System-scale detection of erosion and deposition is crucial in order to assess the transferability of findings from scaled laboratory and small field studies to larger spatial scales. Increasingly, synoptic remote sensing has the potential to provide the necessary data. In this paper, we develop a methodology for channel change detection, coupled to the use of synoptic remote sensing, for erosion and deposition estimation, and apply it to a wide, braided, gravel-bed river. This is based upon construction of digital elevation models (DEMs) using digital photogrammetry, laser altimetry and image processing. DEMs of difference were constructed by subtracting DEM pairs, and a method for propagating error into the DEMs of difference was used under the assumption that each elevation in each surface contains error that is random, independent and Gaussian. Data were acquired for the braided Waimakariri River, South Island, New Zealand. The DEMs had a 1Ð0 m pixel resolution and covered an area of riverbed that is more than 1 km wide and 3Ð3 km long. Application of the method showed the need to use survey-specific estimates of point precision, as project design and manufacturer estimates of precision overestimate a priori point quality. This finding aside, the analysis showed that even after propagation of error it was possible to obtain high quality DEMs of difference for process estimation, over a spatial scale that has not previously been achieved. In particular, there was no difference in the ability to detect erosion and deposition. The estimates of volumes of change, despite being downgraded as compared with traditional cross-section survey in terms of point precision, produced more reliable erosion and deposition estimates as a result of the large improvement in spatial density that synoptic methods provide.
There is growing demand among stakeholders across public and private institutions for spatially-explicit information regarding vulnerability to climate change at the local scale. However, the challenges associated with mapping the geography of climate change vulnerability are non-trivial, both conceptually and technically, suggesting the need for more critical evaluation of this practice. Here, we review climate change vulnerability mapping in the context of four key questions that are fundamental to assessment design. First, what are the goals of the assessment? A review of published assessments yields a range of objective statements that emphasize problem orientation or decision-making about adaptation actions. Second, how is the assessment of vulnerability framed? Assessments vary with respect to what values are assessed (vulnerability of what) and the underlying determinants of vulnerability that are considered (vulnerability to what). The selected frame ultimately influences perceptions of the primary driving forces of vulnerability as well as preferences regarding management alternatives. Third, what are the technical methods by which an assessment is conducted? The integration of vulnerability determinants into a common map remains an emergent and subjective practice associated with a number of methodological challenges. Fourth, who participates in the assessment and how will it be used to facilitate change? Assessments are often conducted under the auspices of benefiting stakeholders, yet many lack direct engagement with stakeholders. Each of these questions is reviewed in turn by drawing on an illustrative set of 45 vulnerability mapping studies appearing in the literature. A number of pathways for placing vulnerability mapping on a more robust footing are also identified.
Climate change, Adaptation, Adaptive capacity, Planning, Evaluation,
Since 1992, there has been a revolution in our ability to quantify the land ice contribution to sea level rise using a variety of satellite missions and technologies. Each mission has provided unique, but sometimes conflicting, insights into the mass trends of land ice. Over the last decade, over fifty estimates of land ice trends have been published, providing a confusing and often inconsistent picture. The IPCC Fifth Assessment Report (AR5) attempted to synthesise estimates published up to early 2013. Since then, considerable advances have been made in understanding the origin of the inconsistencies, reducing uncertainties in estimates and extending time series. We assess and synthesise results published, primarily, since the AR5, to produce a consistent estimate of land ice mass trends during the satellite era . We combine observations from multiple missions and approaches including sea level budget analyses. Our resulting synthesis is both consistent and rigorous, drawing on (i) the published literature, (ii) expert assessment of that literature, and (iii) a new analysis of Arctic glacier and ice cap trends combined with statistical modelling.We present annual and pentad (five-year mean) time series for the East, West Antarctic and Greenland Ice Sheets and glaciers separately and combined. When averaged over pentads, covering the entire period considered, we obtain a monotonic trend in mass contribution to the oceans, increasing from 0.31 ± 0.35 mm of sea level equivalent for 1992-1996 to 1.85 ± 0.13 for 2012-2016. Our integrated land ice trend is lower than many estimates of GRACE-derived ocean mass change for the same periods. This is due, in part, to a smaller estimate for glacier and ice cap mass trends compared to previous assessments. We discuss this, and other likely reasons, for the difference between GRACE ocean mass and land ice trends.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.