1. Freshwater phytoplankton communities are currently experiencing multiple global change stressors, including increasing frequency and intensity of storms. An important mechanism by which storms affect lake and reservoir phytoplankton is by altering the water column’s thermal structure (e.g., changes to thermocline depth). However, little is known about the effects of intermittent thermocline deepening on phytoplankton community vertical distribution and composition or the consistency of phytoplankton responses to varying frequency of these disturbances over multiple years. 2. We conducted whole-ecosystem thermocline deepening manipulations in a small reservoir. We used an epilimnetic mixing system to experimentally deepen the thermocline in two summers, simulating potential responses to storms, and did not manipulate thermocline depth in two succeeding summers. We collected weekly depth profiles of water temperature, light, nutrients, and phytoplankton biomass as well as discrete samples to assess phytoplankton community composition. We then used time-series analysis and multivariate ordination to assess the effects of intermittent thermocline deepening due to both our experimental manipulations and naturally-occurring storms on phytoplankton community structure. 3. We observed inter-annual and intra-annual variability in phytoplankton community response to thermocline deepening. We found that peak phytoplankton biomass was significantly deeper in years with a higher frequency of thermocline deepening events (i.e., years with both manipulations and natural storms) due to weaker thermal stratification and deeper depth distributions of soluble reactive phosphorus. Furthermore, we found that the depth of peak phytoplankton biomass was linked to phytoplankton community composition, with certain taxa being associated with deep or shallow biomass peaks, often according to functional traits such as optimal growth temperature, mixotrophy, and low-light tolerance. 4. Our results demonstrate that abrupt thermocline deepening due to water column mixing affects both phytoplankton depth distribution and community structure via alteration of physical and chemical gradients. In addition, our work supports previous research that phytoplankton depth distribution and community composition interact at inter-annual and intra-annual timescales. 5. Variability in the inter-annual and intra-annual responses of phytoplankton to abrupt thermocline deepening indicates that antecedent conditions and the seasonal timing of surface water mixing may mediate these responses. Our findings emphasize that phytoplankton depth distributions are sensitive to global change stressors and effects on depth distributions should be taken into account when predicting phytoplankton responses to increased storms under global change.
Next to precipitation, secondary water sources emerging from shallow groundwater and lateral redistribution of soil moisture, together with soil properties modulating their accessibility are highly important in water-limited ecosystems. However, effects of these land-associated secondary inputs are not well known over large domains given the mismatch of spatial scales of processes. Here, we quantify the role of land properties on the spatial variations of seasonal decay rate of vegetation cover over water-limited regions of Africa, using machine learning. Over the study domain, 17 % of these variations are directly attributed to land properties, and 16 % are attributed to interaction effects of land properties with climate and vegetation. Locally, total land attributed variations account for more than 60 % in hotspots with different land properties like shallow groundwater, complex topography, and favourable soil properties. Our findings lend empirical evidence for the importance of local-scale secondary water inputs over large domains.
Scene understanding methodologies (e.g., classification, segmentation, and anomaly detection) can be costly when operating on a per-pixel basis. As remote sensing applications rely on imagery with greater resolution, pixels regularly contain similar information to their neighbors. In recent years, scene understanding approaches have leveraged superpixel algorithms to partition imagery into small homogeneous regions for a variety of tasks. Instead of performing operations on millions of pixels, thousands, or in some cases hundreds of superpixels can be used as inputs into various pipelines. Most superpixel algorithms rely solely on RGB color information to produce superpixel maps. However, when multiple co-registered modalities are available, as is the case with the National Ecological Observatory Network (NEON) tree crown dataset, it is possible to combine information from multiple sources to produce a single shared map. In this work we combine airborne hyperspectral imagery, LiDAR point clouds, and RGB imagery to obtain superpixel maps and compare the oversegmentation results to those obtained by fusing individual maps produced from each modality. Superpixels are computed using the Simple Non-Iterative Clustering (SNIC) algorithm. Oversegmentation maps are scored using standard evaluation approaches. We present results on a subset of the National Ecological Observatory Network imagery dataset.
