Evaluation of the GSMaP_Gauge products using rain gauge observations and SWAT model in the Upper Hanjiang River Basin

Atmospheric Research 219 (2019) 153–165

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Evaluation of the GSMaP_Gauge products using rain gauge observations and SWAT model in the Upper Hanjiang River Basin

T

Pengxin Denga, , Mingyue Zhangb, Jianping Binga, Jianwei Jiaa, Dongdong Zhanga ⁎

a b

Bureau of Hydrology, Changjiang Water Resources Commission, Wuhan 430010, China Hubei Provincial Hydrology and Water Resources Investigation Bureau, Wuhan 430071, China

ARTICLE INFO

ABSTRACT

Keywords: GSMaP_Gauge Rainfall Precipitation correction SWAT Hanjiang River Basin

Global Satellite Mapping of Precipitation (GSMaP) product is an important satellite precipitation product of Global Precipitation Measurement (GPM) mission, which provides an alternative means to ground-based rainfall estimates, in which case a rigorous product assessment was required before implementation. The objective of this paper is to evaluate and calibrate the errors of GSMaP_Gauge (GG) products, then SWAT model is chosen to evaluate its performance in hydrological simulation. It was found that the GG has a superior ability in detecting moderate rainfall events of 10–20 mm/d, while other magnitude accuracy needs to be strengthened. The NashSutcliffe coefficient of efficiency (NS), coefficient of determination (R2) and the percent bias (PBIAS) were used to evaluate hydrological model performance in the daily scale, supporting the view that GG still allow considerable room for improvement. SWAT model driving by GG performed relatively poorly which is 0.77 ≤ R2 ≤ 0.84, 0.53 ≤ NS ≤ 0.64 and |PBIAS| < 9%, while the performance of corrected GG is relatively better that is 0.85 ≤ R2 ≤ 0.87, 0.70 ≤ NS ≤ 0.75 and |PBIAS| < 15%. Through the error's correction of GG, it brings a better performance of runoff simulation under SWAT modeling, leading to 11.94% and 6.1% maximum increase in NS and R2 respectively. Certain shortcomings are also noted that improvements to the relative runoff depth errors are limited or worse. But in general, GSMaP product has the potential to be an alternative data source for the research on hydrological simulation and water resources management in data-poor or ungauged basins.

1. Introduction Accurate measurement of precipitation is crucial for modeling the hydrology cycle processes, monitoring extreme weather events, and predicting rainfall-triggered natural hazards and disasters (e.g., floods and landslides) at local, regional, and even global scales (Tan et al., 2017). The spatial and temporal resolutions of rainfall data are interrelated and mutually affected, and both resolutions have significant impacts on the determination of surface hydrology (Masih et al., 2011; Michaelides et al., 2009). It is commonly accepted that more reliable precipitation estimates can be derived in areas of high rain gauge density than in areas of low rain gauge density (Chappell et al., 2013; Yoo, 2000). However, in many areas the ground-based observations are usually sparse or unevenly distributed, due to economic or terrain limitations (Li et al., 2017). Meanwhile, ground-based rain gauges are considered as point measurement within the common problem of uneven distribution and may be unable to capture the spatiotemporal variability of precipitation systems (Anagnostou et al., 2010; Buarque

⁎

et al., 2011). In contrast, satellite-based rainfall estimates provide complement measurement over wide area having little or even no in situ data to overcome the shortcomings of rain gauge networks (Ning et al., 2017). It has been proved to be an operational approach to capture the spatiotemporal variability of the large-scale rainfall from space particularly in remote regions and complex terrain (Peng et al., 2014; Tan et al., 2017). Recent studies have been found that the estimation of spatial rainfall distribution can be improved with the inclusion of ancillary data such as radar, satellite and topography data, and high resolution satellite rainfall estimates have been used in many studies as a valuable data source for hydrology applications and water resources planning purposes (Masih et al., 2011; Wilk et al., 2006; Yan and Gebremichael, 2009; Zhang et al., 2009; Li et al., 2017). In fact, since the Tropical Rainfall Measuring Mission (TRMM) launched in 1997, the rapid development of precipitation datasets based on passive microwave (PMW), calibrated visible and thermal infrared (Vis/IR), and PMW plus Vis/IR observations have resulted in a tremendous amount of quasi-

Corresponding author at: Bureau of Hydrology, Changjiang Water Resources Commission, 1863 Jiefang Avenue, Wuhan, Hubei Province, China. E-mail address: [email protected] (P. Deng).

https://doi.org/10.1016/j.atmosres.2018.12.032 Received 5 September 2018; Received in revised form 30 November 2018; Accepted 31 December 2018 Available online 03 January 2019 0169-8095/ © 2019 Elsevier B.V. All rights reserved.

