Future wave-driven coastal sediment transport along the Catalan coast (NW Mediterranean)

In the context of climate change, this study evaluates the impact on the long-shore and cross-shore sediment transport (LST and CST) along the Catalan coast (NW Mediterranean Sea) derived from climate projections obtained from five combinations of regional and global circulation models (RCMs and GCMs). Special emphasis is given to how inter-model variability translates from wave projections to wave-driven coastal impacts, which is still poorly known. Results show that the uncertainty is in general larger, especially for LST, for which the discrepancies among regional models are more relevant than those associated with the forcing wave parameters. Such increase in the uncertainty can be explained by the nonlinear processes involved, and the role of the forcing wave parameters having sometimes competing effects (e.g. wave height vs. wave direction). This illustrates that the performance of each RCM–GCM can vary from forcing to impact parameters; hence, the suitability of a particular RCM–GCM to evaluate a certain impact should be assessed based on its ability to properly simulate such impact. In this regard, LST and CST rates computed using empirical formulae that integrate several wave climate parameters, as in this study, can be used as a non-computationally expensive tool to assess the suitability of a given RCM–GCM to project changes in coastal dynamics.     

Statistical downscaling for climate change projections in the Mediterranean region: methods and results

Besides dynamical downscaling by regional climate models, statistical downscaling (SD) is a major tool to derive climate change projections on regional or even local scales. For the Mediterranean area, an increasing number of downscaling studies based on different statistical techniques have been published in the last two decades with a broad range of sometimes differing results relating to different variables and regional domains. This paper gives a short review of these Mediterranean downscaling studies mainly considering the following two aspects: (1) what kind of progress has been realized in this field since the early 1990s? The review addresses the inclusion of extremes in downscaling assessments, the development of probabilistic approaches, the extension of predictor sets, the use of ensembles for both dynamical model simulations and statistical model assessments, the consideration of non-stationarities in the predictor–predictand relationships, and some advances related to synoptic downscaling. (2) What are the main regional climate change signals in the Mediterranean area, considering agreed and controversial points also with respect to dynamical models? Best accordance among future projections can be found in seasonal temperatures with lower rates of warming in winter and spring, and, in most cases, higher ones in summer and autumn. Different results are obtained for the intra-annual range of extreme temperatures, but high-temperature conditions are generally expected to increase. Regarding seasonal precipitation, predominant reductions are indicated for spring, summer, and autumn. For winter, however, projections are distinctly different (GCMs: rainfall decrease; RCMs: increase only in the northernmost parts of the Mediterranean region; SD: widespread increases in the northern and western parts in several studies). Different results are obtained for rainfall extremes, but the entire precipitation distribution tends to shift towards higher and lower values. Apart from some sub-regional deviations, there is a predominant increase in future dry period durations. For near-surface winds, only a few studies are available, and they project some decline mainly for the winter season.     

24个CMIP5模式对长江流域模拟能力评估

根据1961~2005年长江流域气象站点的实测月降水量和气温数据,采用第5期全球耦合模式比较计划CMIP5(the Fifth Phase of Coupled Model Intercomparison Project)中24个全球气候模式(GCM)的模拟结果,通过计算模拟变量和观测变量平均值的相对误差、归一化的均方根误差、时间和空间相关系数,采用M-K趋势分析方法,分别选用在长江流域模拟气温和降水较好的5个模式进行集合平均,从时间的演变规律和空间的分布特征两方面,检验该模式集合对长江流域模拟气温和降水的能力。研究结果表明:各个模式模拟气温的能力要明显好于模拟降水的能力,但模拟气温较好的模式模拟降水的能力并不一定突出;模式集合的结果表明:在时间尺度上,模式集合平均结果与观测值拟合程度较好,且模式集合的结果振荡幅度较观测值小;在空间尺度上,模式集合的空间分布趋势与观测值大致相同,说明采用的模式集合结果用于预估未来长江流域降水的时空分布特征和演变规律是可行的。     

