scholarly journals The effects of river inflow and retention time on the spatial heterogeneity of chlorophyll and water–air CO<sub>2</sub> fluxes in a tropical hydropower reservoir

2015 ◽  
Vol 12 (1) ◽  
pp. 147-162 ◽  
Author(s):  
F. S. Pacheco ◽  
M. C. S. Soares ◽  
A. T. Assireu ◽  
M. P. Curtarelli ◽  
F. Roland ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Retention Time ◽  
Spatial Data ◽  
Ghg Emissions ◽  
Temporal Data ◽  
Co2 Fluxes ◽  
Tropical Reservoir ◽  
Dynamic Simulations ◽  
River Temperature ◽  
River Inflow

Abstract. Abundant research has been devoted to understanding the complexity of the biogeochemical and physical processes that are responsible for greenhouse gas (GHG) emissions from hydropower reservoirs. These systems may have spatially complex and heterogeneous GHG emissions due to flooded biomass, river inflows, primary production and dam operation. In this study, we investigated the relationships between the water–air CO2 fluxes and the phytoplanktonic biomass in the Funil Reservoir, which is an old, stratified tropical reservoir that exhibits intense phytoplankton blooms and a low partial pressure of CO2 (pCO2). Our results indicated that the seasonal and spatial variability of chlorophyll concentrations (Chl) and pCO2 in the Funil Reservoir are related more to changes in the river inflow over the year than to environmental factors such as air temperature and solar radiation. Field data and hydro\\-dynamic simulations revealed that river inflow contributes to increased heterogeneity during the dry season due to variations in the reservoir retention time and river temperature. Contradictory conclusions could be drawn if only temporal data collected near the dam were considered without spatial data to represent CO2 fluxes throughout the reservoir. During periods of high retention, the average CO2 fluxes were 10.3 mmol m−2 d−1 based on temporal data near the dam versus −7.2 mmol m−2 d−1 with spatial data from along the reservoir surface. In this case, the use of solely temporal data to calculate CO2 fluxes results in the reservoir acting as a CO2 source rather than a sink. This finding suggests that the lack of spatial data in reservoir C budget calculations can affect regional and global estimates. Our results support the idea that the Funil Reservoir is a dynamic system where the hydrodynamics represented by changes in the river inflow and retention time are potentially a more important force driving both the Chl and pCO2 spatial variability than the in-system ecological factors.

scholarly journals River inflow and retention time affecting spatial heterogeneity of chlorophyll and water–air CO<sub>2</sub> fluxes in a tropical hydropower reservoir

2014 ◽  
Vol 11 (6) ◽  
pp. 8531-8568
Author(s):  
F. S. Pacheco ◽  
M. C. S. Soares ◽  
A. T. Assireu ◽  
M. P. Curtarelli ◽  
F. Roland ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Retention Time ◽  
Spatial Data ◽  
Spatial Information ◽  
Chlorophyll Concentration ◽  
Ghg Emissions ◽  
Co2 Fluxes ◽  
Tropical Reservoir ◽  
River Temperature ◽  
River Inflow

Abstract. Much research has been devoted to understanding the complexity of biogeochemical and physical processes responsible for the greenhouse gas (GHG) emissions from hydropower reservoirs. Spatial complexity and heterogeneity of GHG emission may be observed in these systems because it is dependent on flooded biomass, river inflow, primary production and dam operation. In this study, we investigate the relationships between water–air CO2 fluxes and phytoplanktonic biomass in Funil Reservoir, an old and stratified tropical reservoir, where intense phytoplankton blooms and low partial pressure of CO2 (pCO2) are observed. Our results showed that Funil Reservoir seasonal and spatial variability of chlorophyll concentration (Chl) and pCO2 is more related to changes in river inflow over the year than environmental factor such as air temperature and solar radiation. Field data and hydrodynamic simulations reveal that the river inflow contributes to increased heterogeneity in dry season due to the variation of reservoir retention time and river temperature. Contradictory conclusion can be drawn if temporal data collected only near the dam is considered instead of spatial data to represent CO2 fluxes in whole reservoir. The average CO2 fluxes was −17.6 and 22.1 mmol m−2d−2 considering data collected near the dam and spatial data, respectively, in periods of low retention time. In this case, the lack of spatial information can change completely the role of Funil Reservoir regarding GHG emissions. Our results support the idea that Funil Reservoir is a dynamic system where the hydrodynamics represented by changes in river inflow and retention time is potentially more important force driving both Chl and pCO2 spatial variability than in-system ecological factors.

