European floods during the winter 1783/1784: scenarios of an extreme event during the ‘Little Ice Age’

The Lakagígar eruption in Iceland during 1783 was followed by the severe winter of 1783/1784, which was characterised by low temperatures, frozen soils, ice-bound watercourses and high rates of snow accumulation across much of Europe. Sudden warming coupled with rainfall led to rapid snowmelt, resulting in a series of flooding phases across much of Europe. The first phase of flooding occurred in late December 1783–early January 1784 in England, France, the Low Countries and historical Hungary. The second phase at the turn of February–March 1784 was of greater extent, generated by the melting of an unusually large accumulation of snow and river ice, affecting catchments across France and Central Europe (where it is still considered as one of the most disastrous known floods), throughout the Danube catchment and in southeast Central Europe. The third and final phase of flooding occurred mainly in historical Hungary during late March and early April 1784. The different impacts and consequences of the above floods on both local and regional scales were reflected in the economic and societal responses, material damage and human losses. The winter of 1783/1784 can be considered as typical, if severe, for the Little Ice Age period across much of Europe.     

Channel response to a dam-removal sediment pulse captured at high-temporal resolution using routine gage data

In this study, we captured how a river channel responds to a sediment pulse originating from a dam removal using multiple lines of evidence derived from streamflow gages along the Patapsco River, Maryland, USA. Gages captured characteristics of the sediment pulse, including travel times of its leading edge (~7.8 km yr⁻¹) and peak (~2.6 km yr⁻¹) and suggest both translation and increasing dispersion. The pulse also changed local hydraulics and energy conditions, increasing flow velocities and Froude number, due to bed fining, homogenization and/or slope adjustment. Immediately downstream of the dam, recovery to pre-pulse conditions occurred within the year, but farther downstream recovery was slower, with the tail of the sediment pulse working through the lower river by the end of the study 7 years later. The patterns and timing of channel change associated with the sediment pulse were not driven by large flow or suspended sediment-transporting events, with change mostly occurring during lower flows. This suggests pulse mobility was controlled by process-factors largely independent of high flow. In contrast, persistent changes occurred to out-of-channel flooding dynamics. Stage associated with flooding increased during the arrival of the sediment pulse, 1 to 2 years after dam removal, suggesting persistent sediment deposition at the channel margins and nearby floodplain. This resulted in National Weather Service-indicated flood stages being attained by 3–43% smaller discharges compared to earlier in the study period. This study captured a two-signal response from the sediment pulse: (1) short- to medium-term (weeks to months) translation and dispersion within the channel, resulting in aggradation and recovery of bed elevations and changing local hydraulics; and (2) dispersion and persistent longer-term (years) effects of sediment deposition on overbank surfaces. This study further demonstrated the utility of US Geological Survey gage data to quantify geomorphic change, increase temporal resolution, and provide insights into trajectories of change over varying spatial and temporal scales.     

River flood seasonality in the Northeast United States: Characterization and trends

The New England and Mid‐Atlantic regions of the Northeast United States have experienced climate‐induced increases in both the magnitude and frequency of floods. However, a detailed understanding of flood seasonality across these regions, and how flood seasonality may have changed over the instrumental record, has not been established. The annual timing of river floods reflects the flood‐generating mechanisms operating in a basin, and many aquatic and riparian organisms are adapted to flood seasonality, as are human uses of river channels and flood plains. Changes in flood seasonality may indicate changes in flood‐generating mechanisms, and their interactions, with important implications for habitats, flood plain infrastructure, and human communities. I applied a probabilistic method for identifying flood seasons at a monthly resolution for 90 Northeast U.S. watersheds with natural, or near‐natural, flood‐generating conditions. Historical trends in flood seasonality were also investigated. Analyses were based on peaks‐over‐threshold flood records that have, on average, 85 years of data and three peaks per year—thus providing more information about flood seasonality than annual maximums. The results show rich detail about annual flood timing across the region with each site having a unique pattern of monthly flood occurrence. However, a much smaller number of dominant seasonal patterns emerged when contiguous flood‐rich months were classified into commonly recognized seasons (e.g., Mar–May, spring). The dominant seasonal patterns identified by manual classification were corroborated by unsupervised classification methods (i.e., cluster analyses). Trend analyses indicated that the annual timing of flood‐rich seasons has generally not shifted over the period of record, but 65 sites with data from 1941 to 2013 revealed increased numbers of June–October floods—a trend driving previously documented increases in Northeast U.S. flood counts per year. These months have been historically flood‐poor at the sites examined, so warm‐season flood potential has increased with possible implications for aquatic and riparian organisms.     

Scalar-Flux Similarity in the Layer Near the Surface Over Mountainous Terrain

The scaled standard deviations of temperature and humidity are investigated in complex terrain. The study area is a steep Alpine valley, with six measurement sites of different slope, orientation and roughness (i-Box experimental site, Inn Valley, Austria). Examined here are several assumptions forming the basis of Monin–Obukhov similarity theory (MOST), including constant turbulence fluxes with height and the degree of self-correlation between the involved turbulence variables. Since the basic assumptions for the applicability of the MOST approach—horizontally homogeneous and flat conditions—are violated, the analysis is performed based on a local similarity hypothesis. The scaled standard deviations as a function of local stability are compared with previous studies from horizontally homogeneous and flat terrain, horizontally inhomogeneous and flat terrain, weakly inhomogeneous and flat terrain, as well as complex terrain. As a reference, similarity relations for unstable and stable conditions are evaluated using turbulence data from the weakly inhomogeneous and flat terrain of the Cabauw experimental site in the Netherlands, and assessed with the same post-processing method as the i-Box data. Significant differences from the reference curve and also among the i-Box sites are noted, especially for data derived from the i-Box sites with steep slopes. These differences concern the slope and the magnitude of the best-fit curves, illustrating the site dependence of any similarity theory.     

