Effect of channel morphological changes on wetland transformation.

Keeping aside the traditional approaches to investigating floodplain wetland transformation, the current study investigated various aspects of it through changes in river channel morphology and drainage pattern. The study analyzed wetland transformation using satellite image-based machine learning and intensive fieldwork. Ordinary Least Square (OLS) regression was applied to identify dominant influencing factors among 24 contributing factors under six clusters to eight dependent phenomena of transformation. The result showed that 57 % of wetland area lost since 1991, and existing wetland has also experiencing hydrological scarcity. From 1991 to 2021, the area under low water depth (<1 m.) inflated from 18.55 % to 50.54 %, the hydro-period narrowed down, and the appearance of water become inconsistent. The OLS result showed that changes in channel morphology (bottle neck channel, embankment-driven carrying capacity enhancement, etc.), interruptions in river and wetland connecting channels (source closure, breaching the continuity, conversion in to agricultural land, etc.), and changes in flood ambience (regulated by dam construction, erection of embankments, etc.) majorly contributed to wetland transformation. Very high explainability was found in the cases of rate of wetland loss, decreasing water depth under greater depth, narrowing hydro-period (R2 > 0.9). The findings of this work would be a good policy document for floodplain wetland management.

How hydrological components of urban blue space influence the thermal milieu?

Present study examines the possible improvement of thermal discomfort mitigation. Unlike prior researches, which focused primarily on cooling effects of urban blue space, this study, instead of physical presence of blue space considers its hydrological components. The aim of the study is to better understand the role hydrological components like water consistency depth etc. In temperature regulation. The work uses field surveys and modeling to demonstrate how these hydrological factors influence the cooling effect of blue space, providing insights on urban thermal management. To fulfill the purpose, spatial association of hydrological components blue space with its thermal environment and cooling effects was assessed. The control of hydrological components on the surrounding air temperature was examined by conducting case studies. RESULTS: reveals greater hydro-duration, deeper water, and higher Water Presence Frequency (WPF) produce greater cooling effects. The study demonstrates a favorable correlation between hydrological richness and temperature reduction. The study also analyzes how land use and wetland size affect temperature, emphasizing the significance of hydrological conservation and restoration for successful temperature mitigation. Due to their hydrology, larger wetlands are able to moderate temperature to some extent, whereas smaller, fragmented wetlands being hydrologically poor are not so influential in this regard. With these results, the present study reaches beyond to the general understanding regarding the cooling effects of the urban blue spaces. While the previous studies primarily focused on estimating the cooling effect of urban blue space, the current one shows its synchronization with the hydrological characteristics. Novelty also entrusts here, through the modeling and field survey current study demonstrates deeper and consistent water coverage in the urban blue space for maximum period of a year pronounces the cooling effect. In addition, in this cooling effect, the role of land use which is a strong determinant of many aspects of the urban environment is also highlighted. Since all these findings define specific hydrological feature, the study has several practical implications. Mare restoration of urban blue space is not enough to mitigate the thermal discomfort. In order to optimize the cooling effect, the conservation of the hydrological richness is essential. The hydrological richness of the smaller wetlands and the edge of the larger wetlands is to be improved. The connection of these wetlands with the adjacent mighty may strengthen the hydrology. The vegetation was found to promote the cooling effect whereas shorter building helped in spreading the cooling effect. Such finding drives to incorporate the blue space with the green infrastructure along with restricting the building height atleast at the edge of the blue space.

Inundation dynamics of the natural and manmade wetlands in the Mayurakshi River basin, Eastern India.

