Stream Nitrate Concentrations Research Articles

Effects of changes in precipitation patterns and stream flow on stream DOC and [formula omitted] concentrations in a headwater catchment in a subtropical region of southern China

Study regionThe Guantian headwater watershed is located in the subtropical region of South China. Study focusThis study examined how changes in precipitation and consequent shifts in streamflow affect dissolved organic carbon (DOC) and nitrate (NO3−) concentrations in a headwater stream. New hydrological insights for the regionThe results indicate that stream DOC and NO3− concentrations decrease from spring to winter (0.16 mg/L/month for DOC and 0.18 mg/L/month for NO3−) and have a generally positive relationship with streamflow (p < 0.05). Changes in precipitation-concentration, precipitation-amount and consequent shifts in streamflow remarkably affect stream DOC and NO3− concentrations. When precipitation events are more heavily concentrated (when precipitation concentration index (PCI) changed from 12.81 to 18.33) from May to July, the stream DOC and NO3− concentrations tend to increase from May to July but decrease before and after this period. In addition, an increase in precipitation-amount tends to increase stream DOC and NO3− concentrations before the occurrence of peak flow and in the dry period from September to December, but decreases them in the wet period from June to September. When the precipitation-amount decreases, the trend of the change in concentration is exactly the opposite. These findings suggest that stream DOC and NO3− concentrations in the headwater stream are highly chemodynamic: they are transport-limited before the occurrence of high flow and in the dry period from September to December, while they are supply-limited in the June-September period.

Citizen‐participatory nationwide survey of mountain streamwater chemistry in Japan in 2022: Comparison of nitrate concentrations with the 2003 survey

AbstractMountain streamwater chemistry is an effective indicator of forest condition. In 2022, we conducted a nationwide investigation of mountain streamwater chemistry in Japan, leveraging citizen‐participatory sampling. This approach involved 629 individuals with regular exposure to mountain and natural environments. Although our primary aim was to sample at locations from a 2003 study, we also welcomed samples from new sites. In total, 1414 streamwater samples were collected one time from each forested watershed at the baseflow condition. Our study focused on stream nitrate (NO3−) concentration as a key indicator of anthropogenic nitrogen (N) loading impacts on forests. We compared NO3− concentrations in 2022 with those from 2003 at identical sampling points. After excluding 179 points with evident human‐created features upstream, the mean NO3− concentration in 2022 was 0.328 mg N L−1 (n = 1236). Comparing data from 1088 points sampled in both years, the mean value in 2022 (0.324 mg N L−1) was significantly lower than that in 2003 (0.359 mg N L−1, p &lt; 0.05). Notably, 88.5% of sampling points showed differences within ±0.25 mg N L−1. The spatial distribution pattern of mountain stream NO3− concentrations in 2022 did not consistently align with large cities, industrial areas, or N deposition sources. This unique approach marked the first nationwide participatory survey for collecting mountain streamwater in Japan. Our success in ensuring sample quality through accessible explanations, manuals, and videos demonstrates the potential of citizen science. However, the quantitative evaluation of scientific accuracy remains a forthcoming challenge.

Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins

Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites using the daily nitrate concentration and stream discharge information of a neighboring high-frequency nitrate monitoring site. A Long Short-Term Memory (LSTM) based deep learning (DL) modeling framework was developed to predict daily nitrate concentrations. The DL modeling framework performance was compared with two well-established statistical models, including LOADEST and WRTDS-Kalman, in three selected basins in Iowa, USA: Des Moines, Iowa, and Cedar River. The developed DL model performed well with NSE >0.70 and KGE >0.70 for 67% and 79% nitrate monitoring sites, respectively. DL and WRTDS-Kalman models performed better than the LOADEST in nitrate concentration and load estimation for all low-frequency sites. The average NSE performance of the DL model in daily nitrate estimation is 20% higher than that of the WRTDS-Kalman model at 18 out of 24 sites (75%). The WRTDS-Kalman model showed unrealistic fluctuations in the estimated daily nitrate time series when the model received limited observed nitrate data (less than 50) for simulation. The DL model indicated superior performance in winter months' nitrate prediction (60% of cases) compared to WRTDS-Kalman models (33% of cases). The DL model also better represented the exceedance days from the USEPA maximum contamination level (MCL). Both the DL and WRTDS-Kalman models demonstrated similar performance in annual stream nitrate load estimation, and estimated values are close to actual nitrate loads.

