Streamflow response to regional climate model output in the mountainous watershed: a case study from the Swiss Alps

Regional climate model (RCM) outputs are often used in hydrological modeling, in particular for streamflow forecasting. The heterogeneity of the meteorological variables such as precipitation, temperature, wind speed and solar radiation often limits the ability of the hydrological model performance. This paper assessed the sensitivity of RCM outputs from the PRUDENCE project and their performance in reproducing the streamflow. The soil and water assessment tool was used to simulate the streamflow of the Rhone River watershed located in the southwestern part of Switzerland, with the climate variables obtained from four RCMs. We analyzed the difference in magnitude of precipitation, maximum and minimum air temperature, and wind speed with respect to the observed values from the meteorological stations. In addition, we also focused on the impact of the grid resolution on model performance, by analyzing grids with resolutions of 50 × 50 and 25 × 25 km². The variability of the meteorological inputs from various RCMs is quite severe in the studied watershed. Among the four different RCMs, the Danish Meteorological Institute provided the best performance when simulating runoff. We found that temperature lapse rate is significantly important in the mountainous snow and glacier dominated watershed as compared to other variables like precipitation, and wind speed for hydrological performance. Therefore, emphasis should be given to minimum and maximum temperature in the bias correction studies for downscaling climatic data for impact modeling in the mountainous snow and glacier dominated complex watersheds.     

The influence of model structure on groundwater recharge rates in climate-change impact studies

Numerous modeling approaches are available to provide insight into the relationship between climate change and groundwater recharge. However, several aspects of how hydrological model choice and structure affect recharge predictions have not been fully explored, unlike the well-established variability of climate model chains—combination of global climate models (GCM) and regional climate models (RCM). Furthermore, the influence on predictions related to subsoil parameterization and the variability of observation data employed during calibration remain unclear. This paper compares and quantifies these different sources of uncertainty in a systematic way. The described numerical experiment is based on a heterogeneous two-dimensional reference model. Four simpler models were calibrated against the output of the reference model, and recharge predictions of both reference and simpler models were compared to evaluate the effect of model structure on climate-change impact studies. The results highlight that model simplification leads to different recharge rates under climate change, especially under extreme conditions, although the different models performed similarly under historical climate conditions. Extreme weather conditions lead to model bias in the predictions and therefore must be considered. Consequently, the chosen calibration strategy is important and, if possible, the calibration data set should include climatic extremes in order to minimise model bias introduced by the calibration. The results strongly suggest that ensembles of climate projections should be coupled with ensembles of hydrogeological models to produce credible predictions of future recharge and with the associated uncertainties.     

基于不同排放情景的洞庭湖流域水资源演化研究

流域水资源演化与气候变化和人类活动紧密相关,气候变化与人类活动的加剧极大地改变了流域水文循环。通过相似性和独立性分析,从CMIP5公开发布的47个气候模式中筛选出5个代表性气候模式,然后计算未来高、中、低3种不同排放情景下的气温和降水,构造符合研究区产汇流特性的水文模型,计算洞庭湖流域水资源量并分析其演化规律。结果表明:不论温室气体排放水平如何,洞庭湖流域水资源量在未来60a呈现增加态势,汛期水量增加概率加大,而在高排放情景下枯季水资源量表现为减少趋势;未来洞庭湖流域水资源的时程分配将更趋不均匀化,而温室气体的持续排放将使其变化加剧。     

遥感降水资料后处理研究综述

获取高精度高分辨率的降水数据对于流域水文分析、水资源管理及洪涝干旱监测等均具有重要意义。遥感技术虽然能有效再现降水的时空分布,但原始遥感降水资料无法满足水文领域对高精度高分辨率数据的需求,需要开展遥感降水资料的后处理研究。介绍获取降水资料的主要方法,包括雨量站观测、地面天气雷达估测以及气象卫星反演,讨论各方法的主要优势和当前存在的问题,在此基础上综述遥感降水资料的后处理方法研究进展,包括空间降尺度、偏差校正以及产品融合,并归纳后处理降水产品的评价指标,最后指出今后的研究重点:发展和改进降水估计技术;构建更为合理的多源降水数据融合框架;加强降尺度法对比研究,进一步改进和完善降尺度法;开展降水相关的不确定性分析。     

