An analytical platform for cumulative impact assessment based on multiple futures: the impact of petroleum drilling and forest harvesting on moose (Alces alces) and marten (Martes americana) habitats in northeastern British Columbia

The combined influence on the environment of all projects occurring in a single area is evaluated through cumulative impact assessments (CIA), which consider the consequences of multiple projects, each insignificant on its own, yet important when evaluated collectively. Traditionally, future human activities are included in CIA using an analytical platform, commonly based on complex models that supply precise predictions but with reduced accuracy. To compensate for the lack of accuracy in current CIA approaches, we propose a shift in the paradigm governing CIA. The paradigm shift involves a change in the focus of CIA investigations from the detailed analysis of one unlikely future to the identification of the patterns describing multiple potential future changes in the environment. To illustrate the approach, a set of 144 possible and equally likely futures were developed that aimed to identify the potential impacts of forest harvesting and petroleum drilling on the habitat suitability of moose and marten in northeast British Columbia, Canada. The evolution of two measures of habitat suitability (average habitat suitability index and surface of the stands with habitat suitability index >0.5) revealed that the human activities could induce cycles in the habitat dynamics of moose and marten. The planning period of 100 years was separated into three distinct periods following a sinusoidal pattern (i.e., increase - constant - decrease in the habitat suitability measures). The attributes that could induce significant changes in the assessment of environment are the choice of harvesting age and species.     

An innovative computer design for modeling forest landscape change in very large spatial extents with fine resolutions

Although forest landscape models (FLMs) have benefited greatly from ongoing advances of computer technology and software engineering, computing capacity remains a bottleneck in the design and development of FLMs. Computer memory overhead and run time efficiency are primary limiting factors when applying forest landscape models to simulate large landscapes with fine spatial resolutions and great vegetation detail. We introduce LANDIS PRO 6.0, a landscape model that simulates forest succession and disturbances on a wide range of spatial and temporal scales. LANDIS PRO 6.0 improves on existing forest landscape models with two new data structures and algorithms (hash table and run-length compression). The innovative computer design enables LANDIS PRO 6.0 to simulate very large (>10⁸ ha) landscapes with a 30-m spatial resolution, which to our knowledge no other raster forest landscape models can do. We demonstrate model behavior and performance through application to five nested forest landscapes with varying sizes (from 1 million to 100 million 0.09-ha cells) in the southern Missouri Ozarks. The simulation results showed significant and variable effects of changing spatial extent on simulated forest succession patterns. Results highlighted the utility of a model like LANDIS PRO 6.0 that is capable of efficiently simulating large landscapes and scaling up forest landscape processes to a common regional scale of analysis. The programming methodology presented here may significantly advance the development of next generation of forest landscape models.     

The effects of forest harvest intensity in combination with wind disturbance on carbon dynamics in Lake States Mesic Forests

Total forest carbon (C) storage is determined by succession, disturbances, climate, and the edaphic properties of a site or region. Forest harvesting substantially affects C dynamics; these effects may be amplified if forest harvesting is intensified to provide biofuel feedstock. We tested the effects of harvest intensity on landscape C using a simulation modeling approach that included C dynamics, multiple disturbances, and successional changes in composition. We developed a new extension for the LANDIS-II forest landscape disturbance and succession model that incorporates belowground soil C dynamics derived from the CENTURY soil model. The extension was parameterized and calibrated using data from an experimental forest in northeastern Wisconsin, USA. We simulated a 9800 ha forested landscape over 400 years with wind disturbance combined with no harvesting, harvesting with residual slash left on site (‘standard harvest’), and whole-tree harvesting. We also simulated landscapes without wind disturbance and without eastern hemlock (Tsuga canadensis) to examine the effects of detrital quantity and quality on C dynamics. We estimated changes in live C, detrital C, soil organic C, total C, and forest composition. Overall, the simulations without harvesting had substantially greater total C and continued to sequester C. Standard harvest simulations had more C than the whole tree harvest simulations. Under both harvest regimes, C accrual was not evident after 150 years. Without hemlock, SOC was reduced due to a decline in detritus and a shift in detrital chemistry. In conclusion, if the intensity of harvesting increases we can expect a corresponding reduction in potential C storage. Compositional changes due to historic circumstances (loss of hemlock) may also affect forest C although to a lesser degree than harvesting. The modeling approach presented enabled us to consider multiple, interacting drivers of landscape change and the subsequent changes in forest C.     