Nitrate losses from forest during snowmelt: An underestimated source in mid-high latitude watershed

The forest nitrate cycle is a crucial part of the watershed nitrate load but has received limited attention compared to that of agricultural and residential land. Here, we analyzed the status and sources of riverine nitrate fluxes and identified the characteristics and contribution of forest nitrate loss to the riverine system in a mid-high latitude forested watershed using monthly field sampling and a modified Soil and Water Assessment Tool (SWAT) with enhanced forest nutrient cycle representation. The results indicate that nitrate losses in the headwater stream and downstream exhibit different seasonal characteristics. The nitrate losses in the headwater stream show a bimodal pattern due to lower temperatures and snowmelt runoff. Redundancy analysis (RDA) revealed that, unlike nitrogen (N) fertilizer-induced nitrate loss in the rainy season, forest loss has a positive effect on headwater stream nitrate concentration during the snowmelt season. The modified SWAT was then utilized to simulate nitrate losses in forest lands. The forest nitrate export per unit area of the headwater stream (1.58 ± 1.78 kg/ha/yr) was observed to be higher than that of the downstream (0.67 ± 0.74 kg/ha/yr) due to high snowmelt and mineralization of active organic N. At watershed scale, forest lands contributed 8.18 ± 3.94 % of the total nitrate losses to the water system in the headwater watersheds during the snowmelt season, representing the highest level within the entire basin. A comparison with forest streams in similar low-temperature conditions worldwide revealed that increasing nitrate loss occurred after extreme cold weather or soil freezing events, with an average increment of 6.32 kg/ha/yr. Therefore, forest nitrate losses should be better characterized and included in future watershed N budgets in low-temperature regions, which might help to reduce the N budget uncertainty and improve watershed management.

Delayed recovery of surface water chemistry from acidification in subtropical forest region of China

The three largest acid rain regions of current earth are located in northern and western Europe, eastern North America, and East Asia. Sulfur and nitrate concentrations in headwater streams in Europe and North America decreased as atmospheric sulfur and nitrogen deposition decreased, albeit with a considerable delay. However, how water chemistry responds to the declining sulfur and nitrogen deposition in China is unclear. The regional survey of surface water chemistry during 2010 and 2018 within the Sichuan Basin in southwestern China showed that the recovery of the surface water chemistry was delayed for at least 5 years owing to the release of previously deposited sulfur and nitrogen stored in the soil. After sulfur deposition declined from its peak value, the subregions of purplish soil with low sulfate adsorption capacity still exhibited a net sulfur release in 2010, but this release was no longer evident by 2018. The subregions with the red and yellow soils, which have a high sulfate adsorption capacity, operated as sulfur sinks during 2010 and 2018, indicating a continuous immobilization process through sulfate reduction despite a decrease in sulfur deposition. Additionally, this sulfate reduction countered the release of sulfate caused by sulfur desorption. There was a substantial nitrogen sink within the Sichuan Basin. Nitrogen leaching decreased slowly with the declined nitrogen deposition, except in regions where nitrogen deposition exceeded the critical threshold. Compared to temperate forest regions in Europe, the Sichuan Basin and its surrounding areas have experienced higher decline rates in the leaching of sulfur and nitrogen, highlighting that the subtropical forest region undergoes a faster restoration of surface water chemistry.

The changing nitrogen landscape of United States streams: Declining deposition and increasing organic nitrogen.

Air quality regulations have led to decreased nitrogen (N) and sulfur deposition across the conterminous United States (CONUS) during the last several decades, particularly in the eastern parts. But it is unclear if declining deposition has altered stream N at large scales. We compared watershed N inputs with N chemistry from over 2,000 CONUS streams where deposition was the largest N input to the watershed. Weighted change analysis showed that deposition declined across most watersheds, especially in the Eastern CONUS. Nationally, declining N deposition was not associated with significant large-scale declines in stream nitrate concentration. Instead, significant increases in stream dissolved organic carbon (DOC) and total organic N (TON) were widespread across regions. Possible mechanisms behind these increases include declines in acidity and/or ionic strength drivers, changes in carbon availability, and/or climate variables. Our results also reveal a declining trend of DOC/TON ratio over the entire study period, primarily influenced by the trend in the Eastern region, suggesting the rate of increase in stream TON exceeded the rate of increase in DOC concentration during this period. Our results illustrate the complexity of nutrient cycling that links long-term atmospheric deposition to water quality. More research is needed to understand how increased dissolved organic N could affect aquatic ecosystems and downstream riverine nutrient export.

