Tag Archives: modelling

一篇冰川冻土中文论文

赵奕,南卓铜*,李祥飞,徐毅,张凌. 分布式水文模型DHSVM在西北高寒山区流域的适用性研究. 冰川冻土. 2019, 41(1): 147-157.

分布式水文-土壤-植被模型(Distributed Hydrology Soil Vegetation Model, DHSVM)是基于栅格离散的分布式水文模型,对地表水热循环的各个过程能进行很精细地刻画,被广泛应用于世界各地很多类型的流域的高时空分辨率的水文模拟,然而它在高寒山区的适用性并不清楚。基于300m数字高程模型,应用DHSVM 模型对典型的高寒山区流域八宝河流域2001-2009年的水文过程展开模拟,并采用流域出口祁连站的水文实测数据对模型进行了精度评价。参数敏感性分析表明,土壤横向导水率、田间持水量和植被反照率等是该区域主要的敏感性参数。模型默认参数会高估高寒山区流域的潜在蒸散发量,导致夏季径流量远小于观测值。通过参数率定,模型校准期(2001-2004)的模拟日径流和月径流Nash 效率系数分别达到0.72 和0.87;而模型验证期(2005-2009)分别为0.60 和0.74 。结果表明,DHSVM 模型基本具备了模拟高寒山区流域降水-径流过程的能力。然而,由于DHSVM 模型缺少对高寒山区流域土壤的冻融过程的刻画,春季径流的模拟精度明显受到影响,需要在将来重点改进。

下载 (pdf, ~1.86 MB):

期刊官网:Link

一篇冰川冻土中文论文

植被和土壤参数会较大影响到陆面过程模型的模拟结果,但在青藏高原的相关模拟中,通常并没有对这些参数进行专门的考虑,而相对地其他区域,青藏高原具有植被稀疏和土壤粗颗粒含量高等显著特征,那么这些特点会对多年冻土的模拟结果会有产生多大的影响?我们的研究表明,青藏高原植被土壤特性对Noah模拟结果较大影响。该成果发表在2018年第2期《冰川冻土》。

引用:吴小波,南卓铜*,王维真,赵林. 基于Noah陆面过程模型模拟青藏高原植被和土壤特征对多年冻土影响的模拟. 冰川冻土. 2018, 40(2): 279-287.

下载:官网; Baidu;

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.

Links: Baidu;