95 research outputs found
Double_inversion dataset
No full textDifferent datasets consisting of ground observation, profiles, and satellite observation
A multi-temporal analysis of AMSR-E data for flood and discharge monitoring during the 2008 flood in Iowa
No full textThe objective of this work is to demonstrate the potential of using passive microwave data to monitor flood and discharge
conditions and to infer watershed hydraulic and hydrologic parameters. The case study is the major flood in Iowa in summer
2008. A new Polarisation Ratio Variation Index (PRVI) was developed based on a multi-temporal analysis of 37 GHz satellite
imagery from the Advanced Microwave Scanning Radiometer (AMSR-E) to calculate and detect anomalies in soil moisture
and/or inundated areas. The Robust Satellite Technique (RST) which is a change detection approach based on the analysis of
historical satellite records was adopted. A rating curve has been developed to assess the relationship between PRVI values
and discharge observations downstream. A time-lag term has been introduced and adjusted to account for the changing delay
between PRVI and streamflow. Moreover, the Kalman filter has been used to update the rating curve parameters in near real
time. The temporal variability of the b exponent in the rating curve formula shows that it converges toward a constant value.
A consistent 21-day time lag, very close to an estimate of the time of concentration, was obtained. The agreement between
observed discharge downstream and estimated discharge with and without parameters adjustment was 65 and 95%, respectively.
This demonstrates the interesting role that passive microwave can play in monitoring flooding and wetness conditions and
estimating key hydrologic parameters
La prévision en temps réel des charges de polluants dans un réseau d'assainissement urbain
No full textLe présent travail vise le développement des méthodologies de prévision et de validation, en temps réel, des charges de polluants dans un réseau d'assainissement urbain. La méthodologie de prévision préconisée s'est basée sur le modèle de "rating curve". Le modèle a été modifié afin de surmonter une de ses faiblesses. Le filtre de Kalman a été utilisé pour identifier les paramètres du modèle en temps réel.
L'approche de validation développée se base sur le principe de la redondance de l'information. Un modèle autoregressif a été utilisé comme indicateur de la tendance de variation à court terme. Le modèle de "rating curve" a été également utilisé pour simuler les charges de polluants en temps réel. Entre la valeur mesurée et simulée, celle qui se rapproche le plus de la valeur prévue par le modèle autoregressif est retenue. Les méthodologies développées ont été testées avec succès sur le bassin du secteur I de la ville de Verdun (Québec)
Utilisation de la télédétection pour l'estimation de la réserve hydrique au bassin du Mackenzie au nord ouest canadien
No full textLa présente recherche qui est appliquée au bassin du fleuve Mackenzie, vise l'estimation de l'humidité du sol en utilisant des données de télédétection captées dans le domaine des micro-ondes passives. Compte tenu de l'étendue et l'hétérogénéité du bassin du Mackenzie, un intérêt particulier a été réservé à des approches globales. Un indice d'humidité a été estimé à partir des images SSM/I, en utilisant des températures de brillance verticalement polarisées et mesurées à 19, 37 et 85 GHz. La comparaison des fractions des plans d'eau obtenues aux débits observés a montré l'existence d'une intéressante corrélation. Les micro-ondes passives sont capables de "voir" les plans d'eau et l'humidité du sol. Dans le domaine du visible, seuls les plans d'eau sont captés. Un nouvel indice d'humidité a été donc proposé en se basant sur la différence de ces sensitivités. L'indice a montré une concordance satisfaisante avec les précipitations et les températures observées
A MODIS-based robust satellite technique (RST) for timely detection of oil spilled areas
No full textNatural crude-oil seepages, together with the oil released into seawater as a consequence of oil exploration/production/transportation activities, and operational discharges from tankers (i.e., oil dumped during cleaning actions) represent the main sources of sea oil pollution. Satellite remote sensing can be a useful tool for the management of such types of marine hazards, namely oil spills, mainly owing to the synoptic view and the good trade-off between spatial and temporal resolution, depending on the specific platform/sensor system used. In this paper, an innovative satellite-based technique for oil spill detection, based on the general robust satellite technique (RST) approach, is presented. It exploits the multi-temporal analysis of data acquired in the visible channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite in order to automatically and quickly detect the presence of oil spills on the sea surface, with an attempt to minimize "false detections" caused by spurious effects associated with, for instance, cloud edges, sun/satellite geometries, sea currents, etc. The oil spill event that occurred in June 2007 off the south coast of Cyprus in the Mediterranean Sea has been considered as a test case. The resulting data, the reliability of which has been evaluated by both carrying out a confutation analysis and comparing them with those provided by the application of another independent MODIS-based method, showcase the potential of RST in identifying the presence of oil with a high level of accuracy
