Bruce Malamud | King's College London (original) (raw)

Papers by Bruce Malamud

Earth System Dynamics, 2016

In this chapter, three broad categories of geomorphological models are considered: (1) traditiona... more In this chapter, three broad categories of geomorphological models are considered: (1) traditional physically based computer models; (2) cellular-automata models; and (3) statistical models of observations or simulated data. Nine considerations for constructing and running geomorphological models within these categories are then explored: (1) suitability of the model for the question and observational data at hand; (2) model parsimony; (3) dimensional analysis; (4) benchmarks; (5) sensitivity analysis; (6) calibration; (7) observation and model data exploration; (8) uncertainty assessment; and (9) alternative models, data, and questions. For each consideration, good practices within the context of the literature are highlighted.

International Journal of Disaster Risk Reduction, 2022

The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which wa... more The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which was released in 2019 by ECMWF and benefits from the latest improvements in the field (Hersbach et al., 2020). ERA5 data (ECMWF, 2020) is available 1979 to present (we use up to September 2019), with a spatial resolution of 0.25deg x 0.25deg and an hourly temporal resolution. The data resolves the atmosphere using 137 levels from the surface up to a height of 80 km (ECMWF, 2020). ERA5 data are generated with a short forecast of 18 h twice a day (06:00 and 18:00 UTC) and assimilated with observed data (ECMWF, 2020). more information about ERA5 can be found here. The two following variables are extracted from the product: Extreme precipitation (p): accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's in one hour (mm). This value is averaged over a grid cell. Extreme wind (w): hourly maximum wind gust at a height of 10 m above the surface of the Earth (m...

In this paper, we present a unique 9.5 m palaeolacustrine record of 771 palaeofloods which occurr... more In this paper, we present a unique 9.5 m palaeolacustrine record of 771 palaeofloods which occurred over a period of 9.3 kyr in the Piànico-Sèllere Basin (southern Alps) during an interglacial period in the Pleistocene (sometime from 780 to 393 ka) and analyse its correlation, clustering, and cyclicity properties. We first examine correlations, by applying power-spectral analysis and detrended fluctuation analysis (DFA) to a time series of the number of floods per decade, and find weak long-range persistence: a power-spectral exponent beta-PS approx. 0.39 and an equivalent power-spectral exponent from DFA, beta-DFA approx. 0.25. We then examine clustering using the one-point probability distribution of the inter-flood intervals and find that the palaeofloods cluster in time as they are Weibull distributed with a shape parameter kW = 0.78. We then examine cyclicity in the time series of number of palaeofloods per year, and find a period of about 2030 years. Using these characterizati...

Abstract: The quantification of wildfire regimes, especially the relationship between the frequen... more Abstract: The quantification of wildfire regimes, especially the relationship between the frequency with which events occur and their size, is of particular interest to both ecologists and wildfire managers. Recent studies in cellular automata (CA) and the fractal nature of the frequency-area relationship they produce has led some authors to ask whether the power-law frequency-area st tistics een in the CA might also be present in empirical wildfire data. Here, we outline the history of the debate r garding the statistical wildfire frequency-area models suggested by the CA and their confrontation with empirical data. In particular, the extent o which the utility of these approaches is dependent onbeing placed in the context of self-organized criticality (SOC) is examined. We also consider some of the other heavy-tailed statistical distri-butions used to describe these data. Taking a broadly ecological perspective we suggest that this debate needs to take more interest in the mechani...

