River network alteration of C-N-P dynamics in a mesoscale agricultural catchment

High-intensity rainfall following drought triggers extreme nutrient concentrations in a small agricultural catchment

The profound influence of climate change on the hydrological cycle raises concerns about its potential impacts on water quality, particularly in agricultural catchments. Here, we analysed 200 storm events monitored for nitrate and total phosphorus (TP) at sub-hourly intervals from 2016 to 2023 in the Kervidy-Naizin catchment (north-western France). Using Extreme Value theory, we identified storm events with extreme concentrations and compared their hydroclimatic characteristics to those of non-extreme events. We hypothesised that extreme concentration events occurred under extreme hydroclimatic conditions, which are projected to become more frequent in the future. The extreme events identified showed dilution patterns for nitrate, with concentrations decreasing by up to 41 %, and accretion patterns for TP, with concentrations increasing by up to 1400 % compared to non-extreme events. Hydroclimatic conditions during extreme concentration events were characterised by high rainfall intensities and low antecedent discharge, but no particular conditions for mean discharge. During non-extreme events, nitrate concentration-discharge relationships exhibited primarily clockwise hysteresis, whereas TP displayed an equal mix of clockwise and anticlockwise loops. In contrast, extreme events showed more anticlockwise hysteresis for nitrate and weak hysteresis for TP. We interpreted these dynamics and their hydroclimatic controls as the result of infiltration-excess overland flow diluting nitrate-rich groundwater and exporting large amounts of TP during intensive rainfall events following droughts, while groundwater fluctuations in the riparian zone and streambed remobilization control nutrient exports during non-extreme events. Given the increasing frequency and intensity of hydroclimatic extremes, such retrospective analyses can provide valuable insights into future nutrient dynamics in streams draining agricultural catchments.

How much nutrient reaches a stream: Insights from a hybrid model and implications for watershed nitrogen export and removal

Excess nitrogen (N) discharged into streams and rivers degrades freshwater quality and threatens ecosystems worldwide. Land use patterns may influence riverine N export, yet the effect of location on N export and removal is not fully understood. We proposed a hybrid model to analyze N export and removal within the watersheds. The proposed model is satisfied for the riverine N modelling. The KGE and R2 are 0.75 and 0.72 in the calibration period which are 0.76 and 0.61 in the validation period. Human-impacted land use may modify the N yield in the watershed, and the net N export from built-up to the in-stream system was highest in the urbanized sub-watersheds (0.81), followed by the agricultural sub-watersheds (0.88), and forested sub-watersheds (0.96). Agricultural activities make a large contribution to the N exports in the watersheds, and the mean N input from the agricultural land use to in-stream were 2069-4353 kg km-2 yr-1. Besides, the excess inputs of N by overapplication of fertilizer and manure during the agricultural activities may increase legacy N in soil and groundwater. Biological processes for the riverine N removal may be controlled by the available substrate in the freshwater system, and temperature sensitivity of denitrification is highest in the flood seasons, especially for the human-impacted sub-watersheds. The riverine biological processes may be limited by other competitions. Our model results provide evidence that quantity and location of specific land use may control biogeochemistry within watersheds. We demonstrate the need to understand nutrient export and removal within watersheds by improving the representation of spatial patterns in existing watershed models, and we consider this study to be a new effort for the spatially explicit modeling to support land-use based N management in watersheds.

Development of a two-dimensional model to assess carbon dynamics and anthropogenic effects on CO2 emissions in the Tan river, southern China

Tidal rivers are key biochemical reaction channels along the land-ocean aquatic continuum, receiving carbon from wastewater and agricultural drains, which can considerably affect CO2 emissions. We developed a two-dimensional hydrodynamic and ecological model coupled with an inorganic carbon module along the Tan River in southern China. The simulations of and observations regarding discharge, temperature, total organic carbon (TOC), total inorganic carbon (TIC), and other common water quality variables were generally in good agreement. Based on the validated model, we employed statistical and scenario analyses to evaluate the carbon distribution, TOC and TIC budgets, and the imbalances induced by climatic and anthropogenic changes, providing insights into their potential greenhouse effect. The Tan River was consistently supersaturated with CO2 with an annual mean air-water CO2 emission flux (FCO2) of 226.1 ± 84.9 mmol m-2 d-1, and significant temporal and spatial variations of FCO2, TOC, and TIC were observed. Urban small streams tended to emit additional CO2 during wet seasons, and rural tributaries usually had an increase in TOC concentrations during the dry season. FCO2 was significantly positively correlated with air temperature and negatively correlated with total nitrogen, total phosphorus, and TOC. The annual riverine input of carbon to the urban river network was 17.37 Gg C yr-1, with 59.82% of TOC, and carbon output was 15.31 Gg C yr-1, with 66.87% of TOC. The retention rates for TOC and total carbon were 50.7% and 11.8% in the urban branch, respectively. Furthermore, warming and wastewater treatment could prevent urban river networks and downstream rivers from becoming carbon sources. Therefore, our findings suggest that riverine management strategies change the global CO2 release from tidal rivers and estuarine systems under climate change.