Giscience in the Mountain Geography

2010 ◽  
Vol 27 (1-2) ◽  
pp. 81-90
Author(s):  
Krishna Poudel
Keyword(s):  
Spatial Data ◽  
Environmental Data ◽  
Temporal Data ◽  
Observation Systems ◽  
Research Findings ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics ◽  
Mountain Geography ◽  
Different Sources

Mountains have distinct geography and are dynamic in nature compared to the plains. 'Verticality' and 'variation' are two fundamental specificities of the mountain geography. They possess distinct temporal and spatial characteristics in a unique socio-cultural setting. There is an ever increasing need for spatial and temporal data for planning and management activities; and Geo Information (GI) Science (including Geographic Information and Earth Observation Systems). This is being recognized more and more as a common platform for integrating spatial data with social, economic and environmental data and information from different sources. This paper investigates the applicability and challenges of GISscience in the context of mountain geography with ample evidences and observations from the mountain specific publications, empirical research findings and reports. The contextual explanation of mountain geography, mountain specific problems, scientific concerns about the mountain geography, advances in GIScience, the role of GIScience for sustainable development, challenges on application of GIScience in the contexts of mountains are the points of discussion. Finally, conclusion has been made with some specific action oriented recommendations.

Using computer clusters for processing spatial-temporal data streams in data acquisition systems

2018 ◽  
Vol 935 (5) ◽  
pp. 54-63
Author(s):  
A.A. Maiorov ◽  
A.V. Materuhin ◽  
I.N. Kondaurov
Keyword(s):  
Data Acquisition ◽  
Data Streams ◽  
Spatial Data ◽  
High Performance ◽  
Temporal Data ◽  
Cyberphysical Systems ◽  
Software Environment ◽  
Computer Clusters ◽  
Data Acquisition Systems ◽  
Essential Components

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

scholarly journals A Survey on Spatio-temporal Data Analytics Systems

2022 ◽  
Author(s):  
Md Mahbub Alam ◽  
Luis Torgo ◽  
Albert Bifet
Keyword(s):  
Programming Languages ◽  
Spatial Data ◽  
Data Analytics ◽  
Spatial Databases ◽  
Location Based Services ◽  
Temporal Data ◽  
Wide Range ◽  
Data Volume ◽  
Spatio Temporal ◽  
Gis Software

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

Mapping sediment carbon in a large tropical reservoir: burial of terrestrial and aquatic organic carbon, and occurrence of potential methane ebullition hot spots

2020 ◽  
Author(s):  
Sebastian Sobek ◽  
Raquel Mendonça ◽  
Anastasija Isidorova ◽  
Charlotte Grasset
Keyword(s):  
Organic Carbon ◽  
Spatial Variability ◽  
Accumulation Rate ◽  
Sediment Accumulation ◽  
Sediment Flux ◽  
Lipid Biomarkers ◽  
Gas Bubble ◽  
Tropical Reservoir ◽  
Global Average ◽  
High Sediment

