Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China

Journal Pre-proof Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China

Ning Wang, Wenbin Liu, Fubao Sun, Zhihong Yao, Hong Wang, Wanqing Liu PII:

S0169-8095(19)30802-6

DOI:

https://doi.org/10.1016/j.atmosres.2019.104746

Reference:

ATMOS 104746

To appear in:

Atmospheric Research

Received date:

23 June 2019

Revised date:

25 September 2019

Accepted date:

3 November 2019

Please cite this article as: N. Wang, W. Liu, F. Sun, et al., Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China, Atmospheric Research(2018), https://doi.org/10.1016/ j.atmosres.2019.104746

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Journal Pre-proof Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China Ning Wanga, Wenbin Liub , Fubao Sunb,d , Zhihong Yaoc, Hong Wangb , Wanqing Liua College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Res earch, Chinese Academy of Sciences, Beijing 100101, China c College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450045, China d Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China a

b

Corre sponding authors: [email protected]; [email protected]

Abstract:

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The wide application of satellite-based and reanalysis-based precipitation data has greatly promoted hydrometeorological research in areas where precipitation observations are scarce. However, the suitability of such

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precipitation products needs to be carefully evaluated before applications in certain basins because their inherited errors vary with different climate zones, seasonal cycles and land surface conditions; in addition, precipitation products have not been evaluated in the Xihe River basin, China. In this paper, two representative satellite-based

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precipitation products (Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR)) and two reanalysis-based

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precipitation products (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and National Centers for Environmental Prediction - Climate Forecast System

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Reanalysis (CFSR)) were selected for evaluation and corrected against gauge-observed data (OBS). Furthermore,

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the performances of precipitation products in hydrological simulations were also assessed using the SWAT model calibrated with OBS forcing and not with individual precipitation products. The results show that satellite-based precipitation has a higher quality than reanalysis-based precipitation. The CFSR and CDR overestimate precipitation (the overestimation of CDR precipitation is mainly concentrated in the precipitation intensity range of

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1 mm/d to 5 mm/d), while the TRMM and CMADS underestimate precipitation in the Xihe River basin. The TRMM precipitation performs best during the wet season, while the CDR precipitation performed best during the dry season. After bias correction, the quality of TRMM precipitation improves significantly. The Nash-Sutcliffe coefficient (NS) (the percent bias (|PBIAS|)) increases (decreases) by 0.61 (77.27%) and 0.7 (39.15%) under the two different correction scenarios during 2009 - 2015. Overall, the above original precipitation products cannot be used as supplements to the OBS in the Xihe River basin unless they are corrected by the OBS. Keywords: Satellite-based precipitation; Reanalysis-based precipitation; Error correction; Hydrological model; Xihe River Basin 1. Introduction Precipitation is one of the most significant factors in the intuitive reflection of climate change (Faures et al., 1995). Precipitation is the basic flux of output in the atmospheric process and the fundamental factor driving hydrological processes. Precise measurement of precipitation plays a significant role in simulating the hydrologic cycle of a basin, understanding the water balance, and predicting extreme weather events and natural disasters (such as floods and landslides) (Tan et al., 2018). The precipitation data mainly originate from a ground precipitation station network, and the quality and stability of the precipitation data cannot be guaranteed due to the scarcity of precipitation station sites, uneven spatial distributions, limited time scales, and vulnerability to environmental and human factors (Li et al., 2017). In addition, due to the inability to deploy precipitation observation stations in areas such as high altitudes and remote areas, high-quality precipitation data are needed to compensate for the lack of

Journal Pre-proof measured data. In the new era, the rapid development of remote sensing technology and data assimilation technology has made it possible to obtain high-quality satellite-based and reanalysis-based precipitation products (Liu et al., 2016). Satellite-based and reanalysis-based precipitation products have been widely used in hydrological applications. For example, Fang et al. (2019) evaluated the performances of the TRMM 3B42 and Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG) in extreme precipitation estimations over China, concluding that both products can better describe the spatial pattern of extreme precipitation, but the GPM IMERG performs better than TRMM. Aslami et al. (2019) used statistical indicators to evaluate the GPM IMERG and GSMaP-MVK precipitation products, and the results showed that the IMERG products were closer to the gauge records and can be used as a replacement for gauge observations in study areas that have a lack of weather stations. Awange et al. (2019) assessed the potential applications of Multi-Source Weighted-Ensemble Precipitation (MSWEP) against other precipitation products in Australia and Africa. The MSWEP showed good correlations and cumulative

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distribution with the Bureau of Meteorology (BoM) product over most of Australia but showed no obvious advantages in Africa. Lemma et al. (2019) assessed five satellite-based (ARC2, TAMSAT, TRMM 3B43V7,

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CMORPH and CHIRPSv2) and two reanalysis-based precipitation products (CFSR and ERA-Interim) in Ethiopia, and the results showed that CHIRPSv2 performs better than other precipitation products; the two reanalysis-based

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precipitation products performed the worst. Zhang and Anagnostou (2019) evaluated WRF-adjusted satellite products in Colombia, Peru and Taiwan, and the results showed that the WRF-based satellite adjustment produced considerable improvements in other products. Liu et al. (2018) evaluated and corrected the CDR by using successive correction methods that calculated the deviation between the gauged data and satellite-based data in the

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Brahmaputra River, and the results showed that the accuracy of the original CDR data was low and that the

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corrected CDR data were significantly higher than the initial data. Although precipitation products have been widely used, the performances of precipitation data are inconsistent in different regions. Precipitation products are inevitably subject to errors caused by factors that result from climate zoning, seasonal changes, underlying surface conditions, and so on. Therefore, evaluating the accuracy of precipitation products based on observational data and

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requiring validation before its widespread use are necessary to optimize hydrological simulation and water balance results.

