Distributed hydrological models are important tools for revealing the hydrological processes that occur in a changing environment [1
]. However, a hydrological model includes multiple complex parameters. Hence, various inputs are required to accurately simulate hydrological processes [2
]. Although less effort is needed to calibrate a hydrological model if the input data are reliable and comprehensive [3
], in some regions with data scarce, the calibration of a hydrological model is complex and requires considerable effort. Recently, auto-calibration software and methods for hydrological models have been developed, for example, Parameter ESTimation (PEST) [4
], the Shuffled Complex Evolution algorithm (SCE-UA) [5
], and SWAT Calibration Uncertainty Programs (SWAT-CUP) [6
]. These tools are convenient for model calibration; however, there is more than one set of optimal parameters after a calibration using auto-calibration software or methods [7
]. Moreover, although some of the parameters are reasonable, others are not, and it is difficult to evaluate whether the calibrated parameters are correct. The main output variables of a hydrological model are streamflow, evapotranspiration (ET), soil water content, surface runoff, groundwater flow, and lateral flow [7
]. However, generally, only site-based streamflow data are used to calibrate and validate a hydrological model because it is difficult to observe other variables [9
]. Furthermore, in some watersheds, there are only a few hydrological stations that are heterogeneously distributed. As a result, an auto-calibrated model using site-based streamflow data will yield highly generalised values for all model parameters and hydrological processes of the watershed [10
]. Therefore, to obtain reasonable model parameters and to better simulate watershed hydrological processes, it is necessary to combine the model auto-calibration procedure with both site-based streamflow and other spatially heterogeneous observation data.
ET is one of the most important components of the water balance; approximately 60–70% of precipitation returns to the atmosphere from the land surface. The remaining precipitation may become streamflow or enter other forms of water. With the development of remote sensing, ET data are no longer difficult to obtain. With the help of remote sensing technology, energy data relating to the soil–vegetation–air interface can be extracted, and then combined with site-based meteorological data to calculate the regional ET based on the traditional algorithm. Many regional ET models exist, including the Reg model [4
], Priestley–Taylor jet propulsion laboratory model (PT-JPL) [12
], Penman–Monteith MODIS global terrestrial evapotranspiration algorithm (PM-MOD16) [13
], and Global Land surface Evaporation Amsterdam Methodology (GLEAM) [14
] amongst others.
Due to the different structures of datasets, models perform differently in terms of ET simulation. Models have been validated with observed data and have been reported to perform well in most places in China [15
]. Therefore, most datasets can be used directly to calibrate and validate hydrological models. Immerzeel and Droogers [16
] incorporated remote sensing-derived ET data (based on MODIS data and the SEBAL model) into their calibration of the Soil and water assessment tool (SWAT) for a catchment in the Krishna basin in southern India. After calibration, the performance of the SWAT to simulate ET showed an obvious increase. Rientjes et al. [17
] used streamflow and satellite-based actual ET (based on MODIS data and the SEBAL model) to calibrate the HBV rainfall-runoff model for the Karkheh River basin in Iran. The authors concluded that the catchment water balance was best reproduced when both streamflow and satellite-based ET served as the calibration target. Parajuli et al. [18
] applied time series PM-MOD16 ET data to evaluate the SWAT calibration. They demonstrated the use of satellite-based ET data to evaluate the SWAT performance, which can be applied in watersheds with a lack of meteorological data. In these studies, satellite-based ET data were used to optimise the hydrological model parameters, and the simulated results of the actual ET or streamflow were good. However, many satellite-based ET datasets are missing data for most places in northwest China [15
GLEAM is a series of algorithms to calculate the components of surface ET based on remote sensing data for water and heat [14
]. Compared with other surface ET datasets, GLEAM can not only effectively distinguish soil evaporation, plant emission, plant interception evaporation, snow evaporation, water surface evaporation and other components involved in the process of surface ET, but also considers radiation, temperature, precipitation and the surface layer in the calculation process [14
]. In addition, GLEAM datasets perform well and cover the entire area of China [19
], and can be directly used to calibrate and validate hydrological models.
Many inland rivers exist in northwest China, which generally originates in mountainous areas and dissipates in piedmont plain areas. The land-surface conditions, environment, and climate are therefore different in the upper, middle, and lower reaches. The best way to simulate the hydrological processes in these inland watersheds is to build individual models for each of these reaches [7
]. However, there is often a lack of observational data (e.g., precipitation and streamflow) that are key for building hydrological models successfully. The Bayinhe River, located in the northeast Qaidam basin, is a typical inland river [21
]. The upper reach of the Bayinhe River is situated in Qilian Mountain and the middle reach is in the Zelinggou basin and Delingha City. Only one hydrological station and one meteorological station exist in the entire watershed, both of which are in the middle reach. There is, therefore, an issue regarding how the hydrological processes can be simulated for this basin with data scarce.
