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

A multi-method approach to assess long-term urbanization impacts on an ecologically sensitive urban wetland in Northeast India

Deepor Beel, a natural wetland fringing the outskirts of the sub-Himalayan city of Guwahati in North-East India, has been under threat of urbanization since the past few decades. With a shrinking perimeter, the wetland - a favorite winter halt of migrating Siberian birds, manages to survive between anthropogenic aggression and ecological existence. This study maps the wetland's aerial shrinkage and environmental health from the 1990s to the 2020s using satellite imagery at five-year intervals. The water quality indicators used are Chlorophyll-a (Chl-a), turbidity, and total suspended solids (TSS) - the optically active parameters commonly used in satellite-image supported monitoring of water bodies. The comparisons indicate that while Chl-a or TSS levels in the wetland appears to have not changed significantly over the years, the expanse of the water-body shows a rapid reduction. Landuse and land cover (LULC) classification reveals maximum built-up area expansion during 2000-2010 at 52.38 %, followed by 21.6 % growth from 2010 to 2020. Additionally, two machine learning (ML) algorithms, artificial neural network (ANN) and random forest (RF), are incorporated to identify predictors from Landsat satellite bands and band ratios that reflect water quality characteristics for the different years. The correlations are validated against field-acquired data for three seasons: pre-monsoon, monsoon and post monsoon of 2021 and pre-monsoon as well as monsoon seasons of 2022. The ML models show encouraging predictions with the Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Turbidity Index (NDTI) for evaluation of the Chl-a and TSS parameters. The moderate but increasing Chl-a values indicate the wetland's susceptibility to eutrophication, possibly due to urbanization. Thus, the use of satellite derived data along with machine learning tools and synoptic sampling for water quality assessment and predictions will be beneficial for urban planners and environmental managers for effective wetland management, especially in data poor regions.

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.

Assessing the Impact of Anthropogenically Modified Land Uses on Wetland Health: Case of Witbank Dam Catchment in South Africa

Wetlands are critical ecological infrastructures that improve water quality, serve as habitat for fish and other aquatic life, accumulate floodwaters, and maintain surface water flow during dry periods. However, the health of wetlands has been compromised by anthropogenic activities that affect the constant supply of ecosystem services. This study assessed the impact of anthropogenically modified land use on wetland health in the Witbank Dam Catchment in South Africa, whose land use has been severely modified for agriculture and mining purposes. The study developed a model linking surface runoff generated in the catchment with land use and wetland typology to comprehend diffuse pollution from pollution-source land uses. Runoff data and related wetland spatial information were processed and analysed in a Geographic Information System (GIS) to estimate pollutants (agricultural nutrients and acid mine drainage) from runoff detained and released by wetlands. The analysis facilitated the assessment of the value of wetlands in enhancing water quality, as well as human and environmental health. The runoff volume from pollution-source land uses (urban areas, farmlands, and mining) was used to evaluate annual pollution levels. Wetland types are ranked according to their efficiency levels to filter pollutants. The assumption is that the difference between filtered and unfiltered runoff is the quantity of polluted runoff water discharged into the river system. The analysis has shown that 85% of polluted runoff generated in the catchment ends up in the river system. An important observation is that although wetlands have a substantial ability to absorb excess pollutants, they have finite boundaries. Once they reach their full holding capacity, they can no longer absorb any further pollutants. The excess is discharged into the river system, risking human and environmental health. This explains why the Limpopo River is heavily polluted resulting in the death of fish, crocodiles and other aquatic life.

Impact of wetland health and provisioning services on the livelihood of the fishing community

The present study aimed to explore the linkage between wetland health, provisioning service value (PsV) and livelihood vulnerability of the dependent fishermen community taking examples from the Moribund deltaic wetlands of India. Wetland health including hydrological strength, habitat state, and the water quality of the wetland was assessed using a random forest (RF) and XGBoosing machine learning approach, and the Livelihood Vulnerability Index (LVI) was computed using balanced weighted approach. Ordinary least square (OLS) regression, correlation matrix, and rank correlation matrix were done to make the relationship between wetland health and LVI. Wetland health was found poor (28.38%) in the isolated, smaller, and peripheral parts of the wetland with agricultural and built-up area proximity. Hydrological strength (with r2 of 0.67) was found as the most dominant health determinant followed by habitat state (with r2 of 0.62). OLS reported that in most of the cases, the standard regression residual is low (0.5 to - 0.5) which indicates that there is a strong relation between wetland health and LVI. KDE plot and correlation matrix also figured out the same. From the field survey, it was found that the wetlands with good habitat health are promising for providing more provisioning services like fish which in turn supports the livelihood of the dependent communities. The findings of this study have a deeper insight into livelihood management through wetland management. Hence, it would inspire policymakers and stakeholders to conserve wetlands not only for the sake of ecology but also for society.