Exploring the efficiency of bias corrections of regional climate model output for the assessment of future crop yields in Europe

Excessive summer drying and reduced growing season length are expected to reduce European crop yields in future. This may be partly compensated by adapted crop management, increased CO₂ concentration and technological development. For food security, changes in regional to continental crop yield variability may be more important than changes in mean yields. The assessment of changes in regional and larger scale crop variability requires high resolution and spatially consistent future weather, matching a specific climate scenario. Such data could be derived from regional climate models (RCMs), which provide changes in weather patterns. In general, RCM output is heavily biased with respect to observations. Due to the strong nonlinear relation between meteorological input and crop yields, the application of this biased output may result in large biases in the simulated crop yield changes. The use of RCM output only makes sense after sufficient bias correction. This study explores how RCM output can be bias corrected for the assessment of changes in European and subregional scale crop yield variability due to climate change. For this, output of the RCM RACMO of the Royal Netherlands Meteorological Institute was bias corrected and applied within the crop simulation model WOrld FOod STudies to simulate potential and water limited yields of three divergent crops: winter wheat, maize and sugar beets. The bias correction appeared necessary to successfully reproduce the mean yields as simulated with observational data. It also substantially improved the year-to-year variability of seasonal precipitation and radiation within RACMO, but some bias in the interannual variability remained. This is caused by the fact that the applied correction focuses on mean and daily variability. The interannual variability of growing season length, and as a consequence the potential yields too, appeared even deteriorated. Projected decrease in mean crop yields is well in line with earlier studies. No significant change in crop yield variability was found. Yet, only one RCM is analysed in this study, and it is recommended to extend this study with more climate models and a slightly adjusted bias correction taking into account the variability of larger time scales as well.     

Freshwater stress on small island developing states: population projections and aridity changes at 1.5 and 2 °C

Small island developing states (SIDS) face multiple threats from anthropogenic climate change, including potential changes in freshwater resource availability. Due to a mismatch in spatial scale between SIDS landforms and the horizontal resolution of global climate models (GCMs), SIDS are mostly unaccounted for in GCMs that are used to make future projections of global climate change and its regional impacts. Specific approaches are required to address this gap between broad-scale model projections and regional, policy-relevant outcomes. Here, we apply a recently developed methodology that circumvents the GCM limitation of coarse resolution in order to project future changes in aridity on small islands. These climate projections are combined with independent population projections associated with shared socioeconomic pathways (SSPs) to evaluate overall changes in freshwater stress in SIDS at warming levels of 1.5 and 2 °C above pre-industrial levels. While we find that future population growth will dominate changes in projected freshwater stress especially toward the end of the century, projected changes in aridity are found to compound freshwater stress for the vast majority of SIDS. For several SIDS, particularly across the Caribbean region, a substantial fraction (~?25%) of the large overall freshwater stress projected under 2 °C at 2030 can be avoided by limiting global warming to 1.5 °C. Our findings add to a growing body of literature on the difference in climate impacts between 1.5 and 2 °C and underscore the need for regionally specific analysis.     

Projecting canopy cover change in Tasmanian eucalypt forests using dynamically downscaled regional climate models

Loss of forest cover is a likely consequence of climate change in many parts of the world. To test the vulnerability of eucalypt forests in Australia’s island state of Tasmania, we modelled tree canopy cover in the period 2070–2099 under a high-emission scenario using the current climate–canopy cover relationship in conjunction with output from a dynamically downscaled regional climate model. The current climate–canopy cover relationship was quantified using Random Forest modelling, and the future climate projections were provided by three dynamically downscaled general circulation model (GCM) simulations. Three GCMs were used to show a range of projections for the selected scenario. We also explored the sensitivity of key endemic and non-endemic Tasmanian eucalypts to climate change. All GCMs suggested that canopy cover should remain stable (proportional cover change <10 %) across ~70 % of the Tasmanian eucalypt forests. However, there were geographic areas where all models projected a decline in canopy cover due to increased summer temperatures and lower precipitation, and in addition, all models projected an increase in canopy cover in the coldest part of the state. The model projections differed substantially for other areas. Tasmanian endemic species appear vulnerable to climate change, but species that also occur on the mainland are likely to be less affected. Given these changes, restoration and carbon sequestration plantings must consider the species and provenances most suitable for future, rather than present, climates.     

