基于SWH模型的青藏高原高寒草甸蒸散发及其组分变化分析

基于Shuttleworth-Wallace Hu（SWH）双源蒸散模型对青藏高原那曲、纳木错、藏东南站蒸散发进行估算,在结果验证良好基础上,对青藏高原蒸散发变化特征及各站主要影响因素进行了分析。结果表明：SWH模型在青藏高原3个草甸站适用性良好;年蒸散发介于388~732 mm之间,年内分布呈先增大后减小特征;3站蒸散发组分差异较大,那曲站和纳木错站土壤蒸发对蒸散总量的贡献分别为53%和56%,藏东南站蒸散发则几乎全部由植被蒸腾贡献,占比高达95%;植被叶面积指数为3站蒸散发最主要的影响因素,饱和水汽压差对藏东南站蒸散发影响也较大。研究结果可对青藏高原蒸散发及其组分时空格局与水循环过程研究提供科学依据。     

两种不确定性来源对干旱指数SPEI及干旱评估的影响

全球气候变暖背景下,干旱问题更加频繁、持久,影响范围也逐步扩大。采用干旱指数进行干旱评估与研究时,受多种因素影响,干旱评估结果往往存在一定的不确定性。以黑河流域为研究区,分析概率分布模型和参数估计误差这两种不确定性来源对标准化降水蒸散指数（Standardized precipitation evapotranspiration index,SPEI）和干旱特征变量（干旱强度、干旱峰值和干旱历时）的影响。结果表明：这两种来源均会对SPEI和干旱特征变量产生影响,且SPEI越极端,其影响越大,二者对于极端和严重干旱的影响程度远大于对轻度和中度干旱的影响;对于极端和严重干旱,概率分布模型导致的干旱强度和干旱峰值的不确定性更大,参数估计误差导致的干旱历时的不确定性更大。研究结果可为干旱的准确评估提供支撑,在防旱减灾工作中为制定更加准确有效的抗旱决策提供理论支持,以避免可能造成的减灾能力不足或抗旱资源的浪费。     

应用Cotton2k模型模拟北疆棉花生长结果的分析

Cotton2k模拟模型是一个动态模型,能在生理过程水平上模拟棉花的生长发育和产量形成。通过应用棉花生产管理系统(GOSSYM/COMAY)中一种机理性很强的棉花生长模拟模型Cotton2k,对新疆北部地区棉花的生长状况进行模拟。从生育期的模拟结果分析得出除60%吐絮期误差较大外,其他生育期的模拟结果非常好;从形态发育模拟和实测结果比较表明:形态发育在花铃期前基本吻合;各器官干物质重的模拟和实测结果比较表明:除叶重误差较大外,其他的模拟结果较为满意,特别是植株干重的模拟可以作为精准农业水肥诊断的依据;植株中氮浓度的模拟误差较大,其原因分析有二:(1)棉花在生长发育过程中本身的生物学特性决定了每个生育期对不同肥的吸收量值不同,导致模拟值与观测值有差异;(2)在播种前应对试验地的肥力进行测定,使原始含N量的差异排除,会对模拟结果的精度有所提高。各层土壤含水量的模拟结果表明:耕作层0~45的土壤水分含量差异较大,但是45 cm以下土层的土壤含水量模拟结果与实测值基本吻合;不同水肥处理方式下的产量模拟结果和实测结果比较,产量模拟的误差较大。     

多源再分析降水数据在拉萨河流域应用对比研究

针对资料稀缺地区水文模拟计算难题,开展多源再分析降水数据在拉萨河流域应用对比研究,本文基于HIMS系统构建了拉萨河流域分布式水文模型,以气象站实测数据为参照,对比分析了中国区域地面降水格点日值数据集和中国区域高时空分辨率地面气象要素驱动数据集两套遥感再分析数据集的气象数据在拉萨河流域的径流模拟效果。结果表明：在日和月时间尺度上,气象站实测降水数据的径流模拟精度最好,驱动集降水数据径流模拟结果要好于网格点降水数据。总体上,基于气象站实测降水数据的径流模拟纳西效率系数为0.86(日过程)和0.93(月过程),相关系数均在0.9以上。基于两类再分析数据的降水径流模拟纳西效率系数均在0.7(日过程)和0.8(月过程)以上,相关系数均在0.9左右。对于资料稀缺地区,多源再分析降水数据是重要的可用数据来源。借助于降水—径流模型,探讨多源再分析降水数据对径流模拟精度的影响,是完善多源再分析降水数据产品质量的一个重要环节。     

