One-year Time Series Research Articles

Solar power forecasting using domain knowledge

Integrating renewable energy into the existing energy market is crucial. Solar power forecasting is essential since it depends on weather parameters and must integrate with the central grid to use the produced solar power effectively. Contemporary studies indicate that machine learning has the potential to predict the future generation of solar energy based on past data. This research demonstrates a broad range of solar power forecasting, combining the one-year time series solar power generation data, solar panel physical features, and weather information with the help of machine learning and deep learning tools with domain knowledge. The dataset is curated and preprocessed. We propose a deep learning ensemble model based on BI-LSTM. The proposed model can forecast well regardless of geographical position and is able to predict both short-term and long-term time horizons. We compared the results of the proposed model with the existing dataset and multiple standard deep learning models and found that our model produced better performance than traditional models. We also validated our model using different solar plants in Durgapur, India. For long-term forecasting, our model also outperformed the base model.

Impact of dry density and incomplete saturation on microbial growth in bentonite clay for nuclear waste storage.

Many countries are in the process of designing a deep geological repository (DGR) for long-term storage of used nuclear fuel. For several designs, used fuel containers will be placed belowground, with emplacement tunnels being backfilled using a combination of highly compacted powdered bentonite clay buffer boxes surrounded by a granulated "gapfill" bentonite. To limit the potential for microbiologically influenced corrosion of used fuel containers, identifying conditions that suppress microbial growth is critical for sustainable DGR design. This study investigated microbial communities in powdered and gapfill bentonite clay incubated in oxic pressure vessels at dry densities between 1.1g cm-3 (i.e. below repository target) and 1.6g cm-3 (i.e. at or above repository target) as a 1-year time series. Our results showed an initial (i.e. 1 month) increase in the abundance of culturable heterotrophs associated with all dry densities <1.6g cm-3, which reveals growth during transient low-pressure conditions associated with the bentonite saturation process. Following saturation, culturable heterotroph abundances decreased to those of starting material by the 6-month time point for all 1.4 and 1.6g cm-3 pressure vessels, and the most probable numbers of culturable sulfate-reducing bacteria (SRB) remained constant for all vessels and time points. The 16S rRNA gene sequencing results showed a change in microbial community composition from the starting material to the 1-month time point, after which time most samples were dominated by sequences associated with Pseudomonas, Bacillus, Cupriavidus, and Streptomyces. Similar taxa were identified as dominant members of the culture-based community composition, demonstrating that the dominant members of the clay microbial communities are viable. Members of the spore-forming Desulfosporosinus genus were the dominant SRB for both clay and culture profiles. After initial microbial growth while bentonite was below target pressure in the early phases of saturation, microbial growth in pressure vessels with dry densities of at least 1.4g cm-3 was eventually suppressed as bentonite neared saturation.

Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region

Abstract In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

Coccolith-calcite Sr/Ca as a proxy for transient export production related to Saharan dust deposition in the tropical North Atlantic

Atmospheric dust deposition can modulate the earth’s climate and atmospheric CO2 through fertilising the ocean (nutrient source) and by accelerating the biological carbon pump through fuelling the ballasting process. To distinguish the biogeochemical effects of Saharan dust with respect to fertilization and ballasting, and to gain a broader perspective on the coccolith calcite Sr/Ca in relation to the drivers of coccolith export production, we determined the coccolith-Sr/Ca from a one-year (2012–2013) time-series sediment trap record in the western tropical North Atlantic (M4—49°N/12°W). High Sr/Ca were linked to enhanced export production in the upper part of the photic zone, most notably under windier, dry, and dustier conditions during spring. Attenuated Sr/Ca in the autumn probably reflect a combination of lower Sr-incorporation by dominant but small-size placolith-bearing species and the presence of “aged” coccoliths rapidly scavenged during a highly productive and usually fast export event, likely added by (wet) dust ballasting. Higher Sr/Ca observed in the large coccolith size fractions support the existing notion that larger-sized coccolithophores incorporate more Sr during calcification under the same environmental conditions. The presence of the abnormally Sr-rich species Scyphosphaera apsteinii is also shown in the separated large fraction of our Sr/Ca seasonal data.