Productivity of rainfed wheat as affected by climate change scenario in northeastern Punjab, India

Wheat (Triticum aestivum L.) is grown as a rainfed crop in the sub-mountainous region of the Punjab state of India, with low crop and water productivity. The present study aims to assess the effect of climate change scenario (A1B) derived from PRECIS—a regional climate model—on wheat yield and water productivity. After minimizing bias in the model climate data for mid-century (2021–2050), evapotranspiration (ET) and yield of wheat crop were simulated using Decision Support System for Agrotechnology Transfer, version 4.5, model. In the changed climate, increased temperature would cause reduction in wheat yield to the extent of 4, 32 and 61 % in the mid-century periods between 2021–2030, 2031–2040 and 2041–2050, respectively, by increasing water stress and decreasing utilization efficiency of photosynthetically active radiation. The decreases in crop water productivity would be 40, 56 and 76 %, respectively, which are caused by decreased yield and increased ET. Planting of wheat up to November 25 till the years 2030–2031 seems to be helpful to mitigate the climate change effect, but not beyond that.     

Climate change impacts,vulnerability and adaptive capacity of the electrical energy sector in Cyprus

The purpose of this study was to investigate the climate change impacts, vulnerability and adaptive capacity of the electrical energy sector in Cyprus. Spatial vulnerability of the island was assessed using the degree-day indicator to investigate heating and cooling demands in the near future using daily temperature projections from regional climate models (RCMs). Using daily electrical energy consumption data for the present climate, an impact model linking consumption and temperature was constructed and this relationship was projected to the future climate using the data from the RCMs and assuming the same technology use. Our impact model results showed that for the period between November and April (‘cold period’), a decreasing trend in electrical energy consumption is evident due to warmer conditions in the near future, while for the period between May and October (‘warm period’), an increasing trend in electricity consumption is evident as warmer conditions dominate by 2050. Regarding the spatial vulnerability assessment, the cooling degree-day indicator testified that major increases in cooling demand, between 100 and 200 degree-days, are expected in inland and southern regions during the summer in the near future. In addition, increases of about 20–50 degree-days are anticipated during autumn. Conversely, energy demand for heating is projected to decrease during spring and winter, especially in the higher elevation parts of the island. More precisely, reductions of about 30–75 degree-days are projected during spring, while greater reductions of about 60–90 degree-days are expected during winter in heating demand, especially for in the near future. The ability of the energy sector to adapt and follow these changes was deemed to be satisfactory reducing the overall vulnerability of the sector to future climate change.     

不同RCP情景下未来汉江流域气象干旱变化趋势预估研究

采用来自国际比较计划CMIP5的5个全球气候模式(GCMs)数据，同时这些模式数据也是跨行业影响模式比较计划(ISIMIP)采用的气候模式数据，以RCP2.6和RCP8.5两种情景下的日降雨预估数据，计算了汉江流域不同时段的标准化降水指数(SPI)，以此作为预估未来干旱变化趋势的主要依据。以汉江流域22个气象站点的降雨量观测数据(1960~2004年)和2020~2059年气候模式数据，对比分析了这两个时期干旱事件发生的严重程度、次数和历时特征。结果表明：在干旱严重程度方面，轻度干旱和中度干旱有减轻的趋势，但严重干旱有加重的趋势。由于不同GCMs模拟能力的差异性，对干旱事件的发生次数和历时的分析有一定差异，但总体仍表现为干旱次数减少和历时减短的趋势。通过对比历史时期降水观测数据和GCMs模式预估数据评估结果，表明HadGEM2-ES模式对降水的拟合性最好，而GFDL-ESM2M和IPSL-CM5A-LR在干旱方面表现出较好的模拟能力。     

