Hydro-climatic variability and forest fires in Mexico’s northern temperate forests

Global warming is likely modifying the hydrological cycle of forested watersheds. This report set as objectives to: a) assess the hydrological variables interception loss, I, potential and actual evapo-transpiration, E, Et, runoff, Q, and soil moisture content, θ; b) evaluate whether these variables are presenting consistent trends or oscillations that can be associated to global warming or climate variability; and c) relate θ to the number of wildfires and the burned area in Durango, Mexico. A mass balance approach estimated daily variables of the water cycle using sub-models for I and Et to calculate Q and θ for a time series from 1945 to 2007. Regression and auto-regressive and moving averaging (ARIMA) techniques evaluated the statistical significance of trends. The cumulative standardized z value magnified and ARIMA models projected statistically similar monthly and annual time series data of all variables of the water cycle. Regression analysis and ARIMA models showed monthly and annual P, I, E, and Et, Q, and θ do not follow consistent up or downward linear tendencies over time with statistical significance; they rather follow oscillations that could be adequately predicted by ARIMA models (r² ≥ 0.70). There was a consistent statistical association (p ≤ 0.05) of θ with the number of wildfires and the area burned regardless of the different spatial scales used in evaluating these variables. The analysis shows seasonal variability is increasing over time as magnifying pulses of dryness and wetness, which may be the response of the hydrological cycle to climate change. Further research must center on using longer time series data, testing seasonal variability with additional statistical analysis, and incorporating new variables in the analysis.     

Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery

This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km² and 487 km² in Sicily (Italy), 16,761 plots of size 30-m × 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.     

Divergence of solutions to perturbation-based advection-dispersion moment equations

Moment equation methods are popular and powerful tools for modeling transport processes in randomly heterogeneous porous media, but the application of these methods to advection-dispersion equations often leads to erroneous oscillations. Perturbative methods, required to close systems of moment equations, become inaccurate for large perturbations; however, little quantitative theory exists for determining when this occurs for advection-dispersion equations. We consider three different methods (asymptotic approximation, Eulerian truncation, and iterative solution) for closing and solving advection-dispersion moment equations describing transport in stratified porous media with random permeability. We obtain approximate analytical expressions for time above which the asymptotic approximation to the mean diverges, in particular quantifying the impact that dispersion has on delaying—but not eliminating—divergence. We demonstrate that Eulerian truncation and iterative solution methods do not eliminate divergent behavior either. Our divergence criteria provide a priori estimates that signal a warning to the practitioner of stochastic advection-dispersion equations to carefully consider whether to apply perturbative approaches.     

基于随机二元指标的边坡稳定性分析

考虑岩土体抗剪强度参数的变异性,综合极限平衡法和可靠性理论构建了安全系数概率密度分布函数模型。结合实际工程特点,以安全系数大于临界值为标准构建有效区,将有效区安全系数均值与对应可靠度相乘,得到以安全系数和可靠度为随机二元指标的有效安全系数。以有效安全系数为定量指标,将边坡宏观特征现象与定量分析相联系,构建意义更为明确的边坡稳定性分析及评价方法。通过工程实例对比了有效安全系数与传统评价指标之间的差异。所得结论可为工程建设提供参考和借鉴。     

宁夏及邻区地震活动能量场时空变化特征

运用随机函数理论,将宁夏及邻区地震活动能量场看作时间和空间的随机函数,用自然正交函数展开方法,系统研究宁夏及邻区主要典型能量场的时间权重系数变化曲线以及典型能量场的空间等值线图。结果发现,目标曲线较大幅度的升、降变化预示了可能发生中强地震的时间段,而等值线图的高值变化（危险区）对应了可能发生中强地震的区域。     

Reproductive biology of Spondias tuberosa Arruda (Anacardiaceae), an endemic fructiferous species of the caatinga (dry forest), under different management conditions in northeastern Brazil

In areas under different management conditions (management units, MUs) located either on a slope or at the base of a slope and either in a plantation or in a pasture, we compared the flower and fruit production, floral visitors, visitation rates and pre-emergent reproductive success (PERS) of Spondias tuberosa (Anacardiaceae) in an area of dry forest in NE Brazil. Individuals in the plantation MU produced more flowers per inflorescence than those in other MUs, but there were no statistical differences in mean fruit set among MUs. The only difference in mean visitation rates was between the plantation (65.83 ± 38.49) and the slope sites (11.5 ± 12.8). We observed visits by 19 insect species, including bees (31.6%), butterflies (31.6%), wasps (26.3%) and flies (10.5%). A clustering analysis based on the number of visits showed that a) the plantation site is most different from the other MUs, b) the pasture and the site at the base of the slope are similar to the slope site, and c) the higher frequency of visits of pollinators in the plantation MU was responsible for these differences. Of the 19 species of insects visiting the flowers, 12 were considered pollinators. The pollinator similarity analysis indicated a clear separation between areas and suggested that human activity has resulted in the replacement of native pollinators by exotic species. However, these changes apparently have not affected the pre-emergent reproductive success of S. tuberosa. Our results show that plant reproductive biology in semi-arid ecosystems may be modified by human action and that changes in floral production and pollinator guilds are the most conspicuous effects. However, the traditional management performed by local people may not affect the reproductive success of plant species.     

Quantitative risk assessment of landslide by limit analysis and random fields

Risk may be estimated by multiplying the probability of failure by the consequence. This is acceptable for systems that have a single mode of failure. For systems that have multiple failure modes, such as landslides, the consequences should be assessed individually for each of the failure modes. This paper proposes a new framework of quantitative landslide risk assessment, in which consequences are assessed individually. The framework is generally applicable, and the landslide risk assessments of two typical slopes are presented.     

Calibration of the specific barrier model to Iranian plateau earthquakes and development of physically based attenuation relationships for Iran

Earthquake ground-motion relationships for soil and rock sites in Iran have been developed based on the specific barrier model (SBM) used within the context of the stochastic modeling and calibrated against up-to-date Iranian strong-motion data. A total of 171 strong-motion accelerograms recorded at distances of up to 200 km from 24 earthquakes with moment magnitudes ranging from Mw 5.2 to 7.4 are used to determine the region-specific source parameters of this model. Regression analysis was conducted using the “random effects” methodology that considers both earthquake-to-earthquake (inter-event) variability and within-earthquake (intra-event) variability to effectively handle the problem of weighting observations from different earthquakes. The minimization of the error function in each iteration of the “random effects” procedure was performed using the genetic algorithm method. The residuals are examined against available Iranian strong-motion data to confirm that the model predictions are unbiased and that there are no significant residual trends with distance and magnitude. No evidence of self-similarity breakdown is observed between the source radius and its seismic moment. To verify the robustness of the results, tests were performed to confirm that the results are unchanged if the number of observations is changed by removing different randomly selected datasets from the original database. Stochastic simulations, using the derived SBM, are then performed to predict peak ground-motion and response spectra parameters for a wide range of magnitudes and distances. The stochastic SBM predictions agree well with the new empirical regression equations proposed for Iran, Europe and Middle East in the magnitude–distance ranges well represented by the data. It has been shown that the SBM of this study provides unbiased ground-motion estimates over the entire frequency range of most engineering interests (1–10 Hz) for the Iranian earthquakes. Our results are also important for the assessment of hazards in other seismically active environments in the Middle East and Mediterranean regions.