Space–time variability of denudation rates at the catchment and hillslope scales on the Tyrrhenian side of Central Italy

Abstract The information on the time variability of drop size distributions (DSDs) as seen by a disdrometer is used to illustrate the structure of uncertainty in radar estimates of precipitation. Based on this, a method to generate the space–time variability of the distributions of the size of raindrops is developed. The model generates one moment of DSDs that is conditioned on another moment of DSDs; in particular, radar reflectivity Z is used to obtain rainfall rate R. Based on the fact that two moments of the DSDs are sufficient to capture most of the DSD variability, the model can be used to calculate DSDs and other moments of interest of the DSD. A deterministic component of the precipitation field is obtained from a fixed R–Z relationship. Two different components of DSD variability are added to the deterministic precipitation field. The first represents the systematic departures from the fixed R–Z relationship that are expected from different regimes of precipitation. This is generated using a simple broken-line model. The second represents the fluctuations around the R–Z relationship for a particular regime and uses a space–time multiplicative cascade model. The temporal structure of the stochastic fluctuations is investigated using disdrometer data. Assuming Taylor hypothesis, the spatial structure of the fluctuations is obtained and a stochastic model of the spatial distribution of the DSD variability is constructed. The consistency of the model is validated using concurrent radar and disdrometer data.

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Enahancing the detail on low-level seismicity and swarms in central-southern Italy by template matching

10.5194/egusphere-egu21-16045 ◽

2021 ◽

Author(s):

Luca Carbone ◽

Rita de Nardis ◽

Giusy Lavecchia ◽

Laura Peruzza ◽

Enrico Priolo ◽

...

Keyword(s):

Template Matching ◽

Nearest Neighbor ◽

Matched Filter ◽

Central Italy ◽

Total Duration ◽

Space Time ◽

Risk Area ◽

Low Level ◽

Seismic Catalogue ◽

Completeness Magnitude

&#160;During the seismic sequence which followed the devastating L&#8217;Aquila 2009 earthquake, on 27 May 2009 the OGS (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale) and the GeosisLab (Laboratorio di Geodinamica e Sismogenesi, Chieti-Pescara University) installed a temporary seismometric network around the Sulmona Basin, a high seismic risk area of Central Italy located right at SE of the epicentral one. This area of the central Apennines is generally characterized by low level seismicity organized in low energy clusters, but it experienced destructive earthquakes both in historical and in early instrumental time (Fucino 1915 =XI MCS, Majella 1706 =X-XI MCS, Barrea 1984 =VIII MCS).From the 27 May 2009 to 22 November 2011, the temporary network provided a huge amount of continuous seismic recordings, and a seismic catalogue covering the first seven months of network operation (-1.5&#8804;ML&#8804;3.7, with a completeness magnitude of 1.1) and a spatial area that stretches from the Sulmona Basin to Marsica-Sora area. Aiming to enhance the detection of microearthquakes reported in this catalogue, we applied the matched-filter technique (MFT) to continuous waveforms properly integrated with data from permanent stations belonging to the national seismic network. Specifically, we used the open-source seismological package PyMPA to detect microseismicity from the cross-correlation of continuous data and templates. As templates we used only the best relocated events of the available seismic catalogue. Starting from 366 well located earthquakes we obtain a new seismic catalogue of 6084 new events (-2<ML<4) lowering the completeness magnitude to 0.2. To these new seismic locations, we applied a declustering method to separate background seismicity from clustered seismicity in the area. All the seismicity shows a bimodal behaviour in term of distribution of the nearest-neighbor distance/time with the presence of two statistically distinct earthquake populations. We focused the attention on two of these clusters (C1 and C2) that numerically represent the 60% of the catalogue. They consist in 2619 and 995 events, respectively, with magnitude -2.0<ML<3.6 and -0.5<ML<3.2 occurred in Marsica-Sora area. C1 shows the typical characteristics of a seismic swarm, without a clear mainshock, but with 8 more energetic events (3.0&#8804;ML&#8804;3.5); the temporal evolution is very articulated with a total duration of one month with different bursts of seismicity and characteristic time extension of approximately one week. C2 instead has a different space-time evolution and consists of different swarm-like seismic sequences more discontinuous in comparison with C1. These swarms are described in greater detail to investigate the influence of overpressurized fluids and their space-time distribution.