The present study aimed to measure wetland inundation inconsistency level (IIL) at a spatial scale to appraise the potential serviceability in the Mayurakshi river basin of Eastern India. Inconsistency was used for measuring both wetland water presence area and proxy water depth based on historical satellite images from 1988 to 2022. Applying inconsistency assessment, it was tried to assess how water appearance at a pixel is inconsistent and how average proxy water depth is inconsistent to attain. Four manmade and natural floodplain wetland complexes were taken for this. The study revealed about 51-53% and 59-86% manmade and natural wetland losses respectively and the IIL was also found significantly higher (30-50%) in the cases of natural wetlands in pre and post-monsoon seasons. The scenario is worse in pre-monsoon season in the natural wetlands. Inconsistency of water depth anomaly (IWDA) was also significantly increased almost in the same trend. Discharge control through hydro-engineering structures like dams, barrages, and embankments; river and wetland connecting tie channel loss; and loss of groundwater support are some crucial reasons behind the hydrological inconsistency of wetlands. Growing loss and IIL are caused for concerned economic and ecological adversity. So, the findings would be very useful for taking necessary planning for wetland management and restoration.

Anthropogenic impacts on urban blue space and its reciprocal effect on human and socio-ecological health.

Quantifying anthropogenic impacts on blue space (BS) and its effect on human and socio-ecological health was least explored. The present study aimed to do this in reference to the urban BS transformation scenario of Eastern India. To measure BS transformation, Landsat image-based water indices were run from 1990 to 2021. Anthropogenic impact score (AIS) and 7 components scores of 78 selected BS on 70 parameters related data driven from the field. Total 345 respondents were taken for human and socio-ecological health assessment. For this, depression (DEP), anxiety (ANX), stress (STR), physical activities (PA), social capital (SC), therapeutic landscape (TL) and environment building (EB) parameters were taken. The result exhibited that BS was reduced. About 50% of urban core BS was reported highly impacted. Human and socio-ecological health was identified as good in proximity to BS, but it was observed better in the cases of larger peripheral BS. AIS on BS was found to be positively associated with mental health (0.47-0.63) and negatively associated with PA, SC, TL and EB (-0.50 to -0.90). Standard residual in ordinary least square was reported low (-1.5 to 1.5) in 95% BS. Therefore, BS health restoration and management is crucial for sustaining the living environment.

Resolution effects on ox-bow lake mapping and inundation consistency analysis in moribund deltaic flood plain of India.

Research on investigating spatial resolution effect on image-based wetland mapping was done, and reported finer resolution is more appropriate. But is Sentinel image more effective than Landsat image for delineating ox-bow lake, a cut-off channel of a river, and for mapping inundation frequency? Inundation frequency means regularly, water appears in a pixel. In order to obtain these answers, the present study used frequently used spectral indices like normalized difference water index (NDWI), modified NDWI (MNDWI), re-modified NDWI (RmNDWI) and ensemble vegetation inclusive aggregated water index (ViAWI). For obtaining inundation consistency character, the water presence frequency (WPF) approach was adopted. A set of accuracy matrices was applied for validating the resolution effect. Results revealed that among the used indices, MNDWI was found suitable for ox-bow lake mapping. But this index is not able to map vegetated part of the ox-bow lakes. This problem was resolved using ensemble ViAWI. Inundation frequency analysis exhibited that about 70% of the area is consistent with water presence and therefore is hydro-ecologically and economically viable, and no such major differences were recorded between Sentinel and Landsat images. The study further revealed that finer resolution Sentinel images are more effective in ox-bow lake mapping and characterising inundation frequency, but they were not significantly better. Accuracy difference between them was found at the very minimum. Therefore, the study recommended that in a Sentinel image sparse condition, Landsat images could alternatively be used without much accuracy departure, particularly on those water bodies where water appearance is not highly erratic.

Socio-ecological well-being perspectives of wetland loss scenario: A review.

Previous original research focused on wetland loss and finding out its drivers across different regional units of the world. A few reports also tried to account world's condition on wetland loss. A couple of review articles articulated the causes of wetland loss and services. The present study intended to explore the linkage between wetland loss rate and processes concerning socio-ecological well-being parameters to highlight alternative ways to adopt wetland conservation policies. A total of 132 pieces of Scopus index literature were taken analysing loss rate and drivers of loss from 22 sample countries where publication frequency is relatively high. Meta-analysis was done to explain the publication trend and spatial change in publication polarity. Results distinctly revealed that the rate of wetland loss varies from 0.06% to 4.81% annually, with substantially low in developed countries (DC) than in developing (DeV) and least developed countries (LDC). Six drivers, such as agricultural land expansion, the built-up area, the conversion to grassland area, construction of the dam, climate change and tourism, were the primary drivers. But all these are not equally active across the DC, DeV and LDC. Climate change, tourism development in DC, agriculture and built-up expansions in the Dev and LDC appeared as the major causes behind wetland loss. Socio-ecological well-being parameters like human development, environmental performance, social progression, and economic status were found to be significantly negatively (-0.48 to -0.57), and the poverty rate was positively (0.27) associated with the rates of wetland loss. Drivers also varied with respect to the socio-ecological conditions. These findings are not merely added knowledge to the state-of-arts but are also helpful in re-directing global policies toward wetland conservation.

