Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly-decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over distances of kilometers. The curved shape of peat domes accounts for much of the carbon storage in these landscapes, but their subtle topographic signal is difficult to measure. As many of the world’s tropical peatlands are remote and inaccessible, spaceborne laser altimetry data from missions such as NASA’s Global Ecosystem Dynamics Investigation (GEDI) and the Advanced Topographic Laser Altimeter System (ATLAS) instrument on the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory could help to describe these deposits. However, for better and for worse, tropical peatlands may also support forests with high above-ground biomass—averages of over 200 Mg C / ha have been reported—which increases their carbon stocks but further complicates determination of their surface topography using laser altimetry. In this work, we evaluate retrieval of ground elevations and canopy metrics derived from GEDI waveform data, as well as single-photon data from ATLAS, with reference to an airborne laser scanning dataset covering an area of over 100 km^2 in the Belait District of Brunei Darussalam. We find that despite infrequent ground retrievals, with regularization these spaceborne platforms can provide useful data for tropical peatland surface altimetry.

Climatic changes with warmer temperatures in mid-latitudes require the need to improve the simplified vegetation scheme of the regional climate model COSMO-CLM, which is not capable of modelling complex processes depending on temperature, water availability and day length. Thus, we have implemented the physically based Ball-Berry approach coupled with photosynthesis processes based on Farquhar and Collatz models for C3 and C4 plants in COSMO-CLM (v 5.16). The implementation of the new algorithms includes the replacement of the “one-big leaf” by a “two-big leaf” approach. We performed single column simulations with COSMO-CLM over three observational sites with C3 grass plants in Germany for the period from 1999 to 2015 (Parc, Linden and Lindenberg domain, Fig.1). Hereby, we tested three alternative formulations of the new algorithms. The first formulation (COSMO_v3.5) is based on the Community Land Model (CLM v3.5) algorithms for stomatal resistance, which depend on leaf photosynthesis, CO2 partial and vapor pressure and minimum stomatal conductance. The second one is COSMO_v4.5, which is based on the phenology algorithms of CLM v4.5 including the soil water stress function. The third one is similar to COSMO_v4.5 but with additional equations for dry leaf calculations (COSMO_v4.5e). The results revealed major differences in the annual cycle of stomatal resistance compared to the control simulation (COSMO_orig) with the original algorithm (Fig. 1). The biggest changes are from October to April when stomata are closed. The summer values of experiments are closer to measured values, than COSMO_orig. Further, changes in the stomatal resistance algorithms improve the accuracy of calculated transpiration rate and total evapotranspiration. The results indicate that changes in stomatal resistance and photosynthesis algorithms can improve the accuracy of other parameters of the COSMO-CLM model by comparing them with FLUXNET data and meteorological observations at the sites, and GLEAM datasets. Figure 1: The stomatal resistance based on COSMO-CLM experiments (a - annual cycle; b - daily values from 01.06.2011 to 15.09.2011) for: I – Parc domain, II – Linden domain, III – Lindenberg domain. This research was funded by the German Research Foundation (DFG) through grant number 401857120

Providing environmental flows is challenging in the middle portion of the Rio Grande/Bravo Basin (between Elephant Butte Reservoir in New Mexico and Presidio, Texas) where water demand has continued to increase over time despite limited river water and dropping groundwater levels. Riparian ecosystems in this agriculture-dominated desert environment will likely become more vulnerable as competition over scarce water increases in the face of growing demand and dwindling supply. Little to no water is allocated to riparian ecosystems unless water or water rights are purchased or transferred to sustain those systems. Ongoing debates about providing environmental flows for riparian forest galleries in this water-scarce region have not been backed by quantitative modeling results of potential impacts on surface water and groundwater availability. We quantify water requirements to provide a menu of options for environmental flow allocation to establish cottonwood forest galleries. We apply hydrologic modeling under a projected warm-dry future to determine the frequency of river water availability for providing minimum environmental flows, and to evaluate water budget tradeoffs associated with environmental flow allocations in this region. Results inform water resources management decisions that support riparian habitats.