Simulated climate conditions in Europe during the Marine Isotope Stage 3 stadial

Kjellström, E., Brandefelt, J., Näslund, J.‐O., Smith, B., Strandberg, G., Voelker, A. H. L. & Wohlfarth, B. 2010: Simulated climate conditions in Europe during the Marine Isotope Stage 3 stadial. Boreas, 10.1111/j.1502‐3885.2010.00143.x. ISSN 0300‐9483. State‐of‐the‐art climate models were used to simulate climate conditions in Europe during Greenland Stadial (GS) 12 at 44 ka BP. The models employed for these simulations were: (i) a fully coupled atmosphere–ocean global climate model (AOGCM), and (ii) a regional atmospheric climate model (RCM) to dynamically downscale results from the global model for a more detailed investigation of European climate conditions. The vegetation was simulated off‐line by a dynamic vegetation model forced by the climate from the RCM. The resulting vegetation was then compared with the a priori vegetation used in the first simulation. In a subsequent step, the RCM was rerun to yield a new climate more consistent with the simulated vegetation. Forcing conditions included orbital forcing, land–sea distribution, ice‐sheet configuration, and atmospheric greenhouse gas concentrations representative for 44 ka BP. The results show a cold climate on the global scale, with global annual mean surface temperatures 5 °C colder than the modern climate. This is still significantly warmer than temperatures derived from the same model system for the Last Glacial Maximum (LGM). Regional, northern European climate is much colder than today, but still significantly warmer than during the LGM. Comparisons between the simulated climate and proxy‐based sea‐surface temperature reconstructions show that the results are in broad agreement, albeit with a possible cold bias in parts of the North Atlantic in summer. Given a prescribed restricted Marine Isotope Stage 3 ice‐sheet configuration, with large ice‐free regions in Sweden and Finland, the AOGCM and RCM model simulations produce a cold and dry climate in line with the restricted ice‐sheet configuration during GS 12. The simulated temperature climate, with prescribed ice‐free conditions in south‐central Fennoscandia, is favourable for the development of permafrost, but does not allow local ice‐sheet formation as all snow melts during summer.     

区域尺度近地表气候要素驱动数据研制的研究综述

区域和流域尺度的陆面、水文和生态模拟与同化,都需要高分辨率的大气近地表层风、温、压、湿、辐射、降水等资料作为驱动。介绍了流域尺度驱动数据的重要性,对现阶段大气驱动数据的制备方法进行评论,总结了降水、辐射、气温和其他近地表要素的若干产品,探讨了北美陆面数据同化系统、全球陆面数据同化系统、欧洲陆面数据同化系统和中国西部陆面数据同化系统中大气驱动数据的研究思路和研究成果,最后提出了现阶段制备流域尺度驱动数据存在的问题,并针对中国西部复杂地形的高时空分辨率驱动数据制备提出一些看法。     

Statistically downscaled climate change projections of surface temperature over Northern Italy for the periods 2021–2050 and 2070–2099

Future changes of seasonal minimum and maximum temperature over Northern Italy are assessed for the periods 2021–2050 and 2070–2099 against 1961–1990. A statistical downscaling technique, applied to the ENSEMBLES-Stream1 and CIRCE global simulations (A1B scenario), is used to reach this objective. The statistical scheme consists of a multivariate regression based on Canonical Correlation Analysis. The set-up of the statistical scheme is done using large-scale fields (predictors) derived from ERA40 reanalysis and seasonal mean minimum and maximum temperature (predictands) derived from observational data at around 75 stations, distributed over Northern Italy, over the period 1960–2002. A similar technique is also applied to the number of frost days and ice days at a reduced number of stations in order to construct projections on change of the selected extreme temperature indices for the two future periods. The evaluation of future projections for these extreme indices is relevant due to its impacts on transports, health, and agriculture. The downscaling scheme constructed using observed data is then applied to large-scale fields simulated by global models (A1B scenario), in order to construct scenarios on future change of seasonal temperature, mean and extreme indices, at local scale. The significance of changes is tested from the statistical point of view. The results show that significant increases could be expected to occur under scenario conditions in both minimum and maximum temperature, associated with a decrease in the number of frost and ice days in both periods and more intense to the end of the century.     

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.     