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A suitable hydrological model can aid us in better understanding the water cycle process at the watershed scale (Bhattarai et al., 2019; Zhao et al., 2013). The hydrological model is a mathematical description of the understanding and generalization of the actual hydrological process and a useful tool for studying the law of hydrology and the rational allocation of water resources (Xu, 2009). Current widely used hydrological models mainly include simple lumped hydrological models, complex semi-distributed and distributed hydrological models. Some of the widely used lumped hydrological models are Xinanjiang model (Jiang et al., 2018; Zhang et al., 2019; Zhao, 1992) and Storm Water Management Model (SWMM) (Ketabchy et al., 2019; Xu et al., 2019; Yazdi et al., 2019). Some of the widely used semi-distributed and distributed hydrological models are the TOPgraphy-based hydrological model (TOPMODEL) (Addor and Melsen, 2019; Gao et al., 2019), Hydrologiska Byråns Vattenbalansavdelning model (HBV) (Li et al., 2014; Van Osnabrugge et al., 2019; Worqlul et al., 2018), System Hydrological European model (SHE) (Li et al., 2015; Wang et al., 2012), Noah land surface model with multiparameterization options (Niu et al., 2011), Soil and Water Assessment Tool (SWAT) (Guo et al., 2019; Wang et al., 2019) and Variable Infiltration Capacity model (VIC) (Byun et al., 2019; Liang et al., 1994; Sun et al., 2018). Compared with the lumped hydrological model, the distributed hydrological model considers the spatial distribution of elements and variables, has the characteristics of decentralized input, dispersion or concentrated output, and can quantitatively describe objective hydrological processes on temporal and spatial scales (Wang et al., 2004). Therefore, we choose the representative semi-distributed hydrological model SWAT to analyze the

Journal Pre-proof hydrological simulations. To date, few studies (Bai et al., 2019; Hu et al., 2016; Zhao et al., 2015) have evaluated hydrological simulation results driven by satellite-based and reanalysis-based precipitation products on the Loess Plateau, China. The analysis and correction of multi-source precipitation data and optimizing hydrological simulation results require further research in the Loess Plateau region. As a representative of the mesoscale watershed in the Loess Plateau, the Xihe River is the most important water source in the city of Tianshui, and there has been no research that has evaluated the satellite-based and reanalys is-based precipitation products in this area. Due to the river’s location in the loess hilly and gully region, soil erosion is serious, and natural disasters such as landslides and mudslides often occur, which threaten people's lives and property. Precipitation is the main water resource in the basin, and accurate estimation of precipitation and understanding of the water cycle have played a crucial role in regional sustainable development. In this study, four precipitation products (TRMM, CDR, CMADS and CFSR) were selected for hydrological simulation evaluation and calibration. First, the applicability of the precipitation products is systematically evaluated using multiple

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indicators based on the appropriate time scale in the study area. Then, a corrective model was constructed to improve the quality of the precipitation product. Finally, the applicability of uncorrected and corrected precipitation

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products is explored in the hydrological simulation using SWAT calibrated by the gauge-observed forcing and not with individual precipitation products. Superior to the calibrated methods (Deng et al., 2019; Li et al., 2017;

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Worqlul et al., 2018; Zhao et al., 2015), our method can eliminate the influence of different model parameter settings on the simulation results, and more equitably evaluate the advantages and disadvantages of hydrological simulation results driven by different precipitation products.

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2. Study area and data 2.1 Study area

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The Xihe River is a first-class tributary of the upper and middle reaches of the Weihe River, which is located at the northern foothills of the Qinling Mountains and the southern edge of the Longxi Loess Plateau. The river rises

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at the eastern foot of Jingdongliang Mountain in the Longtai Mountains, Tianshui city, Gansu Province, and joins the Weihe River at the Beidaobu gateway in the Maiji District (Wang et al., 2019). An overview of the Xihe River 2 Basin is shown in Fig. 1. The total length of the river is 85 km, the area of the basin is 1267 km , the altitude is between 1013~2715 m, and the geographical range is between 34°20' N ~ 34°38' N and 105°07' E ~ 106°00' E. The basin belongs to the third sub-region of the Loess hilly-gully region. The terrain of the basin is fragmented and complex with criss-crossing ravines and gullies, and the terrain is high in the northwest and low in the southeast (Fig. 1(a)). The Xihe River Basin belongs to a sub-humid climate in a warm temperate zone. The annual average temperature is 10.5 °C, and the annual average precipitation is 574.6 mm. The precipitation varies greatly between years, is distributed unevenly during the year, and occurs mainly in May-September. The spatial distribution of precipitation is as follows: the amount of precipitation in the south is significantly larger than that in the north, and that in the east is larger than that in the west (Fig. 1(b) and (c)). The main vegetation is deciduous broad-leaved forest in the warm temperate zone, which is in the transition zone from forest grassland to grassland. The natural shrub grassland is mainly distributed in the loess beam and the lower hills, with a coverage rate of 33.05% (Fig. 1(d)). The soil types are complex, and the soil type spatial distribution in the Xihe River Bas in is shown in Fig. 1(e). The main soil types are loessial soil (accounting for 35.9%), cinnamon soil (accounting for 21.5%), and brown soil (accounting for 19.1%). The loessial soil is mainly distributed in the gully area, which is 1100-1500 m above sea level. The soil is thick, crisp, and easily water-eroded during a heavy rain event. The cinnamon soil is mainly distributed approximately 1500-2100 m above sea level on Qinling Mountain. The brown soil is mainly distributed in the southwest at an altitude of 1500-2500 m. This basin experiences severe soil erosion because of long-term unreasonable reclamation.

