Spatially distributed modeling of the long-term carbon balance of a boreal landscape

Spatially and temporally distributed information on the sizes of biomass carbon (C) pools (BCPs) and soil C pools (SCPs) is vital for improving our understanding of biosphere-atmosphere C fluxes. Because the sizes of C pools result from the integrated effects of primary production, age-effects, changes in climate, atmospheric CO₂ concentration, N deposition, and disturbances, a modeling scheme that interactively considers these processes is important. We used the InTEC model, driven by various spatio-temporal datasets to simulate the long-term C-balance in a boreal landscape in eastern Canada. Our results suggested that in this boreal landscape, mature coniferous stands had stabilized their productivity and fluctuated as a weak C-sink or C-source depending on the interannual variations in hydrometeorological factors. Disturbed deciduous stands were larger C-sinks (NEP₂₀₀₄ = 150 gC m⁻² yr⁻¹) than undisturbed coniferous stands (e.g. NEP₂₀₀₄ = 8 gC m⁻² yr⁻¹). Wetlands had lower NPP but showed temporally consistent C accumulation patterns. The simulated spatio-temporal patterns of BCPs and SCPs were unique and reflected the integrated effects of climate, plant growth and atmospheric chemistry besides the inherent properties of the C pool themselves. The simulated BCPs and SCPs generally compared well with the biometric estimates (BCPs: r = 0.86, SCPs: r = 0.84). The largest BCP biases were found in recently disturbed stands and the largest SCP biases were seen in locations where moss necro-masses were abundant. Reconstructing C pools and C fluxes in the ecosystem in such a spatio-temporal manner could help reduce the uncertainties in our understanding of terrestrial C-cycle.     

Surface modelling of global terrestrial ecosystems under three climate change scenarios

A high accuracy and speed method (HASM) of surface modelling is developed to find a solution for error problem and to improve computation speed. A digital elevation model (DEM) is established on spatial resolution of 13.5 km × 13.5 km. Regression formulations among temperature, elevation and latitude are simulated in terms of data from 2766 weather observation stations scattered over the world by using the 13.5 km × 13.5 km DEM as auxiliary data. Three climate scenarios of HadCM3 are refined from spatial resolution of 405 km × 270 km to 13.5 km × 13.5 km in terms of the regression formulations. HASM is employed to simulate surfaces of mean annual bio-temperature, mean annual precipitation and potential evapotranspiration ratio during the periods from 1961 to 1990 (T₁), from 2010 to 2039 (T₂), from 2040 to 2069 (T₃), and from 2070 to 2099 (T₄) on spatial resolution of 13.5 km × 13.5 km. Three scenarios of terrestrial ecosystems on global level are finally developed on the basis of the simulated climate surfaces. The scenarios show that all polar/nival, subpolar/alpine and cold ecosystem types would continuously shrink and all tropical types, except tropical rain forest in scenario A1Fi, would expand because of the climate warming. Especially at least 80% of moist tundra and 22% of nival area might disappear in period T₄ comparing with the ones in the period T₁. Tropical thorn woodland might increase by more than 97%. Subpolar/alpine moist tundra would be the most sensitive ecosystem type because its area would have the rapidest decreasing rate and its mean center would shift the longest distance towards west. Subpolar/alpine moist tundra might be able to serve as an indicator of climatic change. In general, climate change would lead to a continuous reduction of ecological diversity.     

Predicting modes of spatial change from state-and-transition models

State-and-transition models (STMs) can represent many different types of landscape change, from simple gradient-driven transitions to complex, (pseudo-) random patterns. While previous applications of STMs have focused on individual states and transitions, this study addresses broader-scale modes of spatial change based on the entire network of states and transitions. STMs are treated as mathematical graphs, and several metrics from algebraic graph theory are applied—spectral radius, algebraic connectivity, and the S-metric. These indicate, respectively, the amplification of environmental change by state transitions, the relative rate of propagation of state changes through the landscape, and the degree of system structural constraints on the spatial propagation of state transitions. The analysis is illustrated by application to the Gualalupe/San Antonio River delta, Texas, with soil types as representations of system states. Concepts of change in deltaic environments are typically based on successional patterns in response to forcings such as sea level change or river inflows. However, results indicate more complex modes of change associated with amplification of changes in system states, relatively rapid spatial propagation of state transitions, and some structural constraints within the system. The implications are that complex, spatially variable state transitions are likely, constrained by local (within-delta) environmental gradients and initial conditions. As in most applications, the STM used in this study is a representation of observed state transitions. While the usual predictive application of STMs is identification of local state changes associated with, e.g., management strategies, the methods presented here show how STMs can be used at a broader scale to identify landscape scale modes of spatial change.     

Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing

In this paper we describe and test a sub-model that integrates the cycling of carbon (C), nitrogen (N) and phosphorus (P) in the Soil Water Assessment Tool (SWAT) watershed model. The core of the sub-model is a multi-layer, one-pool soil organic carbon (S_C) algorithm, in which the decomposition rate of S_C and input rate to S_C (through decomposition and humification of residues) depend on the current size of S_C. The organic N and P fluxes are coupled to that of C and depend on the available mineral N and P, and the C:N and N:P ratios of the decomposing pools. Tillage explicitly affects the soil organic matter turnover rate through tool-specific coefficients. Unlike most models, the turnover of soil organic matter does not follow first order kinetics. Each soil layer has a specific maximum capacity to accumulate C or C saturation (S_x) that depends on texture and controls the turnover rate. It is shown in an analytical solution that S_x is a parameter with major influence in the model C dynamics. Testing with a 65-yr data set from the dryland wheat growing region in Oregon shows that the model adequately simulates the S_C dynamics in the topsoil (top 0.3 m) for three different treatments. Three key model parameters, the optimal decomposition and humification rates and a factor controlling the effect of soil moisture and temperature on the decomposition rate, showed low uncertainty as determined by generalized likelihood uncertainty estimation. Nonetheless, the parameter set that provided accurate simulations in the topsoil tended to overestimate S_C in the subsoil, suggesting that a mechanism that expresses at depth might not be represented in the current sub-model structure. The explicit integration of C, N, and P fluxes allows for a more cohesive simulation of nutrient cycling in the SWAT model. The sub-model has to be tested in forestland and rangeland in addition to agricultural land, and in diverse soils with extreme properties such high or low pH, an organic horizon, or volcanic soils.     

Estimating the vulnerability of fifteen tree species under changing climate in Northwest North America

In the Pacific northwestern (PNW) region of North America, climatic conditions have significantly warmed since a predominantly cool phase of the Pacific North American circulation patterns between 1950 and 1975. What are the implications of this shift in climate for the vulnerability of native tree species? To address this question, we combined mechanistic and statistical models to assess where a variety of native tree species might be more vulnerable within their recorded ranges and where they might potentially migrate. For long-lived species that are well adapted to compete, seasonal differences in photosynthesis and water use offer insights helpful in predicting their distributions. To evaluate the general response of conifers to climatic variation across the region, we previously applied a process-based model (3-PG), to simulate the growth and maximum leaf area index that Douglas-fir could attain within recognized forested areas. We then constructed automated decision tree models to define and map the ecological distributions of 15 tree species based on differences in how photosynthesis was constrained by drought, daytime temperatures, high evaporative demand, and the frequency of frost. For the baseline climate period (1950-1975), the decision tree models predicted presence and absence of each species at ∼23,000 observations with an average accuracy of 81%, with an average kappa statistic of 0.74. In this paper the same models were run annually for the period between 1976 and 2006 for each species, and the areas defined as remaining suitable or becoming vulnerable to disturbance were identified based on whether more or less than half of the years fell within the originally defined limits. Based on these criteria, 70% of the species recorded ranges remained suitable, with 30% deemed vulnerable. Results varied notably by species with western red cedar and western hemlock remaining highly adapted, with potential for range expansion in area of up to 50% relative to the baseline period. In contrast, ponderosa pine, lodgepole pine, grand, and noble fir were classified as vulnerable with potential net contractions in their ranges. The analysis was extended through the rest of the 21st century using climatic projections from the Canadian global circulation model with a high fossil fuel emission scenario (A2) and compared to other previously published species range predictions.     

Ecosystems emerging. 5: Constraints

Ecosystem constraints are both ontic and epistemic. They limit activity, and as problems to be solved they drive organization, which is our hypothesis:

The driver of organization is constraint.

Solutions proliferate further constraints in an unending spiral of problem (constraint) generation and solution. As constraints proliferate, behavior narrows, and species diversify to compensate (paradox of constraint). Resource enrichment reduces constraints, releases behavior, and reduction of challenges decreases diversity (paradox of enrichment)—high diversity is expressed in low-resource environments and low diversity in high-resource environments. A three-part model of constraints is formulated for non-living systems, and also for goal-directed, problem-solving biota. Mode 1: dynamical means behavior is co-determined by internal states and external inputs. Mode 2: cybernetic employs negative feedback to keep dynamics within goal-oriented operating limits. Mode 3: model-making entails ability to represent (model) physical reality and respond to both phenomenal (modeled) and physical inputs; this property distinguishes living from nonliving systems. Principal sections of the paper elaborate dynamical constraints (three classes), boundary constraints (expressed in edge effects and trophic dynamics), physical constraints (space, time, temperature), chemical constraints (environment fitness, ecological stoichiometry, chemical evolution, limiting factors), coding constraints (environmental vs. genetic coding), network and pathway constraints (connectivity), and natural selection constraints (fitting to the biosphere). Consideration of how the world would look without constraints suggests how fundamental these are in ecosystem emergence, and how the next property in this series, differentiation, would be unmotivated without them. We conclude that constraints as a category are under-studied in ecology, poorly understood in ecological phenomenology, and (our hypothesis) comprise a ubiquitous organizing force in nature.     

