Predicting algal blooms: Are we overlooking groundwater?

Contribution of groundwater-borne nutrients to eutrophication potential and the share of benthic algae in a large lowland river

Groundwater inflow can be a significant source of nutrients for riverine ecosystems, which can affect eutrophication i.e., the elevated primary production and the corresponding accumulation of algal biomass. Experimental and modelling work has shown that benthic algae (autotrophic biofilms) in particular benefit, as they have direct access to the inflowing groundwater-borne nutrients. Primarily the supply of phosphorus (P) enhances pelagic algal biomass, as it is the limiting nutrient for primary production in most freshwater systems. In this study, we estimate the effect of groundwater inflow on overall eutrophication of a large, European lowland river and tested its seasonal effect on biofilms in particular. We calculated the effects on overall eutrophication during summer according to the estimated input of groundwater-borne P and the C:P stoichiometry of planktonic algae in the Elbe River. Our model indicated that these diffuse P inputs have the potential to significantly increase eutrophication. Groundwater-P can contribute up to 1.5 t/d PO4 over the investigated 450 km stretch of the Elbe River under low flow conditions. This would result in an additional planktonic load of about 46 t/d of particulate organic carbon, thereby contributing to eutrophication at the regional scale in this river. In contrast, at the local scale, biofilms were collected seasonally from artificial substrata exposed in the river either in hydrogeologically active areas with groundwater inflow, or in areas of varying hydraulic connectivity. Analyses of biofilm macronutrients, structural components and biofilm community composition show distinct effects of season, hydrogeology and groundwater inflow. The dominant predictors were season and the interaction between hydrogeology and groundwater. Benthic eutrophication is most likely to occur in autumn in areas of loose rock with high groundwater inflow. The strong interaction of environmental factors in determining benthic eutrophication highlights the need to assess these factors in combination rather than in isolation.

Improved eutrophication model with flow velocity-influence function and application for algal bloom control in a reservoir in East China

Improving hydrodynamic conditions is considered an effective method for facilitating the eutrophication management. However, the effect of hydrodynamic conditions on algal growth has rarely been quantified. In this work, a eutrophication model was developed and flow velocity was introduced into the algae growth kinetic formula to simulate the dynamics of algae growth in a drinking water source reservoir in East China. Based on the previous research and model calibration, the flow velocity-influence function f(v) and its parameters were determined. Accordingly, the optimal flow velocity for the dominant algae growth and critical flow velocity for algal growth inhibition were presented to be 0.055 m/s and 0.200 m/s for the study reservoir. Modeled results considering f(v) agreed with better with observations and reproduced the algal overgrowth process more accurately. The spatial-temporal differences in chlorophyll a (Chl a) concentration distribution during the algal proliferation period were analyzed on the basis of simulation results, which corroborated the significant influence of flow velocity on algal growth. The established model was applied to investigate the effect of improvement in hydrodynamic conditions on algal bloom control in the reservoir, and the scenario simulation of the additional sluice was conducted. Results showed that the additional sluice operation inhibited algal overgrowth effectively, resulting in an average decrease of 24.8%, 3.3%, 43.0%, and 37.5% in modeled Chl a concentration upstream north, upstream south, midstream and downstream, respectively. The established model might serve as a practical tool for eutrophication management in the study reservoir and other water bodies with similar hydrological characteristics and geographical features.

Important Role of Overland Flows and Tile Field Pathways in Nutrient Transport

Nitrogen and phosphorus pollution is of great concern to aquatic life and human well-being. While most of these nutrients are applied to the landscape, little is known about the complex interplay among nutrient applications, transport attenuation processes, and coastal loads. Here, we enhance and apply the Spatially Explicit Nutrient Source Estimate and Flux model (SENSEflux) to simulate the total annual nitrogen and phosphorus loads from the US Great Lakes Basin to the coastline, identify nutrient delivery hotspots, and estimate the relative contributions of different sources and pathways at a high resolution (120 m). In addition to in-stream uptake, the main novelty of this model is that SENSEflux explicitly describes nutrient attenuation through four distinct pathways that are seldom described jointly in other models: runoff from tile-drained agricultural fields, overland runoff, groundwater flow, and septic plumes within groundwater. Our analysis shows that agricultural sources are dominant for both total nitrogen (TN) (58%) and total phosphorus (TP) (46%) deliveries to the Great Lakes. In addition, this study reveals that the surface pathways (sum of overland flow and tile field drainage) dominate nutrient delivery, transporting 66% of the TN and 76% of the TP loads to the US Great Lakes coastline. Importantly, this study provides the first basin-wide estimates of both nonseptic groundwater (TN: 26%; TP: 5%) and septic-plume groundwater (TN: 4%; TP: 2%) deliveries of nutrients to the lakes. This work provides valuable information for environmental managers to target efforts to reduce nutrient loads to the Great Lakes, which could be transferred to other regions worldwide that are facing similar nutrient management challenges.

