Nitrogen mass balance and uptake velocity for eutrophic reservoirs in the Brazilian semiarid region

doi:10.21203/rs.3.rs-2711745/v1

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Nitrogen mass balance and uptake velocity for eutrophic reservoirs in the Brazilian semiarid region

https://doi.org/10.21203/rs.3.rs-2711745/v1

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published 09 Aug, 2023

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The nitrogen (N) cycle from the catchment to the downstream reservoir is complex, particularly the quantification of N losses. However, in order to assess the nitrogen impact in a reservoir ecosystem, simplified models may be applicable regarding the TN load production and the magnitude of lake TN removal. This study presented a methodology to perform and validate a TN mass balance to further calibrate a simplified coefficient for TN losses (v_f.) in 29 tropical semiarid reservoirs. The study reservoirs were highly productive ecosystems with an average total nitrogen (TN) concentration, accounting for all measurements in all reservoirs, ranging from 0.59 mg L^− 1 to 3.84 mg L^− 1. Regarding the production of TN load, the median values ranged from 4.35 to 2,499.43 t yr^− 1 with median of 80.34 t yr^− 1. The TN loads were estimated through an annual mass balance over a twenty-four-year period. The median of the estimates was compared with reference values obtained by using the export modelling coefficient. The correlation between the median estimated and reference loads resulted in satisfactory agreement (r² 0.88) and reinforced the reliability of the mass balance alternative. From the validated TN loads, the TN uptake velocity (v_f) was estimated for all reservoirs (44.9 ± 20.1 m yr^− 1) and could be described as a general function of the water residence time. The reservoirs of the study region have demonstrated higher v_f than temperate lakes and reservoirs and similar v_f with Latin America/Caribbean ones. As expected, reservoirs of warmer climates tend to present intensified N loss processes as compared to lakes and reservoirs of temperate regions. The methodology proposed in the present study can be used to potentially improve water quality management in tropical semiarid reservoirs.

Nitrogen losses

Removal process

Tropical semiarid

Nutrient load

Nitrogen (N) is an important nutrient limiting factor for maintaining primary productivity in lake and reservoir ecosystems (Wiegand et al., 2020; Pei et al., 2022). However, recent human interference, such as synthetic fertilizer, animal manure and human waste inputs, have added excessive N discharges into reservoirs, promoting environmental problems, such as eutrophication, and also impacting drinking water supply (Poikane et al., 2022). The terrestrial N cycle, from which derives the external contributions, three phases stand out encompassing different N transportation pathways: (i) upward - plant uptake and gaseous losses, (ii) downward - leaching to groundwater, and (iii) lateral - via surface and subsurface flow to surface waters (Gerling et al., 2016).

The N cycle from the catchment to the reservoir is complex. However, in order to assess the nitrogen impact in a reservoir ecosystem, estimate and predict nitrogen concentration and also evaluate management scenarios, simplified models that can be applicable at the catchment scale are required. Moreover, performing such improvements for water quality management despite detailed data insufficiency is an additional challenge. In this context, simple models have been developed, for instance, the Spatially Referenced Regression on Watershed Attributes (SPARROW). This model accounts for TN removal in a first-order framework using a net loss rate or apparent settling velocity, which has also been a simplified hypothesis adopted by many other models (Hellström, 1996; Ruan et al., 2014). More recent empirical models have also been developed (Steingruber, 2020).

The N concentration in the reservoir depends on several factors: input rates (e.g. catchment contribution, atmospheric deposition, fixation); removal rates (e.g. lake outflow, burial in sediments, microbial removal via denitrification and anaerobic ammonium oxidation), and the internal dynamics (e.g. uptake by phytoplankton and mineralization by heterotrophs (de Andrade et al., 2020). Additional characteristics of the reservoir, such as trophic state, water temperature, size and depth, also impact on these processes (Zhu et al., 2021). For instance, in large reservoirs the nitrogen removal efficiency depends on lake trophic status (Small et al., 2014) as well as reservoirs with warmer waters, such as those in tropical semiarid regions, present denitrification processes enhanced by temperature (Hellstr$\ddot{\text{o}}$m, 1996).

Different forms of nitrogen in the water can also be considered. This includes Dissolved Inorganic Nitrogen (DIN), which is the sum of ammonium, nitrite and nitrate, Dissolved Organic Nitrogen (DON), Particle Organic Nitrogen (PON) and also N in biotic compartments (e.g., phytoplankton and bacteria) (Zhong et al., 2021). Meantime, in face of such a complex speciation, measurements of total nitrogen (TN) concentration, which is the sum of ammoniacal and organic nitrogen, plus the concentrations of nitrite and nitrate, are more widely available in the literature. Then, a complete mass balance may be performed separately for each N speciation in the different compartments (e.g. sediment and water column) or as a total budget (Müller et al., 2021). The N fractions to be considered depends on data availability, level of detailing required in the results, time horizon, and the utilization purpose of the achieved information (Molot and Dillon, 1993).

Regardless of the approach to be adopted for the N budget, N losses are of ultimate importance but of difficult quantification. The amount of N removal from reservoirs depends on the input load and the removal efficiency. Overall, N losses within a system occurs mainly in the sediments via denitrification and accumulation. Losses through ammonia volatilization may also affect the budget as well as the resuspension of accumulated N in lake-bed material. However, as the content of N in old inorganic sediments is small, over many years the N loss is dominated by denitrification (Hellström, 1996). In simplified terms, the N removal mechanisms are controlled by the hydrologic load and the uptake velocity of N removal processes (v_f) (Jiao et al., 2014; Li et al., 2021). The parameter v_f may be considered the biogeochemical metrics of N elimination. It provides reference on N removal efficiency and magnitude in lakes and reservoirs easily through space and time (Harrison et al., 2009).

The parameter v_f may be computed for each loss process separately or aggregating all processes as a total N loss in the system. However, to the authors’ knowledge, the quantification of the magnitude of TN loss for tropical semiarid reservoirs, either by each removal mechanism or as a total loss, is still lacking in the literature. Thus, the objectives of this study are: (1) to propose and validate a simplified mass balance model for TN catchment contribution for several reservoirs located in the Brazilian semiarid region; (2) to quantify the magnitude and efficiency of lake TN removal; (3) to estimate the total v_f for the studied reservoirs. It is expected that the ground basic approaches and simplified tools proposed in the present study will improve the assessment and management of water quality for drinking water supply reservoirs in tropical semiarid regions.

