Assessment of climate change impact on the stream-flow in a typical debris flow watershed of Jianzhuangcuan catchment in Shaanxi Province, China

The study on the stream-flow change associated with future climate change scenarios has a practical significance for local socio-economic development and eco-environmental protection. A study on the Jianzhuangcuan catchments was carried out to quantify the expected impact of climate change on the stream-flow using a multi-model ensemble approach. Climate change scenarios were developed by ensemble four Global Climate Models, which showed good performance for Jianzhuangcuan catchment. Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, was used to investigate the impacts on stream-flow under climate change scenarios. The model was calibrated and validated using daily stream-flow records. The calibration and validation results showed that the SWAT model was able to simulate the daily stream-flow well, with Nash–Sutcliffe efficiency >0.83 for Yaoping Long station, for calibration and validation at daily and monthly scales. Their difference in simulating the stream-flow under future climate scenarios was also investigated. The results indicate a 0.6–0.9 °C increase in annual temperature and changes of 12.6–18.9 mm in seasonal precipitation corresponded to a change in stream-flow of about 0.62–3.67 for 2020 and 2030 scenarios. The impact of the climate change increased in both scenarios.     

未来不同排放情景下气候变化预估研究进展

概述未来不同排放情景下气候变化预估研究的主要进展。首先,对用于开展气候变化预估研究的不同复杂程度的气候系统及地球系统模式及其模拟能力进行了简要的介绍,指出虽然目前气候系统模式在很多方面存在着较大的不确定性,但大体说来可提供当前气候状况的可信模拟结果;进而介绍了IPCC不同的排放情景,以及不同排放情景下全球与东亚区域气候变化预估的主要结果。研究表明,尽管不同模式对不同情景下未来气候变化预估的结果存有差异,但对未来50～100年全球气候变化的模拟大体一致,即全球将持续增温、降水出现区域性增加。在此基础上,概述了全球气候模式模拟结果的区域化技术,并重点介绍了降尺度方法的分类与应用。同时对气候变化预估的不确定性进行了讨论。最后,对气候变化预估的研究前景进行了展望,并讨论了未来我国气候变化预估研究的重点发展方向。     

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.     

气候变化对江河流量变化趋势影响研究进展

气候变化对基于自然稳定气候假定的流量变化趋势的检测和水资源评价方法提出了挑战。在流量变化趋势的检测中分离出气候变化的影响,不仅对水资源管理和水利工程设计有重要的应用价值,而且有助于了解气候变化以何种方式、在何时、何地、已经或尚未对水文循环产生影响,对改进气候模型的模拟与预测有重要的科学价值。 统计方法是检验流量变化趋势显著性的有效工具。直接用气候模型模拟和预测未来径流变化的可靠性取决于模型对当代降水模拟的可信度。多个气候模型集合分析有可能在一定程度上减少模型对降水、径流模拟的不确定性。近年发展起来的多个气候模型集合分析与统计显著性检验技术结合的方法,有可能模拟并预测出气候强迫导致大尺度径流空间分布的变化。随着气候模型尤其是陆—气耦合的区域气候模型对降水模拟的改进,可以预见径流变化的检测、归因和预测的趋同化模拟已为期不远。将温室气体外强迫导致的水文气候变化作为一个因子引入到水资源评价中,对于水资源管理经济与生态评估,以及未来的发展规划将是一件十分重要的变革。      

Impact of climate change on hydrological conditions of Rhine and Upper Danube rivers based on the results of regional climate and hydrological models

The main objective of the Effects of Climate Change On the Inland waterway Networks (ECCONET) EU FP7 project was to assess the effect of climate change on the inland waterway transport network with special emphasis on the Rhine and Upper Danube catchments. The assessment was based on consolidation and analysis of earlier and existing research work as well as application of existing climate change and hydrological modelling tools. A key premise at the planning stage of the project had been that all impact studies conducted within ECCONET should be comparable with each other. This can be guaranteed by the common meteorological and hydrological basis. The climate model simulations, which are the most physics- and process-oriented tools for projecting the future climate evolution, include several uncertainties. In addition, uncertainties exist in the hydrological model simulations. In ECCONET, an effort was made to quantify the uncertainty range by using “representative projections” that represent both the lower and upper signals of hydrological low-flow parameters for 2021–2050 over the Rhine catchment. Their evaluation indicated that the finally chosen two regional climate model simulations could be applied also for the Upper Danube catchments as representative projections. The raw climate model outputs have been corrected to the observation data set through application of the linear scaling and the delta-change method. The first impact studies carried out after validation of the hydrological models resulted in discharge scenarios used as input to the economic models in ECCONET.     