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2.2 Data set

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Fig. 1. The spatial distribution of hydrologic stations, precipitation and underlying surfaces in the Xihe River basin.

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2.2.1 Basic data The basic data involved in this paper are as follows: (1) Digital elevation model (DEM): the 30 m resolution DEM is generated by vectorization of a 1:50 000 paper topographic map and interpolation with the software ANUDEM (the Australian National University-DEM). (2) Land use maps: a land use map with a 30 m resolution was generated with an interpretation of Landsat 8 remote sensing images on May 12, 2015 (https://search.earthdata.nasa.gov/). (3) Soil data: the 1:1 million scale HWSD (Harmonized World Soil Database) was constructed by FAO (Food and Agriculture Organization of the United Nations) and the International Institute for Applied Systems Analysis (http://westdc.westgis.ac.cn/). (4) Hydrometeorological data: daily hydrological data of Tianshui hydrological station and daily precipitation data of Guanzizhe, Huangjizhai and Xujiadian Stations were collected from Volume 7 of the Hydrological Data of the Yellow River Basin (2008-2015) and daily meteorological monitoring data from the Tianshui Station of the National Meteorological Information Center including average temperature, daily maximum temperature, daily minimum temperature, average relative humidity, average wind speed, and sunshine time (http://data.cma.cn/). 2.2.2 Satellite-based and reanalysis-based precipitation In this study, the widely used satellite-based precipitation (TRMM 3B42V7 and CDR) and reanalysis-based precipitation (CMADS and CFSR) were selected with consideration of the time scale. The detailed precipitation data are shown in Table 1.

Journal Pre-proof The Tropical Rainfall Measuring Mission (TRMM) satellite is a meteorological satellite developed by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) for quantitative measurement of tropical and subtropical rainfall. The TRMM daily precipitation data include both microwave and infrared data and have been corrected by ground precipitation data provided by the Global Precipitation Climatology Centre (GPCC). The TRMM data are available approximately 10 - 15 days after the end of each month. The TRMM products range from January 1998 to present time. The spatial resolution is 0.25°, the temporal resolution is daily, and the coverage has a global range of 50° S ~ 50° N (https://mirador.gsfc.nasa.gov/). Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks -Climate Data Record (CDR) was developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI). The aim of using CDR is to address the need for a consistent, long-term, high-resolution and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. CDR is generated from the PERSIANN

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algorithm using GridSat-B1 infrared data and is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product. The CDR product is available to the public as an operational climate data record via the NOAA

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(National Oceanic Atmospheric Administration) NCDC (National Climatic Data Center) CDR program website (www.ncdc.noaa.gov/cdr/operationalcdrs.html) under the Atmospheric CDR category. The CDR spatial resolution

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is 0.25°, and the temporal resolution is daily, covering the global range of 60° S ~ 60° N (http://chrsdata.eng.uci.edu/). The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) is a reanalysis-based product that was established based on CMORPH global precipitation products and is combined with data from the

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China Meteorological Information Center using loop nesting of data, projection of resampling models, pattern

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estimation and bilinear interpolation. The CMADS was completed over a 9-year period from 2008 to 2016. The dataset spatial resolution is 0.25°, the temporal resolution is daily, the time scale is 2008-2016, and the coverage range is over East Asia, 0° N ~ 65° N, 60° E ~ 160° E (http://www.cmads.org/). National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR) data are

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reanalys is data developed by NOAA’s NCEP that couple the atmosphere-ocean-land surface-sea ice system. CFSR has the characteristics of a large time scale, high spatial scale, and convenient data acquisition. It was initially

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completed over the 31-year period from 1979 to 2009 and was extended to July 2014. The CFSR dataset has a spatial resolution of 38 km and a time resolution of days, and coverage is global (https://rda.ucar.edu/datasets/). Table 1

Characteristics of satellite-based and reanalysis-based precipitation datasets. Spatial Temporal Datasets Name Period resolution resolution

Coverage

Gauge-observed

OBS

Point

Daily

2008.01.01-2015.12.31

Xihe River basin

TRM M 3B42V7

TRM M

0.25°

Daily

2008.01.01-2015.12.31

50° S ~ 50° N

PERSIANN-CDR

CDR

0.25°

Daily

2008.01.01-2015.12.31

60° S ~ 60° N

CM ADS

CM ADS

0.25°

Daily

2008.01.01-2015.12.31

East Asia

NCEP-CFSR

CFSR

38 km

Daily

2008.01.01-2014.07.31

Global

3. Methods 3.1 Technical framework This study has two parts. The aim of the first part (precipitation product evaluation) is to evaluate the applicability of satellite-based precipitation (TRMM and CDR) and reanalysis-based precipitation (CMADS and CFSR) and then correct the data error by constructing a calibration model. In the second part (runoff simulation

Journal Pre-proof evaluation), the four precipitation products are used to drive the hydrological model, and the accuracy and applicability of the runoff simulation results are evaluated. Finally, the correction efficiency of the corrected precipitation data is evaluated. The analysis process used in this paper is shown in Fig. 2. Satellite-based precipitation TRMM

Part 1

Part 2

CDR

Evaluation against OBS

SWAT

CMADS CFSR Hydrological simulation and evaluation

Error correction

Corrected data

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Reanalysis-based precipitation

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Construct calibration model

Fig. 2. Flow chart of the technical framework used in this study.

