Abstract
ABSTRACTWetland ecosystems have acquired importance among the scientific community because of their role in biogeochemical cycling and as source and sink of greenhouse gas emissions particularly methane (CH4) in addition to the ecosystem services that they provide. To estimate the CH4 emission from wetlands in spatial domain, models incorporating the geospatial tools are required. Accordingly, main focus of this study is to demonstrate the utility of geospatial techniques in assessing the spatial CH4 emission variability from four different regions of Uttar Pradesh (UP), India, namely, Western, Central, Bundelkhand, and Eastern regions deploying Semi-Automated Empirical CH4 emission Model (SEMEModel) using Moderate Resolution Imaging Spectro-radiometer data of 2010–2012. SEMEModel is a three-tier model which determines the CH4 emissions in spatial domain as a function of remote sensing (RS) and Geographic Information System (GIS) derived wetland components including wetland area and corresponding temperature factors coupled with point CH4 emission coefficients developed via field measurements. Results of the study have shown that eastern region of UP exhibited maximum estimated/modelled CH4 emissions (43.10 Gg yr−1) as compared to other regions due to more area being under wetlands whereas central region was found to be the least contributor (0.266 Gg yr−1) due to the fact that it has minimum wetland area (0.40%) among all the regions. It was observed that estimated/modelled CH4 emissions depicted an increase by 4.96 orders of magnitude in 2010–2011 and 4.04 orders of magnitude in 2011–2012 when estimated by applying literature-based global CH4 emission coefficients for UP in place of CH4 flux values derived in field. It signifies that the upscaling of CH4 flux values using literature-based CH4 flux values of one region to another region may not reflect actual values. Therefore, this study not only helps to improve accuracy of CH4 emission estimates from wetlands but also credibly adjudges that integration of CH4 flux field measurements with modern tools of RS and GIS will immensely assist to reduce the uncertainties in CH4 emission predictions done over larger regions.
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