On the present and potential distribution of Ageratina adenophora (Asteraceae) in South Africa

Invasive alien plants pose a threat to biodiversity worldwide, and the costs of control are ever-escalating. Early detection and prediction of areas potentially at risk is crucial to minimise ecological and socio-economic costs. Maxent was used to predict the area within which Ageratina adenophora can potentially naturalise and spread in South Africa. The model was set up with 1020 occurrence records (10 replicates, 70% of records for calibration:30% for validation), and four climatic predictor variables. Background data were selected using Köppen–Geiger (vegetation-based) climate classification zones. All model replicates performed better than random in both binomial tests of omission and ROC analysis. The model was statistically significant and its mean AUC was 94%. The modeled prevalence was 0.21 and the sensitivity was 0.99. The Eastern Cape, KwaZulu-Natal, Mpumalanga and Gauteng provinces have climatic conditions indicative of a high potential for invasion by A. adenophora, followed by parts of the Western Cape, North West and Limpopo provinces. The model predicted areas beyond the current distribution, suggesting that A. adenophora has potential for further spread, and that searches for it need to be made beyond its currently known distribution. On the other hand it appears not to have spread into some climatically suitable areas near its current occupancy sites, such as throughout the KwaZulu-Natal mist belt, suggesting that unknown biotic (including human) or abiotic factors are also limiting its naturalization and require further study to be identified.     

Establishing climatic constraints shaping the distribution of alien plant species along the elevation gradient in the Alps

In this work, we analyse the role of climatic constraints in shaping the distribution of alien plant species along the elevation gradient in the European Alps. Alien species occurrence was recorded in 278 plots located beside rivers, from 100 to 2,100 m a.s.l. Climate variables were calculated from the data recorded by 145 meteorological stations and interpolated by a multiple regression approach. Both richness and occurrence of aliens were modelled. In particular, relationships between the occurrence of alien plants and (1) elevation or (2) the climatic variables, were tested by applying generalised linear models and generalised linear mixed models; the model parameters obtained were used to estimate upper elevation limits of alien occurrence and their related climate values. Sixty-eight alien species were encountered, the majority (71%) invasive in Italy and worldwide. A steep decrease in alien species richness with elevation was found, with the probability of alien species occurrence decreasing by half for each 100 m increase in elevation. Minimal adequate models based on (1) non-transformed climatic variables and (2) derived PCA values, confirmed that occurrence of alien plant species along the elevation gradient was positively related to the minimum temperature, the mean temperature and the heat sum for the spring season, rather than to the incidence of absolute minimum temperature and frost days, as usually assumed. Although further experimental analyses are needed, these results support the hypothesis that, referring to climate factors, elevation limits along rivers are mainly established by low spring temperatures which operate at the level of population viability rather than plant survival.     

New developments in CLAMP: Calibration using global gridded meteorological data

Climate Leaf Analysis Multivariate Program (CLAMP) is a versatile technique for obtaining quantitative estimates for multiple terrestrial palaeoclimate variables from woody dicot leaf assemblages. To date it has been most widely applied to the Late Cretaceous and Tertiary of the mid- to high latitudes because of concerns over the relative dearth of calibration sites in modern low-latitude warm climates, and the loss of information associated with the lack of marginal teeth on leaves in paratropical to tropical vegetation. This limits CLAMP's ability to quantify reliably climates at low latitudes in greenhouse worlds of the past.One of the reasons for the lack of CLAMP calibration samples from warm environments is the paucity of climate stations close to potential calibration vegetation sites at low latitudes. Agriculture and urban development have destroyed most lowland sites and natural vegetation is now largely confined to mountainous areas where climate stations are few and climatic spatial variation is high due to topographic complexity. To attempt to overcome this we have utilised a 0.5° × 0.5° grid of global interpolated climate data based on the data set of New et al. (1999) supplemented by the ERA40 re-analysis data for atmospheric temperature at upper levels. For each location, the 3-D climatology of temperature from the ECMWF re-analysis project was used to calculate the mean lower tropospheric lapse rate for each month of the year. The gridded data were then corrected to the altitude of the plant site using the monthly lapse rates. Corrections for humidity were also made. From this the commonly returned CLAMP climate variables were calculated. A bilinear interpolation scheme was then used to calculate the climate parameters at the exact lat/long of the site.When CLAMP analyses using the PHYSG3BR physiognomic data calibrated with the climate station based MET3BR were compared to analyses using the gridded data at the same locations (GRIDMET3BR), the results were indistinguishable in that they fell within the range of statistical uncertainty determined for each analysis. This opens the way to including natural vegetation anywhere in the world irrespective of the proximity of a meteorological station.     