Performance of general circulation models and their ensembles for the prediction of drought indices over India during summer monsoon

The drought during the months of June to September (JJAS) results in significant deficiency in the annual rainfall and affects the hydrological planning, disaster management, and the agriculture sector of India. Advance information on drought characteristics over the space may help in risk assessment over the country. This issue motivated the present study which deals with the prediction of drought during JJAS through standardized precipitation index (SPI) using nine general circulation models (GCM) product. Among these GCMs, three are the atmospheric and six are atmosphere–ocean coupled models. The performance of these GCM’s predicted SPI is examined against the observed SPI for the time period of 1982–2010. After a rigorous analysis, it can be concluded that the skill of prediction by GCM is not satisfactory, whereas the ability of the coupled models is better than the atmospheric models. An attempt has been made to improve the accuracy of predicted SPI using two different multi-model ensemble (MME) schemes, viz., arithmetic mean and weighted mean using singular value decomposition-based multiple linear regressions (SVD-MLR) of GCMs. It is found that among these MME techniques, SVD-MLR-based MME has more skill as compared to simple MME as well as individual GCMs.     

Surface Air Temperature Projection Under 1.5 ℃ Warming Threshold Based on Corrected Pattern Scaling Technique

The global mean temperature during the recent decade (2007-2016) has increased above 1 ℃ relative to the pre-industrial period (1861-1890). The climate change and impact under 1.5 ℃ warming in the future have become a great concern in global society. Temperature projections, especially in regional scale, show great uncertainty depending on used climate models. Taking advantage of pattern scaling technique and observed temperature changes during 1951-2005, we tried to project the temperature changes globally under 1.5 ℃ threshold relative to current climate state, i.e. about 1 ℃ warming around 2007-2016. The projections of 21 climate models from the Coupled Model Intercomparison Project - Phase 5 under four Representative Concentration Pathways (RCP2.6, RC4.5, RCP6.0 and RCP8.5) were used to correct the assumptions in pattern scaling. Results showed that the geographical distribution and warming amplitude of surface air temperature changes under 1.5 ℃ threshold are similar in the four scenarios. Warming over most of the land would be above 0.6 ℃, 0.3 ℃ warmer than ocean. The Northern Hemisphere would be 0.2 ℃ warmer than the Southern Hemisphere. The temperature over China region will increase by 0.7 ℃. The warming in the Northern and Central China under RCP2.6 was obviously higher than that in the other scenarios. Ignoring the impact of correction method, uncertainty in temperature projection based on pattern scaling was much smaller than that in climate models, both in global and regional scales.     

Impact of Climate Change on the Regional Hydrology – Scenario-Based Modelling Studies in the German Rhine Catchment

The aim of the study is an impact analysis of global climate change on regional hydrology with special emphasis on discharge conditions and floods. The investigations are focussed on the major part of the German Rhine catchment with a drainage area of approx. 110,000 km². This area is subdivided into 23 subcatchments. In a first step, the hydrological model HBV-D serves to simulate runoff conditions under present climate for the individual subbasins. Simulated, large scale atmospheric fields, provided by two different Global Circulation Models (GCMs) and driven by the emission scenario IS95a (“business as usual”) are then used as input to the method of expanded downscaling (EDS). EDS delivers local time series of scenario climate as input to HBV-D. In a final step, the investigations are focussed on the assessment of possible future runoff conditions under the impact of climate change. The study indicates a potential increase in precipitation, mean runoff and flood discharge for small return intervals. However, the uncertainty range that originates from the application of the whole model chain and two different GCMs is high. This leads to high cumulative uncertainties, which do not allow conclusions to be drawn on the development of future extreme floods.     

1470年以来中国东部季风区降水变化规律及趋势预估

超长系列的降水资料是分析气候变化和预估未来区域水安全形势的重要支撑,但目前观测资料只有几十年时间尺度,利用相关历史文献资料进行系列重建是延长现有观测资料的主要手段。基于《中国近五百年旱涝分布图集》和1959年以来实测降水资料,重建1470—2019年中国东部季风区的长序列降水数据,分析近550 a以来区域降水变化规律,剖析气候自然变异规律和人为气候变化对历史降水的影响,并通过CMIP6中等分辨率气候系统模式下的4种情景降水数据预估未来降水变化趋势。研究表明：(1)东部季风区降水年际分布不均,有明显的丰枯变化,1470—1691年整体处于枯水期,1692—1924年处于丰水期,1925年至今处于枯水期,存在准181 a周期;(2) 1991年后人为气候变化的影响已经显现,海河、黄河下游和长江流域部分降水倾向率发生显著变化,东部季风区总体降水增加趋势加快;(3)在未来气候变化情景下多年平均降水量较基准期(1961—1990年)显著增加,季节性变化加大,区域旱涝风险加剧。由于未来气候情景的不确定性,未来降水趋势预测的可信度尚未可知,需要进一步增强风险分析。     