Freshwater lakes and reservoirs play a disproportionate role in the global organic carbon (OC) budget, as active sites for carbon processing and burial. Associations between OC and iron (Fe) are hypothesized to contribute substantially to the stabilization of OC in sediment, but the magnitude of freshwater Fe-OC complexation remains unresolved. Moreover, global declines in bottom-water oxygen concentrations have the potential to alter OC and Fe cycles in multiple ways, and the net effects of low-oxygen (hypoxic) conditions on OC and Fe are poorly characterized. Here, we measured the pool of Fe-bound OC (Fe-OC) in surficial sediments from two eutrophic reservoirs, and we paired whole-ecosystem experiments with sediment incubations to determine the effects of hypoxia on OC and Fe cycling over multiple timescales. Our experiments demonstrated that short (2–4 week) periods of hypoxia can increase aqueous Fe and OC concentrations while decreasing OC and Fe-OC in surficial sediment by 30%. However, exposure to seasonal hypoxia over multiple years was associated with a 57% increase in sediment OC and no change in sediment Fe-OC. These results suggest that the large sediment Fe-OC pool (~30% of sediment OC in both reservoirs) contains both oxygen-sensitive and oxygen-insensitive fractions, and over multiannual timescales OC respiration rates may play a more important role in in determining the effect of hypoxia on sediment OC than Fe-OC dissociation. Consequently, we anticipate that global declines in oxygen concentrations will alter OC and Fe cycling, with the direction and magnitude of effects dependent upon the duration of hypoxia.
Imaging spectroscopy data is becoming more readily available from different satellite and airborne platforms. As this data becomes more prolific, there is a need for shared data tools and code for wrangling, cleaning, and analyzing it. The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC) pioneers an on-demand science data processing platform with scalable back-end compute. It considers user experience and facilitates open science. ImgSPEC enables users to create data products in areas of interest using default workflows from registered algorithms, while also enabling users to customize scripts and workflows. ImgSPEC seamlessly interfaces with NASA Earthdata Search and tracks appropriate metadata for reproducibility when generating data products to share with others. Users can work in their preferred workspace (e.g., Rstudio, Jupyterlab, or command line) thereby facilitating use of open science software packages and collaborative coding through Git. ImgSPEC leverages existing NASA-funded information technologies such as the hybrid on-premise/cloud science data system (HySDS) and the Multi-mission Algorithm and Analysis Platform (MAAP). It also creates seamless interfaces with NASA-funded ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the NASA Surface Biology and Geology (SBG) Science and Applications Communities. As this technology is more widely adopted the interface with Amazon Web Services and NASA Earthdata search will enable broader use of more data (publicly available or loaded by the user) across more domains.
The largest volcanic eruption of this century, which was submarine, led to a dramatic phytoplankton bloom north of the island of Tongatapu, in the Kingdom of Tonga. In the absence of shipboard observations, we reconstructed the dynamics of this event by using a suite of satellite observations. Two independent bio-optical approaches confirmed that the phytoplankton bloom was a robust observation and not an optical artifact due to volcanogenic material. Furthermore, the timing, size, and position of the phytoplankton bloom suggest that plankton growth was primarily stimulated by nutrients released from volcanic ash rather than by nutrients upwelled through submarine volcanic activity. The appearance of a large region with high chlorophyll a concentrations less than 48 hours after the largest eruptive phase indicates a fast ecosystem response to nutrient fertilization. However, net phytoplankton growth probably initiated before the main eruption, when weaker volcanism had already fertilized the ocean.
Vegetation green leaf phenology directly impacts gross primary productivity (GPP) of terrestrial ecosystems. Satellite observations of land surface phenology (LSP) provide an important means to monitor the key timing of vegetation green leaf development. However, differences between satellite-derived LSP proxies and in-situ measurements of GPP make it difficult to quantify the impact of climate-induced changes in green leaf phenology on annual GPP. Here we used 1,110 site-years of GPP measurements from eddy-covariance towers in association with time series of satellite LSP observations from 2000-2014 to show that while satellite LSP explains a large proportion of variation in annual GPP, changes in green-leaf-based growing season length (GSL) had less impact on annual GPP by ~30% than GSL changes in GPP-based photosynthetic duration. Further, maximum leaf greenness explained substantially more variance in annual GPP than green leaf GSL, highlighting the role of future vegetation greening trends on large-scale carbon budgets. We conclude that satellite LSP-based inferences regarding large-scale dynamics in GPP need to consider changes in both green leaf GSL and maximum greenness.