2.1 Site description and reservoir selection

This study included twenty-nine morphologically different water supply reservoirs scattered over the State of Ceará, Brazilian semiarid, as shown in Fig. (1). The selection considered the strategic importance of the reservoir into the network that supplies water for human, industrial, and agricultural uses in the state. Additionally, if the reservoir is under the continuous monitoring program of water quality, quantity, and catchment characterization of the Water Management Company of the State of Ceará (COGERH). Table 1 details their specific location and general characteristics. The study period covers 2008–2021, with a six-year drought event within this period from 2013–2018 (Freire et al., 2021; Wiegand et al., 2021), which seriously impacted the reservoirs both quantitatively and qualitatively. Due to the tropical semiarid climate and the susceptibility to droughts, the water level, surface area, storage volume and water quality vary significantly. Summarizing important climatic aspects, semiarid reservoirs are under a high and relatively constant incidence of solar radiation, small seasonal variations in water temperature, wind influence and greater temperature variability occurring in a 24-hour period (Souza Filho et al., 2006; Mesquita et al., 2020).

Table 1

Physical characterization and main water quality parameters of the study reservoirs.
Reservoir	ID	Construction date	Catchment Area (km²)^a	Mean Surface Area (km²)^a	Median Volume (hm³)^a	Median Depth (m)^a	Storage capacity (hm³)	Representative trophic state
Acarape do Meio	R1	1924	210	1.16	7.46	14.3	29.6	Eutrophic
Angicos	R2	1998	286	6.39	15.21	10.8	56.05	Eutrophic
Arrebita	R3	1992	79	1.72	4.62	8.0	18.53	Eutrophic
Ayres de Souza	R4	1936	1102	7.66	37.22	18.4	96.8	Eutrophic
Banabuiú	R5	1966	14200	11.48	95.84	23.3	1601	Eutrophic
Cachoeira	R6	2000	143	2.27	6.68	12.5	34.33	Oligotrophic
Canafístula	R7	1992	329	1.15	2.26	5.8	13.11	Eutrophic
Carnaubal	R8	1990	2091	3.53	2.90	5.2	80.07	Eutrophic
Castanhão	R9	2003	44800	99.90	890.50	40.3	6700	Eutrophic
Castro	R10	1997	360	4.15	20.39	14.1	62.31	Eutrophic
Caxitoré	R11	1962	1252	11.82	116.91	19.1	202	Eutrophic
Colina	R12	1998	367	1.79	3.41	10.4	4.29	Eutrophic
Curral Velho	R13	2007	110	3.06	7.80	7.3	12.17	Eutrophic
Edson Queiroz	R14	1987	1778	10.94	77.39	15.1	254	Eutrophic
Flor do campo	R15	1999	663	4.72	6.22	5.3	105	Eutrophic
Forquilha	R16	1927	191	5.16	8.85	7.8	50.13	Eutrophic
General Sampaio	R17	1935	1582	18.47	140.09	25.5	322.2	Eutrophic
Itaúna	R18	2001	771	12.99	56.19	9.1	72.58	Eutrophic
Olho D'água	R19	1998	72	1.20	6.49	11.0	19	Mesotrophic
Orós	R20	1961	24538	93.91	896.56	33.0	1940	Eutrophic
Pacajus	R21	1993	43	16.25	54.00	9.6	232	Eutrophic
Pacoti	R22	1981	1080	30.80	131.93	17.2	380	Eutrophic
Quincoé	R23	1990	168	0.31	0.32	5.4	0.29	Eutrophic
Quixeramobim	R24	1960	7005	0.16	2.64	4.3	7.88	Eutrophic
Riachão	R25	1981	34	4.05	18.92	15.6	47.92	Eutrophic
Rivaldo de Carvalho	R26	1966	318	1.19	2.92	8.8	20.1	Eutrophic
Rosário	R27	2001	337	4.47	21.13	14.7	47.22	Mesotrophic
Serafim Dias	R28	1995	1515	4.18	39.71	20.4	40.94	Eutrophic
Sítios Novos	R29	1999	446	23.23	78.22	14.9	126	Eutrophic

2.2 Data sources

COGERH provided the datasets of TN load from the catchment and TN concentration for several water depths near the reservoir’s outlet. Water samples were collected quarterly and TN concentration was analyzed according to APHA (2005). During the dry period, which encompasses the second semester of the year, the sampling frequency is usually in July/August and November/December. A data consistency was performed to identify possible sampling errors. Additionally, the data related with reservoir morphometry and operation (volume, water level, surface area and released flow) were also provided by COGERH.

With regards to TN load data, COGERH has also elaborated an environmental inventory covering the catchment of each studied reservoir between the years 2011–2016. Since they are highly costing and time demanding, they were elaborated only once. The methodology estimated the annual TP load produced by each catchment based on the Export Coefficient Modelling (ECM) approach (Johnes, 1996). Details on the calculation process of this reference load are available in Ceará (2020).

2.3 Reservoir modeling

2.3.1 Nitrogen budget for total input load

The complete mass balance for nitrogen may be described with N inputs from atmospheric deposition, mineral and organic fertilizers from catchment, biological fixation, internal contribution and imports of food, feed, and products. It is noteworthy that nutrient loading of tropical semiarid reservoirs is a seasonal event due to the highly variable precipitation that occurs mainly in the wet period in the first semester of the year (Goshu et al., 2020; Rocha and Lima Neto, 2021a). Likewise, outputs may be outflow export, biota uptake, denitrification, volatilization, and sedimentation (Boyer and Howarth, 2002). The terms of the complete balance are presented in Eq. (1). It is noteworthy that the nitrogen contribution from the internal processes may result from a rate retained and released in the sediment, similarly to phosphorus (Rocha and Lima Neto, 2021a). Finally, all the losses were grouped as total nitrogen loss in Eq. (2) for which it is expected to find a total nitrogen uptake velocity of TN removal processes (v_f).

Temporary N load = (N_c + N_{fix +} N_{atm +} N_sed) – N_out – (N_d + N_{amx +} N_{sed +} N_{mi +} N_harv) (1)

Temporary N load = (N_c + N_{fix +} N_{atm +} N_sed) – N_out – N_loss (2)

Where the terms are related to the contributions from N_c: Catchment; N_fix: Fixation; N_atm: Atmospheric deposition; N_sed: Advective and/or diffusive fluxes from sediment; N_out: Outflow; N_d: Denitrification; N_amx: Anammox; N_mi: Mineralization; N_harv: Fish harvest and Temporary TN stored: Food web, surface sediment and lake water TN storage (Liu et al., 2021).