Performance of multi-model ensembles for the simulation of temperature variability over Ontario,Canada

Climate ensembles utilize outputs from multiple climate models to estimate future climate patterns. These multi-model ensembles generally outperform individual climate models. In this paper, the performance of seven global climate model and regional climate model combinations were evaluated for Ontario, Canada. Two multi-model ensembles were developed and tested, one based on the mean of the seven combinations and the other based on the median of the same seven models. The performance of the multi-model ensembles were evaluated on 12 meteorological stations, as well as for the entire domain of Ontario, using three temperature variables (average surface temperature, maximum surface temperature, and minimum surface temperature). Climate data for developing and validating the multi-model ensembles were collected from three major sources: the North American Coordinated Regional Downscaling Experiment, the Digital Archive of Canadian Climatological Data, and the Climactic Research Unit’s TS v4.00 dataset. The results showed that the climate ensemble based on the mean generally outperformed the one based on the median, as well as each of the individual models. Future predictions under the Representative Concentration Pathway 4.5 (RCP4.5) scenario were generated using the multi-model ensemble based on the mean. This study provides credible and useful information for climate change mitigation and adaption in Ontario.     

Modeling the potential impact of climate change in northern Mexico using two environmental indicators

Modeling the deterioration of natural resources, especially water and soil that results from the global effects of climate change has become a powerful tool in the search for mitigation and adaptation measures. The objectives of this research were: (1) to model the potential impact of climate change for the period 2010-2039, and (2) to offer advice about future risks based on local radiative forcing or critical areas and taking into account two indicators of environmental quality, the aridity index (AI) and laminar wind erosion (LWE). Evaluation techniques for natural resources, similar to those applied by the Instituto Nacional de Ecología y Cambio Climático (National Institute of Ecology and Climate Change) were used for studies of ecological land use. The inputs include climate information (current and future), soil cover and edaphic properties related to the municipality of Gómez Palacio, Durango, Mexico (25.886° N, 103.476° W). According to calculations estimated from the anomalies for the mean annual rainfall and mean annual temperature, in a future climate change scenario, an average impact of approximately 63% would be caused by LWE, and the AI would change from its historical value of 9.3 to 8.7. It is estimated that the average impact on the AI in the future will be 0.53 ± 0.2.     

Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India

The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982–2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models.     

Projections of temperature changes over South America during the twenty-first century using CMIP6 models

A 22-member ensemble from CMIP6 is used to analyze the projected changes and seasonal behavior in surface air temperature over South America during the twenty-first century. In the future projections, CMIP6 models shown a high dependency to the socioeconomic pathway over each country of South America. The multimodel ensemble projects a continuous increase in the annual mean temperature over South America during the twenty-first century under the three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Besides, it was possible to identify consistent positive trends across all the models, with values between 0.45 ± 0.05 and 2.05 ± 0.31 °C cy⁻¹ under the historical experiment, however largest trends occurs for the projection periods (near, mid and far future), with values between − 0.87 ± 0.84 to 2.88 ± 0.60 °C cy⁻¹ (SSP1-2.6), 1.41 ± 0.88 to 5.32 ± 0.81 °C cy⁻¹ (SSP2-4.5) and 4.75 ± 0.58 to 8.76 ± 0.74 °C cy⁻¹ (SSP5-8.5) with maximum values at Bolivia, Brasil, Paraguay and Venezuela whilst minimum values for Argentina and Uruguay, regardless of the SSP scenario used. From the seasonal behavior analysis was possible to identify maximum values between January and March whilst minimum between June and July, except in Brasil, Venezuela and Guyana–Surinam–French Guayana, with annual range decreasing as the latidude decreases. By the end of the twenty-first century the annual mean temperature over South america is projected to increase between 0.92–2.11 °C, 0.97–3.37 °C and 1.27–6.14 °C under SSP1-2.6, SSP2-4.5 and SSP5-8.5 projection scenarios respectively. This projected increase of temperature across the continent will produce negative repercussions in the social, economic and political spheres. The results obtained in this study provide insights about the CMIP6 performance over this region, which can be used to develop adaptation strategies and might be useful for the adaptation to the climate change.