Ecological consequences of urban blue space transformation.

This study presents the ecological consequences of the blue space conversion and its qualitative degradation in the English Bazar Municipality (EBM) and its surrounding area. The primary blue spaces of the area, the marshy wetland called Chatra and Mohananda river, are the most affected due to urban activities like built-up expansion and sewage and wastewater discharge. Built-up development encroached more than 300 m within wetland territory and caused a 0.57 km2 conversion of wetland area. It is also evident within the bed of the Mohananda river. Agriculture also caused the conversion of the blue space. As a result, the wetland's ecosystem service value (ESV) was reduced by 12.7%, along with a reduction of cultural services by 27.86%. The massive pouring of sewage and wastewater caused hyper-eutrophication in almost the entire wetland area. The trophic state index (TSI) value increased significantly in the last 10 years, causing high growth and areal expansion of water hyacinth. The expanding settlements and agricultural land that captured the river channel face inundation vulnerability during peak discharge. Extreme danger level discharge causes floods in the extensive municipality area. The areal encroachment, water extraction, sewage and wastewater discharge, and water quality deterioration caused severe hydro-ecological degradation of the river. Since blue space is critically essential for urban environmental health, these ecological consequences can cause a crisis for urban wellbeing. Therefore, the anthropogenic adversities towards the urban blue space must be restricted, and the blue space's ecological sustenance must be paid enough attention.

How far the types and wetland hydrological conditions influence its provisioning services in the Indian mature Ganges delta.

Present work intended to explore how far the Provisioning Service Value (PSV) of the mature Ganges deltaic wetlands is determined by its typology and a few physical attributes like hydrology and aquatic vegetations. Firstly, a field investigation was carried out in the representative sample sites, and field-measured PSV was calibrated with wetland types, hydrological security, and aquatic plant biomass to perform spatial estimation and mapping of PSV. The estimation yielded average annual PSV of entire wetlands as 146.5 × 105 Indian Rupee (INR)/km2/year, with the highest over bheries (embankments for fish and shrimp aquaculture) 176 × 105 INR/km2/year and lowest over marshy wetlands 107 × 105 INR/km2/year. Sensitivity analysis of this estimation showed in cases of 55% field visited sites, the field-measured PSV was outside the range of low standard regression residuals (-0.5 to 0.5). While searching for the reason behind such error in the estimation, the variability of the field-measured PSV was measured. Various inequality measures showed high inequality in inter and intra-hydrological conditions of the wetland. Analysis of variance (ANOVA) proved statistical significance of within-class variability. To explain the variability of PSV, Kernel Density Estimation (KDE) plotting was performed, incorporating a few other regional conditioning factors like wetland size, fish and shrimp aquaculture, perenniality, expenditure, and external feeding from the experience of the field. From this excesize, external feeding and expenditure were essential factors that should be incorporated along with the wetland characteristics and physical attributes for accurate estimation. Since producing spatial data layers of these factors with a finer resolution is difficult, the study suggests case-specific estimation of PSV instead of general spatial mapping.

Wetland transformation and its impact on the livelihood of the fishing community in a flood plain river basin of India.