Chaos and Predictability in Ionospheric Time Series

Modelling the Earth's ionosphere is a big challenge, due to the complexity of the system. Different first principle models have been developed over the last 50 years, based on ionospheric physics and chemistry, mostly controlled by Space Weather conditions. However, it is not understood in depth if the residual or mismodelled component of the ionosphere's behaviour is predictable in principle as a simple dynamical system, or is conversely so chaotic to be practically stochastic. Working on an ionospheric quantity very popular in aeronomy, we here suggest data analysis techniques to deal with the question of how chaotic and how predictable the local ionosphere's behaviour is. In particular, we calculate the correlation dimension D2 and the Kolmogorov entropy rate K2 for two one-year long time series of data of vertical total electron content (vTEC), collected on the top of the mid-latitude GNSS station of Matera (Italy), one for the year of Solar Maximum 2001 and one for the year of Solar Minimum 2008. The quantity D2 is a proxy of the degree of chaos and dynamical complexity. K2 measures the speed of destruction of the time-shifted self-mutual information of the signal, so that K2-1 is a sort of maximum time horizon for predictability. The analysis of the D2 and K2 for the vTEC time series allows to give a measure of chaos and predictability of the Earth's ionosphere, expected to limit any claim of prediction capacity of any model. The results reported here are preliminary, and must be intended only to demonstrate how the application of the analysis of these quantities to the ionospheric variability is feasible, and with a reasonable output.

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

The increasing frequency of devastating floods from heavy rainfall—associated with climate change—has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep-learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is a long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with RMSE less than 67 cm for about 10 m of stage peaks, as well as accurately simulating stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision, we found that the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well-simulated, the physical concepts of runoff appear to be soundly embedded in the MLP.

Seasonal Cold-Wave Propagation Into the Near-Surface Ice of Debris-Covered Khumbu Glacier, Nepal

Meltwater from high-elevation debris-covered glaciers—particularly those located in the greater Himalaya and Andes—shapes the water supply of major rivers and nourishes substantial terrestrial, estuarine, and marine habitats (Kraaijenbrink et al., 2017; Immerzeel et al., 2020). However, the relative inaccessibility and high elevation of such glaciers results in a paucity of data relating to their fundamental physical properties and processes, limiting the information available to constrain and evaluate numerical models of their behaviour and project future change. Knowledge of the subsurface properties of such glaciers is particularly deficient because it is largely obscured to satellite and airborne remote sensing; englacial investigations therefore commonly require direct access (Miles et al., 2020). Of the physical properties of glaciers, ice temperature exerts an important control over glaciological processes, such as glacier motion, and their modelled behaviour. For example, ice viscosity is sensitive to temperature such that, under the same stress, ice approaching the melting point deforms 5–10 times more rapidly than it would at −10°C (Deeley and Woodward, 1908; Cuffey and Paterson, 2010). Basal motion depends on lubrication facilitated by the presence of meltwater at the ice-bed interface and/or within the pore space of a subglacial sediment layer. Measurements of near-surface ice temperatures are important for modelling the surface energy balance and projecting the future mass-balance response of glaciers to anticipated climate change. This is especially the case for glaciers with a thick supraglacial debris layer that insulates the underlying ice (according to debris layer thickness and lithology), reducing ablation and potentially extending glacier longevity (Nicholson and Benn, 2006; Nicholson and Benn, 2013; Anderson and Anderson, 2016). Here, we present a one-year time series of near-surface ice temperatures, measured between 1.5 and 7.0 m below the ice surface, in a borehole drilled by hot water into the debris-covered tongue of Khumbu Glacier, Nepal.