In organic soils the availability of electron acceptors determines the ratio of CO2 to CH4 formation under anoxic conditions. While typically only inorganic electron acceptors are considered, the importance of electron accepting capacities of organic matter is increasingly acknowledged. Redox properties of organic matter are yet only investigated for a limited set of peat and reference materials. Therefore, we incubated 60 peat samples of 15 sites located in five major peatland regions covering a variety of both bog and fen samples and characterized their capacities to serve as electron acceptor for anaerobic CO2 production. We quantified CO2 and CH4 formation, and changes in available EAC in anoxic incubations of 56 days. On the time scale of our experiment, on average 36.5 % of CO2 could be attributed to CH4 formation, assuming an CO2/CH4 ratio for methanogenesis of 1:1. Regarding the remaining CO2 formed, for which a corresponding electron acceptor would be needed, we could on average explain 70.8 % by corresponding consumption of EAC from both organic and inorganic electron acceptors, the latter contributing typically less than 0.1 %. When the initial EAC was high, CO2 formation from apparent consumption of EAC was high and outweighed CO2 formation from methanogenesis. A rapid depletion of available EAC resulted in a higher share of CO2 from CH4 formation. Our study demonstrates that EAC provides the most important redox buffer for competitive suppression of CH4 formation in peat soils. Moreover, electron budgets including EAC of organic matter could largely explain anaerobic CO2 production.
Bays within eastern boundary upwelling systems (EBUS) are ecological hot-spots featuring a diverse range of spatio-temporal dynamics. At the EBUSs’ poleward limit, upwelling occurs in short-lived (<1 week) pulses modulated by synoptic wind variability. The circulations in long, narrow bays can respond to these fluctuations within few hours. The short-term biological response to these pulses was investigated in two of these bays (Rias Baixas, NW-Iberia) with a two-week quasi-synoptic spatio-temporal survey in the summer 2018. A four-day-long upwelling pulse caused deep, nutrient-rich isopycnals to rise into the euphotic zone inside the bays, triggering a rapid (~1.7 days) nutrient uptake and formation of a subsurface chlorophyll maximum (~3.8 days). The phytoplankton biomass was transported rapidly toward deep, offshore waters when the winds weakened. These results suggest that high productivity in narrow bays is controlled by the transient exposure of deep, nutrient-rich waters to light during upwelling pulses.
River flow changes on timescales ranging from minutes to millennia. These variations influence fundamental functions of ecosystems, including biogeochemical fluxes, aquatic habitat, and human society. Efforts to describe temporal variation in river flow—i.e. flow regime—have resulted in hundreds of unique descriptors, complicating interpretation and identification of global drivers of overall flow regime. In this study, we used three analytical approaches to investigate three related questions: 1. how interrelated are flow regime metrics, 2. what catchment characteristics are most associated with flow regime at different timescales globally, and 3. what hydrological processes could explain these associations? To answer these questions, we analyzed a new global database of river discharge from 3,685 stations with coverage from 1987 to 2016. We calculated and condensed 189 traditional flow metrics via principal components analysis (PCA). We then used wavelet analysis to perform a frequency decomposition of each time series, allowing comparison with the flow metrics and characterization of variation in flow at different timescales across sites. Finally, we used three machine learning algorithms to relate flow regime to catchment properties, including climate, land-use, and ecosystem characteristics. For both the PCA and wavelet analysis, just a few catchment properties (catchment size, precipitation, and temperature) were sufficient to predict most aspects of flow regime across sites. The wavelet analysis revealed that variability in flow at short timescales was negatively correlated with variability at long timescales. We propose a hydrological framework that integrates these dynamics across daily to decadal timescales, which we call the Budyko-Darcy hypothesis.
