Development of a Regional Regression Model for Estimating Annual Runoff in the Hailar River Basin of China

The Hailar River, a first-grade tributary of the Erguna River that borders China and Russia, is the main water source for the local industry and agriculture. However, because there are only 11 flow gauging stations and those stations cannot monitor all runoff paths, it is hard to directly use the existing flow data to estimate the annual runoffs from all subbasins of interest although such estimation is needed for utilization and protection of the water resources in the Hailar River. Thus, this study implemented an indirect approach (i.e., regional regression model) by correlating annual runoff with annual rainfall and water surface evaporation as well as hydrologic characteristics of the 11 subbasins monitored by the gauging stations. The study used 51 years (from 1956 to 2006) data. The results indicated a significant correlation (R2 > 0.87) between annual runoff and the selected subbasin characteristics and showed the model to be robust because the predicted runoffs for the validation period are compatible with the corresponding observed values. In addition, this model was used to estimate the annual runoffs for the subbasins that are not monitored by the 11 flow gauging stations, which adds new information to existing literature.

Influences of landform as a confounding variable on SOM-NDVI association in semiarid Ordos Plateau

Soil organic matter （SOM） plays an important role in maintaining vegetation cover and thus mitigating land erosion of fragile terrestrial ecosystems such as in the Northern Ordos Plateau of China （NOPC）. However, little information is available on whether and how SOM varies spatially as an intrinsic characteristic of landform in NOPC. The objective of this study was to examine the spatial associations of SOM with landform and vegetation cover. The study was conducted in a 23,000-km2 area within NOPC because this area has landforms of mobile dunes （MD）, flat dunes （FD）, grassy sandy land （GSL）, flat sandy bedrocks （FSB）, and swamps and salt lakes （SW）, which are typical landforms in semiarid ecosystems. SOM was determined using a standard laboratory analysis method for 5 cm topsoil samples collected at 72 locations across the study area. In addition, the 250 m Multitem- poral Moderate Resolution Imaging Spectroradiometer （MODIS） imageries taken in the period from August 2006 to August 2010 were used to extract Normalized Difference Vegetation Index （NDVI） which in turn was used as the surrogate of vegetation cover. Classic and geostatistical methods were used to compare SOM concentration across different landforms. The results indicated that an area with a greater value for NDVI （i.e. better vegetation cover） tended to have a higher SOM concentration regardless of the landform types. However, the association between SOM and NDVI varied from one landform to another. The SW and GSL had a highest SOM concentration, while MD had a lowest concentration. For the study area as a whole and the FD, GSL, and MD, SOM was found to be the sole function of NDVI, whereas, for the FSB, SOM was influenced by several intrinsic variables, namely ground surface altitude, slope, and aspect, as well as NDVI. SOM for the SW landform was found to be a function of NDVI. Furthermore, SOM and NDVI exhibited a consistent spatial pattern of increasing from north to south and from west to east. The highest SOM concentration of 3.5% occurred along an east-westward belt, which is adjacent to water pathways, in the mid part of the study area.