Live fuel moisture content estimation from MODIS: A deep learning approach

Live fuel moisture content (LFMC) is an essential variable to model fire danger and behaviour. This paper presents the first application of deep learning to LFMC estimation based on the historical LFMC ground samples of the Globe-LFMC database, as a step towards operational daily LFMC mapping in the Contiguous United States (CONUS). One-year MODerate resolution Imaging Spectroradiometer (MODIS) time series preceding each LFMC sample were extracted as the primary data source for training. The proposed temporal convolutional neural network for LFMC (TempCNN-LFMC) comprises three 1-D convolutional layers that learn the multi-scale temporal dynamics (features) of one-year MODIS time series specific to LFMC estimation. The learned features, together with a few auxiliary variables (e.g., digital elevation model), are then passed to three fully connected layers to extract the non-linear relationships with LFMC. In the primary training and validation scenario, the neural network was trained using samples from 2002 to 2013 and then adopted to estimating the LFMC from 2014 to 2018, achieving an overall root mean square error (RMSE) of 25.57% and a correlation coefficient (R) of 0.74. Good consistency on spatial patterns and temporal trends of accuracy was observed. The trained model achieved a similar RMSE of 25.98%, 25.20% and 25.93% for forest, shrubland, and grassland, respectively, without requiring prior information on the vegetation type.

A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics

Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral-angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.

REMOTE SENSING IMAGE TIME SERIES METRICS FOR DISTINCTION BETWEEN PASTURE AND CROPLANDS USING THE RANDOM FOREST CLASSIFIER

Abstract. Pasture and croplands play an important role in Brazil’s economic and political scenarios, once its PIB (Raw Internal Product) is mainly based on what is exported from the rural production, such as meat and soybean, and government, with its regulations, is part-responsible for the establishment and maintaining of the conditions so that the trades can go well. In addition, these two types of land use correspond together to aprox. one third of the country extension. Moreover, frequently lands occupation is subject of discussion concerning its potential use for the reason of conflicts including Brazilian traditional communities, landless people and big farmers. Considering it, mapping pasture and croplands accurately is crucial for the country administration, in both economic and political spheres. Certainly, remote sensing is the very manner to tackle this issue, although this may not be an easy task due to the spectral similarity between these patterns. This work, hence, aims to distinct pasture from croplands in an experimental subset area of Brazilian Cerrado biome, using remote sensing metric images derived from one-year time series of the Landsat 8 products. In order to achieve this goal, we utilized six bands of the OLI sensor and calculated seven metrics, attaining a compiled dataset with 42 layers. We performed an object-based supervised classification with the Random Forest algorithm, considering both spectral and geometrical attributes. Results showed global accuracy of 80%, with Kappa index of 0.6, and the potential time series have in separating targets spectrally similar.

Sentinel-2 time series based optimal features and time window for mapping invasive Australian native Acacia species in KwaZulu Natal, South Africa

The spread of invasive Australia native Acacia tree species threatens biodiversity and adversely affecting on vegetative structure and function, including plant community composition, quantity and quality worldwide. It is essential to provide researchers and land managers for biological invasion science and management with accurate information of the distribution of invasive alien species and their dynamics. Remotely sensed data that reveal spatial distribution of the earth’s surface features/objects provide great potential for this purpose. Consistent satellite monitoring of alien invasive plants is often difficult because of lack of sufficient spectral contrast between them and co-occurring plants species. Time series analysis of spectral properties of the species can reveal timing of their variations among adjacent species. This information can improve accuracy of invasive species discrimination and mapping using remote sensing data at large scale. We sought to identify and better understand the optimal time window and key spectral features sufficient to detect invasive Acacia trees in heterogeneous forested landscape in South Africa. We explored one-year (January to December 2018) time series spectral bands and vegetation indices derived from optical Copernicus Sentinel-2 data. The attributes correspond to geographical information of invasive Acacia and native species recorded during a field survey undertaken from 21 February to 25 February 2018 over Kwa-Zulu Natal grasslands landscape, in South Africa. The results showed comparable separability prospects between times series of spectral bands and that of vegetation indices.Substantial differences between Acacia species and native species were observed from spectral indices and spectral bands which are sensitive to Leaf Area Index, canopy chlorophyll and nitrogen concentrations. The results further revealed spectral differences between Acacia species and co-occurring native vegetation in April (senescence for deciduous plants), June-July (dry season), September (peak flowering period of Acacia spp) and December (leaf green-up) with vegetation indices (overall accuracy > 80 %). While spectral bands showed the beginning of the growing season (November–January) and peak vegetation productivity (February-March) as the optimal seasons or dates for image acquisition for discriminating Acacias from its co-occurring native species (overall accuracy > 80 %). In general, the use of Sentinel-2 time series spectral bands and vegetation indices has increased our understanding of Australian Acacias spectral dynamics, and proved that the sentinel-2 data is useful for characterization and monitoring Acacias over a large scale. Our results and approach could assist in deriving detailed geographic information of the species and assessment of a spread invasive plant species and severity of invasion.

