Capability of TMPA products to simulate streamflow in upper Yellow and Yangtze River basins on Tibetan Plateau

Zhen-chun HAO, Kai TONG*,, Xiao-li LIU, Lei-lei ZHANG,

1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, P. R. China

2. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, P. R. China

Capability of TMPA products to simulate streamflow in upper Yellow and Yangtze River basins on Tibetan Plateau

Zhen-chun HAO1, Kai TONG*1,2, Xiao-li LIU1, Lei-lei ZHANG1,2

1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, P. R. China

2. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, P. R. China

Due to the high elevation, complex terrain, severe weather, and inaccessibility, direct meteorological observations do not exist over large portions of the Tibetan Plateau, especially the western part of it. Satellite rainfall estimates have been very important sources for precipitation information, particularly in rain gauge-sparse regions. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products 3B42, RTV5V6, and RTV7 were evaluated for their applicability to the upper Yellow and Yangtze River basins on the Tibetan Plateau. Moreover, the capability of the TMPA products to simulate streamflow was also investigated using the Variable Infiltration Capacity (VIC) semi-distributed hydrological model. Results show that 3B42 performs better than RTV5V6 and RTV7, based on verification of the China Meteorological Administration (CMA) observational precipitation data. RTV5V6 can roughly capture the spatial precipitation pattern but overestimation exists throughout the entire study region. The anticipated improvements of RTV7 relative to RTV5V6 have not been realized in this study. Our results suggest that RTV7 significantly overestimates the precipitation over the two river basins, though it can capture the seasonal cycle features of precipitation. 3B42 shows the best performance in streamflow simulation of the abovementioned satellite products. Although involved in gauge adjustment at a monthly scale, 3B42 is capable of daily streamflow simulation. RTV5V6 and RTV7 have no capability to simulate streamflow in the upper Yellow and Yangtze River basins.

TMPA; CMA precipitation data; VIC hydrological model; streamflow simulation; upper Yellow and Yangtze River basins

1 Introduction

The Tibetan Plateau, with an average elevation of over 4 000 m above sea level and an area of approximate 2.5 × 106km2, is the highest and most extensive highland in the world. The Tibetan Plateau is of considerable importance to the Asian monsoon and global generalcirculation via mechanical and thermal forcings (Duan and Wu 2005; Yanai et al. 1992; Ye and Gao 1979) due to its unique elevation and horizontal extent. The Tibetan Plateau is also the source of major Asian rivers (e.g., the Indus, Ganges, Brahmaputra, Yangtze, and Yellow rivers), which support hundreds of millions of people downstream. The Tibetan Plateau has therefore been called the “Asian water tower” (Immerzeel et al. 2010; Ye and Gao 1979). Precipitation is the most important atmospheric input to the terrestrial hydrologic system, and precipitation variability is a strong component of both hydrological processes and energy cycles. Quantitative evaluation of regional precipitation is one of the important factors in the estimation of latent heat, as it provides an understanding of the water cycle process over this unique highland, and in the evaluation of water resources for major rivers that originate from the plateau (Ueno et al. 2001). A clear understanding of the temporal and spatial variability of precipitation is crucial for studies on climate, hydrology, water budget analysis, glacier mass balance, and soil moisture. The traditional approach to obtaining precipitation information is by gauge observation. Unfortunately, due to its high elevation, complex terrain, severe weather, and inaccessibility, direct meteorological observations do not exist over large portions of the Tibetan Plateau, especially the western part of it.