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3.2 Evaluation metrics

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To quantitatively evaluate the data quality of different precipitation products in this paper, 7 statistical metrics, including the correlation coefficient (CC), mean error (ME), root mean square error (RMSE), percentage bias (PBIAS), probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI), were selected at

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three temporal scales of day, month and year. CC reflects the degree of linear correlation between the two datasets,

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the optimal value is 1; ME reflects the data concentration trend; RMSE measures the deviation of the two datasets and describes the data dispersion degree; PBIAS reflects the dataset system error degree; and the optimal values for ME, RMSE and PBIAS are all 0. POD indicates the ability of precipitation products to accurately capture the actual precipitation occurrence, and the optimal value is 1. FAR indicates that the precipitation products incorrectly predict

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the actual precipitation occurrence, and the optimal value is 0. CSI comprehensively considers the situation of accurate forecasting and false forecasting and reflects the ability of the precipitation data to comprehensively detect

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actual rainfall events, and the optimal value is 1. The expressions of each indicator are shown in Table 2. Table 2

Statistical metrics used for evaluating multiple precipitation products. Function (1) 𝐶𝐶 =

Unit

Description

/

𝐺̅ =

̅ ̅ ∑𝑛 𝑖=1(𝐺𝑖−𝐺)(𝑆𝑖 −𝑆)

𝑛 ̅ 2 ̅ 2 √∑𝑛 𝑖=1(𝐺𝑖−𝐺) ∙√∑𝑖=1(𝑆𝑖 −𝑆)

1 (2) 𝑀𝐸 = 𝑛 ∑ 𝑛𝑖=1 𝑆𝑖 − 𝐺 𝑖

(3) R𝑀𝑆𝐸 =

1 √𝑛 ∑ 𝑛𝑖=1(𝑆𝑖

(4) 𝑃𝐵𝐼𝐴𝑆 =

∑𝑛 𝑖=1 𝑆𝑖 − 𝐺𝑖 ∑𝑛 𝑖=1 𝐺𝑖

mm − 𝐺 𝑖)

2

× 100%

mm

𝑛

𝑛

𝑖=1

𝑖=1

1 1 ∑ 𝐺 𝑖 , 𝑆̅ = ∑ 𝑆𝑖 𝑛 𝑛

𝐺 𝑖 is the gauge-observed precipitation. 𝑆𝑖 is the precipitation from the Satellite-based and reanalysis-based

%

products.

(5) 𝑃𝑂𝐷 = 𝐻+𝑀

/

𝑀 is the observed precipitation not detected.

(6)

/

𝐹 is the precipitation detected but not observed.

/

𝐻 is the observed precipitation correctly detected.

𝐻

(7)

𝐹 𝐹𝐴𝑅 = 𝐻+𝐹 𝐻 𝐶𝑆𝐼 = 𝐻+𝑀+𝐹

3.3 SWAT model The Soil and Water Assessment Tool (SWAT) model was developed by the Agricultural Research Center of the

Journal Pre-proof United States Department of Agriculture. SWAT is mainly used to simulate and predict the impact of land use and various land management strategies on watershed water quality and quantity (Wang et al., 2003). The model has a strong physical mechanism and can simulate various hydrophysical and chemical processes using spatial information provided by GIS and RS (Chen et al., 2014; Zhang et al., 2005). The model has been widely used by scientists in hydrological assessments, environmental change assessments, sensitivity analyses, and in other fields (Golden et al., 2016; Haregeweyn et al., 2017; Muenich et al., 2016; Sarrazin et al., 2016). The watershed was divided into sub-basins with the support of a DEM by using the SWAT, and then the sub-basins were further divided into hydrological response units (HRUs) according to land uses, soil types and topographic factors. The response-unit runoff is calculated by a conceptually lumped model on each HRU, and the total runoff of the basin is calculated by a confluence calculation. The projection coordinate system required by the model was Xian_1980_Albers. The central longitude was 105.5° E, the first standard latitude was 34.6° N, and the second standard latitude was 34.3°N. The soil database

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and meteorological database were established according to the requirements of model database construction. According to the previous research results (Wang et al., 2019), the minimum catchment area of the river was set to 2

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1900 ha (19 km ), and a total of 52 sub-basins and 3301 HRUs were obtained. To improve the simulation accuracy of the model and obtain a good initial state, the warm-up period of the model was set to 2008, the parameter calibration period was set to 2009-2012, and the validation period was set to 2013-2015.