Regional assessment of climate change impacts on maize productivity and associated production risk in Switzerland

A simple model of yield was used along with climate scenarios to assess the impact of climate change on grain maize productivity and associated economic risk in Switzerland. In a first application, changes in the precipitation regime alone were shown to affect the distribution of yield considerably, with shifts not only in the mean but also in the standard deviation and the skewness. Production risk was found to respond more markedly to changes in the long-term mean than in the inter-annual variability of seasonal precipitation amounts. In a further application, yield projections were generated with respect to a full climate scenario, with the emission pathway as specified in the IPCC A2 scenario. Anticipation of the sowing date was found to reduce the negative impact of climate change on yield stability, but was not sufficient to ensure average productivity levels comparable to those observed at present. We argued that this was caused by the reduction in the duration of the growing season, which had a stronger impact than suggested by previous studies. Assuming no change in price relations, the results also revealed a strong increase in production risk with climate change, with more than a doubling in the probability of yield falling short of a critical threshold as compared to today’s situation.     

Variability in crop yields associated with climate anomalies in China over the past three decades

We used simple and explicit methods, as well as improved datasets for climate, crop phenology and yields, to address the association between variability in crop yields and climate anomalies in China from 1980 to 2008. We identified the most favourable and unfavourable climate conditions and the optimum temperatures for crop productivity in different regions of China. We found that the simultaneous occurrence of high temperatures, low precipitation and high solar radiation was unfavourable for wheat, maize and soybean productivity in large portions of northern, northwestern and northeastern China; this was because of droughts induced by warming or an increase in solar radiation. These climate anomalies could cause yield losses of up to 50 % for wheat, maize and soybeans in the arid and semi-arid regions of China. High precipitation and low solar radiation were unfavourable for crop productivity throughout southeastern China and could cause yield losses of approximately 20 % for rice and 50 % for wheat and maize. High temperatures were unfavourable for rice productivity in southwestern China because they induced heat stress, which could cause rice yield losses of approximately 20 %. In contrast, high temperatures and low precipitation were favourable for rice productivity in northeastern and eastern China. We found that the optimum temperatures for high yields were crop specific and had an explicit spatial pattern. These findings improve our understanding of the impacts of extreme climate events on agricultural production in different regions of China.     

Probabilistic impacts of climate change on flood frequency using response surfaces I: England and Wales

The impacts of climate change on hydrology are an important focus of research around the world, but the use of large ensembles to drive impact models is not necessarily straightforward and has to be redone when new projections are released. Here, an alternative sensitivity framework approach is demonstrated, using a set of typical response surfaces alongside the probabilistic UK Climate Projections (UKCP09). These projections comprise sets of 10,000 changes in a number of variables, available for 10 river-basin regions covering England and Wales. Estimates of the potential range of impacts on 20-year return period flood peaks are presented for different types of catchment in each region. Regional average impact ranges are compared for a number of time horizons and emissions scenarios. Results show clear differences in impacts between catchments of different types and between regions. South-East England has the highest impacts with the greatest uncertainty range, while the Dee region has the lowest impacts and smallest uncertainty range. Regional differences are due to both spatial differences in projections and a differing regional balance in the number of catchments of each type. Ease of application of multiple projections is a clear advantage of this sensitivity-based approach to impact assessment, which could be extended to other regions and sectors.     