Comment on Pisarenko et al., “Characterization of the Tail of the Distribution of Earthquake Magnitudes by Combining the GEV and GPD Descriptions of Extreme Value Theory”

In this short note, I comment on the research of Pisarenko et al. (Pure Appl. Geophys 171:1599–1624, 2014) regarding the extreme value theory and statistics in the case of earthquake magnitudes. The link between the generalized extreme value distribution (GEVD) as an asymptotic model for the block maxima of a random variable and the generalized Pareto distribution (GPD) as a model for the peaks over threshold (POT) of the same random variable is presented more clearly. Inappropriately, Pisarenkoet al. (Pure Appl. Geophys 171:1599–1624, 2014) have neglected to note that the approximations by GEVD and GPD work only asymptotically in most cases. This is particularly the case with truncated exponential distribution (TED), a popular distribution model for earthquake magnitudes. I explain why the classical models and methods of the extreme value theory and statistics do not work well for truncated exponential distributions. Consequently, these classical methods should be used for the estimation of the upper bound magnitude and corresponding parameters. Furthermore, I comment on various issues of statistical inference in Pisarenkoet al. and propose alternatives. I argue why GPD and GEVD would work for various types of stochastic earthquake processes in time, and not only for the homogeneous (stationary) Poisson process as assumed by Pisarenko et al. (Pure Appl. Geophys 171:1599–1624, 2014). The crucial point of earthquake magnitudes is the poor convergence of their tail distribution to the GPD, and not the earthquake process over time.     

Can GPS-Derived Surface Loading Bridge a GRACE Mission Gap?

We investigated two ‘gap-filler’ methods based on GPS-derived low-degree surface loading variations (GPS-I and GPS-C) and a more simple method (REF-S) which extends a seasonal harmonic variation into the expected Gravity Recovery and Climate Experiment (GRACE) mission gap. We simulated two mission gaps in a reference solution (REF), which is derived from a joint inversion of GRACE (RL05) data, GPS-derived surface loading and simulated ocean bottom pressure. The GPS-I and GPS-C methods both have a new type of constraint applied to mitigate the lack of GPS station network coverage over the ocean. To obtain the GPS-C solution, the GPS-I method is adjusted such that it fits the reference solution better in a 1.5 year overlapping period outside of the gap. As can be expected, the GPS-I and GPS-C solutions contain larger errors compared to the reference solution, which is heavily constrained by GRACE. Within the simulated gaps, the GPS-C solution generally fits the reference solution better compared to the GPS-I method, both in terms of spherical harmonic loading coefficients and in terms of selected basin-averaged hydrological mass variations. Depending on the basin, the RMS-error of the water storage variations (scaled for leakage effects) ranges between 1.6 cm (Yukon) and 15.3 cm (Orinoco). In terms of noise level, the seasonal gap-filler method (REF-S) even outperforms the GPS-I and GPS-C methods, which are still affected by spatial aliasing problems. However, it must be noted that the REF-S method cannot be used beyond the study of simple harmonic seasonal variations.     

Transient Divergent Flow and Transport in an Infinite Anisotropic Porous Formation

When seeking to predict plume geometry resulting from fluid injection through partially penetrating wells, it is common to assume a steady-state spherically diverging flow field. In reality, the flow field is transient. The steady-flow assumption is likely to cause overestimation of injection plume radius since the accommodation of fluid by increases in porosity and fluid density is ignored. In this paper, a transient solution is developed, resulting in a nonlinear ordinary differential equation expressing plume radius as a function of time. It is shown that the problem can be fully described by one type curve. A critical time, t_c, is identified at which the percentage error of the steady-state flow solution compared to the fully dynamic problem is less than 1%. Only for large injection rates and low permeabilities, does t_c become greater than 1 h. Nevertheless, an improved approximate solution is obtained by a simple linearization procedure. The critical time, t_c for the new approximate solution is 0.3% of that required for the steady-state flow solution.     

Monitoring the regional and temporal variability of winter snowfall in the arid Andes using digital NOAA/AVHRR data

Abstract

This paper deals with the spatial distribution and the temporal variability of snowfall in the most arid part of the Andes (18°‐ 28°S) during southern hemisphere winter (May‐September). As the official precipitation data is of poor quality, analyses were carried out by means of digital image processing techniques, using NOAA/AVHRR satellite‐data. Through analysis of 24 different snowfall events from six winters, a previously unknown spatial and temporal precipitation pattern in this remote and unexplored area was revealed. Snowfall is most abundant in the southernmost part of the research area and on the western side of the Andes, indicating the Pacific origin of the snowfall.

Nevertheless, the typical snowfall pattern is modified during different periods of the winter. Three typical time periods could be defined and distinguished from one another. Each of these three periods is characterized by typical weather conditions (cold fronts and “cut‐offs “) leading to a distinct snowfall pattern.

As this study is part of a broader paleoclimatic project, the results will serve as a basis for paleoclimatic reconstruction of past climate. Only by knowing the modern circulation and precipitation patterns is it possible to interpret paleoclimatic signals and archives found in the study area (e.g. paleosol, moraines) correctly.