Use of remote sensing and GIS to determine recharge potential zones: the case of Occidental Lebanon

Water recharge from land surfaces into subsurface media is an essential element in the hydrologic cycle. For a small-scale assessment, experimental approaches are usually followed, however, on a regional scale, this assessment needs to be made into a comprehensive picture where spatial data of the different contributing factors are treated. The case of Occidental Lebanon, with an area of around 5,000 km², was studied by the integration of all factors influencing this hydrologic process. Contributing factors are: lineaments and drainage frequency density, lithologic character, karstic domains and land cover/land use. The determination of these factors was carried out mainly by the application of remote sensing. Satellite images (Landsat 7 ETM &; SPOT) and aerial photos were subjected to several treatment processes using a miscellany of software, mainly ERDAS Imagine and ESRI’s Arc View software. Furthermore, exogenetic data, such as topographic and geologic maps, were utilized. The extracted information for these factors was plotted on maps. The integration of the maps in a GIS allowed deciding their interactive effects. However, each factor had its own degree of effect, i.e., weight, which was also determined in this study. This study is an approach to better estimate and provide qualitative assessments of recharge potential (RP). The resultant map shows the highest recharge potentials towards the elevated regions where karstification is well development. It was found that around 57% of the study area is terrain with very high to high recharge rate values, which a considerable amount of precipitated water is allowed to percolate into subsurface rocks.     

Effect of ENSO on annual maximum floods and volume over threshold in the southwestern region of Iran

In this paper, the effects of the El Niño-Southern Oscillation (ENSO) on the annual maximum flood (AMF) and volume over threshold (VOT) in two major neighbouring river basins in southwest Iran are investigated. The basins are located upstream of the Dez and Karun-I dams and cover over 40?000 km² in total area. The effects of ENSO on the frequency, magnitude and severity (frequency times magnitude) of flood characteristics over the March–April period were analysed. ENSO indices were also correlated with both AMF and VOT. The results indicate that, in the Dez and Karun basins, the El Niño phenomenon intensifies March–April floods compared with neutral conditions. The opposite is true in La Niña conditions. The degree of the effect is more intense in the El Niño period.     

Climate finance governance in hazard prone riverine islands in Bangladesh: pathway for promoting climate resilience

Bangladesh is one of the topmost climate vulnerable countries in the world where the riverine island (char) dwellers are the most victims of climate change. Climate finance is perceived as the key instruments to address the issues related to climate change impacts and improve the livelihood resilience of the rural people. This article examines the role of climate finance governance at the vulnerable hazard-prone chars in Bangladesh. Climate finance adaptation governance has been assessed through measuring the climate service intervention, behavioral changes and impacts at the local level. The study reveals that the mechanism of climate finance in Bangladesh is good enough, but implementation is not satisfactory. It also observed that there is a lack of participatory governance for disaster management, and there is no direct contribution of climate finance governance in the village level which causes to fail the existing implementation strategy. The study suggests that a riverine island-based long-term development program should be implemented through ensuring effective monitoring system so that climate finance governance can contribute a lot and improve the food security and livelihood resilience of char dwellers.

     

Prediction of hourly actual evapotranspiration using neural networks,genetic programming,and statistical models

The complexity of the evapotranspiration process and its variability in time and space have imposed some limitations on previously developed evapotranspiration models. In this study, two data‐driven models: genetic programming (GP) and artificial neural networks (ANNs), and statistical regression models were developed and compared for estimating the hourly eddy covariance (EC)‐measured actual evapotranspiration (AET) using meteorological variables. The utility of the investigated data‐driven models was also compared with that of HYDRUS‐1D model, which makes use of conventional Penman–Monteith (PM) model for the prediction of AET. The latent heat (LE), which is measured using the EC method, is modelled as a function of five climatic variables: net radiation, ground temperature, air temperature, relative humidity, and wind speed in a reconstructed landscape located in Northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg–Marquardt and Bayesian regularization. The GP technique was used to generate mathematical equations correlating AET to the five climatic variables. Furthermore, the climatic variables, as well as their two‐factor interactions, were statistically analysed to obtain a regression equation and to indicate the climatic factors having significant effect on the evapotranspiration process. HYDRUS‐1D model as an available physically based model was examined for estimating AET using climatic variables, leaf area index (LAI), and soil moisture information. The results indicated that all three proposed data‐driven models were able to approximate the AET reasonably well; however, GP and regression models had better generalization ability than the ANN model. The results of HYDRUS‐1D model exhibited that a physically based model, such as HYDRUS‐1D, might be comparable or even inferior to the data‐driven models in terms of the overall prediction accuracy. Based on the developed GP and regression models, net radiation and ground temperature had larger contribution to the AET process than other variables. Copyright © 2010 John Wiley & Sons, Ltd.     

