Spatial Preference Modelling for equitable infrastructure provision: an application of Sen’s Capability Approach

To determine whether the inclusion of spatial neighbourhood comparison factors in Preference Modelling allows spatial decision support systems (SDSSs) to better address spatial equity, we introduce Spatial Preference Modelling (SPM). To evaluate the effectiveness of this model in addressing equity, various standardisation functions in both Non-Spatial Preference Modelling and SPM are compared. The evaluation involves applying the model to a resource location-allocation problem for transport infrastructure in the Special Province of Yogyakarta in Indonesia. We apply Amartya Sen’s Capability Approach to define opportunity to mobility as a non-income indicator. Using the extended Moran’s I interpretation for spatial equity, we evaluate the distribution output regarding, first, ‘the spatial distribution patterns of priority targeting for allocation’ (SPT) and, second, ‘the effect of new distribution patterns after location-allocation’ (ELA). The Moran’s I index of the initial map and its comparison with six patterns for SPT as well as ELA consistently indicates that the SPM is more effective for addressing spatial equity. We conclude that the inclusion of spatial neighbourhood comparison factors in Preference Modelling improves the capability of SDSS to address spatial equity. This study thus proposes a new formal method for SDSS with specific attention on resource location-allocation to address spatial equity.     

Seasonal variations in the isotopic composition of leaf and stem water from an arid region of Southeast Asia

Abstract

Stable isotopes are powerful research tools in environmental sciences and their use in ecosystem research is increasing. Stable isotope measurements allow the study of evapotranspiration fluxes, soil evaporation and leaf transpiration phenomena. Soil water and leaf water are the sources of the evapotranspiration that transfers large quantities of water from land to the atmosphere; as a result the isotopic composition of water left in the leaves is modified towards enrichment. Evaporation also changes the isotopic composition of water bodies creating a natural isotopic signal. The isotopic identity of soil water affects the oxygen isotopic signature of leaf and stem water. In this paper we present the isotopic data of bulk leaf water, showing the enrichment in isotopic value of oxygen due to evapotranspiration from leaves in conjunction with the isotopic signal of rainwater and other environmental factors such as humidity and temperature. Results suggest that the variation in the values of δ¹⁸O of Eucalyptus citriodora, Dalbergia sissoo, Melia azedarach and Pinus roxburghii is due to the seasonal changes in the δ¹⁸O of the source water for plants, i. e. rain. It is further observed that leaf water δ¹⁸O values are depleted during the months of July, August and September. This occurs due to the following reasons: (a) the sampling areas receive about 50% of the average annual rain during these months, and (b) rainfalls during these months are isotopically depleted compared with winter rains.

Citation Butt, S., Ali, M., Fazil, M. & Latif, Z. (2010) Seasonal variations in the isotopic composition of leaf and stem water from an arid region of Southeast Asia. Hydrol. Sci. J. 55(5), 844–848.     

Rainfall–runoff,flood inundation and sensitivity analysis of the 2014 Pakistan flood in the Jhelum and Chenab river basin

ABSTRACT

The major flood of 2014 in the two eastern, transboundary rivers, the Jhelum and Chenab in Punjab, Pakistan, was simulated using the two-dimensional rainfall–runoff model. The simulated hydrograph showed good agreement with the observed discharge at the model outlet and intervening barrages, with a Nash-Sutcliffe efficiency of 0.86 at the basin outlet. Further, simulated flood inundation extent showed good agreement with the MODIS imagery with a fit (%) of 0.87. For some affected areas that experienced short-duration flooding, local housing damage data confirmed the simulated results. Besides the rainfall–runoff and flood inundation modelling, parameter sensitivity analysis was undertaken to identify the influence of various river and floodplain parameters. The analysis showed that the river channel geometric parameters and the roughness coefficients exerted the primary influence over flood extent and peak flow.     

Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district,Bartın,northern Turkey

The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bart?n province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.     

Statefinder parameter for varying G in three fluid system

In this work, we have considered variable G in flat FRW universe filled with the mixture of dark energy, dark matter and radiation. If there is no interaction between the three fluids, the deceleration parameter and statefinder parameters have been calculated in terms of dimensionless density parameters which can be fixed by observational data. Also the interaction between three fluids has been analyzed due to constant G. The statefinder parameters also calculated in two cases: pressure is constant and pressure is variable.     

集成多源遥感数据的PM2.5浓度空间分布制图

针对传统地面稀疏站点监测PM_2.5浓度以点带面的缺陷,本研究拟借助多源遥感数据开展了地面大气细颗粒物PM_2.5浓度空间分布模拟研究。以京津冀地区2013年的年均、季均PM_2.5浓度模拟图为例,用简化的气溶胶反演算法（SARA）反演了 1 km高分辨率AOD,并结合高分辨率遥感提取污染相关地理要素,对研究区PM_2.5浓度空间分布进行地理统计模拟及优选。结果表明：① SARA算法反演的AOD与地基AERONET相关性达0.99,能准确地反映研究区AOD的时空分布特征;② 集成多源遥感数据的地理加权回归模型拟合度高（平均R²-0.66）,其空间模拟显示研究区平均PM_2.5污染南部城镇最重,中东部城区次之,西北山区较轻;③ 研究区PM_2.5污染程度高,全年平均模拟浓度高达75 μg/m³,在气候环境及主要污染源季节性差异驱动下,浓度分布季节性特征显著,冬季污染最严重,而夏、秋季相对较轻。该成果对于精细把握PM_2.5污染特征,指导污染防控具有重要意义。