Superlinear scaling of riverine biogeochemical function with watershed size

Fire influence on land-water interactions in aridland catchments

Wildfires have increased in size, frequency, and intensity in arid regions of the western United States because of human activity, changing land use, and rising temperature. Fire can degrade water quality, reshape aquatic habitat, and increase the risk of high discharge and erosion. Drawing from patterns in montane dry forest, chaparral, and desert ecosystems, we developed a conceptual framework describing how interactions and feedbacks among material accumulation, combustion of fuels, and hydrologic transport influence the effects of fire on streams. Accumulation and flammability of fuels shift in opposition along gradients of aridity, influencing the materials available for transport. Hydrologic transport of combustion products and materials accumulated after fire can propagate the effects of fire to unburned stream-riparian corridors, and episodic precipitation characteristic of arid lands can cause lags, spatial heterogeneity, and feedbacks in response. Resolving uncertainty in fire effects on arid catchments will require monitoring across hydroclimatic gradients and episodic precipitation.

Toward a metabolic theory of catchments: Scaling of water and carbon fluxes with size

Allometric scaling relations are widely used to link biological processes to body size in nature. Several studies have shown that such scaling laws hold also for natural ecosystems, including individual trees and forests, riverine metabolism, and river network organization. However, the derivation of scaling laws for catchment-scale water and carbon fluxes has not been achieved so far. Here, we focus on scaling relations of catchment green metabolism, defined as the set of ecohydrological and biogeochemical processes through which vegetation assemblages in catchments maintain their structure and react to the surrounding environment. By revising existing plant size-density relationships and integrating them across large-scale domains, we show that the ecohydrological fluxes occurring at the catchment scale are invariant with respect to the above-ground vegetation biomass per unit area of the basin, while they scale linearly with catchment size. We thus demonstrate that the sublinear scaling of plant metabolism results in an isometric scaling at catchment and regional scales. Deviations from such predictions are further shown to collapse onto a common distribution, thus incorporating natural fluctuations due to resource limitations into a generalized scaling theory. Results from scaling arguments are supported by hyperresolution ecohydrological simulations and remote sensing observations.

Ecosystem-size relationships of river populations and communities

Knowledge of ecosystem-size influences on river populations and communities is integral to the balancing of human and environmental needs for water. The multiple dimensions of dendritic river networks complicate understanding of ecosystem-size influences, but could be resolved by the development of scaling relationships. We highlight the importance of physical constraints limiting predator body sizes, movements, and population sizes in small rivers, and where river contraction limits space or creates stressful conditions affecting community stability and food webs. Investigations of the scaling and contingency of these processes will be insightful because of the underlying generality and scale independence of such relationships. Doing so will also pinpoint damaging water-management practices and identify which aspects of river size can be most usefully manipulated in river restoration.

Basin-wide tracking of nitrate cycling in Yangtze River through dual isotope and machine learning

The nitrate (NO3-) input has adversely affected the water quality and ecological function in the whole basin of the Yangtze River. The protection of water sources and implementation of "great protection of Yangtze River" policy require large-scale information on water contamination. In this study, dual isotope and Bayesian mixing model were used to research the transformation and sources of nitrate. Chemical fertilizers contribute 76 % of the nitrate sources in the upstream, while chemical fertilizers were also dominant in the midstream (39 %) and downstream (39 %) of Yangtze River. In addition, nitrification process occurred in the whole basin. Four machine learning models were used to relate nitrate concentrations to explanatory variables describing influence factors to predict nitrate concentrations in the whole basin of Yangtze River. The anthropogenic and natural factors, such as rainfall, GDP and population were chosen to take as predictor variables. The eXtreme Gradient Boosting (XGBoost) model for nitrate has a better predictive performance with an R2 of 0.74. The predictive models of nitrate concentrations will help identify the nitrate distribution and transport in the whole Yangtze River basin. Overall, this study represents the first basin-wide data-driven assessment of the nitrate cycling in the Yangtze River basin.

