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This study examines drought patterns in Ethiopia's 12 major river basins from 1981 to 2018 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Both indices reveal historical drought episodes with slight variations, with significant differences in 1984, 2009, and 2015. Except for the Wabi-Shebelle catchment in southern Ethiopia, all river basins show an increasing trend in SPI12 and SPEI12 indices. The eastern and central regions experience more drought according to SPEI3. Seasonal correlations show that during the March–May rainy season, precipitation is negatively correlated with the Indian Ocean Dipole (IOD) index, while in the June–September season, it negatively correlates with Nino 3.4 and positively with IOD. The study also found that El Niño leads to less rainfall in the Ethiopian highlands, while La Niña results in more rainfall in the central and northern highlands but less in the south. HIGHLIGHTS The research assessed Ethiopia's drought conditions and their environmental implications using the SPI and SPEI, identifying significant drought periods in 1982, 1984, 1987, 1991, 1997, 2002, 2006, 2011, and 2015, with both indices showing a similar drought progression but with slight differences in severity.; The research revealed that the discrepancy between the SPI and SPEI is consistently negative. This underscores the crucial influence of temperature and evapotranspiration processes in assessing the severity of droughts.; The research evaluated the impact of multiple teleconnections on drought conditions. It was observed that distinct precipitation anomaly patterns across Ethiopia were linked to variations in Sea Surface Temperature (SST).; The results highlight the relationship of El Niño with the occurrence of drought conditions in Ethiopia, particularly its effect on cumulative drought conditions for time scales of more than 6 to 12 months. The effects of the La Niña event on drought conditions in Ethiopia are not clearly observed, as several areas experienced abnormally wet conditions during La Niña events.;

The main objective of this work was to develop a viewer with web output, through which the changes experienced by the mangroves of the Gran Humedal del Norte de Ciego de Avila (GHNCA) can be evaluated from remote sensors, contributing to the understanding of the spatiotemporal variability of their vegetative dynamics. The achievement of this objective is supported by the use of open-source technologies such as MapStore, GeoServer and Django, as well as Google Earth Engine, which combine to offer a robust and technologically independent solution to the problem. In this context, it was decided to adopt an action model aimed at automating the workflow steps related to data preprocessing, downloading, and publishing. A visualizer with web output (Geospatial System for Monitoring Mangrove Ecosystems or SIGMEM) is developed for the first time, evaluating changes in an area of central Cuba from different vegetation indices. The evaluation of the machine learning classifiers Random Forest and Naive Bayes for the automated mapping of mangroves highlighted the ability of Random Forest to discriminate between areas occupied by mangroves and other coverages with an Overall Accuracy (OA) of 94.11%, surpassing the 89.85% of Naive Bayes. The estimated net change based on the year 2020 of the areas determined during the classification process showed a decrease of 5138.17 ha in the year 2023 and 2831.76 ha in the year 2022. This tool will be fundamental for researchers, decision makers, and students, contributing to new research proposals and sustainable management of mangroves in Cuba and the Caribbean.

In 2017-2019 southeast Australia experienced one of its most severe droughts since 1900. Rainfall over the region encompassing the Murray-Darling Basin was consistently below average for three consecutive cool seasons, an unprecedented event on record. A strong positive Indian Ocean Dipole event has been previously suggested to have intensified the conditions of the drought in 2019, however the state of the climate drivers cannot fully explain the onset and development of this drought. In this study, we adopt a different approach to investigate processes other than remote climate drivers that may have triggered the drought. Using a Lagrangian model to identify moisture sources to the Murray Darling Basin, we show that local processes were crucial in explaining the onset and development of the drought. We identify the oceanic and terrestrial sources of atmospheric moisture over the Murray Darling Basin and show for the first time a significant decline in rainfall moisture supply from the Tasman Sea in 2017 and 2018. We further show that anomalous atmospheric circulation transported the expected moisture northward toward the Maritime Continent. Our results provide an explanation for the moisture and rainfall deficit that caused the 2017-19 southeast Australian drought. Understanding the processes that led to the 2017-2019 drought is an important step towards improved predictions and planning for future multi-year droughts in Australia.