中国居民食源性疾病的地理分布及影响因素分析

基于2012—2017年全国《中国卫生和计划生育统计年鉴》数据,采用空间自相关分析等方法,刻画中国居民食源性疾病的空间分布格局及其变化,并采用空间滞后面板回归模型（SLM）和时空地理加权模型（GTWR）,识别影响食源性疾病空间分布的主要因素及其变化。结果表明：（1）中国食源性疾病分布具有不均衡性,总体趋势为集中分布于南部以及沿海地区,五年间集中区已出现明显的向北移动的趋势;（2）中国食源性疾病事件数具有一定的空间集聚特征,五年间空间集聚下降。高-高类型区主要分布于西南地区,而低-低类型区集中分布于中国北部。2011—2016年,中国食源性疾病变化趋势与空间分布特征类似;（3）空间滞后面板回归模型分析结果表明,食源性疾病事件具有显著的空间溢出效应。人口密度、突发环境事故的增加将使得食源性疾病增加,而政府财政投入、居民教育水平、城镇化、环境质量的提高将使得食源性疾病减少。除居民教育水平外其余变量也均具有显著的空间溢出效应;（4）时空地理加权模型分析结果表明,食源性疾病的影响因素存在空间差异。地区人口密度对食源性疾病的相对压力由东向西加大。政府财政投入对食源性疾病的降低作用由东南部沿海地区向其余地区递减。居民教育水平提高对食源性疾病降低作用由中部向东西逐步加大。城镇化率对食源性疾病降低作用由东南部沿海地区向其余地区递减。环境质量对食源性疾病降低作用由东南部沿海地区向其余地区递增;突发环境事故对食源性疾病的相对压力由中部向东西加大;年平均气温对食源性疾病的相对压力由南向北减小;年平均降雨量对食源性疾病的相对压力由东部沿海地区向西部内陆地区减小。     

黄河源区径流对气候变化的响应及敏感性分析

Response of the runoff in the headwater region of the Yellow River to climate change and its sensibility are analyzed based on the measured data at the four hydrological stations and ten weather stations during the period 1959–2008. The result indicates that change of temperature in the region has an obvious corresponding relationship with global warming and the changes of annual average temperature in each subregion in the region have been presenting a fluctuant and rising state in the past 50 years. However the change of precipitation is more intricate than the change of temperature in the region because of the influences of the different geographical positions and environments in various areas, and the change of annual precipitation in the main runoff-producing area has been presenting a fluctuant and decreasing state in the past 50 years. And there is a remarkable nonlinear correlativity between runoff and precipitation and temperature in the region. The runoff in the region has been decreasing continuously since 1990 because the precipitation in the main runoff-producing area obviously decreases and the annual average temperature continuously rises. As a whole, the runoff in each subregion of the headwater region of the Yellow River is quite sensitive to precipitation change, while the runoff in the subregion above Jimai is more sensitive to temperature change than that in the others in the region, correspondingly.     

滇西思茅盆地西缘早白垩世沉积物源分析及盆地形成和演化探讨

滇西思茅盆地位于澜沧江断裂带和金沙江—红河断裂带之间,呈北西—南东向带状展布,在构造上处于冈瓦纳与欧亚两大构造域的过渡地带,其形成发展与古特提斯的演化、印度板块的向北漂移和俯冲消亡关系十分密切。通过对思茅盆地西缘早白垩世的沉积物分析表明,滇西思茅盆地西缘早白垩世的沉积环境主要为河湖相,沉积物源为盆地西侧的澜沧江造山带。结合构造背景,认为盆地的形成经历了碰撞后裂谷阶段、陆内坳陷阶段、复合型前陆盆地阶段和走滑拉分盆地四个阶段。     

