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     

基于NCEP CFSv2模式的黑龙江省夏季降水预测研究

基于1982-2017年NCEP_CFSv2（NCEP Climate Forecast System version 2）模式预测资料对黑龙江省夏季降水进行降尺度预测。通过分析黑龙江省夏季降水与同期环流因子的关系、模式对关键区环流因子的预测,选取模式模拟与再分析资料相关较好、黑龙江降水实况与再分析资料关系较好的环流因子作为预测因子,结合最优子集回归法筛选因子,建立降尺度预测模型,最后采用交叉检验法进行预测效果检验和独立样本预测。结果表明：模式降尺度预测与实况的距平符号-致率为69%,6 a独立样本预测中有5 a预测正确,优于目前的业务预测效果。进-步研究发现,在模式能够准确预测环流因子的情况下,模式降尺度可以较好地预测黑龙江省夏季降水的趋势。此外,模式降尺度在拉尼娜年预测效果较好。     

A modified ISBA surface scheme for modeling the hydrology of Athabasca River Basin with GCM-scale data

A soil–vegetation–atmosphere transfer model (SVAT), interactions between the soil–biosphere–atmosphere (ISBA) of Météo France, is modified and applied to the Athabasca River Basin (ARB) to model its water and energy fluxes. Two meteorological datasets are used: the archived forecasts from the Meteorological Survey of Canada’s Global Environmental Multiscale Model (GEM) and the European Centre for Mid-range Weather Forecasts global re-analysis (ERA-40), representing spatial scales typical of a weather forecasting model and a global circulation model (GCM), respectively. The original treatment of soil moisture and rainfall in ISBA (OISBA) is modified to statistically account for sub-grid heterogeneity of soil moisture and rainfall to produce new, highly non-linear formulations for surface and sub-surface runoff (MISBA). These new formulations can be readily applied to most existing SVATs. Stand alone mode simulations using the GEM data demonstrate that MISBA significantly improves streamflow predictions despite requiring two fewer parameters than OISBA. Simulations using the ERA-40 data show that it is possible to reproduce the annual variation in monthly, mean annual, and annual minimum flows at GCM scales without using downscaling techniques. Finally, simulations using a simple downscaling scheme show that the better performance of higher resolution datasets can be primarily attributed to improved representation of local variation of land cover, topography, and climate.     

青藏高原未来30～50年A1B情景下气候变化预估

基于政府间气候变化委员会第四次评估报告(IPCC-AR4)所采用的20个气候模式在未来大气温室气体中等排放情景(A1B)下模拟结果的集合平均以及一个全球气候模式模拟输出驱动下的动力降尺度(downscaling)分析结果,对青藏高原地区未来30～50年的气候变化趋势进行了预估研究.结果表明,从2030-2049年相对于1980-1999年气候平均值的变化来看,青藏高原大部分地区年平均地面气温的升温幅度在1.4～2.2℃之间,高海拔地区的增温一般更为显著,西藏西部的冬季增暖将达到2.4℃以上.降水量的变化相对较小,青藏高原大部分地区和全年多数季节降水可能增加,但未来30～50年青藏高原地区降水率增量通常不超过5%.考虑到未来大气温室气体排放程度、多模式集合预估以及区域尺度气候模拟等多方面均可能存在不确定性,这里给出的青藏高原未来气候变化预估结果应适时检验和修正.     

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster–Shafer and Bayesian approaches

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster–Shafer (D–S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D–S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D–S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D–S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster–Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D–S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D–S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change.     

Downscaling in remote sensing

Downscaling has an important role to play in remote sensing. It allows prediction at a finer spatial resolution than that of the input imagery, based on either (i) assumptions or prior knowledge about the character of the target spatial variation coupled with spatial optimisation, (ii) spatial prediction through interpolation or (iii) direct information on the relation between spatial resolutions in the form of a regression model. Two classes of goal can be distinguished based on whether continua are predicted (through downscaling or area-to-point prediction) or categories are predicted (super-resolution mapping), in both cases from continuous input data. This paper reviews a range of techniques for both goals, focusing on area-to-point kriging and downscaling cokriging in the former case and spatial optimisation techniques and multiple point geostatistics in the latter case. Several issues are discussed including the information content of training data, including training images, the need for model-based uncertainty information to accompany downscaling predictions, and the fundamental limits on the representativeness of downscaling predictions. The paper ends with a look towards the grand challenge of downscaling in the context of time-series image stacks. The challenge here is to use all the available information to produce a downscaled series of images that is coherent between images and, thus, which helps to distinguish real changes (signal) from noise.     

