Modeling riparian flood plain wetland water richness in pursuance of damming and linking it with a methane emission rate

Abstract

Modeling water richness in dam-induced riparian wetland and linking methane (CH₄) emission rate has immense importance to understand the regime of methanogenesis. Multi-model machine learning approaches have been adopted for developing water richness (WR) models and an equation-based method has been followed for estimating CH₄ emission from the wetland. REPTree model is found to be the best representative WR model. About 194.14 km² wetland areas have been squeezed; very high water richness area has been reduced from 47.37 to 29.59% in the post-dam period. Along with CH₄ emission has also been reduced by 52.61%. The high CH₄ emission rate is attributed to the higher WR area. Linking CH₄ emission with WR demonstrates a positive relation (0.59 for pre-dam and 0.23 for post-dam at >0.001 level of statistical significance) establishing the very vivid effect of WR on CH₄ emission.

Keywords:

• Riparian wetland
• machine learning model
• water richness
• methane emission
• methanogenesis
• damming effect

Additional information

Funding

Rajesh Sarda would like to acknowledge University Grants Commission (UGC Ref. No.: 3430/(NET-DEC 2018)), New Delhi, India for providing financial support as a Junior Research fellowship to conduct the research work presented in this paper. We would like to extend my gratitude to USGS for providing Landsat imageries.