This paper examines the linear relationship between climate events in the La Plata Basin (LP) from 1980 to 2018 and atmospheric moisture transport from major sources using a Lagrangian approach. The standardized precipitation evapotranspiration index (SPEI-1) was utilized to assess climate events, as monthly water balance variations may be related to changes in atmospheric moisture transport. A total of 49 dry and 46 wet events were identified through sequences of negative and positive SPEI-1 values, respectively. Lagrangian analysis tracked changes in moisture uptake and supply from sources to the LP during these events. Simple linear regression analysis revealed a relationship between moisture transport from the Amazonas (AM), North Atlantic (NA), and Tocantins (TO) basins and the severity and duration of climate events. Increased moisture supply from the São Francisco basin (SF) and Eastern Brazil oceanic (EBO) sources intensified the duration and severity of wet events. Peak wet events were linked to increased moisture supply from the northern South America basins (AM, NA, and TO), while peak droughts were related to decreased moisture uptake from eastern sources (TO, SF, and EBO). Our findings highlight how the water balance in distant regions affects the LP via moisture transport, emphasizing the need for interconnected adaptive strategies.

The function of cerebral small vessels can be assessed using cerebral vasomotor reactivity (VMR). Our aim in this retrospective cross-sectional study was to investigate a correlation between carotid artery stenosis measured through ultrasonographic morphological and hemodynamic parameters and cerebral VMR. A total of 285 patients (125 males; mean age 54) were included. The breath-holding index (BHI) was used to evaluate cerebral VMR. Ultrasonographic carotid artery parameters were collected: the presence and characteristics of carotid plaques, the degree of carotid diameter stenosis, intima–media thickness (IMT), peak systolic velocity (PSV), and end diastolic velocity (EDV). Additionally, hemodynamic parameters of the middle cerebral artery (MCA) were evaluated, including the mean flow velocity (MFV) and pulsatility index (PI). The following was collected from patients’ medical histories: age, gender, and vascular risk factors. A negative correlation between the BHI and age (r = −0.242, p < 0.01), BHI and the presence of carotid plaques, BHI and IMT (r = −0.203, p < 0.01), and BHI and the PI of MCA on both sides (r = −0.268, p < 0.01) was found. We found a positive correlation between the BHI in the left MCA and EDV in the left internal carotid artery (r = 0.121, p < 0.05). This study shows the correlation between cerebral VMR and carotid stenosis but indicates a higher influence of morphological parameters on VMR values.

In this article, we focus on studying the nexus between moisture transport deficit and drought occurrence in nine key regions across the world where the magnitude of meteorological drought is projected to increase from 1850 to 2100 under a high anthropogenic emission scenario. These regions are central America, southwestern South America, northern Brazil, the Amazon, northeastern Brazil, the western Mediterranean, southern Africa, the eastern Mediterranean, and southwestern Australia. Using the Lagrangian particle dispersion model FLEXPART, we identify the specific moisture sources of the regions (the own region, the nearby continental source and the oceanic sources) and obtain their contributions to the precipitation in the regions for the period 1980–2018. For each region and specific moisture source, the conditional probability of meteorological drought occurrence given an equivalent contribution deficit from the source is estimated using copula models, a statistical methodology that allows us to capture complex relationships between variables. We identify the dominant moisture source in each region, which is the source for which the contribution deficit maximises drought probability. A variety of cases are found: in three regions, the dominant source is the region itself, in one region, it is the nearby terrestrial source, and in five regions, it is an oceanic source. In general, contribution deficits from specific moisture sources are associated with only slightly greater drought probabilities than those from major global moisture sources. We also reveal that the source that contributes the most to precipitation in a given region is not necessarily the dominant source of drought in the region. These results highlight the importance of understanding the role of dominant moisture sources and moisture transport deficits on meteorological drought occurrence at a regional scale.