The state of the practice for large area land cover mapping is based on the application of supervised classifiers to multi-temporal multi-spectral optical wavelength data. Deep learning approaches have been developed but are typically applied to individual images with less research on their application to satellite time series. The recent availability of 30 m Landsat analysis ready data (ARD) has significantly increased the ease of using Landsat data. Irregular gaps in the Landsat image time series reduce the easy application of deep learning to time series. This study proposes a novel solution based on a two dimensional (2-D) array (one spectral dimension and one temporal dimension) derived at each ARD pixel time series and using the 2-D convolutional neural network (CNN) deep learning algorithm. Classification results are presented for all of the Conterminous United States (CONUS) considering a year of Landsat 5 and 7 data. The 30 m USGS National Land Cover Database (NLCD) (15 classes after filtering) and USDA Crop Data Layer (CDL) (22 classes after filtering) products are filtered conservatively across the CONUS and sampled to construct >3.31 and >0.48 million training pixels respectively defined with reliable accuracy and reduced spatial autocorrelation. The CNN and a conventional Random Forest (RF) classifier are trained using 10%, 50% and 90% of the training samples, and used to classify the remaining samples. Classification experiments are undertaken independently using the NLCD and CDL training data. Different CNN structures with different learnable coefficients are used and the accuracy results compared with conventional RF results. The main findings were (1) application of the CNN with different structures provided only about 1% accuracy difference, the optimal CNN structure was dependent on the number of training samples, and increasing the number of CNN learnable coefficients beyond the number of training samples was not helpful; (2) although the CNN training time was up to two orders of magnitude slower than the RF, the classification time was an order of magnitude faster, (3) the CNN provided 2-5% higher accuracy than the RF which is notable given the large number of classes and that the overall classification accuracies were >80%.

Arctic plants are small in stature and spectrally diverse, which presents challenges to current NASA missions to visualize effects of disturbance or directional vegetation change via mapping. Remotely sensed data having fine spatial (ca. 10 cm pixels) and spectral grain (eg. “hyperspectral”) will therefore help resolve patches of many arctic plant groups, such as dwarf shrubs, bryophytes and lichens and separate them from litter, wood or rock/soil. To address these challenges, in summer 2018 we sampled vegetation at 15 different sites around Fairbanks, Alaska using ground-based and airborne hyperspectral sensors under eight different AVIRIS ng flight lines next gen flight lines (circa 2017-2018). At each AVIRIS flight line, we estimated percent cover of plant functional types in eleven 1m2 quadrats every 10 m along a 100m transect. We then flew our UAV and imaging spectrometer (Headwall Micro A-series VNIR, 400-1000 nm, 330 bands, 10 cm pixels). Spectral signatures of any surfaces were sampled using a field spectroradiometer (PSR+ Spectral Evolution, 400-2500 nm, 1nm bands). We collected 600+ georeferenced scans from 70+ species/plant functional types at 25+ different sites around Alaska. Spectral profiles showed many different plant species have similar to indistinguishable signatures (eg. Paper birch and Alder) while many plant functional types that have been grouped together (eg. Moss) were very spectrally heterogenous. UAV-based hyperspectral imagery (ca. 4-10 cm pixels) resolved pure pixels of many artic plants. Our approach resolves fine grained ecological features, such as networks of circular patches (mostly lichens, brophytes and mineral soil) over very large areas (ca. 10,000 m2), created by small cryoturbation features (frost boils). We explore spectral unmixing and other statistical approaches to compare mapping results using our spectral library with AVIRIS ng (4 m pixels) and our UAV-based VNIR hyperspectral imagery.