Implications of climate change in sustained agricultural productivity in South Asia

One of the targets of the United Nations ‘Millennium Development Goals’ adopted in 2000 is to cut in half the number of people who are suffering from hunger between 1990 and 2015. However, crop yield growth has slowed down in much of the world because of declining investments in agricultural research, irrigation, and rural infrastructure and increasing water scarcity. New challenges to food security are posed by accelerated climatic change. Considerable uncertainties remain as to when, where and how climate change will affect agricultural production. Even less is known about how climate change might influence other aspects that determine food security, such as accessibility of food for various societal groups and the stability of food supply. This paper presents the likely impacts of thermal and hydrological stresses as a consequence of projected climate change in the future potential agriculture productivity in South Asia based on the crop simulation studies with a view to identify critical climate thresholds for sustained food productivity in the region. The study suggests that, on an aggregate level, there might not be a significant impact of global warming on food production of South Asia in the short term (<2°C; until 2020s), provided water for irrigation is available and agricultural pests could be kept under control. The increasing frequency of droughts and floods would, however, continue to seriously disrupt food supplies on year to year basis. In long term (2050s and beyond), productivity of Kharif crops would decline due to increased climate variability and pest incidence and virulence. Production of Rabi crops is likely to be more seriously threatened in response to 2°C warming. The net cereal production in South Asia is projected to decline at least between 4 and 10% under the most conservative climate change projections (a regional warming of 3°C) by the end of this century. In terms of the reference to UNFCCC Article 2 on dangerous anthropogenic (human-induced) interference with the climate system, the critical threshold for sustained food productivity in South Asia appears to be a rise in surface air temperature of ~2°C and a marginal decline in water availability for irrigation or decrease in rainfall during the cropping season.     

Heat stress and crop yields in the Mediterranean basin: impact on expected insurance payouts

Insurance programmes have been indicated as a tool to reduce the economic risk associated with climate change, and crop growth simulation models can be used effectively to assess future trends in crop insurance payouts. This paper assesses the economic role of increasing weather extremes under future climate change on the expected insurance payouts for durum wheat (Triticum turgidum L. spp. durum) over the Mediterranean basin, focusing attention on the effects of heat stresses (HSs). A crop growth simulation, Sirius Quality version 2 (SQ2), calibrated for three varieties (long, medium and short growth cycle) was applied on seven sites under present (1975–1990) and future climate conditions (2030–2050) obtained from five regional circulation models under SRES scenario A1b. The intensity of HSs at anthesis was included as reducing factor of yield originally simulated by SQ2 calculated according to a specific empirical model. Simulated yields were then fitted to the most appropriate distribution, which was used to calculate the expected payouts according to the probability of yields being below a guaranteed level. We found that the simulated crop yields were, in general, negatively skewed and that Weibull probability density function (PDF), admitting negative skewing, provided the best performances in their fitting. The simulation of HSs modified the original shape of the Weibull PDF by increasing the skewness of the distribution. The results of the insurance model indicated that the modification of crop PDFs induced by HSs led to a general increase in payouts with respect to unstressed conditions, with a marked difference between present (+11 %, on average for the selected sites) and future periods (+25 %). When compared to the present, a general decrease in payouts (?1.1 %) was observed when HSs were not included in the simulations. Conversely, HSs impact resulted in a general increase in payouts (+10.3 %) where the highest increase was detected for the long growth cycle variety (+16.6 %) and the lowest for that with short growth cycle (?1.6 %). These results emphasize the importance of the appropriate characterization of crop yield distribution, the economic implications of HSs in a risk management context and a possible strategy to cope with climate change and variability.     

Looking beyond the average agricultural impacts in defining adaptation needs in Europe

The effects of climate change on agriculture are often characterised by changes in the average productivity of crops; however, these indicators provide limited information regarding the risks associated with fluctuations in productivity resulting from future changes in climate variability that may also affect agriculture. In this context, this study evaluates the combined effects of the risks associated with anomalies reflected by changes in the mean crop yield and the variability of productivity in European agro-climatic regions under future climate change scenarios. The objective of this study is to evaluate adaptation needs and to identify regional effects that should be addressed with greater urgency in the light of the risks and opportunities that are identified. The results show differential effects on regional agriculture and highlight the importance of considering both regional average impacts and the variability in crop productivity in setting priorities for the adaptation and maintenance of rural incomes and agricultural insurance programmes.     