High frequency (millimeter wave and higher) systems are being used for curb-to-home services and indoor networks with fixed transmitter and receivers. However, the environment between them can shift due to moving flora and fauna causing sudden blockages. To mitigate outages due to blocking, we investigate the use of a two-beam system rather than a conventional single line-of-sight (LOS) beam. A two-beam system requires a design that can adjust both the transmit phase and delay on one of the beams to ensure a strong signal when both beams are not blocked, and the ability to adapt the transmission rate when the receiver power drops due to intermittent blockages on either beam. We propose a low-complexity co-phasing strategy in which the transmitter guesses the phase and delay offset between the two beams until the receiver indicates a satisfactory channel has been established. Exact co-phasing of the beams is not required and the average number of guesses needed to find an appropriate delay and phase is relatively small. Once the link has been established, the transmitted signal power is split between the two beams to achieve maximum throughput for a fixed total power budget limit. The resulting scheme is not only computationally efficient, but is also robust to channel estimation errors that typically plague transmit adaptation strategies. Numerical results demonstrate the effectiveness of the approach, showing that a capacity within 0.1 dB of perfect co-phasing can be achieved with fewer than 100 guesses. In 90 % of the cases, 25 guesses were required on average to achieve a power that is within 0.1 dB of the optimum.

In this study, we conducted a global assessment of the sensitivity of vegetation greenness (VGS) to precipitation and to the estimated Lagrangian precipitation time series of oceanic (PLO) and terrestrial (PLT) origin. The study was carried out for terrestrial ecosystems consisting of 9 biomes and 139 ecoregions during the period of 2001–2018. This analysis aimed to diagnose the vegetative response of vegetation to the dominant component of precipitation, which is of particular interest considering the hydroclimatic characteristics of each ecoregion, climate variability, and changes in the origin of precipitation that may occur in the context of climate change. The enhanced vegetation index (EVI) was used as an indicator of vegetation greenness. Without consideration of semi-arid and arid regions and removing the role of temperature and radiation, the results show the maximum VGS to precipitation in boreal high-latitude ecoregions that belong to boreal forest/taiga: temperate grasslands, savannas, and shrublands. Few ecoregions, mainly in the Amazon basin, show a negative sensitivity. We also found that vegetation greenness is generally more sensitive to the component that contributes the least to precipitation and is less stable throughout the year. Therefore, most vegetation greenness in Europe is sensitive to changes in PLT and less to PLO. In contrast, the boreal forest/taiga in northeast Asia and North America is more sensitive to changes in PLO. Finally, in most South American and African ecoregions, where PLT is crucial, the vegetation is more sensitive to PLO, whereas the contrast occurs in the northern and eastern ecoregions of Australia.

In this study, we identified and investigated the annual climatological moisture sources for the Ethiopian river basins during 1980–2018. First, according to cluster analysis, the 12 river basins of this country were grouped into four regions: northeast (NE), southeast (SE), southwest (SW), and west (W), which were characterised by similar annual precipitation features. Global outputs from the Lagrangian FLEXPART model were used to investigate the air mass humidity gain before reaching each region. This revealed five main oceanic moisture sources located in the Mediterranean Sea, Red Sea, Indian Ocean, Persian Gulf, and the Arabian Sea, in addition to three main terrestrial moisture sources located in the African continent, Arabian Peninsula, and the regions themselves. Once the main climatological sources of moisture for each region were identified, a forward-in-time analysis of air masses over each source was performed to calculate the moisture contribution to precipitation (E – P) < 0 over the defined regions. The most important source at the annual scale for the NE, SW, and W regions is the African continent, while for the SE, it is the Indian Ocean. Indeed, terrestrial moisture sources are the major contributors (>50%) to the precipitation over the NE, SW, and W, whereas oceanic sources are the major contributors to the SE. Another analysis identified the years affected by drought conditions in the regions. The role of the sources was evaluated for those years affected by severe and extreme drought, revealing the heterogeneous and also direct influences on the regions. Finally, according to the normalised difference vegetation index, the impacts of annual severe and extreme droughts were more prominent in areas of the NE and SE, but also in the SW during 1984.