Breeding colony contraction of Asian openbill stork (Anastomus oscitans): an eco-spatial monitoring from Keleghai River Bank, India

Asian openbill stork (Anastomus oscitans) colonies and also the numbers were enormous before some decades in some villages beside Keleghai River bank. However, there has been a significant decline in both the colonies and their populations. This present study investigates the rapid decline in the number of breeding colonies and birds, exploring the reasons behind this phenomenon. To address these inquiries, the study collects perceptions from various stakeholders through extensive field observations and interviews. Expeditions are conducted to comprehend the prevailing situation and associated circumstances. Qualitative observations reveal that human intervention, both direct and indirect, is limiting the development of stork colonies. Data analysis indicates that 37.5% of respondents attribute the contraction of bird colonies to changes in land use, while 26.3% point to the extermination of larger trees along the colony periphery. Additionally, 18.05% of opinions implicate the heavy use of pesticides and fertilizers in the wetland, 6.94% cite local climate change, and 11.11% cite other reasons. To validate the gathered information, the study employed temporal land use land cover (LULC) classification techniques. Random point pattern analysis from Google Image of 2023 is also utilized to reinforce the survey results. It enhances understanding of the spatio-temporal relationship. Subsequently, the study presents the correlation matrix to elucidate the relationship between stork colonies and contributing factors. It provides a clear insight into the underlying situations. Finally, a comparative analysis of the surveyed and spatial analysis information is conducted to detect and validate the perception of stakeholders. Based on these observations, the study formulates conservation measures for the bird habitat and their colonies in the study area.

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.

Measuring anthropogenic impact of the ox-bow lakes in moribund Ganges deltaic India

Measuring the anthropogenic impact score (AIS) of the ox-bow lakes in order to explore the present situation and future ways of restoration is very necessary, particularly in highly populated areas. The present work targeted to do this considering 68 contributing parameters under eight AIS constituting components like pollution impact score (PIS), habitat alteration impact score (HAIS), hydrological alteration impact score (HYAIS), landscape alteration impact score (LAIS), etc. and tried to explore the major determinants behind. Machine learning (ML) algorithms were applied for computing component level and overall, AIS. A supervised correlation attribute evaluator (CAE) was applied for detecting major determinants. The result revealed out of total 44 major ox-bow lakes 40.90 % to 59.09 % (9.97 km2 to 14.69 km2) were identified as highly impacted both at the component level and overall scale as per the best predicted Random Forest (RF) model. Hydrologically connected lakes were less impacted than isolated ones. Genetically main river (Bhagirathi-Hooghly) left ox-bow lakes are less affected than those of the off-shoot channel. Larger size lakes witnessed less impact than medium and smaller lakes. Pollution, habitat, and hydrological components were found as the most dominant components of AIS. Reclamation, pollution, and eutrophication factors were identified as the dominant factors. AIS is negatively associated with fish yield and positively associated with the livelihood vulnerability of the dependent fishermen community. Since hydrological connectivity is a big issue; maintenance of it could be a good approach to its sustainability.

Riverine connectivity influences the phytoplankton ecology in the open floodplain wetland of the lower river Ganga

The river Ganga has several floodplain wetlands that support its ecology and ecosystem. Phytoplankton is an important component of the aquatic ecosystem, which plays an important role as a bioindicator for the assessment of aquatic health. The present study was conducted between 2018 and 2019 to understand the seasonal variation in the phytoplankton diversity of the Charaganga wetland and, parallelly, in the river Ganga in Nabadweep, India. The study explains how riverine connectivity affects the structure of the algal community in the wetland ecosystem. In the study, it has been observed that in the wetland, maximum mean phytoplankton density was noticed during pre-monsoon, i.e., 4079 unit l-1 followed by post-monsoon 3812 unit l-1 and monsoon 550 unit l-1, respectively. In the river system, the phytoplankton density varied from 78 unit l-1 to 653 unit l-1 seasonally, i.e., highest during monsoon and lowest during pre-monsoon. In both the ecosystems, i.e., wetland and river, the supreme influential group was Cyanophyceae followed by diatoms. One-way ANOVA showed a significant variation (p > 0.05) of three algal groups of phytoplankton (Bacillariophyceae, Coscinodiscophyceae, Chlorophyceae) in the river, while in the wetland, no significant variation (p > 0.05) was found among the other algal groups. The observed higher Shannon and Margalef's species richness value in the wetland was observed than in the river defines the significance and importance of the wetland ecosystem, which may support the growth and conservation of various aquatic organisms as well. The study highlighted that the influencing abiotic factors like water temperature, dissolved oxygen, pH, and nutrients have affected the phytoplankton community in both the water bodies, i.e., wetland and river. We concluded that river connectivity is required to restore the biotic flora of the wetland ecosystem.