On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basins on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. The evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.
Precipitation is one of the essential meteorological inputs of a hydrologic
model and the key driving force for a hydrologic cycle. Errors in
precipitation estimation can bring significant uncertainties in streamflow
simulation and prediction (Sorooshian et al., 2011). Three methods are
generally used to measure precipitation: traditional gauge observations,
meteorological radar observations, and satellite observations (Ashouri et
al., 2015). In many remote regions and mountainous areas, rain gauges and
meteorological radar networks are either sparse or non-existent. Thus,
satellite-based precipitation is of great importance in such regions. For
instance, there is a great potential for using satellite-based precipitation
estimates on the Tibetan Plateau known as the “roof of the world” with an
average elevation of over 4000 m (Yao et al., 2012). Owing to a harsh
environment, the existing meteorological stations managed by the Chinese
Meteorological Administration only form an extremely sparse network, which
creates great challenges for water resources management and operation. For
example, on average, there is only 0.3 and 1 station per grid of
1
The selected river basins (the upper Yellow River and Yangtze River basin) on the Tibetan Plateau and location of rainfall stations and river outlets.
According to Kidd and Levizzani (2011), during the last decade
satellite-based precipitation estimates have reached a good level of
maturity. Currently, many satellite rainfall products are available and have
been extensively used globally (e.g., Sorooshian et al., 2000; Huffman et
al., 2001; Adler et al., 2003; Xie et al., 2003; Joyce et al., 2004; Turk and
Miller, 2005; Miao et al., 2010, 2011). Recently, a new satellite-based
precipitation product was released by the National Climatic Data Center
(NCDC), which is termed Precipitation Estimation from Remotely Sensed
Information Using Artificial Neural Networks – Climate Data Record
(PERSIANN-CDR) (Ashouri et al., 2015). PERSIANN-CDR is a multi-satellite,
high-resolution and post-time rainfall product that provides daily
precipitation estimates at 0.25
Many studies have been carried out to evaluate the suitability of a number of satellite-based precipitation estimate products in forcing hydrologic models and simulating streamflow for various regions around the world (e.g., Yilmaz et al., 2005; Artan et al., 2007; Su et al., 2011; Bitew et al., 2012; Yong et al., 2012, Yang et al., 2015). However, there are few evaluation studies focusing on hydrological modeling driven by satellite rainfall products on the Tibetan Plateau. Among a limited number of studies, Tong et al. (2014) evaluated the streamflow simulation capability of four satellite products (TRMM-3B42-V7, TRMM-3B42RT-V7, PERSIANN, and CMORPH) using the variable infiltration capacity (VIC) hydrologic model in two sub-basins over the Tibetan Plateau and concluded that the TRMM-3B42-V7 and CMORPH datasets have relatively better performance than the others. One of the limitations is that the data length of many satellite precipitation products, such as TRMM-3B42RT-V7 and CMORPH, start from 2000 to the present, which is rather short. In this study, there is no such limitation because the PERSIANN-CDR daily rainfall product includes more than 33 years of data and the length of data grows every year. In Tong et al. (2014), the rain gauge is set to be the reference to compare different satellite-based rainfall products. However, given the fact that (1) density of rain gauges on the Tibetan Plateau is rather low as compared to other regions in China, (2) distribution of gauges are uneven according to Miao et al. (2015), and (3) rain gauges are located in low-elevation river channels (Fig. 1), the authors have the similar concern as Miao et al. (2015) that the use of a sparse rain gauge as reference to compare satellite products is questionable. Therefore, in this study, precipitation from a limited gauge network, GLDAS precipitation, and PERSIANN-CDR precipitation are used as the inputs of a hydrologic model for streamflow simulation on two major river basins, the upper Yangtze River basin and the upper Yellow River basin, on the Tibetan Plateau. Then, the simulation results are compared with observed streamflow, which is believed to be a more reliable reference than the limited rainfall observation to judge the qualities of satellite rainfall products on the Tibetan Plateau. Potential sources of uncertainties are also discussed with regard to the parameterization of the hydrological model and the length of data used for calibration.
