in the Haihe River system， the Yangtze River system， the Liaohe River system， the tributaries and the downstream main stream areas of Yellow River system， and the downstream area of the Pearl River system， whereas non-point source pollution caused by the loss of fertilizer and soil nitrogen should be controlled in the Huaihe River system， the Songhua River system， the middle reaches of the main stream area of the Yellow River system， and the middle and upper reaches of the Pearl River system. The results can provide a scientific basis for the effective control of nitrate pollution in the river systems in China.

Evaluation of three prevalent global riverine nutrient transport models.

Global riverine nitrogen (N) and phosphorus (P) transport models offer important insights into basin nutrient cycling. However, appropriate model selection for a given research objective remains ambiguous. This study conducted a meta-analysis to evaluate the performance and applicability of three prevalent global riverine nutrient transport models: Global NEWS, IMAGE-GNM, and WorldQual. According to performance criteria (satisfactory: R2 > 0.50 and NSE > 0.50), the Global NEWS model performs satisfactorily in simulating dissolved organic nitrogen (DON; n = 101, R2 = 0.58, NSE = 0.57) and dissolved organic phosphorus loads (DOP; n = 80, R2 = 0.59, NSE = 0.59). The model falls short in simulating dissolved inorganic nitrogen (DIN; n = 644, R2 = 0.56, NSE = - 0.80) and dissolved inorganic phosphorus loads (DIP; n = 450, R2 = 0.33, NSE = - 0.12). The IMAGE-GNM model shows satisfactory accuracies in simulating riverine total nitrogen (TN; n = 831, R2 = 0.56, NSE = 0.53) and total phosphorus (TP; n = 902, R2 = 0.59, NSE = 0.48) concentrations, particularly in European basins. The WorldQual model presented unsatisfactory performance in simulating riverine TN (n = 11, R2 = 0.76, NSE = 0.34) and TP (n = 13, R2 = 0.71, NSE = - 0.25) concentrations. Using a two-segment linear model, we recommend the Global NEWS model for basins larger than 2.2 × 104 km2 for DIN and 3.2 × 104 km2 for DIP. The IMAGE-GNM model is best suited for basins with long-term datasets and high latitudes (TN > 21 years and > 53.8 °N; TP > 22 years and > 54.5 °N). For model improvements, both the Global NEWS and WorldQual models could benefit from enhanced in-stream nutrient retention/release modules. The Global NEWS model could be further improved with a better chemical weathering module. For the IMAGE-GNM model, refining the soil erosion module is warranted to enhance model performance. Addressing legacy nutrient effects is crucial for all three models. This study provides valuable guidance for selecting and improving nutrient transport models based on specific research needs.

Quantifying groundwater phosphorus flux to rivers in a typical agricultural watershed in eastern China.

Increasing evidence indicates that groundwater can contain high dissolved phosphorus (P) concentrations, thereby contributing as a potential pollution source for surface waters. However, limited quantitative knowledge is available concerning groundwater P fluxes to rivers. Based on monthly hydrochemical monitoring data for rivers and groundwater in 2017-2020, this study combined baseflow separation methods and a load apportionment model (LAM) to quantify contributions from point sources, surface runoff, and groundwater/subsurface runoff to riverine P pollution in a typical agricultural watershed of eastern China. In the studied Shuanggang River, most total P (TP) and dissolved P (DP) concentrations exceeded targeted water quality standards (i.e., TP ≤ 0.2 mg P L-1, DP ≤ 0.05 mg P L-1), with DP (76 ± 20%) being the major riverine P form. Observed DP concentrations in groundwater were generally higher than those of river waters. There was a strong correlation between river and groundwater P concentrations, implying that groundwater might be a considerable P pollution source to rivers. The nonlinear reservoir algorithm estimated that baseflow/groundwater contributed 66-68% of monthly riverine water discharge on average, which was consistent with results estimated by an isotope-based sine-wave fitting method. The LAM incorporating point sources, surface runoff, and groundwater effectively predicted daily riverine TP [calibration: coefficient of determination (R2) = 0.76-0.82, Nash-Sutcliffe Efficiency (NSE) = 0.61-0.77; validation: R2 = 0.88-0.98, NSE = 0.54-0.64] and DP loads (calibration: R2 = 0.73-0.84, NSE = 0.67-0.72; validation: R2 = 0.88-0.97, NSE = 0.56-0.83). The LAM estimated point source, surface runoff, and groundwater contributions to riverine loads were 15-18%, 14-35%, and 46-70% for TP loads and 7-9%, 10-32%, and 59-82% for DP loads, respectively. Groundwater was the dominant riverine P source due to long-term accumulation of P from excess fertilizer and farmyard manure applications. The developed methodology provides an alternative method for quantifying P pollution loads from point sources, surface runoff, and groundwater to rivers. This study highlights the importance of controlling groundwater P pollution from agricultural lands to address riverine water quality objectives and further implies that decreasing fertilizer P application rates and utilizing legacy soil P for crop uptake are required to reduce groundwater P loads to rivers.

Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay, China.

Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes ([Formula: see text] and [Formula: see text]) and water quality parameters, this study combined the dual stable isotope-based MixSIAR model and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture, and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. The dual stable isotope-based MixSIAR model identified soil N, aquaculture tailwater, domestic wastewater, and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine [Formula: see text] in the Cao'e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and the dual stable isotope-based MixSIAR model showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. This study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.

[Estimation of Nitrous Oxide Emission from River System Based on Water Discharge and Dissolved Nitrous Oxide Concentration].

Due to increasing active nitrogen pollution loads, river systems have become an important source of nitrous oxide (N2O) in many areas. Due to the lack of monitoring data in many studies as well as the difficulty in estimating intermediate parameters and expressing temporal-spatial variability in current methods, a high level of uncertainty remains in the estimates of riverine N2O emission quantity. Based on the monthly monitoring efforts conducted for 10 sampling sites across the Yonganxi River system in Zhejiang Province from June 2016 to July 2019, the temporal and spatial dynamics of riverine N2O dissolved concentrations ρ(N2O), N2O fluxes, and their influencing factors were addressed. A multiple regression model was then developed for predicating riverine N2O emission flux to estimate annual N2O emission quantity for the entire river system. The results indicated that observed riverine ρ(N2O) (0.03-2.14 µg·L-1) and the N2O fluxes[1.32-82.79 µg·(m2·h)-1] varied by 1-2 orders of magnitude of temporal-spatial variability. The temporal and spatial variability of ρ(N2O) were mainly influenced by the concentrations of nitrate, ammonia, and dissolved organic carbon, whereas the N2O emission fluxes were mainly affected by river water discharges and ρ(N2O). A multiple regression model that incorporates variables of river water discharge and ρ(N2O) could explain 90% of the variability in riverine N2O emission fluxes and has high accuracy. The model estimated N2O emission quantity from the entire Yonganxi River system of 3.67 t·a-1, with 29% from the main stream and 71% from the tributaries. The IPCC default emission factor method might greatly overestimate and underestimate N2O emission quantities for rivers impacted by low and high pressures of human activities, respectively. This study advances our quantitative understanding of N2O emission for the entire river system and provides a reference method for estimating riverine N2O emission with more accuracy.

Modeling Riverine N2O Sources, Fates, and Emission Factors in a Typical River Network of Eastern China.

Estimates of riverine N2O emission contain great uncertainty because of the lack of quantitative knowledge concerning riverine N2O sources and fates. Using a 3.5-year record of monthly N2O measurements from the Yongan River network of eastern China, we developed a mass-balance model to address the riverine N2O source and sink processes. We achieved reasonable model efficacies (R2 = 0.44-0.84, Nash-Sutcliffe coefficients = 0.40-0.80) across three tributaries and the entire river system. Estimated riverine N2O loads originated from groundwater (38-88%), surface runoff (3-26%), and in-stream production (4-48%). Estimated in-stream losses via atmospheric release + complete denitrification accounted for 76, 95, 25, and 89% of riverine N2O fate for the agricultural, residential, forest, and entire river system, respectively. Considering limited complete denitrification, the model estimated an upper-bound riverine N2O emission rate of 2.65 ton N2O-N km-2 year-1 for the entire river system. Riverine N2O emission estimates were of comparable magnitude to those estimated with a power-law scaling model. Riverine N2O emissions using the IPCC default emission factor (0.26%) overestimated emissions by 3-15 times, whereas the dissolved N2O concentration-based emission factor overestimated or underestimated emissions. This study highlights the importance of combining comprehensive information on N2O sources and fates to achieve accurate riverine N2O emission estimates.