Satellite-based land surface phenology information (e.g. time-series spectral metrics and seasonal composites) can be used to model tropical tree canopy cover (TCC) but is limited by missing observations caused by clouds and cloud shadows and the sensor failure. Gap-filling (i.e. reconstruction of missing data in images) provides a key to solving the limitation, but its effectiveness and necessity for a given application, e.g. TCC modelling have been rarely explained. The main goal of this study was to examine the effectiveness and necessity of gap-filling, applied to the TCC modelling using Landsat time series. We chose Missing Observation Prediction Based on Spectral-Temporal Metrics (MOPSTM) method for its simple tuning, fast computation, and good performance in Landsat missing data reconstruction. MOPSTM models the relationship between valid observations in time series and spectral-temporal metrics in the k-Nearest Neighbor regression to predict the missing observations. We provided a quantitative comparison of TCC modelling using predictor variables (e.g. seasonal composite, spectral-temporal metrics, and harmonic regression coefficients) derived from Landsat time series that included gap-filling versus those that did not include gap-filling. With 1-year Landsat 8 Surface Reflectance time series acquired from a tropical area in Taita Hills, Kenya throughout 2015 and a reference TCC map scaled in 0 – 100 derived from the Airborne Laser Scanning (ALS) data between 2014 and 2015, we applied random forest regression to model TCC using the predictor variables. The results indicated that TCC modelling using gap-filled predictors yielded smaller root mean square error than that using the non-gap-filled predictors, which proved the effectiveness of gap-filling in tropical TCC modelling. The effects of gap-filling might be reduced when the predictor variables were of high quality, e.g. median composite derived from the time series where sufficient observations exist. We concluded that gap-filling has a positive effect on the accuracy of TCC modelling.

This study investigates the characteristics and climate impacts of the quasi-biweekly oscillation (QBWO) over the western North Pacific (WNP) in boreal winter based on observational and reanalysis data and numerical experiments with a simplified model. The wintertime convection over the WNP is dominated by significant biweekly variability with a 10-20-day period, which explains about 66% of the intraseasonal variability. Its leading mode on the biweekly timescale is a northwestward-propagating convection dipole over the WNP, which oscillates over a period of about 12 days. When the convection-active center of this QBWO is located to the east of the Philippines, it can generate an anticyclonic vorticity source to the south of Japan via inducing upper-tropospheric divergence and excite a Rossby wave train propagating towards North America along the Pacific rim. The resultant lower-tropospheric circulation facilitates cold advection and leads to cold anomalies over central North America in the following week. This result highlights a cause-effect relationship between the WNP convection and the North American climate on the quasi-biweekly timescale and may provide some prediction potential for the North American climate.

Based on the observational dataset SA-OBS and model outputs from the Community Earth System Model Large Ensemble project, this study investigates heatwaves in Southeast Asia in the current and future warmer climate. A heatwave is detected when the daily maximum temperature exceeds the 90th percentile threshold at each grid for at least three consecutive days. Three characteristics describing the frequency, duration, and amplitude of heatwaves are examined, including the sum of heatwave days per year (HWF) satisfying the heatwave definition, the length of the longest yearly heatwave event (HWD), and the hottest amplitude of the hottest yearly heatwave event (HWA). Results indicate that increased global warming is associated with substantial changes in heatwave characteristics over Southeast Asia, with more frequent heatwaves, longer heatwave duration, and higher extreme temperatures. The increase in HWA has a linear growth against global warming levels with distinct regional differences between the Maritime Continent and the Indochina Peninsula due to their different heat content of lower atmospheric boundaries. In contrast, those in HWF and HWD have nonlinear growth characteristics. The projected warmer future tends to be associated with a higher risk ratio value with the occurrence of rarer extreme heatwaves relative to the current climate. These results reiterate the potential risks of extreme regional heatwaves if global warming is unrestricted.

We quantify the shape of hooked hairs which is a newly observed phenotype of epidermal cell extensions [1] in the common bean genotype L88-57 (Phaseolus vulgaris). The hooked hairs emerge below-ground before the root hairs and have a distinct ‘hooking’ morphology. We generated a dataset capturing their full distribution under the microscope within 3-5 days of germination. We quantify their shape by a novel computational pipeline that can automatically phenotype morphology. Our phenotyping pipeline quantifies traits like length, curvature, perimeter, area, and ‘hooking.’ Our objective is to quantify the response of hooked hairs to nutrient-stress to determine their function in common bean during early development. We used the pipeline for analyzing our dataset of hydroponically grown beans and observed statistically significant responses compared to the control for length, curvature, perimeter, and area to nitrogen (p<0.001**) and phosphorus (p<0.001**) stress treatments. The calculation of ‘hooking’ for our dataset is still ongoing. We are simultaneously developing a landmark-free method for the two-dimensional shape analysis of our dataset and believe that our phenotyping efforts will enable the high-throughput analysis of morphological root hair traits for any plant species.