Optimizing model for land use/land cover retrieval from remote sensing imagery based on variable precision rough sets

The suitable spectral mode in remote sensing is often desirable to facilitate the inversion of ecological environment and landscape. This paper put forward an optimizing model based on variable precision rough sets (VPRS) for the land cover discrimination in wetland inventory. In the case study of Lake Baiyangdian which has important ecological functions to the northern China, this model is established successfully according to the domain-experts knowledge. The procedure is as follows. First step is data collection, including remote-sensing data (e.g., Landsat-5 TM bands), the digitized relief maps, and statistical yearbooks. Second, the remote sensing imagery (RSI) and relief maps are co-registered into the same resolution. Third, a condition set, including various attributes is derived from spectral bands, band math or ratio indices based on previous studies, at the same time, the decision set is derived from true land types after investigation and validation. Then, the remote sensing decision table (RSDT) is constructed by linking condition set with decision set according to the sequential pixels in RSI. Fourth, we create one forward greedy searching algorithm based on VPRS to handle this RSDT. After adjusting parameters such as β and knowledge granularity diameter (KGD), we obtain the stable optimized results. Comparative experiments and evaluation show that the discrimination or retrieval accuracy of VPRS model is satisfying (overall accuracy: 87.32% and KHAT: 0.84) and better than original data. Moreover, data dimension has been decreased dramatically (from 12 to 3) and key attributes found by the model may be useful for specific retrieval in wetland inventories.     

Modelling forest management within a global vegetation model—Part 2: Model validation from a tree to a continental scale

The construction of a new forest management module (FMM) within the ORCHIDEE global vegetation model (GVM) allows a realistic simulation of biomass changes during the life cycle of a forest, which makes many biomass datasets suitable as validation data for the coupled ORCHIDEE-FM GVM. This study uses three datasets to validate ORCHIDEE-FM at different temporal and spatial scales: permanent monitoring plots, yield tables, and the French national inventory data. The last dataset has sufficient geospatial coverage to allow a novel type of validation: inventory plots can be used to produce continuous maps that can be compared to continuous simulations for regional trends in standing volumes and volume increments. ORCHIDEE-FM performs better than simple statistical models for stand-level variables, which include tree density, basal area, standing volume, average circumference and height, when management intensity and initial conditions are known: model efficiency is improved by an average of 0.11, and its average bias does not exceed 25%. The performance of the model is less satisfying for tree-level variables, including extreme circumferences, tree circumference distribution and competition indices, or when management and initial conditions are unknown. At the regional level, when climate forcing is accurate for precipitation, ORCHIDEE-FM is able to reproduce most productivity patterns in France, such as the local lows of needleleaves in the Parisian basin and of broadleaves in south-central France. The simulation of water stress effects on biomass in the Mediterranean region, however, remains problematic, as does the simulation of the wood increment for coniferous trees. These pitfalls pertain to the general ORCHIDEE model rather than to the FMM. Overall, with an average bias seldom exceeding 40%, the performance of ORCHIDEE-FM is deemed reliable to use it as a new modelling tool in the study of the effects of interactions between forest management and climate on biomass stocks of forests across a range of scales from plot to country.     

Examining Flood Frequency Distributions in the Midwest U.S.1

Villarini, Gabriele, James A. Smith, Mary Lynn Baeck, and Witold F. Krajewski, 2011. Examining Flood Frequency Distributions in the Midwest U.S. Journal of the American Water Resources Association (JAWRA) 47(3):447‐463. DOI: 10.1111/j.1752‐1688.2011.00540.x Abstract: Annual maximum peak discharge time series from 196 stream gage stations with a record of at least 75 years from the Midwest United States is examined to study flood peak distributions from a regional point of view. The focus of this study is to evaluate: (1) “mixtures” of flood peak distributions, (2) upper tail and scaling properties of the flood peak distributions, and (3) presence of temporal nonstationarities in the flood peak records. Warm season convective systems are responsible for some of the largest floods in the area, in particular in Nebraska, Kansas, and Iowa. Spring events associated with snowmelt and rain‐on‐snow are common in the northern part of the study domain. Nonparametric tests are used to investigate the presence of abrupt and slowly varying changes. Change‐points rather than monotonic trends are responsible for most violations of the stationarity assumption. The abrupt changes in flood peaks can be associated with anthropogenic changes, such as changes in land use/land cover, agricultural practice, and construction of dams. The trend analyses do not suggest an increase in the flood peak distribution due to anthropogenic climate change. Examination of the upper tail and scaling properties of the flood peak distributions are examined by means of the location, scale, and shape parameters of the Generalized Extreme Value distribution.     

