Category Archives: Recent publications

基于机器学习模型的青藏高原日降水数据的订正研究

[1] H Chen, C Ning, Z Nan, et al. Correction of Daily Precipitation Data over the Qinghai-Tibetan Plateau with Machine Learning Models[J]. 2017, 39(3): 583—592.[陈浩,宁忱,南卓铜,等. 基于机器学习模型的青藏高原日降水数据的订正研究[J]. 冰川冻土. 2017, 39(3): 583—592.]

选择了5种机器学习模型,即k最近邻方法(KNN)、多元自回归样条方法(MARS)、支持向量机(SVM)、多项对数线性模型(MLM)和人工神经网络(ANN),利用海拔、相对湿度、坡向、植被、风速、气温和坡度等因子订正ITPCAS和CMORPH两种常用的青藏高原日降水数据集。五折交叉验证表明,KNN的订正精度最高。在三个验证站点(唐古拉、西大滩和五道梁)的误差分析,以及对青藏高原年降水量的空间分析均表明,KNN对CMORPH的订正效果显著,对ITPCAS在局部区域有一定订正效果,ITPCAS及其订正值的降水空间分布准确度高于CMORPH的订正值。主成分分析法表明降水订正是气象和环境因子综合作用的结果。

下载:Link 1 (from冰川冻土); precip.machine.learning-wyd-2017 (Local)

一个洪水预报论文

强德霞,赵彦博,南卓铜*,吴小波. 基于参数实时优化的洪水预报系统研究:以黑河干流洪水为例. 水利水电技术. 2017, 48(4): 13-17.

另:对于里面使用不同模型进行不同场次洪水预报我有不同意见,因为我们无法知道下一场次洪水到底适合何种模型,从而不能实际用起来。但使用实测数据,对给定模型参数进行实时率定,从而优化使用该模型的洪水预报精度,是本文主要想传达的。

Full text available upon request.

A paper on evaluation of some simple permafrost models on QTP

Zhao S, Nan Z*, Huang Y, Zhao L. The application and evaluation of simple permafrost distribution models on the Qinghai-Tibet Plateau. Permafrost and Periglacial Processes. 2017, 28(2): 391-404. DOI:10.1002/ppp.1939.

ABSTRACT

The performance of simple permafrost distribution models widely used on the Qinghai–Tibet Plateau (QTP) has not been fully evaluated. In this study, two empirical models (the elevation model and mean annual ground temperature model) and three semi-physical models (the surface frost number model, the temperature at the top of permafrost model and the Kudryavtsev model) were investigated. The simulation results from the models were compared to each other and validated against existing permafrost maps of the entire QTP and in three representative areas investigated in the field. The models generally overestimated permafrost distribution in the investigated areas, but they captured the broad characteristics of permafrost distribution on the entire QTP, and performed best in areas with colder, continuous permafrost. Large variations in performance occurred at elevations of 3800–4500 m asl and in areas with thermally unstable permafrost. The two empirical models performed best in areas where permafrost is strongly controlled by elevation, such as eastern QTP. In contrast, the three semi-physical models were better in southern island permafrost areas with relatively flat terrain, where local factors considerably impact the distribution of permafrost. Model performance could be enhanced by explicitly considering the effects of elevation zones and regional conditions.

PDF available upon request.

三篇IGARSS 2016会议论文:关于多层土壤数据和降水较正

1. Wu X, Nan Z.A multilayer soil texture dataset for permafrost modeling over Qinghai-Tibetan Plateau.In Proceeding of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2016. 4917-4920. (wu et al. 2016, igarss )
2. Wang Y, Nan Z*, Chen H, Wu X.Correction of daily precipitation data of ITPCAS dataset over the Qinghai-Tibetan Plateau with KNN model.In Proceeding of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2016. 593-596. (wang et al. 2016, igarss)
3. Ning C, Wang Y, Nan Z*, Chen H, Liu C.Study on correction of daily precipitation data of the Qinghai-Tibetan plateau with machine learning models.In Proceeding of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2016. 517-520. (ning et al. 2016, igarss)

王玉丹等,2016,遥感技术与应用

王玉丹,南卓铜,陈浩,吴小波. 基于K最近邻模型的青藏高原CMORPH日降水数据的订正研究. 遥感技术与应用. 2016, 31(3): 607-616.