Discretely wetland transformations and livelihood vulnerability related works are profoundly found worldwide, but their linkage is not investigated often. The present study aimed to explore the after damming transformation of wetland's eco-hydrological status and water quality and assessed its effects on livelihood vulnerability state of the fishermen community in the lower part of the Tangon river basin. Based on 15 field and satellite image-driven indicators of transformation, multiple machine learning (ML) algorithms were used to model the eco-hydrological state (EHS) of the wetland. Livelihood Vulnerability Index (LVI) of 45 fishing-dominated villages was computed using a balanced weighted LVI score. The result revealed that 60.55 % wetland area was obliterated between the pre- dam and post-dam periods, and the existing wetland area (21.06 km2) witnessed noticeable eco-hydrological and water quality degradation. Correlation and kernel density estimation (KDE) plot clearly revealed that rate of EHS degradation and water quality changes was negatively associated (at ≤0.01 level of significance) and both controlled LVI. So, such changes not only pose pressure on the aquatic species like fishes but also hampered the well-being of the fishermen communities evolving. The findings of the work would be useful in this transition while deciding the alternative strategies to build a resilient community. Moreover, since the eco-hydrological state were explored this would be effective for wetland restoration planning.

Linking river flow modification with wetland hydrological instability, habitat condition, and ecological responses.

Flow modification pursuing dams is widely found. Some works also focused on its impact on floodplain wetland hydrology. However, how this change can pose an impact on habitat conditions, ecological conditions, and trophic state is also a matter of investigation. The very least attention has been paid to this so far. Therefore, the present study focused on these, taking the dam-induced Lower Tangon river basin of India and Bangladesh as a case. The degree of flow alteration in the river was presented in a heat map. Multi-parametric machine learning (ML) approaches were applied to model hydrological instability and habitat condition. The ecological consequences like evaluating eco-deficit using flow duration curve (FDC) approach, trophic state using trophic state index (TSI), fish habitat zone using image-based hydrological parameters, etc. were measured. The study exhibited that after damming, the degree of river flow modification was about 41%. Consequently, the wetland hydrological instability and habitat conditions were degraded. In the post-dam period, > 50% of wetland area was lost, and hydrological instability was enhanced considerably over wider parts of the wetland. Habitat conditions of the existing wetland also witnessed fragility (poor and very poor areas increased by about 22.23 and 9.34%). As a result of this, adverse ecological responses were found. For instance, the eco-deficit area was increased by 36.19%, a good proportion (100%) of wetlands was witnessed the transformation of TSI from oligotrophic to mesotrophic state, and optimum fish habitat area was declined. The ecological strength map integrating all the cause-effect model parameters showed that good ecological strength was reduced from 49 to 2% in the post-dam. The result of the study would be very useful for wetland restoration for ecological and human well-being.

Trend analysis and forecasting of streamflow using random forest in the Punarbhaba River basin.

Streamflow rate changes due to damming are hydro-ecologically sensitive in present and future times. Very less studies have done an investigation of the damming effect on the streamflow along with future forecasting, which can be the solution for the existing problems. Therefore, this study aims to use the Pettitt test as well as standard normal homogeneity test (SNHT) to discover trends in streamflow with the future situation in the Punarbhaba River in Indo-Bangladesh from 1978 to 2017. Trend was spotted using Mann-Kendall test, Spearman's rank correlation approach, innovative trend analysis, and a linear regression model. The current work additionally uses advanced machine learning techniques like random forest (RF) to estimate flow regimes using historical time series data. 1992 appears to be a yard mark in this continuum of time series datasets, indicating a significant transformation in the streamflow regime. The MK test as well as Spearman's rho was used to find a significant negative trend for the average (-0.57), maximum (-0.62), and minimum (-0.48) flow regimes. The consistency of the flow regime has been losing consistency, and the variability of flow regime has increased from 2.1 to 6.7% of the average water level, 1.5 to 6.5% of the maximum streamflow, and 3.1 to 5.8% of the minimum streamflow in the post-change point phase. The forecast trend using random forest for streamflow up to 2030 are negative for all four seasons with a flow volume likely to be reduced by 0.67% to-5.23%. Annual and monthly streamflows revealed very negative tendencies, according to the conclusions of unique trend analysis. Flow declination of this magnitude impacts downstream habitat and environment. According to future estimates, the seasonal flow will decrease. Furthermore, the outcome of this research will give a wealth of data for river management and other places with comparable environment.

