Hydrological models are important tools for simulating hydrological cycling processes for water resource assessment, management, and utilization [1
]. Since the concept of unit hydrograph was produced by Sherman [2
], the development of hydrological models experienced three stages. The first one is about the knowledge exploration of the infiltration, evaporation, and streamflow concentration, which are the critical parts of hydrological cycling processes. Based on the accumulation of hydrological knowledge, lumped hydrological models are developed including Stanford model [3
], Soil Conservation Service model (SCS) [4
], Xinanjing Model [5
]. After the concept of distributed hydrological model was introduced by Freeze and Harlan [6
], distributed models are developed and widely applied, such as Soil and Water Assessment Tool (SWAT) [7
], Hydrologic System Program Fortran (HSPF) [8
], and Variable Infiltration Capacity model (VIC) [9
Hydrological model development considers the spatial heterogeneity with the model structure and description of hydrological processes [10
]. The study area has been discretized into sub-watersheds [11
], grid cells [9
], representative elementary areas (REA) [12
], and hydrological response units (HRU) [7
] based on digital elevation models (DEMs), land cover, and soil datasets with the technologies of geographical information system and remote sensing. Hydrological models define them as the basic spatial simulating unit, on which the hydrological cycling process is simulated. The spatial heterogeneity of hydrological model input about precipitation is presented with the monitoring meteorological stations together with the interpolation methods of Kriging, ridge regression, and inverse distance weighted (IDW) [13
Spatial heterogeneity is considered when representing hydrological processes by hydrological models about the runoff generation and streamflow channel routing. Xinanjiang model presents the soil infiltration with the probability distribution function based on the variable infiltration capacity accounting for the soil properties [5
]. Soil Conservation Service developed the curve number method to determine the rainfall–runoff relationship and coefficients based on the soil, land cover, and slope [4
]. The runoff generation mechanisms of saturation excess dominated in the humid area and intensity excess in the arid area are combined with time compression analysis method [9
] to increase the runoff predicting accuracy in semi-arid areas. Regarding channel routing, the kinematic wave model described the overland flow concentration process accounts for the distance of hillslope, surface roughness, and slope. The lumped overland flow concentration method GIUH adds the geomorphological characteristics into the instantaneous unit hydrograph [15
] in estimating the streamflow concentration time, velocity, and quantity. Distributed channel routing models, such as Saint-Venant equations, estimate the flow rate, velocity, and depth along the channels considering distance along watercourse, cross section area, and the watercourse bottom slope with the numerical solutions working under different basin hydrological conditions [17
Even though spatial heterogeneity has been considered in hydrological model development, no single hydrological model can be applied under all conditions. Therefore, exclusive models are developed in describing the hydrological processes with distinctive characteristics and applied in appropriate areas. For urban areas with subsurface pipe-nets, storm water management model (SWMM) [18
] is applied to simulate the runoff quantity and quality by describing the streamflow concentration and subsurface water movement through pipe-nets. In the ground water dominated watersheds, groundwater model MODFLOW model is developed and applied to estimate the groundwater quantity through describing the water balance processes and physical mechanism of the groundwater dynamics [19
]. In glacierized watersheds, the streamflow is estimated through describing the processes of melt water from glaciers draining to the catchment outlet with the coupled model HBV and the glacier retreat model developed by Li et al. [21
The characteristics of an inland river basin in regards to the hydrological cycling process are not consistent over the entire basin, with the upper subbasin as the contributing area, and middle and lower subbasins as the dissipative areas. It is hard to obtain excellent simulating results utilizing one hydrological model, much less to provide scientific support about the water allocation among different subbasins and water resource management within the subbasins, even though spatial heterogeneity is considered in hydrological models such as VIC, SWAT, or exclusive models. Therefore, this study presents a solution to simulate the hydrological process of an inland river basin with multiple hydrological models by providing a case study of the hydrological process simulation of the Heihe River basin in China. Three hydrological models are selected, applied, and tested in simulating the hydrological processes of the upper, middle, and lower reaches of Heihe River basin respectively, according to the distinctive hydrological characteristics of the three subbasins of Heihe River basin.
It is hard to simulate the hydrological processes of an inland river basin due to the inconsistency of the hydrological characteristics over the entire basin. This paper provides a solution to this problem by presenting a case study of Heihe River basin hydrological process simulation using multiple hydrological models. This inland basin is divided into three subbasins and three hydrological models are selected to simulated the hydrological processes of each reach based on the distinctive hydrological characteristics. It has been proven that this solution is practical and effective based on the three models’ excellent performances and the reasonable simulation of water balance status for each reach.
The upper reach locates in the semi-arid mountain area with runoff generated by the two mechanisms of intensity excess and saturation excess, therefore, HIMS is utilized to simulate the streamflow of the contributing area due to its structure of the combined runoff generation mechanisms. Simulated streamflow with HIMS model has greater predicting accuracy than those simulated with other models such as VIC and SWAT in the same study period. Water balance is closed in the study period for the upper reach with precipitation and snowmelt as the major water sources.
The middle reach is the dissipative area located in the arid area and a conceptual water balance model is utilized in simulating its streamflow accounting for the frequent interactions between the surface and subsurface flows under the impacts of the intensive human activities. Water balance is kept for the six land use types and the entire middle reach with the major inflow of precipitation and outflow of ET. More downstream flow can be expected through reducing ET and infiltration with effective irrigation methods and low water consuming crops. The lower reach is the other dissipative area located in the extreme arid climate area and the groundwater dynamics is well simulated with the GMS based on the statistical results in the calibration and validation periods for 15 wells. Water balance of the lower reach is kept in the study period between the inflow and outflow, even though the lower reach locates in the extreme arid area. Percolation and evaporation are identified as the key factors influencing the groundwater level, as evaporation decreases and percolation increases the groundwater level. The groundwater level over the entire lower reach keeps elevating at the average annual pace of 0.02 m due to the ecological division with negative water storage change values in dry years such as 2001, 2004, 2009, and 2010. The groundwater level increases by 0.04 m/year in the near-river channel area and decreases in the areas far away from the channels.