     

气候变化下中国主要流域气象水文干旱潜在风险传播

全球气候变化影响了气象水文要素的时空分布特性,气象水文干旱事件的转化关系及风险传播特征亟待研究。基于站点、栅格观测资料和CMIP5（Coupled Model Inter-comparison Project Phase5）的19个气候模式输出数据,采用新安江等4个水文模型模拟了中国135个流域历史（1961—2005年）和未来时期（2011—2055年,2056—2100年）的水文过程,计算了SPI（Standard Precipitation Index）和SRI（Standard Runoff Index）干旱指标,通过游程理论识别了气象干旱与水文干旱事件,利用Copula函数与最大可能权函数度量二维干旱风险特征,定量评估了气象干旱至水文干旱的潜在风险传播特性。结果表明:①气象-水文干旱对气候变化响应强烈,华北和东北地区的干旱联合重现期增大,干旱潜在风险减小,华中和华南地区的干旱联合重现期减少60%~80%,干旱潜在风险增加;②气象干旱与水文干旱风险在历史和未来时段均存在显著的正相关关系,相关系数超过0.99;③各流域水文干旱风险变化对气象干旱风险变化的敏感程度不会随气候变暖发生较大变化,但未来北方地区水文干旱同气象干旱同时发生的概率将会小幅度增加。     

Hydrological and nitrogen distributed catchment modeling to assess the impact of future climate change at Trichonis Lake, western Greece

According to regional climatic models, climate change may affect Mediterranean lakes significantly in terms of water availability and quality. Trichonis Lake catchment covers a semi-mountainous area of 403 km² including the largest Greek lake by volume (2.6?×?10⁹?m³), located in western Greece. The impact of climate change on the hydrology and water quality of the lake, in terms of lake water level and nutrient concentrations, has been assessed. Water balance estimates and geographical information system tools were then used to set up a physically based, spatially distributed model. The calibrated model was simulated for two future scenarios specified by the Intergovernmental Panel on Climate Change: A2 (pessimistic) and B2 (more optimistic), which involved temperature/evaporation/evapotranspiration increase and small precipitation decrease. The model was calibrated efficiently for the 1990–1992 period. The two basic climatic scenarios illustrated that the responses of the lake water levels will show a decrease of 24.2 and 12 cm, respectively, and an increase of total nitrogen concentrations by 3.4 and 10%, in relation to the early 1990s values. These important findings suggest that mitigation and optimum management plans should be developed to eliminate the aforementioned climate change impacts and further research should follow.     

A review of observed and projected changes in climate for the islands in the Caribbean

Observed and projected changes in climate have serious socio-economic implications for the Caribbean islands. This article attempts to present basic climate change information—based on previous studies, available observations and climate model simulations—at spatial scales relevant for islands in the Caribbean. We use the General Circulation Model (GCM) data included in the Coupled Model Intercomparison Project phase 3 (CMIP3) and the UK Hadley Centre regional climate model (RCM) data to provide both present-day and scenario-based future information on precipitation and temperature for individual island states. Gridded station observations and satellite data are used to study 20th century climate and to assess the performance of climate models. With main focus on precipitation, we also discuss factors such as sea surface temperature, sea level pressure and winds that affect seasonal variations in precipitation. The CMIP3 ensemble mean and the RCM successfully capture the large-scale atmospheric circulation features in the region, but show difficulty in capturing the characteristic bimodal seasonal cycle of precipitation. Future drying during the wet season in this region under climate change scenarios has been noted in previous studies, but the magnitude of change is highly uncertain in both GCM and RCM simulations. The projected decrease is more prominent in the early wet season erasing the mid-summer drought feature in the western Caribbean. The RCM simulations show improvements over the GCM mainly due to better representation of landmass, but its performance is critically dependent on the driving GCM. This study highlights the need for high-resolution observations and ensemble of climate model simulations to fully understand climate change and its impacts on small islands in the Caribbean.     