Douglas-fir plantations impact stream and groundwater chemistry in western Europe: Insights from three case studies in France and Luxembourg

In rural areas, nitrate concentrations in surface waters most often originate from the leaching of excess N fertilizer in agricultural lands, whereas forested catchments often have good water quality. However, Douglas-fir plantations may induce nitrogen cycle unbalances which may lead to an excess of nitrate production in the soil. We hypothesize that the excess of production of nitrate in the soil and nitrate leaching to streamwater is greater in catchments planted with Douglas fir. We used paired catchments in both France and Luxembourg with different land covers (Douglas-fir, Spruce, Deciduous, Grassland and clearcut) which were monitored over a 3–5 year period in order to assess the effect of Douglas-fir plantations on the chemical composition of surface water. Nitrate concentration in the soil and groundwater were also monitored. The results show that nitrate concentrations in streams draining Douglas-fir catchments were two to ten times higher than in streams draining other land covers, but were similar to the clearcut catchment. Nitrate concentrations under Douglas-fir in groundwater (up to 50 mg L−1) and in the soil were also higher than under all other land covers. Soil nitrate concentration was related to stream nitrate concentration. This suggests that soil processes, through excessive nitrate production under Douglas-fir, are driving the nitrate concentration in the stream water and our hypothesis of a transfer of a fairly large proportion of this excessive production from the soil to the stream is supported. This study also shows that nitrate concentrations in surface and ground waters in rural areas could also originate from Douglas fir forested catchments. The impact of Douglas-fir is nevertheless reduced downstream through a dilution effect: mixing tree species at the catchment scale could thus be a solution to mitigate the effect of Douglas-fir on nitrate concentration in surface waters.

Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data.

Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data from in-situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive-moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly.

Memory and Management: Competing Controls on Long‐Term Nitrate Trajectories in U.S. Rivers

AbstractExcess nitrogen from intensive agricultural production, atmospheric N deposition, and urban point sources elevates stream nitrate concentrations, leading to problems of eutrophication and ecosystem degradation in coastal waters. A major emphasis of current US‐scale analysis of water quality is to better our understanding of the relationship between changes in anthropogenic N inputs within watersheds and subsequent changes in riverine N loads. While most water quality modeling assumes a positive linear correlation between watershed N inputs and riverine N, many efforts to reduce riverine N through improved nutrient management practices result in little or no short‐term improvements in water quality. Here, we use nitrate concentration and load data from 478 US watersheds, along with developed N input trajectories for these watersheds, to quantify time‐varying relationships between N inputs and riverine N export. Our results show substantial variations in watershed N import‐export relationships over time, with quantifiable hysteresis effects. Our results show that more population‐dense urban watersheds in the northeastern U.S. more frequently show clockwise hysteresis relationships between N imports and riverine N export, with accelerated improvements in water quality being achieved through the implementation of point‐source controls. In contrast, counterclockwise hysteresis dynamics are more common in agricultural watersheds, where time lags occur between the implementation of nutrient management practices and water‐quality improvements. Finally, we find higher tile‐drainage densities to be associated with more linear relationships between N inputs and riverine N. The empirical analysis in this study is bolstered by modeled simulations to reproduce and further explain drivers behind the hysteretic relationships commonly observed in the monitored watersheds.