Spatial characteristics of nutrient budget on town scale in the Three Gorges Reservoir area, China

Accurately quantifying nutrient budget is an essential step toward sustainable nutrient management in large watersheds increasingly disturbed by human activity. A town-scale nutrient budget framework based on the Soil and Water Assessment Tool was developed for 2010-2012 in the Three Gorges Reservoir area in China (TGRA). Moran's I spatial correlation test and Geodetector spatial heterogeneity test were employed to systematically analyze the spatial characteristics of the resulting nutrient budget. The Moran's I value of total nitrogen (TN) and total phosphorus (TP) gradually increased from input to output in the range of 0.091-0.232 and 0.102-0.484, respectively. Towns with higher TN and TP inputs were largely concentrated in the main urban area of Chongqing because of its high population density. By contrast, towns with higher TN and TP outputs were concentrated in the head of the TGRA. The Moran's I values of the TN and TP retention coefficients (R) were 0.433 and 0.524, respectively, demonstrating clear spatial consistency. Towns with a "High-high" spatial consistency pattern and positive R value were concentrated in the tail and hinterland, while those with a "Low-low" spatial consistency pattern and negative coefficient value were located mainly in the head of the TGRA. This phenomenon was mostly caused by differences in regional elevation, the normalized difference vegetation index, and soil erosion factor. The interaction effect between any two of these three factors on nutrient retention (Geodetector q-value) was greater than 60%. Therefore, future nutrient management should be based on a full understanding of regional biophysical conditions, especially in large areas. These findings provide a new perspective on fine nutrient management.

Phosphate removal from actual wastewater via La(OH)3-C3N4 adsorption: Performance, mechanisms and applicability

In this study, La(OH)₃ nanoparticles were immobilized on C₃N₄ to effectively restrict their aggregation and subsequently enhance the La utilization efficiency to promote phosphate adsorption. The prepared La(OH)₃-C₃N₄ nanocomposite was characterized by SEM, XRD, FTIR, XPS, BET and Zeta potential analysis. Batch and continuously-fed (fixed-bed column) experiments to assess the adsorption performance of La(OH)₃-C₃N₄ showed that the composite exhibits superior utilization efficiency, resulting to relatively quick adsorption with a short equilibrium time of 30 min. The theoretical maximum P adsorption capacity reached the 148.35 mg·g^-1, efficiency that remained unaffected by the anions and HA present. The adsorption mechanism showed stability in a wide pH range (4.0-11.0) and is considered effective even after extensive use (five-cycles). The dynamics of the adsorption capacity and the half-penetration time values were estimated by 'Thomas' and 'Yoon-Nelson' models showed that are better represented from the experimental values obtained from the fixed-bed column trial. The adsorption mechanisms were attributed to surface precipitation, electrostatic attraction, and inner-sphere complexation via ligand exchange. Furthermore, La(OH)₃-C₃N₄ demonstrated high efficiency in scavenging phosphate from both diluted and concentrated wastewater (natural pond and swine wastewater respectively). The above confirm that La(OH)₃-C₃N₄ is a promising composite material for phosphate management in aqueous environments.

A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates

Riparian forest buffers have multiple benefits for biodiversity and ecosystem services in both freshwater and terrestrial habitats but are rarely implemented in water ecosystem management, partly reflecting the lack of information on the effectiveness of this measure. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. We aim to develop a Bayesian belief network (BBN) model for application as a learning tool to simulate and assess the reach- and segment-scale effects of riparian vegetation properties and land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and subcatchment land use information from geographic information system data, and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania, and Sweden). We modelled the ecological condition based on the Average Score Per Taxon (ASPT) index, a macroinvertebrate-based index widely used in European bioassessment, as a function of different riparian variables using the BBN modelling approach. The results of the model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing the ecological condition, with reach-scale riparian vegetation quality associated with the strongest improvements in ecological status. Specifically, reach-scale buffer vegetation of score 3 (i.e. moderate quality) generally results in the highest probability of a good ASPT score (99-100%). In contrast, a site with a narrow width of riparian trees and a small area of trees with reach-scale buffer vegetation of score 1 (i.e. low quality) predicts a high probability of a bad ASPT score (74%). The strengths of the BBN model are the ease of interpretation, fast simulation, ability to explicitly indicate uncertainty in model outcomes, and interactivity. These merits point to the potential use of the BBN model in workshop activities to stimulate key learning processes that help inform the management of riparian zones.

Is the Cyanobacterial Bloom Composition Shifting Due to Climate Forcing or Nutrient Changes? Example of a Shallow Eutrophic Reservoir

Cyanobacterial blooms in eutrophic freshwater is a global threat to the functioning of ecosystems, human health and the economy. Parties responsible for the ecosystems and human health increasingly demand reliable predictions of cyanobacterial development to support necessary decisions. Long-term data series help with identifying environmental drivers of cyanobacterial developments in the context of climatic and anthropogenic pressure. Here, we analyzed 13 years of eutrophication and climatic data of a shallow temperate reservoir showing a high interannual variability of cyanobacterial development and composition, which is a less occurring and/or less described phenomenon compared to recurrant monospecific blooms. While between 2007–2012 Planktothrix agardhii dominated the cyanobacterial community, it shifted towards Microcystis sp. and then Dolichospermum sp. afterwards (2013–2019). The shift to Microcystis sp. dominance was mainly influenced by generally calmer and warmer conditions. The later shift to Dolichospermum sp. was driven by droughts influencing, amongst others, the N-load, as P remained unchanged over the time period. Both, climatic pressure and N-limitation contributed to the high variability of cyanobacterial blooms and may lead to a new equilibrium. The further reduction of P-load in parallel to the decreasing N-load is important to suppress cyanobacterial blooms and ameliorate ecosystem health.