气候模式应用中的不确定性分析

为了分析和评价气候变化对水文水资源的影响,根据中国东部地区1956～2000年实测年降水量系列数据,采用多年均值、系列趋势变化指标和反映系列可持续特性的Hurst系数等3项指标,对CGCMA3、MPI-ECHAM5和平均GCM等3种气候模式模拟的同期年降水系列数据进行了检验。检验结果表明,3种气候模式模拟结果与实测值之间,以及不同模式模拟结果之间,不仅定量方面差异很大,而且在定性方面甚至出现相悖的结果,不确定性十分显著。同样,根据3种气候模式预测的未来2001～2050年年降水量和季平均气温推得的未来年径流量系列数据,其多年均值、系列趋势变化指标和干旱年年径流量多项指标也存在明显的不确定性。据此对气候模式及其应用提出了相应的建议。     

Construction of Statistical-Dynamic Prediction Model for the Asian-Australian Summer Monsoon Based on the Predictable Mode Analysis Method and Assessment of Its Predictive Skills

Due to the limitations of model performances, the predictive skills of current climate models for the Asian-Australian summer monsoon precipitation are still poor. The prediction based on the combination of statistical and dynamic approaches is an effective way to improve the predictive skills. We used such method to identify the predictable modes of the Asian-Australian summer monsoon precipitation with clear physical interpretation from the historical observational data. Then we combined the principal components time series of these modes predicted by the coupled models, which is derived from the seasonal prediction experiments in the ENSEMBLES project, and the corresponding spatial patterns derived from the above observational analysis to reconstruct the precipitation field. These formed a statistical-dynamic seasonal prediction model for the Asian-Australian summer monsoon precipitation. We analyzed the predictive skills of the model at 1-, 4-and 7-month leads. The result shows that the forecast skills of the statistical-dynamic prediction model are higher than those of the simple dynamic predictions. In addition, the predictive skills of the Multi-Model Ensemble (MME) mean are superior to those of any individual models. Therefore, it is very necessary to implement multi-model ensemble prediction for the monsoon precipitation.     

气候自然变异在气候变化对水资源影响 评价中的贡献分析——Ⅰ.基准期的模型与方法

基于气候变化影响的水资源评价对水资源规划和管理具有重要意义,随着全球气候变化影响的加剧,这一研究显得越来越紧迫。在目前的气候变化研究中,很少考虑气候自然波动的影响(气候自然变异),常将所有的变化单独归因于气候变化的影响,这在气候变化的影响评价中可能导致错误的理解与判断。气候自然变异分析由于缺乏超长系列的数据资料而长期被人为避开。针对这一问题,本研究提出模型方法体系,通过历史基准期的长系列模拟来分析气候自然变异的影响。选取常用的1961~1990年水文系列作为基准期,提出一种基于拉丁超立方体抽样技术的季节分段抽样模拟方法,实现对气候自然变异的模拟。应用水文模型TOPMODEL对基准期的径流系列进行模拟,基于不确定性分析GLUE方法对基准期内水文模型参数不确定性进行分析,并探讨了气候自然变异的影响。研究结果表明,在气候变化影响评价中,气候自然变异的影响不可忽略,应在气候变化的影响中加以区分和界定。     

Environmental change over the last millennium recorded in two contrasting crater lakes in western Uganda,eastern Africa (Lakes Kasenda and Wandakara)

The last millennium is a key period for understanding environmental change in eastern Africa, as there is clear evidence of marked fluctuations in climate (effective moisture) that place modern concern with future climate change in a proper context, both in terms of environmental and societal impacts and responses. Here, we compare sediment records from two small, nearby, closed crater lakes in western Uganda (Lake Kasenda and Lake Wandakara), spanning the last 700 (Wandakara) and 1200 years (Kasenda) respectively. Multiproxy analyses of chemical sedimentary parameters (including C/N ratios, δ¹³C of bulk organic matter and δ¹³C and δ¹⁸O of authigenic carbonates) and biotic remains (diatoms, aquatic macrofossils, chironomids) suggest that Kasenda has been sensitive to climate over much of this period, and has shown substantial fluctuations in conductivity, while Wandakara has a more muted response, likely due to the increasing dominance of human activity as a driver of change within the lake and catchment over the length of our record. Evidence from both records, however, supports the idea that lake levels were low from ～AD 700–1000 AD, with increasing aridity from AD 1100–1600, and brief wet phases around AD 1000 and 1400. Wetter conditions are recorded in the 1700s, but drought returned by the end of the century and into the early 1800s, becoming wetter again from the mid-1800s. Comparison with other records across eastern Africa suggests that while some events are widespread (e.g. aridity beginning ～ AD 1100), at other times there is a more complex spatial signature (e.g. in the 1200s to 1300s, and from the 1400s to 1600s). This study highlights the important role of catchment-specific factors (e.g. lakemorphometry, catchment size, and human impact) in modulating the sensitivity of proxies, and lake records, as indicators of environmental change, and potential hazards when regional inference is based on a single site or proxy.