K 近邻非参数回归概率预报技术及其应用

针对参数回归技术制作概率预报存在拟合好、但预报结果不稳定的现象, 提出了用K近邻非参数回归技术制作概率预报的新途径。K 近邻非参数回归技术包括历史样本数据库、近邻子集生成和优化以及预报量估计4 个主要部分。利用该技术进行了单要素概率预报(主要包括云量和降水)和多维联合概率预报(降水、总云量、风速和气温)试验, 并对试验结果进行了检验。实例研究结果表明:该文所给出的计算方案预报稳定性好, 准确率较高,具有良好的业务应用价值。     

卫星估计降水量产品的优化处理及分区检验

经检验发现,由国家卫星气象中心反演并投入业务使用的GMS-5卫星红外估计降水量产品性能在我国存在一定的区域差异。利用实测雨量对卫星估计降水量产品进行优化处理,结果显示优化后的卫星估计降水量在全国各气候分区都有不同程度的改善。     

黄河中下游沿线城市水资源利用效率的时空演变及影响因素

采用超效率DEA模型、GIS空间分析方法、动态面板的系统GMM估计方法对2010—2017年黄河中下游沿线城市水资源利用效率时空格局及影响因素进行分析,结果表明:①黄河中下游沿线城市水资源利用效率整体由2010年的0.70波动下降为2017年的0.67,且区域内70%以上的城市水资源利用效率变动趋势与其一致;②黄河中下游沿线城市水资源利用效率呈"U"型区域两侧相对较高,中间区域相对较低的空间分布态势,且空间分布重心始终位于地理重心的北方;③环境规制、经济发展水平、产业结构优化、技术水平对水资源利用效率呈显著正向影响,农田水利设施建设、城镇化对水资源利用效率呈显著负向影响。研究结论可为黄河流域生态保护与高质量发展的推进提供决策支持。     

Incorporating GIS Building Data and Census Housing Statistics for Sub-Block-Level Population Estimation

This article presents a deterministic model for sub-block-level population estimation based on the total building volumes derived from geographic information system (GIS) building data and three census block-level housing statistics. To assess the model, we generated artificial blocks by aggregating census block areas and calculating the respective housing statistics. We then applied the model to estimate populations for sub-artificial-block areas and assessed the estimates with census populations of the areas. Our analyses indicate that the average percent error of population estimation for sub-artificial-block areas is comparable to those for sub-census-block areas of the same size relative to associated blocks. The smaller the sub-block-level areas, the higher the population estimation errors. For example, the average percent error for residential areas is approximately 0.11 percent for 100 percent block areas and 35 percent for 5 percent block areas.     

Predicting demand for 311 non-emergency municipal services: An adaptive space-time kernel approach

Many cities in the United States and Canada offer a 311 helpline to their residents for submitting requests for non-emergency municipal services. By dialing 311, urban residents can report a range of public issues that require governmental attention, including potholes, graffito, sanitation complaints, and tree debris. The demand for these municipal services fluctuates greatly with time and location, which poses multiple challenges to effective deployment of limited resources. To address these challenges, this study uses a locally adaptive space-time kernel approach to model 311 requests as an inhomogeneous Poisson process and presents an analytical framework to generate predictions of 311 demand in space and time. The predictions can be used to optimally allocate resources and staff, reduce response time, and allow long-term dynamic planning. We use a bivariate spatial kernel to identify the spatial structure and weigh each kernel by corresponding past observations to capture the temporal dynamics. Short-term serial dependency and weekly temporality are modeled through the temporal weights, which are adaptive to local community areas. We also transform the computation-intensive parameter estimation procedure to a low dimensional optimization problem by fitting to the autocorrelation function of historical requests. The presented method is demonstrated and validated with sanitation service requests in Chicago. The results indicate that it performs better than common industry practice and conventional spatial models with a comparable computational cost.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.     

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.     

Micromechanical approach to swelling behavior of capillary-porous media with coupled physics

A detailed multiscale analysis is presented of the swelling phenomenon in unsaturated clay-rich materials in the linear regime through homogenization. Herein, the structural complexity of the material is formulated as a three-scale, triple porosity medium within which microstructural information is transmitted across the various scales, leading ultimately to an enriched stress-deformation relation at the macroscopic scale. As a side note, such derived relationship leads to a tensorial stress partitioning that is reminiscent of a Terzaghi-like effective stress measure. Otherwise, a major result that stands out from previous works is the explicit expression of swelling stress and capillary stress in terms of micromechanical interactions at the very fine scale down to the clay platelet level, along with capillary stress emerging due to interactions between fluid phases at the different scales, including surface tension, pore size, and morphology. More importantly, the swelling stress is correlated with the disjoining forces due to electrochemical effects of charged ions on clay minerals and van der Waals forces at the nanoscale. The resulting analytical expressions also elucidate the role of the various physics in the deformational behavior of clayey material. Finally, the capability of the proposed formulation in capturing salient behaviors of unsaturated expansive clays is illustrated through some numerical examples.     

模拟三维裂纹问题的多尺度扩展有限元法

扩展有限元法模拟裂纹时独立于网格,因此该方法是目前求解裂纹问题最有效的数值方法。为了在计算代价不大的情况,实现大型结构分析中考虑小裂纹或提高裂纹附近精度,在裂纹附近一般采用小尺度单元,其他区域采用大尺度单元。提出了分析三维裂纹问题的多尺度扩展有限元法,在需要的地方采用小尺度单元。基于点插值构造了六面体任意节点单元。所有尺度单元都采用8节点六面体单元,这样六面体任意节点单元可方便有效地连接不同尺度单元。采用互作用积分法计算三维应力强度因子。边裂纹和中心圆裂纹算例分析结果表明,该方法是正确和有效的。     

冰川区不同气温估算方法评估 ——以藏东南帕隆4号冰川为例

冰川作为地表特殊的下垫面,冰川区内气温明显低于同高度非冰川区大气温度。如何利用低海拔非冰川区观测资料精确估算高海拔冰川区气温,直接关系着青藏高原冰川消融估算及其水文效应的评估。利用架设在藏东南帕隆藏布4号冰川不同高度带的四台自动气象站资料,分析了冰川区与非冰川区气温的波动特征,评估了迄今为止通用的线性递推模型（DT模型）、分段拟合模型（SM模型）和简化热力学模型（GB模型）三种方法在藏东南冰川区气温估算方面的应用效果。对比研究发现：SM模型在帕隆4号冰川上的模拟效果最为理想且操作相对简单;传统DT模型在消融区存在严重的高估,帕隆4号冰川表面夏季（6-8月）正积温的高估比例接近39%;GB模型由于受到诸如冰川风边界层厚度等不确定性的影响,降低了大范围温度估算的可操作性。