Nitrate sources and transformation processes in groundwater of a coastal area experiencing various environmental stressors

In coastal salinized groundwater systems, contamination from various nitrate (NO3) inputs combined with complex hydrogeochemical processes make it difficult to distinguish NO3 sources and identify potential NO3 transformtation processes. Effective field-based NO3 studies in coastal areas are needed to improve the understanding of NO3 contamination dynamics in groundwater of such complex coastal systems. This study focuses on a typical Mediterranean coastal agricultural area, located in Tunisia, experiencing substantial NO3 contamination from multiple anthropogenic sources. Here, multiple isotopic tracers (δ18OH2O, δ2HH2O, δ15NNO3, δ18ONO3, and δ11B) combined with a Bayesian isotope MixSIAR model are used (i) to identify the major NO3 sources and their contributions, and (ii) to describe the potential NO3 transformation processes. The measured NO3 concentrations in groundwater are above the natural baseline threshold, suggesting anthropogenic influence. The measured isotopic composition of NO3 indicates that manure, soil organic matter, and sewage are the potential sources of NO3, while δ11B values constrain the NO3 contamination to manure; a finding that is supported by the results of MixSIAR model revealing that manure-derived NO3 dominates over other likely sources. Nitrate derived from manure in the study area is attributed to organic fertilizers used to promote crop growth, and livestock that deposit manure directly on the ground surface. Evidence for ongoing denitrification in groundwaters of the study area is supported by an enrichment in both 15N and 18O in the remaining NO3, although isotopic mass balances between the measured and the theoretical δ18ONO3 values also suggest the occurrence of nitrification. The simultaneous occurrence of these biogeochemical processes with heterogeneous distribution across the study area reflect the complexity of interactions within the investigated coastal aquifer. The multiple isotopic tracer approach used here can identify the effect of multiple NO3 anthropogenic activities in coastal environments, which is fundamental for sustainable groundwater resources management.

Deep denitrification: Stream and groundwater biogeochemistry reveal contrasted but connected worlds above and below

Excess nutrients from agricultural and urban development have created a cascade of ecological crises around the globe. Nutrient pollution has triggered eutrophication in most freshwater and coastal ecosystems, contributing to a loss in biodiversity, harm to human health, and trillions in economic damage every year. Much of the research conducted on nutrient transport and retention has focused on surface environments, which are both easy to access and biologically active. However, surface characteristics of watersheds, such as land use and network configuration, often do not explain the variation in nutrient retention observed in rivers, lakes, and estuaries. Recent research suggests subsurface processes and characteristics may be more important than previously thought in determining watershed-level nutrient fluxes and removal. In a small watershed in western France, we used a multi-tracer approach to compare surface and subsurface nitrate dynamics at commensurate spatiotemporal scales. We combined 3-D hydrological modeling with a rich biogeochemical dataset from 20 wells and 15 stream locations. Water chemistry in the surface and subsurface showed high temporal variability, but groundwater was substantially more spatially variable, attributable to long transport times (10-60 years) and patchy distribution of the iron and sulfur electron donors fueling autotrophic denitrification. Isotopes of nitrate and sulfate revealed fundamentally different processes dominating the surface (heterotrophic denitrification and sulfate reduction) and subsurface (autotrophic denitrification and sulfate production). Agricultural land use was associated with elevated nitrate in surface water, but subsurface nitrate concentration was decoupled from land use. Dissolved silica and sulfate are affordable tracers of residence time and nitrogen removal that are relatively stable in surface and subsurface environments. Together, these findings reveal distinct but adjacent and connected biogeochemical worlds in the surface and subsurface. Characterizing how these worlds are linked and decoupled is critical to meeting water quality targets and addressing water issues in the Anthropocene.

Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States

Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. The Medium Resolution Imaging Spectrometer (MERIS) and United States (US) National Lakes Assessments are leveraged to model the probability among lakes of exceeding lower and higher demonstration thresholds for microcystin toxin, cyanobacteria, and chlorophyll a. By leveraging the large spatial variation among lakes using two national-scale data sources, rather than focusing on temporal variability, this approach avoids many of the previous challenges in relating satellite imagery to cyanotoxins. For every satellite-derived lake-level Cyanobacteria Index (CI_cyano) increase of 0.01 CI_cyano/km2, the odds of exceeding six bloom thresholds increased by 23-54 %. When the models were applied to the 2192 satellite monitored lakes in the US, the number of lakes identified with ≥75 % probability of exceeding the thresholds included as many as 335 lakes for the lower thresholds and 70 lakes for the higher thresholds, respectively. For microcystin, the models identified 162 and 70 lakes with ≥75 % probability of exceeding the lower (0.2 μg/L) and higher (1.0 μg/L) thresholds, respectively. This approach represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.