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global information for research and applications (Ning et al., 2017; Tan et al., 2017). Currently, many precipitation satellite products with various temporal and spatial resolutions (0.25° and 3-h scales) have been conducted to application, including the TRMM Multi-satellite Precipitation Analysis product (TMPA) (George et al., 2007),Naval Research Laboratory (NRL) Global Blended-Statistical Precipitation Analysis (NRL-Blended)(Li et al., 2013), the PERSIANN Cloud Classification System (PERSIANN-CCS) (Mahrooghy et al., 2012) and the Global Satellite Mapping of Precipitation product (GSMaP) (Kubota et al., 2007). As the successor to TRMM, the GSMaP project starting in 2002 with the support of the Japan Science and Technology Agency and the Japanese Aerospace Exploration Agency (JAXA) aims to produce high-precision and high-resolution global precipitation estimates by using almost all available passive microwave radiometer data (Okamoto et al. 2005). Since then, there are three GSMaP products, i.e., the real-time GSMaP_NRT, the post-real-time GSMaP_MVK, and the gauge-adjusted research-grade product GSMaP_Gauge (Tan et al., 2017). Among them, GSMaP_Gauge is a product that adjusts the GSMaP_MVK estimate with global gauge analysis supplied by NOAA, with more measurement accurate. GSMaP_Gauge can provide 1 hourly, 0.1° × 0.1° latitude/longitude global gridded rain rate for the latitude band 60°N–60°S (Aonashi et al., 2009). To date, several preliminary studies have been done to compare and validate the GSMaP and other precipitation estimates at global, regional, or basin scale. Although it is generally believed that GSMaP has higher monitoring accuracy than other satellite-based rainfall estimates with only small biases compared to gauge data (Kubota et al., 2009; Dinku et al., 2009, 2010; Qin et al., 2014; Prakash et al., 2016), there are some shortcomings. For example, Kubota et al. (2009) systematically investigated and found that GSMaP evidently performed worse for light rainfall during the warm season and for heavy rainfall during any season. Dinku et al. (2009) even found that GSMaP products overestimate rainfall occurrences and have higher false alarm ratio over the drier region than wetter region. The results of these studies demonstrate that the quality of satellite rainfall products varies with region, season, and elevation. So, the absolute accuracy of satellite rainfall products is questionable, and needs a thorough validation before it can be used extensively in simulation and water balance analysis provides useful information for hydrology research and water management purposes (Li et al., 2017). Soil and Water Assessment Tool (SWAT) model (Arnold et al., 1998) is widely used physically based semi-distributed continuous hydrological model for estimating water budget components at a watershed scale. The performance of SWAT model relies on precipitation input parameters, namely accuracy and spatial distribution (Tobin and Bennett, 2009). For example, Li et al. (2017) investigated the TRMM product's role in forcing data for hydrology simulations over the Tiaoxi catchment (Taihu lake basin, China), and found the TRMM rainfall data showed a superior performance at the monthly and annual scales, fitting well with surface observation-based frequency rainfall distributions. Ruan et al. (2016) also validate the precision of high-resolution gridded precipitation for hydrological simulation in data-scarce regions. To date, there is less study about the GSMaP products for hydrological simulation in China, especially in driving the SWAT modeling. At the view of precipitation error, it is to be noted that evaluation and calibration of the GSMaP products for hydrological simulation still need to be further researched. As the water source of the central line of the South-North Water Diversion Project and the important economic corridor of China, Hanjiang River Basin (HRB) is in a very important position in terms of flood control and water supply (Deng et al., 2018). Precipitation is the main source of water resources in the watershed, which is directly related to the regional development and utilization of water resources in HRB. Due to the vast terrain and undulating topography of the basin, the spatial distribution of rainfall and water resources needs to be carried out more reliable assessment relying on satellite data with better spatial continuity. The accuracy of GSMaP products for rainfall

monitoring in HRB needs to be further evaluated and confirmed. In view of the above considerations, there are two main purposes of this paper. One is to assess and build the errors calibration model for GSMaP products. The other is to establish the SWAT model to evaluate the performance of GSMaP in hydrological simulation and then verify the validity of the rainfall errors calibration model. We selected one of GSMaP products with best measurement accurate as the main evaluation and calibration object, and present work consists of four sections. Firstly, analyze comprehensive error structures of daily GSMaP_Gauge products (Ver. 06) over Upper Hanjiang River Basin (UHRB) at 0.1∘spatial resolution. Secondly, construct the error calibration relationship to further correct GSMaP to decrease the rainfall monitoring errors. Third, build the SWAT model, and comparative assessment of SWAT Model performance among different precipitation input, has been done. Last, the results of the analysis are demonstrated and summarizing remarks and conclusions are drawn. 2. Study area and data 2.1. Study area With an area of 159,000 km2, Hanjiang River Basin (HRB) is one of the largest tributary regions of the Yangtze River Basin. Its upper reaches (106.0°–110.5°E longitude, 31.5°–34.5°N latitude and 157–3508 m a.s.l) named UHRB, is the main water source area of the inter-basin water transfer project with a drainage area of about 61,703 km2. Hanjiang river originates in the south of Qinling Mountain in the southwest of Shanxi Province, and flows east across the southern part of Shanxi Province into Hubei Province (Wang et al., 2012; Yang et al., 2017). The terrain shows high in the west and low in the east, which the total drop is 3351 m. The main stream is accompanied by the development of unbalanced tributaries. UHRB belongs to the northern sub-tropic monsoon climatic region with a relative humidity > 60%. The annual mean temperature is 12–16 °C with the highest and the lowest temperature of 43 °C and −13 °C, respectively. The average annual precipitation during the period of 1956–2016 was 867 mm of which 80% concentrate in the period from May to October. Maximum continuous four-month precipitation accounts for about 55%–65% of the annual total. Simultaneously, the annual mean runoff of the UHRB is about 234.1 × 108 m3, accounting for 60%–70% of the total runoff of the whole basin with large inter-annual variability. Usually, Late-June to late-July is the summer flood season and late-August to mid-October is autumn flood season with abundant water resources (Li et al., 2009, Yang et al., 2017). The UHRB has been selected as the main modeling study region because of less impact of human activities and higher density vegetation. Vegetation in the UHRB have a typical gradient with deciduous forest, mixed deciduous and conifer forest, coniferous forest, sub-alpine meadow and farmland from low to high elevation. Farmland (AGRL) and forest (FRST) cover is respectively approximately 60% and 30%. Orchard (ORCD), grassland (PAST), surface water (WATR), bare areas (UINS) and urban areas (URBN) occupy the rest of catchment (Fig. 1(b)). Urban and farmlands are distributed along the river networks (areas with lower elevation). Soil in UHRB is composed of umber soil, fuscous soil, and the bedrock. According to the soil characteristics, 22 types can be divided. As Fig. 1(c) shown, the study area is dominated by Haplic Lixisols and Eutric Cambisols, occupying 52.3% and 13.6% respectively. Other 20 soil groups include Calcaric Cambisols, Calcic Luvisols, Calcaric Fluvisols, Leptosols, Ferric Lixisols, Rendzic Leptosols, Chromic Luvisols, Dystric Cambisols, Calcaric Regosols, Eutric Regosols, Eutric Vertisols, Humic Cambisols, Cumulic Anthrosols, Eutric Gleysols, Mollic Leptosols, Eutric Planosols, Dystric Planosols, Albic Luvsiols, Gleyic Luvisols, and Water bodies. 154

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Fig. 1. Basic profile of UHRB and distribution of hydrologic gauges.

