A Global Data Analysis for Representing Sediment and Particulate Organic Carbon Yield in Earth System Models


Although sediment yield (SY) from water erosion is ubiquitous and its environmental consequences are well recognized, its impacts on the global carbon cycle remain largely uncertain. This knowledge gap is partly due to the lack of soil erosion modeling in Earth System Models (ESMs), which are important tools used to understand the global carbon cycle and explore its changes. This study analyzed sediment and particulate organic carbon yield (CY) data from 1,081 and 38 small catchments (0.1-200 km2), respectively, in different environments across the globe. Using multiple statistical analysis techniques, we explored environmental factors and hydrological processes important for SY and CY modeling in ESMs. Our results show clear correlations of high SY with traditional agriculture, seismicity and heavy storms, as well as strong correlations between SY and annual peak runoff. These highlight the potential limitation of SY models that represent only interrill and rill erosion because shallow overland flow and rill flow have limited transport capacity due to their hydraulic geometry to produce high SY. Further, our results suggest that SY modeling in ESMs should be implemented at the event scale to produce the catastrophic mass transport during episodic events. Several environmental factors such as seismicity and land management that are often not considered in current catchment-scale SY models can be important in controlling global SY. Our analyses show that SY is likely the primary control on CY in small catchments and a statistically significant empirical relationship is established to calculate SY and CY jointly in ESMs.




Tan, Zeli, L. Ruby Leung, HongYi Li, Teklu Tesfa, Matthias Vanmaercke, Jean Poesen, Xuesong Zhang, Hui Lu, and Jens Hartmann. "A Global Data Analysis for Representing Sediment and Particulate Organic Carbon Yield in Earth System Models." Water Resources Research 53, no. 12 (December 2017): 10674-10700. DOI: 10.1002/2017WR020806.
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