空间尺度转换与跨尺度信息链接:区域生态水文模拟研究空间尺度转换方法综述

空间尺度转换是近年来区域生态水文研究领域的一个基本研究问题。其需要主要是源于模型的输入数据与所能提供的数据空间尺度不一致以及模型所代表的地表过程空间尺度与所观测的地表过程空间尺度不吻合。综述了目前区域生态水文模拟研究中常用的空间尺度转换研究方法,包括向上尺度转换和向下尺度转换。详细论述了2种向下尺度转换方法: 统计学经验模型和动态模型。前者是通过将GCM大尺度数据与长期的历史观测数据比较从而建立统计学相关模型, 然后利用这个统计学经验模型进行向下的空间尺度转换. 然而动态模型并不直接对GCM数据进行向下尺度的转换,而是对与GCM进行动态耦合的区域气候模型(RCM) 的输出数据进行空间尺度转换. 通常后者所获得的数据精度要比前者高,但是一个主要缺点就是并不是全球所有的研究区域都有对应的RCM。还详细论述了2种向上尺度转换方法: 统计学经验模型和斑块模型。前者是建立一个能代表小尺度信息在大尺度上分布的密度分布概率函数, 然后利用这个函数在所需的大尺度上进行积分而求得大尺度所需的信息。而后者是根据相似性最大化原则将大尺度划分为若干个可操作的小尺度斑块,然后将计算的每个小尺度斑块的信息平均化得到大尺度所需的信息。通常在计算这种斑块化的小尺度信息的时候,对每个小尺度也会采用统计学经验模型来计算代表整个斑块小尺度的信息。建议用斑块模型与统计学经验模型相集合的方法来实现向上的空间尺度转换     

水文模型与陆面模式耦合研究进展

水文模型与陆面模式耦合是目前全球变化研究中的热点问题,如何实现分布式水文模型与陆面过程模式的双向耦合,并将其有机嵌入大气模式中,是未来大气环流模式(GCM)和区域气候模式(RCM)发展和完善的重要目标之一.在简单介绍陆面过程模式和水文模型发展历程的基础上,对水文模型和陆面过程耦合研究的国内外进展进行了综述,指出了模式耦合中存在的共同问题和未来工作的研究要点.最后,探讨了分布式水文模型与陆面模式耦合在全球变化研究框架中的地位与意义,并展望了陆面水文过程发展的主流趋势和研究方向.     

基于ERA5-Land数据集的玛纳斯河径流模拟研究

气象数据是水文过程研究的关键要素,再分析数据的发展为资料缺乏地区的径流模拟提供了新的解决方案。为研究ERA5-Land再分析数据集在径流模拟中的适用性,本文以玛纳斯河流域肯斯瓦特水文站以上流域为研究区,选取多个评价指标对ERA5-Land降水和温度进行准确性评价,并采用经验模态分解(EOF)分析其在研究区内的分布特点。在准确性方面,ERA5-Land与实测数据具有较好相关性,降水探测率为0.96,能反映大多数的降水事件,但与实测数据相比总体偏高21.81%,气温准确性好于降水,总体拟合效果较好,最优范围为-520 ℃,在极值部分不确定性有所增加。EOF决定性模态表明研究区内降水、气温变化趋势基本一致,即易受大尺度天气系统影响。利用该数据集驱动SWAT模型在月、日尺度上对玛纳斯河流域进行径流模拟,在验证期纳什系数(NSE)分别为0.88和0.82,具有较好的模拟效果。ERA5-Land再分析数据集可为西北缺乏实测气象资料地区径流模拟提供参考。     

基于分布式水文模型的雅鲁藏布江径流水源组成解析

为开展河川径流的水源解析,构建过程描述和本构参数两方面均有较强物理性的分布式水文模型。以雅鲁藏布江为对象,利用水文分区曲线对降雨、融雪和融冰等不同水源主导的流量过程进行划分,以划分的流量过程线子集对相应水文过程参数进行分步率定,提高了水文模型参数的物理性,以此构建了雅鲁藏布江流域分布式水文模型及参数集,内部多个水文站点和流域雪水当量的验证表明模型具有良好的性能。基于模型解析了2001-2015年间雅鲁藏布江的径流水源组成,降雨、融雪、融冰水源对总径流量贡献的比例分别为66%、20%和14%。本文方法对高山寒区径流的水源解析有普遍意义,结果对理解气候变化下雅鲁藏布江径流变化趋势有参考价值。     