Biotic interactions improve prediction of boreal bird distributions at macro-scales

Aim The role of biotic interactions in influencing species distributions at macro‐scales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro‐scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models. Location Finland, northern Europe. Methods Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four‐fold cross‐validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot. Results At 10‐km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross‐validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10‐km resolution, although these improvements were less apparent at 40‐km resolution for both land cover and biotic interactions. Main conclusions Predictions of species distributions at macro‐scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species–climate impact models.     

A predictive model relating North American plant formations and climate

A discriminant model was produced that predicts North American plant formations with basic climatic variables (monthly mean temperatures, monthly precipitation, and latitude). The model is based on data from 176 weather stations. Climatic variables from 30 additional randomly-selected weather stations were used to test the model. The predicted formation and actual formation at each site were compared; four sites were classified into the wrong formations (87% accuracy). This predictive model indicates a strong correlation between climate and formations in North America. Vegetation-climate models produced by canonical discriminant analysis may be useful in detecting geographical localities where non-climatic factors are particularly influential.     

Can climate data from METEOSAT improve wildlife distribution models?

Global climate change generated by human activities is likely to affect agroecosystems in several ways: reinforcing intensification in northern and western Europe, and extensification in the Mediterranean countries. If we are to predict the consequences of global warming for wildlife, distribution models have to include climate data. The METEOSAT temporal series from EWBMS offers an attractive alternative to using climatic surfaces derived from ground stations. The aim of this paper is to test whether this climatic satellite data can improve the distribution models obtained previously by Suárez-Seoane et al. using habitat variables for three agro-steppe bird species: great bustard, little bustard and calandra lark in Spain. Rainfall, radiation balance, evapotranspiration and soil moisture images were incorporated together with the other variables used as predictors in the published stepwise GAM models. Changes in the predicted distributions from the habitat only and climate-habitats models were assessed by reference to the CORINE land cover categories. Inclusion of climatic variables from METEOSAT led to statistically superior models for all three species. There were large differences in the climatic variables selected and the original variables dropped among the species. Evapotranspiration variables were the most frequently selected. Maps of the differences between the habitat and climate-habitat models showed very different patterns for the three species. Inclusion of climate variables led to a wider range of land cover types being deemed suitable. Despite the statistical superiority of models, care is needed in deciding whether to use climatic variables because they may emphasize the fundamental rather than the realized niche. Used together, however, habitat and climate models can provide new insights into factors limiting species distributions and how they may respond to climate change.     

A numerical phytoclimatic classification of China

The relationship is examined between vegetation and climate using climatic variables collected from 644 meteorological stations located throughout China. Multivariate methods are applied directly to the raw climatic data in order to define climatic clusters and to examine the relationship between the clusters and vegetation types. This approach is based on the concept of multidimensional climatic space defined by the combination of climatic variables. Phytoclimatic classes are defined on the basis of the distribution of vegetation types in climatic clusters and a new phytoclimatic classification of China is proposed. Patterns of climatic changes between neighbouring phytoclimatic classes are described. Two indexes of the influence of climate on vegetation are proposed based on discriminant analysis.     

Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude

Aims We examine the relationships between the distribution of British ground beetle species and climatic and altitude variables with a view to developing models for evaluating the impact of climate change. Location Data from 1684 10‐km squares in Britain were used to model species–climate/altitude relationships. A validation data set was composed of data from 326 British 10‐km squares not used in the model data set. Methods The relationships between incidence and climate and altitude variables for 137 ground beetle species were investigated using logistic regression. The models produced were subjected to a validation exercise using the Kappa statistic with a second data set of 30 species. Distribution patterns for four species were predicted for Britain using the regression equations generated. Results As many as 136 ground beetle species showed significant relationships with one or more of the altitude and climatic variables but the amount of variation explained by the models was generally poor. Models explaining 20% or more of the variation in species incidence were generated for only 10 species. Mean summer temperature and mean annual temperature were the best predictors for eight and six of these 10 species respectively. Few models based on altitude, annual precipitation and mean winter temperature were good predictors of ground beetle species distribution. The results of the validation exercise were mixed, with models for four species showing good or moderate fits whilst the remainder were poor. Main conclusions Whilst there were many significant relationships between British ground beetle species distributions and altitude and climatic variables, these variables did not appear to be good predictors of ground beetle species distribution. The poor model performance appears to be related to the coarse nature of the response and predictor data sets and the absence of key predictors from the models.     

A plant's perspective of extremes: terrestrial plant responses to changing climatic variability

We review observational, experimental, and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied, although potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heatwaves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational, and/or modeling studies have the potential to overcome important caveats of the respective individual approaches.     

Urbanisation induces early flowering: evidence from <Emphasis Type="Italic">Platanus acerifolia</Emphasis> and <Emphasis Type="Italic">Prunus cerasus</Emphasis>

The effect of towns on plant phenology, i.e. advancement of spring development compared with a rural environment, via the urban heat island (UHI) phenomenon, has been shown for many towns in many countries. This work combines experimental and observational methodology to provide a better and deeper view of climatic habitat in an urban context with a view to understanding the relationship between plant development and urban climate on the intra-urban scale (by taking into account town structure). A dense network of 17 meteorological stations was set up in Rennes, France, enabling us to identify and quantify climatic changes associated with the UHI. Meanwhile, phenological observations were made during early spring (March and April) in 2005 on Platanus acerifolia and Prunus cerasus to study the relationship between climatic and phenological data. The results show that there is both a climatic gradient and a developmental gradient corresponding to the type of urbanisation in the town of Rennes. The town influences plant phenology by reducing the diurnal temperature range and by increasing the minimum temperature as one approaches the town centre. The influence of ground cover type (plants or buildings) on development is also shown. The developmental phases of preflowering and flowering are influenced to differing extents by climatic variables. The period during which climatic variables are effective before a given developmental phase varies considerably. The preflowering phases are best correlated with the mean of the minimum air temperature for the 15-day period before the observation, whereas flowering appears to be more dependent on the mean of the daily diurnal temperature range for the 8 days preceding the observation.     

Growth ring response of two Atlantic Forest tree species pre- and post-dam operation in Southern Brazil

Hydropower plants are important sources of renewable energy, but the climatic impacts of their constructions remain poorly explored. Considering that tree growth analysis is a useful tool to identify environmental impacts, this study aimed at using climate records and tree-ring chronologies to understand possible local climate changes caused by the construction of a hydropower plant in the 1980s in the State of Paraná, Southern Brazil. Historical climatic data were obtained from the local meteorological station and surrounding municipalities and analyzed using ANOVA and means tests. The Pettitt test was additionally used to identify change-points in the meteorological data. Wood samples from a total of 60 trees from Araucaria angustifolia (Bertol.) Kuntze (Araucariaceae) and Cedrela fissilis Vell. (Meliaceae) were collected, and tree-ring chronologies were built using dendrochronological standard procedures. Chronologies for A. angustifolia and C. fissilis represented time periods from 1800 to 2016 and 1899–2015, respectively. Tree-ring growth responses to climatic variables were evaluated by adjusting generalized mixed linear models and the Spearman correlations. Our results evidenced that the hydropower plant altered the local climate, mostly influencing the hydrological cycle by increasing local rainfall, with monthly rain volumes being statistically higher than in other meteorological stations. Significant responses in the growth of A. angustifolia were found to be associated with the water level increase caused by the dam and of C. fissilis due to the increase in cloud cover.     