Two river basins on the northern Tibetan Plateau, namely, the upper Yangtze
River (UYZR) and upper Yellow River (UYLR) basins are selected, which have a
long daily streamflow record from 1983 to 2012. As shown with red squares in
Fig. 1, two hydrological stations, Tangnaihai and Zhimenda, are the outlet
stations of the UYZR and UYLR, which have total drainage areas of 121 972
and 137 704 km
The observed daily streamflow data from 1983 to 2012 at the outlets of the
two basins are provided by the Ministry of Water Resources of China. The
runoff is calculated by dividing streamflow by corresponding basin area. The
daily gauge meteorological data in the two basins from 1983 to 2012 are
obtained from the China Meteorological Administration
(
The hydrologic model used in this study is the Hydroinformatic Modeling System (HIMS) rainfall–runoff model (Liu et al., 2006, 2008, 2010a, b),
which is one of the operational hydrological models by the Tibet Government
in China. The HIMS model is a grid-based hydrologic model, which is able to
simulate the dominant hydrological processes such as actual
evapotranspiration, infiltration, runoff, groundwater recharge, and channel
routing. In the HIMS model, a catchment is divided into grids, and grids are
linked throughout the stream network based on topological relationships of
channel network and properties of soil, vegetation, and land use. In each
grid, actual evaporation is calculated by a formulation between soil water
content and potential evapotranspiration. Potential evapotranspiration
ET
The infiltration process is modeled using an empirical relationship, which has
been confirmed through analysis of data measured in a number of experimental
watersheds and various physical geographic factors in China (Liu et al.,
2006):
Description of HIMS model parameters and allowable ranges.
According to the saturation excess mechanism and spatial variability of
watershed characteristics, interflow and groundwater recharge are estimated
as linear functions of soil wetness (soil moisture amount divided by soil
moisture capacity). Baseflow is simulated based on the linear reservoir
assumption, in which the relationship between groundwater storage and
outflow is linear. Interflow (RI; mm), groundwater recharge
(REC; mm), baseflow (RG; mm), and total runoff (TR; mm) are determined by
Average monthly precipitation and runoff in the upper Yangtze and Yellow River basins.
Note: Rain_gauge, Rain_GLDAS and Rain_CDR indicate gauge-based precipitation GLDAS precipitation and PERSIANN-CDR precipitation (mm), respectively. Runoff_OBS indicates observed runoff (mm). Ratio means the percentage of precipitation and streamflow during May to November to annual values.
The monthly average runoff observed at the river outlet of the upper Yangtze River and Yellow River basin, and the precipitation data retrieved from ground-based observation, GLDAS, and PERSIANN-CDR product.
The HIMS model is set up at 0.25
Figure 2 and Table 2 show the average monthly amounts of precipitation and runoff in the UYZR and UYLR from 1983 to 2012. These two river basins have distinct dry and wet seasons, which are from September to February, and March to October, respectively. According to Table 2, precipitation between May and October (wet season) accounts for 92.5 and 90.1 % of the annual total precipitation for the UYZR and UYLR, respectively. Similar to the temporal distribution of precipitation, runoff during May to October accounts for 87.6 and 78.4 % of annual runoff in the UYZR and UYLR, respectively. Given the seasonal concurrence of precipitation and runoff, precipitation in the wet season plays a dominant role in annual runoff generation in these two river basins. The runoff coefficients are 0.22, 0.27, and 0.26 in the UYZR based on gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation, respectively. In the UYLR, the runoff coefficients are 0.29, 0.31, and 0.29 based on the three precipitation datasets, respectively.
Figure 3 shows the spatial distribution of average annual values of
1.0
The spatial distribution of average annual values of 1.0
The calculated CDF of daily gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation in the upper Yangtze River basin and upper Yellow River basin.
Calibrated parameter values in the HIMS model for the upper Yangtze and Yellow River basins.
The performances of streamflow simulations driven by gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation in the two basins.
Note: Q_obs indicates observed runoff (m
The comparison between the simulated daily streamflow (red) with
ground-based, GLDAS, and PERSIANN-CDR precipitation and the observed data
(black) at the outlets of the upper Yangtze River basin
The comparison between the observed streamflow (black) and the simulated streamflow using ground-based precipitation (red), GLDAS precipitation (green), and PERSIANN-CDR precipitation (blue) in the upper Yangtze River basin and upper Yellow River basin.
Due to the previously mentioned concern that a sparse gauge network and its interpolation cannot perfectly describe the spatial and temporal rainfall characteristics at river basin scale, the alternative is to evaluate the streamflow simulated instead of treating the sparse gauge network as reference. In this section, the streamflow simulated by gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation is derived from HIMS, and compared with observed streamflows at the outlet in the UYZR and UYLR. The HIMS model is separately calibrated by maximizing the NSE between observed streamflow and simulated streamflow driven by gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation from 1983 to 1997. Table 3 shows the calibrated parameter values of the HIMS model for the two basins. Figure 5 shows daily observed streamflow and simulated streamflow driven by gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation for the two basins from 1983 to 2012. In the UYZR (Fig. 5a, b and c), the NSE values are 0.63, 0.78, and 0.77 in the calibration period driven by gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation, respectively, whereas they are 0.60, 0.71, and 0.73 in the verification period. In both the calibration and verification period, the NSE values from GLDAS precipitation and PERSIANN-CDR precipitation are greater than that from gauge-based precipitation, which indicates that using GLDAS precipitation and PERSIANN-CDR precipitation as input to the HIMS model is able to generate more accurate streamflow than using gauge-based precipitation in the UYZR. In the UYLR (Fig. 5d, e and f), the NSE values between daily observed streamflow and simulated streamflow are 0.82, 0.78, and 0.80 in the calibration period driven by gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation, respectively. In the verification period, the NSE values are 0.81, 0.77, and 0.78 for the three types of data. The high NSE values in both the calibration and verification periods suggest that gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar performances as the drivers of streamflow simulation in the UYLR.