2.2. Data collection

GSMaP product which is based on GSMaP-MVK (a pure satellite based GSMaP product without correction by gauge data) and adjusted by the Climate Prediction Center (CPC) global daily gauge data analysis (Xie et al., 2007; Ushio et al., 2009). GG have native 0.1° spatial resolution and 1-h temporal resolution, covering the latitude region of 60°N to 60°S and the longitude region of 180°W to 180°E. The time series of GG is from January 2001 to December 2016. More documents about the GSMaP products are distributed through the G-Portal data service system (http://www.gportal.jaxa.jp) (Zhao et al., 2018). GG provides additional accurate precipitation information across CPC-gauged correction compared to GSMaP-MVK (Deng et al., 2018). In our study, GG is selected and aggregated into daily temporal resolutions to keep consistent with the ground reference dataset (GO), which the period is from 2001 to 2016. The precipitation intensity value on a pixel is a single value of the satellite rainfall estimates (Fig. 1(a)).

2.2.1. Gauge-observed data The daily data of 33 rainfall gauges and 13 meteorological stations across the whole UHRB were collected as the ground rain reference dataset. In addition, the daily discharge series of 2 hydrological stations (XJP and BH) were chosen as the modeling reference dataset. BH represents the amount of water coming from the whole UHRB, and XJP represents the amount of water from one of the tributaries. The collection time of gauge-observed data is from 2001.01.01 to 2016.12.31. Those gauge-observed data were provided by the Bureau of Hydrology (BOH), Changjiang Water Resources Commission (CWRC) and Climatic Data Center, National Meteorological Information Center, China Meteorological Administration (http://data.cma.cn/). Fig. 1(a) displays the spatial distribution of hydrology gauges. To accurately reflect the true ground rain and flow events, the gauge-observed rainfall (GO) and discharge data (Q) had been conducted the quality control by providers, including the analysis of consistency, reliability and representation. There is no missing data during the 16 years study period, and all the gauges are uniformly distributed throughout the basin, which is beneficial for the detection of extreme precipitation events and the errors calibration. It is worth noting that all the 33 + 13 reference gauges were not belonging to CPC-gauged data, which are used to construct the GSMaP_Gauge product (Xie and Arkin, 2001; Xie et al., 2007). So, they can be regarded as independent with the gauge-adjusted GSMaP.

3. Methods 3.1. Validation metrics To understand the pattern of GG estimated errors at different time scales, this study validated the satellite-based precipitation of daily, monthly and annual scales. Daily GG data were produced from the original hourly rainfall estimates. The monthly and annual data were produced from daily data. To further quantify the accuracy of GSMaP, we used some types of statistical indices including Correlation Coefficient (CC), Mean Error (ME), relative mean error (RMSE) and percent bias (PBIAS) The definitions of these statistics can be found in Chen et al. (2013) and Guo et al. (2016). CC takes account of the degree

2.2.2. Remote sensing precipitation data Characteristics of latest GSMaP_Gauge data (GG) used in this study for detail analysis are described in Table 1. GG is one error-corrected 155

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n (G G )(S S ) i i=1 i n (G G )2 n (S i=1 i i=1 i n (S G ) i i PBIAS = i = 1n × 100% i = 1 Gi

CC =

GG

GO Q P

60°N–60°S

UHRB

2001.01.01 to 2016.12.31

Name

of linear correlation between satellite-based precipitation and gauge observations, while PBIAS assesses the systematic bias of satellite precipitation estimates. ME scales the average difference, and RMSE also measures the average error magnitude but gives greater weight to larger errors relative to ME (Ushio et al., 2009; Tan et al., 2017). The formulas are as follows:

ME =

1 n

n i = 1 (Si 1 n

S )2

, G=

1 n

n i=1

Gi, S =

1 n

n i=1

(1)

Si

(2) (3)

Gi)

n i = 1 (Si

(4)

Gi )2

where G is the GO data, S is the GG data, n is number of the samples. Deng et al. (2018) have found that the daily precipitation intensity (PI) has a strong nonlinear relationship with ME, which is a polynomial fitting. The formulas are as follows:

/ / /

0.1°

RMES =

(5)

where A, B1, B2 and B3 are the model parameters, determined by measured data. Then the GG will be corrected as:

Daily Daily Daily

1 hourly

MEi = A + B1 PIi + B 2 PIi2 + B3 PIi3

Pi = MEi + Gi

(6)

Morphing and Kalman filter, by forward and backward process / / / PMW imagers, PMW sounders, GEO IR radiometers, CPC global gauge data analysis 33 rainfall gauges 2 discharge stations 13 meteorological stations GSMaP_Gauge

/ / /

3.2. Hydrological model The Soil and Water Assessment Tool (SWAT) is a well-established, semi-distributed, eco-hydrologic model operating on daily, monthly or yearly time-step (Arnold et al., 1998), which has been widely used. SWAT divides the entire basin into various sub-watersheds and constructs hydrology response units (HRUs). HRUs are the basic unit of SWAT model measurement, which is the unique combination of soil and land use characteristics as well as slope, and are hydrologically homogeneous. The SWAT model simulates hydrology as a two-component system, comprised of land hydrology and channel hydrology. The land portion of the hydrology cycle is based on a water mass balance (Wang et al., 2012). The water balance equation is as follows: t

SWti = SWoi +

(Pi

i=1

Qsurfi

Ei

Wseepi

(7)

Qgwi )

where Swti is the final soil water content(mm),Swoi is the initial soil water content on time i (mm),t is the time, Pi is the amount of precipitation on time i (mm),Qsurfi is the surface runoff amount on time i (mm),Ei is the amount of evapotranspiration on time i (mm),Wseepi is the amount of water that enters the vadose zone from the soil profile on time i (mm),and Qgwi is the return flow amount on day i (mm). SWAT partitions groundwater into two shallow aquifer systems. One is unconfined aquifer and the other is confined aquifer. The water balance for the shallow aquifer is: aqsh, i = aqsh,

i‐1 +

wrchrg,

sh −

Qgw − wrevap − wrump,

sh

(8)

where aqsh,i is the amount of water stored in the shallow aquifer on day i (mm), aqsh,i-1 is the amount of water stored in the shallow aquifer on day i (mm),wrchrg,sh is the amount of recharge entering the shallow aquifer on day i (mm), Qgw is groundwater flow, or base flow, into the main channel on day i (mm), wrevap is the amount of water moving into the soil zone in response to water deficiencies on day i (mm), and wpump,sh is the amount of water removed from the shallow aquifer by

Gauge-observed

IR technique Data source

Yes

where Gi is the GO data, Pi is the GG_g data, i is number of the samples.