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3.4 Model calibration

The steps of the sensitivity analysis and calibration are essential to hydrological simulation. Sensitivity analyses

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can help identify parameters that have a significant impact on hydrological simulation and improve hydrological

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simulation efficiency. A total of 12 parameters with higher sensitivity were selected by the LH-OAT (Latin-hypercube and one-factor-at-a-time sampling) method for calibration, as shown in Table 3. Parameter calibration can make the simulation results closer to the gauge-observed data. To obtain better model simulation results, the SUFI-2 (Sequential Uncertainty Fitting, ver.2) algorithm was used to calibrate the

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hydrological results driven by gauge-observed data. Wu and Chen (2015) showed that the SUFI-2 method performs better than the generalized likelihood uncertainty estimation (GLUE) and the parameter solution (ParaSol) methods

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in hydrological s imulation. The deterministic coefficient R² and Nash-Sutcliffe efficiency coefficient (NS) were used to evaluate the applicability of the simulated results. When the deterministic coefficient R² approaches 1, the simulated results were more accurate. NS indicates the degree of deviation between the simulated and observed values. When NS approaches 1, the simulated values were closer to the observed values. The NS was calculated using the equation shown below: 𝑁𝑆 = 1 −

2 ∑𝑛 𝑖=1 (𝑄𝑜−𝑄𝑠)

(8)

∑𝑛 ̅ )2 𝑖=1(𝑄𝑜−𝑄

In the equation, 𝑄𝑜 is the observed value, 𝑄𝑠 is the simulated value, and 𝑄̅ is the average observed value. Generally, when NS is greater than or equal to 0.5, the simulation results are good; when NS is greater than or equal to 0.65, the simulation results are very good (Moriasi et al., 2007). The hydrological s imulation of precipitation products used SWAT calibrated by the gauge-observed forcing and not individual precipitation products, and the other meteorological data (average temperature, daily maximum temperature, daily minimum temperature, average relative humidity, average wind speed, and sunshine time) required for the simulation were gauge-observed data. The calibration strategy is as follows: First, the model is 2

constructed based on the results of Section 3.3. Then, R , PBIAS and NS are used to evaluate the calibration results, and the calibration is continued until the three indicators are no longer increased (the number of times is greater than 1500). According to the calibration results of the SUFI-2 method, the values of the selected parameters are

Journal Pre-proof shown in Table 3. Table 3 Sensitivity parameters for the runoff and parameter calibration results in the Xihe River Basin. Physical M eanings

Calibration Range

Optimum Calibration Value

r_CN2

SCS runoff curve value

-0.5~0.5

-0.06

v_ALPHA_BF v_GW_DELAY

Base flow regression coefficient Hysteresis coefficient of groundwater

0~1 0~500

0.662 136.5

v_GWQM N v_CANM X

Runoff coefficient of shallow groundwater M aximum canopy interception

0~5000 0~100

1.029 17.80

v_ESCO v_SM FM X

Soil evaporation compensation factor M aximum melt rate for snow during the year

0.1~0.9 0~20

0.911 1.879

v_SURLAG v_SOL_K v_ALPHA_BNK

Surface runoff lag time Saturated hydraulic conductivity of soil Reservoir coefficient of the main channel

0.05~24 0~20 0~0.8

5.502 4.726 0.478

v_SOL_AWC

Available water content of surface soil

0~1

v_SOL_BD

Surface soil bulk density

0.9~2.5

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Parameters

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0.604 1.745

Note: “r_” means that the parameter is multiplied by (1 + calibration value), and “v_” means that the parameter is replaced by the

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calibration value.

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4. Results and discussion

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4.1 Comparison of multi-precipitation products with gauge-observed data

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4.1.1 Evaluation of multi-precipitation products Based on the monthly scale, the precipitation products are first compared by computing the average monthly precipitation at four gauge-observed stations in the basin (AMPB) and the average monthly precipitation during the

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year (AMPY), which is the average of the AMPB in one year. As shown in Fig. 3, the precipitation is overestimated by CFSR and CDR, especially for CFSR, which shows poor performance (similar results were found in Lemma et

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al. (2019)). In 2008-2013, the overestimation of CFSR is more obvious than the other data. The AMPB is evenly overestimated by 18.5 mm, and the AMPY is evenly overestimated by 17.5 mm. The precipitation trend of CFSR is quite different from those of other datasets because the CFSR has a critical flaw of a high single-point observation error (Yu et al., 2019). The results show that the applicability of the CFSR in the Xihe River basin are not as good as in previous study areas (Dile and Srinivasan, 2014; Essou et al., 2017; Fuka et al., 2014). TRMM and CMADS show precipitation underestimations, and the CMADS underestimation is more obvious. For CMADS, AMPB and AMPY were both underestimated by 11.7 mm. The findings in our study are different from those of previous studies (Gao et al., 2018; Zhou et al., 2019), which reported CMADS to have a better performance in the study area. These results indicate that the regional heterogeneity of CMADS is severe and that it is necessary to evaluate the accuracy of CMADS before its widespread use. The TRMM precipitation trend is most similar to the OBS data trend. The reason TRMM precipitation has a better performance is that it integrated with the Special Sensor Microwave Imager Sounder (SSMIS) on the Defense Meteorological Satellite Program (DMSP) F-16 satellites and the new Global Precipitation Climatology Centre (GPCC V2.2), which have further improved the data accuracy of precipitation estimates (Yong et al., 2014).

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Fig. 3. Comparison of multiple precipitation products from an average multi-station perspective:

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The solid lines represent AM PB; the dashed lines represent AM PY.

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Fig. 4 also shows the deviation in the four precipitation products against the OBS data in the total monthly cumulative precipitation. The slope of the curve in the graph represents the estimated deviation in the monthly

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cumulative total of the precipitation product. The deviation in the reanalys is-based precipitation increases in comparison to the OBS. As of July 2014, the CFSR overestimated by 1465 mm, CMADS underestimated by 882 mm, and the satellite-based precipitation has a small degree of deviation with a high estimation quality. As of July 2014, the CDR overestimated by 298 mm, and the TRMM underestimated by 399 mm. Previous studies have also

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found obvious reanalysis-based precipitation overestimates or underestimates by comparing reanalysis-based

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precipitation and OBS (Ghodichore et al., 2018; Graham et al., 2019; Saha et al., 2014). The detailed reason for the deviation in reanalys is-based products can be found in previous studies (Blacutt et al., 2015; Reichle et al., 2011; Soares et al., 2012).