A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds

High-frequency stream nitrate concentration provides critical insights into nutrient dynamics and can help to improve the effectiveness of management decisions to maintain a sustainable ecosystem. However, nitrate monitoring is conventionally conducted through lab analysis using in situ water samples and is typically at coarse temporal resolution. In the last decade, many agencies started collecting high-frequency (5–60 min intervals) nitrate data using optical sensors. The hypothesis of the study is that the data-driven models can learn the trend and temporal variability in nitrate concentration from high-frequency sensor-based nitrate data in the region and generate continuous nitrate data for unavailable data periods and data-limited locations. A Long Short-Term Memory (LSTM) model-based framework was developed to estimate continuous daily stream nitrate for dozens of gauge locations in Iowa, USA. The promising results supported the hypothesis; the LSTM model demonstrated median test-period Nash-Sutcliffe efficiency (NSE) = 0.75 and RMSE = 1.53 mg/L for estimating continuous daily nitrate concentration in 42 sites, which are unprecedented performance levels. Twenty-one sites (50 % of all sites) and thirty-four sites (76 % of all sites) demonstrated NSE > 0.75 and 0.50, respectively. The average nitrate concentration of neighboring sites was identified as a crucial determinant of continuous daily nitrate concentration. Seasonal model performance evaluation showed that the model performed effectively in the summer and fall seasons. About 26 sites showed correlations >0.60 between estimated nitrate concentration and discharge. The concentration-discharge (c-Q) relationship analysis showed that the study watersheds had four dominant nitrate transport patterns from landscapes to streams with increasing discharge, including the flushing pattern being the most dominant one. Stream nitrate estimation impedes due to data inadequacy. The modeling framework can be used to generate temporally continuous nitrate at nitrate data-limited regions with a nearby sensor-based nitrate gauge. Watershed planners and policymakers could utilize the continuous nitrate data to gain more information on the regional nitrate status and design conservation practices accordingly.

Stable isotopic evidence for the excess leaching of unprocessed atmospheric nitrate from forested catchments under high nitrogen saturation

Abstract. Owing to the elevated loading of nitrogen through atmospheric deposition, some forested ecosystems become nitrogen saturated, from which elevated levels of nitrate are exported. The average concentration of stream nitrate eluted from upstream and downstream of the Kasuya Research forested catchments (FK1 and FK2 catchments) in Japan were more than 90 µM, implying that these forested catchments were under nitrogen saturation. To verify that these forested catchments were under the nitrogen saturation, we determined the export flux of unprocessed atmospheric nitrate relative to the entire deposition flux (Matm/Datm ratio) in these catchments; because the Matm/Datm ratio has recently been proposed as a reliable index to evaluate nitrogen saturation in forested catchments. Specifically, we determined the temporal variation in the concentrations and stable isotopic compositions, including Δ17O, of stream nitrate in the FK catchments for more than 2 years. In addition, for comparison, the same parameters were also monitored in the Shiiba Research forested catchment (MY catchment) in Japan during the same period, where the average stream nitrate concentration was low, less than 10 µM. While showing the average nitrate concentrations of 109.5, 90.9, and 7.3 µM in FK1, FK2, and MY, respectively, the catchments showed average Δ17O values of +2.6 ‰, +1.5 ‰, and +0.6 ‰ in FK1, FK2, and MY, respectively. Thus, the average concentration of unprocessed atmospheric nitrate ([NO3-atm]) was estimated to be 10.8, 5.1, and 0.2 µM in FK1, FK2, and MY, respectively, and the Matm/Datm ratio was estimated to be 14.1 %, 6.6 %, and 1.3 % in FK1, FK2, and MY, respectively. The estimated Matm/Datm ratio in FK1 (14.1 %) was the highest ever reported from temperate forested catchments monitored for more than 1 year. Thus, we concluded that nitrogen saturation was responsible for the enrichment of stream nitrate in the FK catchments, together with the elevated NO3-atm leaching from the catchments. While the stream nitrate concentration ([NO3-]) can be affected by the amount of precipitation, the Matm/Datm ratio is independent of the amount of precipitation; thus, the Matm/Datm ratio can be used as a robust index for evaluating nitrogen saturation in forested catchments.

Evaluating the Influence of Environmental Factors on Mountain Stream Nitrate Concentrations in Miyazaki Prefecture

Evaluating the Influence of Environmental Factors on Mountain Stream Nitrate Concentrations in Miyazaki Prefecture

Use of geostatistical models to evaluate landscape and stream network controls on post‐fire stream nitrate concentrations