Keeping the most relevant input processes for the budget, which will be further addressed, and aggregating the gain and all the losses regarding the internal processes in the reservoir under a total uptake velocity (v_f), the mass balance can be simplified as expressed in Eq. (3) and rewritten in Eq. (4). The processes involved in nitrogen transformations, such as nitrogen fixation, were disregarded. Organisms capable of fixing molecular nitrogen, blue-green algae and photosynthetic bacteria, are most likely present in the lake. However, as much as their quantitative role in the nitrogen cycle is unknown, it is probably of minor importance. Only in the metalimnion and the upper part of the hypolimnion, where they sometimes form considerable populations in rather thin strata, might they locally contribute to the lake's nitrogen metabolism (Verdouw and Dekkers, 1982).

$$\text{V}\frac{\text{d}{\text{C}}_{\text{N}}}{\text{dt}}\text{ = W - }{\text{Q}}_{\text{out}}{\text{.}\text{C}}_{\text{N}}\text{ -} {\text{v}}_{\text{f}}.{\text{A}}_{\text{s}}.{\text{C}}_{\text{N}}$$

$$\text{V}\frac{\text{d}{\text{C}}_{\text{N}}}{\text{dt}}\text{ = W - }{\text{Q}}_{\text{out}}\text{.}{\text{C}}_{\text{N}}\text{ - }{\text{k}}_{\text{s}}\text{.V.}{\text{C}}_{\text{N}}$$

Where V: Average volume (m³); $\frac{\text{d}{\text{C}}_{\text{N}}}{\text{dt}}$: nutrient concentration variation over time (kg yr^− 1); W: nutrient input load (kg yr^− 1); Q_out: outflow (m³ yr^− 1); ${\text{k}}_{\text{s}}$: first order settling loss rate (yr^− 1); v_f: nitrogen uptake velocity of TN removal processes (m yr^− 1); A_s: surface area (m²); C_N: average nutrient concentration in the water column (kg m^− 3).

2.3.2 Driving factors on TN uptake velocity (v_f)

The parameter v_f, nitrogen uptake velocity of TN removal processes, is recognized as the biogeochemical metrics of TN elimination processes and stabilizes at a constant in certain temperature and substrate concentration (Liu et al., 2021). Combining Eqs. (3) and (4) leads to Eq. (5). Then, the TN uptake velocity can be described as a function of the first order settling loss rate and the average reservoir height ($\frac{\text{V}}{{\text{A}}_{\text{s}}})$. Accordingly, previous studies have also highlighted the impact reservoir depth in internal process, specifically the nitrogen denitrification rate (Zhu et al., 2021)

$${\text{v}}_{\text{f}}={\text{k}}_{\text{s}}. \text{H}$$

Then, the k_s was calibrated through the application of Eq. (6), which is the transient analytical solution for Eq. (4) proposed by Vollenweider (1968). It was originally applied to estimate phosphorus concentration considering a complete mix hypothesis (Rocha and Lima Neto, 2021a,b, 2022).

$$\text{T}{\text{N}}_{\text{w}}\left(\text{t}\right)\text{ = T}{\text{N}}_{\text{w,0}}.\text{e}\text{x}\text{p}(-\text{(}\frac{{\text{Q}}_{\text{s}}}{\text{V}}\text{+}{\text{k}}_{\text{s}}\text{).t)}\text{ }\text{+ [L/(}{\text{Q}}_{\text{s}}\text{ + }{\text{k}}_{\text{s}}\text{V)].[1 – }\text{e}\text{x}\text{p}(-\text{( }\frac{{\text{Q}}_{\text{s}}}{\text{V}}\text{+}{\text{k}}_{\text{s}}\text{)}\text{.}\text{t)]}$$

Where TN_w(t): TN concentration at the reservoir outlet at a given time (kg m⁻³); TN_w,o: Initial TP concentration (kg m⁻³); t: Elapsed time (month); V: Average reservoir volume (m³); L: TN load (kg month^− 1); Q_s: Released flow (m³ month^− 1)

With k_s calibrated, the adjustment of the empirical model presented in Eq. (7) was evaluated if suitable to nitrogen modeling. Note that this equation was originally developed to predict k_s for total phosphorus (TP) in tropical semiarid reservoirs. It is important to highlight that the hydrologic residence time (RT) is a main influencing variable on N cycling (Small et al., 2014; Gerling et al., 2016; Li et al., 2021). Previous studies found significant correlation between the annual nitrate removed and the reservoir water retention time (Tomaszek and Koszenilk, 2003; David et al., 2006). Finally, RT was calculated as proposed by ( Rocha and Lima Neto, 2021b).

${\text{k}}_{\text{s}}$ = $\frac{\text{4}}{\sqrt{\text{RT}}}$ (7)

2.3.3 Assumptions for fixation and atmospheric deposition

The processes of fixation and atmospheric deposition were removed from the budget as shown in Eq. (2). These processes are overall disregarded due to the environmental conditions which favor or not a significant amount of nitrogen input, the lack of measured data and the lack adequate estimation methods. The amount of fixed nitrogen is widely related as a function of the N/P ratio of the loading. It is reported that N fixation occurs in lakes with N/P loading ratio less than approximately 8.1 (Ruan et al. 2014; Lewtas et al., 2017). Overall, it is considered that atmospheric deposition and N₂ fixation contribute quite little (less than 10%) to the total nutrients loading (Wu et al., 2017). However, to support the hypothesis of removing the fixation term for the total mass balance, the N/P ratio was evaluated for each reservoir. Regarding atmospheric deposition, it is related with the return to the surface of all nitrogen emitted to the atmosphere through the form of wet deposition, dissolved in rainwater, and dry deposition, in the form of gases and particles (Jiang et al. 2022). There was no available data regarding this input and, although there are available methods to estimate this contributions for temperate regions (Lewtas et al., 2017), to the authors’ knowledge, there is no estimation method for reservoirs in tropical semiarid regions. Then, this contribution was removed from the budget.

2.3.4 Assumption for sediment fluxes and internal load

As described in Eq. (1), the N cycle in aquatic environments comprises of N₂ fixation, denitrification, nitrification, anammox, dissimilatory nitrate reduction to ammonium, comammox, and organic nitrogen remineralization to ammonium (Müller et al., 2021). These processes occurring in the sediment compartment are the most relevant regarding N loss (Yang et al., 2020). The sediment has the additional complication in which certain N cycle processes act as a source (e.g. N fixation) while others represent a sink (e.g. denitrification) and some represent either a source or sink according to the environmental conditions (e.g. N mineralization and inorganic N diffusion) (Zhong et al., 2021).