Climate change adaptation and mitigation in smallholder crop–livestock systems in sub-Saharan Africa: a call for integrated impact assessments

African mixed crop–livestock systems are vulnerable to climate change and need to adapt in order to improve productivity and sustain people’s livelihoods. These smallholder systems are characterized by high greenhouse gas emission rates, but could play a role in their mitigation. Although the impact of climate change is projected to be large, many uncertainties persist, in particular with respect to impacts on livestock and grazing components, whole-farm dynamics and heterogeneous farm populations. We summarize the current understanding on impacts and vulnerability and highlight key knowledge gaps for the separate system components and the mixed farming systems as a whole. Numerous adaptation and mitigation options exist for crop–livestock systems. We provide an overview by distinguishing risk management, diversification and sustainable intensification strategies, and by focusing on the contribution to the three pillars of climate-smart agriculture. Despite the potential solutions, smallholders face major constraints at various scales, including small farm sizes, the lack of response to the proposed measures and the multi-functionality of the livestock herd. Major institutional barriers include poor access to markets and relevant knowledge, land tenure insecurity and the common property status of most grazing resources. These limit the adoption potential and hence the potential impact on resilience and mitigation. In order to effectively inform decision-making, we therefore call for integrated, system-oriented impact assessments and a realistic consideration of the adoption constraints in smallholder systems. Building on agricultural system model development, integrated impact assessments and scenario analyses can inform the co-design and implementation of adaptation and mitigation strategies.F     

Regionalization of climate scenarios impacts on maize production and the role of cultivar and planting date as an adaptation strategy

Understanding climate change and its impacts on crops is crucial to determine adaptation strategies. Simulations of climate change impacts on agricultural systems are often run for individual sites. Nevertheless, the scaling up of crop model results can bring a more complete picture, providing better inputs for the decision-making process. The objective of this paper was to present a procedure to assess the regional impacts of climate scenarios on maize production, as well as the effect of crop cultivars and planting dates as an adaptation strategy. The focus region is Santa Catarina State, Brazil. The identification of agricultural areas cultivated with annual crops was done for the whole state, followed by the coupling of soil and weather information necessary for the crop modeling procedure (using crop model and regional circulation models). The impact on maize yields, so as the effect of adaptation strategies, was calculated for the 2012–2040 period assuming different maize cultivars and planting dates. Results showed that the exclusion of non-agricultural areas allowed the crop model to correctly simulate local and regional production. Simulations run without adaptation strategies for the 2012–2040 period showed reductions of 11.5–13.5 % in total maize production, depending on the cultivar. By using the best cultivar for each agricultural area, total state production was increased by 6 %; when using both adaptation strategies—cultivar and best planting date—total production increased by 15 %. This analysis showed that cultivar and planting date are feasible adaptation strategies to mitigate deleterious effects of climate scenarios, and crop models can be successfully used for regional assessments.     

Range contraction and loss of genetic variation of the Pyrenean endemic newt <Emphasis Type="Italic">Calotriton asper</Emphasis> due to climate change

Many studies have identified climate warming to be among the most important threats to biodiversity. Climate change is expected to have stronger effects on species with low genetic diversity, ectothermic physiology, small ranges, low effective populations sizes, specific habitat requirements and limited dispersal capabilities. Despite an ever-increasing number of studies reporting climate change-induced range shifts, few of these have incorporated species’ specific dispersal constraints into their models. Moreover, the impacts of climate change on genetic variation within populations and species have rarely been assessed, while this is a promising direction for future research. Here we explore the effects of climate change on the potential distribution and genetic variation of the endemic Pyrenean newt Calotriton asper over the period 2020–2080. We use species distribution modelling in combination with high-resolution gridded climate data while subsequently applying four different dispersal scenarios. We furthermore use published data on genetic variation of both mtDNA and AFLP loci to test whether populations with high genetic diversity (nucleotide diversity and expected heterozygosity) or evolutionary history (unique haplotypes and K clusters) have an increased extinction risk from climate change. The present study indicates that climate change drastically reduces the potential distribution range of C. asper and reveals dispersal possibilities to be minimal under the most realistic dispersal scenarios. Despite the major loss in suitable climate, the models highlight relatively large stable areas throughout the species core distribution area indicating persistence of populations over time. The results, however, show a major loss of genetic diversity and evolutionary history. This highlights the importance of accounting for intraspecific genetic variation in climate change impact studies. Likewise, the integration of species’ specific dispersal constraints into projections of species distribution models is an important step to fully explore the effects of climate change on species potential distributions.     