AbstractForested watersheds provide many ecosystem services, such as the filtration of sediment, pollutants, and nutrients, which are increasingly threatened by wildfire. For example, stream nutrient concentrations often increase following wildfire and can remain elevated for decades, making downstream waters susceptible to eutrophication. We investigated the drivers of persistent elevated stream nutrients, specifically nitrate (NO3−), in nine watersheds that were burned 16 years prior by the Hayman fire, Colorado, USA. We evaluated the ability of multiple linear regression and spatial stream network modeling approaches to predict observed concentrations of the biologically active solute NO3− and the conservative solute sodium (Na+) which serves as a partial control. Specifically, we quantified the degree to which landscape and stream network characteristics predict stream solute concentrations. Stream Na+ exhibited strong spatial autocorrelation that was primarily controlled by topography and hydrology. In contrast, stream NO3− had higher spatial variability and was inversely correlated to vegetation cover, measured as mean normalized differenced moisture index (NDMI). Spatially heterogeneous wildfire behaviour left intact forest patches interspersed with high burn severity patches dominated by shrubs and grasses which contributes to the spatial variability in stream NO3− concentrations. Post‐fire vegetation also interacts with watershed structure to influence stream NO3− patterns. For example, severely burned convergent hillslopes in headwaters positions were associated with the highest stream NO3− concentrations due to the high proportional influence of hillslope water in these locations. Our findings suggest that targeted reforestation in severely burned convergent hillslopes in headwater positions may enhance the recovery of stream NO3− concentrations to pre‐fire levels.

Tracing the source of nitrate in a forested stream showing elevated concentrations during storm events

Abstract. To clarify the source of nitrate increased during storm events in a temperate forested stream, we monitored temporal variation in the concentrations and stable isotopic compositions including Δ17O of stream nitrate in a forested catchment (KJ catchment, Japan) during three storm events: I, II, and III (summer). The stream showed a significant increase in nitrate concentration, from 24.7 to 122.6 µM, from 28.7 to 134.1 µM, and from 46.6 to 114.5 µM, during the storm events I, II, and III, respectively. On the other hand, the isotopic compositions (δ15N, δ18O, and Δ17O) of stream nitrate showed a decrease in accordance with the increase in the stream nitrate concentration, from +2.5 ‰ to −0.1 ‰, from +3.0 ‰ to −0.5 ‰, and from +3.5 ‰ to −0.1 ‰, for δ15N; from +3.1 ‰ to −3.4 ‰, from +2.9 ‰ to −2.5 ‰, and from +2.1 ‰ to −2.3 ‰ for δ18O; and from +1.6 ‰ to +0.3 ‰, from +1.4 ‰ to +0.3 ‰, and from +1.2 ‰ to +0.5 ‰, for Δ17O during the storm events I, II, and III, respectively. Besides, we found strong linear relationships between the isotopic compositions of stream nitrate and the reciprocal of stream nitrate concentrations during each storm event, implying that the temporal variation in the stream nitrate can be explained by simple mixing between two distinctive endmembers of nitrate having different isotopic compositions. Furthermore, we found that both concentrations and the isotopic compositions of soil nitrate obtained in the riparian zone of the stream were plotted on the nitrate-enriched extension of the linear relationship. We concluded that the soil nitrate in the riparian zone was primarily responsible for the increase in stream nitrate during the storm events. In addition, we found that the concentration of unprocessed atmospheric nitrate in the stream was stable at 1.6 ± 0.4, 1.8 ± 0.4, and 2.1 ± 0.4 µM during the storm events I, II, and III, respectively, irrespective of the significant variations in the total nitrate concentration. We concluded that the storm events have fews impacts on the concentration of unprocessed atmospheric nitrate in the stream, and thus the annual export flux of unprocessed atmospheric nitrate relative to the annual deposition flux can be a robust index to evaluate nitrogen saturation in forested catchments, irrespective to the variation in the number of storm events and/or the variation in the elapsed time from storm events to sampling.