The performance of a single and separate TN budget regarding the internal processes is largely applied mainly to find the net rate of capture or release of the nutrient from the sediment. However, as the objective of this work was to perform the total budget, the internal budget was integrated to the global one, which is also a widely applied alternative (Müller et al., 2021). Additionally, considering that each loss process likely presents a different removal rate efficiency, regarding the internal processes and in face of the available data, it was decided to determine the total net uptake velocity of the overall N loss rate of a system. This approach is an alternative to determine individual v_f values for each N removal process (Li et al., 2021).

2.4 Uncertainty analysis and model validation

Firstly, a data accuracy and an outlier analysis were performed followed by removal of non-specific values below or over predefined limits according to literature. Secondly, regarding validation of the estimated parameters, the main parameter to be validated was the first order settling loss rate (k_s) which was firstly obtained through a mass balance and then adjusted into Eq. (7). The validation of this adjustment was possible by performing a correlation analysis between the mean modeled annual TN load that applied k_s such as presented in Eq. (7) with the reference load available by Ceará (2020).

3.1 TN concentration among and within reservoirs

Filled contour plots of the vertical profile of the TN concentration are shown in Fig. 2 indicating the mixing status of this nutrient in some study sites over the study period. The reservoirs in Fig. 2 were selected as they presented the most complete dataset regarding water depth and TN concentration over the study period. The variability in TN concentration among reservoirs is noticeable. R14, R23 and R28 presented the lowest average concentrations (about 0.03 mg L^− 1) while R16 and R26 presented the highest ones (21.6 and 22.5 mg L^− 1, respectively). Overall, the average concentration accounting all measurements in all reservoirs ranged from 0.59 mg L^− 1 to 3.84 mg L^− 1. Additionally, the reservoirs have been predominantly mixed when they were full or shallow, but, in the first situation with lower concentrations and the, in the second, with higher concentrations. As the water depth decreased, anomalous periods of peak concentration were more recurrent. However, is important to highlight the limited temporal resolution of the sampling campaigns. The variability in a nutrient concentration among and within reservoirs may be influenced for factors such as the size and depth (Håkanson, 2005), volume variation, inflow and input load during the prior wet period and the exposure to marked variability in climatic forcings (Rocha and Lima Neto et al., 2022).

3.2 Assessment of TN pollution sources from catchment landuse and modeled TN load

Figure 3 presents the percentage of contribution of TN pollution for each catchment from the different sources estimated by COGERH. The main sources evaluated were agriculture, soil, and livestock, considered non-point sources (NPS), as well as fish farming, and sewer, considered point sources (PS). The total PS load exceeded NPS loads in 65% of the catchments, which suggests that the low coverage of sanitation facilities common in these catchments potentially leads to untreated sewage inputs and a significant amounts of TN pollution (Ceará, 2020). Additionally, the among-catchment heterogeneity regarding the TN sources derives mostly from the differentiated anthropogenic uses dominant in each catchment (Ceará, 2020).

The second estimation method for the TN load produced in the catchments was performed through the application of Eq. (6). The values are presented in Fig. 4 along with the reference TN load values estimated by COGERH. The variability results mostly from changes in TN concentration and water balance over the periods of modeling which may incur in different catchment runoff and nutrient supply. The median of the estimated values ranged from 4.35 to 2,499.43 t yr^− 1 with median of 80.34 t yr^− 1. The estimation of nitrogen production in the catchment either by mass balance or by export coefficients or even by other methods has been widely performed worldwide. For instance, Le et al. (2014) estimated through the export coefficient method a TN production from different land uses varying from 3 to 2,365 t yr^− 1 for a catchment in North Vietnam. Müller et al. (2021) estimated in average 223 t yr^− 1 of TN load to a Swiss catchment applying the catchment model MODIFFUS.

Considering the reference load, the maximum contribution was 9,201 t yr^− 1 for the catchment of R9, the minimum was 3.5 t yr^− 1 for R23 and median of all estimates was 45 t yr^− 1. For 27% of the catchments, the reference load and the median estimated load were the closest, while, for the remaining, the reference value was somewhere between the range of modeled loads. For the catchments whose estimates were higher than the reference load, it should be highlighted that the catchment may have undergone land use changes over the years and that the reference load, although relevant as a measure of comparison, was the result of a single estimation. The reconstruction of TN load through mass balance modeling approach and the comparison with the reference load obtained from the export modelling coefficient, which is one additional estimation method, resulted in a satisfactory correlation (r² 0.88) (See Fig. 3b). The reproducibility and precision of analytical TN determination were crucial for the calculation of the budget and these results reinforces the mass balance as a reliable alternative when that is mostly what the available data can accomplish.

3.3. Estimated settling loss rate (k_s)

Figure 5 presents the estimated settling loss rate for TN removal from the water column. The total of the estimates for the twenty-nine reservoirs obtained in the different years over the studied period were displayed together. Additionally, the curve of the reference model was obtained be the application of Eq. (7) as an alternative to fit the estimated values and to be used for further applications. The adjustment of the reference model was evaluated as a new approach for the estimation of k_s. The variability of the estimates in comparison to the reference model was measured by the mean average percentual error (MAPE). The average MAPE was 73%, mainly influenced by the exceptionally lower values. As will be further addressed, the TN load was estimated by applying Eq. (7) to estimate k_s. The results of the estimated load was compared with a reference load as a further validation method of the adequacy of Eq. (7). This is noteworthy because, despite the variability when modeling k_s alone, the integrated result for the TN load when combined in the total mass balance provides satisfactory results.

Ultimately, it was also intended to compare the modeled k_s for nitrogen with the ${\text{k}}_{\text{s}}$ reported in literature for phosphorus removal. Initially, it can be seen that almost all estimated ${\text{k}}_{\text{s}}$ values for nitrogen were under the maximum limit reported for phosphorus. On the other hand, several estimated values were below the minimum reported limit. This aspect may indicate that the phosphorus removal processes occur faster than nitrogen removal. In fact, in reservoirs of tropical semiarid regions the phosphorus removal process is more intense than in any other region, and phosphorus is usually the limiting nutrient for biological processes (Rocha and Lima Neto, 2021b, 2022).