National estimates of environmental thresholds for upland soil phosphorus in China based on a meta-analysis.

The environmental threshold for upland soil phosphorus (P) content (ETSP, i.e., inflection point of soil P content leading to enhanced P loss) provides an important metric for guiding agricultural nonpoint source P pollution control. This study achieved the first meta-analysis to determine ETSP values for upland soils in China. The estimated national-level ETSP based on 472 field experimental observations of Olsen-P content and P loss rate was 30.1 ± 4.0 mg P kg-1, which was lower than the average ETSP value (52.1 ± 5.0 mg P kg-1) but higher than the average agronomic threshold values (16.0 ± 6.4 mg P kg-1) previously reported. Lower upland ETSP values occurred in acidic soils and soils having higher organic matter content (SOM), precipitation and slope (ETSP: 30.5 for pH < 7.0 versus 46.1 for pH ≥ 7.0; >56.4 for SOM < 2%, 49.9 for SOM = 2%-3%, and <3 for SOM > 3%; 33 for precipitation < 1000 mm yr-1, 27.5 for precipitation = 1000-1200 mm yr-1 and <5 for precipitation > 1200 mm yr-1; and 39.8 for slopes < 5° versus <9 for slopes ≥ 5°). A multiple regression model that incorporates SOM, pH, precipitation and slope was developed to predict upland ETSP values (R2 = 0.73, p < 0.01). The model estimated national upland ETSP values ranging from ~0 to 100 mg P kg-1 with an areal-weighted average of 60.6 mg P kg-1 and 15% of national upland soils having ETSP values <30 mg P kg-1. Upland soil P contents in Guangdong, Fujian and Zhejiang provinces largely exceeded their corresponding ETSP values by 1-22 mg P kg-1, indicating high P loss risks. Controlling upland P loss requires integrated management of soil P content, SOM, pH and erosion control. This study provides the first national estimate of upland soil ETSP, providing critical quantitative information for designing management practices to attenuate agricultural nonpoint source P pollution.

Estimation of nitrogen runoff loss from croplands in the Yangtze River Basin: A meta-analysis.

Nitrogen (N) runoff loss from croplands due to excessive anthropogenic N additions is a principal cause of non-point source water pollution worldwide. Quantitative knowledge of regional-scale N runoff loss from croplands is essential for developing sustainable agricultural N management and efficient water N pollution control strategies. This meta-analysis quantifies N runoff loss rates and identifies the primary factors regulating N runoff loss from uplands (n = 570) and paddy (n = 434) fields in the Yangtze River Basin (YRB). Results indicated that total N (TN) runoff loss rates from uplands and paddy fields consistently increased from upstream to downstream regions. Runoff depth, soil N content and fertilizer addition rate (chemical fertilizer + manure) were the major factors regulating variability of TN runoff loss from uplands, while runoff depth and fertilizer addition rate were the main controls for paddy fields. Multiple regression models incorporating these influencing factors effectively predicted TN runoff loss rates from uplands (calibration: R2 = 0.60, n = 242; validation: R2 = 0.55, n = 104) and paddy fields (calibration: R2 = 0.70, n = 189; validation: R2 = 0.85, n = 82). Models estimated total cropland TN runoff loss load in YRB of 0.54 (95% Cl: 0.23-1.33) Tg, with 0.30 (95% Cl: 0.15-0.56) Tg from uplands and 0.24 (95% Cl: 0.08-0.77) Tg from paddy fields in 2017. Guangxi, Jiangxi, Fujian, Hunan and Henan provinces within the YRB were identified as cropland TN runoff loss hotspots. Models predicted that TN runoff loss loads from croplands in YRB would decrease by 0.8-13.7% for five scenarios, with higher TN load reductions occurring from scenarios with decreased runoff amounts. Reducing upland TN runoff loss should focus primarily on soil N utilization and runoff management, while reducing N fertilizer addition and runoff provided the most sensitive strategies for paddy fields. Integrated management of water, soil and fertilizer is required to effectively reduce cropland N runoff loss.

Long-term (1980-2015) changes in net anthropogenic phosphorus inputs and riverine phosphorus export in the Yangtze River basin.

