Inicio  /  Water  /  Vol: 10 Par: 9 (2018)  /  Artículo
ARTÍCULO
TITULO

An Improved Grid-Xinanjiang Model and Its Application in the Jinshajiang Basin, China

Changqing Meng    
Jianzhong Zhou    
Deyu Zhong    
Chao Wang and Jun Guo    

Resumen

A modified form of the distributed Grid-Xinanjiang model (GXAJ) characterizing the infiltration excess and saturation excess runoff mechanisms coupled to a two-source potential evapotranspiration model (TSPE) was proposed to simulate the hydrological process and study the spatiotemporal pattern of the precipitation, evapotranspiration, and soil moisture in the Jinshajiang River basin. In the flow routing module, the flow is routed by the physically nonlinear Muskingum?Cunge method. The TSPE model can calculate the spatiotemporal variation of the potential canopy transpiration (CT), interception evaporation (IE), and potential soil evaporation (SE). Subsequently, the calculated potential evapotranspiration (PE) is coupled to the GXAJ model to calculate the water budget in each grid. An a priori parameter estimation was developed to obtain the spatially varied parameters from geographical data, including digital elevation model (DEM) data, soil data, vegetation data, and routing data. Hydrometeorological data were interpolated to 4750 grids with cell sizes of 10 km × 10 km by the Thiessen Polygon method. The DEM data was used to extract the flow direction, river length, hillslope, and channel slopes and to adjust the altitude-related meteorological variables. The reprocessed Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) from the Beijing Normal University (BNU) dataset, which has a spatial resolution of 1 km × 1 km, was used to obtain the spatiotemporal variation in the LAI. The developed GXAJ model was applied to three sub-basins in the Jinshajiang River basin and was compared to the traditional GXAJ model. The developed GXAJ model satisfactorily reproduced the streamflow at each catchment outlet and matched the peak discharges better than the traditional GXAJ model for both the dry and wet seasons. The uneven distribution of the simulated mean annual evapotranspiration in the whole watershed was closely related to the vegetation types, ranging from 189.81 to 585.45 mm. Forest and woodland, shrubland, grassland, and cropland were shown to have mean annual evapotranspiration values of 485.6, 289.4, 275.9, and 392.3 mm, respectively. The ratios of the annual evapotranspiration to precipitation (E/P) of the forest, woodland, shrubland, grassland, and cropland were 54, 83, 53, and 48%, respectively.

 Artículos similares

       
 
Yaru Guo, Tian-Chyi Jim Yeh and Yonghong Hao    
Karst aquifers are prominent sources of water worldwide; they store large amounts of water and are known for their beautiful springs. However, extensive groundwater development and climate variation has resulted in a decline in the flow of most karst spr... ver más
Revista: Water

 
Zhaoxin Wang, Tiejun Wang and Yonggen Zhang    
Knowledge of both state (e.g., soil moisture) and flux (e.g., actual evapotranspiration (ETa) and groundwater recharge (GR)) hydrological variables across vadose zones is critical for understanding ecohydrological and land-surface processes. In this stud... ver más
Revista: Water

 
Ludek Bures, Petra Sychova, Petr Maca, Radek Roub and Stepan Marval    
An appropriate digital elevation model (DEM) is required for purposes of hydrodynamic modelling of floods. Such a DEM describes a river?s bathymetry (bed topography) as well as its surrounding area. Extensive measurements for creating accurate bathymetry... ver más
Revista: Water

 
Zuhier Alakayleh, Xing Fang and T. Prabhakar Clement    
This study aims at furthering our understanding of the Modified Philip?Dunne Infiltrometer (MPDI), which is used to determine the saturated hydraulic conductivity Ks and the Green?Ampt suction head ? at the wetting front. We have developed a forward-mode... ver más
Revista: Water

 
Haoran Liu, Kehui Xu, Bin Li, Ya Han and Guandong Li    
Machine learning classifiers have been rarely used for the identification of seafloor sediment types in the rapidly changing dredge pits for coastal restoration. Our study uses multiple machine learning classifiers to identify the sediment types of the C... ver más
Revista: Water