Fig. 4. Comparison of monthly cumulative precipitation from a multi-station average perspective among different products.

Table 4 shows the multi-station average precipitation of the precipitation products and OBS at daily, monthly and annual scales. The daily average satellite-based precipitation products show little difference from OBS, and the TRMM and CDR values against OBS are 0.16 mm and 0.14 mm, respectively. The daily average reanalysis-based precipitation products are quite different from OBS, and the CMADS and CFSR values against OBS are 0.38 mm and 0.62 mm, respectively. The monthly scale calculation results show that the TRMM has the smallest deviation during the wet season (May-Nov.), while the CDR has the smallest deviation during the dry season (Dec.-Apr.). On the annual scale, the average annual precipitation deviation of CDR is 51.6 mm, followed by TRMM at 60.9 mm,

Journal Pre-proof and the worst is CFSR at 236.1 mm. Table 4 M ulti-station average precipitation at various time scales (daily, monthly and yearly, unit: mm) for different precipitation products. Datasets

Day

Jan

Feb

M ar

Apr

M ay

Jun

Jul

Aug

Sept

Oct

Nov

Dec

Year

OBS

1.57

7.3

9.7

26.0

44.6

73.4

65.1

99.7

86.5

96.5

40.6

20.7

4.5

574.6

TRM M

1.41

2.7

2.1

16.8

34.3

68.7

67.8

101.1

83.6

89.2

31.2

14.5

1.7

513.7

CDR

1.71

8.1

9.9

26.0

58.2

88.2

67.2

82.5

91.0

107.2

54.3

28.8

4.7

626.2

CM ADS

1.19

0.5

0.8

6.5

34.4

59.2

57.4

78.1

81.7

78.3

30.9

5.4

1.0

512.0

CFSR

2.19

12.9

18.8

31.4

50.9

88.8

76.2

109.3

86.6

103.6

51.8

23.1

6.2

810.7

To further reflect the difference between the precipitation products and the OBS, the CC, ME, RMSE and PBIAS of the precipitation products and the OBS are counted at different time scales. The specific calculation results are shown in Fig. 5. As seen from Fig. 5, the CMADS has the best correlation with the OBS on the daily scale, CC is

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0.93, RMSE is 1.88 mm, followed by TRMM, with a CC of 0.73 and RMSE of 3.36 mm, and the TRMM and CMADS have the same correlations with the OBS at the monthly scale, with a value of 0.96, but the quality of

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TRMM is higher with an RMSE of 13.35 mm. The correlation between each dataset and OBS is generally higher during the wet season (March to November) than in the dry season. The RMSE results during the wet season are

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larger than those of the dry season, but the results of PBIAS show that the deviation during the wet season is smaller than that of the dry season. The statistical results of the four metrics and the results in Table 4 further indicate that TRMM has better prediction results during the wet season, and CDR performs best during the dry season. However, these findings in our study area are different from the findings of a previous study (Ma et al., 2019), which found

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that the TRMM is the closest to the OBS during the dry season and that the CDR is the least accurate precipitation

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product among the TRMM, CDR and CFSR. The reason for this phenomenon may be due to the regional heterogeneity of the precipitation products. At the annual scale, the statistical metrics indicate that the satellite-based precipitation products are of higher quality than the reanalysis-based precipitation products (similar results were found in Lemma et al. (2019)). Although the TRMM performs best among the precipitation products

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selected in our study, it still has much room for improvement (consistent with the findings of Yong et al. (2014)).

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Fig. 5. The metrics of multi-station average precipitation comparing the OBS with the (a) TRM M , (b) CDR, (c) CM ADS,

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and (d) CFSR at various time scales in the Xihe River basin.

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Fig. 6 shows the accuracy of precipitation event detection for each product based on daily data from January 1, 2008 to July 31, 2014. The POD and FAR of the TRMM are the highest, which are 0.67 and 0.39, respectively;

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followed by CMADS, where the POD and FAR are 0.57 and 0.17, respectively. The CSI of the CMADS is the highest at 0.51, followed by TRMM and CDR, both of which are 0.47, and the worst is for CFSR (0.46). The results show that the CMADS has the highest ability to detect precipitation events, followed by TRMM and CDR, with CFSR being the worst.

Fig. 6. Heat map of precipitation detection capability among different products in the Xihe River basin.

To further explore whether different products can capture precipitation events within various precipitation intensity (PI, unit: daily precipitation, mm/d) groups, we divided PI into 7 groups (0≤PI<1, 1≤PI<5, 5≤PI<10,10≤PI<20, 20≤PI<30, 30≤PI<50, and PI>50). Fig. 7 shows the total precipitation of the four precipitation products from 2008 to 2015 under different PI groups. The precipitation overestimation by CDR is mainly concentrated in the range of 1 mm/d ~ 5 mm/d, while the ability to predict a large PI is insufficient. The estimated capacity of TRMM is the closest to the OBS under different PI groups, and the optimal range is between 1 mm/d and 5 mm/d.

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Fig. 7. Precipitation amount under different PI groups during the study period.

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The total precipitation results under different PI groups show only the overestimation and underestimation of

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precipitation products; however, these products cannot represent the accuracy of precipitation products for detecting the actual precipitation. Therefore, in this study, the precipitation products are further grouped according to the PI of the OBS, and the CC and RMSE of each dataset under different PI grouping conditions are calculated. The results are shown in Table 5.