Precipitation datasets

Table 1 Characteristics of GSMaP_Gauge data and Gauge-observed data.

Corrected by gauges

Temporal resolution

Spatial resolution

Period

Coverage

P. Deng et al.

156

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pumping on day i (mm) (Arnold et al., 1998). In our study, delineating watershed into sub-basins and HRUs requires three essential input: a digital elevation model (DEM, 30 m × 30 m, https://glovis.usgs.gov/), a land use cover map (LULC, http://westdc.westgis.ac.cn/), and a soil map (http://westdc.westgis. ac.cn/). Daily time-series of measured air temperature, pan evaporation, and relative humidity were obtained at 13 meteorological stations. The sequential uncertainty fitting (SUFI-2) algorithm of the SWAT-CUP software was used for auto-calibration and uncertainty analysis (Abbaspour et al., 2007). Three goodness-of-fit measures, namely, R2, percent bias (PBIAS) and Nash–Sutcliffe efficiency (NS), were used (Abbaspour et al., 2007; Nash and Sutcliffe, 1970). According to Moriasi et al. (2007), the performances of models are divided into 2 levels, which are the satisfactory simulations (0.50 < NS < 0.65) and adequate simulations (0.65 < NS < 0.75). It is worth noting that the GSMaP data is the gridded precipitation, which needs to be inputed to the SWAT model in form of gauge data. Thus, grids of gridded precipitation data are treated as virtual precipitation station, and virtual station is a common method that grids data input to the SWAT model (Zou et al., 2016; Sood et al., 2013). Virtual precipitation stations were under construction for each subbasin that adopts the mean precipitation of the grid within each subbasin. Constructing steps of virtual precipitation station can refer to Ruan et al., 2016. To further explore water balance errors of different discharge levels, the best simulations based on GG and GG_g have been chosen to divide into some different classes by the probability of occurrence of the measured flow (0.0–0.1%, 0.1–0.2%, 0.2–0.5%, 0.5–5.0%, 5.0–20%, 20–50%, 50–100%). Calculation of the mainstay discharge by the frequency method is as follows:

P (X

x) =

m × 100% n+1

4. Results and discussion 4.1. Comparison of GSMaP with rain gauges data 4.1.1. Spatial and temporal distribution The rain gauge observations were interpolated to grid data with a spatial resolution of 0.1°using Inverse Distance Weighted (IDW) interpolation algorithm for ArcGIS platform. IDW may produce some errors of the grid cells that do not have a rain gauge, but it is possible to make some qualitative comparisons by spatial continuity, and still get some preliminary understanding. Fig. 3 shows the spatial pattern of average daily precipitation for the GO and GG during the 2001–2016 periods. Since 2000, the pattern of higher precipitation in the southern region and lower precipitation over the northern part of UHBR is noted. Obviously, GG can capture the entire precipitation spatial patterns but their differences are also notable. Rainfall overestimation mainly focuses on the mid-northern parts of the basin within less rain, while the underestimation across the southern heavy rain region. Next, we use individual rain gauges against the gridded GSMaP for evaluation. Rainfall intensity of GG was calculated by the satellite pixel (0.1° × 0.1°) in which the rain gauge stations are located. Then the average values of multi-station precipitation regarding the GG and GO datasets under consideration are listed in Table 2. Fig. 4 shows the time series of average and max daily rainfall. At multi-station average perspective, though the overall difference between GG and GO in multi-annual average daily estimates is small, the GG daily rainfall in the period of 2008–2016 (ranging 2.32–3.44 mm/d) is higher than the GO (ranging 2.07–3.31 mm/d). In addition, comparison between the max 1-day and max 5-day rainfall calculated based on two datasets shows larger rainfall estimates differences than that of average daily scale above, especially during the period of 2008–2011 and 2015–2016. Obviously, there are some random errors of rainfall estimation of GSMaP in different years, and its capability of detecting a heavy rain event is deteriorated relatively. The failure to describe the occurrence of max 1-day and 5-day rainfall accurately in some years may lead to extremum flow production differences. In terms of monthly scale, it is easy to find that GG show overestimation in March-to-October, while underestimation in Novemberto-February. But, it is worthy to recognize that the estimated errors during the flood season from May to October are relatively small, which the PBIAS are not exceeded 10%. In contrast, the underestimation of precipitation in December and March is relatively large, which the PBIAS are both approximately 18%. As for annual precipitation, the average annual precipitation of GG is about 49 mm higher than GO, with PBIAS of about 5.41%. Obviously, there are small errors in annual precipitation estimate for GG. Fig. 5 shows the spatial distribution of CC, BIAS, RMSE indices to further analyze the spatial difference of daily rainfall estimation. It needs to be pointed out that, compared to ME, the RMSE retain the difference in magnitude as they can avoid the fact that positive and negative differences cancel each other out to some degree, reflecting the randomness of the errors (Yong et al., 2010). Fig. 5(a) shows that better CC values appear in the southern high elevation region, and those areas are usually the precipitation centers. As for PBIAS, it seems that the low value region basically corresponds to the high-value area of CC, showing northern overestimation and southern underestimation. In terms of RMSE, some high value points (RMSE > 5.97 mm/d) are mainly concentrated in the heavy rainfall center in the southern area. Obviously, GG has the relatively great random estimation error existed in the extreme precipitation and may has a certain impact on the runoff production. Table 3 lists the metrics of multi-station average precipitation assessment of GG and GO. It shows that GG has a well performance with PBIAS values varying from −17.96% to 9.98% and CC values ranging