Figure 6 and Table 4 compare the simulated and observed average monthly
streamflow for the two basins. In the UYZR, the relative bias between
observed streamflow and simulated streamflow driven by gauge-based
precipitation is 10.3 % in the wet season, which suggests a considerable
overestimate of streamflow. Comparably, the relative bias between observed
streamflow and simulated streamflow driven by GLDAS precipitation and
PERSIANN-CDR precipitation is
In summary, the streamflow simulated by GLDAS precipitation and PERSIANN-CDR precipitation has a good agreement with the observed streamflow in the UYZR and UYLR. The good agreement between observed streamflow and PERSIANN-CDR simulated streamflow reveals a strong streamflow simulation capability of PERSIAN-CDR product, which also gives community certain confidence in using PERSIANN-CDR product to study hydrological cycle and climate change on the Tibetan Plateau.
In this study, model parameters are separately calibrated in terms of the
highest NSE between observed streamflow and simulated streamflow driven by
gauge-based precipitation, GLDAS precipitation and PERSIANN-CDR
precipitation. Therefore, these parameter values are highly dependent on the
precipitation inputs. When the precipitation input changes, the parameter
values may change accordingly in order to match the streamflow. Table 3 shows
the values of calibrated parameters separately driven by gauge-based
precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation in the two
basins. Parameter sensitivity study of the HIMS model indicates that the HIMS
model is most sensitive to parameters of the maximum soil storage capacity
(SMSC) and the infiltration coefficients (
The simulated daily streamflow (red) forced by PERSIANN-CDR rainfall
product in different scenarios and the observed daily streamflow (black) at
the outlets of the upper Yangtze River basin and upper Yellow River basin.
Panels
In a previous study, Tong et al. (2014) evaluated the streamflow simulation
capabilities of four satellite-based precipitation products (TRMM-3B42-V7,
TRMM-3B42RT-V7, PERSIANN, and CMORPH) using the VIC hydrologic model in the
UYZR and UYLR from 2006 to 2012. Different from the PERSIANN product that
Tong et al. (2014) used, PERSIANN-CDR is a different product that provides
over 33 years of daily and high-resolution precipitation with GPCP monthly
information incorporated. In addition, the parameters in the VIC hydrologic
model are calibrated by the input of interpolated gauge-based precipitation.
The calibrated parameter values are then kept fixed when the VIC model are
rerun by inputs of satellite-based precipitation datasets to evaluate the
streamflow simulation capabilities of satellite-based precipitation
datasets. Rerunning the hydrologic model with the fixed parameters
calibrated by gauge-based precipitation partly indicates that Tong et al. (2014)
assumed that the sparse gauge observations are a more reliable dataset
than satellite-based precipitation datasets. However, this is a questionable
assumption. As we mentioned in the introduction, not only the
location of rain gauges is conditioned (relatively low elevations) but also
the sparse distribution of rainfall stations over the Tibetan Plateau could
bring large errors and uncertainties in regional rainfall measurement.