Drivers of multi-decadal nitrate regime shifts in a large European catchment

Long-term monitoring shows evidence of persistent changes in the magnitude and timing of the seasonal pattern of nitrate concentrations in streams, with possibly grave effects on aquatic ecosystems. Seasonal patterns of stream nutrient concentrations are determined by a complex interplay of inputs, transport, and turnover. Over multi-decadal periods, each of these factors may change due to socio-economic factors such as consumption patterns, governance regimes, or technological control measures. Here we test the hypothesis that observed multi-decadal changes in stream nitrate seasonality could be explained by changes in the relative importance of catchment nutrient sources over time. We analyze 66 years of shifting nitrate seasonality in a large, central-European river (Elbe) during a period of significant socio-political changes (1954–2019), with correspondingly significant changes in the sources of anthropogenic nitrate emissions. We develop a mixed-source succession model to test how the multi-decadal evolution of the composition of nitrate sources (point and diffuse) influences in-stream seasonality. We show that the in-stream nitrate seasonality of the River Elbe changed significantly from a weak seasonal pattern with peak concentrations during summer in the 1950s to a strong seasonal pattern with peak concentrations during winter in the 1990s. We link these shifts to a succession of technical and political developments which influence the contribution of point and diffuse sources over time. Such shifts in seasonal concentration patterns can significantly impact the macronutrient (carbon, nitrogen, phosphorus) ratios in rivers, which in turn highly affect the health of aquatic ecosystems.

Patterns and Drivers of Groundwater and Stream Nitrate Concentrations in Intensively Managed Agricultural Catchments

The environmental loss of nitrogen in agricultural landscapes has pervasive consequences, including human health implications, eutrophication, loss of habitat biodiversity and greenhouse gas emissions. The efficacy of mitigation strategies designed to control or prevent nitrate contamination of waterbodies requires an understanding of catchment scale pressures and processes. Groundwater and stream nitrate concentrations fluctuate over temporal scales ranging from the daily to the decadal. Identifying spatiotemporal trends and dominant drivers of nitrate in water is challenging as the drivers are intertwined. The effects of agronomic, meteorological and hydrogeological drivers on groundwater and stream nitrate were investigated over seven years in two well-drained agricultural catchments, dominated by tillage and grassland farming, respectively. A significant positive temporal trend in nitrate concentration was observed in the tillage catchment, whereas no long-term trend was observed in the grassland catchment. Agronomic, meteorological and hydrogeological factors were significantly related to temporal nitrate changes across both catchments. Clearly identifying the drivers influencing temporal changes in nitrate concentrations is critical to improving water quality. The study highlighted that to reduce groundwater nitrate levels in areas of high risk (thin soils, low clay content and shallow groundwater), nitrogen applications need to be reduced and/or tailored, particularly at times of restricted crop growth.

Fire effects on riparian vegetation recovery and nutrient fluxes in Brazilian Cerrado

AbstractFire can change gallery forest vegetation structure, thereby altering nutrient fluxes between terrestrial and aquatic ecosystems. In Brazilian savannas, which are fire‐prone ecosystems, the fire regime is changing due to human activities such as converting native vegetation to farmland and urban areas. Uncontrolled wildfires in these savannas can reach gallery forests, which are more sensitive to fire impacts, leading to concerns about the effects of fire on gallery forest vegetation structure and freshwater ecosystems. We analysed the relationships between fire severity (the degree to which the fire has changed an area), percentage of burned area, variation in precipitation with gallery forest and savanna vegetation structure recovery and input of nutrients (NH4, , and ) to streams for 16 months after a fire event in five watersheds associated with small streams. One year after the fire, vegetation recovery (NDVI) was lower in gallery forest areas than in savanna woodlands, despite the more severe fires in savannas. The short‐term effects of fire on gallery forest vegetation included increased nitrate concentrations in streams, which were also influenced by increased precipitation and the extent of the burned area. The nutrient inputs into the stream stabilized within 1 year. However, gallery forest vegetation did not fully recover at that time and may continue to alter the functioning of the aquatic ecosystem. Together, these results demonstrate the need for an integrated fire management plan that considers both gallery forests and the surrounding savannas in the landscape to address consequences to aquatic ecosystems.

Chaotic Modeling of Stream Nitrate Concentration and Transportation via IFPA-ESN and Turning Point Analyses