Although zooplankton play a substantial role in the biological carbon pump and serve as a crucial link between primary producers and higher trophic level consumers, the skillful representation of zooplankton is not often a focus of ocean biogeochemical models. Systematic evaluations of zooplankton in models could improve their representation, but so far, ocean biogeochemical skill assessment of Earth system model (ESM) ensembles have not included zooplankton. Here we use a recently developed global, observationally-based map of mesozooplankton biomass to assess the skill of mesozooplankton in six CMIP6 ESMs. We also employ a biome-based assessment of the ability of these models to reproduce the observed relationship between mesozooplankton biomass and surface chlorophyll. The combined analysis found that most models were able to reasonably simulate the large regional variations in mesozooplankton biomass at the global scale. Additionally, three of the ESMs simulated a mesozooplankton-chlorophyll relationship within the observational bounds, which we used as an emergent constraint on future mesozooplankton projections. We highlight where differences in model structure and parameters may give rise to varied mesozooplankton distributions under historic and future conditions, and the resultant wide ensemble spread in projected changes in mesozooplankton biomass. Despite differences, the strength of the mesozooplankton-chlorophyll relationships across all models was related to the projected changes in mesozooplankton biomass globally and in regional biomes. These results suggest that improved observations of mesozooplankton and their relationship to chlorophyll will better constrain projections of climate change impacts on these important animals.
Species Distribution Modelling (SDM) is widely used by ecologists to monitor biodiversity and manage wildlife. In the last decades, Artificial Intelligence (AI) and Machine Learning (ML) techniques became popular and were successfully applied for different tasks, including SDM. The objective of this article was to evaluate Machine Learning models for Species Distribution Modeling in the Amazon Basin region near Manaus (AM), based on meteorological and aerosol data collected by the GoAmazon 2014/15 project. The techniques were evaluated regarding their accuracy and the Decision Tree Classifier and the Maximum Entropy Model obtained good predictive performances.
Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past five years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in early stages of development (i.e., non-operational), despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed, and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events five days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts necessitates substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.
Globally-significant quantities of carbon (C), nitrogen (N), and phosphorus (P) enter freshwater reservoirs each year. These inputs can be buried in sediments, respired, taken up by organisms, emitted to the atmosphere, or exported downstream. While much is known about reservoir-scale biogeochemical processing, less is known about spatial and temporal variability of biogeochemistry within a reservoir along the continuum from inflowing streams to the dam. To address this gap, we examined longitudinal variability in surface water biogeochemistry (C, N, and P) in two small reservoirs throughout a thermally-stratified season. We sampled total and dissolved fractions of C, N, and P, and chlorophyll-a from each reservoir’s major inflows to the dam. We found that time was generally a more important driver of heterogeneity in biogeochemical concentrations than space. However, dissolved nutrient and organic carbon concentrations had high site-to-site variability within both reservoirs, potentially as a result of shifting biological activity or environmental conditions. When considering spatially explicit processing, we found that certain locations within the reservoir, most often the stream-reservoir interface, acted as ‘hotspots’ of change in biogeochemical concentrations. Our study suggests that spatially explicit metrics of biogeochemical processing could help constrain the role of reservoirs in C, N, and P cycles in the landscape. Ultimately, our results highlight that biogeochemical heterogeneity in small reservoirs is driven more by seasonality than longitudinal spatial gradients, and that some sites within reservoirs play critically important roles in whole-ecosystem biogeochemical processing.
Laboratory studies have shown that rhizodeposits could lead to either soil structural formation or dispersion depending on plant species, soil conditions, and microbial activity. However, these studies have usually been conducted in dry soils and rarely considered the combined effect of rhizodeposit and organic residues on soil structure. This study hypothesizes that root exudates promote soil dispersion initially, but over time decomposition of root exudates produce binding agents that promote stable soil structure in the rhizosphere. To test this hypothesis, a sandy loam soil sieved to < 500 µm particle size was first amended with root exudate compounds (14.4 mg C g-1), δ13C-barley residue (0.44 mg C g-1 soil), or both. Six replicate samples per treatment were packed in cores to a bulk density of 1.27 g cm-3 and then equilibrated on a tension table at -2 kPa matric potential. Rheological measurements of flow characteristics (dynamic viscosity) and strength (storage modulus, loss modulus, tan δ, and yield stress) of the control and amended soils were obtained immediately after amendment and after twelve days of incubation at 20 oC. Only root exudate compounds initially decreased the capacity of soil to retain water at -2 kPa by 21% and by 49% after incubation. Likewise, the yield stress of root exudate amended soil was significantly (P < 0.05) lower than that of the unamended soil, reflecting dispersion of soil. However, microbial decomposition/activities significantly (P < 0.05) increased yield stress over the corresponding pre-incubation values for these treatments by 200% (root exudate) and 230% (root exudate + δ13C-barley residue). These results confirmed the hypothesized dual effect of root exudates on rhizosphere structure. The initial soil dispersion may facilitate root growth by augmenting soil penetrability and releasing nutrients that were occluded in soil aggregates, whereas stable soil structure is achieved upon decomposition of root exudates.