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AXIS—An Autonomous Expendable Instrument System

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-17-0054.1 ◽

2017 ◽

Vol 34 (12) ◽

pp. 2673-2682 ◽

Cited By ~ 2

Author(s):

D. M. Fratantoni ◽

J. K. O’Brien ◽

C. Flagg ◽

T. Rossby

Keyword(s):

Thermal Structure ◽

New Technology ◽

Space Time ◽

Time Variability ◽

Upper Ocean ◽

Time Stamp ◽

Merchant Marine ◽

Marine Vessels ◽

System Axis

AbstractExpendable bathythermographs (XBT) to profile upper-ocean temperatures from vessels in motion have been in use for some 50 years now. Developed originally for navy use, they were soon adapted by oceanographers to map out upper-ocean thermal structure and its space–-time variability from both research vessels and merchant marine vessels in regular traffic. These activities continue today. This paper describes a new technology—the Autonomous Expendable Instrument System (AXIS)—that has been developed to provide the capability to deploy XBT probes on a predefined schedule, or adaptively in response to specific events without the presence of an observer on board. AXIS is a completely self-contained system that can hold up to 12 expendable probes [XBTs, XCTDs, expendable sound velocimeter (XSV)] in any combination. A single-board Linux computer keeps track of what probes are available, takes commands from ashore via Iridium satellite on what deployment schedule to follow, and records and forwards the probe data immediately with a time stamp and the GPS position. This paper provides a brief overview of its operation, capabilities, and some examples of how it is improving coverage along two lines in the Atlantic.

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Integrated investigation of space-time variability in bed load transport rates using remote sensing

River Sedimentation ◽

10.1201/9781315623207-30 ◽

2016 ◽

pp. 145-145

Author(s):

M. Bakker ◽

S.N. Lane

Keyword(s):

Remote Sensing ◽

Space Time ◽

Time Variability ◽

Bed Load ◽

Load Transport ◽

Bed Load Transport

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Space-time variability of hydrological drought and wetness in Iran using NCEP/NCAR and GPCC datasets

Hydrology and Earth System Sciences ◽

10.5194/hess-14-1919-2010 ◽

2010 ◽

Vol 14 (10) ◽

pp. 1919-1930 ◽

Cited By ~ 21

Author(s):

T. Raziei ◽

I. Bordi ◽

L. S. Pereira ◽

A. Sutera

Keyword(s):

Time Series ◽

Singular Spectrum Analysis ◽

Standardized Precipitation Index ◽

Principal Component ◽

Reanalysis Data ◽

Space Time ◽

Hydrological Drought ◽

Time Variability ◽

Climate Conditions ◽

Good Agreement

Abstract. Space-time variability of hydrological drought and wetness over Iran is investigated using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and the Global Precipitation Climatology Centre (GPCC) dataset for the common period 1948–2007. The aim is to complement previous studies on the detection of long-term trends in drought/wetness time series and on the applicability of reanalysis data for drought monitoring in Iran. Climate conditions of the area are assessed through the Standardized Precipitation Index (SPI) on 24-month time scale, while Principal Component Analysis (PCA) and Varimax rotation are used for investigating drought/wetness variability, and drought regionalization, respectively. Singular Spectrum Analysis (SSA) is applied to the time series of interest to extract the leading nonlinear components and compare them with linear fittings. Differences in drought and wetness area coverage resulting from the two datasets are discussed also in relation to the change occurred in recent years. NCEP/NCAR and GPCC are in good agreement in identifying four sub-regions as principal spatial modes of drought variability. However, the climate variability in each area is not univocally represented by the two datasets: a good agreement is found for south-eastern and north-western regions, while noticeable discrepancies occur for central and Caspian sea regions. A comparison with NCEP Reanalysis II for the period 1979–2007, seems to exclude that the discrepancies are merely due to the introduction of satellite data into the reanalysis assimilation scheme.

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