OPTIMAL DATES FOR DECIDUOUS TREE SPECIES MAPPING USING FULL YEARS SENTINEL-2 TIME SERIES IN SOUTH WEST FRANCE

Abstract. The free to use Sentinel-2 (S2) sensors with 5-day revisit time at high spatial resolution in 10 spectral bands is a revolution in the remote sensing domain. Including 6 spectral bands in the near infrared, with 3 dedicated for the red-edge (where the vegetation significatively increases), these european satellites are very promising for mapping tree species distribution at a national scale. Here, we study the contribution of three one-year S2 Satellite Image Time Series (SITS) for mapping deciduous species distribution in the southwest of France. The annual cycle of vegetation (called phenology) can contribute to the identification of tree species. For some specific dates, species can have different phenological behaviours (senesence, flowering…). To train and validate the maps, we used the Support Vector Machine algorithm with a spatial cross-validation method. To train the algorithm with the same number of samples per species, we decided to undersample each class to the smallest class using a K-means clustering method. Moreover, a Sequential Feature Selection (SFS) has been implemented to detect the optimal dates per species. Our results are promising with high accuracy for Red oak andWillow (average score of the three one-year respectively F1 = 0.99, F1 = 0.94) based on the optimal dates. However, it appears that the performances when using the each full SITS are far below the optimal dates models (average ΔF1 = 0.32). We did not find, except for Willow and Red oak, that the optimal dates were the same for each year. Perspectives is to find an algorithm robust to temporal or spectral noise and to smooth the time series.

Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping

The potential use of the interferometric coherence measured with Sentinel-1 satellites as input feature for crop classification is explored in this study. A one-year time-series of Sentinel-1 images acquired over an agricultural area in Spain, in which 17 crop species are present, is exploited for this purpose. Different options regarding temporal baselines, polarization, and combination with radiometric data (backscattering coefficient) are analyzed. Results show that both radiometric and interferometric features provide notable classification accuracy when used individually (overall accuracy lies between 70% and 80%). It is found that the shortest temporal baseline coherences (6 days) and the use of all available intensity images perform best, hence proving the advantage of the 6-day revisit time provided by the Sentinel-1 constellation with respect to longer revisit times. It is also shown that dual-pol data always provide better classification results than single-pol ones. More importantly, when both coherence and backscattering coefficient are jointly used, a significant increase in accuracy is obtained (greater than 7% in overall accuracies). Individual accuracies of all crop types are increased, and an overall accuracy above 86% is reached. This proves that both features provide complementary information, and that the combination of interferometric and radiometric radar data constitutes a solid information source for this application.

Determination of biogas process efficiency - a practice-oriented alternative to the biomethane potential test

The energy efficiency of biogas plants is fundamentally based on the biochemical degradation of input materials. This paper introduces a novel method of efficiency determination based on the gross calorific value (GCV), a very common method in conventional power generation. GCV investigation of five commercial biogas plants in northern Schleswig Holstein were conducted by a one-year time series analysis with weekly sample taking. Although initially a simple model was used to estimate the lignin content, the plausible results indicate the suitability of the proposed method. For comparison, methods like the FoDM and the classical biomethane potential test are highlighted to point out their traditional usage and their adaption for the biogas sector. Also the laboratory efforts they cause is taken into account.