Shape is foundational to biology. Observing and documenting shape has fueled biological understanding as the shape of biomolecules, cells, tissues, and organisms arise from the effects of genetics, development, and the environment. The vision of Topological Data Analysis (TDA), that data is shape and shape is data, will be relevant as biology transitions into a data-driven era where meaningful interpretation of large datasets is a limiting factor. We focus on quantifying the morphology of X-ray CT scans of a diverse panel of citrus and their tissues. In total, we have access to 146 different citrus samples comprising 58 different species, including samples of all fundamental citrus species. First, using the power of X-rays and image processing, we are able to compare and contrast size ratios between different tissues, such as the size of exocarp compared to the mesocarp or the endocarp. Second, using Persistent Homology we focus on characterizing the size and distribution of oil glands across different citrus species, and the relation of such distribution to other phenotypes such as exocarp surface area. This shape characterization will allow us later to link genotype with phenotype, furthering our understanding on how the physical shape is genetically coded in DNA.

Anthropogenic factors such as climate change, harsh agricultural practices, and mining have contributed to increases in soil salinization and heavy metal contamination. Highly saline environments drastically lower yield for crop species and elevated levels of toxic metals like cadmium are carcinogenic in the environment. Some plants that have evolved in high-salinity habitats or in soils with heavy metals could be used to remediate contaminated soils. Halophytes are plants with various adaptations that allow them to survive and reproduce in saline conditions. General mechanisms for salt tolerance/uptake in halophytes are hypothesized to help deal with other stresses like heavy metals. Plants with these traits could be utilized to extract salt and heavy metals from affected soils in a process called phytoremediation. We plan to develop Sea Rocket (Cakile maritima) in the Mustard family as a model system to understand mechanisms of salt (NaCl) and cadmium uptake and tolerance, as it has been shown to accumulate both. As part of this study, we will hydroponically grow C. maritima in different stress treatments using salt and cadmium. As the plants uptake the pollutants, the conductivity of the solution will change. We will develop an automated pipeline to track these changes in real-time using conductivity sensors. In addition, we will sample root and leaf tissues at various time points to measure salt and cadmium uptake using ICP-OES elemental analysis. This data will provide insights into salt and cadmium uptake/tolerance and paves a path toward efficient and viable solutions improving phytoremediation approaches.

Emergence timing of a plant, i.e., the time at which the plant is first visible from the surface of the soil, is an important phenotypic event as an indicator of seed quality and plant growth. Uneven emergence timing is associated with lower yield and poor farmer acceptance. The research introduces a novel deep learning based method called EmergeNet with a custom-designed loss function for coleoptile emergence timing detection and tracking its growth from a time-lapse video sequence in presence of cluttered background and extreme variations in illumination. EmergeNet uses a novel ensemble technique that integrates SEResNet, InceptionV3 and VGG19 to detect the coleoptile at its first tiny appearance on the soil surface. Emergence is an important phenotype which not only helps determine the dormancy of seeds for different genotypes in different conditions but also helps determine various aspects of the plant growth at an early stage. To develop and evaluate the algorithm, a benchmark dataset is indispensable. Thus, we introduce and publicly release a benchmark dataset called University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED). A visible light camera was fitted to capture the top view time-lapse images to form UNL-MED, where imaging started before the emergence and continued until maize seedlings are about 1 inch tall. Experimental evaluation on UNL-MED demonstrates the efficacy of the EmergeNet to detect the emergence timing with 100% accuracy when compared with human perceived groundtruth.