摘要:

青藏高原的降水数据主要由遥感产品和多源观测数据融合产生,由于青藏高原的观测站点分布稀疏不均,遥感数据误差较大,因此常用的CMORPH(Climate Prediction Center Morphing Technique)等降水数据集精度有限.通过K 最近邻(K-Nearest Neighbor,简称KNN)模型,可以建立环境(海拔、坡度、坡向、植被)、气象因子(气温、湿度、风速)和日降水量的关系,从而订正青藏高原的CMORPH 日降水数据集,提高数据精度.对CMORPH 日降水数据的误差分析表明,采用KNN 模型订正后的CMORPH 降水数据优于原始数据和采用PDF(Probability Density Function Matching Method)法订正的CMORPH 数据,且空间分布较好地符合青藏高原的降水分布特征.

下载 (~11MB, pdf) Link

一篇冻土建模的中文论文

马启民,黄滢冰,南卓铜*,吴小波. 青藏高原典型多年冻土区的一维水热过程模拟研究. 冰川冻土. 2016, 38(2): 341-350.

摘要:

了解多年冻土内部的水热过程对寒区工程规划和建设的辅助决策具有重要意义。冻土的水分迁移与温度变化密切相关,然而传统的经验模型局限性大,对水热物理过程考虑不足;陆面过程模型所需的驱动数据多且很难准确模拟深层土温,尽管数值模型在工程上应用的比较多,但很少应用到冻土的演化过程中。基于非饱和土壤渗流和热传导理论,实现了冻土水分场与温度场的水热耦合数值模拟。以唐古拉综合观测场为例,将数值模拟结果与观测数据进行对比,验证水热耦合数值模拟的有效性。结果表明:模型对土壤温度模拟效果较好,15 m以上R2在0.88以上,RMSE在1℃以内;水分模拟尚可,但仍存在一定误差,R2在0.7以上,RMSE在7.65%以内。模拟的活动层厚度约3.6 m,年平均地温所在的深度约为15 m,与实测值基本一致。该水热耦合模型可用于研究多年冻土区土壤水热变化规律.

下载 (pdf, ~10.8 MB): mqm.et al. bcdt.2016

A Plos One Paper

Zhang L, Nan Z, Xu Y, Li S. Hydrological Impacts of Land Use Change and Climate Variability in the Headwater Region of the Heihe River Basin, Northwest China. PloS One. 2016, 11(6): e158394. DOI:10.1371/journal.pone.0158394.

ABSTRACT:

Land use change and climate variability are two key factors impacting watershed hydrology, which is strongly related to the availability of water resources and the sustainability of local ecosystems. This study assessed separate and combined hydrological impacts of land use change and climate variability in the headwater region of a typical arid inland river basin, known as the Heihe River Basin, northwest China, in the recent past (1995–2014) and near future (2015–2024), by combining two land use models (i.e., Markov chain model and Dyna-CLUE) with a hydrological model (i.e., SWAT). The potential impacts in the near future were explored using projected land use patterns and hypothetical climate scenarios established on the basis of analyzing long-term climatic observations. Land use changes in the recent past are dominated by the expansion of grassland and a decrease in farmland; meanwhile the climate develops with a wetting and warming trend. Land use changes in this period induce slight reductions in surface runoff, groundwater discharge and streamflow whereas climate changes produce pronounced increases in them. The joint hydrological impacts are similar to those solely induced by climate changes. Spatially, both the effects of land use change and climate variability vary with the sub-basin. The influences of land use changes are more identifiable in some sub-basins, compared with the basin-wide impacts. In the near future, climate changes tend to affect the hydrological regimes much more prominently than land use changes, leading to significant increases in all hydrological components. Nevertheless, the role of land use change should not be overlooked, especially if the climate becomes drier in the future, as in this case it may magnify the hydrological responses.

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158394