Open-ocean-interior moored sensor turbulence estimates, below a Meddy

A one-year time series of moored high-resolution temperature T-sensor data from 1455 ± 65 m depth on a 3900 m long line in about 5300 m of water in the NE-Atlantic Canary Basin are dominated by salinity (over-)compensated intrusions arising from the effects of Mediterranean outflow waters, which are commonly organized as underwater eddy-lenses ‘Meddies’. During the passage of a Meddy-core above the T-sensors, no intrusions were observed, thereby making it possible to use the temperature records to quantify turbulence parameters. Mean turbulence values confirm previous shipborne profiler estimates of O(10-10) m2 s-3 and O(10-5) m2 s-1 for dissipation rate and eddy diffusivity, respectively. The present data show that these ocean-interior turbulence estimates are from short-lived (<0.5 h) rather intense overturning cells with vertical scales of <5 m. Because the turbulence inertial subrange is found to extend into the internal wave band, the overturns are predominantly driven by shear associated with inertial currents. Kinetic energy, current shear and temperature variance peak at sub-inertial frequencies during the Meddy passage, suggesting wave trapping in the warm anti-cyclonic eddy and/or weakly stratified layers. The observations further show that internal wave displacements are coherent over vertical scales of up to 40 m during the presence of the Meddy compared with vertical coherence scales of <25 m during the more common no-Meddy conditions of double diffusion intrusions.

Seasonal time-series reveal the impact and rapid recovery in richness, abundance and community structure of benthic macroinvertebrates following catchment wildfire

Catchment wildfire can decimate freshwater benthos when burnt material washes through river channels. We conducted detailed seasonal surveys over the course of one-year at N = 9 sites following summer-time wildfire in 2009 and N = 3 sites following summer-time wildfire in 2010 (constituting one-year time-series of 9 ∗ 5 and 3 ∗ 5 samples, respectively). Basic one-year-on surveys were conducted at an additional 12 sites following 2009 wildfires (Sumpostfire:Sum+one.yr; N = 12 ∗ 2). Burnt sites were compared with 6 reference sites surveyed seasonally between autumn and summer, 2009–2010 (N = 6 ∗ 4), supplemented by 6 additional sites surveyed in summer 2004 (total reference samples: N = 6 ∗ 5). While benthic macroinvertebrates were largely unaffected by the fire event, richness and abundance were decimated during the winter, yet recovered to initial survey levels by the following summer. The differential response of assemblages in the exceptionally wet winter of 2009–10 versus 2010–11 highlighted the catalytic role of rainfall as a driver of benthic disturbance. Ecological disturbance was proportionally greater for less abundant taxa with community evenness peaking at the time of maximum disturbance. Seasonal dynamics in fire-impacted and reference sites followed a similar pattern, implying that despite the major reduction in macroinvertebrate standing crop the general character of benthic processes was sustained. One year after the wildfire event community structure was similar to the immediate post-fire assemblages and generally indistinguishable from reference samples. The statistical importance of habitat parameters at the landscape and local scale (catchment size, landuse, slope, bank management and benthic substrate) were indicative of mechanistic processes underlying wildfire disturbance-recovery and define the scope for mitigation management. The remarkable resilience of community structure in these Mediterranean streams marks an emphatic contrast to the response of benthic macroinvertebrates to comparable disturbance processes in temperate regions. Given the increasing geographic scale and frequency of fires accompanying global warming wildfire-risk may become a leading issue for river management.

Observations of Deep Current at the Western Boundary of the Northern Philippine Basin

One-year time series of current velocities and hydrographic parameters based on four deep moorings deployed east of the Luzon Strait are employed to study the deep current at the western boundary (DCWB) of the northern Philippine Basin. While the mean current is relatively weak, the DCWB is highly variable on an intraseasonal time scale, with dominant periods ranging between 30 and 80 days. During the period of observation (October 2011–October 2012), the DCWB reversed its direction at early April, and pointed southward (−2.4 cm/s) in summer/autumn and northward (1.7 cm/s) in winter/spring. This annual reversal of the DCWB is consistent with the water property distribution in the deep Philippine Basin, with relatively cold and fresh water to the north and relatively warm and salty water to the south. The moored time series also allow for discussion on the stratification of the deep Luzon Strait, which indicates the lower interface of Pacific deep water capable of furnishing the deepwater overflow in the Luzon Strait.