Nitrate transformation and source tracking of rivers draining into the Bohai Sea using a multi-tracer approach combined with an optimized Bayesian stable isotope mixing model

Excessive levels of NO3- can result in multiple eco-environmental issues due to potential toxicity, especially in coastal areas. Accurate source tracing is crucial for effective pollutant control and policy development. Bayesian models have been widely employed to trace NO3- sources, while limited studies have utilized optimized Bayesian models for NO3- tracing in the coastal rivers. The Bohai Rim is highly susceptible to ecological disturbances, particularly N pollution, and has emerged as a critical area. Therefore, identification the N fate and understanding their sources contribution is urgent for pollution mitigation efforts. In addition, understanding the influenced key driven factors to source dynamic in the past ten years is also implication to environmental management. In this study, water samples were collected from 36 major river estuaries that drain into the Bohai Sea of North China. The main transformation processes were analyzed and quantified the sources of NO3- using a Bayesian stable isotope mixing model (MixSIAR) with isotopic approach (δ15N-NO3- and δ18O-NO3-). The overall isotopic composition of δ15N-NO3- and δ18O-NO3- in estuary waters ranged from -0.8-19.3‰ (9.3 ± 4.6‰) and from -7.1-10.5‰ (5.0 ± 4.3‰), respectively. The main sources of nitrate in most river estuaries were manure & sewage, and chemical fertilizer, while weak denitrification and mixed processes were observed in Bohai Rim region. A temporal decrease in the nitrogen load entering the Bohai Sea indicates an improvement in water quality in recent years. By incorporating informative priors and utilizing the calculated coefficients, the accuracy of sourcing results was significantly improved. This study highlighted the optimized MixSIAR model enhanced its accuracy for sourcing analysis and providing valuable insights for policy formulation. Future efforts should focus on improving management strategies to reduce nitrogen into the bay.

Large-stream nitrate retention patterns shift during droughts: Seasonal to sub-daily insights from high-frequency data-model fusion

High-frequency nitrate-N (NO3--N) data are increasingly available, while accurate assessments of in-stream NO3--N retention in large streams and rivers require a better capture of complex river hydrodynamic conditions. This study demonstrates a fusion framework between high-frequency water quality data and hydrological transport models, that (1) captures river hydraulics and their impacts on solute signal propagation through river hydrodynamic modeling, and (2) infers in-stream retention as the differences between conservatively traced and reactively observed NO3--N signals. Using this framework, continuous 15-min estimates of NO3--N retention were derived in a 6th-order reach of the lower Bode River (27.4 km, central Germany), using long-term sensor monitoring data during a period of normal flow from 2015 to 2017 and a period of drought from 2018 to 2020. The unique NO3--N retention estimates, together with metabolic characteristics, revealed insightful seasonal patterns (from high net autotrophic removal in late-spring to lower rates, to net heterotrophic release during autumn) and drought-induced variations of those patterns (reduced levels of net removal and autotrophic nitrate removal largely buffered by heterotrophic release processes, including organic matter mineralization). Four clusters of diel removal patterns were identified, potentially representing changes in dominant NO3--N retention processes according to seasonal and hydrological conditions. For example, dominance of autotrophic NO3--N retention extended more widely across seasons during the drought years. Such cross-scale patterns and changes under droughts are likely co-determined by catchment and river environments (e.g., river primary production, dissolved organic carbon availability and its quality), which resulted in more complex responses to the sequential droughts. Inferences derived from this novel data-model fusion provide new insights into NO3- dynamics and ecosystem function of large streams, as well as their responses to climate variability. Moreover, this framework can be flexibly transferred across sites and scales, thereby complementing high-frequency monitoring to identify in-stream retention processes and to inform river management.

Advances in Catchment Science, Hydrochemistry, and Aquatic Ecology Enabled by High-Frequency Water Quality Measurements

High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function.