&lt;p&gt;Istanbul is a major global urban centre. With city expansion expected to... more &lt;p&gt;Istanbul is a major global urban centre. With city expansion expected to continue over the next few decades there is a real opportunity for urban growth that incorporates disaster risk reduction (DRR). But in order to develop DRR inclusive urban development strategies we need to understand the breadth of hazards that can affect the city and their potential interactions.&lt;/p&gt;&lt;p&gt;To create a single hazard overview for the city we searched through peer-reviewed literature, reports, government websites and international disaster databases for hazard occurrences. Of the 34 natural hazards in our global hazard table encompassing five major hazard groups (geophysical, shallow process, meteorological, hydrological, climatological and extraterrestrial), we found 27 of these had occurred or had the potential to occur in Istanbul. Notable absences were snow avalanches, glacial outburst floods and direct volcanic hazards. However, ash dispersal models show that ash from volcanic eruptions in the Mediterranean can affect the city.&lt;/p&gt;&lt;p&gt;Additionally, we present an interaction matrix for hazards relevant to the city that shows how one hazard may trigger or increase the probability of another. We adapted the global hazard interaction matrix of Gill and Malamud (2014) by removing hazards that were not relevant to Istanbul and supplementing it with specific examples that have occurred in the city. We found 85 such interactions that reveal the potential for interacting chains of natural hazards.&lt;/p&gt;&lt;p&gt;We discuss how multi-hazard scenarios, developed through expert stakeholder engagement and based on the hazard interaction matrix, are an effective way to explore and communicate the dynamic variability of exposure, vulnerability and therefore, multi-hazard risk.&lt;/p&gt;

The study analyzes long-term trends of the hourly temperatures and the DTR at the Mauna Loa obser... more The study analyzes long-term trends of the hourly temperatures and the DTR at the Mauna Loa observatory. This location best represents an open area with no anthropogenic influence. The nocturnal warming and the decrease of the DTR found in Mauna Loa strengthens some previous studies arguing that regional and global warming are not a reflection of urban heat island (UHI, e.g., Parker 2006, 2010). Thus, the study can contribute to the scientific discussion on global warming and deserves publication, subjected to a major revision based on the following comments.

Natural Hazards and Earth System Sciences, 2016

The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based q... more The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, <i>L</i>. Here we examine the spatial–temporal clustering of severe tornadoes, which we define as having path lengths <i>L</i> ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial–temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial–temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe…

Agu Fall Meeting Abstracts, Dec 1, 2003

Agu Fall Meeting Abstracts, 2002

Earthquakes are a major cause of landslides. Landslides, in turn, are a major contributor to the ... more Earthquakes are a major cause of landslides. Landslides, in turn, are a major contributor to the damage and causalities associated with earthquakes. It is desirable to quantify this association between landslides and earthquakes. A major step in this direction was taken by Keefer (1994) who obtained an empirical relationship between the magnitude (moment) of an earthquake and the total volume of the landslides generated by the earthquake. In order to extend this quantification, we have fit a frequency-area probability distribution to three recent very well-documented and ``complete'' landslide event inventories, from different parts of the world and with different triggering mechanisms. Our proposed ``universal'' landslide probability distribution consists of a three-parameter inverse gamma distribution. One implication of this universal distribution is that the mean area of the landslides in the distribution is 3,070 m2, and is independent of both the total number (NLT) and total area (ALT) of landslides that occurred during the triggered landslide event. To further quantify landslide events we introduce the landslide-event magnitude scale ML = log(NLT). Using our universal probability distribution, ML can be determined from a partial inventory of the largest landslides that occur during a landslide event. Using the largest 10% of landslides that occur during our ``complete'' inventories, the predicted values for NLT and ALT are within 0.1--15% of the actual values for the complete inventory. Finally, we relate our landslide magnitude to earthquake magnitude, convert landslide areas to volumes using an approximate relationship, and determine an analytic expression for the rate of erosion, based on the Gutenberg-Richter frequency-magnitude of earthquakes in the region.

Proquest Dissertations and Theses Thesis Cornell University 1998 Publication Number Aai9818484 Isbn 9780591699258 Source Dissertation Abstracts International Volume 58 12 Section B Page 6453 323 P, 1998

Agu Fall Meeting Abstracts, Dec 1, 2008

Agu Fall Meeting Abstracts, Dec 1, 2009