Modern seismotectonic studies are aimed at obtaining a self-consistent explanation of fault zone heterogeneity, the rupture process, recurrence times and rupture mode of large earthquake sequences. In subduction regions large earthquakes are often characterized by very long source zones and complex long-term postseismic processes following the coseismic release of accumulated elastic stresses. A set of mechanical models was proposed to describe the generation of strongest earthquakes based on the idea of the synchronous failure of several adjacent asperities. In this study we propose a model which is based on verified numerical schemes, which allows us to quantitatively characterize the process of generation of strong earthquakes. The model takes into account the fault-block structure of the continental margin and combined the ideas of a possible synchronous destruction of several adjacent asperities, mutual sliding along a fault plane with a variable coefficient of friction and subsequent healing of medium defects under high pressure conditions. The applicability of the proposed model is shown by the example of the recent seismic history of the Kuril subduction zone. Kuril island arc is one of the most tectonically active regions of the world due to very high plate convergence rate. Heterogeneities in the mechanical coupling of the interplate interface in this region lead to the formation of the block structure of the continental margin, which is confirmed by various geological and seismological studies. GPS observations recorded at different stages of seismic cycle related to the 2006--2007 Simushir earthquakes allow us to model geodynamic processes of slow strain accumulation and its rapid release during the earthquake and the subsequent posteseismic process. We use parameters describing the regional tectonic structure and rheology obtained from the inversion of geodetic data to construct a 2D model of generation of large earthquakes in central Kurils. Analysis of paleoseismic data on dates and rupture characteristics of previous major earthquakes shows a good agreement between the modeled and observed seismic cycle features. The predicted horizontal displacements of the seismogenic block at the coseismic stage are consistent with satellite geodetic data recorded during the 2006 Simushir earthquake. The proposed model provides new insights into the geodynamic processes controlling the occurrence of strong subduction earthquakes.

The Zagros thrust belt formed due to the convergence between the Arabian and Eurasian plates during the closure of the Neo-Tethys Ocean. GPS measurements show that the northward relative motion of the Arabian Plate is still active today. This active drift produces high-magnitude earthquakes along the Zagros thrust belt, such as the Iran-Iraq border seismic event of November 12, 2017 (Mw = 7.3), nucleated along the “Mountain Front Fault”. The aim of this work is to study the inter-seismic and co-seismic deformation that eventually leads to the nucleation of important seismic events in the study area. A 2-D finite element model has been performed starting from published geological sections and relevant seismological datasets, integrated with geo-mechanical parameters of the rocks. The results of finite element modeling (FEM), including predicted uplift pattern and rate along the analysed crustal section, are compared with a tectonic geomorphology analysis in order to unravel the most reliable tectonic scenario for the active tectonic setting of the study area.

In this work we examine the seismicity of Montefeltro, a historical region of central Italy located between the northern Apennines and the Republic of San Marino towards the northern Adriatic coast. The seismicity of the northern Apennines region is mainly concentrated along the chain within a seismogenic layer that extends from about 5 to 15 km depth. Recently, important seismic sequences occurred in the central-northern Apenninic chain with magnitudes exceeding 5.5 (Colfiorito 1997, Mw 6.0; L’Aquila 2009 Mw 6.3; Emilia 2012, Mw 6.1; Amatrice 2016, Mw 6.5). Instead, the seismicity of Montefeltro, as recorded in the past three decades, appears quite widespread and discontinuous over time, except in some phases of intense activity. The magnitudes detected so far vary essentially from low to moderate, lower than 4.5 for the largest events. Unfortunately, the region that includes the Montefeltro is still not adequately monitored, therefore a detailed analysis of the background seismicity, the calculation of focal mechanisms for low magnitude events, and a more reliable estimate of the b-value is limited. To better define these parameters, a temporary seismic network has been set up starting from December 2018 to integrate the instrumental coverage provided by the Italian National Seismic Network (RSN). The analysis of the seismic activity occurred in the period January 2005 – up to now shows a sporadic, rather diffuse background seismicity marked by small magnitude seismic sequences strongly clustered in time and space. The most important sequences occurred in September-October 2005 (M=3.2) and August-September 2006 (M=3.7), nearby the towns of Macerata Feltria and Casteldelci, respectively. The spatio-temporal evolution of these main episodes highlights an energy release consistent with swarm-like activity. Others clusters of less intensity and duration were observed in January 2011 (ML 2.2, 15 events), September 2012 (ML 2.8, 6 events), and July 2015 (ML 2.3, 10 events). These bursts are confined in the upper crust contrary to the swarm-like seismicity which extends up to ~25 km depth. Another major feature of the seismicity pattern is represented by the occurrence of events even in the lower crust and in the upper mantle down to at least 50 km depth.

