The regional-scale consistency between four precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the Arabian Peninsula was assessed. Their macroscale relationships were inter-compared with soil moisture and total water storage (TWS) estimates from AMSR-E and GRACE. The consistency analysis was studied with multivariate statistical hypothesis testing and Pearson correlation metrics for the period from January 2000 to December 2010. The products and GRACE estimates were assessed over a representative sub-domain (United Arab Emirates) with available in situ well observations. Next, geographically temporally weighted regression (GTWR) was employed to examine the interdependencies among the peninsula’s hydrological components. The results showed GPCC-TRMM recording the highest correlation (0.85) with insignificant mean differences over more than 90% of the peninsula. The highest GTWR predictive performance of TWS (R2 = 0.84) was achieved with TRMM forcing, which indicates its potential to monitor changes in TWS over the arid peninsular region. 相似文献
To assess heavy metals in mangrove swamps of Sehat and Tarut coastal areas along the Arabian Gulf, 18 sediment samples were collected for Al, V, Cr, Mn, Cu, Zn, Cd, Pb, Hg, Sr, As, Fe, Co, and Ni analysis. The results indicated that the distribution of some metals was largely controlled by anthropogenic inputs, while others were of terrigenous origin and most strongly associated with distribution of aluminum and total organic carbon in sediments. Mangrove sediments were extremely severe enriched with Sr (EF?=?67.59) and very severe enriched with V, Hg, Cd, Cu, As (EF?=?44.28, 37.45, 35.77, 25.97, and 11.53, respectively). Average values of Sr, V, Hg, Cd, Cu, Ni, As, and Cr were mostly higher than the ones recorded from the Mediterranean Sea, the Red Sea, the Gulf of Aqaba, the Caspian Sea, the Arabian and Oman gulfs, coast of Tanzania, sediment quality guidelines, and the background shale and the earth crust. Landfilling due to coastal infrastructure development around mangrove forests, oil spills and petrochemical and desalination effluents from Al-Jubail industrial city to the north were the anthropogenic activities that further enhanced heavy metals in the studied mangrove sediments. 相似文献
Understanding and developing groundwater resources in arid regions such as El Salloum basin, along the northwestern coast of Egypt, remains a challenging issue. One-dimensional (1D) electrical sounding (ES), two-dimensional (2D) electrical resistivity imaging (ERI), and very low frequency electromagnetic (VLF-EM) measurements were used to investigate the hydrogeological framework of El Salloum basin with the aim of determining the potential for extraction of potable water. 1D resistivity sounding models were used to delineate geoelectric sections and water-bearing layers. 2D ERI highlighted decreases in resistivity with depth, attributed to clay-rich limestone combined with seawater intrusion towards the coast. A depth of investigation (DOI) index was used to constrain the information content of the images at depths up to 100 m. The VLF-EM survey identified likely faults/fractured zones across the study area. A combined analysis of the datasets of the 1D ES, 2D ERI, and VLF-EM methods identified potential zones of groundwater, the extent of seawater intrusion, and major hydrogeological structures (fracture zones) in El Salloum basin. The equivalent geologic layers suggest that the main aquifer in the basin is the fractured chalky limestone middle Miocene) south of the coastal plain of the study area. Sites likely to provide significant volumes of potable water were identified based on relatively high resistivity and thickness of laterally extensive layers. The most promising locations for drilling productive wells are in the south and southeastern parts of the region, where the potential for potable groundwater increases substantially.
Historically, paired watershed studies have been used to quantify the hydrological effects of land use and management practices by concurrently monitoring 2 similar watersheds during calibration (pretreatment) and post‐treatment periods. This study characterizes seasonal water table and flow response to rainfall during the calibration period and tests a change detection technique of moving sums of recursive residuals (MOSUM) to select calibration periods for each control–treatment watershed pair when the regression coefficients for daily water table elevation were most stable to minimize regression model uncertainty. The control and treatment watersheds were 1 watershed of 3–4‐year‐old intensely managed loblolly pine (Pinus taeda L.) with natural understory, 1 watershed of 3–4‐year‐old loblolly pine intercropped with switchgrass (Panicum virgatum), 1 watershed of 14–15‐year‐old thinned loblolly pine with natural understory (control), and 1 watershed of switchgrass only. The study period spanned from 2009 to 2012. Silvicultural operational practices during this period acted as external factors, potentially shifting hydrologic calibration relationships between control and treatment watersheds. MOSUM results indicated significant changes in regression parameters due to silvicultural operations and were used to identify stable relationships for water table elevation. None of the calibration relationships developed using this method were significantly different from the classical calibration relationship based on published historical data. We attribute that to the similarity of historical and 2010–2012 leaf area index on control and treatment watersheds as moderated by the emergent vegetation. Although the MOSUM approach does not eliminate the need for true calibration data or replace the classic paired watershed approach, our results show that it may be an effective alternative approach when true data are unavailable, as it minimizes the impacts of external disturbances other than the treatment of interest. 相似文献
The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks
with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in
the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as
well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing.
Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature;
2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage;
and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural
network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation
training method has been used for the selection of the five different random training sites in order to calculate the factor’s
weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard
maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide
test locations that were not used during the training phase of the neural network. Our findings of verification results show
an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently
analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis.
The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide
areas. 相似文献
Seismic site response analysis is commonly used to predict ground response due to local soil effects. An increasing number of downhole arrays are deployed to measure motions at the ground surface and within the soil profile and to provide a check on the accuracy of site response analysis models. Site response analysis models, however, cannot be readily calibrated to match field measurements. A novel inverse analysis framework, self-learning simulations (SelfSim), to integrate site response analysis and field measurements is introduced. This framework uses downhole array measurements to extract the underlying soil behavior and develops a neural network-based constitutive model of the soil. The resulting soil model, used in a site response analysis, provides correct ground response. The extracted cyclic soil behavior can be further enhanced using multiple earthquake events. The performance of the algorithm is successfully demonstrated using synthetically generated downhole array recordings. 相似文献
In the current study, the shuttle radar topography mission (SRTM) data, with~90 m horizontal resolution, were used to delineate the paleodrainage system and their mega basin extent in the East Sahara area. One mega-drainage basin has been detected, covering an area of 256 000 km2. It is classified into two sub mega basins. The Uweinate sub mega basin, which is composed of four main tributaries, collected water from a vast catchment region and drained eastward from the north, west, and southwest, starting at... 相似文献