&lt;p&gt;Reservoirs efficiently trap the riverine sediment flux, and therefore rapidly accumulate sediment. Since the sediments contain organic carbon (OC), reservoirs globally store significant amounts of OC in their sediments. The source of the OC buried in reservoir sediments is currently not well-known, but has important implications for the accounting of reservoir C burial as a new anthropogenic C sink. On the other hand, sediment OC can be degraded to the greenhouse gas methane (CH4) in anoxic sediment layers, and at high sediment accumulation rates, CH4 reaches oversaturation and forms gas bubbles which efficiently transport CH4 to the atmosphere. Accordingly, CH4 ebullition (bubble emission) is the main pathway of the globally significant CH4 emission by reservoirs. Both sediment OC accumulation and CH4 production is spatially extremely heterogeneous in reservoirs, and we currently lack understanding of the drivers of this spatial variability. We therefore mapped the spatial variability of sediment OC accumulation and of gas bubble-rich, CH4-oversaturated sediments in a large (1300 km2) tropical reservoir in Brazil, using both seismic sub-bottom profiling and sediment coring. In addition, we performed analyses of the sediment stable isotopic signature (13C and 15N) and lipid biomarkers (alkanes, alkanols, and acids) in order to discern the origin of the buried OC. We found that the OC accumulation rate was strongly dependent on the sedimentation rate, which in turn varied with water depth, bottom slope and proximity to river inflows. The spatially-resolved mean OC burial rate was 44 g C m-2 yr-1, twice as high as the global average for natural lakes, but lower than the global average for reservoirs. Gas bubble-containing sediment was detected in 30% of the sub-bottom survey length and occurred along the whole reservoir, but was most abundant in areas of high primary productivity, high sediment accumulation rate, and &lt; 25 m water column depth. Evidence from stable isotopes and lipid biomarkers indicates that a significant share of the OC accumulating in the reservoir sediment is of aquatic origin, and therefore is accountable as a new C sink that results from reservoir construction. These results indicate that the spatial variability of both the burial of OC from terrestrial and aquatic origin, and of gas bubble-rich sediments prone for CH4 ebullition can be understood from the reservoir characteristics.&lt;/p&gt;

scholarly journals Semi-stationary measurement as a tool to refine understanding of the soil temperature spatial variability

2015 ◽  
Vol 29 (4) ◽  
pp. 449-457 ◽  
Author(s):  
Michal Lehnert ◽  
Miroslav Vysoudil ◽  
Petr Kladivo
Keyword(s):  
Spatial Variability ◽  
Soil Temperature ◽  
Temperature Measurement ◽  
Computer Technology ◽  
Spatial Data ◽  
Local Level ◽  
Limited Area ◽  
Using Data ◽  
Further Development ◽  
Stationary Measurement

AbstractUsing data obtained by soil temperature measurement at stations in the Metropolitan Station Network in Olomouc, extensive semi-stationary measurement was implemented to study the spatial variability of the soil temperature. With the development of the research and computer technology, the study of the temperature is not limited by the complexity of the processes determining the soil temperature, but by the lack of spatial data. This study presents simple semi-stationary soil temperature measurement methods, which can contribute to the study of the spatial variability of soil temperature. By semi-stationary measurement, it is possible to determine the average soil temperature with high accuracy and the minimum soil temperature with sufficient accuracy at a depth of 20 cm. It was proven that the spatial variability of the minimum soil temperature under grass at a depth of 20 cm can reach up to several degrees Celsius at the regional level, more than 1°C at the local level, and tenths of °C at the sublocal level. Consequently, the standard stationary measurement of the soil temperature can be regarded as representative only for a very limited area. Semi-stationary soil temperature measurement is, therefore, an important tool for further development of soil temperature research.

scholarly journals Thermal functioning of a tropical reservoir assessed through three-dimensional modelling and high-frequency monitoring

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
Denis Furstenau Plec ◽  
Talita Fernanda das Graças Silva ◽  
Brigitte Vinçon-Leite ◽  
Nilo Nascimento
Keyword(s):  
Water Temperature ◽  
High Frequency ◽  
Three Dimensional ◽  
Frequency Measurement ◽  
Three Dimensional Model ◽  
Anthropogenic Pressures ◽  
Tropical Reservoir ◽  
River Temperature ◽  
Lake Morphology ◽  
The Impact

