Sparse Randomized Maximum Likelihood (SpRML) for subsurface flow model calibration and uncertainty quantification

Despite their apparent high dimensionality, spatially distributed hydraulic properties of geologic formations can often be compactly (sparsely) described in a properly designed basis. Hence, the estimation of high-dimensional subsurface flow properties from dynamic performance and monitoring data can be formulated and solved as a sparse reconstruction inverse problem. Recent advances in statistical signal processing, formalized under the compressed sensing paradigm, provide important guidelines on formulating and solving sparse inverse problems, primarily for linear models and using a deterministic framework. Given the uncertainty in describing subsurface physical properties, even after integration of the dynamic data, it is important to develop a practical sparse Bayesian inversion approach to enable uncertainty quantification. In this paper, we use sparse geologic dictionaries to compactly represent uncertain subsurface flow properties and develop a practical sparse Bayesian method for effective data integration and uncertainty quantification. The multi-Gaussian assumption that is widely used in classical probabilistic inverse theory is not appropriate for representing sparse prior models. Following the results presented by the compressed sensing paradigm, the Laplace (or double exponential) probability distribution is found to be more suitable for representing sparse parameters. However, combining Laplace priors with the frequently used Gaussian likelihood functions leads to neither a Laplace nor a Gaussian posterior distribution, which complicates the analytical characterization of the posterior. Here, we first express the form of the Maximum A-Posteriori (MAP) estimate for Laplace priors and then use the Monte-Carlo-based Randomize Maximum Likelihood (RML) method to generate approximate samples from the posterior distribution. The proposed Sparse RML (SpRML) approximate sampling approach can be used to assess the uncertainty in the calibrated model with a relatively modest computational complexity. We demonstrate the suitability and effectiveness of the SpRML formulation using a series of numerical experiments of two-phase flow systems in both Gaussian and non-Gaussian property distributions in petroleum reservoirs and successfully apply the method to an adapted version of the PUNQ-S3 benchmark reservoir model.     

Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM + ) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS’ coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCA_MG), with reference SCA maps (SCA_REF) generated from binary classification of TM imagery. SCA_MG showed strong agreement with SCA_REF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCA_REF and SCA_MG ranged between −2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCA_REF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCA_MG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91.In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km²) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.     

半湿润流域水文模型比较与集合预报

选择7种水文模型分别在中国北部3个半湿润流域做模拟对比,分析不同水文模型在各流域的适用性,并使用贝叶斯模型平均法对不同模型集合,比较各种集合方法的优势,研究贝叶斯模型平均法的应用效果.研究结果表明,以蓄满产流模式为主的模型在半湿润流域应用效果较好,针对不同流域特点对传统模型进行改进可以提高模拟精度.贝叶斯模型平均法能提供较好的确定性预报结果和概率预报结果,仅对少数模拟效果好的模型进行集合,并不能有效提高预报精度,适当增加参与集合的模型数量能使贝叶斯模型平均法更好地综合各模型优势,提高预报结果的精度.     

晋中市气候变暖的特征及对策建议

全球气候变暖已经成为不争的事实。近百年全球平均地表温度上升了0．74℃,中国地表气温增暖0．5℃～0．8℃,山西近50年平均温度每10年上升0．25℃,晋中近50年平均温度每10年上升0．2℃。由于气候变暖将涉及到政治、经济和社会问题,所以引起了政府及专家的极大关注。     

土壤质地对中国区域陆面过程模拟的影响

利用陆面过程模式(CLM3.5)和中国区域两种土壤质地数据(分别来自第二次中国土壤调查SNSS和联合国粮食农业组织FAO),研究了土壤质地变化对于模式模拟的陆表水热变量的影响。结果显示,土壤质地对土壤水文学变量的影响远大于对土壤热力学变量的影响,尤其是对于饱和土壤含水量和饱和水力传导率的影响。对于模式的输出,土壤质地影响比较明显的有土壤湿度、总径流和土壤渗透等水文学变量以及地表潜热、地表感热和土壤热通量等热力学变量,而影响相对较小的有地面吸收的太阳辐射和地表反照率。同时,发现基于SNSS模拟的土壤湿度与站点观测值更加接近。因此,本研究认为基于SNSS土壤质地数据可以有效地改进模式模拟结果,建议以后在陆面模式试验中尽可能使用以观测为基础的SNSS土壤质地数据。     

