排序方式： 共有14条查询结果，搜索用时 15 毫秒
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Salif Diop Aliou Guisse Claude Sene Birane Cisse Ndeye Rokhaya Diop Sokhna Di Ka Amady Gnagna Cisse Saly Sambou Ousmane Ndiaye Adand Belarmain Fandohan FU Chao WANG Guoqin WANG Yongdong 《资源与生态学报(英文版)》2018,9(3):257-265
The Great Green Wall Initiative (GGWI) has an overall objective of fighting desert encroachment through proven practices of sustainable management of land, and the reinforcement and protection of natural resources and systems of production and transformation, while also ensuring socio-economic development of local communities through multi-purpose activity platforms. The activities described in the present study are designed to accomplish several goals: (1) generate wealth, (2) strengthen access to basic social services, (3) manage the transition to a green economy as a means of creating suitable conditions for the emergence of rural production centers, (4) integrate sustainable development in order to eradicate poverty and food insecurity, and (5) strengthen adaptation and resilience capacities of local populations. The present study was undertaken on the basis of a wide variety of available publications and documentation, including articles and scientific papers, thesis, meeting summaries and reports, concerning the implementation of the Great Green Wall Initiative/GGWI in Senegal. 相似文献
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Mads Olander Rasmussen Frank-M. Göttsche Doudou Diop Cheikh Mbow Folke-S. Olesen Rasmus Fensholt Inge Sandholt 《International Journal of Applied Earth Observation and Geoinformation》2011
A tree survey and an analysis of high resolution satellite data were performed to characterise the woody vegetation within a 10 × 10 km2 area around a site located close to the town of Dahra in the semi-arid northern part of Senegal. The surveyed parameters were tree species, height, tree crown radius, and diameter at breast height (DBH), for which allometric models were determined. An object-based classification method was used to determine tree crown cover (TCC) from Quickbird data. The average TCC from the tree survey and the respective TCC from remote sensing were both about 3.0%. For areas beyond the surveyed areas TCC varied between 3.0% and 4.5%. Furthermore, an empirical correction factor for tree clumping was obtained, which considerably improved the estimated number of trees and the estimated average tree crown area and radius. An allometric model linking TCC to tree stem crosssectional area (CSA) was developed, which allows to estimate tree biomass from remote sensing. The allometric models for the three main tree species found performed well and had r2-values of about 0.7–0.8. 相似文献
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This paper deals with the use, for seismic applications, of a Maxwell element in parallel with a low damping isolator. The study of the properties of the frequency response function shows that this isolator is capable to reduce the base displacement of isolated structures with no considerable amplification of the non‐isolated modes. This is, also, confirmed by the floor response spectra under earthquake excitations. Hence, the previously mentioned isolator does not present the drawbacks met when base displacement is reduced by increasing damping. Moreover, it seems that its performance is comparable with that of more elaborated and expensive techniques combining passive and semi‐active devices. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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Joseph G. Alfieri Dev Niyogi Margaret A. LeMone Fei Chen Souleymane Fall 《Pure and Applied Geophysics》2007,164(8-9):1789-1809
With increasing computational resources, environmental models are run at finer grid spacing to resolve the land surface characteristics. The land use/land cover (LULC) data sets input into land surface models are used to assign various default parameters from a look-up tables. The objective of this study is to assess the potential uncertainty in the LULC data and to present a reclassification method for improving the accuracy of LULC data sets. The study focuses on the Southern Great Plains and specifically the Walnut River Watershed in southeastern Kansas, USA. The uncertainty analysis is conducted using two data sets: The National Land Cover Dataset 1992 (NLCD 92) and the Gap Analysis Program (GAP) data set, and a reclassification logic tree. A comparison of these data sets showed that they do not agree for approximately 27% of the watershed. Moreover, an accuracy assessment of these two data sets indicated that neither had an overall accuracy as high as 80%. Using the relationships between land-surface characteristics and LULC, a reclassification of the watershed was conducted using a logical model. This model iteratively reclassified the uncertain pixels according to their surface characteristics. The model utilized normalized difference vegetation index (NDVI) measurements during April and July 2003, elevation, and slope. The reclassification yielded a revised LULC dataset that was substantially improved. The overall accuracy of the revised data set was nearly 93%. The study results suggest: (i) as models adopt finer grid spacings, the uncertainty in the LULC data will become significant; (ii) assimilating NDVI into the land-surface models can reduce the uncertainty due to LULC assignment; (iii) the standard LULC data sets must be used with caution when the focus is on local scale; and (iv) reclassification is a valuable means of improving the accuracy of LULC data sets prior to applying them to local issues or phenomena. 相似文献