基于SWAT模型的流溪河流域土地利用与气候变化对径流的影响

选用国内外广泛应用的SWAT分布式水文模型,定量分析流溪河流域土地利用与气候变化对径流的影响,采用情景模拟分析方法设置3类情景进行定量分析.对上中下游的温泉、太平场和南岗3个水文站依次校正与验证得出：除温泉站在验证期的3个系数刚达标之外,其他的相对误差＜15%、相关系数＞0.8、Nash Sutcliffe效率系数＞0.75,说明SWAT模型在流溪河流域的径流量模拟中具有较高的适用性.综合型情景模拟分析得出：以1991-2000年为基准期,2001-2010年土地利用与气候变化综合引起年均径流量增加11.23 m³·s^-1,土地利用变化引起年均径流量减少0.62 m³·s^-1,气候变化引起年均径流量增加11.85 m³·s^-1,气候变化的影响强度强于土地利用变化的影响强度.极端土地利用情景模拟分析得出：与2000年土地利用现状模拟径流量相比,耕地情景和草地情景的径流量分别增加2.7%和0.5%,林地情景的径流量减少0.7%,证明林地有一定的截流能力.气候变化情景模拟分析得出：流域径流量变化与降水变化呈正相关关系（降水每升高10%,径流平均增加11.6%）,与气温变化呈负相关关系（气温每升高1 ℃,径流平均降低0.8%）,降水变化的影响强度强于气温变化的影响强度.在气候变化环境下,需要重视对强降雨的预测和灾害预防,可通过优化土地利用结构与空间布局来减缓气候变化带来的水文负效应,如洪涝灾害.     

Effects of geographic distance and climatic dissimilarity on species turnover in alpine meadow communities across a broad spatial extent on the Tibetan Plateau

Understanding the underlying mechanisms that generate species turnover or beta diversity among biological communities is a central theme in ecology. Here, we distinguish the effects of geographic distance and climatic dissimilarity on species turnover of vascular plants in alpine meadow communities on the Tibetan Plateau in China. We calculated species turnover between each pair of 17 sites, using the Jaccard??s and Simpson??s indices. We selected six variables to quantify climate at each site, and subjected values of the climatic variables to a principal component analysis. We applied a variance partitioning approach to disentangle the effects of geographic distance and climatic dissimilarity on species turnover in alpine meadow communities. We also examined the effect of elevation variation on species turnover. Geographic distance and climate dissimilarity together explained 49.1?% of the variation in compositional difference between alpine meadow communities; the amount of the variation explained purely by geographic distance and purely by climatic dissimilarity was 6.8?% and 2.8?%, respectively. When geographic distance, climate dissimilarity, and elevation difference were included in an analysis, they together explained 55?% of the variation in compositional difference between alpine meadow communities; the pure effect of each of the three sets of explanatory variables was 4.8, 4.3, and 3.5?%, respectively. The fact that the vast majority of the variation explained by geographic distance and climatic dissimilarity cannot be independently attributed to either factor suggests that the two factors operate together in determining regional patterns of species composition in alpine meadows on the Tibetan Plateau.     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.     