Modelling hydrological strength and alteration in moribund deltaic India.

The Ganga-Brahmaputra moribund deltaic floodplain region hosted many socio-ecologically precious freshwater wetland ecosystems experiencing hydrological alteration. The present study aimed to model hydrological strength (HS) to show the spatial difference and account for the degree and direction of hydrological alteration of Indian moribund deltaic wetland in three phases e.g. (1) phase I (1988-1997), (2) phase II (1998-2007) and phase III (2008-2017). Three key hydrological parameters, such as Water Presence Frequency (WPF), water depth, and hydro-period were considered for hydrological strength modelling using two ensemble Machine Learning (ML) techniques (Random Forest (RF) and XGBoost). Image algebra was employed for phasal change detection. Hydrological strength models show that around 75% of the wetland area was lost in-between phases I to III and the loss was found more intensive in moderate and weak HS zones. Existing wetland shows a clear spatial difference of HS between wetland core and periphery and river linked and delinked or not linked wetlands. Regarding the suitability of the ML models, both are acceptable, however, the XGBoost outperformed in reference to applied 15 statistical validation techniques and field evidence. HS models based on change detection clarified that more than 22% and 55% of the weak HS zone in phases II and III respectively were turned into non-wetland. The degree of alteration revealed that about 40% of wetland areas experienced a negative alteration during phases I to II, and this proportion increased to 63% in between phases II to III. Since the study figured out the spatial nature of HS, degree and direction of alteration at a spatial scale, these findings would be instrumental for adopting rational planning towards wetland conservation and restoration.

Image-driven hydrological parameter coupled identification of flood plain wetland conservation and restoration sites.

A good many works focus on wetland vulnerability; some works also explore restoration sites at a very limited spatial extent. But the satellite image-driven hydrological data-based approach adopted in this work is absolutely new. Moreover, existing work only focused on identifying restoration sites in the present context, but for devising long-term sustainable planning, predicted hydrological parameters based on possible restoration sites may be an effective tool. Considering this, the present work focused on exploring hydrological data (water presence frequency (WPF), hydro-period (HP) and water depth (WD)) from time-series satellite images. This exploration may resolve the hydrological data scarcity of wetland over the wider geographical areas. Using these parameters, we developed wetland restoration and conservation sites for different historical years (2008, 2018) and predicted years (2028) using ensemble machine learning (EML) models. From the analysis, it was found that water depth, hydro-period and WPF became poorer over the period, and the trend may seem to continue in predicted years. Among the applied EML models, Random Subspace (RS) predicted wetland restoration and conservation sites precisely over others. The predicted area under high-priority restoration sites is 34% in 2018, which was 14% in 2008. In 2028, 12% more areas may fall in this priority level. Wetland away from main streams (mainly ortho-fluvial wetland) and fringe wetland parts should be given more priority for restoration. These present and predicted information will effectively help to frame sustainable wetland restoration planning.

Identifying sensitivity of factor cluster based gully erosion susceptibility models.

The present study has attempted to address the issue of sensitivity of different clusters of factors towards gully erosion in the Mayurakshi river basin. Firstly, the gully erosion susceptibility of the basin area has been mapped by integrating using 18 parameters divided into four factor-cluster, viz. erodibility, erosivity, resistance, and topographical cluster, with the help of four machine learning (ML) models such as random forest (RF), gradient boost (GBM), extreme gradient boost (XGB), and support vector machine (SVM). Results show that almost 20% and 25% of the upper catchment of the basin belongs to extreme and high gully erosion susceptibility. Among the applied algorithms, RF is appeared as the best performing model. The spatial association of factor cluster-based models with the final susceptibility model is found the highest for the erosivity cluster, followed by the erodibility cluster. From the sensitivity analysis, it becomes clear that geology and soil texture are dominant contributing factors to gully erosion susceptibility. The geological formation of unclassified granite gneiss and geomorphological formation of denudational origin pediment-pediplain complex is dominant over the entire upper catchment of the basin, and therefore, can be considered regional factors of importance. Since the study has figured out the different grades of susceptible areas with dominant factors and factor cluster, it would be useful for devising planning for gully erosion check measures. From economic particularly food security purpose, it is very essential since it is concerned with precious soil loss and negative effects on agriculture.