Increased concentrations of nitrogenous compounds in stream networks are detrimental to the health of both humans and ecosystems. Monitoring, modeling, and forecasting nitrate concentration in the temporal domain are essential for an in-depth understanding of nitrate dynamics and transformation within stream networks. In this study, an advanced chaotic modeling and forecasting approach integrated with turning point analysis is proposed. First, the time-series daily nitrate concentrations in the form of nitrate-nitrite were reconstructed based on the chaotic characteristics and then input into the forecasting models. Second, an echo state network (ESN) was developed for one-day-ahead nitrate concentration forecasting, and the hyperparameters were optimized through an improved flower pollination algorithm (IFPA) to achieve a high efficiency. Furthermore, turning point analysis was performed to quantify the relationship between discharge and peak nitrate concentration. The Ricker function was fitted, and the parameters were estimated for turning points using the forecasted nitrate concentration and measured discharge. Field data, including daily stream nitrate concentration and information on discharge collected from eight different monitoring sites in the southern Sichuan Basin, China, were utilized for case studies. A comparative analysis was performed under three modeling scenarios, viz. conventional time-series modeling, temporal signal decomposition, and data reconstruction and embedding with chaotic characteristics. Four benchmark time-series forecasting algorithms were compared against the proposed IFPA-ESN in the above-mentioned scenarios. For each site, parameters of the Ricker functions were estimated, and turning points were computed based on the forecasted nitrate concentration and discharge. Computational results validated the superiority of the proposed approach in improving the accuracy of stream nitrate concentration prediction. The limitations to the supply and transportation of nitrogenous compounds were quantified, which would be valuable for pollution mitigation in the future.

Diel patterns in stream nitrate concentration produced by in-stream processes

Abstract. Diel variability in stream NO3- concentration represents the sum of all processes affecting NO3- concentration along the flow path. Being able to partition diel NO3- signals into portions related to different biochemical processes would allow calculation of daily rates of such processes that would be useful for water quality predictions. In this study, we aimed to identify distinct diel patterns in high-frequency NO3- monitoring data and investigated the origin of these patterns. Monitoring was performed at three locations in a 5.1 km long stream reach draining a 430 km2 catchment. Monitoring resulted in 355 complete daily recordings on which we performed a k-means cluster analysis. We compared travel time estimates to time lags between monitoring sites to differentiate between in-stream and transport control on diel NO3- patterns. We found that travel time failed to explain the observed lags and concluded that in-stream processes prevailed in the creation of diel variability. Results from the cluster analysis showed that at least 70 % of all diel patterns reflected shapes typically associated with photoautotrophic NO3- assimilation. The remaining patterns suggested that other processes (e.g., nitrification, denitrification, and heterotrophic assimilation) contributed to the formation of diel NO3- patterns. Seasonal trends in diel patterns suggest that the relative importance of the contributing processes varied throughout the year. These findings highlight the potential in high-frequency water quality monitoring data for a better understanding of the seasonality in biochemical processes.

Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape

Abstract. Surface waters are under pressure from diffuse pollution from agricultural activities, and groundwater is known to be a connection between the agricultural fields and streams. This paper is one of the first to calculate long-term in-stream concentrations of tritium, chloride, and nitrate using dynamic groundwater travel time distributions (TTDs) derived from a distributed, transient, 3D groundwater flow model using forward particle tracking. We tested our approach in the Springendalse Beek catchment, a lowland stream in the east of the Netherlands, for which we collected a long time series of chloride and nitrate concentrations (1969–2018). The Netherlands experienced a sharp decrease in concentrations of solutes leaching to groundwater in the 1980s due to legislations on the application of nitrogen to agricultural fields. Stream measurements of chloride and nitrate showed that the corresponding trend reversal in the groundwater-fed stream occurred after a time lag of 5–10 years. By combining calculated TTDs with the known history of nitrogen and chloride inputs, we found that the variable contribution of different groundwater flow paths to stream water quality reasonably explained the majority of long-term and seasonal variation in the measured stream nitrate concentrations. However, combining only TTDs and inputs underestimated the time lag between the peak in nitrogen input and the following trend reversal of nitrate in the stream. This feature was further investigated through an exploration of the model behaviour under different scenarios. A time lag of several years, and up to decades, can occur due to (1) a thick unsaturated zone adding a certain travel time, (2) persistent organic matter with a slow release of N in the unsaturated zone, (3) a long mean travel time (MTT) compared to the rate of the reduction in nitrogen application, (4) areas with a high application of nitrogen (agricultural fields) being located further away from the stream or drainage network, or (5) a higher presence of nitrate attenuating processes close to the stream or drainage network compared to the rest of the catchment. By making the connection between dynamic groundwater travel time distributions and in-stream concentration measurements, we provide a method for validating the travel time approach and make the step towards application in water quality modelling and management.