Quantitative information on long-term net anthropogenic phosphorus inputs (NAPI) and its relationship with riverine phosphorus (P) export are critical for developing sustainable and efficient watershed P management strategies. This is the first study to address long-term (1980-2015) NAPI and riverine P flux dynamics for the Yangtze River basin (YRB), the largest watershed in China. Over the 36-year study period, estimated NAPI to the YRB progressively increased by ∼1.4 times, with NAPIA (chemical fertilizer input + atmospheric deposition + seed input) and NAPIB (net food/feed imports + non-food input) contributing 65% and 35%, respectively. Higher population, livestock density and agricultural land area were the main drivers of increasing NAPI. Riverine total phosphorus (TP), particulate phosphorus (PP) and suspended sediment (SS) export at Datong hydrological station (downstream station) decreased by 52%, 75% and 75% during 1980-2015, respectively. In contrast, dissolved phosphorus (DP) showed an increase in both concentration (∼7-fold) and its contribution to TP flux (∼16-fold). Different trends in riverine P forms were mainly due to increasing dam/reservoir construction and changes in vegetation/land use and NAPI components. Multiple regression models incorporating NAPIA, NAPIB, dam/reservoir storage capacity and water discharge explained 84% and 92% of the temporal variability in riverine DP and PP fluxes, respectively. Riverine TP flux estimated as the sum of DP and PP fluxes showed high agreement with measured values (R2 = 0.87, NSE = 0.84), indicating strong efficacy for the developed models. The model forecasted an increase of 50% and 7% and a decrease of 15% and 22% in riverine DP flux from 2015 to 2045 under developing, dam building, NAPIA and NAPIB reduction scenarios, respectively. This study highlights the importance of including enhanced P transformation from particulate to bioavailable forms due to river regulation and changes in land-use, input sources and legacy P pools in development of P pollution control strategies.

Coupling stable isotopes and water chemistry to assess the role of hydrological and biogeochemical processes on riverine nitrogen sources.

Accurate source identification is critical for optimizing water pollution control strategies. Although the dual stable isotope (15N-NO3-/18O-NO3-) approach has been widely applied for differentiating riverine nitrogen (N) sources, the relatively short-term (<1 yr) 15N-NO3-/18O-NO3- records typically used in previous studies often hinders rigorous assessment due to high temporal variability associated with watershed N dynamics. Estimated contributions of legacy N sources in soils and groundwater to riverine N export by modeling approaches in many previous studies also lack validation from complementary information, such as multiple stable isotopes. This study integrated three years of multiple stable isotope (15N-NO3-/18O-NO3- and 2H-H2O/18O-H2O) and hydrochemistry measurements for river water, groundwater and rainfall to elucidate N dynamics and sources in the Yongan watershed (2474 km2) of eastern China. Nonpoint source N pollution dominated and displayed considerable seasonal and spatial variability in N forms and concentrations. Information from δ15N-NO3- and δ18O-NO3- indicated that riverine N dynamics were regulated by contributing sources, nitrification and denitrification, as well as hydrological processes. For the three examined catchments and entire watershed, slow subsurface and groundwater flows accounted for >75% of river discharge and were likely the major hydrological pathways for N delivery to the river. Riverine NO3- sources varied with dominant land use (p < 0.001), with the highest contributions of groundwater (60%), wastewater (35%), and soil (50%) occurring in agricultural, residential and forest catchments, respectively. For the entire watershed, groundwater (∼50%) and soil N (>30%) were the dominant riverine NO3- sources, implying considerable potential for N pollution legacy effects. Results were consistent with observed nitrous oxide dynamics and N sources identified in previous modeling studies. As the first attempt to apply multiple isotope tracers for exploring and quantifying N transformation and transport pathways, this study provides an integrated approach for verifying and understanding the N pollution legacy effects observed in many watersheds worldwide. This study highlights that river N pollution control in many watersheds requires particular attention to groundwater restoration and soil N management in addition to N input control strategies.

Modeling nitrous oxide emission from rivers: a global assessment.