HIMS模型参数的不确定性及其影响因素

模型参数的不确定性及影响因素分析对无资料流域水文预报具有重要意义。本文以澳大利亚3 个流域为例,采用GLUE方法分析HIMS模型参数的不确定性,在此基础上探讨流域物理属性对参数取值的影响。研究发现:① HIMS模型9个参数的不确定性都比较大,属于敏感参数;② 降雨量、森林覆盖度和表层土壤最大有效蓄水量大的流域,土壤蓄水容量W_sm取值大;土壤饱和导水率和森林覆盖度高的流域,产流系数R、r的取值较大;温度低、森林覆盖度小的流域,实际蒸散发系数ε取值大;易透水、森林覆盖度高的流域,马斯京根汇流系数C₂取值大。这些结论能为今后在无资料流域应用HIMS模型进行水文预报提供一定参考。     

Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model

This study presents a methodology for conducting sensitivity and uncertainty analysis of a GIS-based multi-criteria model used to assess flood vulnerability in a case study in Brazil. The paper explores the robustness of model outcomes against slight changes in criteria weights. One criterion was varied at-a-time, while others were fixed to their baseline values. An algorithm was developed using Python and a geospatial data abstraction library to automate the variation of weights, implement the ANP (analytic network process) tool, reclassify the raster results, compute the class switches, and generate an uncertainty surface. Results helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. Overall, the criteria ‘houses with improper building material’ and ‘evacuation drills and training’ are the most sensitive ones, thus, requiring more accurate measurements. The sensitivity of these criteria is explained by their weights in the base run, their spatial distribution, and the spatial resolution. These findings can support decision makers to characterize, report, and mitigate uncertainty in vulnerability assessment. The case study results demonstrate that the developed approach is simple, flexible, transparent, and may be applied to other complex spatial problems.     

科尔沁湿草甸参考作物蒸散发模拟分析

为探明气候变化下干旱半干旱地区湿草甸参考作物蒸散发(ET₀)影响因子,使用FAO 56 P-M模型对科尔沁湿草甸ET₀进行模拟,利用涡度相关系统对模型的适用性进行评价,并通过通径分析及指标敏感性分析对ET₀的影响因子进行辨识。结果表明:(1)小时尺度模拟精度最高,日尺度次之,月尺度较差,小时尺度上晴、阴、雨3种天气条件下模拟效果不同,晴天最优,阴雨天较差。(2)ET₀年内变化呈单峰曲线状,生长季明显高于非生长季,集中在3—10月,占全年89.79%。生长季典型晴天ET₀逐小时分布特征遵循倒“U”单峰型变化规律。(3)通径分析结果显示,对ET₀的通径系数以及对回归方程估测可靠程度E的总贡献均表现为VPD(饱和水汽压差) > T_min(最低气温) > R_n(冠层表面净辐射)>u₂(2 m高度风速),即VPD为影响ET₀最重要的因子;指标敏感性分析中,在去除VPD后引起的E变化最大,说明ET₀对VPD的变化最为敏感,其次为u₂、T_min和R_n。     

干旱区芦苇蒸散量计算模式研究

从蒸散量和水面蒸发量之比与主要根系层平均含水率的关系出发,利用在中国科学院阿克苏水平衡试验站芦苇试验小区监测的土壤水分和蒸散资料,建立了干旱区芦苇蒸散量的计算模式,并利用芦苇实际蒸散量的测量值,对模式进行了验证。结果表明,模式计算精度较高,可以作为计算干旱区芦苇蒸散量的一种计算方法而使用。     

1960-2017年艾比湖流域实际蒸散量与气象要素的变化特征

传统估算蒸散发的方法大都基于局地尺度,而在生态水文发生剧烈变化的资料稀缺流域背景下,充分考虑流域下垫面的空间变异性的陆面过程模型为流域长时序、大尺度及连续模拟实际蒸散量提供了新途径.以艾比湖流域为研究区,应用可变下渗能力模型(VIC)模拟1960-2017年艾比湖流域的水文过程,探讨研究区值实际蒸散发量的年、月、日时空...     