(9)

where P(X ≥ x) is the probability of occurrence of variable X (flow discharge) greater than x m3/s. m is data rattings, n is the amount of data. X is the series of discharge data, and x is the reliably discharge if the probability in accordance with the allocation. 3.3. Analysis process There are two main parts of this paper. The first part (Rainfall part) is to evaluate the error of GG and construct error calibration model to correct the GG's errors. Then GG will be corrected by the obtain calibrated model and named GG_g; The second part (Runoff part) is that using three kinds of precipitation dataset of GO, GG and GG_g respectively drives the SWAT model to evaluate the accuracy and application potential in runoff simulation. Simultaneously, the difference between GG and GG_g in runoff simulation was compared to evaluate the effectiveness of the error calibration model. The main analytical process of this paper is shown in Fig. 2.

Fig. 2. Flow chart of analytical process. 157

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Fig. 3. Spatial distributions of average daily precipitation from GSMaP and Gauge-observed.

from 0.14 to 0.91. The GG overestimates the March to October precipitation with ME values ranging from 2.44 mm to 8.05 mm and RMSE ranging from 7.79 mm to 24.16 mm. Moreover, GG underestimates the November, December, January, and February precipitation with ME values ranging from −3.08 mm to −0.55 mm and RMSE ranging from 3.33 mm to 6.81 mm. In general, the GG has better performance in flood season precipitation measurements with CC values > 0.70 and PBIAS values below 10%. But, what caused the seasonal reversal of GG's performance between March to October and November to February? It mainly due to the difference in estimation errors of GG for different precipitation intensities. To further reveal the estimation error of rainfall, the daily precipitation intensity (PI) evaluation was conducted using the Probability Density Function (PDF) approach by volume (Yang et al., 2016). Generally, the precipitation intensity of rain gauge measurements values is divided into six different classes (0.1–2 mm/day, 2–10 mm/day, 10–20 mm/day, 20–50 mm/day, 50–100 mm/day, 100–200 mm/day) based on the World Meteorological Organization standard with slightly modification based on local climate system (Tan et al., 2017). Fig. 6 shows PDF for GG and GO in volume. Statistical summary of average PI for different classes is listed in Table 4. As the Fig. 6 and Table 4 shown, GG tends to underestimate the mid and high rain events of > 10 mm/day, and to seriously overestimate the light precipitation events of 0–10 mm/day. Especially, there are large deviation of rain estimates in the classes of No.1(0–2 mm/day) with the total bias of 8.28 × 104 mm and the PBIAS is up to 534%. Simultaneously, the serious underestimation happened on the rain rate range of 20–50 mm/d with the total bias of −8.54 × 104 mm. In contrast, it has a superior ability in detecting the moderate rain rate of 10–20 mm/d with PBIAS values of −8.3%. Although the PBIAS of the rainstorm (PI > 100 mm/day) is the largest, the bias of the total amount is relatively small, only −0.61 × 104 mm.

measurements values above 0 were separated into some groups with 0.5 mm/day per step. Then ME in different groups were further calculated in accordance with the precipitation measured by rain gauges. Through multiple analysis studies, the relationship between PI and ME could be characterized by a cubic polynomial fitting. Simultaneously, dividing ME into two segments according to the PI threshold of 50 mm/d is beneficial to polynomial fitting. The polynomial fit between PI and ME is showed in Fig. 7. Under the cubic polynomial fitting, the deterministic coefficient (R-square) in the group of 0 < PI ≤ 50 mm/day is 0.825, while that of other group (50 < PI mm/day) is 0.823. Obviously, it provided useful information to calibrate the ME of GG. According to the cubic polynomial fitting, the ME was chosen to be corrected. The revised GG is recorded as GG_g. Fig. 8(a) shows the scatter plots of multi-station daily precipitation of GO~GG_g and GO~GG. The random errors of daily rainfall estimates can be visually compared by plotting their RMSE and CC with the gauge analysis in a “Taylor” diagram (Taylor, 2001), as Fig. 8(b) shown. The standard deviation represents the difference of daily precipitation changes. After calibration, the linear slopes of GO~GG_g have been improved from 0.693 to 0.923, the R-square increases from 0.625 to 0.795, reducing the random errors. Taylor diagram clearly shows the changes of overall accuracy before and after GG correction from individual gauge perspective. Before correction, CC values of most gauges are in the range of 0.63–0.94 and RMSE ranged from 2.75% to 7.07%. After correction, RMSE values of most sites are in the range of 2.15%–5.84%, with correlations of 0.76–0.98. Obviously, rainfall accuracy of GG has been enhanced. 4.2. Hydrologic process evaluation 4.2.1. Model calibration and performance Based on the above analysis, hydrologic process evaluation is carried out using the SWAT model for different rainfall input. During the SWAT model building process, the whole UHRB is divided into 27 sub basins according to the distribution of rainfall gauges, to ensure that each sub basin has at least one set of gauge-observed data. Combined with landuse and soil data, 789 HRU numbers have been delineated. The calibrated

4.1.2. Error decomposition and correction ME of GG was further decomposed according to PI of the rain gauge measurements, and to investigate any quantitative or qualitative relationships exist between them. All GG samples with rain gauge Table 2 The metrics of multi-station average precipitation in UHRB Unit: mm. Dataset

Day

Max 1-day

Max 5-day

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Year

GG GO

2.61 2.48

45.6 41.6

104 101

6.02 6.47

13.7 16.7

32.0 29.4

59.5 54.1

112 107

115 111

185 177

147 140

159 153

76.6 70.5

34.2 34.4

7.75 9.38

954 905

158

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Fig. 4. Time series of daily, Max 1-day, Max 5-day rainfall between multi-station average GG vs. GO.