水深梯度对荆三棱(Scirpus yagara)生长的影响

研究湿地植物对水深梯度的响应对于湿地修复具有重要的指导意义.在南四湖湖边滩地上的人工修复湿地中开展水深梯度对荆三棱(Scirpus yagara)生长的影响研究,试验观测水深分别为-10、0、10、20、30 cm共5个梯度.结果显示:随着水位的升高,1)荆三棱的株高、基茎、叶长和叶宽均有所增加,当水位大于10 cm时各项指标增加更为显著;2)生物量呈增加趋势,而根茎比则逐渐减小,水位为30 cm时的生物量和根茎比分别为-10 cm时的2.67倍和58.15%;3)叶绿素含量总体呈增加趋势,但在不同水位间的差异不明显(P>0.05);4)叶绿素荧光方面,最大光化学量子产量Fv/Fm均在0.75以上,光化学淬灭qP和非光化学淬灭qN分别呈现出升高和下降的趋势.结果表明,在试验所设置的水位条件下(-10～30 cm),荆三棱的生长没有受到水深梯度增加的显著影响,相反表现出随着水深的增加而生长更好的趋势.可见,在试验设置的水深梯度范围内,相对较高的水位有利于荆三棱的生长,因此在湿地管理中应控制适当的高水位以利于其种群的生长和稳定.     

东江流域1959-2009年气候变化及其对径流的影响

依据东江流域21个气象站1959-2009年逐年平均降雨、蒸发、日照、湿度及气温等气象要素序列,选择常用的线性倾向估计及非参数M．K等趋势分析方法,分析了东江流域近50年来气温、降水、蒸发、日照及湿度等气象要素的变化趋势。选择降雨和蒸发两个气候要素两两组合,构成未来气候变动的36种假想情景,运用改进的SCS月模型模拟计算了顺天流域年径流量的变化幅度。结果表明：在过去的50年间,流域降雨量呈不显著增长,而气温则为显著上升,其他气候要素如蒸发、日照及湿度等均呈不同程度减少趋势;关联度分析表明降雨在所有气象要素中与径流的关联度为最大,说明了在东江流域降雨是径流量变化的主要驱动因子;未来流域降雨增加,蒸发减少的气候情景模式下,径流量会有所增加,反之亦然;由降雨变化引起的流域月径流量的增幅较由蒸发变化引起的相应流量的增幅变化大。     

近50年三江源地表湿润指数变化特征及其影响因素

基于三江源区1959—2008年月平均气温、最高气温、最低气温、相对湿度、降水量、风速和日照百分率等气候要素资料,应用修订的Penman-Monteith（P-M）模型计算了最大潜在蒸散量和地表湿润指数,分析其空间分布、年际和年代际变化特征及其主要气象因子的影响。结果表明：1959—2008年间,研究区年降水量呈增加趋势,降水量变化曲线线性拟合倾向率为5.316~13.047 mm.（10a）-1,春夏季增幅较大;最大潜在蒸散量呈增加趋势,年最大潜在蒸散量变化曲线线性拟合倾向率在5.073~10.712 mm.（10a）-1,夏季增幅最大;地表湿润指数变化也呈增加趋势,年地表湿润指数变化曲线线性拟合倾向率0.011~0.026（10a）-1,冬季增幅最大,在15年周期附近,出现了3~5个干湿交替期,1984年之后为偏湿期,在中高频区,1998—2006年有偏干振荡;影响三江源区地表湿润指数的主要因子是降水量、相对湿度和平均最高气温。     

西安市降水频率变化特征分析

利用1951-2005年西安市的逐日降水资料,分析了西安市降水量和降水频率的年、季变化特征,探讨了各等级降水的频率变化对总降水量变化的贡献。结果表明:(1)50多年来西安市降水量有所减小,降水日数显著减少且在春夏秋冬四季均有反映,降水日数的减少速率以秋季为最大;(2)5mm以下小雨的降水频率有显著的逐年代降低趋势,而5～10mm的小雨和中雨的降水频率基本上没有变化,大雨和暴雨及以上的降水频率低,且其变化具有随机性;(3)自20世纪80年代以来,春夏秋冬四季的小雨降水频率均有所降低,其中夏季小雨降水频率的减少系主要由微量降水雨日的减少引起;(4)小雨降水的频率比重和总量比重自20世纪80年代以来显著降低,中雨降水总量比重也有所降低,而大雨和暴雨及以上的降水量比重基本没有变化。所以小雨和中雨降水总量减少是多年来总降水量减少的主要原因。研究结果显示,人类活动排放的气溶胶可能是造成西安市降水频率降低和总量减少的主要原因之一。