OpenStreetMap data quality enrichment through awareness raising and collective action tools—experiences from a European project

ABSTRACT

Nowadays, several research projects show interest in employing volunteered geographic information (VGI) to improve their systems through using up-to-date and detailed data. The European project CAP4Access is one of the successful examples of such international-wide research projects that aims to improve the accessibility of people with restricted mobility using crowdsourced data. In this project, OpenStreetMap (OSM) is used to extend OpenRouteService, a well-known routing platform. However, a basic challenge that this project tackled was the incompleteness of OSM data with regards to certain information that is required for wheelchair accessibility (e.g. sidewalk information, kerb data, etc.). In this article, we present the results of initial assessment of sidewalk data in OSM at the beginning of the project as well as our approach in awareness raising and using tools for tagging accessibility data into OSM database for enriching the sidewalk data completeness. Several experiments have been carried out in different European cities, and discussion on the results of the experiments as well as the lessons learned are provided. The lessons learned provide recommendations that help in organizing better mapping party events in the future. We conclude by reporting on how and to what extent the OSM sidewalk data completeness in these study areas have benefited from the mapping parties by the end of the project.     

Experimental study on the phase transformation point of crushable and noncrushable soils

Two marine calcareous deposits as crushable soils and a siliceous sand as a noncrushable soil were used in this study to compare their monotonic response. Undrained monotonic triaxial tests were conducted on samples, which were prepared in different relative densities and consolidated under various confining pressures. The location of phase transformation point in undrained response of the sands in different initial conditions was evaluated. The effect of important parameters including relative density, confining pressure, particle shape, and particle breakage on phase transformation point was assessed. The input energy applied per unit volume of the soils was used to interpret the shearing response of crushable and noncrushable soils. The results showed that calcareous sands have more tendencies in contraction. Particle shape and breakage play a key role in engineering behavior of crushable soils.     

A comparative study of kriging and simulation-based methods in classifying ore and waste blocks

Ore grade is the most important source of uncertainty in a mining operation which plays an important role to classify run-of-mine (ROM) material into ore and waste parcels. As a widely used method, kriging estimator is used to estimate the grade of ore blocks. In conventional mining practices, if the estimated grade of a parcel is above the cut-off grade, this parcel is classified as ore, otherwise, is labelled as a waste parcel. An alternative approach is to simultaneously consider the grade of parcels and the economic consequences of sending parcels to destinations by applying simulation-based methods. In this study, kriging and simulation-based methods including loss and profit functions are applied on a real-world case study to classify ore/waste material based on the initial exploration data. Then, the actual known data, collected from blast holes samples, are compared with the estimated results in order to validate the performance of the presented methods. Outcomes show that simulation-based methods can perform better and show more adjustability with real data.     

234Th/238U activity ratios in Pacific Ocean bottom waters

The²³⁴Th/²³⁸U activity ratios in the near-bottom waters at a station in the South Pacific have been measured. The activity ratios are close to the secular equilibrium value, ranging between 0.9 and 1.13 (± 8%), suggesting that the rate of removal of²³⁴Th by bottom-water scavenging processes at this station is slow compared to its rate of radioactive decay. The mean²³⁴U/²³⁸U activity ratios in these waters is 1.14 ± 0.02, the same as the reported values for the world oceans.     

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

Shock waves and the chemical structure of interstellar clouds

In this paper we study the effect of shock waves on the chemical structure of the interstellar clouds. A model of molecular cloud has been assumed. The chemistry is investigated in a time dependent model. Our chemical network contains 56 species in 251 reactions to including molecules of the elements H, O, C, N, S, and Si.The results indicate that the calculated fractional abundance of the molecules NS, H₂O, CN, NH, CO, and SO agrees well with the observations. The molecules OH, H₂S, CS, H₂CS, HS, NO, SiO, CH, CH₂, CH₃, HCO, C₂, and HCN reach high post-shock abundances.