Similar arguments are also raised by Miao et al. (2015). In this study, we
rather cautiously believe that gauge-based precipitation could not be
reliable, especially in the UYZR where there is only one station per
34 426 km
Besides of the uncertainties due to hydrological model calibration, another factor that influences the accuracy of streamflow simulation is the length of precipitation records used for calibration. As mentioned before, one of the advantages of PERSIANN-CDR product is the provision of more than 33 years of continuous sequences of precipitation data, which can allow for more extensive streamflow simulation in the Tibetan Plateau. In this study, comparison experiments (Fig. 7) were designed to test the influences of precipitation record length on the accuracy of streamflow simulation. In the designed experiments, we investigate the accuracy of streamflow simulation during 2008 to 2012 with two different calibration scenarios. In the first scenario, the calibration period is from 2003 to 2007 for both the UYZR (Fig. 7a) and the UYLR (Fig. 7b). In the second scenario (Fig. 7c and d), 15 years of data from 1983 to 1997 are used for calibration, which are longer than that in the first scenario. As it is shown in Fig. 7a and b, in the first scenario the NSE values between daily observed and simulated streamflow are 0.75 and 0.66 during the verification period (from 2008 to 2012) for the UYZR and UYLR, respectively. Comparatively, in the second scenario the NSE values during the verification period (from 2008 to 2012) are 0.81 and 0.82 for the two basins, respectively. The NSE values in the second scenario are consistently higher than that in the first scenario in the two basins. For the UYLR in the second scenario (Fig. 7d), the NSE value during the verification period is significantly greater than that in the first scenario. Figure 7b also shows that the HIMS hydrological model significantly underestimates the flow peaks during the summer of 2010 and 2012 when calibrated by 5 years of data from 2003 to 2007. The disagreement between the observed and simulated flow peaks is partly because the magnitudes of flood events during the calibration period are all smaller than that during the verification period and the HIMS hydrological model cannot be well trained during the calibration period. Therefore, when using a short-length precipitation data as input for a hydrological model, the accuracy of streamflow simulation could be limited, especially when precipitation data used for calibration cannot cover the flood and drought conditions of a basin. However, when the HIMS hydrological model is calibrated by the longer dataset from 1983 to 1997, as it is shown in Fig. 7c and d, there is a greater potential that the characteristics of extreme events can be captured by the hydrological model than using only 5 years of data from 2003 to 2007. Given the availability of long-term precipitation records (over 33 years) provided by PERSIANN-CDR product, the extreme events in the historical period could be well captured by a hydrological model. Therefore, using such a product with long-term records, the confidence of simulating streamflow over the Tibetan Plateau will correspondingly increase.
As it is compared to radar-based precipitation measurement and gauge networks, the main advantage of satellite-based precipitation estimate is the broader coverage at global scale. This allows for a comprehensive understanding of the driving force of hydrologic cycle, especially for the gauge-sparse area. To verify the accuracy of satellite-based precipitation estimate products, the comparison with ground observation is necessary. However, in a gauge-sparse area, a direct comparison on precipitation temporal and spatial variation will be questionable due to the limited gauge information. This study provides an alternative way to evaluate satellite-based precipitation products by forcing both rainfall estimates from satellite and limited gauge network into hydrological model. Given the confidence in streamflow measurements, which are more reliable and well monitored than the limited ground-based rainfall measurements, the comparison of simulated streamflow enables an indirect way to evaluate satellite-based precipitation products.
In this study, PERSIANN-CDR precipitation, GLDAS precipitation, and gauge-based precipitation have good agreements in the UYLR, whereas the three datasets have different values in the UYZR. Streamflow simulation capabilities of PERSIANN-CDR precipitation, GLDAS precipitation, and gauge-based precipitation are evaluated as the inputs of the HIMS hydrologic model in the two basins. All the three datasets have similar good performances in the UYLR, whereas PERSIANN-CDR precipitation and GLDAS precipitation have slightly better performance than gauge-based precipitation in the UYZR. Gauge-based precipitation tends to produce larger streamflow in the wet season in the UYZR. This indicates that in the UYZR, a sparse gauge network could not be fully reliable when used as the reference for streamflow simulation due to the fact that the locations of the limited gauge stations cannot be representative for measuring the precipitation patterns at the river basin scale. In addition, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation all generate smaller streamflow in the dry season probably because of the lack of a frozen soil algorithm in HIMS model. This may bring certain uncertainties in the discharge comparisons by different precipitation inputs (Xue et al., 2013b). Further studies should be conducted to improve the frozen soil simulation of HIMS model.
Lack of rainfall gauge stations has brought a great challenge to hydrological and climate studies over the Tibetan Plateau (e.g., Yao et al., 2012; Zhang et al., 2013). Based on the demonstration in this study that PERSIANN-CDR is able to produce reasonably good streamflow in the UYZR and UYLR as compared to observed streamflow, we can speculate that the PERSIANN-CDR rainfall product has the potential to be a useful dataset and an alternative for the sparse gauge network in climate change and hydrological studies on the Tibetan Plateau considering the needs for long-term (more than 33 years) and high-resolution records.
The GLDAS precipitation dataset is available at the website
This research was supported by the Natural Science Foundation of China (41330529, 41571024, 41201034), the program for “Bingwei” Excellent Talents in Institute of Geographic Sciences and Natural Resources Research, CAS (project no. 2013RC202), the NOAA NCDC/Climate Data Record program (prime award NA09NES440006), and the DOE (prime award no. DE-IA0000018). Edited by: D. Yang Reviewed by: two anonymous referees