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Wetlands are dynamic ecosystems that require continuous monitoring and assessment of degradation status to design strategies for their sustainable management. While hydrology provides the primary functional control for the wetland ecosystem, the loss of landscape connectivity influences wetland degradation in a major way as it leads to fragmentation. This article aims to integrate hydrogeomorphic and ecological concepts for the assessment of degradation status and its causal factors for a large wetland in the western Ganga plains, India, the Haiderpur, using a wetlandscape approach. We have used a remote-sensing-based approach, which offers a powerful tool for assessing and linking cross-scale structures, functions, and controls in a wetlandscape. The Haiderpur, a Ramsar site since December 2021, is an artificial wetland located on the right bank of the Ganga River wherein the inflows are controlled by a barrage constructed on the Ganga River apart from smaller tributaries flowing in from the north. A novel aspect of this work is the integration of river dynamics and its connectivity to the wetlandscape to understand the spatiotemporal variability in the waterspread area in the wetland. In this work, we have developed an integrated wetlandscape assessment approach by evaluating wetland's geomorphic and hydrological connectivity status for the period 1993-2019 (25 years) across three different spatial scales - regional, catchment, and wetland. We have highlighted the ecological implications of connectivity and patch dynamics for developing sustainable wetland management plans.
Floodplain wetlands are critical for sustaining various ecological and hydrological functions in a riverine environment. Severe anthropogenic alterations and human occupation of floodplains have threatened these wetlands in several parts of the world. A major handicap in designing sustainable restoration and monitoring strategies for these wetlands is the lack of scientific process-based understanding and information on the basin-scale controls of their degradation. Here, we offer a novel approach to integrate the connectivity of the wetlands with the surrounding landscape along with other attributes such as stream density, hydrometeorological parameters, and groundwater dynamics to explain their degradation and then to prioritise them for restoration and monitoring.
We hypothesise that the best possible connectivity scenario for the existence of a wetland would be if (a) the wetland has a high connectivity with its upslope area, and (b) the wetland has a low connectivity with its downslope region.
The first condition ensures the flow of water into the wetland and the second condition allows longer water residence time in the wetland. Accordingly, we define four connectivity-based wetland health scenarios-good, no impact, bad, and worst.
We have implemented the proposed method in 3226 wetlands in the Ramganga Basin in north India. Further, we have applied specific selection criteria, such as distance from the nearest stream and stream density, to prioritise the wetlands for restoration and monitoring.
We conclude that the connectivity analysis offers a quick process-based assessment of wetlands' health status and serves as an important criterion to prioritise the wetlands for developing appropriate management strategies.
The wetland cover is defined as the spatially homogenous region of a wetland attributed to the underlying biophysical conditions such as vegetation, turbidity, hydric soil, and the amount of water.
Here, we present a novel method to derive the wetland-cover types (WCTs) combining three commonly used multispectral indices, NDVI, MNDWI, and NDTI, in three large Ramsar wetlands located in different geomorphic and climatic settings across India. These wetlands include the Kaabar Tal, a floodplain wetland in east Ganga Plains, Chilika Lagoon, a coastal wetland in eastern India, and Nal Sarovar in semi-arid western India.
The novelty of our approach is that the derived WCTs are stable in space and time, and therefore, a given WCT across different wetlands or within different zones of a large wetland will imply similar underlying biophysical attributes.
The WCTs can therefore provide a novel tool for monitoring and change detection of wetland cover types.
We have automated the proposed WCT algorithm using the Google Earth Engine (GEE) environment and by developing ArcGIS tools. The method can be implemented on any wetland and using any multispectral imagery dataset with visible and NIR bands.
The proposed methodology is simple yet robust and easy to implement and, therefore, holds significant importance in wetland monitoring and management.