Double_inversion dataset
No full textDifferent datasets consisting of ground observation, profiles, and satellite observation
An empirical algorithm for retreiving salinity in the Arabian Gulf: Application to Landsat-8 data
No full textApplication of a Nighttime Fog Detection Method Using SEVIRI Over an Arid Environment
No full textFog degrades horizontal visibility causing significant adverse impacts on transport systems. The detection of fog from satellite data remains challenging especially in the presence of higher clouds, dust, mist, or unknown underlying soil conditions. Observations from Meteosat second generation Spinning-Enhanced Visible and Infrared Imager (MSG SEVIRI) over the United Arab Emirates (UAE), an arid area on the Arabian Peninsula, from 2016 to 2018 (two fog seasons) are used in this study. We implement an adaptive threshold-based technique using pseudo-emissivity values to detect nocturnal fog from SEVIRI. The method allows the threshold to vary spatially and temporally. Low clouds are detected with the analysis of the vertical temperature gradient. Fog classification was verified against four stations in the UAE, namely Abu Dhabi, Dubai, Al Ain, and Al Maktoum, where visibility and meteorological observations are available. The probability of detection (POD) (false alarm ratio (FAR)) was 0.81 (0.40), 0.83 (0.50), 0.83 (0.33), and 0.77 (0.44) at Abu Dhabi, Dubai, Al Ain, and Al Maktoum, respectively. In addition, the spatial frequency of fog is presented, which provides new insights into the fog dynamics in the region
A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula
No full textA better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), precipitation, and soil moisture; (ii) characterize their temporal variability and spatial distribution; and (iii) infer recent trends and change points within their time series. Remote sensing data for TWS, precipitation, and soil moisture are obtained from the Gravity Recovery and Climate Experiment (GRACE), the Tropical Rainfall Measuring Mission (TRMM), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), respectively. The study relies on trend analysis, the modified Mann–Kendall test, and change point detection statistics. We first derive 10-year (2002–2011) seasonal averages from each of the datasets and intercompare their spatial organization. In the absence of large-scale in situ data, we then compare trends from GRACE TWS retrievals to in situ groundwater observations locally over the subdomain of the United Arab Emirates (UAE). TWS anomalies vary between −6.2 to 3.2 cm/month and −6.8 to −0.3 cm/month during the winter and summer periods, respectively. Trend analysis shows decreasing precipitation trends (−2.3 × 10−4 mm/day) spatially aligned with decreasing soil moisture trends (−1.5 × 10−4 g/cm3/month) over the southern part of the AP, whereas the highest decreasing TWS trends (−8.6 × 10−2 cm/month) are recorded over areas of excessive groundwater extraction in the northern AP. Interestingly, change point detection reveals increasing precipitation trends pre- and post-change point breaks over the entire AP region. Significant spatial dependencies are observed between TRMM and GRACE change points, particularly over Yemen during 2010, revealing the dominant impact of climatic changes on TWS depletion
Analysis of the Long-Term Variability of Poor Visibility Events in the UAE and the Link with Climate Dynamics
No full textThe goal of this study is to investigate the variability of poor visibility events occurring hourly in the UAE and their relationship to climate dynamics. Hourly visibility observation data spanning more than three decades from ten stations across the country were used. Four intervals of low visibility, between 0.10 km and 5.0 km, were considered. Poor visibility records were analyzed under wet and dry weather conditions. The Mann–Kendall test was used to assess the inferred trends of low visibility records. The relationships between poor visibility measurements and associated meteorological variables and climate oscillations were also investigated. Results show that Fujairah city has the highest average visibility values under wet weather conditions, while Abu Dhabi city has the lowest average visibility values under both wet and dry conditions. Wet weather conditions had a greater impact than dry weather conditions on visibility deterioration in seven out of the ten stations. Results confirm that fog and dust contribute significantly to the deterioration of visibility in the UAE and that Abu Dhabi has been more impacted by those events than Dubai. Furthermore, the numbers of fog and dust events show steep increasing trends for both cities. A change point in poor visibility records triggered by fog and dust events was detected around the year 1999 at Abu Dhabi and Dubai stations after the application of the cumulative sum method. Increasing shifts in the means and the variances were noticed in the total annual fog events when Student’s t-test and Levene’s test were applied. In Abu Dhabi, the mean annual number of dust events was approximately 112.5 before 1999, increasing to 337 dust events after 1999. In Dubai, the number of dust events increased from around 85.5 to 315.6 events. The inferred fog and dust trends were compared to four climate indices. Results showed a significant correlation (positive and negative) between four climate indices and the occurrence of fog and dust events in the UAE
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