Estimates of global riverine nitrous oxide (N2 O) emissions contain great uncertainty. We conducted a meta-analysis incorporating 169 observations from published literature to estimate global riverine N2 O emission rates and emission factors. Riverine N2 O flux was significantly correlated with NH4 , NO3 and DIN (NH4  + NO3 ) concentrations, loads and yields. The emission factors EF(a) (i.e., the ratio of N2 O emission rate and DIN load) and EF(b) (i.e., the ratio of N2 O and DIN concentrations) values were comparable and showed negative correlations with nitrogen concentration, load and yield and water discharge, but positive correlations with the dissolved organic carbon : DIN ratio. After individually evaluating 82 potential regression models based on EF(a) or EF(b) for global, temperate zone and subtropical zone datasets, a power function of DIN yield multiplied by watershed area was determined to provide the best fit between modeled and observed riverine N2 O emission rates (EF(a): R2  = 0.92 for both global and climatic zone models, n = 70; EF(b): R2  = 0.91 for global model and R2  = 0.90 for climatic zone models, n = 70). Using recent estimates of DIN loads for 6400 rivers, models estimated global riverine N2 O emission rates of 29.6-35.3 (mean = 32.2) Gg N2 O-N yr-1 and emission factors of 0.16-0.19% (mean = 0.17%). Global riverine N2 O emission rates are forecasted to increase by 35%, 25%, 18% and 3% in 2050 compared to the 2000s under the Millennium Ecosystem Assessment's Global Orchestration, Order from Strength, Technogarden, and Adapting Mosaic scenarios, respectively. Previous studies may overestimate global riverine N2 O emission rates (300-2100 Gg N2 O-N yr-1 ) because they ignore declining emission factor values with increasing nitrogen levels and channel size, as well as neglect differences in emission factors corresponding to different nitrogen forms. Riverine N2 O emission estimates will be further enhanced through refining emission factor estimates, extending measurements longitudinally along entire river networks and improving estimates of global riverine nitrogen loads.

A lagged variable model for characterizing temporally dynamic export of legacy anthropogenic nitrogen from watersheds to rivers.

Legacy nitrogen (N) sources originating from anthropogenic N inputs (NANI) may be a major cause of increasing riverine N exports in many regions, despite a significant decline in NANI. However, little quantitative knowledge exists concerning the lag effect of NANI on riverine N export. As a result, the N leaching lag effect is not well represented in most current watershed models. This study developed a lagged variable model (LVM) to address temporally dynamic export of watershed NANI to rivers. Employing a Koyck transformation approach used in economic analyses, the LVM expresses the indefinite number of lag terms from previous years' NANI with a lag term that incorporates the previous year's riverine N flux, enabling us to inversely calibrate model parameters from measurable variables using Bayesian statistics. Applying the LVM to the upper Jiaojiang watershed in eastern China for 1980-2010 indicated that ~97% of riverine export of annual NANI occurred in the current year and succeeding 10 years (~11 years lag time) and ~72% of annual riverine N flux was derived from previous years' NANI. Existing NANI over the 1993-2010 period would have required a 22% reduction to attain the target TN level (1.0 mg N L(-1)), guiding watershed N source controls considering the lag effect. The LVM was developed with parsimony of model structure and parameters (only four parameters in this study); thus, it is easy to develop and apply in other watersheds. The LVM provides a simple and effective tool for quantifying the lag effect of anthropogenic N input on riverine export in support of efficient development and evaluation of watershed N control strategies.

Influence of lag effect, soil release, and climate change on watershed anthropogenic nitrogen inputs and riverine export dynamics.

This study demonstrates the importance of the nitrogen-leaching lag effect, soil nitrogen release, and climate change on anthropogenic N inputs (NANI) and riverine total nitrogen (TN) export dynamics using a 30-yr record for the Yongan River watershed in eastern China. Cross-correlation analysis indicated a 7-yr, 5-yr, and 4-yr lag time in riverine TN export in response to changes in NANI, temperature, and drained agricultural land area, respectively. Enhanced by warmer temperature and improved agricultural drainage, the upper 20 cm of agricultural soils released 270 kg N ha(-1) between 1980 and 2009. Climate change also increased the fractional export of NANI to river. An empirical model (R(2) = 0.96) for annual riverine TN flux incorporating these influencing factors estimated 35%, 41%, and 24% of riverine TN flux originated from the soil N pool, NANI, and background N sources, respectively. The model forecasted an increase of 45%, 25%, and 6% and a decrease of 13% in riverine TN flux from 2010 to 2030 under continued development, climate change, status-quo, and tackling scenarios, respectively. The lag effect, soil N release, and climate change delay riverine TN export reductions with respect to decreases in NANI and should be considered in developing and evaluating N management measures.