Precipitation products derived from satellite observations have reached a level of maturity over the last decade (Kidd and Levizzani 2011). Various satellite precipitation products with different temporal and spatial resolutions are available (Adler et al. 2003; Huffman et al. 2001; Joyce et al. 2004; Sorooshian et al. 2000; Turk and Miller 2005; Xie et al. 2003). Satellite rainfall estimates are becoming very important sources of rainfall information, particularly in regions where the rain gauge distribution is very sparse. However, satellite rainfall estimates are subject to a variety of error sources (gaps in revisit times, poor direct relationships between remotely sensed signals and the rainfall rate, and atmospheric effects that modify the radiation field) and require a thorough validation (Bitew and Gebremichael 2011a, 2011b). Some global and regional validations have been reported for different satellite precipitation products (Adler et al. 2001; Bowman et al. 2003; Brown 2006; Chokngamwong and Chiu 2008; Dinku et al. 2007, 2008; Ebert et al. 2007; Gebremichael et al. 2005; Hirpa et al. 2010; Hong et al. 2007; Xie and Arkin 1995). However, on the Tibetan Plateau, which is characterized by a very complex topography and high elevation, there have been very few validation works (Yin et al. 2008; Barros et al. 2000; Bai et al. 2008; Gao and Liu 2013).

In this study, we evaluated the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) (Huffman et al. 2007) with gauge observations from the China Meteorological Administration (CMA) in the upper Yellow River Basin (UYERB) and the upper Yangtze River Basin (UYARB), which have relatively high-density gauge observations, and then used TMPA precipitation as hydrological model inputs for streamflow simulation within a hydrological modeling framework. The purpose of this work was to: (1) evaluate the performance of satellite precipitation products over this mountainregion; and (2) assess the capability and limitations of satellite precipitation products as inputs into the Variable Infiltration Capacity (VIC) distributed hydrological model for streamflow simulation of large watersheds on the Tibetan Plateau.

2 Data and methodology

2.1 Study region

The Tibetan Plateau, surrounded by the Earth’s highest mountains, such as the Himalayas, Pamir, and Kunlun, is the highest and most extensive plateau in the world and has long been known as the roof of the world (Liu and Chen 2000). Two basins (the UYERB and UYARB) are shown in Fig. 1. The drainage areas upstream of Tangnaihai (the UYERB) and Zhimenda (the UYARB) hydrological stations (the green dots in Fig. 1) are 121 972 km2and 137 704 km2, respectively. The elevation of the UYERB ranges from 2 728 to 5 969 m above sea level, and the UYARB ranges from 3 804 to 5 959 m.

Fig. 1 Location and topography of UYERB, UYARB, and Tibetan Plateau

2.2 TMPA and CMA precipitation data

Satellite-based instruments have been designed to collect observations mainly at thermal infrared (IR) and passive microwave (MW) wavelengths that can be used to estimate rainfall rates. The concept behind the high-resolution satellite rainfall algorithms is to combine information from the more accurate (but infrequent) MW with the more frequent (but indirect) IR to take advantage of their complementary strengths. The TMPA uses MW data to calibrate the IR-derived estimates and creates estimates that contain MW-derived rainfall estimates when and where MW data are available and the calibrated IR estimates where MW data are not available (Huffman et al. 2007). The TMPA products are available in two versions: the post real-time research version (3B42, ftp://disc2.nascom.nasa.gov/data/TRMM/Gridded/) and the real-time version (3B42 RT, ftp://trmmopen.nascom.nasa.gov/pub/merged/). The main difference between the two versions is the use of monthly rain gauge data for bias adjustmentin the research product. In addition, the research version uses the TRMM Combined Instrument (TCI) as a calibrator and the real-time version uses the TRMM Microwave Imager (TMI).

TMPA real-time products were upgraded to version 7 (RTV7) in 2012. RTV7 has incorporated several important changes from version 6 (Huffman and Bolvin 2012): version 7 real-time datasets, available since March 1, 2000; records from additional satellites, including the early parts of the various records and the entire operational Special Sensor Microwave Imager/Sounder (SSMIS) record; uniformly reprocessed input data using current algorithms, most notably for the Advanced Microwave Sounding Unit (AMSU) and Microwave Humidity Sounder (MHS), but also including TCI, TMI, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Special Sensor Microwave Imager (SSMI); use of version 7 TCI and 3B43 for climatological calibration; and use of a latitude-band calibration scheme for all satellites. In this study, we used the previous version TMPA RT version 5, RT version 6 (RTV5V6), and the latest version of TMPA RTV7 (Table 1). Three-hour satellite data with Universal Time Coordinates (UTC) were transformed to those with Local Standard Time (LST) Coordinates and accumulated to daily precipitation with the consistency of the rain gauges.

