Extreme rainfalls in SE South America

Heavy rainfall trends in a region of south-eastern South America during 1959–2002 were discussed using daily data of 52 meteorological stations of Argentina, Brazil and Uruguay. Changes in intensity and frequency were both studied with different statistical tests and approaches to check the significance of trends of single and regional aggregated rainfall series. There were predominant positive trends in the annual maximum rainfalls, as well as a remarkable increment in the frequency of heavy rainfalls over thresholds ranging from 50 to 150 mm. However, significant positive trends were not shown in the series of annual maximums and shown only in 15% to 30% of the series of frequencies over thresholds. This lack of significance is due to the high variability of heavy rainfalls in space and time, which makes difficult their capture by single rain gauges. Thus, when the assessment of the heavy rainfall indicators of intensity and frequency were conducted at the regional and sub-regional level, it showed significant trends, both in intensity and frequency over thresholds, with a clearer signal in central and eastern Argentina between 30° and 40° S.     

A very rare triple-peaked type-I X-ray burst in the low-mass X-ray binary 4U 1636−53

We have discovered a triple-peaked X-ray burst from the low-mass X-ray binary (LMXB) 4U 1636−53 with the Rossi X-ray Timing Explorer ( RXTE ). This is the first triple-peaked burst reported from any LMXB using RXTE , and it is only the second burst of this kind observed from any source. (The previous one was also from 4U 1636−53, and was observed with EXOSAT .) From fits to time-resolved spectra, we find that this is not a radius-expansion burst, and the same triple-peaked pattern seen in the X-ray light curve is also present in the bolometric light curve of the burst. Similar to what was previously observed in double-peaked bursts from this source, the radius of the emitting area increases steadily during the burst, with short periods in between during which the radius remains more or less constant. The temperature first increases steeply, and then decreases across the burst also showing three peaks. The first and last peak in the temperature profile occur, respectively, significantly before and after the first and last peaks in the X-ray and bolometric light curves. We found no significant oscillations during this burst. This triple-peaked burst, as well as the one observed with EXOSAT and the double-peak bursts in this source, all took place when 4U 1636−53 occupied a relatively narrow region in the colour–colour diagram, corresponding to a relatively high (inferred) mass-accretion rate. No model presently available is able to explain the multiple-peaked bursts.     

Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

Background

Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them.

Results

Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m^?2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R² ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha^?1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha^?1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R² ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha^?1 [between 0.69–0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha^?1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R² was between 0.58–0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha^?1 for the echo-based model, whereas for the CHM R² was between 0.37–0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha^?1.

Conclusions

Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m^?2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m^?2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m^?2.

     

Long-Term RST Analysis of Anomalous TIR Sequences in Relation with Earthquakes Occurred in Greece in the Period 2004–2013

Real-time integration of multi-parametric observations is expected to accelerate the process toward improved, and operationally more effective, systems for time-Dependent Assessment of Seismic Hazard (t-DASH) and earthquake short-term (from days to weeks) forecast. However, a very preliminary step in this direction is the identification of those parameters (chemical, physical, biological, etc.) whose anomalous variations can be, to some extent, associated with the complex process of preparation for major earthquakes. In this paper one of these parameters (the Earth’s emitted radiation in the Thermal InfraRed spectral region) is considered for its possible correlation with M ≥ 4 earthquakes occurred in Greece in between 2004 and 2013. The Robust Satellite Technique (RST) data analysis approach and Robust Estimator of TIR Anomalies (RETIRA) index were used to preliminarily define, and then to identify, significant sequences of TIR anomalies (SSTAs) in 10 years (2004–2013) of daily TIR images acquired by the Spinning Enhanced Visible and Infrared Imager on board the Meteosat Second Generation satellite. Taking into account the physical models proposed for justifying the existence of a correlation among TIR anomalies and earthquake occurrences, specific validation rules (in line with the ones used by the Collaboratory for the Study of Earthquake Predictability—CSEP—Project) have been defined to drive a retrospective correlation analysis process. The analysis shows that more than 93 % of all identified SSTAs occur in the prefixed space–time window around (M ≥ 4) earthquake's time and location of occurrence with a false positive rate smaller than 7 %. Molchan error diagram analysis shows that such a correlation is far to be achievable by chance notwithstanding the huge amount of missed events due to frequent space/time data gaps produced by the presence of clouds over the scene. Achieved results, and particularly the very low rate of false positives registered on a so long testing period, seems already sufficient (at least) to qualify TIR anomalies (identified by RST approach and RETIRA index) among the parameters to be considered in the framework of a multi-parametric approach to t-DASH.     