We review 25+ years of published climate impacts projections for the continental United States under a variety of climate scenarios and compare them with observations from field and remote sensing. Summers have become warmer and drier, fire season longer, drought stress more severe, endemic pests more lethal, vegetation shifts obvious. We present results from a dynamic global vegetation model that has been used since 1995 to simulate ecosystem responses to a variety of climate futures. Strengths and weaknesses of the vegetation model and its drivers are listed but overall results show that despite its obvious shortcomings, the model simulated fairly well the observed trends and the order of magnitude of the changes. One major conclusion of the study is that uncertainty with climate and vegetation model projections is much less than that of human choices and sociopolitical decisions which affect land use and greenhouse gas emissions.

Geochemical analyses applied to lake sedimentary records can reveal the history of pollution by metals and the effects of remedial efforts. Lakes provide ideal environments for geochemical studies because they have steady deposition of fine grained material suitable for fixation of pollutants. The Laurentian Great Lakes are the most studied system in this field and they have well-preserved chronological profiles. To date this important system has been considered in parts, hampering basin-wide conclusions regarding metal contamination. Therefore, we synthesized all relevant literature to create a central understanding of geochemical research to date in the Great Lakes. We also filled spatial and temporal gaps in a comprehensive geochemical analysis of 11 sediment cores collected from all five Great Lakes. Hierarchical cluster analysis of all Great Lakes samples divided the metal analytes into five functional groups: (1) carbonate elements; (2) metals and oxides with diverse natural sources, including a subgroup of analytes known to be anthropogenically enriched (Cd, Pb, Sn, Zn, and Sb); (3) common crustal elements; (4) metals related to coal and nuclear power generation; and (5) all of the rare earth elements. Two contamination indices (Igeo and EF) applied to sedimentary metals indicated that Na, Co, Mn, Cd, Pb, Ta, and Cu were the most enriched metal pollutants in this system, at some point during the Anthropocene. Land uses correlated with the metal analytes, such as increases in contaminant metals with the rise in catchment population and increases in carbonate elements (e.g. Ca) with agriculture. Certain contamination trends were observed basin-wide, such as for the atmospheric pollutant Pb, which followed a rise associated with fossil fuel combustion and a decline following the ban of leaded gasoline. Other trends were lake-specific such as recent high concentrations of Na in Lake Superior due to road salt applications and a late-20th century peak in Ca associated with algal whiting events in Lake Ontario. The Great Lakes are sensitive to environmental changes such as pollution by metals and it clear that, while there has been remedial success, results from the uppermost intervals of cores indicate ongoing problems.

Mountainous landscape evolution under tropical and alpine environments is mainly dictated by climatic forcing which influences underlying mechanisms of geomorphic transport (e.g., soil formation, river dynamics, slope stability and mass wasting). The time scale over which this influence acts ranges from seasonal to decennial time span. On the seasonal time scale, for accessible locations and when manpower is available, direct observations and field survey are the most useful and standard approaches. While very limited studies have been focused on the the decennial and century scale due to observational constrains. Here, we present an open and reproducible pipeline based on historical aerial images (up to 70yrs time span) that includes sensor calibration, dense matching and elevation reconstruction over two areas of interest that represent pristine examples for tropical and alpine environments: The Rempart Canyon in Reunion Island, and the Bossons glacier in the French Alps share a limited accessibility (in time and space) that can be overcome only from remote-sensing. We reach unprecedented resolution: the aero-triangulation falls at sub-metric scale based on ground truth, which is comparable to the initial images spatial sampling. This provides elevation time series with a better resolution to most recent satellite images such as Pleiades. In the case of the Rempart Canyon, we identified and quantified the results of 2 landslides that occurred in 1965 and 2001, and characterized the landslides dynamics. As for the alpine case, we highlight the effect of the temperature plateau occurred during 1939-1970 in Europe before the well known accelerated retreat during the post-industrial period. In both cases, we emphasize the strong effect of extreme events over multi-decennial to century time-scales.