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Jize Zhang Alexandros A. Taflanidis Norberto C. Nadal-Caraballo Jeffrey A. Melby Fatimata Diop 《Natural Hazards》2018,94(3):1225-1253
This paper establishes various advancements for the application of surrogate modeling techniques for storm surge prediction utilizing an existing database of high-fidelity, synthetic storms (tropical cyclones). Kriging, also known as Gaussian process regression, is specifically chosen as the surrogate model in this study. Emphasis is first placed on the storm selection for developing the database of synthetic storms. An adaptive, sequential selection is examined here that iteratively identifies the storm (or multiple storms) that is expected to provide the greatest enhancement of the prediction accuracy when that storm is added into the already available database. Appropriate error statistics are discussed for assessing convergence of this iterative selection, and its performance is compared to the joint probability method with optimal sampling, utilizing the required number of synthetic storms to achieve the same level of accuracy as comparison metric. The impact on risk estimation is also examined. The discussion then moves to adjustments of the surrogate modeling framework to support two implementation issues that might become more relevant due to climate change considerations: future storm intensification and sea level rise (SLR). For storm intensification, the use of the surrogate model for prediction extrapolation is examined. Tuning of the surrogate model characteristics using cross-validation techniques and modification of the tuning to prioritize storms with specific characteristics are proposed, whereas an augmentation of the database with new/additional storms is also considered. With respect to SLR, the recently developed database for the US Army Corps of Engineers’ North Atlantic Comprehensive Coastal Study is exploited to demonstrate how surrogate modeling can support predictions that include SLR considerations. 相似文献
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High nitrate concentrations, above the WHO guideline of 50 mg l−1, were observed in samples of shallow wells reaching the Yeumbeul suburb (Senegal) area groundwater. This groundwater is exploited by 7000 houses and therefore there are health implications. Correlations between parameters such as nitrate content (NO3−) in the groundwater and soil water, the distance between shallow wells and family latrines, and soil water chloride (Cl−) and colon bacillus content led to two possible sources of groundwater pollution: first, contamination by non impervious and shallow latrines; and second, the leaching of soil NO3− from waste organic matter carried in groundwater. 相似文献
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Heavy off-season rains in the tropics pose significant natural hazards largely because they are unexpected and the popular
infrastructure is ill-prepared. One such event was observed from January 9 to 11, 2002 in Senegal (14.00° N, 14.00°␣W), West
Africa. This tropical country is characterized by a long dry season from November to April or May. During this period, although
the rain-bearing monsoonal flow does not reach Senegal, the region can occasionally experience off-season rains. We conducted
a numerical simulation of the January 9–11, 2002 heavy off-season rain using the Fifth-Generation NCAR/Pennsylvania State
University Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) model. The objective was to delineate the
meteorological set-up that led to the heavy rains and flooding. A secondary objective was to test the model’s performance
in Senegal using relatively simpler (default) model configurations and local/regional observations. The model simulations
for both MM5 and WRF agree satisfactorily with the observations, particularly as regards the wind patterns, the intensification
of the rainfall, and the associated drop in temperatures. This situation provided the environment for heavy rainfall accompanied
by a cold wave. The results suggest that off-the-shelf weather forecast models can be applied with relatively simple physical
options and modest computational resources to simulate local impacts of severe weather episodes. In addition, these models
could become part of regional hazard mitigation planning and infrastructure. 相似文献
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In order to manage local rock aggregates efficiently, an inventory has been made of superficial sand materials in the Senegalese basin. A detailed geological study of sandy formations has been undertaken, and geotechnical tests were also performed in order to characterize the sands according to their mechanical properties. The geotechnical characteristics of the sands can be related to their geological histories thus allowing the geotechnical parameters to be estimated by field mapping. 相似文献