Fig. 6. Probability density function (%) for GG and GO in volume and occurrence. Table 4 Statistical summary of average PI for nine different classes. No.

Volume (104 mm)

PI (mm/day)

1 2 3 4 5 6 Total

0.1–2 2–10 10–20 20–50 50–100 100–200

GO

GG

Δ

PBIAS (%)

1.55 10.9 11.1 16.3 13.2 1.02 46.6

9.82 12.5 10.2 12.2 8.72 0.41 48.7

8.28 1.69 −0.92 −4.05 −4.49 −0.61 2.09

534 15.5 −8.30 −24.9 −34.0 −59.7 4.48

Fig. 5. Spatial distributions of indices computed from GSMaP and Gauge-observed in daily scale.

Note: Δ stands for GG – GO.

time of the model is from 2002 to 2010, and the validated period is from 2011 to 2016. Warm up period for the model is one year. To assess runoff process obtained from the GO, GG and GG_g, some experiments based on the SWAT model were conducted with input from those datasets across the UHRB. SWAT model contains parameters that need to be determined by means of calibration, and calibrated values were affected by correlations between model parameters and observed

data. To build reliable hydrological model and avoid the calibration effects of different rainfall datasets, SWAT model used the GO, GG and GG_g as inputs named Scenario 1, Scenario 2 and Scenario 3 respectively and simulated the daily runoff at two hydrological stations (XJP and BH). Three goodness-of-fit measures, namely, coefficient of determination(R2), relative runoff depth errors (PBIAS) and Nash–Sutcliffe efficiency (NS), were used as the objective function to evaluate the

Table 3 The metrics of multi-station average precipitation in UHRB. Statistical

Day

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Year

CC PBIAS (%) ME (mm) RMSE (mm)

0.74 5.41 0.13 5.37

0.14 −6.96 −0.55 3.33

0.21 −17.96 −3.08 6.81

0.64 8.84 2.44 7.79

0.85 9.98 5.15 8.81

0.89 4.67 4.36 14.49

0.91 3.60 3.78 12.64

0.78 4.52 8.05 24.16

0.71 5.00 6.35 19.42

0.87 3.92 4.82 19.30

0.83 8.65 6.02 12.45

0.69 −0.58 −0.14 7.03

0.26 −17.91 −1.70 4.64

0.89 5.41 46.4 113

159

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represented the groundwater, soil, runoff, evaporation and channel components of the watershed hydrology process. Other parameters remained in the range set by the original SWAT model. The SUFI-2 algorithm was used for autocalibration and uncertainty analysis by SWAT-CUP. Wu and Chen (2015) had shown that the SUFI-2 method is able to provide more reasonable and balanced predictions than the generalized likelihood uncertainty estimation (GLUE) and the parameter solution (ParaSol) methods. Through multiple loop simulations (> 2000 times), numerical parameter optimization results in the study area are given in Table 5. The simulation results of different scenarios under the daily scale from January 1st 2002 to December 31st 2016 are presented in Figs. 9–10. Fig. 9 shows the observed and simulated daily scatter plots in the calibration period of 2002–2010. Fig. 10 shows the observed and simulated daily hydrographs in the validation period of 2011–2016. SWAT model using conventional rain gauge data produced a relatively good overall fit of hydrology processes in the case of Scenario 1, which the R2 values of XJP and BH are both > 0.65, and the absolute value of PBIAS (named |PBIAS|) < 15%. So, the relatively high R2 and NS of scenario 1 have demonstrated that the SWAT model can describe well the observed streamflow variation. Under the comparison of Scenario 1 vs. Scenario 2 and Scenario 3, it is indicated that the model performance utilizing GG rainfall dataset was acceptable, which is the R2 > 0.70, NS > 0.50 and |PBIAS| < 20%. It is worth pointing out that the Scenario 3 shows better performance than Scenario 2. In the calibration periods of Scenario 3, NS values obtained were surprisingly high at XJP(NS = 0.77) and a little lower at BH (NS = 0.71). The calculated relative runoff depth errors were − 14.3% (XJP) and − 8.28% (BH). Also, the relatively high R2 values (0.87–0.88) showed that the model described well the observed daily streamflow variation in calibration time. In the validation periods of Scenario 3, GG_g-based model also shows well simulation performance, which is the R2 = 0.85, 0.68 ≤ NS ≤ 0.73 and |PBIAS| ≤ 16%. In contrast, the GG-based model in scenario 2 performed relatively poorly, which is the 0.74 ≤ R2 ≤ 0.81, 0.52 ≤ NS ≤ 0.63 and |PBIAS| < 20%. According to the performances of three scenarios, GO and GG_g rainfall-based models have relatively adequate simulations, while GG rainfall-based model has relatively satisfactory simulations. But GG still brings relatively high relative runoff depth errors.

Fig. 7. Scatter plots between precipitation intensity and ME contained in(a) 0 < PI ≤ 50 mm/day and (b) 50 < PI mm/day.

simulation accuracy (Nash and Sutcliffe, 1970). When the three goodness-of-fit measures have not been improved, calibrations of the model were stopped (Wang et al., 2012). Prior to model calibration, based on SWAT sensitivity analysis procedure, the parameters listed in Table 5 were identified as the most sensitive and used to calibrate and validate the SWAT model. These parameters

4.2.2. Evaluation of different rainfall inputs Based on the above three calibrated models, three scenarios have been set to evaluate the differences of runoff among the rainfall inputs. Three scenarios are described below: In Scenario 1, daily GG and GG_g were used to run the GO calibrated model and the simulated runoffs for the two inputs were compared. In

Fig. 8. (a) Scatter plots of GO~GG and GO~GG_g;(b) Taylor diagram of multi-year average statistics of CC, RMSE and standard deviation for GG_g, and GG. 160

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Table 5 Optimal parameters selected by the calibration process. Parameter

r__CN2 v__GW_DELAY v__CH_N2 v__ALPHA_BF r__Sol_AWC v__CH_K2 v__ESCO v__GW_REVAP v__Revapmn r__Sol_K r__Sol_BD v__RCHRG_DP

