Abstract
Wetlands are complex and diverse ecosystems, substantially contributing to natural capital. Projected variations in natural and anthropogenic scenarios are likely to alter wetland dynamics by substantially impacting the hydrological and ecosystem functions. This study focuses on monitoring the probable impacts of land use change and El Nino Southern Oscillation (ENSO) variations on wetlands dynamics by analysing the magnitude and extent of global wetland inundation trends during the study period. A microwave dataset was used to study the trends and interannual variability of surface inundation extent of global lakes, rivers, reservoirs and wetland classes. Between 1995 and 2015, the average rate of increase in surface inundation extent of global waterbodies and wetlands of 7029.6 km2 per year occurred with the average inundation of 2.9 million km2. Three periods of significant inter–annual variability 1998–2004, 2005–2009, and 2010–2015 have been observed during 21 years. The maximum contribution of 3.06 million km2 inundation has been recorded during the strong El Nino year of 2010. Swamps flooded forests and coastal wetlands has shown the most significant increase in surface inundation extent. Our results showed a positive lag correlation between Nino 3.4 SST anomalies and surface inundation of different GLWD classes. Moreover, lakes, rivers, reservoirs and wetlands have revealed varying responses to different anthropogenic drivers like cropland, natural vegetation and urban land. Cropland, natural vegetation and barren land buffered region has presented strong negative connection with coastal inundation extent. Whereas swamps demonstrated strongly positive correlation with urban-land and coastal with shrub/grassland land cover type. Through this study the extent of impact projected by climatic oscillations and anthropogenic drivers to water bodies and wetlands can be analysed for well–informed conflict management and decision–making practices for minimizing the human driven impact on natural water systems.
Highlights
Wetlands, the ecosystem known for its natural recycling strength, contributes in controlling and distribution of the nutrient loads, with the extensive ability to support biodiversity greater than that of tropical rainforests (Raisin et al, 1999; Jones et al, 2009; Ramsar Convention on Wetlands, 2015)
Swamps flooded forests are concentrated around Amazon and Congo Basin of Central Africa (20◦S to 10◦N brackish wetlands are concentrated around Middle Eastern countries and southeast Australia (40◦S to 50◦N) and bogs fens and mires are mostly located around Hudson Bay in the north west and Lena and Ob in northeastern Russia (45◦N to 75◦N)
Our results presented grasslands and shrublands conversion to other land has shown agreement with the fragmentation and loss as the surface inundation increased over time (Yu et al, 2017) which could be associated with overgrazing (Sekercioglu et al, 2011; Huang et al, 2012)
Summary
The ecosystem known for its natural recycling strength, contributes in controlling and distribution of the nutrient loads, with the extensive ability to support biodiversity greater than that of tropical rainforests (Raisin et al, 1999; Jones et al, 2009; Ramsar Convention on Wetlands, 2015). Wetlands with larger stocks of mineral and organic matter provides suitable environment for methanogenesis emitting methane as a byproduct (Zhu et al, 2014; Zhang et al, 2017). The significance of this ecosystem is embedded into its unique property of both as carbon source and sink under varying environmental and meteorological conditions (Erwin, 2009). Wetland ecosystems are considered to be both ecologically significant and vulnerable due to the climatic changes observed globally and are considered robust ecosystems because of their contribution to earth’s methane budget (Lee and Yeh, 2009; Mitsch et al, 2013; Zhu et al, 2014). The contribution of this ecosystem to climate forcing has been reflected, attributing to the complex biogeochemical processes, highlighting their vital impact on radiative feedback, and forcing (Dlugokencky et al, 2011; Mitsch et al, 2013; Zhu et al, 2014; Hu et al, 2017; Vizza et al, 2017)
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