Journal Paper Digests

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Journal Paper Digests 2020 #4

  • Quantification of Peat Thickness and Stored Carbon at the Landscape Scale in Tropical Peatlands: A Comparison of Airborne Geophysics and an Empirical Topographic Method
  • Hydrogeological Modeling and Water Resources Management: Improving the Link Between Data, Prediction, and Decision Making

Quantification of Peat Thickness and Stored Carbon at the Landscape Scale in Tropical Peatlands: A Comparison of Airborne Geophysics and an Empirical Topographic Method

By:Silvestri, S (Silvestri, Sonia)[ 1,2 ] ; Knight, R (Knight, Rosemary)[ 3 ] ; Viezzoli, A (Viezzoli, Andrea)[ 4 ] ; Richardson, CJ (Richardson, Curtis J.)[ 2 ] ; Anshari, GZ (Anshari, Gusti Z.)[ 5 ] ; Dewar, N (Dewar, Noah)[ 3 ] ; Flanagan, N (Flanagan, Neal)[ 2 ] ; Comas, X (Comas, Xavier)[ 6 ]

JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE

Volume: 124 Issue: 12 Pages: 3107-3123 Published: DEC 2019

Document Type: Article

Abstract Peatlands play a key role in the global carbon cycle, sequestering and releasing large amounts of carbon. Despite their importance, a reliable method for the quantification of peatland thickness and volume is still missing, particularly for peat deposits located in the tropics given their limited accessibility, and for scales of measurement representative of peatland environments (i.e., of hundreds of km(2)). This limitation also prevents the accurate quantification of the stored carbon as well as future greenhouse gas emissions due to ongoing peat degradation. Here we present the results obtained using the airborne electromagnetic (AEM) method, a geophysical surveying tool, for peat thickness detection at the landscape scale. Based on a large amount of data collected on an Indonesian peatland, our results show that the AEM method provides a reliable and accurate 3-D model of peatlands, allowing the quantification of their volume and carbon storage. A comparison with the often used empirical topographic approach, which is based on an assumed correlation between peat thickness and surface topography, revealed larger errors across the landscape associated with the empirical approach than the AEM method when predicting the peat thickness. As a result, the AEM method provides higher estimates (22%) of organic carbon pools than the empirical method. We show how in our case study the empirical method tends to underestimate the peat thickness due to its inability to accurately detect the large variability in the elevation of the peat/mineral substrate interface, which is better quantified by the AEM method.

By: Harken, Bradley; Chang, Ching-Fu; Dietrich, Peter; et al. WATER RESOURCES RESEARCH Volume: 55 Issue: 12 Pages: 10340-10357 Published: DEC 2019 Context Sensitive Links Full Text from Publisher Close Abstract

A risk-based decision-making mechanism capable of accounting for uncertainty regarding local conditions is crucial to water resources management, regulation, and policy making. Despite the great potential of hydrogeological models in supporting water resources decisions, challenges remain due to the many sources of uncertainty as well as making and communicating decisions mindful of this uncertainty. This paper presents a framework that utilizes statistical hypothesis testing and an integrated approach to the planning of site characterization, modeling prediction, and decision making. Benefits of this framework include aggregated uncertainty quantification and risk evaluation, simplified communication of risk between stakeholders, and improved defensibility of decisions. The framework acknowledges that obtaining absolute certainty in decision making is impossible; rather, the framework provides a systematic way to make decisions in light of uncertainty and determine the amount of information required. In this manner, quantitative evaluation of a field campaign design is possible before data are collected, beginning from any knowledge state, which can be updated as more information becomes available. We discuss the limitations of this approach by the types of uncertainty that can be recognized and make suggestions for addressing the rest. This paper presents the framework in general and then demonstrates its application in a synthetic case study. Results indicate that the effectiveness of field campaigns depends not only on the environmental performance metric being predicted but also on the threshold value in decision-making processes. The findings also demonstrate that improved parameter estimation does not necessarily lead to better decision making, thus reemphasizing the need for goal-oriented characterization.

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