Description

Lower bound

Initial SCS runoff curve number for moisture condition II Groundwater delay time (days) Manning's “n” value for the main channel. Baseflow alpha factor-Baseflow recession constant Available water capacity of soil layer (mm H2O/mm soil) Effective hydraulic conductivity in main channel alluvium (mm/h) Soil evaporation compensation factor Groundwater “revap” coefficient Threshold depth of water in shallow aquifer for “revap” to occur (mm) Saturated hydraulic conductivity Moist bulk density Deep aquifer percolation fraction

−0.1 0 0.01 0.01 −0.8 0 0.01 0 0 −1.0 −0.5 0

Upper bound

0.1 350 0.3 1 0.8 500 1 0.2 500 1.0 0.5 1

Optimal value Scenario 1

Scenario 2

Scenario 3

−0.607 18.73 0.161 0.826 −0.034 61.936 0.808 0.020 250.0 −0.924 −0.259 0.493

−0.734 27.51 0.176 0.946 0.091 112.0 0.533 0.059 210.0 −0.932 −0.297 0.106

−0.678 46.00 0.025 0.852 −0.015 325.0 0.550 0.047 225.0 −0.919 −0.362 0.090

Note: Calibration periods stand for 2002–2010.

Fig. 9. Comparison of observed vs. simulated daily scatter plots at (a) XJP and (b) BH during the calibration periods.

Scenario 2, daily GO and GG_g were utilized to drive the GG calibrated model, and in Scenario 3, the daily GO and GG were used to drive the GG_g calibrated model. Table 6 shows the simulation results of different scenarios under the daily scale from January 1st 2002 to December 31st 2016. Among them, as for R2 and NS, Δ1 and Δ2 stand for the percent bias of GG vs. GO and GG_g vs.GO. As for PBIAS, Δ1 and Δ2 stand for the absolute bias of GG vs. GO and GG_g vs.GO. The simulation results of models in three Scenarios expound the impact of GG revision. Compared with GG_g, after the ME of GG have been corrected, model performances based on GG_g are slightly improved, which is the NS and R2 increase about 0.0–11.94% and 0.0–6.1% respectively. In contrast, GG performed poorly. For example, in Scenario 1 of XJP, the NS of GG was reduced from 0.67 to 0.56 while that of GG_g was increased from 0.67 to 0.75. Other scenarios are similar. However, the correction of GG did not significantly reduce the

relative runoff depth errors. Only at the XJP of the Scenario 2 and Scenario 3, simulations based on GG_g bring the PBIAS of 14.6–15.6% reduction. In the other scenarios, the PBIAS increase 1.2–6.49%. Obviously, although GG_g can improve the NS and R2 values, which it can better simulate the process of runoff change, there are still large relative runoff depth errors from the perspective of total water balance. Besides the streamflow hydrograph comparisons, water balance analysis provides another important indicator for testing rainfall data validity. SWAT model partitions precipitation into evaporation, transpiration, surface runoff, and groundwater recharge (includes base flow). The differences of those components from daily streamflow simulations using three rainfall inputs have been examined. A numerical comparison of the averaged water balance components in the whole study area from 2001 to 2016 is shown in Table 7. In the case of the GO driven calculations, 59.7% of precipitation was exhausted through evaporation and transpiration, whereas in the case 161

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Fig. 10. Comparison of observed vs. simulated daily hydrographs at (a) XJP and (b) BH during the validation periods.

of GG and GG_g rainfall data the corresponding rates were 58.8% and 61.2% respectively. The groundwater recharge was 18.4% (GO),19.7%(GG) and 19.1% (GG_g), which is a key component that determines the amount of base flow. As for the total runoff, less precipitation was distributed into runoff in the GO (310 mm) and GG_g (302 mm) rainfall case than in the GG (343 mm) case. The GG_g-based model has a similar total runoff to the GO-based model. As a matter of fact, this difference between GO and GG_g is mainly due to the base flow volume difference, which is 115 mm (GO) vs. 127 mm (GG_g). The GG_g-based model has relatively high base flow volumes, which account for 42% of total runoff. As the above analysis, the reason why the GG_g-based model still brings large relative runoff depth errors is mainly reflected in the difference of the base flow. Then the runoff PBIAS of different grouping between observed and simulated was calculated. Statistical summary of average discharge and PBIAS for different classes is listed in Table 8. Comparisons of reliably discharge curves in different frequency are shown in Fig. 11. As Table 8 showed, in the 0–0.2% and 5–20% class, the average daily discharges are underestimated both for GG and GG_g-based models with the PBIAS ranging from −29.8% to −5.98%. Compared with GG, there are smaller PBIAS in the class of 5–20% under the GG_gbased model, showing that the errors correction of GG had a positive effect on the observed mid-discharge describing. As for the 50–100% class, the performances of two products are generally unsatisfactory, because GG shows seriously overestimation with the PBIAS of 61.9–93.2% and GG_g shows seriously underestimation with the PBIAS

of −57.5~ − 46.4%. However, in the class of 0.2–20%, GG_g-based models have better performance than GG, with the absolute values of PBIAS of 5.42–26.3%. In summary, the GSMaP_Gauge rainfall data-driving model can generate relatively satisfied daily runoff simulation. So, GSMaP_Gauge rainfall data have the potential to be an alternative data source in cases of data-poor or ungauged basins, particularly in developing countries or at remote locations. Moreover, given that satellite-based rainfall data cover a large part of the world, satellite-based product assessment and estimated bias calibration are all necessary in different climatic areas. In this paper, through the construction of the mean bias correction model, the linear slopes between GG and GO, have been improved from 0.693 to 0.923, the R2 increased from 0.625 to 0.795. This also brings the improvement of runoff simulation, leding to an increase of NS and lower relative bias ratio of total discharge under SWAT model. Obviously, to revise GG can effectively provide data support for the research on hydrological simulation and water resources management of Hanjiang River Basin. However, we also noticed that after GG correction, GG_g will also increase the runoff errors in some relative duration. Therefore, it is necessary to collect more gauge-observed rainfall data in the future to carry out the in-depth work of precipitation revisions. 5. Conclusions In this paper, we firstly take GSMaP_Gauge (GG) dataset of GSMaP products as the assessment objects to assess the accuracy of remote