基于分布式水文模型的雅鲁藏布江径流水源组成解析

为开展河川径流的水源解析，构建过程描述和本构参数两方面均有较强物理性的分布式水文模型。以雅鲁藏布江为对象，利用水文分区曲线对降雨、融雪和融冰等不同水源主导的流量过程进行划分，以划分的流量过程线子集对相应水文过程参数进行分步率定，提高了水文模型参数的物理性，以此构建了雅鲁藏布江流域分布式水文模型及参数集，内部多个水文站点和流域雪水当量的验证表明模型具有良好的性能。基于模型解析了2001—2015年间雅鲁藏布江的径流水源组成，降雨、融雪、融冰水源对总径流量贡献的比例分别为66%、20%和14%。本文方法对高山寒区径流的水源解析有普遍意义，结果对理解气候变化下雅鲁藏布江径流变化趋势有参考价值。     

Evaluating multiple performance criteria to calibrate the distributed hydrological model of the upper Neckar catchment

Performance criteria are used in the automated calibration of hydrological models to determine and minimise the misfit between observations and model simulations. In this study, a multiobjective model calibration framework is used to analyse the trade-offs between Nash–Sutcliffe efficiency of flows (NSE), the NSE of log-transformed flows (NSE_logQ), and the sum-squared error of monthly discharge sums (SSE_MQ). These criteria are known to put different emphasis on average and high flows, low flows, and average volume-balance components. Twenty-two upper Neckar subbasins whose catchment area ranges from 56 to 3,976 km² were modelled with the distributed mesoscale hydrological model (mHM) to investigate these trade-offs. The 53 global parameters required for each instance of the mHM model were estimated with the global search algorithm AMALGAM. Equally weighted compromise solutions based on the selected criteria and extreme ends of all bi-criterion Pareto fronts were used after each calibration run to analyse the trade-off between different performance criteria. Calibration results were further analysed with ten additional criteria commonly used for evaluating hydrological model performance. Results showed that the trade-off patterns were similar for all subbasins irrespective of catchment size and that the largest trade-offs were consistently observed between the NSE and NSE_logQ criteria. Simulations with the compromise solution provided a well-balanced fit to individual characteristics of the streamflow hydrographs and exhibited improved volume balance. Other performance criteria such as bias, the Pearson correlation coefficient, and the relative variability remained largely unchanged between compromise solutions and Pareto extremes. Parameter sets of the best NSE fit and the compromise solution of the largest basin (gauge at Plochingen) were used to simulate streamflow at the other 21 internal subbasins for a 10-year evaluation period without re-calibration. Both parameter sets performed well in the individual basins with median NSE values of 0.74 and 0.72, respectively. The compromise solution resulted in similar NSE_logQ-ranges and a 14.6 % lower median volume-balance error which indicates an overall better model performance. The results demonstrate that the performance criteria for hydrological model calibration should be selected in accordance with the anticipated model predictions. The compromise solution provides an advance to the use of single criteria in model calibration.     

Impact of vegetation on the simulation of seasonal monsoon rainfall over the Indian subcontinent using a regional model

The change in the type of vegetation fraction can induce major changes in the local effects such as local evaporation, surface radiation, etc., that in turn induces changes in the model simulated outputs. The present study deals with the effects of vegetation in climate modeling over the Indian region using the MM5 mesoscale model. The main objective of the present study is to investigate the impact of vegetation dataset derived from SPOT satellite by ISRO (Indian Space Research Organization) versus that of USGS (United States Geological Survey) vegetation dataset on the simulation of the Indian summer monsoon. The present study has been conducted for five monsoon seasons (1998–2002), giving emphasis over the two contrasting southwest monsoon seasons of 1998 (normal) and 2002 (deficient). The study reveals mixed results on the impact of vegetation datasets generated by ISRO and USGS on the simulations of the monsoon. Results indicate that the ISRO data has a positive impact on the simulations of the monsoon over northeastern India and along the western coast. The MM5-USGS has greater tendency of overestimation of rainfall. It has higher standard deviation indicating that it induces a dispersive effect on the rainfall simulation. Among the five years of study, it is seen that the RMSE of July and JJAS (June–July–August–September) for All India Rainfall is mostly lower for MM5-ISRO. Also, the bias of July and JJAS rainfall is mostly closer to unity for MM5-ISRO. The wind fields at 850 hPa and 200 hPa are also better simulated by MM5 using ISRO vegetation. The synoptic features like Somali jet and Tibetan anticyclone are simulated closer to the verification analysis by ISRO vegetation. The 2 m air temperature is also better simulated by ISRO vegetation over the northeastern India, showing greater spatial variability over the region. However, the JJAS total rainfall over north India and Deccan coast is better simulated using the USGS vegetation. Sensible heat flux over north-west India is also better simulated by MM5-USGS.     