Table 1 Information on precipitation products

Observational precipitation data from 130 meteorological stations located on the Tibetan Plateau and its peripheral areas (Fig. 1) were provided by the CMA. The CMA data have undergone quality control procedures to eliminate erroneous and homogenous assessment. All the CMA station-gauged precipitation data were interpolated to 1/12° grids with the inverse distance weighting (IDW) interpolation method. The IDW method enjoys a long history of usage and reliability, due primarily to its simplicity of formulation and its persistent application in operational settings (Kurtzman et al. 2009). IDW has been demonstrated to be efficient and reliable in many precipitation interpolation applications (Chen and Liu 2012; Garcia et al. 2008; Ly et al. 2011; Nalder and Wein 1998).

To facilitate direct comparisons between the satellite precipitation data and CMA data, and maintain consistency with the VIC hydrological model setup over the Tibetan Plateau, all the gridded satellite data sets were re-gridded to 1/12° × 1/12° grids using the nearest neighbor method, which means that the value of the small grid (1/12°) was assigned the value of the large grid (0.25°) that is nearest to the small one. Therefore, the resulting grids usually preserved the spatial pattern and topographic features of the data at the original resolutions.

2.3 Methodology

2.3.1 Statistical methods

In the evaluation, TMPA precipitation estimates were compared with the CMA precipitation data. Three statistical indices were introduced to assess the performance of the satellite products. Bias (E) and the correlation coefficient (R) were used to describe the agreements between the CMA and satellite data. For evaluating the performance of the VIC hydrological model in streamflow simulations, the Nash-Sutcliffe efficiency (NSE) (Nash and Sutcliffe 1970), which describes the prediction skill of the simulated streamflow as compared to observed streamflow, was introduced as another statistical index. The definitions of E, R, and NSE are as follows:

where n is the number of samples, Gi and G denote the individual and mean CMA gauge precipitation data, respectively, Si and S denote the individual and mean satellite retrievals data, respectively, Qoiand Qsiare the observed and simulated streamflow, respectively, and Qois the mean observed stream flow.

2.3.2 Hydrological model

In this study, the VIC model (Liang et al. 1994, 1996) was used to evaluate the capability of satellite precipitation retrievals to simulate streamflow in the UYERB and UYARB on the Tibetan Plateau. The VIC model is a grid-based land surface scheme that parameterizes the dominant hydrometeorological processes taking place at the land surface-atmosphere interface. The model solves both surface water and energy balances over a grid. The VIC model uses a mosaic representation of land cover and a parameterization for infiltration that accounts for subgrid-scale heterogeneities in land surface hydrological processes. The data and information of the land surface characteristics required by the VIC model include soil texture, topography, and vegetation types. The meteorological inputs for the VIC model include daily precipitation, maximum temperature (Tmax), minimum temperature (Tmin), and wind speed from interpolated CMA station data. The model setup, with a spatial resolution of 1/12° in latitude and longitude was taken from Zhang et al. (2013).

3 Results and discussion

3.1 Evaluation of satellite precipitation products

In this study, the overlap period of January 2006 to December 2011 was chosen for evaluation. Fig. 2 shows the spatial distribution of annual mean precipitation over the UYERB and UYARB from the CMA and satellite estimates over the period of 2006 to 2011. Annual precipitation from the CMA estimates exhibits a southeast to northwest gradient, ranging from over 700 to 900 mm/year in the southeast to less than 300 mm/year in the northwest (Fig. 2(a)). The annual spatial variations of the 3B42 estimates are closely resemble those of the CMA estimates (Fig. 2(b)), probably due to the monthly gauge adjustments included in the 3B42 estimates (Huffman et al. 2007). RTV5V6 can also roughly capture the large-scale spatial patterns of annual precipitation with the highest precipitation predominantly in the southeast of the region (reaching 1 500 mm/year). The tendency of large overestimation compared to the CMA is clearly visible (Fig. 2(c)). RTV7 is the latest real-time product from the TMPA and incorporates many important improvements, such as algorithms and data sources. However, the anticipated improvements of RTV7 relative to RTV5V6 have not been realized in this study. More than half of the study region’s annual precipitation exceeds 1 500 mm/year from RTV7 (Fig. 2(d)). Gao and Liu (2013) inferred that positive bias in TMPA RT products over the Tibetan Plateau may be attributed to the impact of topography on IR observations. Tian et al. (2009) consider that current satellite-based precipitation products are more reliable over areas with strong convective precipitation and flat surfaces while having larger measurement uncertainties over complex terrains, coastlines and inland water bodies, cold surfaces, and high-latitude areas.