基于MODIS数据的无定河流域蒸散模拟

利用黄土高原无定河流域水文气象资料、MODIS数据及GIS背景信息,应用分布式生态水文模型（VIP模型）,按250m空间分辨率模拟了该流域水量平衡各分量的时空分布。结果表明:2000²003年无定河流域年蒸散量分别为300 mm、397 mm、460 mm和443 mm;流域蒸散有明显的由南向北,由东到西的梯度递减特征,降水量和地表植被覆盖度的差异是其空间变异的主要因素;蒸腾与蒸发空间分异显著,但两者的变化相互补偿,降低了蒸散的空间变异性。整个流域平均而言,不同植被类型间的年蒸散总量差异不明显。白家川等9个子流域年蒸散量的模拟结果与水量平衡法估计结果具有较好的一致性。     

Uncertainty analysis of runoff and sedimentation in a forested watershed using sequential uncertainty fitting method

The Soil and Water Assessment Tool(SWAT) was implemented in a small forested watershed of the Soan River Basin in northern Pakistan through application of the sequential uncertainty fitting(SUFI-2) method to investigate the associated uncertainty in runoff and sediment load estimation. The model was calibrated for a 10-year period(1991–2000) with an initial 4-year warm-up period(1987–1990), and was validated for the subsequent 10-year period(2001–2010). The model evaluation indices R~2(the coefficient of determination), NS(the Nash-Sutcliffe efficiency), and PBIAS(percent bias) for stream flows simulation indicated that there was a good agreement between the measured and simulated flows. To assess the uncertainty in the model outputs, p-factor(a 95% prediction uncertainty, 95PPU) and r-factors(average wideness width of the 95 PPU band divided by the standard deviation of the observed values) were taken into account. The 95 PPU band bracketed 72% of the observed data during the calibration and 67% during the validation. The r-factor was 0.81 during the calibration and 0.68 during the validation. For monthly sediment yield, the model evaluation coefficients(R~2 and NS) for the calibration were computed as 0.81 and 0.79, respectively; for validation, they were 0.78 and 0.74, respectively. Meanwhile, the 95 PPU covered more than 60% of the observed sediment data during calibration and validation. Moreover, improved model prediction and parameter estimation were observed with the increased number of iterations. However, the model performance became worse after the fourth iterations due to an unreasonable parameter estimation. Overall results indicated the applicability of the SWAT model with moderate levels of uncertainty during the calibration and high levels during the validation. Thus, this calibrated SWAT model can be used for assessment of water balance components, climate change studies, and land use management practices.     

A case study of model selection and parameter inference by maximum likelihood with application to uncertainty analysis

One of the uses of geostatistical conditional simulation is as a tool in assessing the spatial uncertainty of inputs to the Monte Carlo method of system uncertainty analysis. Because the number of experimental data in practical applications is limited, the geostatistical parameters used in the simulation are themselves uncertain. The inference of these parameters by maximum likelihood allows for an easy assessment of this estimation uncertainty which, in turn, may be included in the conditional simulation procedure. A case study based on transmissivity data is presented to show the methodology whereby both model selection and parameter inference are solved by maximum likelihood.     

Multi-criteria spatial screening and uncertainty analysis applied to direct-use geothermal projects

ABSTRACT

The focus of this work is general methods for prioritization or screening of project sites based on the favorability of multiple spatial criteria. We present a threshold-based transformation of each underlying spatial favorability factor into a continuous scale with a common favorability interpretation across all criteria. We compare several methods of computing site favorability and propagating uncertainty from the data to the favorability metrics. Including uncertainty allows decision makers to determine if seeming differences among sites are significant. We address uncertainty using Taylor series approximations and analytical distributions, which are compared to computationally intensive Monte Carlo simulations. Our methods are applied to siting direct-use geothermal energy projects in the Appalachian Basin, where our knowledge about any particular site is limited, yet sufficient data exist to estimate favorability. We consider four factors that contribute to site favorability: the thermal resource described by the depth to 80°C rock, natural reservoir productivity described by rock permeability and thickness, potential for induced seismicity, and the estimated cost of surface infrastructure for heat distribution. Those factors are combined in three ways. We develop favorability uncertainty propagation and sensitivity analysis methods. All methods are general and can be applied to other multi-criteria spatial screening problems.