The objective of this research is to evaluate the advantages of using the ET data derived from the GLEAM to separately calibrate the widely used SWAT model for the upper and middle reaches of the Bayinhe River. We combine actual streamflow data in one calibration as a means of simplifying the calibration process and improving the model performance.
The performance of the non-calibrated SWAT model was quite poor because the precipitation data were obtained from the only meteorological station in the watershed—in the middle reaches of the Bayinhe River, which is not representative of the rainfall across the entire upper and middle reaches. This indicates that a great effort was required to calibrate the SWAT model to obtain a better model performance. The study area of the upper and middle reaches includes variations in climate, terrain, and land use. However, as mentioned, only one hydrological station exists in the entire watershed, at the outlet of the middle reaches. Therefore, we first calibrated the SWAT model (SWAT1) using only the observed streamflow at the outlet of the middle reach. Compared to the non-calibrated SWAT model, the performance of the calibrated SWAT1 model to simulate the monthly streamflow improved obviously. The traditional calibration using the site-based streamflow grouped the hydrological processes together, which was primarily due to there being only one gauging station. Hence, this situation illustrates the need for more data with a high spatial resolution to calibrate the SWAT model for such a data scarce area.
To address this issue, the SWAT2 model was calibrated with both the site-based streamflow and satellite-based ET data. In addition, the upper and middle reaches were calibrated separately (SWAT2U and SWAT2M). The SWAT2U model simulated the water balance of the upper reach of the Bayinhe River and was calibrated using the GLEAM-based ET data, whereas the SWAT2M model simulated the water balance of the middle reach of the Bayinhe River and was calibrated using the GLEAM-based ET data and observed stream outflow from the middle reach. For the SWAT2M, the simulated stream outflow from the upper reach was used directly as the inflow to the middle reach. The performances of the SWAT2U and SWAT2M models for simulating the monthly ET were very good. The performance of the SWAT2M model for simulating the monthly streamflow at the outlet of the middle reach was better than that of the SWAT1 model. Although other similar studies [16
] have used different ET data to calibrate their hydrological models, our results, which used the satellite-based ET data to improve our model’s performance, were essentially the same as these previous studies. In our research, the GLEAM-based ET data played four roles in the calibration process, whereby the data: (1) distributed the hydrological processes of the study area (compared to SWAT1); (2) reduced the uncertainty of the SWAT model in this data scarce area; (3) improved the performance of the SWAT model to simulate the streamflow and water balance; (4) improved the reliability of the model parameters.
As mentioned, the precipitation data used in the present study were obtained from the only meteorological station in the study area; thus, the spatial heterogeneity of precipitation was not considered. This may have been a factor for the discrepancy between the simulated and observed streamflow in some specific months. Hence, if the spatial heterogeneity of precipitation had been taken into account, the model uncertainties may have been reduced. Although the use of the GLEAM-based ET data to calibrate the SWAT model improved the model’s performance for simulating the streamflow, the performance was not very good. Consequently, validated satellite-based precipitation data are needed for hydrological modelling in such data scarce areas.
In this research, we assumed that the GLEAM-based ET data could provide an independent measure of ET. Although the dataset was validated for the entire area of China, the GLEAM is just a series of algorithms to calculate ET, and some deviation still exists in comparison to field observed ET. Moreover, the spatial resolution of the GLEAM-based ET data is 0.25° × 0.25°, with one data point corresponding to one or more sub-basins. The use of the GLEAM dataset was able to reduce the grouping of hydrological processes that occurred during the model calibration. However, an equifinality issue may still have occurred at the HRU scale [16
]. Further study is therefore required to downscale the GLEAM-based ET data and improve the calibration results in HRUs.
In this research, three SWAT models (SWAT1, SWAT2U, and SWAT2M) were built to evaluate the advantages of using ET data derived from the GLEAM to separately calibrate the widely used SWAT model for the upper and middle reaches of a scare data area: The Bayinhe River. The results showed that:
(1) A great effort was required to calibrate the SWAT model for the Bayinhe River basin to obtain a better model performance;
(2) The performance of the SWAT model to simulate the streamflow and water balance was reliable when calibrated with streamflow only; however, this calibration method grouped the hydrological processes together and caused an equifinality issue;
(3) The combination of the streamflow and GLEAM-based ET data for the SWAT model calibration improved the model’s performance for simulating the streamflow and water balance. However, the equifinality issue remained at the HRU level.