Climate change in Algeria and its impact on durum wheat

According to IPCC reports, the Mediterranean basin and particularly the North African area are amongst the most vulnerable regions to climate change. However, the information concerning the North African zone is very limited, and studies on climate change have never been conducted in Algeria up to now. This paper aims at bridging this information gap and initiates a first research on the impact of climate change on durum wheat cropping, the most strategic commodity in the food system and in the national economy of Algeria. Climate projections for the distant future (2071–2100), obtained from the ARPEGE-Climate model of Météo-France run under the medium A1B SRES scenario, are introduced into a simple agrometeorological crop model previously validated with field data. Two options for the sowing date are assessed: a dynamical date, chosen within the traditional sowing window by means of a rainfall criterion, or a prescribed date with supplemental irrigation on the same day. Crop development is modelled using thermal time, and maximum yield is determined from the accumulation of solar radiation. A water stress index is inferred from a daily water balance model, and actual yield is estimated from potential yield corrected by the water stress index. The model also takes into account the occurrence of dry periods during the growing season, which can induce partial or total failure of the crop cycle. Two stations, representative of two of the three agroclimatic areas where durum wheat is grown, were chosen: Algiers in the central northern region and Bordj Bou Arreridj in the eastern high plains. Climate change is not similar for both areas, but a tendency towards aridity is clear especially in spring. Future temperature and potential evapotranspiration increase in both regions with a maximum in spring and summer. In Algiers, rainfall will decrease throughout the year and mainly in spring and summer. Conversely, summer precipitation in Bordj Bou Arreridj will increase significantly. In both regions, the autumn rains will increase in the future climate, the possibilities of early sowing will be improved, crop cycle will be reduced, and harvest will take place earlier. In Algiers, yields tend to decrease in the future climate, whereas in Bordj Bou Arreridj, a dynamical (earlier) sowing will tend to keep yields at their current level.     

Analyzing the carbon dynamics of central European forests: comparison of Biome-BGC simulations with measurements

Biogeochemical models are often used for making projections of future carbon dynamics under scenarios of global change. The aim of this study was to assess the accuracy of the process-based biogeochemical model Biome-BGC for application in central European forests from the lowlands to upper treeline as a pre-requisite for environmental impact assessments. We analyzed model behavior along an altitudinal gradient across the alpine treeline, which provided insights on the sensitivity of simulated average carbon pools to changes in environmental factors. A second set of tests included medium-term (30 years) simulations of carbon fluxes, and a third set of tests focused on daily carbon and water fluxes. Model results were compared to aboveground biomass measurements, leaf area index recordings as well as net ecosystem exchange (NEE) and actual evapotranspiration (AET) measurements. The simulated medium-term forest growth agreed well with measured data. Also daily NEE fluxes were simulated adequately in most cases. Problems were detected when simulating ecosystems close to the upper timberline (overestimation of measured growth and pool sizes), and when simulating daily AET fluxes (overestimation of measured fluxes). The results showed that future applications of Biome-BGC could benefit much from an improvement of model algorithms (e.g., the Q₁₀ model for respiration) as well as from a detailed analysis of the ecological significance of crucial parameters (e.g., the canopy water interception coefficient).