Impact of river flow modification on wetland hydrological and morphological characters.

A good number of researchers investigated the impact of flow modification on hydrological, ecological, and geomorphological conditions in a river. A few works also focused on hydrological modification on wetland with some parameters but as far the knowledge is concerned, linking river flow modification to wetland hydrological and morphological transformation following an integrated modeling approach is often lacking. The current study aimed to explore the degree of hydrological alteration in the river and its effect on downstream riparian wetlands by adopting advanced modeling approaches. After damming, maximally 67 to 95% hydrological alteration was recorded for maximum, minimum, and average discharges. Wavelet transformation analysis figured out a strong power spectrum after 2012 (damming year). Due to attenuation of flow, the active inundation area was reduced by 66.2%. After damming, 524.03 km2 (48.9% of total pre-dam wetland) was completely obliterated. Hydrological strength (HS) modeling also reported areas under high HS declined by 14% after post-dam condition. Wetland hydrological security state (WSS) and HS matrix, a new approach, are used to explore wetland characteristics of inundation connectivity and hydrological security state. WSS was defined based on lateral hydrological connectivity. HS under critical and stress WWS zones deteriorated in the post-dam period. The morphological transformation was also well recognized showing an increase in area under the patch, edge, and a decrease in the area under the large core area. All these findings established a clear linkage between river flow modification and wetland transformation, and they provided a good clue for managing wetlands.

Predicting wetland area and water depth in Barind plain of India.

The present study attempts to delineate wetlands in the lower Tangon river basin in the Barind flood plain region using spectral water body extraction indices. The main objectives of this present study are simulating and predicting wetland areas using the advanced artificial neural network-based cellular automata (ANN-CA) model and water depth using statistical (adaptive exponential smoothing) as well as advanced machine learning algorithms such as Bagging, Random Subspace, Random Forest, Support vector machine, etc. The result shows that RmNDWI and NDWI are the representative wetland delineating indices. NDWI map was used for water depth prediction. Regarding the prediction of wetland areas, a remarkable decline is likely to be identified in the upcoming two decades. The small wetland patches away from the master stream are expected to dry out during the predicted period, where the major wetland patches nearer to the master stream with greater water depth are rather sustainable, but their depth of water is predicted to be reduced in the next decades. All models show satisfactory performance for wetland depth mapping, but the random subspace model was identified as the best-suited water depth predicting method with an acceptable prediction accuracy (root mean square error <0.34 in all the years) and the machine learning models explored better result than adaptive exponential smoothing. This recent study will be very helpful for the policymakers for managing wetland landscape as well as the natural environment.

Revisiting air quality during lockdown persuaded by second surge of COVID-19 of megacity Delhi, India.

Is the impact of city-scale lockdown in response to 2nd surge of COVID-19, behavioural changes in people owing to yearlong cohabitation with COVID-19, and partial vaccination on air quality different from the impact of nationwide lockdown during COVID-19's 1st surge in March 2020? Targeting this objective, the present work has selected four phases pre-lockdown and lockdown of 1st and 2nd cycles of lockdown taking average air quality index (NAQI) from Central Pollution Control Board (CPCB). The results clearly show that both the nationwide lockdown and the city-scale restriction are responsible for improving air quality in India's megacity Delhi, but the rate of improvement was higher (39%) during the first cycle of lockdown (nationwide) than during the second cycle of lockdown (city-scale). During city-scale lockdown, the disparity in NAQI between the core and the periphery is obvious. Due to the effect of economic activities surrounding Delhi, around 10 km of the city's interior has experienced high NAQI. The reason for the lower NAQI improvement during the second lockdown cycle is likely due to relief from initial fear following a year of cohabitation with COVID-19, partial vaccination, and partial relaxation in industrial sectors to avoid the economic hardships experienced during the first lockdown cycle.

Integration of satellite image-derived temperature and water depth for assessing fish habitability in dam controlled flood plain wetland.