Table 6 Simulation results of different scenarios under the daily scale. Stations

XJP BH

Indexes

R2 NS PBIAS (%) R2 NS PBIAS (%)

Scenario 1

Scenario 2

Scenario 3

GO

GG

GG_g

Δ1(%)

Δ2(%)

GO

GG

GG_g

Δ1(%)

Δ2(%)

GO

GG

GG_g

Δ1(%)

Δ2(%)

0.82 0.67 −12.7 0.84 0.69 3.81

0.77 0.56 −2.13 0.83 0.61 1.87

0.87 0.75 −14.7 0.85 0.71 −10.3

−6.10 −16.4 −10.6 −1.19 −11.6 −1.94

6.10 11.94 2.00 1.19 2.90 6.49

0.85 0.71 −30.0 0.85 0.70 −8.10

0.75 0.53 6.40 0.81 0.64 8.28

0.87 0.75 −15.4 0.85 0.71 −9.30

−11.8 −25.4 −23.6 −4.71 −8.57 −0.82

2.35 5.63 −14.6 0.00 1.43 1.20

0.85 0.71 −30.3 0.85 0.70 −8.40

0.77 0.55 −4.13 0.84 0.62 2.80

0.86 0.71 −14.7 0.85 0.70 −10.3

−9.41 −22.5 −26.2 −1.18 −11.43 −5.60

1.18 0.00 −15.6 0.00 0.00 1.90

Note: Simulation periods stand for 2002–2016. 162

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Table 7 Components of the three rainfall-based models. Components

Precipitation Evaporation and Transpiration Groundwater recharge Total runoff Surface runoff Base flow

GO-based model

GG-based model

Volume (mm/y)

r1 (%)

892 533 164 310 195 115

59.7 18.4 34.8 21.9 12.9

GG_g-based model

r2 (%)

Volume (mm/y)

r1 (%)

63.0 37.0

941 553 185 343 202 141

58.8 19.7 36.5 21.5 15.0

r2 (%)

Volume (mm/y)

r1(%)

r2(%)

59.0 41.0

887 543 169 302 175 127

61.2 19.1 34.1 19.8 14.3

58.0 42.0

Note: r1 stands for the composition ratio of precipitation; r2 stands for the composition ratio of total runoff. Table 8 Statistical summary of average daily discharge and PBIAS for different classes. No.

Class of frequency (%)

XJP

BH 3

1 2 3 4 5 6 7

0.0–0.1 0.1–0.2 0.2–0.5 0.5–5.0 5.0–20 20–50 50–100

Average daily discharge (m /s)

PBIAS (%)

Observed

GG-based

GG_g-based

R1

2674 1856 1248 422 112 43.7 16.2

1999 1606 1277 442 80.6 47.5 31.4

2079 1477 1176 445 105 37.6 6.91

−25.2 −13.5 2.34 4.82 −28.0 8.56 93.2

Average daily discharge (m3/s)

PBIAS (%)

R2

Observed

GG-based

GG_g-based

R1

R2

−22.3 −20.4 −5.74 5.42 −5.98 −14.0 −57.5

19,460 15,640 10,226 3150 1410 588 217

13,896 11,996 10,015 4292 1159 542 352

13,660 11,756 9392 3978 1309 523 117

−28.6 −23.3 −2.07 36.2 −17.8 −7.86 61.9

−29.8 −24.8 −8.15 26.3 −7.16 −11.1 −46.4

Note: R1stands for the percent bias between observed and GG-based; R2 stands for the percent bias between observed and GG_g-based.

sensing precipitation, based on constructing some types of statistical indices. Secondly, the mean error of GG is corrected to build new GG data, named GG_g. Finally, driving SWAT model for runoff simulation based on different rainfall inputs and evaluating the difference of simulation accuracy. The main research results are as follows. (1) GG have relatively high monitoring accuracy in monthly and annual scale. They overestimate the March to October rainfall and underestimate that of other months. Although GG can capture the entire precipitation spatial patterns in the Upper Hanjiang River Basin (UHRB), their differences are notable. The overestimation occurs in the mid-northern less rain parts of the basin, while the underestimation is across southern rainy region. (2) The main manifestations of GG are seriously overvaluation of light rainfall of 1–10 mm/day and seriously underestimate the high and heavy rain rate of > 20 mm/day in the UHRB. It has a superior ability in detecting moderate precipitation events of 10 to 20 mm/ d, which PBIAS values are between −10% and 10%. (3) Error calibration model has been constructed to correct the GG's errors. After revised, rainfall accuracy of GG has been increased, which could be beneficial for further correction of this new satellite precipitation dataset. (4) SWAT model driving by GO rainfall dataset could describe well the runoff volume of the 0.82 ≤ R2 ≤ 0.83, 0.65 ≤ NS ≤ 0.67 and |PBIAS| < 15%. But GG-based model performed relatively poorly. Through the correction of mean errors, GG_g brings the improvement of runoff simulation, leading to 0.0–11.94% increase in NS and 0.0–6.1% increase in R2. (5) From the perspective of water balance, certain shortcomings are noted that whether it is GG or GG_g, there are still large relative runoff depth errors, which mainly reflected in the difference of the base flow. Specifically reflecting on the magnitude is that GG may tend to seriously overestimate the daily discharges in the frequency of 50–100% while GG_g shows seriously underestimation. Simultaneously, due to the extreme rainfalls cannot be detected precisely, GG and GG_g underestimate the discharges of the frequency of 0–0.5% about 13.5–29.8%. Simultaneously, it also shows

Fig. 11. Comparison of the reliably discharge curves in different frequency. 163

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that the GG error correction model constructed this time still has room for improvement.

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