Motivated by recent experimental observations, where differentially coherent detection outperforms coherent detection on certain underwater acoustic channels, we revisit the performance of these two methods with respect to the achievable rate in orthogonal frequency-division multiplexing systems. Our comparison is based on a class of time-varying channels, where coherent quadrature phase shift keying (QPSK) detection requires channel estimation through a set of designated pilot symbols, while differentially coherent QPSK (DQPSK) enjoys an almost pilot-free detection. We show that given the cost of pilot overhead and channel estimation errors, the average rate achievable with DQPSK can be higher than that of QPSK. We use analytical results to identify channels for which this is the case, and expand the results to larger constellation sizes such as 8PSK, 16PSK, and 16QAM. In addition to acoustic channels, wireless radio channels with unusually long delay spread or high Doppler spread, such as those found in unmanned aerial systems and high-speed trains, may also benefit from using differentially coherent detection. In general, differentially coherent detection is favored in rapidly time-varying channels which require large pilot overhead and/or whose bit error rate performance is dominated by channel estimation errors. Our analysis includes the results for array receivers and soft detectors, showing that the favorable parameter range for differentially coherent detection expands significantly in both cases.

This paper discusses asynchronous distributed inference in object tracking. Unlike many studies, which assume that the delay in communication between partial estimators and the central station is negligible, our study focuses on the problem of asynchronous distributed inference in the presence of delays. We introduce an efficient data fusion method for combining the distributed estimates, where delay in communications is not negligible. To overcome the delay, predictions are made for the state of the system based on the most current available information from partial estimators. Simulation results show the efficacy of the methods proposed.

A large part of the population and the economic activities of South America are located in eastern regions of the continent, where extreme climate events are a recurrent phenomenon. This study identifies and characterizes the dry and wet climate periods at domain-scale occurring over the eastern South America (ESA) during 1980–2018 through the multi-scalar Standardized Precipitation–Evapotranspiration Index (SPEI). For this study, the spatial extent of ESA was defined according to a Lagrangian approach for moisture analysis. It consists of the major continental sink of the moisture transported from the South Atlantic Ocean throughout the year, comprising the Amazonia, central Brazil, and the southeastern continental areas. The SPEI for 1, 3, 6, and 12 months of accumulation was calculated using monthly precipitation and potential evapotranspiration time series averaged on ESA. The analysis of the climate periods followed two different approaches: classification of the monthly SPEI values as mild, moderate, severe, and extreme; the computation of the events and their respective parameters (duration, severity, intensity, and peak). The results indicate that wet periods prevailed in the 1990s and 2000s, while dry conditions predominated in the 2010s, when the longest and more severe dry events have been identified at the four scales.

The climate in Central Europe (CEU) during the 20th century is characterized by an overall temperature increase. Severe and prolonged drought events began occurring towards the end and these have continued into the 21st century. This study aims to analyze variations in the moisture supply from the Mediterranean Sea (MDS) during meteorological drought episodes occurring over the CEU region over the last three decades. A total of 51 meteorological drought episodes (22 with summer onsets, and 29 with winter) are identified over the CEU during the period 1980–2015 through the one-month Standardized Precipitation Evapotranspiration Index (SPEI-1), and their respective indicators, including duration, severity, intensity, and peak values, are then computed. Lagrangian forward-in-time analysis reveals that negative anomalies of moisture coming from the MDS prevail in all episodes except seven. Linear regression analysis between variations in the MDS anomalies and indicators of the drought episodes shows a significant linear relationship between severity, duration, peak values (winter), and MDS anomalies, which implies that drought episodes last longer and are more severe with an increase in the negative anomaly of moisture supply from the MDS. Nevertheless, no linear relationship is found between the intensity and peak values (annual, summer) of drought episodes and anomalies in the moisture contribution from the MDS.

