Hao Fanghua

Impact of land-cover and climate changes on runoff of the source regions of the Yellow River

After dividing the source regions of the Yellow River into 38 sub-basins, the paper made use of the SWAT model to simulate streamflow with validation and calibration of the observed yearly and monthly runoff data from the Tangnag hydrological station, and simulation results are satisfactory. Five land-cover scenario models and 24 sets of temperature and precipitation combinations were established to simulate annual runoff and runoff depth under different scenarios. The simulation shows that with the increasing of vegetation coverage annual runoff increases and evapotranspiration decreases in the basin. When temperature decreases by 2°C and precipitation increases by 20%, catchment runoff will increase by 39.69%, which is the largest situation among all scenarios.

无测站流域水文预测（PUB）的研究方法

根据国内外研究成果,结合自身的科研体会,借用＂巧妇面临无米之炊＂时可能用到的＂借、替和种米＂的应对逻辑,将无测站流域水文预测（Predictions in Ungauged Basins,PUB）的研究方法归纳为移植法、替代法和生成法。将移植法归类为两种,一是当研究地区没有测站资料（简称无资料）而周边某个地区有测站资料（简称有资料）时,如果有资料区的自然地理环境与研究地区相似,直接移用该有资料地区的资料到无资料区,即直接移植法;二是倘若周边不只一个区域有资料,那么就借用所有这些点的信息,采用插值法得到无资料区的资料,即间接移植法。定义替代法为如果本研究区域或者相似区域没有资料,但有可能＂求出＂资料的辅助信息,则挖掘这些信息的方法。将替代法分为两类,一是根据本研究区域其他信息,通过模型模拟、同化、融合、从本学科和多学科领域挖掘信息等技术,得到本研究区所需资料。二是根据其它研究区域其他信息得到本研究区所需资料,包括就地外延和对比流域法。定义生成法为通过获取第一手资料开展水文预测的方法,包括野外实验和室内实验。通过梳理PUB的这些研究途径,探索了PUB方法发展的可能突破口,旨为推动PUB的研究提供思路。

Complex vegetation cover classification study of the Yellow River Basin based on NDVI data

In order to better reflect the response of natural geographical environment factors, such as hydrology, climate and soil, to the changes of land use/land cover, land cover classification using multi-temporal and multi-spectral remote sensing observation data has been an effective technique. This paper adopts a multi-dimension classification method to classify the vegetation cover of the Yellow River Basin (YRB) in China by using NOAA/AVHRR remote sensing image as data source. Better classification resolution, which Mill provide scientific basis to study on increasingly prominent ecological-environmental problems, hydrology and water resources of the YRB, is acquired by this complex classification method. With the support of geography information system (GIS) and remote sensing (RS), this paper makes use of principal component transformation (KL transformation) to classify the vegetation cover using the Normalized Difference Vegetation Index (NDVI) data of NOAA/AVHRR remote sensing image as data source with spatial and temporal resolution at 1 km times 1 km and a vegetation growth cycle. Principal component transformation is first carried out on 3 normalized images of bio-temperature (BT), potential evapotranspiration rate (PER) and annual precipitation (P) which are the controlling factors on vegetation distribution pattern and the first principal component is chosen as the first classification vector of the digital image of the vegetation cover classification of the YRB. Because the basin topography is complex and underlaying surface condition has great impact on vegetation growth, Digital Elevation Model (DEM) image of the YRB became the second classification vector. Then, the first 3 principal components from the time series of the 12 monthly NDVI images are combined with the first and the second classification vectors to consist of the integrated image with 5 bands. After integrating the multidimension information, the vegetation cover of the YRB is finally classified into two grads with iterative self-organizing data analysis technique A (ISODATA) and unsupervised classification method, including 8 vegetation types and 25 vegetation subtypes