Implications of methane emissions in biogeochemical budgeting: A study from a eutrophic tropical lake of South India

Greenhouse gases (GHGs), especially, methane (CH4) emissions from the littoral zones of the lakes play an important role in regional biogeochemical budgets. Only a few studies are available in literature highlighting the direct flux measurements of CH4 &#160;&#160;from the aquatic systems. In the present study, an attempt has been made to quantify the spatio-temporal variations of CH4&#160;efflux and the key physical factors controlling the emission rate, from the vegetated littoral zones of lake Vellayani (5.55Km2), located in the urbanized area of Thiruvananthapuram city, Kerala, South-West India. CH4&#160;efflux were collected from different vegetations in littoral zones, using a static chamber, during the peak growing seasons from March to October in 2016 and further analyses were carried out by using Gas Chromatograph (PE Clarus 500, PerkinElmer, Inc.). The mean efflux rate of CH4 &#160;&#160;from the emergent plant species (Phragmites australis and Typha spp.) was 114.4 mg CH4 m-2h-1; while, in the floating leaved species (Nymphaea spp. and Nelumbo Spp.), it &#160;&#160;was &#160;&#160;observed to be 32.6 mgCH4 m-2h-1. The results reveal that CH4 efflux in the zone of emergent vegetation was significantly higher than the floating-leaved zone indicating the importance of plant biomass and standing water depths for the spatial variations of CH4 efflux. However, no significant temporal variations were noticed in the physical factors during the peak growing seasons. These results indicate that the vegetated littoral zones of lake, especially the emergent plant zones were supersaturated with CH4, facilitating the production of carbon for CH4 emission, but also enable the release of CH4 by the diffusion from the intercellular gas lacunas. We conclude that the atmospheric CH4 emissions will be affected by the growth of exotic species in the lake systems and may be the reason for enhancing the climate warming in local/regional scale.

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Tropospheric temperature variation over India and links with the Indian summer monsoon : 1971-2000

MAUSAM ◽

10.54302/mausam.v53i3.1646 ◽

2022 ◽

Vol 53 (3) ◽

pp. 289-308

Author(s):

D. R. KOTHAWALE ◽

K. RUPA KUMAR

Keyword(s):

Summer Monsoon ◽

Southern Oscillation ◽

Monsoon Season ◽

Temporal Variations ◽

Mean Annual Temperature ◽

Tropospheric Temperature ◽

Temperature Variations ◽

Annual Cycles ◽

The Mean ◽

Spatio Temporal

In the context of the ever increasing interest in the regional aspects of global warming, understanding the spatio-temporal variations of tropospheric temperature over India is of great importance. The present study, based on the data from 19 well distributed radiosonde stations for the period 1971-2000, examines the seasonal and annual mean temperature variations at the surface and five selected upper levels, viz., 850, 700, 500, 200 and 150 hPa. An attempt has also been made to bring out the association between tropospheric temperature variations over India and the summer monsoon variability, including the role of its major teleconnection parameter, the El Niño/Southern Oscillation (ENSO). Seasonal and annual mean all-India temperature series are analyzed for surface and five tropospheric levels. The mean annual cycles of temperature at different tropospheric levels indicate that the pre-monsoon season is slightly warmer than the monsoon season at the surface, 850 hPa and 150 hPa levels, while it is relatively cooler at all intermediate levels. The mean annual temperature shows a warming of 0.18° C and 0.3° C per 10 years at the surface and 850 hPa, respectively. Tropospheric temperature anomaly composites of excess (deficient) monsoon rainfall years show pronounced positive (negative) anomalies during the month of May, at all the levels. The pre-monsoon pressure of Darwin has significant positive correlation with the monsoon temperature at the surface and 850 hPa.

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Spatio-temporal variations of land surface temperature and precipitation due to climate change in the Jhelum river basin, India

MAUSAM ◽

10.54302/mausam.v71i4.54 ◽

2021 ◽

Vol 71 (4) ◽

pp. 661-674

Author(s):