Pressure cookers or pressure valves: Do roads lead to deforestation in China?

The effect of roads on forests is ambiguous. Many studies conclude that building and upgrading roads increases pressure on forests but some find that new and better roads may reduce the rate of deforestation. In this paper we use satellite remote sensing images of forest cover in Jiangxi Province, China, to test whether the existence and the size of roads (ranging from expressways to tertiary roads) in 1995 affected the level of forest cover in 2000 or the rate of change between 1995 and 2000. To account for road access for each of our 1 km² (“pixel”) units of forest cover we measure whether or not and what type of roads penetrate the “watershed” in which the pixel lies. These watersheds allow more plausible measures of accessibility than do traditional “crowfly” distance measures that ignore topography. To account for possible confounding we also use 12 additional covariates: geographic and climatic variables (e.g., elevation, slope, rainfall, temperature, soil properties); demographic and economic variables (e.g., local population and GDP per square kilometer); and distance variables (e.g., distance to the nearest provincial capital). Although simple univariate OLS regressions show that forest levels are lower and deforestation rates higher either when there is a road, or when there is a higher quality road, these results are not robust. Controlling for all of the covariates and also using recently developed covariate matching techniques to estimate treatment effects, we find that roads in China’s Jiangxi Province can most safely be described as having no impact on the level of forests and no impact on the rate of deforestation.     

季节性雪被对青藏高原东缘高寒草甸土壤氮矿化的影响

依据自然雪被分布的差异,在青藏高原东缘高寒草甸中设置3条样带(即深雪、中等厚度雪被和浅雪),于2008年的秋冬过渡期,连续监测各样带中的雪被厚度和土壤温度,并采用原位培养法测定每月的土壤氮素氨化、硝化和矿化速率,以研究不同厚度雪被对高寒草甸土壤氮矿化的影响.结果表明,月均土温、每月日最高土温均值分别与雪被厚度极显著相关,二次函数关系拟合较好(R2=0.576,0.685),且根据每月日最高土温均值与雪被厚度的二次函数关系方程可知,25 cm厚的雪被可以起到较好的隔绝效果;土壤含水量受雪被厚度和土壤温差两个因素的显著影响.在秋冬过渡期末,浅雪梯度下土壤硝态氮含量显著降低,且雪被下的净氮矿化速率与月均土温、每月日最高土温均值、每月日最低土温均值都分别呈极显著相关,二次函数关系拟合较好(R2=0.589,0.541,0.601).研究表明,不同厚度的雪被对土壤温度和含水量影响显著,从而显著地影响着土壤氮的矿化,深雪更有利于氨化、硝化和氮矿化.图7表2参36     

环境卫星CCD与Landsat TM影像质量及生态监测应用比对研究——以青海湖区域为例

针对环境卫星 CCD 影像,结合影像质量评价、专题制图、土地利用/覆被解译以及常用植被指数构建,以青海湖区域为例,与Landsat TM影像进行比对研究。结果表明,在与TM影像质量评价参数比较中,TM影像较优,而环境卫星影像具有很大的改善空间;在对地物判译中,环境卫星影像色彩稍暗淡,但对大多数地物解译判读的边界更清晰;环境卫星覆盖度大,区域制图的优势非常明显;生态监测定量遥感常用的植被指数比较中两种数据大致相同。     

Effects of Climate and Land Cover on Hydrology in the Southeastern U.S.: Potential Impacts on Watershed Planning

The hydrologic response to statistically downscaled general circulation model simulations of daily surface climate and land cover through 2099 was assessed for the Apalachicola‐Chattahoochee‐Flint River Basin located in the southeastern United States. Projections of climate, urbanization, vegetation, and surface‐depression storage capacity were used as inputs to the Precipitation‐Runoff Modeling System to simulate projected impacts on hydrologic response. Surface runoff substantially increased when land cover change was applied. However, once the surface depression storage was added to mitigate the land cover change and increases of surface runoff (due to urbanization), the groundwater flow component then increased. For hydrologic studies that include projections of land cover change (urbanization in particular), any analysis of runoff beyond the change in total runoff should include effects of stormwater management practices as these features affect flow timing and magnitude and may be useful in mitigating land cover change impacts on streamflow. Potential changes in water availability and how biota may respond to changes in flow regime in response to climate and land cover change may prove challenging for managers attempting to balance the needs of future development and the environment. However, these models are still useful for assessing the relative impacts of climate and land cover change and for evaluating tradeoffs when managing to mitigate different stressors.