Tree stomata conductance estimates of a wax myrtle-tree heath (fayal-brezal) cloud forest as affected by fog

Direct transpiration measurements of tree species obtained with Granier’s heat dissipation probes, combined with micrometeorological data were used to derive tree conductance estimates in a cloud-immersed wax myrtle-tree heath forest located in the Garajonay National Park (La Gomera, Canary Islands, Spain). The one-year period time series were analysed distinguishing between foggy and fog-free conditions in order to evaluate the vegetation response to fog. The presence of fog was found to reduce transpiration (T) in both species investigated (E. arborea and M. faya), such that the yearly medians of the hourly T values were 6–15.6 times higher when no fog was present as compared to cloud immersed periods. By contrast the gc estimates exhibit larger values in the afternoon during foggy conditions when compared in terms of the hourly gc medians at each time of the day, and were clearly greater on a daily basis. During foggy conditions, gc followed a monotonically increasing trend. Large variability of conductance estimates was observed across the ranges of micrometeorological conditions explored. The largest conductance values were associated with low solar radiations, air temperatures between 8 and 15°C, low VPD (<0.5kPa), and wind velocities of 2–4ms−1. Laurel forest trees, previously referred as following a profligate water use strategy, may thus profit from the foggy environment by maintaining the stomata opened during the day without significant water losses, while benefiting with the associated carbon gain.

Spatio-Temporal Decomposition of Satellite-Derived SST–SSH Fields: Links Between Surface Data and Ocean Interior Dynamics in the Agulhas Region

In this paper, we develop a novel observation-driven methodology to explore spatio-temporal dependencies between satellite-derived sea surface height (SSH) and sea surface temperature (SST) fields. Level-set-based and registration-based criteria are defined to evaluate and detect spatial links between SSH and SST anomaly fields. The method is applied to one-year SST and SSH time series in the highly dynamical Aghulas return current region. The analysis evidences a seasonal variation of the overall correlation between SST and SSH fields. As further revealed, the coldest SST anomalies are reported to efficiently trace the lowest SSH anomalies for all seasons, while the warmest SST anomalies solely match the largest SSH anomalies during winter. The second criterion relies on the registration of SSH and SST anomaly fields. The registration energy is shown to corresponds to the seasonal influence of the mixed-layer depth revealed here by atmospheric forcing. These results show us that the SST-derived SSH reconstruction using the surface quasigeostrophic approximation should take into account stratification effects, especially during summer. As discussed, the proposed methodology can enforce the reconstruction of sea surface current from a joint analysis of satellite altimetry data and high-resolution satellite-derived SST data.

Concentrations and abundance ratios of long-chain alkenones and glycerol dialkyl glycerol tetraethers in sinking particles south of Java

In this study, we obtained concentrations and abundance ratios of long-chain alkenones and glycerol dialkyl glycerol tetraethers (GDGTs) in a one-year time-series of sinking particles collected with a sediment trap moored from December 2001 to November 2002 at 2200m water depth south of Java in the eastern Indian Ocean. We investigate the seasonality of alkenone and GDGT fluxes as well as the potential habitat depth of the Thaumarchaeota producing the GDGTs entrained in sinking particles. The alkenone flux shows a pronounced seasonality and ranges from 1µgm−2d−1 to 35µgm−2d−1. The highest alkenone flux is observed in late September during the Southeast monsoon, coincident with high total organic carbon fluxes as well as high net primary productivity. Flux-weighted mean temperature for the high flux period using the alkenone-based sea-surface temperature (SST) index U37K′ is 26.7°C, which is similar to satellite-derived Southeast (SE) monsoon SST (26.4°C). The GDGT flux displays a weaker seasonality than that of the alkenones. It is elevated during the SE monsoon period compared to the Northwest (NW) monsoon and intermonsoon periods (approximately 2.5 times), which is probably related to seasonal variation of the abundance of Thaumarchaeota, or to enhanced export of GDGTs by aggregation with sinking phytoplankton detritus. Flux-weighted mean temperature inferred from the GDGT-based TEX86H index is 26.2°C, which is 1.8°C lower than mean annual (ma) SST but similar to SE monsoon SST. As the time series of TEX86H temperature estimates, however, does not record a strong seasonal amplitude, we infer that TEX86H reflects ma upper thermocline temperature at approximately 50m water depth.