HIMAYOUN DAR ◽

ROSHNI THENDIYATH ◽

MOHSIN FAROOQ

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

21St Century ◽

Temporal Variations ◽

Average Increase ◽

Temperature And Precipitation ◽

Rcp 8.5 ◽

The Mean ◽

Spatio Temporal ◽

Precipitation And Temperature

The present study investigated the spatio-temporal variations of precipitation and temperature for the projected period (2011-2100) in the Jhelum basin, India. The precipitation and temperature variables are projected under RCP 8.5 scenario using statistical down scaling techniques such as Artificial Neural Network (ANN) and Wavelet Artificial Neural Network (WANN) models. Firstly, the screened predictors were downscaled to predictand using ANN and WANN models for all the study stations. On the basis of the performance criteria, the WANN model is selected as an efficient model for downscaling of precipitation and temperature. The future screened predictor data pertaining to RCP 8.5 of CanESM2 model were downscaled to monthly temperature and precipitation for future periods (2011-2100) using WANN models. The investigation of the future projections revealed an average increase of 17-25% in the mean annual precipitation and 20-25% average increase in the monthly mean precipitation for all the selected stations towards the end of 21st century. The monthly mean temperature also showed an increase of 2-3 °C for all the study stations towards the end of 21st century. The mean seasonal temperature of the projected period is found to be increasing for all the four seasons in most parts of the basin.

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An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator

The Cryosphere ◽

10.5194/tc-15-2803-2021 ◽

2021 ◽

Vol 15 (6) ◽

pp. 2803-2818

Author(s):

Joan Antoni Parera-Portell ◽

Raquel Ubach ◽

Charles Gignac

Keyword(s):

Sea Ice ◽

Regional Scale ◽

Ice Cover ◽

Detection Algorithm ◽

Temporal Variations ◽

The Arctic ◽

Sea Ice Extent ◽

Monitoring Tools ◽

Spatio Temporal ◽

Ice Detection

Abstract. The continued loss of sea ice in the Northern Hemisphere due to global warming poses a threat to biota and human activities, evidencing the necessity of efficient sea ice monitoring tools. Aiming at the creation of an improved sea ice extent indicator covering the European regional seas, the new IceMap500 algorithm has been developed to classify sea ice and water at a resolution of 500 m at nadir. IceMap500 features a classification strategy built upon previous MODIS sea ice extent algorithms and a new method to reclassify areas affected by resolution-breaking features inherited from the MODIS cloud mask. This approach results in an enlargement of mapped area, a reduction of potential error sources and a better delineation of the sea ice edge, while still systematically achieving accuracies above 90 %, as obtained by manual validation. Swath maps have been aggregated at a monthly scale to obtain sea ice extent with a method that is sensitive to spatio-temporal variations in the sea ice cover and that can be used as an additional error filter. The resulting dataset, covering the months of maximum and minimum sea ice extent (i.e. March and September) over 2 decades (from 2000 to 2019), demonstrates the algorithm's applicability as a monitoring tool and as an indicator, illustrating the sea ice decline at a regional scale. The European sea regions located in the Arctic, NE Atlantic and Barents seas display clear negative trends in both March (−27.98 ± 6.01 × 103 km2yr−1) and September (−16.47 ± 5.66 × 103 km2yr−1). Such trends indicate that the sea ice cover is shrinking at a rate of ∼ 9 % and ∼ 13 % per decade, respectively, even though the sea ice extent loss is comparatively ∼ 70 % greater in March.

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Imaging the spatio-temporal variations in deep tremor activity using cluster analysis techniques

10.5194/egusphere-egu21-14542 ◽

2021 ◽

Author(s):

Anca Opris ◽

Sumanta Kundu ◽

Takahiro Hatano

Keyword(s):