ABSTRACT Urban lakes and reservoirs provide important ecosystem services. However, their water quality is being affected by anthropogenic pressures. The thermal regime is a strong driver of the vertical transport of nutrients, phytoplankton and oxygen. Thermal stratification can modify biogeochemical processes. In this paper, a three-dimensional hydrodynamic model was implemented and validated with high-frequency measurement of water temperature. The simulation results were in agreement with the measurements. For all simulation period, the model performance was evaluated based on hourly values, presenting a maximum RMSE of 0.65 ºC and Relative Error of 2.08%. The results show that high-frequency measurement associated with a three-dimensional model could help to understand and identify the reasons for the changes in the thermal condition of a shallow urban lake. The impact of the stream inflow on the temperature was highlighted, showing that during higher discharge events, when the river temperature is colder than the lake water, it flows into the lake deeper layers. The inflow water sank to the deeper layers where the lake morphology changes. The model showed an impact along the entire lake, showing the importance of monitoring the inflow water temperature. This modelling tool could be further used to study specific patterns of reservoir hydrodynamics.

scholarly journals Software to compare images of the vegetation index obtained by satellite devices and unmanned aircraft

2020 ◽  
Vol 23 (4) ◽  
pp. 63-70
Author(s):  
M. Yu. Kataev ◽  
◽  
M. O. Krylov ◽  
P. P. Geiko ◽  
◽  
...  
Keyword(s):  
Spatial Data ◽  
Vegetation Index ◽  
Spatial Information ◽  
Measurement Data ◽  
Unmanned Aircraft ◽  
Software System ◽  
Temporal Data ◽  
Sources Of Information ◽  
Data Infrastructure ◽  
Spatio Temporal

At present, the practice of supporting many types of human activities requires the use of the spatial data infrastructure. Such an infrastructure integrates spatio-temporal sets from many sources of information within itself, providing the user with various types of processing, analysis and visualization methods. This article describes the architecture of the software system and the processes for managing sets of spatio-temporal data to solve agricultural problems. Measurement data using multispectral satellite systems, unmanned aerial vehicles (UAVs), as well as a priori information (meteorology, agrochemical information, etc.) are taken as input information. The User of the Software System is provided with the opportunity to control the spatial information of the territory of agricultural fields, sets of temporal data from various spatial data. An important achievement of the work is the combination of the results of satellite and UAV images according to the controlled parameters, that makes possible to expand the area of use of UAVs and verify them. The results of real data processing are presented.

scholarly journals Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 182
Author(s):  
Elias Dritsas ◽  
Andreas Kanavos ◽  
Maria Trigka ◽  
Gerasimos Vonitsanos ◽  
Spyros Sioutas ◽  
...  
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Privacy Preservation ◽  
Nearest Neighbor ◽  
Data Representation ◽  
Privacy Preserving ◽  
Temporal Data ◽  
K Nearest Neighbor ◽  
Trajectory Data ◽  
Spatio Temporal

Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection. The k-anonymity is considered a key solution that has been widely employed to prevent data re-identifcation and concerns us in the context of this work. Data modeling has also gained significant attention from the big data perspective. It is believed that the advancing distributed environments will provide users with several solutions for efficient spatio-temporal data management. GeoSpark will be utilized in the current work as it is a key solution that has been widely employed for spatial data. Specifically, it works on the top of Apache Spark, the main framework leveraged from the research community and organizations for big data transformation, processing and visualization. To this end, we focused on trajectory data representation so as to be applicable to the GeoSpark environment, and a GeoSpark-based approach is designed for the efficient management of real spatio-temporal data. Th next step is to gain deeper understanding of the data through the application of k nearest neighbor (k-NN) queries either using indexing methods or otherwise. The k-anonymity set computation, which is the main component for privacy preservation evaluation and the main issue of our previous works, is evaluated in the GeoSpark environment. More to the point, the focus here is on the time cost of k-anonymity set computation along with vulnerability measurement. The extracted results are presented into tables and figures for visual inspection.

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