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Table 5

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Statistical summary of multiple precipitation products against OBS under different PI groups. TRM M CDR CM ADS CFSR PI (mm/d) CC RMSE CC RMSE CC RMSE CC RMSE [0, 1) 0.21 1.57 0.15 2.15 0.35 0.38 0.24 2.38 [1, 5) 0.15 5.05 0.18 3.50 0.49 1.65 0.17 5.33 [5, 10) 0.08 5.75 -0.02 6.50 0.33 3.56 0.002 7.77 [10, 20) 0.29 9.25 0.13 11.34 0.61 5.21 0.06 9.28 [20, 30) 0.50 15.91 0.21 29.50 0.51 13.66 -0.04 29.97 0.48 16.32 0.33 25.69 0.60 9.58 0.31 23.89 [30, +∞)

Under different PI grouping conditions, Table 5 shows that the correlation between CMADS and OBS is the

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highest, and the RMSE is the smallest. When 0≤PI<1, the precipitation estimation is the most accurate, with an RMSE of 0.38, followed by 1≤PI<5 and an RMSE of 1.65. With an increase in PI, the prediction deviation of precipitation products becomes larger. However, the correlation between each precipitation product and OBS based on different PI grouping conditions is not as good as that based on time scale conditions (the results are different from previous findings (Deng et al., 2019), where the relationship between PI and ME could be well characterized by a cubic polynomial fit). Therefore, an error correction was established based on the high correlation between the TRMM and OBS under time scale conditions. 4.1.2 Error correction The results of the multiple precipitation product evaluation indicate that the TRMM quality during the wet season (May-Nov.) is high and is highly correlated with OBS, while CDR performs best during the dry season (Dec.-Apr. of the following year) compared with other datasets. Fig. 8(a) shows the scatter plot between TRMM and OBS during the wet season. The TRMM-g dataset was obtained by corrected TRMM data using the results of the linear fit for the wet season and combined with the orig inal TRMM data during the dry season. Because the CDR has better data quality during the dry season, the TRMM-c dataset was obtained by corrected TRMM data using the results of linear fit for the wet season and combined with CDR data during the dry season. To quantitatively analyze the correction quality of TRMM data, the ME, RMSE and PBIAS for the annual precipitation of TRMM before and after correction relative to OBS were calculated. The TRMM ME, RMSE and PBIAS results

Journal Pre-proof were -60.96 mm, 72 mm and -10.6%, respectively; the TRMM-g results were -34.45 mm, 49.91 mm and -6.0%,

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respectively; and the TRMM-c results were 14.75 mm, 52.07 mm and -2.57%, respectively. The results show that the quality of corrected TRMM is greatly improved. Fig. 8(b) is a further analysis of the multi-year average statistics using the Taylor chart (Taylor, 2001), which clearly shows that the corrected TRMM has the highest quality, and TRMM-g performs better than TRMM-c.

Fig. 8. (a) Scatter plots between TRM M and OBS during the M ay to November period;

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(b) Taylor diagram of multi-year average statistics among different products.

4.2 Hydrological simulation evaluation

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Based on the results of the model construction and calibration (Section 3.3 and 3.4), the simulation results for different precipitation inputs at the monthly scale are shown in Figs. 9-10. Fig. 9 shows a comparison of the

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observed runoff data and the simulation results of uncorrected precipitation data between 2009 and 2015. Fig. 10 shows a comparison of the observed runoff data and the simulation results of the corrected TRMM between 2009

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and 2015. Fig. 9 shows that the CFSR runoff simulation result is the worst. The simulation of gauge-based precipitation data (OBS) is closest to the observed runoff. The other simulation results driven by TRMM, CDR and CMADS severely underestimate the runoff, especially for the peak flood flow. Regarding the above analys is, the reason why the simulation results of the precipitation products bring large errors in the peak flood flow is mainly due to the inaccurate prediction of heavy precipitation (this point matches the findings of Table 4). Fig. 10 shows that the runoff simulation quality of corrected TRMM has been significantly improved, and the peak flood flow simulation is more accurate. More importantly, TRMM-g has a better runoff simulation than TRMM-c.

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Fig. 9. The hydrograph of SWAT simulations against the observed runoff at the monthly scale in the Xihe River basin.

Fig. 10. The hydrograph of SWAT simulations driven by corrected precipitation products against the observed runoff at the monthly scale in the Xihe River basin.

To clearly quantify the runoff simulation results of each precipitation dataset, Fig. 11 shows the scatter plots of the runoff simulation results and the observed runoff for each precipitation dataset from 2009 to 2015 at a monthly scale. Since the CFSR simulation result is significantly different from the observed runoff, scatter statistics are not performed. Fig. 11 shows that the runoff simulation results based on the OBS are the best, and under calibration (validation), R 2 is 0.84 (0.97), NS is 0.83 (0.96) and PBIAS is 9.3% (2.3%). Following TRMM, the statistical metrics of TRMM during 2009-2015 are R 2 = 0.89, NS = -0.16 and PBIAS = 89.56%. The simulation results of the corrected TRMM, which were corrected using two deviation correction methods, show a better performance. The NS (|PBIAS|) values of TRMM-c and TRMM-g increased (decreased) by 0.61 (77.27%) and 0.70 (39.15%) during 2009-2015. The results of the post-correction simulation show that the TRMM-g and OBS qualities are comparable, and the TRMM that is corrected during the wet season can be used as a supplement to the OBS for hydrological analyses and simulations in watersheds with scarce precipitation observations. Finally, an important conclusion is obtained: although during the dry season, TRMM-g performs poorly and was merged with CDR, which has a good performance, the simulation result was not further improved, and the data quality was reduced. This finding also