3.4 Total nitrogen uptake velocity (v_f)

Figure 6 shows a boxplot representation of the estimated TN uptake velocity (v_f) for each reservoir since the estimates varied for the years over the study period. Additionally, a thorough research was carried out in literature to find representative values of v_f for other waterbodies and regions around the world. Thus, Fig. 5 also presents v_f values for global lakes and reservoirs, global temperate reservoirs, global tropical reservoirs (Li et al., 2021), Tropical Lakes in Latin America/Caribbean and Tropical Reservoirs in Latin America/Caribbean (Salas and Martino, 1991). Although this values were selected as comparison thresholds, several studies have attempted to determine v_f values for lakes/reservoirs of specific countries worldwide (Nõges, 2005; Patoine et al. 2006; Harrison et al. 2009; Jian et al. 2012). It is noteworthy that few values over 200 m yr^− 1 were observed, only in reservoirs R5 and R9. Settling velocities over this limit were previously reported in a temperate lake in Switzerland (Mengis et al. 1997). Contrastingly, values under 21 m yr^− 1 were observed in R23.

Considering the median of estimates, the results showed that, firstly, the reservoirs of tropical semiarid regions demonstrate a higher v_f than tropical lakes and temperate reservoirs, excepting R23. The combination of the waterbody type and the region most likely triggers different processes and intensity of reactions regarding N losses (Harrison et al., 2009). It is noteworthy that faster decay processes were also reported for P concentration in the reservoirs of the study region when compared with lakes/reservoirs of tropical and temperate regions (Salas and Martino, 1991; Vollenweider, 1968; Rocha and Lima Neto, 2021a). Although reservoirs are expected to present intensified loss processes than lakes, the same is expected when comparing waterbodies in warmer with colder regions. Thus, the proximity of the reported values for tropical lakes and temperate reservoirs may be explained by this unbalance on waterbody type and region. The estimated values were better placed among the v_f reported for lakes and reservoirs located in Latin America/Caribbean. The median estimated v_f values was 44.9 m yr^− 1 (± 20.1) while the threshold for tropical lake in Latin America/Caribbean was 26.4 m yr^− 1 and the threshold for tropical reservoirs in Latin America/Caribbean was 81 m yr^− 1.

Among the physical and environmental factors that interfere with removal processes there are the temperature, pH, total alkalinity, dissolved oxygen, phosphorus (P) concentration, water depth and also the trophic status (Gerling et al., 2016). As for the processes, while there are several such as the mineralization, biomass uptake, adsorption and desorption, the denitrification is the most relevant one for nitrogen removal. Denitrification contributes with approximately 30% of the total N loss and can occur in the water column and/or at the water-sediment interface (Hellström, 1996; Wu et al., 2017; Liu et al. 2021). The denitrification rate tends to be higher in eutrophic waterbodies as well as shallow reservoirs (Wu et al. 2017, Müller et al., 2021). Shallow reservoirs are more likely to present accelerated nitrification-denitrification processes influenced by the strong turbulence effect. Turbulence can promote faster transport of oxygen to sediment, creating favorable oxic conditions in sediment surface for nitrification (Zhu et al., 2021). Reinforcing this aspect, strong negative correlation between the denitrification rate and the anoxic factor has already been reported (Molot and Dillon, 1993).

Among the aforementioned aspects, the study sites gather several characteristics to support the elevated v_f values. Firstly, the reservoirs remained eutrophic/hypereutrophic for most of the study period (see Table 1), in part due to the high and continuous P load contribution that worsens the trophic state (Rocha and Lima Neto, 2022). Secondly, they are also under favorable environmental conditions which are a high intensity of solar radiation with consequent warmer waters (Santos et al., 2014) and shallow waters due to the volume reduction under dry conditions (Rocha Junior et al., 2018). Additionally, the estimated values placed between the thresholds for tropical lakes or reservoirs of Latin America reinforce the results.

3.5 Seasonal variation in TN removal rate

Figure 7 presents the variability of the estimated TN uptake velocity (v_f) for each reservoir and period, whenever available, over the study period. The estimates for the wet period presented more variability, which can be expressed as a reflect of the variability in the input variables, mainly the inflow and TN concentration, over the years. Comparing the median estimate between the periods, 35% of the reservoirs presented higher v_f during the dry period. The reservoirs in which the median v_f of the wet period was significantly higher than the v_f of the dry period (R1, 2, 5, 16 and 18) were the reservoirs with the better dissolved oxygen concentration as presented by Rocha and Lima Neto (2022). In fact, the seasonal increase in TN removal rates in eutrophic lakes correlates significantly with seasonal oxygen fluxes measured across the water–sediment interface (Ma et al., 2021; Müller et al. 2021).

As for the higher TN uptake velocity in the warmer season, it is expected that warmer waters increase nutrient uptake by the phytoplankton community, which in some lakes causes nitrogen limitation later in the year (Vinçon-Leite and Casenave, 2019). However, it was already reported that the nitrogen may be the limiting factor in some reservoirs of the study region, especially when they are with higher water levels (Wiegand et al., 2020), which is usually at the end of the wet period and the beginning of the dry period. This may explain the high values of v_f estimated even for the wet period.

3.6 Assessment of load production and dynamic between phosphorus and nitrogen

Figure 8 presents (a) the variability of the main two nutrients produced by the studied catchments, phosphorus and nitrogen, and (b) their correlation considering the median estimated values by the mass balance model and the values reported by COGERH (2011). The comparison of the total load production suggests a greater production of nitrogen in all catchments implying that phosphorus is usually the limiting nutrient in these waterbodies, which is widely supported by the local literature (de Lacerda et al., 2018; Gondim Rocha et al., 2019); Vidal and Capelo Neto, 2014). However, as can be seen in Fig. 8(a), the intersections in the boxplots suggests the existence of periods in which nitrogen is scarcer. Indeed, nitrogen was found to be the limiting nutrient under specific conditions in several reservoirs of the study region (Wiegand et al., 2020).

Analyzing the correlation between these two nutrients presented in Fig. 8(b) it can be highlighted initially that both methodologies resulted in a strong correlation between the load produced by the catchments for each nutrient. This can be supported by the fact that the same sources likely produce both loads, for instance animal husbandry, crop, and sediment. Then, depending on catchment size, flow, and transferring pathways these nutrients come together to the reservoir. An additional important aspect is that the estimated values by mass balance resulted slightly higher than the values already estimated by Cogerh (2011). For such, it is important to note that, although the value estimated by COGERH is a useful reference they resulted from only one attempt of quantification. Then, it represents the measurement conditions of one specific year a certain time ago, not considering different hydrologic conditions and the impact of the runoff as well as not accounting possible changes in the catchment landuse over the years.