The present study attempted to investigate the changes in temperature conducive to fish habitability during the summer months in a hydrologically modified wetland following damming over a river. Satellite image-driven temperature and depth data calibrated with field data were used to analyse fish habitability and the presence of thermally optimum habitable zones in some fishes, such as labeo rohita, cirrhinus mrigala, tilapia fish, small shrimp, and catfish. The study was conducted both at the water's surface and at the optimum depth of survival. It is very obvious from the analysis that a larger part of the wetland has become an area that destroyed aquatic habitat during the post-dam period, and existing wetlands have suffered significant shallowing of water depth. This has resulted in a shrinking of the thermally optimum area of fish survival in relation to surface water temperature (from 100.09 to 74.24 km2 before the dam to 93.97 to 0 km2 after the dam) and an improvement in the optimum habitable condition in the comfortable depth niche of survival. In the post-dam period, it increased from 75.49 to 99.76%. Since the damming effect causes a 30.53 to 100% depletion of the optimum depth niche, improving the thermal environment has no effect on fish habitability. More water must be released from dams for restoration. Image-driven depth and temperature data calibrated with field information has been successfully applied in data sparse conditions, and it is further recommended in future work.

Methane emissions only negligibly reduce the ecosystem service value of wetlands and rice paddies in the mature Ganges Delta.

Wetland provides a wide range of ecosystem services with immense value. However, methane (CH4) emissions adversely affect ecosystem services, and it requires fixation cost. The objective of the present study was to estimate CH4 emissions and ecosystem services value (ESV) and how much the fixation cost of CH4 reduces the ESV. Since rice cultivation is a very common practice here, the paddy fields were also incorporated in this study. CH4 flux and satellite data were employed for estimating the emissions with the help of two-factor (temperature and water availability) model. Global coefficients of ecosystem service value (ESV) that is defined as the monetary valuation of materialistic and non-materialistic services were adapted for estimating the ecosystem service of the CH4 emitting sources. Results show that during the boro season (pre-monsoon summer paddy cultivation season), average monthly emissions of paddy fields are equal to the wetlands which are 0.16 t/km2. During amon season (monsoon paddy cultivation season), this emissions is 0.7 t/km2 and 0.53 t/km2, respectively, from wetlands and paddy fields. Both wetlands and paddy fields emit a greater amount of CH4 during amon season than boro season. Behind this seasonal variation, water availability in terms of precipitation-evaporation ratio plays a more vital role than temperature. Total estimated ESV is 928.51 million US$, and CH4 fixation cost is 6.64 million US$ which is only 0.71% to total ESV. So, considering such huge net ESV, emphasis on wetland conservation and restoration are necessary.

Assessing the role of deltaic flood plain wetlands on regulating methane and carbon balance.

Present study deals with the role of wetland for regulating greenhouse gases (GHG) particularly methane (CH4) emission and carbon (C) sequestration in mature Ganges deltaic environment. The annual total amount of emission and sequestration in wetlands of varying types was estimated along with the seasonal variation. Result showed that the streams were the highest emitter of CH4 followed by ox-bow lakes in all the seasons whereas the bheries (embanked pisciculture arresting tidal water) consistently exhibited the lowest average emission. The average sequestration of C was the highest in ox-bow lakes followed by marshes and mudflats. The average emission in monsoon season was 43% and 78% higher than the average emission of pre and post-monsoon seasons respectively. The yearly total emission was 8.01 × 103 ton and yearly total sequestration was estimated 908.98 × 103 ton. From the perspective of GHG regulation, the wetlands were found to yearly uptake four times higher carbon dioxide (CO2) than the CO2 equivalent (CO2e) of emitted CH4. After offsetting the fixation cost of emitted CH4, the yearly surplus sequestrated C in the wetlands of the entire region was worthy of 68.46 million US dollar (USD). So, wetland plays positive role for reducing greenhouse gas effect and associated temperature rise which is considered to be serious issue. Such result has made a good agreement on the debated issue of wetland CH4 emission and C sequestration and will encourage restoring wetland for even mediating GHG issue.