Investigating relationships between climate controls and nutrient flux in surface waters, sediments, and subsurface pathways in an agricultural clay catchment of the Great Lakes Basin

Water quality within agricultural catchments is governed by management practices and climate conditions that control the transport, storage, and exchange of nutrients between components of the hydrologic cycle. This study aims to improve knowledge of nitrogen (N) and phosphorus (P) transport in low permeability agricultural watersheds by considering spatial and temporal trends of surface water nutrient concentrations in relation to hydroclimatic drivers, sediment quality, shallow hyporheic exchange, groundwater quality, and tile drain discharge over a 14-month field study in a clay hydrosystem of the Lake Huron basin, one of the five Great Lakes. Results found that events of varying magnitude and intensity enhanced nutrient release from overland flow and subsurface pathways. Tile drain discharge was found to be a consistent and elevated source of P and N to surface waters when flowing, mobilizing both diffuse nutrients from fertilizer application and legacy stores in the vadose zone. Surface water quality was temporally variable at the seasonal and event scale. Targeted sampling following fertilization periods, snowmelt, and moderate precipitation events revealed catchment wide elevated nutrient concentrations, emphasizing the need for targeted sampling regimes. Controls other than discharge magnitude and overland flow were found to contribute to peak nutrient concentrations, including internal nitrate loading, soil-snowmelt interaction, catchment wetness, and freeze thaw cycles. Sediments were found to store P in calcium minerals and have a high P storage capacity. Instream mechanisms such as sediment P fixation and hyporheic exchange may play a role in mediating surface water quality, but currently have no discernable benefit to year-round surface water nutrient concentrations. Best management practices need to focus on reducing sources of agricultural nutrients (e.g., field phosphorus concentrations and tile drain discharge loading) at the watershed scale to reduce nutrient concentrations and export in flashy clay catchments.

Remote sensing for mapping algal blooms in freshwater lakes: a review

A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sensing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms, such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.

Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers

Nitrate is one of the most widespread and persistent pollutants in our time. Our understanding of nitrate dynamics has advanced substantially in the past decades, although its predominant drivers across gradients of climate, land use, and geology have remained elusive. Here we collated nitrate data from 2061 rivers along with 32 watershed characteristic indexes and developed machine learning models to reconstruct long-term mean (multi-year average) nitrate concentrations in the contiguous United States (CONUS). The trained models show similarly satisfactory model performance and can predict nitrate concentrations in chemically-ungauged places with about 70% accuracy. Further analysis revealed that five (out of 32) indexes (drivers) can explain about 70% of spatial variations in mean nitrate concentrations. The five influential drivers are nitrogen application rates N_rate and urban area A_urban% (human drivers), mean annual precipitation and temperature (climate drivers), and sand percent Sand% (soil property driver). Nitrate concentrations in undeveloped sites are primarily modulated by climate and soil property; they decrease with increasing mean discharge and Sand%. Nitrate concentrations in agriculture and urban sites increase with N_rate and A_urban% until reaching their apparent maxima around 10,000 kg/km²/yr and around 25%, respectively. Results indicate that nitrate concentrations may remain similar or increase with growing human population. In addition, nitrate concentrations can increase even without human input, as warming escalates water demand and reduces mean discharge in many places. These results allude to a conceptual model that highlights the impacts of distinct drivers: while human drivers predominate nitrogen input to land and rivers, climate drivers and soil properties modulate its transport and transformation, the balance of which determine long-term mean concentrations. Such mechanism-based insights and forecasting capabilities are essential for water management as we expect changing climate and growing agriculture and urbanization.

Reconceptualizing the hyporheic zone for nonperennial rivers and streams

Nonperennial streams dominate global river networks and are increasing in occurrence across space and time. When surface flow ceases or the surface water dries, flow or moisture can be retained in the subsurface sediments of the hyporheic zone, supporting aquatic communities and ecosystem processes. However, hydrological and ecological definitions of the hyporheic zone have been developed in perennial rivers and emphasize the mixing of water and organisms, respectively, from both the surface stream and groundwater. The adaptation of such definitions to include both humid and dry unsaturated conditions could promote characterization of how hydrological and biogeochemical variability shape ecological communities within nonperennial hyporheic zones, advancing our understanding of both ecosystem structure and function in these habitats. To conceptualize hyporheic zones for nonperennial streams, we review how water sources and surface and subsurface structure influence hydrological and physicochemical conditions. We consider the extent of this zone and how biogeochemistry and ecology might vary with surface states. We then link these components to the composition of nonperennial stream communities. Next, we examine literature to identify priorities for hydrological and ecological research exploring nonperennial hyporheic zones. Lastly, by integrating hydrology, biogeochemistry, and ecology, we recommend a multidisciplinary conceptualization of the nonperennial hyporheic zone as the porous subsurface streambed sediments that shift between lotic, lentic, humid, and dry conditions in space and time to support aquatic-terrestrial biodiversity. As river drying increases in extent because of global change, we call for holistic, interdisciplinary research across the terrestrial and aquatic sciences to apply this conceptualization to characterize hyporheic zone structure and function across the full spectrum of hydrological states.