This study presented a methodology to perform a simple TN mass balance in order to provide a comprehensive insight of N removal processes and to calibrate a simplified coefficient for TN losses in reservoirs. The methodology was applied to twenty-nine reservoirs of tropical semiarid regions with the specific goals of estimating and validating a simplified mass balance for TN catchment contribution, quantifying the magnitude of N removal by estimating the TN apparent settling velocity (v_f.). Although the parameter v_f may be computed for each loss process separately or aggregating all process as a total N loss, its quantification for reservoirs of tropical semiarid regions, either if by each removal mechanism or as a total loss, is still lacking in the literature.

The study reservoirs are majorly highly productive ecosystems with an average TN concentration, accounting all measurements in all reservoirs, ranging from 0.59 mg L^− 1 to 3.84 mg L^− 1. Additionally, the reservoirs have been predominantly mixed when they were full or shallow, but, in the first situation with lower concentrations and the, in the second one, with higher concentrations. As for the estimated TN load, the median values ranged from 4.35 to 2,499.43 t yr^− 1 with median of 80.34 t yr^− 1. The comparison between the TN load estimated through the mass balance approach and the reference load obtained from the export modelling coefficient resulted in a satisfactory correlation (r² 0.88). The results reinforce the idea that the mass balance is a reliable alternative under data limitation. Additional comparisons were performed to compare TN and TP load produced by each catchment, indicating a greater production of nitrogen in all catchments.

Once estimated and validated the TN loads, the TN uptake velocity (v_f) was estimated. The study sites have demonstrated a higher v_f than tropical lakes and temperate reservoirs, with only one exception. As expected, reservoirs, and particularly reservoirs of warmer climates, tend to present intensified loss processes as compared to lakes and reservoirs of temperate regions. The estimated values were better placed among the v_f reported for lakes and reservoirs located in Latin America/Caribbean with a median of 44.9 m yr^− 1 (± 20.1). The results also revealed that v_f can described as a general function of the water residence time of the reservoirs. Several characteristics such as high temperature, phosphorus concentration, water depth and also the trophic status support higher v_f. It is expected that the results of this work’s ground basic approaches and simplified tools will support the assessment and management of water quality for drinking water reservoirs with regards to evaluating complex nutrients such as nitrogen under data scarcity.

- Ethics approval and consent to participate: Not applicable

- Consent for publication: Not applicable

- Availability of data and materials: Data available upon request

- Competing interests: The authors declare that they have no competing interests

- Funding: The present study was supported through the National Council for Scientific and Technological Development – CNPq and the Ceará State Research Foundation – FUNCAP [Research Grant PNE-0112-00042.01.00/16]

- Authors’ contributions: Maria de Jesus Delmiro Rocha: Conceptualization; Formal analysis, Investigation; Methodology, Writing – original draft preparation. Iran Eduardo Lima Neto: Conceptualization, Formal analysis, Writing – Reviewing and Editing, Supervision, Project administration.

- Acknowledgements: The authors thank the Water Resources Management Company of Ceará (COGERH) for providing the field data.