Machine Learning ◽

Cluster Analysis ◽

Regional Scale ◽

Temporal Variations ◽

Machine Learning Algorithms ◽

Temporal Behaviour ◽

Slow Earthquakes ◽

Short Period ◽

Spatial Correlation Structure ◽

Spatio Temporal

<div>More than 15 years of seismic observations on slow earthquakes are available for the Nankai and Cascadia regions, due to the high density of seismic stations and constant improvements. It was observed that deep tremor activity exhibits highly non-Poissonian behaviour, consisting of short-period bursts separated by long periods of inactivity, as well as significant spatial variations throughout a tectonic region (Obara, 2011). Tremor activity in these regions has shown episodic behaviour with different recurrence interval. Modelling the space-time variations can help the unified understanding of the phenomenon. Catalogues with more than 30.000 (Idehara et al., 2014) and 130000 LFE&#8217;s (Mizuno et al, 2019) are available for the world tremor databese. If we consider LFE&#8217;s source as a spatial correlation structure which is evolving in time, in order to reveal the characteristics of this structure, we used the Grassberger Procaccia algorithm to calculate the combined correlation dimension (Tosi et al.,2008) of tremor activity (Cc (r, &#964;)), at both local and regional scale. The integral representation is shown as contour map (facilitating the possibility of using machine learning algorithms based on image processing for identifying the characteristic image of each tremor patch). Thus, implementing machine learning methods for LFE cluster analysis is required. After performing the cluster analysis, we could identify the specific spatio-temporal behaviour of each of the tremor patches in the studied regions, not just the features which were described in previous studies, such as recurrence intervals for short-term slow slip events (Idehara et al., 2014), tremor migration (for monitoring purposes), but also new features which could be used for forecasting.</div><div>&#160;</div><div><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.0b8b5af57a0068928921161/sdaolpUECMynit/12UGE&app=m&a=0&c=4087202a58071c68d39e6d5f305ba0b4&ct=x&pn=gepj.elif&d=1" alt=""></div>

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Correcting for Systematic Underestimation of Topographic Glacier Aerodynamic Roughness Values From Hintereisferner, Austria

Frontiers in Earth Science ◽

10.3389/feart.2021.691195 ◽

2021 ◽

Vol 9 ◽

Author(s):

Joshua R. Chambers ◽

Mark W. Smith ◽

Thomas Smith ◽

Rudolf Sailer ◽

Duncan J. Quincey ◽

...

Keyword(s):

Laser Scanning ◽

Regional Scale ◽

Laser Scanner ◽

Turbulent Energy ◽

Temporal Variations ◽

Glacier Surface ◽

Order Of Magnitude ◽

Spatio Temporal ◽

Aerodynamic Roughness ◽

Elevation Model

Spatially-distributed values of glacier aerodynamic roughness (z0) are vital for robust estimates of turbulent energy fluxes and ice and snow melt. Microtopographic data allow rapid estimates of z0 over discrete plot-scale areas, but are sensitive to data scale and resolution. Here, we use an extensive multi-scale dataset from Hintereisferner, Austria, to develop a correction factor to derive z0 values from coarse resolution (up to 30 m) topographic data that are more commonly available over larger areas. Resulting z0 estimates are within an order of magnitude of previously validated, plot-scale estimates and aerodynamic values. The method is developed and tested using plot-scale microtopography data generated by structure from motion photogrammetry combined with glacier-scale data acquired by a permanent in-situ terrestrial laser scanner. Finally, we demonstrate the application of the method to a regional-scale digital elevation model acquired by airborne laser scanning. Our workflow opens up the possibility of including spatio-temporal variations of z0 within glacier surface energy balance models without the need for extensive additional field data collection.

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Hydrologic processes of groundwater in a small monsoon-influenced mountainous watershed

Hydrology Research ◽

10.2166/nh.2018.030 ◽

2018 ◽

Vol 49 (6) ◽

pp. 2016-2029 ◽

Cited By ~ 3

Author(s):

Ruiqiang Yuan ◽

Shiqin Wang ◽

Lihu Yang ◽

Jianrong Liu ◽

Peng Wang ◽

...

Keyword(s):

Water Table ◽

Northern China ◽

Temporal Variations ◽

Hydrologic Processes ◽

Mountain Valley ◽

Mountain Watersheds ◽

Mountainous Watershed ◽

The Mean ◽

Spatio Temporal ◽

In The Beginning

Abstract Mountain block recharge is the least well quantified owing to the lack of a thorough understanding of mountain block hydrological processes. Observations of spatio-temporal variations of groundwater were employed to clarify hydrologic processes in a semi-arid mountainous watershed of northern China. Results showed that the annual feeding rate of precipitation changed between 21% and 40%. However, infiltration of precipitation was mainly drained as interflow on slopes and recharged into the mountain valley as focused recharge. As a result, the mean correlation coefficient between precipitation and groundwater level was only 0.20 and seasonal variations were reduced. Mountain slope is essentially impermeable with no bedrock percolation under arid circumstances. Only a bedrock percolation event occurred after multiple closely-spaced heavy rains during the four-year observation, which induced a local rapid ascending of the water table and an enhanced lateral recharge from upgradient watersheds. The influence of the enhanced lateral recharge lasted three years, suggesting a huge groundwater catchment overcoming local watershed divides in mountain blocks. The average of the gradual recession of the water table was 5.1 mm/d with a maximum of 11.4 mm/d in the beginning stage. Both interflow and bedrock percolation are important. Our results highlight the changeability of hydrologic processes in mountain watersheds.

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