Small stream catchments in a developing city context: The importance of land cover changes on the ecological status of streams and the possibilities for providing ecosystem services

Urban expansion and the transformation of rural areas into suburban areas along with increasing human pressure can cause major changes in the environment. Typical effects of urbanization include degradation of aquatic ecosystems. However, urban expansion is often accompanied by an effort to provide residents with some contact with nature in the city. Natural waterbodies are the elements of the environment predestined to ensure appropriate conditions for recreation and other ecosystem services key to human well-being. In this study, the changes in the forms of development of a submontane stream catchment area (Carpathians, Poland) against the background of the gradual extension of city boundaries were analysed. In addition, the impact of urbanization on the ecological conditions of the urban stream, and therefore the potential ecosystem to provide ecosystem services, was assessed. Although the extension of the city entailed the development of settlements, it was accompanied by the expansion of the municipal sewage infrastructure, thus improving sewage management. The disappearance of arable lands from the stream catchment due to the decline in agricultural activities initiated the spontaneous process of buffer restoration along the stream. All the biotic indices such as Shannon diversity index, Margalef species richness index and BMWP-PL index, based on macroinvertebrate communities increased, and the fish were more numerous in the late stage of city development compared with the early (rural) stage. Surprisingly, the system has rehabilitated well with no revitalisation actions. It is necessary, however, to improve morphological in-stream conditions, and to make further efforts to improve water quality for the system to develop a semi-natural state. Only reaching full ecological potential can lead to an increase in the ecosystem capacity to provide ecosystem services. The studied ecosystem may provide many different services simultaneously, but their availability depends on the protection of the ecosystem's functioning as a whole.

The Importance of Groundwater in Critical Zone Science

The critical zone (CZ)-from treetops to groundwater-is an increasingly studied part of the earth system, where scientists study interactions between water, air, rock, soil, and life. Groundwater is both a boundary and an essential store in this integrated system, but is often not well considered in part because of the difficulty in accessing it and its slow movement relative to other parts of the system. Here, we describe some fundamental areas where groundwater hydrology is of fundamental importance to CZ science, including sustaining streamflow and vegetation, reacting with minerals to produce dissolved solutes and regolith, and influencing energy fluxes across the land-atmosphere interface. As the timing and type of precipitation change with climate, groundwater may play an even more important role in CZ processes as a sustainable water source for plants and streamflow. Many open questions also exist about the role of CZ processes on groundwater. Many data streams are needed and important to quantifying the integrated response of the CZ to groundwater and vice versa, but long-term data records are often incomplete or discontinued due to limited funding. We argue that the long timescales of processes that involve groundwater necessitate data collection efforts beyond typical federal funding timespans. Sustaining monitoring networks and developing new ones aimed at testing hypotheses related to slow-moving, groundwater-controlled CZ processes should be a scientific priority, and here we outline some open questions that we hope will motivate groundwater scientists to get involved in CZ science.

The Chesapeake Bay Program Modeling System: Overview and Recommendations for Future Development

The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.

From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?

Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality measure. Our capabilities of forecasting DO however remain elusive. Water quality data, specifically DO data here, often have large gaps and sparse areal and temporal coverage. Earth surface and hydrometeorology data, on the other hand, have become largely available. Here we ask: can a Long Short-Term Memory (LSTM) model learn about river DO dynamics from sparse DO and intensive (daily) hydrometeorology data? We used CAMELS-chem, a new data set with DO concentrations from 236 minimally disturbed watersheds across the U.S. The model generally learns the theory of DO solubility and captures its decreasing trend with increasing water temperature. It exhibits the potential of predicting DO in "chemically ungauged basins", defined as basins without any measurements of DO and broadly water quality in general. The model however misses some DO peaks and troughs when in-stream biogeochemical processes become important. Surprisingly, the model does not perform better where more data are available. Instead, it performs better in basins with low variations of streamflow and DO, high runoff-ratio (>0.45), and winter precipitation peaks. Results here suggest that more data collections at DO peaks and troughs and in sparsely monitored areas are essential to overcome the issue of data scarcity, an outstanding challenge in the water quality community.