APHA, 2005. Standard Methods for the Examination Water and Wastewater, twenty-first ed. APHA, Washington, p. 1083. American Public Health association.
Boyer, W. E; Howarth, W. R. 2002. The nitrogen cycle at regional to global scales. Biogeochemistry. 57/58. Springer Netherlands. http://dx.doi.org/10.1007/978-94-017-3405-9
Ceará. Companhia de Gestão dos Recursos Hídricos: Portal hidrológico do Ceará. Disponível em: <http://www.hidro.ce.gov.br/>.
Cogerh. Companhia de Gestão dos Recursos Hídricos: Inventários ambientais. 2011. Disponível em: <http://www.hidro.ce.gov.br/hidro-ce-zend/mi/midia/show/150>.
David, M.B., Wall, L.G., Royer, T. V., Tank, J.L., 2006. Denitrification and the nitrogen budget of a reservoir in an agricultural landscape. Ecol. Appl. 16, 2177–2190. https://doi.org/10.1890/1051-0761(2006)016[2177:DATNBO]2.0.CO;2
de Andrade, E.M., Ferreira, K.C.D., Lopes, F.B., Araújo, I.C. da S., da Silva, A.G.R., 2020. Balance of nitrogen and phosphorus in a reservoir in the tropical semiarid region: Balanço de nitrogênio e fósforo em um reservatório na região semi-árida tropical. Rev. Cienc. Agron. 51, 1–10. https://doi.org/10.5935/1806-6690.20200020
de Lacerda, L.D., Santos, J.A., Marins, R. V., Da Silva, F.A.T.F., 2018. Limnology of the largest multi-use artificial reservoir in NE Brazil: The Castanhão Reservoir, Ceará State. An. Acad. Bras. Cienc. 90, 2073–2096. https://doi.org/10.1590/0001-3765201820180085
dos Santos, J.C.N., de Andrade, E.M., de Araújo Neto, J.R., Meireles, A.C.M., de Queiroz Palácio, H.A., 2014. Land use and trophic state dynamics in a tropical semi-arid reservoir. Rev. Cienc. Agron. 45, 35–44. https://doi.org/10.1590/s1806-66902014000100005
Freire, L.L., Costa, A.C., Lima Neto, I.E. 2021. Spatio-temporal Patterns of River Water Quality in the Semiarid Northeastern Brazil. Water Air and Soil Pollution, 232(452). https://doi.org/10.1007/s11270-021-05406-7
Gerling, A.B., Munger, Z.W., Doubek, J.P., Hamre, K.D., Gantzer, P.A., Little, J.C., Carey, C.C., 2016. Whole-Catchment Manipulations of Internal and External Loading Reveal the Sensitivity of a Century-Old Reservoir to Hypoxia. Ecosystems 19, 555–571. https://doi.org/10.1007/s10021-015-9951-0
Gondim Rocha, S.M., Mesquita, J.B.F, Lima Neto, I. E., 2019. Analysis and modeling of the relations between nutrients and phytoplankton in reservoirs of Ceará. Brazilian Journal of Environmental Sciences, https://doi.org/10.5327/Z2176-947820190536 (in Portuguese).
Goshu, G., Strokal, M., Kroeze, C., Koelmans, A.A., de Klein, J.J.M., 2020. Assessing seasonal nitrogen export to large tropical lakes. Sci. Total Environ. 731, 139199. https://doi.org/10.1016/j.scitotenv.2020.139199
Håkanson, L., 2005. The importance of lake morphometry and catchment characteristics in limnology - Ranking based on statistical analyses. Hydrobiologia 541, 117–137. https://doi.org/10.1007/s10750-004-5032-7
Harrison, J.A., Maranger, R.J., Alexander, R.B., Giblin, A.E., Jacinthe, P.A., Mayorga, E., Seitzinger, S.P., Sobota, D.J., Wollheim, W.M., 2009. The regional and global significance of nitrogen removal in lakes and reservoirs. Biogeochemistry 93, 143–157. https://doi.org/10.1007/s10533-008-9272-x
Hellström, T., 1996. An empirical study of nitrogen dynamics in lakes. Water Environ. Res. 68, 55–65. https://doi.org/10.2175/106143096x127208
Jiang, X., Gao, G., Deng, J., Zhu, G., Tang, X., Shao, K., Hu, Y., 2022. Nitrogen concentration response to the decline in atmospheric nitrogen deposition in a hypereutrophic lake. Environmental Pollution, 300, p. 118952, http://dx.doi.org/10.1016/j.envpol.2022.118952.
Jiao, J., Duan, X., Lang, C., Yang, J. 2014. Study on apparent settling velocity of nutrients for Banchengzi reservoir, China. Acta Ecologica Sinica, Beijing, v. 34, n. 14, p. 1-2, jan. 2014. Acta Ecologica Sinica. http://dx.doi.org/10.5846/stxb201306101627
Johnes, P.J., 1996. Evaluation and management of the impact of land use change on the nitrogen and phosphorus load delivered to surface waters: the export coefficient modelling approach, J. Hydrol., 183, 323–349.
Le, T.P.Q., Ho, C.T., Duong, T.T., Rochelle-Newall, E., Dang, D.K., Hoang, T.S., 2014. Nutrient budgets (N and P) for the Nui Coc reservoir catchment (North Vietnam). Agric. Water Manag. 142, 152–161. https://doi.org/10.1016/j.agwat.2014.04.014
Lewtas, K., Osman, H., Gunn, G., Roy, D., 2017. Manitoba Prairie Lakes: Mass balance budget for nutrient management at Pelican Lake, Manitoba 36.
Li, Q., Yu, Q., Wang, F., Yan, W., Wang, J., 2021. Nitrogen removal in the Chaohu Lake, China: Implication in estimating lake N uptake velocity and modelling N removal efficiency of large lakes and reservoirs in the Changjiang River network. Ecol. Indic. 124, 107353. https://doi.org/10.1016/j.ecolind.2021.107353
Ma, S.N., Wang, H.J., Wang, H.Z., Zhang, M., Li, Y., Bian, S.J., Liang, X.M., Søndergaard, M., Jeppesen, E., 2021. Effects of nitrate on phosphorus release from lake sediments. Water Res. 194, 116894. https://doi.org/10.1016/j.watres.2021.116894
Mesquita, J.B. de F., Lima Neto, I.E., Raabe, A., de Araújo, J.C., 2020. The influence of hydroclimatic conditions and water quality on evaporation rates of a tropical lake. J. Hydrol. 590, 125456. https://doi.org/10.1016/j.jhydrol.2020.125456
Müller, B., Thoma, R., Baumann, K.B.L., Callbeck, C.M., Schubert, C.J., 2021. Nitrogen removal processes in lakes of different trophic states from on-site measurements and historic data. Aquat. Sci. 83, 1–13. https://doi.org/10.1007/s00027-021-00795-7
Nõges P (2005) Water and nutrient mass balance of the partly meromictic temperate Lake Verevi. Hydrobiologia 547:21–31. doi:10.1007/s10750-005-4140-3
Patoine A, Graham MD, Leavitt PR (2006) Spatial variation of nitrogen fixation in lakes of the northern Great Plains. Limnol Oceanogr 51:1665–1677
Pei, W., Yan, T., Lei, Q., Zhang, T., Fan, B., Du, X., Luo, J., Lindsey, S., Liu, H., 2022. Spatio-temporal variation of net anthropogenic nitrogen inputs (NANI) from 1991 to 2019 and its impacts analysis from parameters in Northwest China. J. Environ. Manage. 321. https://doi.org/10.1016/j.jenvman.2022.115996
Poikane, S., Kelly, M.G., Várbíró, G., Borics, G., Erős, T., Hellsten, S., Kolada, A., Lukács, B.A., Lyche Solheim, A., Pahissa López, J., Willby, N.J., Wolfram, G., Phillips, G., 2022. Estimating nutrient thresholds for eutrophication management: Novel insights from understudied lake types. Sci. Total Environ. 827. https://doi.org/10.1016/j.scitotenv.2022.154242
Rocha Junior, C.A.N. da, Costa, M.R.A. da, Menezes, R.F., Attayde, J.L., Becker, V., 2018. Water volume reduction increases eutrophication risk in tropical semi-arid reservoirs. Acta Limnol. Bras. 30. https://doi.org/10.1590/s2179-975x2117
Rocha, M. J.D., Lima Neto, I.E., 2021a. Modeling flow-related phosphorus inputs to tropical semiarid reservoirs. J. Environ. Manage. 295, 113123. https://doi.org/10.1016/j.jenvman.2021.11312
Rocha, M. de J., Lima Neto, I.E., 2021b. Phosphorus mass balance and input load estimation from the wet and dry periods in tropical semiarid reservoirs. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-021-16251-w
Rocha, M. de J, Lima Neto, I.E., 2022. Internal phosphorus loading and its driving factors in the dry period of Brazilian semiarid reservoirs. J. Environ. Manage. 312, 114983. https://doi.org/10.1016/j.jenvman.2022.114983
Ruan, X., Schellenger, F., Hellweger, F.L., 2014. Accounting for nitrogen fixation in simple models of lake nitrogen loading/export. Environ. Sci. Technol. 48, 5667–5673. https://doi.org/10.1021/es405707n
Salas, H., Martino, P. A., 1991. Simplified phosphorus trophic state model for warm-water tropical lakes. Water Research, 25, 341-350. https://doi.org/10.1016/0043-1354(91)90015-I
Small, G.E., Cotner, J.B., Finlay, J.C., Stark, R.A., Sterner, R.W., 2014. Nitrogen transformations at the sediment-water interface across redox gradients in the Laurentian Great Lakes. Hydrobiologia 731, 95–108. https://doi.org/10.1007/s10750-013-1569-7
Santos, J.C.N., de Andrade, E.M., de Araújo Neto, J.R., Meireles, A.C.M., de Queiroz Palácio, H.A., 2014. Land use and trophic state dynamics in a tropical semi-arid reservoir. Rev. Cienc. Agron. 45, 35–44. https://doi.org/10.1590/s1806-66902014000100005
Souza Filho, F. A., Martins E., S., P., R., and Porto M., 2006. The Mixing process in semiarid reservoirs and its implication on water quality. Rev Bras. Recur Hídricos 11(4): 109-119. (in Portuguese).
Steingruber, S.M., 2020. Improved empirical models for predicting nitrogen retention in lakes and reservoirs. Ecol. Modell. 416. https://doi.org/10.1016/j.ecolmodel.2019.108853
Tomaszek, J.A., Koszelnik, P., 2003. A simple model of nitrogen retention in reservoirs. Hydrobiologia 504, 51–58. https://doi.org/10.1023/B:HYDR.0000008507.66924.23
Verdouw, H.; Dekkers, E. M. J. 1982. Nitrogen cycle of Lake Vechten: concentration patterns and internal mass-balance. Hydrobiologia. 95. 1, p. 191-197, set. 1982. Springer Science and Business Media LLC. http://dx.doi.org/10.1007/bf00044483.
Vidal, T.F., Capelo Neto, J., 2014. Dinâmica de nitrogênio e fósforo em reservatório na região semiárida utilizando balanço de massa. Rev. Bras. Eng. Agrícola e Ambient. 18, 402–407. https://doi.org/10.1590/s1415-43662014000400007
Vinçon-Leite, B., Casenave, C., 2019. Modelling eutrophication in lake ecosystems: A review. Sci. Total Environ. 651, 2985–3001. https://doi.org/10.1016/j.scitotenv.2018.09.320
Verdouw, H.; Dekkers, E. M. J. 1982. Nitrogen cycle of Lake Vechten: concentration patterns and internal mass-balance. Hydrobiologia. 95, 1, p. 191-197. http://dx.doi.org/10.1007/bf00044483.
Vollenweider, R. A., 1968. Water management research. Scientific fundamentals of the eutrophication of lakes and flowing waters with particular reference to nitrogen and phosphorus as factors in eutrophication. Organization for Economic Co-operation and De. Limnology and Oceanography, 15, 169–170. https://doi.org/10.4319/lo.1970.15.1.0169
Wiegand, M.C.; Nascimento, A. T. P.; Costa, A.C.; Lima Neto, I. E., 2021. Trophic state changes of semi-arid reservoirs as a function of the hydro-climatic variability. Journal of Arid Environments. http://dx.doi.org/10.1016/j.jaridenv.2020.104321
Wiegand, M.C., Pinheiro, A.T., Costa, A.C., 2020. Evaluation of Limiting Nutrient of Algal Production in Reservoirs of the Brazilian Semiarid Algal Em Reservatórios Do Semiárido Brasileiro. Rev. Bras. Ciências Ambient. Brazilian J. Environ. Sci. 23.
Wu, Z., Liu, Y., Liang, Z., Wu, S., Guo, H., 2017. Internal cycling, not external loading, decides the nutrient limitation in eutrophic lake: A dynamic model with temporal Bayesian hierarchical inference. Water Res. 116, 231–240. https://doi.org/10.1016/j.watres.2017.03.039
Yang, C., Yang, P., Geng, J., Yin, H., Chen, K., 2020. Sediment internal nutrient loading in the most polluted area of a shallow eutrophic lake (Lake Chaohu, China) and its contribution to lake eutrophication. Environ. Pollut. 262, 114292. https://doi.org/10.1016/j.envpol.2020.114292
Zhong, J., Wen, S., Zhang, Lu, Wang, J., Liu, C., Yu, J., Zhang, Lei, Fan, C., 2021. Nitrogen budget at sediment–water interface altered by sediment dredging and settling particles: Benefits and drawbacks in managing eutrophication. J. Hazard. Mater. 406, 124691. https://doi.org/10.1016/j.jhazmat.2020.124691
Zhu, L., Shi, W., Zhou, J., Yu, J., Kong, L., Qin, B., 2021. Strong turbulence accelerates sediment nitrification-denitrification for nitrogen loss in shallow lakes. Sci. Total Environ. 761. https://doi.org/10.1016/j.scitotenv.2020.143210

