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.
Rock moisture can be an important contributor to forest transpiration and growth. Limited work has been done studying the effects of rock moisture (subsurface water stored in fractured, weathered rock) on transpiration rates — especially in water-limited environments. Semi-arid forests like the Gordon Gulch catchment (west of Boulder, CO) exhibit complex water budget systems where water sources are not completely understood. Here, we compare transpiration rates from plots on opposing aspects with regard to soil moisture and potential rock moisture storage as inferred from shallow seismic refraction surveys. We calculated the transpiration rates of ponderosa pine and lodgepole pine trees with sap flow data collected from June to September 2014. Potential storage for rock moisture is estimated based on qualitative analysis of shallow seismic refraction line data. While one would expect areas with higher soil moisture on average to have higher transpiration rates, our results showed the contrary: the plot with less soil moisture on average exhibited 25% higher transpiration rates. By qualitatively analyzing the seismic line images, we found that this phenomenon could possibly be explained by rock moisture. The plot with higher transpiration also had more fractured, weathered bedrock below that could potentially store more water in rock moisture. Rock moisture is an important component of the complex water budget system in Gordon Gulch. Further imaging of the subsurface is key to advance our understanding on how water is being used and moved in similar environments. Our research provides insight into rock moisture’s potential effects on water usage via transpiration in water-limited environments.
Dinitrogen (N2) fixers (diazotrophs) fuel primary productivity by providing reactive nitrogen into the ocean ecosystem and promoting CO2 sequestration. N2 fixation has been extensively studied in the low latitudes of the Atlantic and Pacific Oceans. By comparison, the Indian Ocean remains the least explored and most enigmatic ocean basin. This is particularly the case for the Southern Indian Ocean (SIO). Here we explore N2 fixation activity and diazotroph community composition, diversity, and abundance from 20 to 60ºS in the SIO. While this region plays a key biogeochemical role serving as a link between the Atlantic and South Pacific Ocean waters, its N2 fixation potential remains unknown. Our results provide new insights into diazotrophy in a poorly studied region and expand the range of biomes where diazotrophy may be observed.
Rice (Oryza sativa) is a major staple food crop in India occupying about 44 million ha (Mha) of cropped land in meeting food requirements for about 65% of the population. As water scarcity has become a major concern in changing climatic scenarios precise measurements of actual evapotranspiration (ETa) and crop coefficients (Kc) are needed to better manage the limited water resources and improve irrigation scheduling. The eddy covariance (EC) method was used to determine ETa and Kc of tropical lowland rice in eastern India over two years. Reference evapotranspiration (ETa) was estimated by four different approaches– the Food and Agriculture Organization-Penman-Monteith (FAO-PM) method, the Hargreaves, and Samani (HS) method, the Mahringer (MG) method, and pan evaporation (Epan) measurements. Measurements of turbulent and available energy fluxes were taken using EC during two rice growing seasons: dry season (January-May) and wet season (July-November) and also in the fallow period where no crop was grown. Results demonstrated that the magnitude of average ETa during dry seasons (2.86 and 3.32 mm d-1 in 2015 and 2016, respectively) was higher than the wet seasons (2.3 and 2.2 mm d-1) in both the years of the experiment. The FAO-PM method best-represented ETa in this lowland rice region of India as compared to the other three methods. The energy balance was found to be more closed in the dry seasons (75–84%) and dry fallow periods (73–81%) as compared to the wet seasons (42–48%) and wet fallows (33-69%) period of both the years of study, suggesting that lateral heat transport was an important term in the energy balance calculation. The estimated Kc values for lowland rice in dry seasons by the FAO-PM method at the four crop growth stages; namely, initial, crop development, reproductive, and late-season were 0.23, 0.42, 0.64, and 0.90, respectively, in 2015 and 0.32, 0.52, 0.76 and 0.88, respectively, in 2016. The FAO-PM, HS, and MG methods produced reliable estimates of Kc values in dry seasons, whereas Epan; performed better in wet seasons. The results further demonstrated that the Kc values derived for tropical lowland rice in eastern India are different from those suggested by the FAO implying revision of Kc values for regional-scale irrigation planning.
Abstract Background, Aims and Scope. Wetland soil is one of the largest natural contributors to methane emissions, which is a prevalent greenhouse gas. The vertical flux pattern of methane in soils is unclear. To investigate the relationship between methane vertical flux, soil total organic carbon (TOC) and methanogen, we were monitored in the riparian buffer of a wetland park from August 2018 to January 2020. The objectives of this study were to (1) analyze the vertical variation in methane fluxes within the riparian buffer and (2) investigate the vertical space relationships between methane fluxes, TOC and methanogens. Furthermore, the results of this study could provide better information for understanding the vertical linkage between methane, TOC and methanogens. Methods. The study area is the Living Water Garden (LWG), a wetland park in Chengdu, Sichuan Province, western China (30◦40’ N, 104◦05’ E), which is a city park using a constructed wetland system (CW) to treat polluted water from the Jin River. The sampling site is close to the park inlet, located on the bank of the Jin River, a flat riparian buffer with an area of about 100 m2 (Fig. 1c, the red dashed box). Methane flux was measured once per month (in mid-month) using a portable greenhouse gas flux measurement system (WS-L1820, WEST Ltd., Italy). The sampling frequency is one time on a selected day and the sampling time was between 11:00 am and 12:00 am. There are 6 selected monitoring sites with 4 monitoring depths including surface, 5cm, 10cm and 15cm. Soil sampling and methane flux measurements are performed on the same days. After methane flux monitoring, soil samples were collected. Soil sampling sites were also same as the methane monitoring sites. At each sampling site, soil samples were excavated at depths of 0-5 cm, 5-10 cm and 10-15 cm. Gene copies of the methanogens (mcrA) were determined by q-PCR on the ABI 9700 Real-Time PCR system. Sequencing data was processed using the quantitative insights into the microbial ecology (QIIME) pipeline. SPSS software (version 21, SPSS Inc., USA) and the software package R (R Foundation for Statistical Computing, Austria) were used for statistical analyses and data graphing. Structural equation model (SEM) was conducted using the AMOS statistical software (version 21, IBM SPSS, USA). Results. During the study period, the average surface methane emission was 81.86 mg m-2 h-1 and ranged from 20.42 mg m-2 h-1 to 190.75 mg m-2 h-1. Cumulative methane emissions from studied area was 7.26 kg CO2eq m−2 year−1 and the global warming potential (GWP) was at a moderate level. The results reveal that the Methanobacteriaceae, Methanosarcinaceae and Methanoregulaceae were the major methanogenic microorganisms in the study area. The mathematical regression of methane flux (z, mg m-2 h-1) with soil depth (x, cm) and TOC (y, g kg-1) was as follows: z = 52860.66 + 54.44x - 2.96x2 - 26788.64y + 4487.80y2 - 249.34y3 (R2=0.82). It indicates that the relatio