Fig. 2 Spatial distribution of annual mean precipitation over UYERB and UYARB from CMA and satellite estimates from 2006 to 2011

Fig. 3 shows the monthly basin-wide precipitation time series from the CMA and satellite retrievals from 2006 to 2011. The line of 3B42 compares closely with that of the CMA in both basins. RTV5V6 slightly overestimates the data and cannot capture the precipitation seasonalcycle before 2009. Although RTV7 makes an obvious overestimation, it captures the precipitation seasonal cycle in the two basins, i.e., most of annual precipitation falls from May to October and a little falls from November to April.

Fig. 3 Monthly precipitation time series from CMA and satellite estimates in UYERB and UYARB from 2006 to 2011

Fig. 4 shows the scatter plots of daily basin-wide CMA and TMPA precipitation data in the UYERB and UYARB from 2006 to 2011. The statistical validation indices are also given in the figure. The 3B42 estimates exhibit the best correspondence with CMA data, with a correlation coefficient of 0.802/0.746 and a bias of –3.2%/–2.4% in the UYERB/UYARB. RTV5V6 and RTV7 show comparable performance but are worse than 3B42. Most of the dots between RTV5V6, RTV7, and CMA lie on the left of the 1:1 line, which indicates an overestimation (with a bias of 90.6%/48.6% from RTV5V6 and 123.0%/146.1% from RTV7 in the UYERB/UYARB). RTV7 obtained a higher correlation coefficient with the CMA (0.655/0.598) in the two river basins than RTV5V6 (0.378/0.292) did.

3.2 Capability of satellite precipitation products to simulate streamflow

In land surface hydrology, errors in precipitation measurements can cause significant uncertainties in predicting processes such as surface runoff (Nijssen and Lettenmaier 2004). Evaluation of satellite rainfall estimates based on their ability to predict the streamflow in a hydrological modeling framework has two advantages (Bitew and Gebremichael 2011b). First, since the evaluation is performed at the watershed scale, it is not subject to the scale discrepancyproblem that arises when using rain gauge data for validation. Second, the satellite rainfall estimates are evaluated with respect to a specific application, as a driving input variable in a hydrologic model.

Fig. 4 Scatter plots of daily basin-wide precipitation between CMA and satellite estimates in UYERB and UYARB from 2006 to 2011

3.2.1 Calibration period

In this study, the VIC model was calibrated with CMA precipitation data for the period from 2000 to 2005. Fig. 5 shows daily observed and simulated streamflow forced by the CMA precipitation for the UYERB (Fig. 5(a)) and monthly streamflow in the UYARB (Fig. 5(b)) for the period from 2000 to 2005.

The CMA-driven model simulations generally closely follow the daily hydrograph for the UYERB with an NSE of 0.716 and a bias of 5.3% for the period from 2000 to 2005 (Fig. 5(a)), although underestimates/overestimates of peak floods exist in some cases. In the UYARB, we did not obtain daily streamflow data at the Zhimenda Station. The monthly streamflow driven by CMA precipitation overestimates observed flow peaks in the first half of the calibration period (2000 to 2002), but slightly underestimates it in the second half (2003 and 2005). Overall, there was general agreement between the simulated and observed hydrographs. The performance of the model was acceptable, with an NSE of 0.773 and a bias of −3.9% at monthly scale.