Stream diatoms exhibit weak niche conservation along global environmental and climatic gradients

Niche conservatism (NC) describes the scenario in which species retain similar characteristics or traits over time and space, and thus has potentially important implications for understanding their biogeographic distributions. Evidence consistent with NC includes similar niche properties across geographically distant regions. We investigated whether NC was evident in stream diatom morphospecies by modeling species responses to environmental and climatic variables in a set of calibration sites (from the US) and then evaluated the models with test sets (from France, Finland, New Zealand, Antilles and La Réunion). We also examined whether diatom species showed congruency in environmental niche optima and niche breadths between the study regions, and whether species occupancy and functional traits influenced the observed patterns. We used boosted regression tree models with local environmental variables and climatic variables as predictors. We detected low NC in both environmental and climate models and a lack of consistent differences in NC between widely distributed and regionally rare species and among functional groups. For all species, diatom environmental and climatic optima varied clearly between the regions but showed some positive relationships especially for pH and total phosphorus. Diatom niche breadths were only weakly correlated between the US and the other regions. We demonstrated that diatoms showed overall relatively little NC globally, and NC was especially low for climatic variables. Collectively, these findings suggest that there may exist locally adapted lineages within the diatom morphospecies or diatoms possess some adaptation potential for differences in temperature. We argue that in diatoms, environmental and especially climate models may not be transferrable in space globally but need regional diatom data for calibration because species niches seem to differ among geographical regions.     

SPEI指数在中国东北地区干旱研究中的适用性分析

干旱指数的区域适用性是准确表征区域干旱的重要前提,本文以中国东北地区为典型研究区,探讨标准化降水蒸散指数(SPEI)在该地区应用的有效性。基于研究区90个气象台站的逐日气象资料,计算1961—2014年多时间尺度的SPEI指数。从Kolmogorov-Smirnov(K-S)拟合优度检验、SPEI与典型干旱事件核准、SPEI与农作物受旱灾面积及与土壤湿度相关性分析等方面,验证SPEI指数在东北地区的适用性。分析结果表明:1)东北地区多时间尺度的累积水分亏缺量符合Log-logistic分布,SPEI指数在东北地区的应用具备数学统计理论基础;2)生长季平均SPEI值与黑龙江省、吉林省和辽宁省农作物受旱灾面积比例均呈极显著负相关(P0.01);3)在1、3、6和12个月尺度下,SPEI与土壤湿度呈显著正相关(P0.05)的站点比例分别为90.2%、92.16%、90.2%和88.24%。综上所述,SPEI指数不仅满足数学理论统计的要求,而且与干旱灾情数据和土壤水分监测值均具有极度的关联性,说明其在东北地区干旱预测和定量化研究中具有较好的适用性。     

Potential Effects of Climate Change on the Distribution of Cold-Tolerant Evergreen Broadleaved Woody Plants in the Korean Peninsula

Climate change has caused shifts in species’ ranges and extinctions of high-latitude and altitude species. Most cold-tolerant evergreen broadleaved woody plants (shortened to cold-evergreens below) are rare species occurring in a few sites in the alpine and subalpine zones in the Korean Peninsula. The aim of this research is to 1) identify climate factors controlling the range of cold-evergreens in the Korean Peninsula; and 2) predict the climate change effects on the range of cold-evergreens. We used multimodel inference based on combinations of climate variables to develop distribution models of cold-evergreens at a physiognomic-level. Presence/absence data of 12 species at 204 sites and 6 climatic factors, selected from among 23 candidate variables, were used for modeling. Model uncertainty was estimated by mapping a total variance calculated by adding the weighted average of within-model variation to the between-model variation. The range of cold-evergreens and model performance were validated by true skill statistics, the receiver operating characteristic curve and the kappa statistic. Climate change effects on the cold-evergreens were predicted according to the RCP 4.5 and RCP 8.5 scenarios. Multimodel inference approach excellently projected the spatial distribution of cold-evergreens (AUC = 0.95, kappa = 0.62 and TSS = 0.77). Temperature was a dominant factor in model-average estimates, while precipitation was minor. The climatic suitability increased from the southwest, lowland areas, to the northeast, high mountains. The range of cold-evergreens declined under climate change. Mountain-tops in the south and most of the area in the north remained suitable in 2050 and 2070 under the RCP 4.5 projection and 2050 under the RCP 8.5 projection. Only high-elevations in the northeastern Peninsula remained suitable under the RCP 8.5 projection. A northward and upper-elevational range shift indicates change in species composition at the alpine and subalpine ecosystems in the Korean Peninsula.     