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Nitrogen mass balance and uptake velocity for eutrophic reservoirs in the Brazilian semiarid region

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Methodology

2.1 Site description and reservoir selection

2.2 Data sources

2.3 Reservoir modeling

2.3.1 Nitrogen budget for total input load

2.3.2 Driving factors on TN uptake velocity (v_f)

2.3.3 Assumptions for fixation and atmospheric deposition

2.3.4 Assumption for sediment fluxes and internal load

2.4 Uncertainty analysis and model validation

3. Results And Discussion

3.1 TN concentration among and within reservoirs

3.2 Assessment of TN pollution sources from catchment landuse and modeled TN load

3.3. Estimated settling loss rate (k_s)

3.4 Total nitrogen uptake velocity (v_f)

3.5 Seasonal variation in TN removal rate

3.6 Assessment of load production and dynamic between phosphorus and nitrogen

4. Conclusions

5. Declarations

References

Status:

Journal Publication

Version 1

Nitrogen mass balance and uptake velocity for eutrophic reservoirs in the Brazilian semiarid region

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Methodology

2.1 Site description and reservoir selection

2.2 Data sources

2.3 Reservoir modeling

2.3.1 Nitrogen budget for total input load

2.3.2 Driving factors on TN uptake velocity (vf)

2.3.3 Assumptions for fixation and atmospheric deposition

2.3.4 Assumption for sediment fluxes and internal load

2.4 Uncertainty analysis and model validation

3. Results And Discussion

3.1 TN concentration among and within reservoirs

3.2 Assessment of TN pollution sources from catchment landuse and modeled TN load

3.3. Estimated settling loss rate (ks)

3.4 Total nitrogen uptake velocity (vf)

3.5 Seasonal variation in TN removal rate

3.6 Assessment of load production and dynamic between phosphorus and nitrogen

4. Conclusions

5. Declarations

References

Status:

Journal Publication

Version 1

2.3.2 Driving factors on TN uptake velocity (v_f)

3.3. Estimated settling loss rate (k_s)

3.4 Total nitrogen uptake velocity (v_f)