3.2.2 Validation period

The calibrated VIC model was then used to evaluate the satellite estimates in streamflow simulations over the two selected basins without any further adjustment of parameters for theperiod from 2006 to 2011. Maintaining the same parameters allows us to investigate how differences between precipitation estimates affect the accuracy of VIC streamflow simulations.

Fig. 5 Observed and simulated streamflow in UYERB and UYARB for calibration period (2000-2005)

Fig. 6 shows the performance of precipitation products for streamflow simulation from 2006 to 2011. We collected streamflow data from 2006 to 2009 for the UYERB and from 2007 to 2008 for the UYARB. Table 2 shows the statistical summary of model performance in the validation period. Compared to observations, streamflow simulated using the CMA data is acceptable, with an NSE of 0.524 and a bias of 10.2% in the UYERB, and an NSE of 0.669 and a bias of 11.2% in the UYARB. Gauge corrections to satellite products significantly enhance their skill by greatly reducing the bias in hydrologic predictions, especially over mountainous areas (Pan et al. 2010). 3B42 showed a comparable performance to the CMA with an NSE of 0.632 and a bias of 2.6% in the UYERB at the daily scale. This indicates that, although involved in gauge adjustment at the monthly scale, 3B42 has the capability of daily streamflow simulation. In the UYARB, 3B42 had an NSE of 0.589 and a bias of 19.5% at a monthly scale. However, other satellite retrievals showed dissatisfactory performance in streamflow simulation. Simulated streamflow driven by RTV5V6 and RTV7 obviously overestimated observations and obtained a negative NSE in both river basins during the validation period. This indicates that RTV5V6 and RTV7 have no capability to simulate streamflow in the two river basins.

Fig. 6 Observed and simulated streamflow in UYERB and UYARB for validation period (2006-2011)

Table 2 Comparison between observed and simulated streamflow over UYERB and UYARB in validation period

4 Conclusions

The performances of TMPA precipitation products (3B42, RTV5V6, and RTV7) were evaluated against the CMA gauge observational precipitation data in the UYERB and UYARB from 2006 to 2011. The potential capability of TMPA precipitation products to simulate streamflow with a VIC hydrological model in the two basins was also investigated. The main findings of our research are as follows:

(1) Compared to CMA precipitation data, 3B42 performs better than RTV5V6 and RTV7. RTV5V6 can roughly capture the spatial precipitation pattern but overestimation exists throughout the entire study region. The anticipated improvements in RTV7 relative to RTV5V6 have not been realized in this study. Our results suggest that RTV7 obviously overestimates the precipitation over the two river basins, though it captures precipitation seasonal cycle features.

(2) 3B42 shows the best performance of the satellite products in streamflow simulation.Although involved in gauge adjustment at a monthly scale, 3B42 shows a capability for daily streamflow simulation. RTV5V6 and RTV7 show no capability of streamflow simulation in the UYERB and UYARB.

To sum up, TMPA precipitation products provide an alternative source of precipitation data for the data-sparse Tibetan Plateau region, especially for the western part of it. Moreover, TMPA 3B42 precipitation products show satisfactory capability as the forcing of the VIC hydrological model to simulate streamflow in basins on the Tibetan Plateau. However, for the RT products, more evaluations are needed of the feasibility of its application in the Tibetan Plateau region, where the topography is complex and the rainfall rate is highly variable. Also more efforts, such as correction according to gauged data, are necessary before it is used for the forcing of the hydrological model for streamflow simulation in the Tibetan Plateau region.

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(Edited by Yun-li YU)

This work was supported by the National Basic Research Program of China (the 973 Program, Grant No. 2010CB951101), the Special Fund of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering of Hohai University (Grant No. 1069-50985512), and the “Strategic Priority Research Program” of the Chinese Academy of Sciences (Grant No. XDA05110102).

*Corresponding author (e-mail: ktong@hhu.edu.cn)

Received Jul. 12, 2013; accepted Jun. 10, 2014