Stepwise selection of functional covariates in forecasting peak levels of olive pollen

High levels of airborne olive pollen represent a problem for a large proportion of the population because of the many allergies it causes. Many attempts have been made to forecast the concentration of airborne olive pollen, using methods such as time series, linear regression, neural networks, a combination of fuzzy systems and neural networks, and functional models. This paper presents a functional logistic regression model used to study the relationship between olive pollen concentration and different climatic factors, and on this basis to predict the probability of high (and possibly extreme) levels of airborne pollen, selecting the best subset of functional climatic variables by means of a stepwise method based on the conditional likelihood ratio test.     

Seismic vulnerability of bridges in transport networks subjected to environmental deterioration

This paper investigates the problem of management, maintenance and planning of interventions in transport networks located in seismic zones, in relation to the actual state of degradation of their most vulnerable elements, as bridges. The study consists in two phases: the first phase is concerned with definition of the seismic vulnerability of a typical bridge in the network, through the construction of fragility curves calculated taking into account the corrosion of the reinforcing steel as the main cause of environmental deterioration. Once the fragility curves of the deteriorated bridges are computed, the second phase consists in the analysis of the vulnerability of the transport network in which the bridges are included taking into account the modification of the traffic flows when bridge infrastructures are damaged. The results of this pilot study can be used as a first step for a proper allocation of economic resources in the planning of seismic retrofit interventions to minimize the overall risk and manage the immediate post-earthquake emergency phase and guide rescuers in reaching the affected and critical areas.     

Terrestrial laser scanning to estimate plot-level forest canopy fuel properties

This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R² = 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R² = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R² = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R² = 0.87.     

Aridity changes in the Temperate-Mediterranean transition of the Andes since ad 1346 reconstructed from tree-rings

The Andes Cordillera acts as regional ??Water Towers?? for several countries and encompasses a wide range of ecosystems and climates. Several hydroclimatic changes have been described for portions of the Andes during recent years, including glacier retreat, negative precipitation trends, an elevation rise in the 0° isotherm, and changes in regional streamflow regimes. The Temperate-Mediterranean transition (TMT) zone of the Andes (35.5°?C39.5°S) is particularly at risk to climate change because it is a biodiversity hotspot with heavy human population pressure on water resources. In this paper we utilize a new tree-ring network of Austrocedrus chilensis to reconstruct past variations in regional moisture in the TMT of the Andes by means of the Palmer Drought Severity Index (PDSI). The reconstruction covers the past 657?years and captures interannual to decadal scales of variability in late spring?Cearly summer PDSI. These changes are related to the north?Csouth oscillations in moisture conditions between the Mediterranean and Temperate climates of the Andes as a consequence of the latitudinal position of the storm tracks forced by large-scale circulation modes. Kernel estimation of occurrence rates reveals an unprecedented increment of severe and extreme drought events during the last century in the context of the previous six centuries. Moisture conditions in our study region are linked to tropical and high-latitude ocean-atmospheric forcing, with PDSI positively related to Ni?o-3.4 SST during spring and strongly negatively correlated with the Antarctic Oscillation (AAO) during summer. Geopotential anomaly maps at 500-hPa show that extreme dry years are tightly associated with negative height anomalies in the Ross?CAmundsen Seas, in concordance with the strong negative relationship between PDSI and AAO. The twentieth century increase in extreme drought events in the TMT may not be related to ENSO but to the positive AAO trend during late-spring and summer resulting from a gradual poleward shift of the mid-latitude storm tracks. This first PDSI reconstruction for South America demonstrates the highly significant hindcast skill of A. chilensis as an aridity proxy.