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。     

Influences of subsurface heterogeneity and vegetation cover on soil moisture, surface temperature and evapotranspiration at hillslope scales

Physical processes are at the root of determining hydrologic response at all scales. Here, the physical mechanisms linking (1) subsurface heterogeneities to soil moisture and (2) resulting land-surface energy feedbacks to the atmosphere, are examined at the hillslope scale using a fully coupled surface-subsurface-land-surface model, ParFlow. A hillslope with a heterogeneous subsurface and uniform topography was modeled numerically using summer atmospheric conditions and a single precipitation event under controlled boundary conditions in order to isolate the contribution of hydraulic conductivity to land-surface hydrological processes and energy interactions. Patterns of subsurface hydraulic conductivity are shown to govern soil-moisture distribution at the hillslope scale following precipitation. This variability in soil moisture is closely linked to the variability in land-surface energy feedbacks. The role that vegetation plays in subsurface soil moisture and land energy communications is also examined. Results show that hillslope soil moisture variation is first established by patterns in vertical hydraulic conductivity, while later on in the dry-down period, vegetation exerts greater control on the land-surface energy fluxes and controls the rate of hillslope dry down. Furthermore, as compared to bare-soil simulations, grass-cover simulations show an increase in near-surface soil moisture despite water up-take along the rooting depth.     

Expected impacts of climate change on extreme climate events

An overview of the expected change of climate extremes during this century due to greenhouse gases and aerosol anthropogenic emissions is presented. The most commonly used methodologies rely on the dynamical or statistical downscaling of climate projections, performed with coupled atmosphere–ocean general circulation models. Either of dynamical or of statistical type, downscaling methods present strengths and weaknesses, but neither their validation on present climate conditions, nor their potential ability to project the impact of climate change on extreme event statistics allows one to give a specific advantage to one of the two types. The results synthesized in the last IPCC report and more recent studies underline a convergence for a very likely increase in heat wave episodes over land surfaces, linked to the mean warming and the increase in temperature variability. In addition, the number of days of frost should decrease and the growing season length should increase. The projected increase in heavy precipitation events appears also as very likely over most areas and also seems linked to a change in the shape of the precipitation intensity distribution. The global trends for drought duration are less consistent between models and downscaling methodologies, due to their regional variability. The change of wind-related extremes is also regionally dependent, and associated to a poleward displacement of the midlatitude storm tracks. The specific study of extreme events over France reveals the high sensitivity of some statistics of climate extremes at the decadal time scale as a consequence of regional climate internal variability.     

Impact of water demand on hydrological regime under climate and LULC change scenarios

The present study focuses on an assessment of the impact of future water demand on the hydrological regime under land use/land cover (LULC) and climate change scenarios. The impact has been quantified in terms of streamflow and groundwater recharge in the Gandherswari River basin, West Bengal, India. dynamic conversion of land use and its effects (Dyna-CLUE) and statistical downscaling model (SDSM) are used for quantifying the future LULC and climate change scenarios, respectively. Physical-based semi-distributed model Soil and Water Assessment Tool (SWAT) is used for estimating future streamflow and spatiotemporally distributed groundwater recharge. Model calibration and validation have been performed using discharge data (1990–2016). The impacts of LULC and climate change on hydrological variables are evaluated with three scenarios (for the years 2030, 2050 and 2080). Temperature Vegetation Dyrness Index (TVDI) and evapotranspiration (ET) are considered for estimation of water-deficit conditions in the river basin. Exceedance probability and recurrence interval representation are considered for uncertainty analysis. The results show increased discharge in case of monsoon season and decreased discharge in case of the non-monsoon season for the years 2030 and 2050. However, a reverse trend is obtained for the year 2080. The overall increase in groundwater recharge is visible for all the years. This analysis provides valuable information for the irrigation water management framework.