表面粗糙度对海浪成长率的影响

本文在Miles准层流风生波模型基础上，引入表面粗糙度滑波面不均匀分布的线性模型，通过不稳定性分析，导出了平均压力随表面粗糙度变化和海浪成长率之间的关系，表明表面粗糙度的不均匀分布是影响海浪成长率的一个重要因素。     

Issues and challenges in spatio-temporal application of an ecosystem services framework to UK seas

The concepts of ecosystem services and human welfare provide strong integrative frameworks that can be used to inform marine policy and management decisions that support sustainable development. A theoretical framework has been developed and applied to create a model for UK seas to measure changes in final ecosystem services, in terms of human welfare. The model that has been developed is explicitly spatial and temporal to facilitate its use in supporting marine planning decisions. The development and application of this framework to UK seas necessarily requires many assumptions to be made. The paper describes the development and population of the framework and discusses the practical limitations and challenges in seeking to develop and apply such models. Significant differences in long-term values of different services were identified under the different scenarios. All scenarios highlight the projected decline in oil and gas revenues which provide particular intense values at sites of extraction. These values are partially replaced by revenues from offshore renewables in some of the scenarios. Values associated with carbon sequestration, maritime transport, tourism and pollution assimilation are also very significant but more spatially diffuse. The study has demonstrated that it is possible to develop spatio-temporal models to evaluate changes in final ecosystem service benefits using existing data, although the approach necessarily requires many assumptions to be made.     

Regional Oceanic Impact on Circulation and Direct Radiative Effect of Aerosol over East Asia

The Regional Integrated Environmental Model System(RIEMS 2.0) coupled with a chemistry-aerosol model and the Princeton Ocean Model(POM) is employed to simulate regional oceanic impact on atmospheric circulation and the direct radiative effect(DRE) of aerosol over East Asia.The aerosols considered in this study include both major anthropogenic aerosols(e.g.,sulfate,black carbon,and organic carbon) and natural aerosols(e.g.,soil dust and sea salt) .The RIEMS 2.0 is driven by NCEP/NCAR reanalysis II,and the simulated period is from 1 January to 31 December 2006.The results show the following:(1) The simulated annual mean sea-level pressure by RIEMS 2.0 with POM is lower than without POM over the mainland and higher without POM over the ocean.(2) In summer,the subtropical high simulated by RIEMS 2.0 with POM is stronger and extends further westward,and the continental low is stronger than without POM in summer.(3) The aerosol optical depth(AOD) simulated by RIEMS 2.0 with POM is larger in the middle and lower reaches of the Yangtze River than without POM.(4) The direct radiative effect with POM is stronger than that without POM in the middle and lower reaches of the Yangtze River and parts of southern China. Therefore,the authors should take account of the impact of the regional ocean model on studying the direct climate effect of aerosols in long term simulation.     

宜章界牌岭锡多金属矿床地球化学异常模式

界牌岭锡多金属矿床是一个与浅源重熔花岗岩有关的热液多金属隐伏矿床。研究表明：矿床自上至下具有Ｆ－Ｂｅ－Ｐｂ、Ｚｎ－Ｐｂ－Ｓｎ、Ｃｕ－Ｎｂ、Ｔａ、ＴＲ的矿化分带和细脉状－层状（似层状）－面状的矿体形态分带,成晕具有Ｈｇ、Ｂ、Ｂａ、Ｃｒ、Ａｓ离心半环状晕－Ｆ、Ｂｅ、Ｌｉ、Ｐｂ、Ａｇ同心环状上偏心晕－Ｓｎ、Ｃｕ、Ｚｎ、同心环状下偏心晕－Ｎｂ、Ｇａ离心卫星晕的分布规律。据此,建立了本矿床“三环－帽壳式”地球化学异常模式。经模式识别,总结了不同剥蚀程度矿床（浅,中,深）异常评价的地质－地球化学指标。