Long-term dependencies may be one of the reasons for the spatial variability in precipitation frequencies. This study assesses the long-term dependencies in precipitation time series at a basin-scale using the wavelet-based fractal decomposition technique. The gridded precipitation datasets (0.25deg x 0.25deg) from the India Meteorological Department (IMD) for the year, 1901 to 2018 have been used. In order to find the climate change point (i.e., the year in terms of annual series) from each grid point, the mean-based change point detection is performed. Based on the change points, the input for the wavelet analysis is generated into two series, the series -1 (before change point) and the series – 2 (after change point). The results of the climate change points are different for every location, and the corresponding length of the series also gets changed. In order to handle the non-stationarity associated with the time series datasets, the method of wavelet decomposition is used. The Discrete Wavelet Transform (DWT) based fractal decomposition of time series is performed by taking the Daubechies (db1 to db10) mother wavelet along with the varying scale and translation parameters. Both the results of the series wavelet coefficients are compared using the scaled ratio method and the relative shift in the cumulative distribution functions (CDF). Comparing the time series datasets before and after the change point reveals the significance of long-term dependencies at each location. The results of the spatial variability and its patterns explain the long-term dependencies and their significance at a basin-scale, which may support various scientific studies and development.
Catchment studies provide foundational scientific knowledge that is relevant to policy, resource management, education, outreach, and public awareness of the environment and environmental problems. Through a concerted effort to highlight the catchments and catchment studies behind a rich legacy of scientific findings, we have led several efforts to promote long-term and place-based research at monitoring, observatory, and ecosystem study sites. Our efforts have included sessions at AGU and other professional meetings, a special issue of Hydrological Processes, and a CUAHSI Cyberseminar series on monitoring and observation at research catchments that span the globe. In this poster, we continue to promote the catchments and innovative research at those sites by summarizing our efforts and updating a map of the catchment studies that we have identified. Our primary objective is to stimulate discussion about the vibrant state of the catchment sciences, the sites that make the science possible, and how to move forward in coming decades.
Microbial-induced calcium carbonate precipitation (MICP) is an innovative technique used for soil improvement, for controlled reduction of permeability in porous media or immobilization of soil contaminants. The application of MICP in the field is influenced by the environmental factors. In the present study, the main purpose is to explore the effectiveness of MICP in treating porous media at different environmental temperatures and reveal the underlying mechanisms. The microstructure characteristics were investigated via SEM imaging, EDS and XRD analyses and consolidated drained triaxial compression tests were performed to examine the performance of MICP-treated samples. Results indicate that the shear strength depends heavily on the treatment temperature, which was mainly due to the different content, size and distribution of CaCO3 in samples at different conditions. The observations of pore-scale characteristics revealed that low temperature (4℃) and high temperature (50℃) produced less CaCO3 precipitation, resulted in smaller carbonate crystals precipitation and thus lower strength. In contrast, samples treated at room temperature and 35 ℃ show more CaCO3 precipitation and greater strength. The crystal forms, though, were not influenced by the temperature. The climate conditions are a very important parameter that needs to be tuned specifically for the purposes of each MICP application (whether controlled alteration of permeability or for soil stabilization). However, in most MICP field applications, temperature is nearly impossible to control, and in such conditions where bacterial activity is reduced, the alteration of the MICP recipe is required, and specifically the number of bacterial solution injections are worth to be considered.