In this paper, we provide a Lagrangian analysis of the anomalies in the moisture transport during two important drought events (1989/1990 and 2003) configured over the Danube River Basin (DRB) region. Firstly, we identified the drought episodes that occurred over the DRB in the period of 1980–2014 through the Standardized Precipitation Evapotranspiration Index (SPEI). SPEI was calculated using monthly Climatic Research Unit (CRU) Time-Series (TS) Version 3.23 precipitation and potential evapotranspiration (PET) datasets with a spatial resolution of 0.5 degrees. The monthly SPEI-1 index was applied to identify the drought episodes and their respective indicators, including duration, severity, and intensity. Two significant drought events were selected: 1989/1990 (presenting dry conditions during October 1989–March 1990) and 2003 (presenting dry conditions during April 2003–September 2003). These events were associated with the two most severe SPEI-1 episodes identified over the DRB during 1980–2014. Then, an analysis of anomalies in the moisture transport was conducted in order to verify possible changes in the moisture supply from the climatological sources for the DRB during these episodes. The moisture transport analysis was performed through a Lagrangian approach, which uses the outputs of the FLEXiblePARTicle dispersion model FLEXPART integrated with one of the reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF): the ECMWF Re-Analysis (ERA)-Interim dataset. The DRB receives moisture from seven different moisture source regions: the North Atlantic Ocean (NATL), North Africa (NAF), the Mediterranean Sea (MED), the Black Sea (BS), the Caspian Sea (CS), the DRB, and Central and Eastern Europe (Rest of Land (RestL)). The analysis of drought events shows that the precipitation and moisture supply from the selected sources weakened mainly during both drought events. Anomalous subsidence and an increased PET also prevailed over the DRB during these SPEI-1 episodes. RestL and MED registered the most intensive reduction in the moisture supply over the DRB during both periods.

In this study, we investigate the sources of moisture (and moisture for precipitation) over the Danube River Basin (DRB) by means of a Lagrangian approach using the FLEXPART V9.0 particle dispersion model together with ERA-Interim reanalysis data to track changes in atmospheric moisture over 10-day trajectories. This approach computes the budget of evaporation-minus-precipitation by calculating changes in specific humidity along forward and backward trajectories. We considered a time period of 34 years, from 1980 to 2014, which allowed for the identification of climatological sources and moisture transport towards the basin. Results show that the DRB mainly receives moisture from seven different oceanic, maritime, and terrestrial moisture source regions: North Atlantic Ocean, North Africa, the Mediterranean Sea, Black Sea, Caspian Sea, the Danube River Basin, and Central and Eastern Europe. The contribution of these sources varies by season. During winter (October–March) the main moisture source for the DRB is the Mediterranean Sea, while during summer (April–September) the dominant source of moisture is the DRB itself. Moisture from each source has a different contribution to precipitation in the DRB. Among the sources studied, results show that the moisture from the Mediterranean Sea provides the greatest contribution to precipitation in the basin in both seasons, extending to the whole basin for the winter, but being more confined to the western side during the summer. Moisture from the Caspian and Black Seas contributes to precipitation rather less.

This paper investigates the signal-to-interference ratio and the achievable rates of underwater acoustic (UA) OFDM systems over channels where time and frequency dispersion are high enough that (i) neither the transmitter nor the receiver can have a priori knowledge of the channel state information and (ii) intersymbol/intercarrier interference (ISI/ICI) cannot be neglected in the information-theoretic treatment. The goal of this study is to obtain a better understanding of the interplay between interference and the achievable transmission rates. Expressions for these rates take into account the “cross-channels” established by the ISI/ICI and are based on lower bounds on mutual information that assume independent and identically distributed input data symbols. In agreement with recent statistical analyses of experimental shallow-water data, the channel is modeled as a multivariate Rician fading process with a slowly time-varying mean and with potentially correlated scatterers, which is more general than the common wide-sense stationary uncorrelated scattering model. Numerical assessments on real UA channels with spread factors around 10−1 show that reliable OFDM transmissions at 2 to 4 bits/sec/Hz are achievable provided an average signal-to-noise ratio of 15 to 20 dB.