Journal Pre-proof shows that there is some potential connection between the results of the precipitation data in the retrieval, and simply splicing between data does not effectively improve the data quality. Given the above analysis, the simulation results driven by TRMM-g still have a large error, indicating that more meteorological data from observation sites should be collected to further explore the relationship between precipitation products and gauge-observed data. To date, precipitation products are not a substitute for gauge-observed data, and it is still vital to establish a more systematic precipitation observation network for the study of the water cycle and climate change in the Loess

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Plateau.

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5. Conclusions

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Fig. 11. Scatter plots of simulated runoff against observed runoff at the monthly scale in the Xihe River basin during 2009 - 2015.

In this study, the applicability of TRMM, CDR, CMADS and CFSR in the mesoscale watershed of the Loess Plateau is first evaluated. Then, a correction model is constructed to correct the deviation in the precipitation products based on the correlations between the precipitation products and OBS. Finally, the SWAT model is driven by uncorrected and corrected precipitation products to assess the accuracy of the hydrological simulation results for multi-source precipitation data. The main conclusions are as follows: (1) At various time scales (daily, monthly and yearly), the quality of satellite-based precipitation products is higher than that of the reanalysis-based precipitation products. The CFSR and CDR overestimate precipitation. The CFSR overestimation is more significant and shows the worst performance among the precipitation products selected in this study. The overestimation of precipitation by CDR is mainly concentrated in the range of 1 mm/d ~ 5 mm/d, while the ability to predict a large PI is insufficient. The TRMM and CMADS underestimate precipitation, and CMADS underestimates precipitation more obviously. (2) Compared with other precipitation products, the TRMM performed the best during the wet season (May-Nov.), and the CDR performed best during the dry season (Dec.-Apr. of the following year). In addition, all precipitation products have a higher deviation against the OBS in the wet season than in the dry season. (3) At the daily scale, CMADS has the highest accuracy for detecting precipitation events, with a CSI of 0.51, followed by TRMM and CDR. TRMM has the highest ability to capture actual precipitation, with a POD of

Journal Pre-proof 0.67. (4) The statistical results of different PI groupings indicate that the estimated capacity of TRMM is closest to that of the OBS under different PI groups, with an optimal range between 1 mm/d and 5 mm/d. With an increase in PI, the prediction deviation in the precipitation products becomes larger. (5) A new method for correcting precipitation data was proposed. This method corrected the precipitation data during the dry and wet seasons separately and improved the accuracy of the data significantly. Compared with the difference between the TRMM and OBS in terms of annual precipitation after correction according to the corrective plan, the ME, RMSE and PBIAS of the TRMM-c (TRMM-g) decreased by 46.21 mm (26.51 mm), 19.93 mm (22.09 mm) and 8.03% (4.6%). (6) The SWAT simulations driven by different precipitation datasets show that the simulation results of the OBS are optimal. All of the simulation results driven by the uncorrected precipitation products based on the parameters calibrated by gauge-observed forcing show poor performance. The hydrological simulation results

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of the corrected TRMM are effectively improved. The NS (|PBIAS|) of the TRMM-c and TRMM-g increased (decreased) by 0.61 (77.27%) and 0.70 (39.15%) during 2009-2015, respectively. We also found that different

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parameter value settings result in the same simulation effects. This result is subject to in-depth research. In summary, although the satellite-based precipitation products represented by TRMM have been widely used

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in hydrological simulations, the data quality of these products will inevitably be affected by observational bias, spatial scale and retrieval method. Therefore, more in-depth work on the regional differences among various precipitation products and the applicability of hydrological simulations is necessary to optimize the hydrological simulation results and clarify the water cycle process in the basin. Acknowledgments

This research is financially supported by the National Key Research and Development Program of China

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(2016YFC0401401 and 2016YFA0602402), the Key Research Program of the Chinese Academy of Sciences (ZDRW-ZS-2019-3, ZDRW-ZS-2017-3-1), the National Natural Science Foundation of China (41601035) and

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Top-Notch Young Talents Program of China. The authors appreciate the Yellow River Institute of Hydraulic Research and Xi’an University of Technology for the support of data. We also thank the editor and the three anonymous reviewers for their invaluable comments.

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hydrological simu lation using a distributed hydrological model in the Weihe River catch ment in China. J. Geogr. Sci., 25(2): 177-195.

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Zhao, R.J., 1992. The Xinanjiang model applied in China. J. Hydrol., 135(1-4): 371-381. Zhou, Z.H., Gao, X.C., Yang, Z.Y. et al., 2019. Evaluation of Hydrological Application of CMADS in Jinhua River Basin,

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China. Water, 11(1): 22.

Journal Pre-proof Highlights  

Four precipitation products (TRMM, PERSIANN-CDR, CMADS and NCEP-CFSR) were selected for evaluation and corrected against gauge-observed data (OBS). The performances of precipitation products in hydrological simulations were assessed using the SWAT model. The results show that satellite-based precipitation has a relatively higher quality than reanalysis-based precipitation. The TRMM precipitation performed best during the wet season, while the CDR precipitation performed best

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during the dry season. After bias correction, the quality of the TRMM precipitation improves significantly.

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