MODES系统对贵州月气温、降水预测初步评估

通过对2013年1月—2015年6月(MODES)发布的最优月预测产品在贵州省月平均气温距平和降水距平百分率的预测检验评估,发现MODES对全省平均气温有较好的预报,分析时段内预测与实况的相关系数为0.24,距平同号率为65.5%,且对气温偏高预测的可参考性高于其对气温偏低的预测。相比于气温,MODES对降水预测能力较弱,参考性也相对较低,其中对贵州全省平均降水偏多趋势的预测技巧要优于对全省平均偏少趋势的预报技巧。逐站分析显示,MODES对贵州气温预测效果较好的地区在西部、北部和东部,对降水偏多的预测效果较好的地区位于除西北部和北部边缘地区外的其余大部地区。通过对MODES与预报员综合预报的结果评估发现,MODES月预测总体效果较预报员好,且稳定性高于预报员,可为预报员提供参考信息。     

降尺度方法中初始资料处理的研究

从 GCM模式预测结果获取区域尺度特征的气候变化的统计方法被称为 Downscaling方法 ,它主要是通过区域气候尺度的预报量与 GCM模式输出或大尺度地面观测资料建立统计模式。在建立模式之初 ,研究各种尺度资料的匹配是至关重要的问题。本文在不同的站点数量、区域网格分辨率和差值影响半径的条件下 ,对资料处理方法的可行性和结果的可靠性作了分析探讨。结果说明 :一个大尺度网格箱内用 2 0个站点资料代替 4 0个站点的资料是可行的 ,并且确定了最佳插值方案 :当次网格分辨率取 1°× 1°时 ,插值影响半径应取 1;当次网格分辨率取 0 .5°× 0 .5°时 ,插值影响半径应取为 2。本文还对温度场、降水场及其对应的 EOF场进行了比较试验 ;并做了温度场、降水场与同步海平面气压场 (SL P)的单点线性相关分析     

统计降尺度方法和Delta方法建立黄河源区气候情景的比较分析

大气环流模型(GCMs)预测的气候变化情景,必须经降尺度处理得出小尺度上未来气候变化的时空分布资料,才能满足气候变化对资源、环境和社会经济等影响进行评估的需要。文中研究同时应用Delta方法和统计降尺度(SDS)方法对黄河源区的日降水量和日最高、最低气温进行降尺度处理,建立起未来3个时期(2006—2035、2036—2065和2066—2095年,简记为2020s、2050s和2080s)的气候变化情景,并比较分析两种方法的优缺点和适用性。结果表明,未来降水量有一定的增加趋势,但是增幅不大,而日最高、最低气温存在明显的上升趋势,且增幅较大。与基准期相比,Delta方法模拟的未来3个时期降水量将分别增加8.75%、19.70%和18.49%;日最高气温将分别升高1.41、2.42和3.44℃,同时,日最低气温将分别升高1.49、2.68和3.76℃,未来极值气温变幅减小。SDS法借助站点实测数据和NCEP再分析资料建立GCM强迫条件下的降尺度模型,模拟结果表明,未来3个时期降水量将分别增加3.47%、6.42%和8.67%,季节变化明显;气温随时间推移增幅明显,未来3个时期的日最高气温将分别升高1.34、2.60和3.90℃,最低气温增幅相对较小,3个时期将分别升高0.87、1.49和2.27℃,由此模拟的未来时期无霜期将延长。在降尺度方法的应用上,SDS方法存在明显的优势,但同时也存在不可避免的缺陷。因此,在实际的气候变化影响评估中,需要多种方法综合比较,以期为决策部门提供参考和依据。     

Modelling the main features of the Algarve coastal circulation during July 2004: A downscaling approach

This article presents a methodology for simulating the Algarve coastal circulation using realistic forcing (e.g. low-frequency circulation, tide, high-resolution atmospheric forcing). Low-frequency open boundary conditions are defined via a downscaling of the HYCOM-US operational solution for the Gulf of Cadiz. Atmospheric forcing is imposed using the MM5 high-resolution mesoscale model (9 km resolution near the coast). A 3-level nesting system based on the MOHID numerical system is implemented. The higher nesting level has a horizontal resolution of 0.02° along the Algarve coast. The methodology is first validated qualitatively. A comparison between the numerical results and the conceptual model of the circulation described in the literature is presented. A quantitative validation is also performed, based mainly on remote sensing data (sea surface temperature and altimetry) available for July 2004. The numerical system is able to reproduce many of the circulation features described in the literature (e.g. Azores current recirculation in the Gulf of Cadiz, the upwelling jet, Mediterranean Water undercurrent, Mediterranean Outflow splitting, generation of meddies) and observed with remote-sensing data (e.g. the signature in sea surface temperature (SST) during a regime of upwelling relaxation).