Late Holocene palaeoenvironment change in central Tianshan of Xinjiang,northwest China

The northern slopes of central Tianshan Mountains in Xinjiang, northwestern China can provide an ideal database to research palaeoclimate as disturbance by human impact is relatively low and the vegetation zones reflect climatic gradients. In order to establish the correlation between modern climatic factors and surface pollen assemblages and to reconstruct palaeoclimate on the northern slope of central Tianshan Mountains, three Holocene sections in Daxigou, Huashuwozi and Sichanghu located at different elevations and vegetation zones were chosen for study. A total of 80 surface pollen samples in 86 vegetation quadrats were collected for pollen‐vegetation relationship analysis. The Warmth Index (WI) and Moisture Index (MI) were calculated based on averaged modern climate data during 1951 – 2000 at eight weather stations in the study area. Pollen percentages of Picea, Artemisia, Chenopodiaceae, Ephedra, and Tamarix, as well as A/C (Artemisia/Chenopodiaceae) and AP/NAP (arboreal/nonarboreal pollen) ratio were selected as pollen variables and WI and MI were chosen as climatic variables. The relationship between pollen percentages (Picea, Artemisia, Chenopodiaceae and Tamarix), A/C, AP/NAP ratio, WI and MI values were estimated (95% confidence interval) using stepwise multiple linear regression analysis. WI and MI values for the three sections were calculated using these regression equations, and palaeoclimate for the study area could be reconstructed. The results showed periods of both cool‐humid and warm‐dry conditions on the northern slopes of Tianshan Mountain during the late Holocene.     

应用SWAT模型研究潮河流域土地利用和气候变化对径流的影响

为定量分析潮河流域土地利用和气候变化对流域径流变化的影响,应用SWAT模型对流域上游至下游的大阁、戴营和下会3个水文站径流进行模拟,采用情景法分析径流对土地利用和气候变化的响应。在模型校准期和验证期采用两个参数:p因子和r因子来评价模拟的拟合度及不确定性。结果表明,3个水文站在校准期和验证期的p因子值分别为:0.70和0.77,0.87和0.82,0.92和0.78,r因子值分别为0.63和0.90,0.97和0.79,0.88和0.92,评价整个流域模拟有效性的模型目标函数g最佳值为0.66,说明该模型对潮河流域的产水量模拟具有很好的适用性。以1981—1990年为基准期,1991—2000年流域土地利用变化造成年径流量减少了4.10 mm,而气候变化导致年径流增加了29.68 mm;2001—2009年土地利用变化造成年径流量减少2.98mm,气候变化造成年径流量减少了14.30 mm。与1999年土地利用条件模拟径流值相比,几种极端情景法模拟分析结果表明:灌木林地情景下年径流增加了158.2%,草地情景下年径流增加了4.1%,林地和耕地情景下年径流分别减少23.7%和41.7%;不同气候变异情景模拟结果显示,径流对降水的变化敏感性高于对温度变化的敏感性,降水每增加10%,径流平均增加23.9%。温度每增加12%,径流平均减少6%。因此,在气候变化背景下,优化土地利用结构与方式是实现流域水资源科学管理的途径之一。     

Instrumental test to estimate a model of forecast yield: a non-parametric application to the pollen index in South Italy

A statistical test is described to verify the characteristics of the biological information contained in the dynamics of the flowering process. The test focuses on interactions between the pollen index and climatic variables to investigate if the biological indicator can synthesise the information of the pre-flowering phases. The multiple-regression model is built upon two pre-flowering climate macro-indicators extracted by Principal Component Analysis (PCA) and the optimised pollen index is obtained by non-parametric estimation. The empirical analysis is applied to 15 stations located in southern Italy in regions that have a longstanding tradition of olive production. Using the variance explained, we find that an optimised pollen index is fairly well predicted by the pre-flowering climatic data. We conclude that the optimised pollen index makes more parsimonious the modelling for predicting olive production.