Anthropogenic risk assessment of riverine habitat using geospatial modelling tools for conservation and restoration planning: a case study from a tropical river Pranhita, India.

The riverine ecosystem provides multiple benefits to human community and contributes to the sustainable development of the ecoregion. The growing dependency on these ecosystems has largely contributed to aggravating the ecological risks, habitat degradation, and loss of ecosystem services. The present study evaluates the ecological risk emanating from nine anthropogenic stressors including river use, hydro-morphology, catchment pollution, and biological stressor on river Pranhita in Godavari Basin of Peninsular India using InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Habitat Risk Assessment model. The primary field survey, remote sensing, and secondary data-assisted spatial modelling results revealed low ecological risk (R = 0.65 of 3) in river Pranhita due to anthropogenic activities. Sediment loading, the inflow of nitrogen, and habitat fragmentation were the major stressors with relatively higher risk score (> 1); influence on a sizeable portion of riverine habitat (29-75% of the total area under high-risk zone) indicates the mounting threat from catchment activities. The low-risk value observed in protected river reaches as compared to unprotected areas is likely to be influenced by the abundant presence of intact riparian vegetation which mitigate the catchment stressors and minimal anthropogenic activity within protected areas. This study demonstrates the application of InVEST HRA model for ecological risk assessment of riverine ecosystems and fish assemblages along with their input data generation framework. This has the potential for prioritization of sensitive habitats based on computed ecological risk and stressor identification based on their exposure and consequences for developing appropriate mitigation measures. This model is spatially explicit and accommodates user-defined criteria for ecosystem-level assessment at a regional and national scale to facilitate the resource managers and policymakers for conservation and restoration planning and implementation of targeted management measures for sustainable development.

Applications of Sentinel-2 satellite data for spatio-temporal mapping of deep pools for monitoring the riverine connectivity and assessment of ecological dynamics: a case from Godavari, a tropical river in India (2016-2021).

Rivers are dynamic ecosystems with diverse habitats that require ample connectivity to ensure the flow of ecosystem services, thus empowering the sustainable development of an entire basin. Geo-spatial tools offer powerful prospects for monitoring of aquatic ecosystems. The usefulness of Sentinel-2 datasets to assess river connectivity has been explored for an un-gauged seasonal river system. The present study was undertaken in five ecologically unique river reaches viz. Wainganga, Wardha, Pranhita, Godavari-mid and Manair in Godavari Basin in the Indian Deccan Plateau to map water spread dynamics at various time scales, i.e., fortnightly, monthly, seasonal, annual and demi-decadal during 2016-2021. The maximum value of perennial water spread per square kilometre of total floodplain area (2016-2021), determined using Sentinel-2 imageries, was observed in river Wardha (0.18) followed by Pranhita (0.12) and Wainganga (0.11). The water spread showed a decreasing trend, while the number of patches in the river corridor increased over time from post-monsoon to pre-monsoon season. The copious perennial habitat with relatively larger patches, incessant flow in river Pranhita and obstructed flow, large-sized patches reported in river Wardha during summer months, hold importance in terms of providing refuge to aquatic biota. This study provides evidence for the impact of water projects on spatio-temporal water spread dynamics in Godavari Basin. The demonstrated utility of Sentinel-2 imagery coupled with gauge station measurements for river continuity assessment and deep pool mapping would aid in enhancing our understanding on environmental flow at a spatial scale, which in turn would aid in effective river management to achieve the Sustainable Development Goals. The implications of this study for sustainable environmental management and limitations are also discussed.

Framework for mapping the drivers of coastal vulnerability and spatial decision making for climate-change adaptation: A case study from Maharashtra, India.

The impacts of climate change are of particular concern to the coastal region of tropical countries like India, which are exposed to cyclones, floods, tsunami, seawater intrusion, etc. Climate-change adaptation presupposes comprehensive assessment of vulnerability status. Studies so far relied either on remote sensing-based spatial mapping of physical vulnerability or on certain socio-economic aspects with limited scope for upscaling or replication. The current study is an attempt to develop a holistic and robust framework to assess the vulnerability of coastal India at different levels. We propose and estimate cumulative vulnerability index (CVI) as a function of exposure, sensitivity and adaptive capacity, at the village level, using nationally comparable and credible datasets. The exposure index (EI) was determined at the village level by decomposing the spatial multi-hazard maps, while sensitivity (SI) and adaptive capacity indices (ACI) were estimated using 23 indicators, covering social and economic aspects. The indicators were identified through the literature review, expert consultations, opinion survey, and were further validated through statistical tests. The socio-economic vulnerability index (SEVI) was constructed as a function of sensitivity and adaptive capacity for planning grassroot-level interventions and adaptation strategies. The framework was piloted in Sindhudurg, a coastal district in Maharashtra, India. It comprises 317 villages, spread across three taluks viz., Devgad, Malvan and Vengurla. The villages in Sindhudurg were ranked based on this multi-criteria approach. Based on CVI values, 92 villages (30%) in Sindhudurg were identified as highly vulnerable. We propose a decision tool for identifying villages vulnerable to changing climate, based on their level of sensitivity and adaptive capacity in a two-dimensional matrix, thus aiding in planning location-specific interventions. Here, vulnerability indicators are classified and designated as 'drivers' (indicators with significantly high values and intervention priority) and 'buffers' (indicators with low-to-moderate values) at the village level. The framework provides for aggregation or decomposition of CVI and other sub-indices, in order to plan spatial contingency plans and enable swift action for climate adaptation.